SBMLBioModels: T - W

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T


A Robust Model of DNA Damage Dynamics. Rasgou Talemi and Schaber, 12.20.2014.

Mathematical modelling has been instrumental to understand kinetics of radiation-induced DNA damage repair and associated secondary cancer risk. The widely accepted two-lesion kinetic (TLK) model assumes two kinds of double strand breaks, simple and complex ones, with different repair rates. Recently, persistent DNA damage associated with telomeres was reported as a new kind of DNA damage. We therefore extended existing versions of the TLK model by new categories of DNA damage and re-evaluated those models using extensive data. We subjected different versions of the TLK model to a rigorous model discrimination approach. This enabled us to robustly select a best approximating parsimonious model that can both recapitulate and predict transient and persistent DNA damage after ionizing radiation. Models and data argue for i) nonlinear dose-damage relationships, and ii) negligible saturation of repair kinetics even for high doses. Additionally, we show that simulated radiation-induced persistent telomere-associated DNA damage foci (TAF) can be used to predict excess relative risk (ERR) of developing secondary leukemia after fractionated radiotherapy. We suggest that TAF may serve as an additional measure to predict cancer risk after radiotherapy using high dose rates. This may improve predicting risk-dose dependency of ionizing radiation especially for long-term therapies. link: http://identifiers.org/pubmed/26359627

Parameters: none

States: none

Observables: none

MODEL1606100000 @ v0.0.1

Talemi2016 - Yeast osmo-homoestasisThis model is described in the article: [Systems Level Analysis of the Yeast Osmo-St…

Adaptation is an important property of living organisms enabling them to cope with environmental stress and maintaining homeostasis. Adaptation is mediated by signaling pathways responding to different stimuli. Those signaling pathways might communicate in order to orchestrate the cellular response to multiple simultaneous stimuli, a phenomenon called crosstalk. Here, we investigate possible mechanisms of crosstalk between the High Osmolarity Glycerol (HOG) and the Cell Wall Integrity (CWI) pathways in yeast, which mediate adaptation to hyper- and hypo-osmotic challenges, respectively. We combine ensemble modeling with experimental investigations to test in quantitative terms different hypotheses about the crosstalk of the HOG and the CWI pathways. Our analyses indicate that for the conditions studied i) the CWI pathway activation employs an adaptive mechanism with a variable volume-dependent threshold, in contrast to the HOG pathway, whose activation relies on a fixed volume-dependent threshold, ii) there is no or little direct crosstalk between the HOG and CWI pathways, and iii) its mainly the HOG alone mediating adaptation of cellular osmotic pressure for both hyper- as well as hypo-osmotic stress. Thus, by iteratively combining mathematical modeling with experimentation we achieved a better understanding of regulatory mechanisms of yeast osmo-homeostasis and formulated new hypotheses about osmo-sensing. link: http://identifiers.org/pubmed/27515486

Parameters: none

States: none

Observables: none

Tan2012 - Antibiotic Treatment, Inoculum EffectThe efficacy of many antibiotics decreases with increasing bacterial dens…

The inoculum effect (IE) refers to the decreasing efficacy of an antibiotic with increasing bacterial density. It represents a unique strategy of antibiotic tolerance and it can complicate design of effective antibiotic treatment of bacterial infections. To gain insight into this phenomenon, we have analyzed responses of a lab strain of Escherichia coli to antibiotics that target the ribosome. We show that the IE can be explained by bistable inhibition of bacterial growth. A critical requirement for this bistability is sufficiently fast degradation of ribosomes, which can result from antibiotic-induced heat-shock response. Furthermore, antibiotics that elicit the IE can lead to 'band-pass' response of bacterial growth to periodic antibiotic treatment: the treatment efficacy drastically diminishes at intermediate frequencies of treatment. Our proposed mechanism for the IE may be generally applicable to other bacterial species treated with antibiotics targeting the ribosomes. link: http://identifiers.org/pubmed/23047527

Parameters:

Name Description
kd=1.0 Reaction: c => ; c, Rate Law: kd*c
kappa=0.5 Reaction: => c; c, Rate Law: c/(kappa+c)
gamma=1.0E-5; phi=5.0E-6; delta=1.0E-5 Reaction: c => ; c, Rate Law: phi*c/(delta+gamma*c)
alpha=0.001 Reaction: => c, Rate Law: alpha

States:

Name Description
c [30S ribosomal protein S1; 50S ribosomal protein L1]

Observables: none

BIOMD0000000285 @ v0.0.1

This a model from the article: Experimental and computational analysis of polyglutamine-mediated cytotoxicity. Tang…

Expanded polyglutamine (polyQ) proteins are known to be the causative agents of a number of human neurodegenerative diseases but the molecular basis of their cytoxicity is still poorly understood. PolyQ tracts may impede the activity of the proteasome, and evidence from single cell imaging suggests that the sequestration of polyQ into inclusion bodies can reduce the proteasomal burden and promote cell survival, at least in the short term. The presence of misfolded protein also leads to activation of stress kinases such as p38MAPK, which can be cytotoxic. The relationships of these systems are not well understood. We have used fluorescent reporter systems imaged in living cells, and stochastic computer modeling to explore the relationships of polyQ, p38MAPK activation, generation of reactive oxygen species (ROS), proteasome inhibition, and inclusion body formation. In cells expressing a polyQ protein inclusion, body formation was preceded by proteasome inhibition but cytotoxicity was greatly reduced by administration of a p38MAPK inhibitor. Computer simulations suggested that without the generation of ROS, the proteasome inhibition and activation of p38MAPK would have significantly reduced toxicity. Our data suggest a vicious cycle of stress kinase activation and proteasome inhibition that is ultimately lethal to cells. There was close agreement between experimental data and the predictions of a stochastic computer model, supporting a central role for proteasome inhibition and p38MAPK activation in inclusion body formation and ROS-mediated cell death. link: http://identifiers.org/pubmed/20885783

Parameters:

Name Description
kproteff = 1.0; kalive = 1.0; kdegMisP = 0.01 Reaction: MisP_Proteasome => Proteasome, Rate Law: kdegMisP*MisP_Proteasome*kalive*kproteff
kalive = 1.0; kgenROSAggP = 5.0E-6 Reaction: AggP3 => AggP3 + ROS, Rate Law: kgenROSAggP*AggP3*kalive
kdegPolyQ = 0.0025; kproteff = 1.0; kalive = 1.0 Reaction: PolyQ_Proteasome => Proteasome, Rate Law: kdegPolyQ*PolyQ_Proteasome*kalive*kproteff
kalive = 1.0; kgenROSp38 = 7.0E-4; kp38act = 1.0 Reaction: p38_P => p38_P + ROS, Rate Law: kgenROSp38*p38_P*kp38act*kalive
kalive = 1.0; ksynNatP = 2.4 Reaction: Source => NatP, Rate Law: ksynNatP*Source*kalive
kalive = 1.0; kdisaggMisP5 = 1.0E-7 Reaction: AggP5 => MisP + AggP4, Rate Law: kdisaggMisP5*AggP5*kalive
kalive = 1.0; kseqPolyQProt = 5.0E-7 Reaction: PolyQ_Proteasome + SeqAggP => SeqAggP, Rate Law: kseqPolyQProt*PolyQ_Proteasome*SeqAggP*kalive
kalive = 1.0; kPIdeath = 2.5E-8 Reaction: AggP_Proteasome => AggP_Proteasome + PIdeath, Rate Law: kPIdeath*AggP_Proteasome*kalive
kalive = 1.0; kbinmRFPu = 5.0E-7 Reaction: mRFPu + Proteasome => mRFPu_Proteasome, Rate Law: kbinmRFPu*mRFPu*Proteasome*kalive
kproteff = 1.0; kalive = 1.0; kdegmRFPu = 0.005 Reaction: mRFPu_Proteasome => Proteasome, Rate Law: kdegmRFPu*mRFPu_Proteasome*kalive*kproteff
kalive = 1.0; kdisaggMisP2 = 4.0E-7 Reaction: AggP2 => MisP + AggP1, Rate Law: kdisaggMisP2*AggP2*kalive
kalive = 1.0; kbinPolyQ = 5.0E-8 Reaction: PolyQ + Proteasome => PolyQ_Proteasome, Rate Law: kbinPolyQ*PolyQ*Proteasome*kalive
kalive = 1.0; kgenROS = 0.0017 Reaction: Source => ROS, Rate Law: kgenROS*Source*kalive
krelMisPProt = 1.0E-8; kalive = 1.0 Reaction: MisP_Proteasome => MisP + Proteasome, Rate Law: krelMisPProt*MisP_Proteasome*kalive
kdisaggPolyQ3 = 3.0E-7; kalive = 1.0 Reaction: AggPolyQ3 => PolyQ + AggPolyQ2, Rate Law: kdisaggPolyQ3*AggPolyQ3*kalive
kactp38 = 5.0E-6; kalive = 1.0 Reaction: ROS + p38 => ROS + p38_P, Rate Law: kactp38*ROS*p38*kalive
kalive = 1.0; kagg2MisP = 1.0E-10 Reaction: MisP + AggP1 => AggP2, Rate Law: kagg2MisP*MisP*AggP1*kalive
kdisaggPolyQ1 = 5.0E-7; kalive = 1.0 Reaction: AggPolyQ1 => PolyQ, Rate Law: kdisaggPolyQ1*AggPolyQ1*kalive
kalive = 1.0; kdisaggPolyQ4 = 2.0E-7 Reaction: AggPolyQ4 => PolyQ + AggPolyQ3, Rate Law: kdisaggPolyQ4*AggPolyQ4*kalive
kalive = 1.0; ksynmRFPu = 0.138 Reaction: Source => mRFPu, Rate Law: ksynmRFPu*Source*kalive
kalive = 1.0; krelmRFPu = 1.0E-8 Reaction: mRFPu_Proteasome => mRFPu + Proteasome, Rate Law: krelmRFPu*mRFPu_Proteasome*kalive
kalive = 1.0; kbinMisPProt = 5.0E-8 Reaction: MisP + Proteasome => MisP_Proteasome, Rate Law: kbinMisPProt*MisP*Proteasome*kalive
kalive = 1.0; kseqmRFPuProt = 5.0E-7 Reaction: mRFPu_Proteasome + SeqAggP => SeqAggP, Rate Law: kseqmRFPuProt*mRFPu_Proteasome*SeqAggP*kalive
kalive = 1.0; kinhprot = 5.0E-9 Reaction: AggP3 + Proteasome => AggP_Proteasome, Rate Law: kinhprot*AggP3*Proteasome*kalive
kdisaggMisP3 = 3.0E-7; kalive = 1.0 Reaction: AggP3 => MisP + AggP2, Rate Law: kdisaggMisP3*AggP3*kalive
kalive = 1.0; kgenROSSeqAggP = 1.0E-7 Reaction: SeqAggP => SeqAggP + ROS, Rate Law: kgenROSSeqAggP*SeqAggP*kalive
kalive = 1.0; kaggPolyQ = 5.0E-8 Reaction: PolyQ + ROS => AggPolyQ1 + ROS, Rate Law: kaggPolyQ*PolyQ*(PolyQ-1)*0.5*ROS^2/(10^2+ROS^2)*kalive
kseqMisPProt = 5.0E-7; kalive = 1.0 Reaction: MisP_Proteasome + SeqAggP => SeqAggP, Rate Law: kseqMisPProt*MisP_Proteasome*SeqAggP*kalive
kmisfold = 2.0E-6; kalive = 1.0 Reaction: NatP + ROS => MisP + ROS, Rate Law: kmisfold*NatP*ROS*kalive
kalive = 1.0; kremROS = 2.0E-4 Reaction: ROS => Sink, Rate Law: kremROS*ROS*kalive
kalive = 1.0; kdisaggMisP1 = 5.0E-7 Reaction: AggP1 => MisP, Rate Law: kdisaggMisP1*AggP1*kalive
kseqmRFPu = 1.0E-10; kalive = 1.0 Reaction: mRFPu + SeqAggP => SeqAggP, Rate Law: kseqmRFPu*mRFPu*SeqAggP*kalive
kdisaggPolyQ2 = 4.0E-7; kalive = 1.0 Reaction: AggPolyQ2 => PolyQ + AggPolyQ1, Rate Law: kdisaggPolyQ2*AggPolyQ2*kalive
krelPolyQ = 1.0E-9; kalive = 1.0 Reaction: PolyQ_Proteasome => PolyQ + Proteasome, Rate Law: krelPolyQ*PolyQ_Proteasome*kalive
kseqPolyQ = 8.0E-7; kalive = 1.0 Reaction: PolyQ + SeqAggP => SeqAggP, Rate Law: kseqPolyQ*PolyQ*SeqAggP*kalive
kaggMisP = 1.0E-11; kalive = 1.0 Reaction: MisP => AggP1, Rate Law: kaggMisP*MisP*(MisP-1)*0.5*kalive
kseqAggPProt = 5.0E-7; kalive = 1.0 Reaction: AggP_Proteasome + SeqAggP => SeqAggP, Rate Law: kseqAggPProt*AggP_Proteasome*SeqAggP*kalive
kseqMisP = 1.0E-9; kalive = 1.0 Reaction: MisP + SeqAggP => SeqAggP, Rate Law: kseqMisP*MisP*SeqAggP*kalive
kalive = 1.0; kinactp38 = 0.002 Reaction: p38_P => p38, Rate Law: kinactp38*p38_P*kalive
kdisaggMisP4 = 2.0E-7; kalive = 1.0 Reaction: AggP4 => MisP + AggP3, Rate Law: kdisaggMisP4*AggP4*kalive
kdisaggPolyQ5 = 1.0E-7; kalive = 1.0 Reaction: AggPolyQ5 => PolyQ + AggPolyQ4, Rate Law: kdisaggPolyQ5*AggPolyQ5*kalive

States:

Name Description
AggP3 AggP3
Proteasome [proteasome complex]
ROS ROS
AggP4 AggP4
mRFPu mRFPu
AggP Proteasome AggP_Proteasome
p38 P p38_P
AggPolyQ4 AggPolyQ4
PolyQ [Huntingtin]
AggP1 AggP1
SeqAggP SeqAggP
AggP2 AggP2
mRFPu Proteasome mRFPu_Proteasome
PolyQ Proteasome PolyQ_Proteasome
AggPolyQ3 AggPolyQ3
Source Source
MisP Proteasome MisP_Proteasome
AggPolyQ5 AggPolyQ5

Observables: none

# Aurora Kinase B and ZAK interaction model Equivalent of the stochastic model used in "Network pharmacology model pred…

Cancer cells with heterogeneous mutation landscapes and extensive functional redundancy easily develop resistance to monotherapies by emerging activation of compensating or bypassing pathways. To achieve more effective and sustained clinical responses, synergistic interactions of multiple druggable targets that inhibit redundant cancer survival pathways are often required. Here, we report a systematic polypharmacology strategy to predict, test, and understand the selective drug combinations for MDA-MB-231 triple-negative breast cancer cells. We started by applying our network pharmacology model to predict synergistic drug combinations. Next, by utilizing kinome-wide drug-target profiles and gene expression data, we pinpointed a synergistic target interaction between Aurora B and ZAK kinase inhibition that led to enhanced growth inhibition and cytotoxicity, as validated by combinatorial siRNA, CRISPR/Cas9, and drug combination experiments. The mechanism of such a context-specific target interaction was elucidated using a dynamic simulation of MDA-MB-231 signaling network, suggesting a cross-talk between p53 and p38 pathways. Our results demonstrate the potential of polypharmacological modeling to systematically interrogate target interactions that may lead to clinically actionable and personalized treatment options. link: http://identifiers.org/pubmed/31312514

Parameters:

Name Description
k_pten = 0.2 Reaction: => PTEN; TP53, Rate Law: Cell*k_pten*TP53
kd_mapk13 = 1.4 Reaction: MAPK13 =>, Rate Law: Cell*kd_mapk13*MAPK13
kd_tp53 = 2.0 Reaction: TP53 =>, Rate Law: Cell*kd_tp53*TP53
k_prkaca = 2.0 Reaction: => PRKACA; AURKB, Rate Law: Cell*k_prkaca*AURKB
k_tp53 = 0.6 Reaction: => TP53; SRC, Rate Law: Cell*k_tp53*SRC
k_src = 0.2 Reaction: => SRC; CSF1R, Rate Law: Cell*k_src*CSF1R
k_mapk13 = 2.0 Reaction: => MAPK13; MAP2K4, Rate Law: Cell*k_mapk13*MAP2K4
kd_src = 1.0 Reaction: SRC =>, Rate Law: Cell*kd_src*SRC
kd_parp1 = 0.005 Reaction: PARP1 =>, Rate Law: Cell*kd_parp1*PARP1
kd_csf1r = 30.0 Reaction: CSF1R =>, Rate Law: Cell*kd_csf1r*CSF1R
k_shc1 = 2.0 Reaction: => SHC1; TGFBR1, Rate Law: Cell*k_shc1*TGFBR1
kd_pik3r1 = 10.0 Reaction: PIK3R1 => ; PTEN, Rate Law: Cell*kd_pik3r1*PIK3R1*PTEN
kd_aurkb = 4.5 Reaction: AURKB =>, Rate Law: Cell*kd_aurkb*AURKB
k_tgfbr1 = 0.5 Reaction: => TGFBR1; ZAK, Rate Law: Cell*k_tgfbr1*ZAK
kd_ywhaz = 0.072 Reaction: YWHAZ =>, Rate Law: Cell*kd_ywhaz*YWHAZ
kd_brca1 = 20.0 Reaction: BRCA1 =>, Rate Law: Cell*kd_brca1*BRCA1
k_ywhaz = 0.9 Reaction: => YWHAZ; ZAK, Rate Law: Cell*k_ywhaz*ZAK
kd_bad = 0.04; const=0.0133 Reaction: BAD => ; YWHAZ, Rate Law: Cell*kd_bad*BAD*YWHAZ*const
kd_prkaca = 6.0 Reaction: PRKACA =>, Rate Law: Cell*kd_prkaca*PRKACA
kd_pten = 0.5 Reaction: PTEN =>, Rate Law: Cell*kd_pten*PTEN
k_brca1 = 2.0 Reaction: => BRCA1; PARP1, Rate Law: Cell*k_brca1*PARP1
k_atm = 0.1 Reaction: => ATM; MAPK14, Rate Law: Cell*k_atm*MAPK14
k_aurkb = 3.0 Reaction: => AURKB; PARP1, Rate Law: Cell*k_aurkb*PARP1
kd_map2k4 = 0.6 Reaction: MAP2K4 =>, Rate Law: Cell*kd_map2k4*MAP2K4
k_pkn1 = 0.5 Reaction: => PKN1, Rate Law: Cell*k_pkn1
k_map2k4 = 0.2 Reaction: => MAP2K4; MAP2K3, Rate Law: Cell*k_map2k4*MAP2K3
k_pik3r1 = 2.0 Reaction: => PIK3R1; SHC1, Rate Law: Cell*k_pik3r1*SHC1
kd_atm = 3.0 Reaction: ATM =>, Rate Law: Cell*kd_atm*ATM
kd_zak = 0.5 Reaction: ZAK =>, Rate Law: Cell*kd_zak*ZAK
k_map2k3 = 0.2 Reaction: => MAP2K3; PKN1, Rate Law: Cell*k_map2k3*PKN1
kd_tgfbr1 = 0.45 Reaction: TGFBR1 =>, Rate Law: Cell*kd_tgfbr1*TGFBR1
k_mapk14 = 2.0 Reaction: => MAPK14; MAP2K3, Rate Law: Cell*k_mapk14*MAP2K3
kd_tp53 = 2.0; const=0.0067 Reaction: TP53 => ; AURKB, Rate Law: Cell*kd_tp53*TP53*AURKB*const
kd_mapk14 = 5.0 Reaction: MAPK14 =>, Rate Law: Cell*kd_mapk14*MAPK14
kd_shc1 = 0.06 Reaction: SHC1 => ; PTEN, Rate Law: Cell*kd_shc1*SHC1*PTEN
k_csf1r = 2.0 Reaction: => CSF1R; PIK3R1, Rate Law: Cell*k_csf1r*PIK3R1
kd_pkn1 = 0.005 Reaction: PKN1 =>, Rate Law: Cell*kd_pkn1*PKN1
k_bad = 5.0 Reaction: => BAD, Rate Law: Cell*k_bad
k_zak = 0.1 Reaction: => ZAK; PKN1, Rate Law: Cell*k_zak*PKN1
k_parp1 = 0.5 Reaction: => PARP1, Rate Law: Cell*k_parp1
kd_map2k3 = 0.6 Reaction: MAP2K3 =>, Rate Law: Cell*kd_map2k3*MAP2K3
kd_bad = 0.04 Reaction: BAD =>, Rate Law: Cell*kd_bad*BAD

States:

Name Description
ATM [Serine-protein kinase ATM]
MAPK13 [Mitogen-activated protein kinase 13]
SHC1 [SHC-transforming protein 1]
TP53 [Cellular tumor antigen p53]
SRC [Proto-oncogene tyrosine-protein kinase Src]
PARP1 [Poly [ADP-ribose] polymerase 1]
PTEN [Phosphatidylinositol 3,4,5-trisphosphate 3-phosphatase and dual-specificity protein phosphatase PTEN]
CSF1R [P07333]
BRCA1 [P38398]
PKN1 [Q16512]
BAD [Bcl2-associated agonist of cell death]
MAP2K3 [Dual specificity mitogen-activated protein kinase kinase 3]
MAPK14 [Mitogen-activated protein kinase 14]
TGFBR1 [TGF-beta receptor type-1]
MAP2K4 [Dual specificity mitogen-activated protein kinase kinase 4]
PRKACA [cAMP-dependent protein kinase catalytic subunit alpha]
PIK3R1 [Phosphatidylinositol 3-kinase regulatory subunit alpha]
AURKB [Q96GD4]
ZAK [Q75JK0]
YWHAZ [P63104]

Observables: none

Since the emergence of the first cases in Wuhan, China, the novel coronavirus (2019-nCoV) infection has been quickly spr…

Since the emergence of the first cases in Wuhan, China, the novel coronavirus (2019-nCoV) infection has been quickly spreading out to other provinces and neighboring countries. Estimation of the basic reproduction number by means of mathematical modeling can be helpful for determining the potential and severity of an outbreak and providing critical information for identifying the type of disease interventions and intensity. A deterministic compartmental model was devised based on the clinical progression of the disease, epidemiological status of the individuals, and intervention measures. The estimations based on likelihood and model analysis show that the control reproduction number may be as high as 6.47 (95% CI 5.71-7.23). Sensitivity analyses show that interventions, such as intensive contact tracing followed by quarantine and isolation, can effectively reduce the control reproduction number and transmission risk, with the effect of travel restriction adopted by Wuhan on 2019-nCoV infection in Beijing being almost equivalent to increasing quarantine by a 100 thousand baseline value. It is essential to assess how the expensive, resource-intensive measures implemented by the Chinese authorities can contribute to the prevention and control of the 2019-nCoV infection, and how long they should be maintained. Under the most restrictive measures, the outbreak is expected to peak within two weeks (since 23 January 2020) with a significant low peak value. With travel restriction (no imported exposed individuals to Beijing), the number of infected individuals in seven days will decrease by 91.14% in Beijing, compared with the scenario of no travel restriction. link: http://identifiers.org/pubmed/32046137

Parameters: none

States: none

Observables: none

The basic reproduction number of an infectious agent is the average number of infections one case can generate over the…

The basic reproduction number of an infectious agent is the average number of infections one case can generate over the course of the infectious period, in a naïve, uninfected population. It is well-known that the estimation of this number may vary due to several methodological issues, including different assumptions and choice of parameters, utilized models, used datasets and estimation period. With the spreading of the novel coronavirus (2019-nCoV) infection, the reproduction number has been found to vary, reflecting the dynamics of transmission of the coronavirus outbreak as well as the case reporting rate. Due to significant variations in the control strategies, which have been changing over time, and thanks to the introduction of detection technologies that have been rapidly improved, enabling to shorten the time from infection/symptoms onset to diagnosis, leading to faster confirmation of the new coronavirus cases, our previous estimations on the transmission risk of the 2019-nCoV need to be revised. By using time-dependent contact and diagnose rates, we refit our previously proposed dynamics transmission model to the data available until January 29th, 2020 and re-estimated the effective daily reproduction ratio that better quantifies the evolution of the interventions. We estimated when the effective daily reproduction ratio has fallen below 1 and when the epidemics will peak. Our updated findings suggest that the best measure is persistent and strict self-isolation. The epidemics will continue to grow, and can peak soon with the peak time depending highly on the public health interventions practically implemented. link: http://identifiers.org/pubmed/32099934

Parameters: none

States: none

Observables: none

Mathematical model of the blood coagulation cascade including interaction of snake venom and antivenom.

Many snake venoms contain procoagulant toxins that activate the coagulation cascade and cause venom-induced consumptive coagulopathy (VICC). We developed a semi-mechanistic model of the clotting cascade in order to explore the effects of the procoagulant toxin from taipan venom on this system as well as the effects of antivenom. Simulations of the time course in the change of clotting factors were compared to data collected from taipan envenomed patients. The model accurately predicted the observed concentration of clotting factors over time following taipan envenomation. Investigations from the model indicated that the upper limit of the half-life of the procoagulant toxin was 1h. Simulations from the model also suggest that antivenom for Australasian elapids has negligible effect on reducing the recovery time of the coagulation profile unless administered almost immediately after envenomation. The model has generality to be expanded to describe the effects of other venoms and drugs on the clotting cascade. link: http://identifiers.org/pubmed/18831981

Parameters: none

States: none

Observables: none

This model is from the article: A multiscale modeling approach to investigate molecular mechanisms of pseudokinase act…

Multiscale modeling provides a powerful and quantitative platform for investigating the complexity inherent in intracellular signaling pathways and rationalizing the effects of molecular perturbations on downstream signaling events and ultimately, on the cell phenotype. Here we describe the application of a multiscale modeling scheme to the HER3/ErbB3 receptor tyrosine kinase (RTK) signaling network, which regulates critical cellular processes including proliferation, migration and differentiation. The HER3 kinase is a topic of current interest and investigation, as it has been implicated in mechanisms of resistance to tyrosine kinase inhibition (TKI) of EGFR and HER2 in the treatment of many human malignancies. Moreover, the commonly regarded status of HER3 as a catalytically inactive 'pseudokinase' has recently been challenged by our previous study, which demonstrated robust residual kinase activity for HER3. Through our multiscale model, we investigate the most significant molecular interactions that contribute to potential mechanisms of HER3 activity and the physiological relevance of this activity to mechanisms of drug resistance in an ErbB-driven tumor cell in silico. The results of our molecular-scale simulations support the characterization of HER3 as a weakly active kinase that, in contrast to its fully-active ErbB family members, depends upon a unique hydrophobic interface to coordinate the alignment of specific catalytic residues required for its activity. Translating our molecular simulation results of the uniquely active behavior of the HER3 kinase into a physiologically relevant environment, our HER3 signaling model demonstrates that even a weak level of HER3 activity may be sufficient to induce AKT signaling and TKI resistance in the context of an ErbB signaling-dependent tumor cell, and therefore therapeutic targeting of HER3 may represent a superior treatment strategy for specific ErbB-driven cancers. link: http://identifiers.org/pubmed/21509365

Parameters: none

States: none

Observables: none

MODEL0393108880 @ v0.0.1

This a model from the article: A model for human ventricular tissue. ten Tusscher KH, Noble D, Noble PJ, Panfilov AV…

The experimental and clinical possibilities for studying cardiac arrhythmias in human ventricular myocardium are very limited. Therefore, the use of alternative methods such as computer simulations is of great importance. In this article we introduce a mathematical model of the action potential of human ventricular cells that, while including a high level of electrophysiological detail, is computationally cost-effective enough to be applied in large-scale spatial simulations for the study of reentrant arrhythmias. The model is based on recent experimental data on most of the major ionic currents: the fast sodium, L-type calcium, transient outward, rapid and slow delayed rectifier, and inward rectifier currents. The model includes a basic calcium dynamics, allowing for the realistic modeling of calcium transients, calcium current inactivation, and the contraction staircase. We are able to reproduce human epicardial, endocardial, and M cell action potentials and show that differences can be explained by differences in the transient outward and slow delayed rectifier currents. Our model reproduces the experimentally observed data on action potential duration restitution, which is an important characteristic for reentrant arrhythmias. The conduction velocity restitution of our model is broader than in other models and agrees better with available data. Finally, we model the dynamics of spiral wave rotation in a two-dimensional sheet of human ventricular tissue and show that the spiral wave follows a complex meandering pattern and has a period of 265 ms. We conclude that the proposed model reproduces a variety of electrophysiological behaviors and provides a basis for studies of reentrant arrhythmias in human ventricular tissue. link: http://identifiers.org/pubmed/14656705

Parameters: none

States: none

Observables: none

MODEL7910499126 @ v0.0.1

This a model from the article: Alternans and spiral breakup in a human ventricular tissue model. ten Tusscher KH, Pa…

Ventricular fibrillation (VF) is one of the main causes of death in the Western world. According to one hypothesis, the chaotic excitation dynamics during VF are the result of dynamical instabilities in action potential duration (APD) the occurrence of which requires that the slope of the APD restitution curve exceeds 1. Other factors such as electrotonic coupling and cardiac memory also determine whether these instabilities can develop. In this paper we study the conditions for alternans and spiral breakup in human cardiac tissue. Therefore, we develop a new version of our human ventricular cell model, which is based on recent experimental measurements of human APD restitution and includes a more extensive description of intracellular calcium dynamics. We apply this model to study the conditions for electrical instability in single cells, for reentrant waves in a ring of cells, and for reentry in two-dimensional sheets of ventricular tissue. We show that an important determinant for the onset of instability is the recovery dynamics of the fast sodium current. Slower sodium current recovery leads to longer periods of spiral wave rotation and more gradual conduction velocity restitution, both of which suppress restitution-mediated instability. As a result, maximum restitution slopes considerably exceeding 1 (up to 1.5) may be necessary for electrical instability to occur. Although slopes necessary for the onset of instabilities found in our study exceed 1, they are within the range of experimentally measured slopes. Therefore, we conclude that steep APD restitution-mediated instability is a potential mechanism for VF in the human heart. link: http://identifiers.org/pubmed/16565318

Parameters: none

States: none

Observables: none

Mathematical model of the mechanism and kinetics of blood coagulation factor XII.

Surface-induced activation of factor XII is critical part of the intrinsic pathway of blood coagulation. The mechanism of this process remains unclear: in particular, it is not known whether the initial amounts of factor XIIa, an active form of factor XII, are produced purely by factor XII contacting a surface or if traces of factor XIIa pre-exist. Furthermore, it is not known whether factor XII first has to bind to a surface before it can interact with the surface-bound factor XIIa in a two-dimensional process to become activated ("bound-substrate model") or if surface-bound factor XIIa activates a fluid-delivered form of factor XII ("free-substrate model"). To investigate these possibilities, we used mathematical modeling to implement various hypotheses. Time courses of factor XII production were generated under different initial conditions and matched with experimental data. We established that only the "bound-substrate model" fits with the majority of experimental data, whereas the "free-substrate model" does not. We also addressed the question of spontaneous activation and found that measurable differences between the models with and without spontaneous activation appear only under limiting conditions (deficit or excess of surface). As there are insufficient data regarding the system's behavior upon such variations of surface concentration in the literature, we designed new experiments to answer this question. link: http://identifiers.org/pubmed/26187095

Parameters: none

States: none

Observables: none

MODEL7893871775 @ v0.0.1

This a model from the article: Using Physiome standards to couple cellular functions for rat cardiac excitation-contra…

Scientific endeavour is reliant upon the extension and reuse of previous knowledge. The formalization of this process for computational modelling is facilitated by the use of accepted standards with which to describe and simulate models, ensuring consistency between the models and thus reducing the development and propagation of errors. CellML 1.1, an XML-based programming language, has been designed as a modelling standard which, by virtue of its import and grouping functions, facilitates model combination and reuse. Using CellML 1.1, we demonstrate the process of formalized model reuse by combining three separate models of rat cardiomyocyte function (an electrophysiology model, a model of cellular calcium dynamics and a mechanics model) which together make up the Pandit-Hinch-Niederer et al. cell model. Not only is this integrative model of rat electromechanics a useful tool for cardiac modelling but it is also an ideal framework with which to demonstrate both the power of model reuse and the challenges associated with this process. We highlight and classify a number of these issues associated with combining models and provide some suggested solutions. link: http://identifiers.org/pubmed/18344258

Parameters: none

States: none

Observables: none

BIOMD0000000253 @ v0.0.1

This is the model described in the article: The danger of metabolic pathways with turbo design Teusink B, Walsh MC,…

Many catabolic pathways begin with an ATP-requiring activation step, after which further metabolism yields a surplus of ATP. Such a 'turbo' principle is useful but also contains an inherent risk. This is illustrated by a detailed kinetic analysis of a paradoxical Saccharomyces cerevisiae mutant; the mutant fails to grow on glucose because of overactive initial enzymes of glycolysis, but is defective only in an enzyme (trehalose 6-phosphate synthase) that appears to have little relevance to glycolysis. The ubiquity of pathways that possess an initial activation step, suggests that there might be many more genes that, when deleted, cause rather paradoxical regulation phenotypes (i.e. growth defects caused by enhanced utilization of growth substrate). link: http://identifiers.org/pubmed/9612078

Parameters:

Name Description
KFru16P2=1.0 mM; KADP=0.1 mM; Vlower=20.0 mM per min Reaction: Fru16P2 + ADP => ATP, Rate Law: cell*Vlower*Fru16P2*ADP/(KFru16P2*KADP)/((1+Fru16P2/KFru16P2)*(1+ADP/KADP))
VATPase=68.0 mM per min; KATP=3.0 mM Reaction: ATP => ADP, Rate Law: cell*VATPase*ATP/(KATP+ATP)
wild_type=1.0 dimensionless; VHK=68.0 mM per min; KATP=0.15 mM; KGlc=1.0 mM; KiTre6P=4.422 mM Reaction: Glc + ATP => HMP; Tre6P, Rate Law: cell*VHK*Glc*ATP/(KGlc*KATP)/((1+Glc/KGlc+wild_type*Tre6P/KiTre6P)*(1+ATP/KATP))
L = NaN; gR = 10.0; lambda1 = NaN; lambda2 = NaN; T = NaN; VPFK=30.0 mM per min; R = NaN Reaction: HMP + ATP => Fru16P2, Rate Law: cell*VPFK*gR*lambda1*lambda2*R/(R^2+L*T^2)

States:

Name Description
ATP [ATP]
Tre6P [alpha,alpha-trehalose 6-phosphate]
HMP [beta-D-fructofuranose 6-phosphate; alpha-D-glucose 6-phosphate]
Fru16P2 [keto-D-fructose 1,6-bisphosphate]
Glc [alpha-D-glucose]
ADP [ADP]

Observables: none

BIOMD0000000064 @ v0.0.1

**Can yeast glycolysis be understood in terms of in vitro kinetics of the constituent enzymes? Testing biochemistry.**…

This paper examines whether the in vivo behavior of yeast glycolysis can be understood in terms of the in vitro kinetic properties of the constituent enzymes. In nongrowing, anaerobic, compressed Saccharomyces cerevisiae the values of the kinetic parameters of most glycolytic enzymes were determined. For the other enzymes appropriate literature values were collected. By inserting these values into a kinetic model for glycolysis, fluxes and metabolites were calculated. Under the same conditions fluxes and metabolite levels were measured. In our first model, branch reactions were ignored. This model failed to reach the stable steady state that was observed in the experimental flux measurements. Introduction of branches towards trehalose, glycogen, glycerol and succinate did allow such a steady state. The predictions of this branched model were compared with the empirical behavior. Half of the enzymes matched their predicted flux in vivo within a factor of 2. For the other enzymes it was calculated what deviation between in vivo and in vitro kinetic characteristics could explain the discrepancy between in vitro rate and in vivo flux. link: http://identifiers.org/pubmed/10951190

Parameters:

Name Description
KTREHALOSE=2.4 mMpermin Reaction: G6P + P => Trh, Rate Law: cytosol*KTREHALOSE
KeqGLT=1.0 mM; KmGLTGLCo=1.1918 mM; VmGLT=97.264 mmolepermin; KmGLTGLCi=1.1918 mM Reaction: GLCo => GLCi, Rate Law: VmGLT/KmGLTGLCo*(GLCo-GLCi/KeqGLT)/(1+GLCo/KmGLTGLCo+GLCi/KmGLTGLCi+0.91*GLCo*GLCi/(KmGLTGLCo*KmGLTGLCi))
KSUCC=21.4 Reaction: ACE + NAD + P => NADH + SUCC, Rate Law: cytosol*KSUCC*ACE
KmG3PDHGLY=1.0 mM; KeqG3PDH=4300.0 dimensionless; KmG3PDHDHAP=0.4 mM; KmG3PDHNADH=0.023 mM; KeqTPI = 0.045 dimensionless; KmG3PDHNAD=0.93 mM; VmG3PDH=70.15 mMpermin Reaction: TRIO + NADH => NAD + GLY, Rate Law: cytosol*VmG3PDH/(KmG3PDHDHAP*KmG3PDHNADH)*(1/(1+KeqTPI)*TRIO*NADH-GLY*NAD/KeqG3PDH)/((1+1/(1+KeqTPI)*TRIO/KmG3PDHDHAP+GLY/KmG3PDHGLY)*(1+NADH/KmG3PDHNADH+NAD/KmG3PDHNAD))
KmPGIG6P_2=1.4 mM; VmPGI_2=339.677 mMpermin; KmPGIF6P_2=0.3 mM; KeqPGI_2=0.314 dimensionless Reaction: G6P => F6P, Rate Law: cytosol*VmPGI_2/KmPGIG6P_2*(G6P-F6P/KeqPGI_2)/(1+G6P/KmPGIG6P_2+F6P/KmPGIF6P_2)
KmGLKADP=0.23 mM; KmGLKGLCi=0.08 mM; VmGLK=226.452 mMpermin; KmGLKG6P=30.0 mM; KeqGLK=3800.0 dimensionless; KmGLKATP=0.15 mM Reaction: GLCi + P => G6P; ATP, ADP, Rate Law: cytosol*VmGLK/(KmGLKGLCi*KmGLKATP)*(GLCi*ATP-G6P*ADP/KeqGLK)/((1+GLCi/KmGLKGLCi+G6P/KmGLKG6P)*(1+ATP/KmGLKATP+ADP/KmGLKADP))
KATPASE=33.7 permin Reaction: P => ; ATP, Rate Law: cytosol*KATPASE*ATP
KPFKF26BP = 6.82E-4 mM; KmPFKF6P = 0.1 mM; CiPFKATP = 100.0 dimensionless; CPFKATP = 3.0 dimensionless; CPFKAMP = 0.0845 dimensionless; KPFKF16BP = 0.111 mM; Lzero = 0.66 dimensionless; VmPFK=182.903 mMpermin; CPFKF26BP = 0.0174 dimensionless; CPFKF16BP = 0.397 dimensionless; KPFKAMP = 0.0995 mM; gR = 5.12 dimensionless; KiPFKATP = 0.65 mM; KmPFKATP = 0.71 mM Reaction: F6P + P => F16P; AMP, ATP, F26BP, Rate Law: cytosol*VmPFK*gR*F6P/KmPFKF6P*ATP/KmPFKATP*R_PFK(KmPFKF6P, KmPFKATP, gR, ATP, F6P)/(R_PFK(KmPFKF6P, KmPFKATP, gR, ATP, F6P)^2+L_PFK(Lzero, CiPFKATP, KiPFKATP, CPFKAMP, KPFKAMP, CPFKF26BP, KPFKF26BP, CPFKF16BP, KPFKF16BP, ATP, AMP, F16P, F26BP)*T_PFK(CPFKATP, KmPFKATP, ATP)^2)
VmGAPDHf=1184.52 mMpermin; VmGAPDHr=6549.8 mMpermin; KmGAPDHNAD=0.09 mM; KmGAPDHBPG=0.0098 mM; KmGAPDHGAP=0.21 mM; KeqTPI = 0.045 dimensionless; KmGAPDHNADH=0.06 mM Reaction: TRIO + NAD => BPG + NADH, Rate Law: cytosol*(VmGAPDHf*KeqTPI/(1+KeqTPI)*TRIO*NAD/(KmGAPDHGAP*KmGAPDHNAD)-VmGAPDHr*BPG*NADH/(KmGAPDHBPG*KmGAPDHNADH))/((1+KeqTPI/(1+KeqTPI)*TRIO/KmGAPDHGAP+BPG/KmGAPDHBPG)*(1+NAD/KmGAPDHNAD+NADH/KmGAPDHNADH))
KeqENO=6.7 dimensionless; KmENOP2G=0.04 mM; KmENOPEP=0.5 mM; VmENO=365.806 mMpermin Reaction: P2G => PEP, Rate Law: cytosol*VmENO/KmENOP2G*(P2G-PEP/KeqENO)/(1+P2G/KmENOP2G+PEP/KmENOPEP)
KmPGMP3G=1.2 mM; KeqPGM=0.19 dimensionless; VmPGM=2525.81 mMpermin; KmPGMP2G=0.08 mM Reaction: P3G => P2G, Rate Law: cytosol*VmPGM/KmPGMP3G*(P3G-P2G/KeqPGM)/(1+P3G/KmPGMP3G+P2G/KmPGMP2G)
KGLYCOGEN_3=6.0 mMpermin Reaction: G6P + P => Glyc, Rate Law: cytosol*KGLYCOGEN_3
KmPGKBPG=0.003 mM; KmPGKATP=0.3 mM; KeqPGK=3200.0 dimensionless; VmPGK=1306.45 mMpermin; KmPGKP3G=0.53 mM; KmPGKADP=0.2 mM Reaction: BPG => P3G + P; ATP, ADP, Rate Law: cytosol*VmPGK/(KmPGKP3G*KmPGKATP)*(KeqPGK*BPG*ADP-P3G*ATP)/((1+BPG/KmPGKBPG+P3G/KmPGKP3G)*(1+ATP/KmPGKATP+ADP/KmPGKADP))
KmALDGAP=2.0 mM; VmALD=322.258 mMpermin; KeqALD=0.069 dimensionless; KmALDDHAP=2.4 mM; KeqTPI = 0.045 dimensionless; KmALDGAPi=10.0 mM; KmALDF16P=0.3 mM Reaction: F16P => TRIO, Rate Law: cytosol*VmALD/KmALDF16P*(F16P-KeqTPI/(1+KeqTPI)*TRIO*1/(1+KeqTPI)*TRIO/KeqALD)/(1+F16P/KmALDF16P+KeqTPI/(1+KeqTPI)*TRIO/KmALDGAP+1/(1+KeqTPI)*TRIO/KmALDDHAP+KeqTPI/(1+KeqTPI)*TRIO*1/(1+KeqTPI)*TRIO/(KmALDGAP*KmALDDHAP)+F16P*KeqTPI/(1+KeqTPI)*TRIO/(KmALDGAPi*KmALDF16P))
KeqAK = 0.45 dimensionless Reaction: ADP = (SUM_P-(P^2*(1-4*KeqAK)+2*SUM_P*P*(4*KeqAK-1)+SUM_P^2)^0.5)/(1-4*KeqAK), Rate Law: missing
VmPDC=174.194 mMpermin; KmPDCPYR=4.33 mM; nPDC=1.9 dimensionless Reaction: PYR => ACE + CO2, Rate Law: cytosol*VmPDC*PYR^nPDC/KmPDCPYR^nPDC/(1+PYR^nPDC/KmPDCPYR^nPDC)
KmADHNAD=0.17 mM; KiADHETOH=90.0 mM; KiADHNADH=0.031 mM; KiADHACE=1.1 mM; KmADHETOH=17.0 mM; KeqADH=6.9E-5 dimensionless; KmADHNADH=0.11 mM; KiADHNAD=0.92 mM; VmADH=810.0 mMpermin; KmADHACE=1.11 mM Reaction: ACE + NADH => NAD + ETOH, Rate Law: (-cytosol)*VmADH/(KiADHNAD*KmADHETOH)*(NAD*ETOH-NADH*ACE/KeqADH)/(1+NAD/KiADHNAD+KmADHNAD*ETOH/(KiADHNAD*KmADHETOH)+KmADHNADH*ACE/(KiADHNADH*KmADHACE)+NADH/KiADHNADH+NAD*ETOH/(KiADHNAD*KmADHETOH)+KmADHNADH*NAD*ACE/(KiADHNAD*KiADHNADH*KmADHACE)+KmADHNAD*ETOH*NADH/(KiADHNAD*KmADHETOH*KiADHNADH)+NADH*ACE/(KiADHNADH*KmADHACE)+NAD*ETOH*ACE/(KiADHNAD*KmADHETOH*KiADHACE)+ETOH*NADH*ACE/(KiADHETOH*KiADHNADH*KmADHACE))
VmPYK=1088.71 mMpermin; KmPYKATP=1.5 mM; KeqPYK=6500.0 dimensionless; KmPYKPYR=21.0 mM; KmPYKADP=0.53 mM; KmPYKPEP=0.14 mM Reaction: PEP => PYR + P; ATP, ADP, Rate Law: cytosol*VmPYK/(KmPYKPEP*KmPYKADP)*(PEP*ADP-PYR*ATP/KeqPYK)/((1+PEP/KmPYKPEP+PYR/KmPYKPYR)*(1+ATP/KmPYKATP+ADP/KmPYKADP))

States:

Name Description
ATP [ATP; ATP]
P [ADP; ATP; ADP; ADP; ATP]
Trh [trehalose; alpha,alpha-Trehalose]
GLY [glycerol; Glycerol]
AMP [AMP; AMP]
F16P [keto-D-fructose 1,6-bisphosphate; D-Fructose 1,6-bisphosphate]
GLCi [glucose; C00293]
P2G [2-phospho-D-glyceric acid; 2-Phospho-D-glycerate]
P3G [3-phospho-D-glyceric acid; 3-Phospho-D-glycerate]
GLCo [glucose; C00293]
NADH [NADH; NADH]
SUCC [succinate(2-)]
PYR [pyruvate; Pyruvate; pyruvic acid]
TRIO [dihydroxyacetone phosphate; D-glyceraldehyde 3-phosphate; D-Glyceraldehyde 3-phosphate; Glycerone phosphate; D-glyceraldehyde 3-phosphate]
Glyc [glycogen; Glycogen]
F6P [keto-D-fructose 6-phosphate; beta-D-Fructose 6-phosphate]
CO2 [carbon dioxide; CO2]
BPG [3-phospho-D-glyceroyl dihydrogen phosphate; 3-Phospho-D-glyceroyl phosphate]
G6P [alpha-D-glucose 6-phosphate; alpha-D-Glucose 6-phosphate]
PEP [Phosphoenolpyruvate; phosphoenolpyruvate; phosphoenolpyruvate]
NAD [NAD(+); NAD+]
ETOH [ethanol; Ethanol]
ADP [ADP; ADP]
ACE [acetaldehyde; Acetaldehyde]

Observables: none

Teusink2006 - Genome-scale metabolic network of Lactobacillus plantarum (iBT721)This model is described in the article:…

A genome-scale metabolic model of the lactic acid bacterium Lactobacillus plantarum WCFS1 was constructed based on genomic content and experimental data. The complete model includes 721 genes, 643 reactions, and 531 metabolites. Different stoichiometric modeling techniques were used for interpretation of complex fermentation data, as L. plantarum is adapted to nutrient-rich environments and only grows in media supplemented with vitamins and amino acids. (i) Based on experimental input and output fluxes, maximal ATP production was estimated and related to growth rate. (ii) Optimization of ATP production further identified amino acid catabolic pathways that were not previously associated with free-energy metabolism. (iii) Genome-scale elementary flux mode analysis identified 28 potential futile cycles. (iv) Flux variability analysis supplemented the elementary mode analysis in identifying parallel pathways, e.g. pathways with identical end products but different co-factor usage. Strongly increased flexibility in the metabolic network was observed when strict coupling between catabolic ATP production and anabolic consumption was relaxed. These results illustrate how a genome-scale metabolic model and associated constraint-based modeling techniques can be used to analyze the physiology of growth on a complex medium rather than a minimal salts medium. However, optimization of biomass formation using the Flux Balance Analysis approach, reported to successfully predict growth rate and by product formation in Escherichia coli and Saccharomyces cerevisiae, predicted too high biomass yields that were incompatible with the observed lactate production. The reason is that this approach assumes optimal efficiency of substrate to biomass conversion, and can therefore not predict the metabolically inefficient lactate formation. link: http://identifiers.org/pubmed/17062565

Parameters: none

States: none

Observables: none

Tham2008 - PDmodel, Tumour shrinkage by gemcitabine and carboplatin This model is described in the article: [A pharmaco…

PURPOSE: This tumor response pharmacodynamic model aims to describe primary lesion shrinkage in non-small cell lung cancer over time and determine if concentration-based exposure metrics for gemcitabine or that of its metabolites, 2',2'-difluorodeoxyuridine or gemcitabine triphosphate, are better than gemcitabine dose for prediction of individual response. EXPERIMENTAL DESIGN: Gemcitabine was given thrice weekly on days 1 and 8 in combination with carboplatin, which was given only on day 1 of every cycle. Gemcitabine amount in the body and area under the concentration-time curves of plasma gemcitabine, 2',2'-difluorodeoxyuridine, and intracellular gemcitabine triphosphate in white cells were compared to determine which best describes tumor shrinkage over time. Tumor growth kinetics were described using a Gompertz-like model. RESULTS: The apparent half-life for the effect of gemcitabine was 7.67 weeks. The tumor turnover time constant was 21.8 week.cm. Baseline tumor size and gemcitabine amount in the body to attain 50% of tumor shrinkage were estimated to be 6.66 cm and 10,600 mg. There was no evidence of relapse during treatment. CONCLUSIONS: Concentration-based exposure metrics for gemcitabine and its metabolites were no better than gemcitabine amount in predicting tumor shrinkage in primary lung cancer lesions. Gemcitabine dose-based models did marginally better than treatment-based models that ignored doses of drug administered to patients. Modeling tumor shrinkage in primary lesions can be used to quantify individual sensitivity and response to antitumor effects of anticancer drugs. link: http://identifiers.org/pubmed/18594002

Parameters:

Name Description
Keq = NaN per_week; Exposure = NaN mg Reaction: Ce = Exposure/1-Ce*Keq, Rate Law: Exposure/1-Ce*Keq

States:

Name Description
Ce Ce

Observables: none

Its a Deterministic ODE model showcasing mechanism of PDL1 induced TCR and CD38 signalling inhibition. The model also co…

Programmed cell death-1 (PD-1) is an inhibitory immune checkpoint receptor that negatively regulates the functioning of T cell. Although the direct targets of PD-1 were not identified, its inhibitory action on the TCR signaling pathway was known much earlier. Recent experiments suggest that the PD-1 inhibits the TCR and CD28 signaling pathways at a very early stage ─ at the level of phosphorylation of the cytoplasmic domain of TCR and CD28 receptors. Here, we develop a mathematical model to investigate the influence of inhibitory effect of PD-1 on the activation of early TCR and CD28 signaling molecules. Proposed model recaptures several quantitative experimental observations of PD-1 mediated inhibition. Model simulations show that PD-1 imposes a net inhibitory effect on the Lck kinase. Further, the inhibitory effect of PD-1 on the activation of TCR signaling molecules such as Zap70 and SLP76 is significantly enhanced by the PD-1 mediated inhibition of Lck. These results suggest a critical role for Lck as a mediator for PD-1 induced inhibition of TCR signaling network. Multi parametric sensitivity analysis explores the effect of parameter uncertainty on model simulations. link: http://identifiers.org/pubmed/30356330

Parameters:

Name Description
Kpa_i = 1.0E-6 1/s Reaction: LCKi => LCKya, Rate Law: Cell*Kpa_i*LCKi
Kpi_i = 6.0E-7 1/s Reaction: LCKi => LCKyi, Rate Law: Cell*Kpi_i*LCKi
Kdpi_yi = 0.0 l/(nmol*s) Reaction: LCKyi => LCKi; CPactive, Rate Law: Cell*Kdpi_yi*CPactive*LCKyi
Kd_slp = 0.12 1/s; Ka_slp = 0.015 l/(nmol*s) Reaction: GADSa + SLP76 => SLP76i, Rate Law: Cell*(Ka_slp*GADSa*SLP76-Kd_slp*SLP76i)
Ka_shp = 0.0065 l/(nmol*s); Kd1_shp = 10.0 1/s Reaction: PD1p1 + SHP2 => CP1, Rate Law: Cell*(Ka_shp*PD1p1*SHP2-Kd1_shp*CP1)
Ka_zap = 7.0E-5 l/(nmol*s); Kd_zap = 0.001 1/s Reaction: CD3a + ZAP70 => ZAP70i, Rate Law: Cell*(Ka_zap*CD3a*ZAP70-Kd_zap*ZAP70i)
Ka_gads = 5.0E-4 l/(nmol*s); Kd_gads = 1.5 1/s Reaction: LATa + GADS => GADSa, Rate Law: Cell*(Ka_gads*LATa*GADS-Kd_gads*GADSa)
Kd2_shp = 1.0 1/s Reaction: CP2 => SHP2 + PD1p1, Rate Law: Cell*Kd2_shp*CP2
Kdp_cd28 = 5.0 1/s; KMdp_cd28 = 500.0 nmol/l Reaction: CD28a => CD28i; CPactive, Rate Law: Cell*Kdp_cd28*CPactive*CD28a/(KMdp_cd28+CD28a)
Kdpi_yiya = 0.0 l/(nmol*s) Reaction: LCKyiya => LCKya; CPactive, Rate Law: Cell*Kdpi_yiya*CPactive*LCKyiya
Kdpa_pi = 0.0 l/(nmol*s) Reaction: LCKpi => LCKyi; CPactive, Rate Law: Cell*Kdpa_pi*CPactive*LCKpi
KMp_pd1 = 1000.0 nmol/l; Kp_pd1 = 7.5 1/s Reaction: PD1p1 => PD1p2; LCKactive, Rate Law: Cell*Kp_pd1*LCKactive*PD1p1/(KMp_pd1+PD1p1)
Kp_slp = 0.003 l/(nmol*s) Reaction: SLP76i => SLP76a; ZAP70a2, Rate Law: Cell*Kp_slp*ZAP70a2*SLP76i
Kp2_zap = 3.0E-5 l/(nmol*s) Reaction: ZAP70a1 => ZAP70a2; LCKactive, Rate Law: Cell*Kp2_zap*LCKactive*ZAP70a1
Kp_cd28 = 1.0 1/s; KMp_cd28 = 1000.0 nmol/l Reaction: CD28i => CD28a; LCKactive, Rate Law: Cell*Kp_cd28*LCKactive*CD28i/(KMp_cd28+CD28i)
Kpa_yi = 7.5E-4 1/s Reaction: LCKyi => LCKpi, Rate Law: Cell*Kpa_yi*LCKyi
KMp_cd3 = 80.0 nmol/l; Kp_cd3 = 3.29 1/s Reaction: CD3i => CD3a; LCKactive, Rate Law: Cell*Kp_cd3*LCKactive*CD3i/(KMp_cd3+CD3i)
KMdp_cd3 = 150.0 nmol/l; Kdp_cd3 = 5.0 1/s Reaction: CD3a => CD3i; CPactive, Rate Law: Cell*Kdp_cd3*CPactive*CD3a/(KMdp_cd3+CD3a)
Kdp_cp2 = 5.0E-8 1/s Reaction: CP2 => CP1, Rate Law: Cell*Kdp_cp2*CP2
Kpi_ya = 6.0E-5 1/s Reaction: LCKya => LCKyiya, Rate Law: Cell*Kpi_ya*LCKya
KMp_pd1 = 1000.0 nmol/l; Kp_pd1 = 7.5 1/s; k = 41.0 Reaction: PD1 => PD1p1; LCKactive, PD1p1, PD1p2, LCKt, Rate Law: Cell*Kp_pd1*LCKactive*PD1/(KMp_pd1+PD1)*(1-(PD1p1+PD1p2)/(LCKt*k))
Kp1_zap = 2.0E-6 l/(nmol*s) Reaction: ZAP70i => ZAP70a1; LCKactive, Rate Law: Cell*Kp1_zap*LCKactive*ZAP70i
Kdpa_ya = 0.0 l/(nmol*s) Reaction: LCKya => LCKi; CPactive, Rate Law: Cell*Kdpa_ya*CPactive*LCKya
Ka_pi3k = 1.4E-6 l/(nmol*s); Kd_pi3k = 9.0E-4 1/s Reaction: CD28a + PI3K => PI3Kb, Rate Law: Cell*(Ka_pi3k*CD28a*PI3K-Kd_pi3k*PI3Kb)
Kp_lat = 0.001 l/(nmol*s) Reaction: LATi => LATa; ZAP70a2, Rate Law: Cell*Kp_lat*ZAP70a2*LATi

States:

Name Description
CD3a [T-cell surface glycoprotein CD3 zeta chain; 0002220]
SHP2 [Tyrosine-protein phosphatase non-receptor type 11]
PD1p1 [Programmed cell death 1 ligand 1; 0002220]
LCKinactive [Tyrosine-protein kinase Lck; 0002355]
CP1 [Tyrosine-protein phosphatase non-receptor type 11; Programmed cell death 1 ligand 1; 0002220]
SLP76 [Lymphocyte cytosolic protein 2]
GADSt [GRB2-related adapter protein; Growth factor receptor-bound protein 2]
CD3t [T-cell surface glycoprotein CD3 zeta chain]
CD28a [T-cell-specific surface glycoprotein CD28; 0002220]
CD28i [T-cell-specific surface glycoprotein CD28; 0002355]
GADSa [Linker for activation of T-cells family member 1; Growth factor receptor-bound protein 2; GRB2-related adapter protein]
ZAP70 [Tyrosine-protein kinase ZAP-70]
PI3Kt [Phosphatidylinositol 3-kinase regulatory subunit alpha; Phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit alpha isoform]
ZAP70t [Tyrosine-protein kinase ZAP-70]
SLP76t [Lymphocyte cytosolic protein 2]
LCKpi [Tyrosine-protein kinase Lck; 0002220]
ZAP70a1 [Tyrosine-protein kinase ZAP-70; 0002220]
LATa [Linker for activation of T-cells family member 1; 0002220]
LCKya [Tyrosine-protein kinase Lck; 0002220]
GADS [Growth factor receptor-bound protein 2; GRB2-related adapter protein]
PI3K [Phosphatidylinositol 3-kinase regulatory subunit alpha; Phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit alpha isoform]
SLP76i [Lymphocyte cytosolic protein 2]
CD28t [T-cell-specific surface glycoprotein CD28]
LCKactive [Tyrosine-protein kinase Lck; 0002220]
LCKyiya [Tyrosine-protein kinase Lck; 0002220]
CPactive [Tyrosine-protein phosphatase non-receptor type 11; Programmed cell death 1 ligand 1; 0002220]
LCKyi [Tyrosine-protein kinase Lck; 0002355]
SLP76a [Lymphocyte cytosolic protein 2]
LCKt [Tyrosine-protein kinase Lck]
PD1p2 [Programmed cell death 1 ligand 1; 0002220]
LATt [Linker for activation of T-cells family member 1]
ZAP70i [T-cell surface glycoprotein CD3 zeta chain; Tyrosine-protein kinase ZAP-70; 0002355]
ZAP70a2 [Tyrosine-protein kinase ZAP-70; 0002220]
CD3i [T-cell surface glycoprotein CD3 zeta chain; 0002355]
LATi [Linker for activation of T-cells family member 1; 0002355]
PI3Kb [Phosphatidylinositol 3-kinase regulatory subunit alpha; Phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit alpha isoform; 0002220]
CP2 [Programmed cell death 1 ligand 1; Tyrosine-protein phosphatase non-receptor type 11; 0002220]
PD1 [Programmed cell death 1 ligand 1; 0002355]
LCKi [Tyrosine-protein kinase Lck; 0002355]

Observables: none

Henry2016 Folate pathway model with induced PanB reactionThis model is described in the article: [Experimental and Meta…

Tetrahydrofolate (THF) and its one-carbon derivatives, collectively termed folates, are essential cofactors, but are inherently unstable. While it is clear that chemical oxidation can cleave folates or damage their pterin precursors, very little is known about enzymatic damage to these molecules or about whether the folate biosynthesis pathway responds adaptively to damage to its end-products. The presence of a duplication of the gene encoding the folate biosynthesis enzyme 6-hydroxymethyl-7,8-dihydropterin pyrophosphokinase (FolK) in many sequenced bacterial genomes combined with a strong chromosomal clustering of the folK gene with panB, encoding the 5,10-methylene-THF-dependent enzyme ketopantoate hydroxymethyltransferase, led us to infer that PanB has a side activity that cleaves 5,10-methylene-THF, yielding a pterin product that is recycled by FolK. Genetic and metabolic analyses of Escherichia coli strains showed that overexpression of PanB leads to accumulation of the likely folate cleavage product 6-hydroxymethylpterin and other pterins in cells and medium, and-unexpectedly-to a 46% increase in total folate content. In silico modeling of the folate biosynthesis pathway showed that these observations are consistent with the in vivo cleavage of 5,10-methylene-THF by a side-activity of PanB, with FolK-mediated recycling of the pterin cleavage product, and with regulation of folate biosynthesis by folates or their damage products. link: http://identifiers.org/pubmed/27065985

Parameters:

Name Description
k1=10029.0 Reaction: L_Glutamate + ATP + H2_Pteroate => DHF + ADP + Phosphate, Rate Law: compartment*k1*L_Glutamate*ATP*H2_Pteroate
k1=8000.0 Reaction: p_ABA + H2_HMPterinPP => PPi + H2_Pteroate, Rate Law: compartment*k1*p_ABA*H2_HMPterinPP
v=2.35E-7 Reaction: => H2_HMPt, Rate Law: compartment*v
k1=0.008 Reaction: CH2_THF => H2_HMPt + p_ABA, Rate Law: compartment*k1*CH2_THF
k1=86170.0 Reaction: DHF + NADPH => NADP + THF, Rate Law: compartment*k1*DHF*NADPH
k1=4080.0; k2=2000.0 Reaction: THF + L_serine => CH2_THF + Glycine, Rate Law: compartment*(k1*THF*L_serine-k2*CH2_THF*Glycine)
Km=5.921E-5; V=1.726E-7 Reaction: CH2_THF =>, Rate Law: compartment*V*CH2_THF/(Km+CH2_THF)
k1=24.8 Reaction: ATP + H2_HMPt => AMP + H2_HMPterinPP, Rate Law: compartment*k1*ATP*H2_HMPt
V=1.243E-7; Km=1.571E-4 Reaction: THF =>, Rate Law: compartment*V*THF/(Km+THF)

States:

Name Description
H2 Pteroate [Dihydropteroate; pteroate]
ATP [ATP; ATP]
NADP [NADP; NADP+]
THF [tetrahydrofolate; Tetrahydrofolate]
AMP [AMP; AMP]
DHF [Dihydrofolate; dihydrofolate(2-)]
Phosphate [phosphate]
H2 HMPterinPP [6-(Hydroxymethyl)-7,8-dihydropterin; phosphorylation; dihydropterin]
NADPH [NADPH; NADPH]
L Glutamate [L-Glutamate; glutamate(2-)]
Glycine [glycine; Glycine]
ADP [ADP; ADP]
PPi [Diphosphate]
CH2 THF [5,10-methylenetetrahydrofolate(2-); 5,10-Methylenetetrahydrofolate]
L serine [serine; L-Serine]
H2 HMPt [dihydropterin; 6-(Hydroxymethyl)-7,8-dihydropterin]
p ABA [4-Aminobenzoate; 4-aminobenzoate]

Observables: none

Henry2016 Folate pathway model with induced PanB reactionThis model is described in the article: [Experimental and Meta…

Tetrahydrofolate (THF) and its one-carbon derivatives, collectively termed folates, are essential cofactors, but are inherently unstable. While it is clear that chemical oxidation can cleave folates or damage their pterin precursors, very little is known about enzymatic damage to these molecules or about whether the folate biosynthesis pathway responds adaptively to damage to its end-products. The presence of a duplication of the gene encoding the folate biosynthesis enzyme 6-hydroxymethyl-7,8-dihydropterin pyrophosphokinase (FolK) in many sequenced bacterial genomes combined with a strong chromosomal clustering of the folK gene with panB, encoding the 5,10-methylene-THF-dependent enzyme ketopantoate hydroxymethyltransferase, led us to infer that PanB has a side activity that cleaves 5,10-methylene-THF, yielding a pterin product that is recycled by FolK. Genetic and metabolic analyses of Escherichia coli strains showed that overexpression of PanB leads to accumulation of the likely folate cleavage product 6-hydroxymethylpterin and other pterins in cells and medium, and-unexpectedly-to a 46% increase in total folate content. In silico modeling of the folate biosynthesis pathway showed that these observations are consistent with the in vivo cleavage of 5,10-methylene-THF by a side-activity of PanB, with FolK-mediated recycling of the pterin cleavage product, and with regulation of folate biosynthesis by folates or their damage products. link: http://identifiers.org/pubmed/27065985

Parameters:

Name Description
k1=0.0121 Reaction: CH2_THF => H2_HMPt + p_ABA, Rate Law: compartment*k1*CH2_THF
k1=3602.18 Reaction: L_Glutamate + ATP + H2_Pteroate => DHF + ADP + Phosphate, Rate Law: compartment*k1*L_Glutamate*ATP*H2_Pteroate
k1=4000.0 Reaction: p_ABA + H2_HMPterinPP => PPi + H2_Pteroate, Rate Law: compartment*k1*p_ABA*H2_HMPterinPP
k1=31170.0 Reaction: DHF + NADPH => NADP + THF, Rate Law: compartment*k1*DHF*NADPH
k1=4080.0; k2=2000.0 Reaction: THF + L_serine => CH2_THF + Glycine, Rate Law: compartment*(k1*THF*L_serine-k2*CH2_THF*Glycine)
v=1.66E-7 Reaction: => p_ABA, Rate Law: compartment*v
Km=5.921E-5; V=1.726E-7 Reaction: CH2_THF =>, Rate Law: compartment*V*CH2_THF/(Km+CH2_THF)
k1=15.8 Reaction: ATP + H2_HMPt => AMP + H2_HMPterinPP, Rate Law: compartment*k1*ATP*H2_HMPt
V=1.243E-7; Km=1.571E-4 Reaction: THF =>, Rate Law: compartment*V*THF/(Km+THF)

States:

Name Description
H2 Pteroate [pteroate; Dihydropteroate]
ATP [ATP; ATP]
NADP [NADP+; NADP]
AMP [AMP; AMP]
THF [Tetrahydrofolate; tetrahydrofolate]
DHF [dihydrofolate(2-); Dihydrofolate]
Phosphate [phosphate]
H2 HMPterinPP [6-(Hydroxymethyl)-7,8-dihydropterin; phosphorylation; dihydropterin]
NADPH [NADPH; NADPH]
L Glutamate [L-Glutamate; glutamate(2-)]
Glycine [Glycine; glycine]
ADP [ADP; ADP]
PPi [Diphosphate]
CH2 THF [5,10-Methylenetetrahydrofolate; 5,10-methylenetetrahydrofolate(2-)]
L serine [serine; L-Serine]
H2 HMPt [6-(Hydroxymethyl)-7,8-dihydropterin; dihydropterin]
p ABA [4-aminobenzoate; 4-Aminobenzoate]

Observables: none

Thiaville2016 - Wild type folate pathway model with proposed PanB reactionThis is a wild type E. coli model, and is one…

Tetrahydrofolate (THF) and its one-carbon derivatives, collectively termed folates, are essential cofactors, but are inherently unstable. While it is clear that chemical oxidation can cleave folates or damage their pterin precursors, very little is known about enzymatic damage to these molecules or about whether the folate biosynthesis pathway responds adaptively to damage to its end-products. The presence of a duplication of the gene encoding the folate biosynthesis enzyme 6-hydroxymethyl-7,8-dihydropterin pyrophosphokinase (FolK) in many sequenced bacterial genomes combined with a strong chromosomal clustering of the folK gene with panB, encoding the 5,10-methylene-THF-dependent enzyme ketopantoate hydroxymethyltransferase, led us to infer that PanB has a side activity that cleaves 5,10-methylene-THF, yielding a pterin product that is recycled by FolK. Genetic and metabolic analyses of Escherichia coli strains showed that overexpression of PanB leads to accumulation of the likely folate cleavage product 6-hydroxymethylpterin and other pterins in cells and medium, and-unexpectedly-to a 46% increase in total folate content. In silico modeling of the folate biosynthesis pathway showed that these observations are consistent with the in vivo cleavage of 5,10-methylene-THF by a side-activity of PanB, with FolK-mediated recycling of the pterin cleavage product, and with regulation of folate biosynthesis by folates or their damage products. link: http://identifiers.org/pubmed/27065985

Parameters:

Name Description
k1=15.8 Reaction: ATP + H2_HMPt => AMP + H2_HMPterinPP, Rate Law: compartment*k1*ATP*H2_HMPt
k1=0.004 Reaction: CH2_THF => H2_HMPt + p_ABA, Rate Law: compartment*k1*CH2_THF
k1=4080.0; k2=2000.0 Reaction: THF + L_serine => CH2_THF + Glycine, Rate Law: compartment*(k1*THF*L_serine-k2*CH2_THF*Glycine)
k1=4000.0 Reaction: p_ABA + H2_HMPterinPP => PPi + H2_Pteroate, Rate Law: compartment*k1*p_ABA*H2_HMPterinPP
Km=5.921E-5; V=1.726E-7 Reaction: CH2_THF =>, Rate Law: compartment*V*CH2_THF/(Km+CH2_THF)
k1=6184.0 Reaction: L_Glutamate + ATP + H2_Pteroate => DHF + ADP + Phosphate, Rate Law: compartment*k1*L_Glutamate*ATP*H2_Pteroate
k1=31170.0 Reaction: DHF + NADPH => NADP + THF, Rate Law: compartment*k1*DHF*NADPH
v=1.66E-7 Reaction: => H2_HMPt, Rate Law: compartment*v
V=1.243E-7; Km=1.571E-4 Reaction: THF =>, Rate Law: compartment*V*THF/(Km+THF)

States:

Name Description
H2 Pteroate H2-Pteroate
ATP ATP
NADP NADP
AMP AMP
THF THF
DHF DHF
Phosphate Phosphate
H2 HMPterinPP H2-HMPterinPP
NADPH NADPH
L Glutamate L-Glutamate
Glycine Glycine
ADP ADP
PPi PPi
CH2 THF CH2-THF
p ABA p-ABA
H2 HMPt H2-HMPt
L serine L-serine

Observables: none

Thiele2005 - Genome-scale metabolic network of Helicobacter pylori (iIT341)This model is described in the article: [Exp…

Helicobacter pylori is a human gastric pathogen infecting almost half of the world population. Herein, we present an updated version of the metabolic reconstruction of H. pylori strain 26695 based on the revised genome annotation and new experimental data. This reconstruction, iIT341 GSM/GPR, represents a detailed review of the current literature about H. pylori as it integrates biochemical and genomic data in a comprehensive framework. In total, it accounts for 341 metabolic genes, 476 intracellular reactions, 78 exchange reactions, and 485 metabolites. Novel features of iIT341 GSM/GPR include (i) gene-protein-reaction associations, (ii) elementally and charge-balanced reactions, (iii) more accurate descriptions of isoprenoid and lipopolysaccharide metabolism, and (iv) quantitative assessments of the supporting data for each reaction. This metabolic reconstruction was used to carry out in silico deletion studies to identify essential and conditionally essential genes in H. pylori. A total of 128 essential and 75 conditionally essential metabolic genes were identified. Predicted growth phenotypes of single knockouts were validated using published experimental data. In addition, in silico double-deletion studies identified a total of 47 synthetic lethal mutants involving 67 different metabolic genes in rich medium. link: http://identifiers.org/pubmed/16077130

Parameters: none

States: none

Observables: none

Thiele2011 - Genome-scale metabolic network of Salmonella Typhimurium (STM_v1_0)This model is described in the article:…

Metabolic reconstructions (MRs) are common denominators in systems biology and represent biochemical, genetic, and genomic (BiGG) knowledge-bases for target organisms by capturing currently available information in a consistent, structured manner. Salmonella enterica subspecies I serovar Typhimurium is a human pathogen, causes various diseases and its increasing antibiotic resistance poses a public health problem.Here, we describe a community-driven effort, in which more than 20 experts in S. Typhimurium biology and systems biology collaborated to reconcile and expand the S. Typhimurium BiGG knowledge-base. The consensus MR was obtained starting from two independently developed MRs for S. Typhimurium. Key results of this reconstruction jamboree include i) development and implementation of a community-based workflow for MR annotation and reconciliation; ii) incorporation of thermodynamic information; and iii) use of the consensus MR to identify potential multi-target drug therapy approaches.Taken together, with the growing number of parallel MRs a structured, community-driven approach will be necessary to maximize quality while increasing adoption of MRs in experimental design and interpretation. link: http://identifiers.org/pubmed/21244678

Parameters: none

States: none

Observables: none

Thiele2013 - Adrenal gland glandular cellsThe model of adrenal gland glandular cells metabolism is derived from the comm…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

MODEL1310110038 @ v0.0.1

Thiele2013 - Appendix glandular cellsThe model of appendix glandular cells metabolism is derived from the community-driv…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

MODEL1310110032 @ v0.0.1

Thiele2013 - Appendix lymphoid tissueThe model of appendix lymphoid tissue metabolism is derived from the community-driv…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

Thiele2013 - Bone marrow hematopoietic cellsThe model of bone marrow hematopoietic cells metabolism is derived from the…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

MODEL1310110023 @ v0.0.1

Thiele2013 - Breast glandular cellsThe model of breast glandular cells metabolism is derived from the community-driven g…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

Thiele2013 - Bronchus respiratory epithelial cellsThe model of bronchus respiratory epithelial cells metabolism is deriv…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

Thiele2013 - Cerebellum cells in granular layerThe model of cerebellum cells in granular layer metabolism is derived fro…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

Thiele2013 - Cerebellum cells in molecular layerThe model of cerebellum cells in molecular layer metabolism is derived f…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

MODEL1310110050 @ v0.0.1

Thiele2013 - Cerebellum Purkinje cellsThe model of cerebellum Purkinje cells metabolism is derived from the community-dr…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

MODEL1310110064 @ v0.0.1

Thiele2013 - Cerebral cortex glial cellsThe model of cerebral cortex glial cells metabolism is derived from the communit…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

Thiele2013 - Cerebral cortex neuronal cellsThe model of cerebral cortex neuronal cells metabolism is derived from the co…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

Thiele2013 - Cervix uterine glandular cellsThe model of cervix uterine glandular cells metabolism is derived from the co…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

Thiele2013 - Cervix uterine squamous epithelial cellsThe model of cervix uterine squamous epithelial cells metabolism is…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

MODEL1310110043 @ v0.0.1

Thiele2013 - Colon glandular cellsThe model of colon glandular cells metabolism is derived from the community-driven glo…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

MODEL1310110012 @ v0.0.1

Thiele2013 - Duodenum glandular cellsThe model of duodenum glandular cells metabolism is derived from the community-driv…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

MODEL1310110054 @ v0.0.1

Thiele2013 - Epididymis glandular cellsThe model of epididymis glandular cells metabolism is derived from the community-…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

Thiele2013 - Esophagus squamous epithelial cellsThe model of esophagus squamous epithelial cells metabolism is derived f…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

Thiele2013 - Fallopian tube glandular cellsThe model of fallopian tube glandular cells metabolism is derived from the co…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

Thiele2013 - Gall bladder glandular cellsThe model of gall bladder glandular cells metabolism is derived from the commun…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

MODEL1310110056 @ v0.0.1

Thiele2013 - Heart muscle myocytesThe model of heart muscle myocytes metabolism is derived from the community-driven glo…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

MODEL1310110005 @ v0.0.1

Thiele2013 - Hippocampus glial cellsThe model of hippocampus glial cells metabolism is derived from the community-driven…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

MODEL1310110034 @ v0.0.1

Thiele2013 - Hippocampus neuronal cellsThe model of hippocampus neuronal cells metabolism is derived from the community-…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

Thiele2013 - Human metabolism global reconstruction (Recon 2)Community-driven global reconstruction of human metabolism…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

MODEL1310110053 @ v0.0.1

Thiele2013 - Kidney cells in glomeruliThe model of kidney cells in glomeruli metabolism is derived from the community-dr…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

MODEL1310110047 @ v0.0.1

Thiele2013 - Kidney cells in tubulesThe model of kidney cells in tubules metabolism is derived from the community-driven…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

Thiele2013 - Lateral ventricle glial cellsThe model of lateral ventricle glial cells metabolism is derived from the comm…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

Thiele2013 - Lateral ventricle neuronal cellsThe model of lateral ventricle neuronal cells metabolism is derived from th…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

MODEL1310110041 @ v0.0.1

Thiele2013 - Liver bile duct cellsThe model of liver bile duct cells metabolism is derived from the community-driven glo…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

MODEL1310110014 @ v0.0.1

Thiele2013 - Liver hepatocytesThe model of liver hepatocytes metabolism is derived from the community-driven global reco…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

MODEL1310110051 @ v0.0.1

Thiele2013 - Lung macrophagesThe model of lung macrophages metabolism is derived from the community-driven global recons…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

MODEL1310110010 @ v0.0.1

Thiele2013 - Lung pneumocytesThe model of lung pneumocytes metabolism is derived from the community-driven global recons…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

Thiele2013 - Lymph node germinal center cellsThe model of lymph node germinal center cells metabolism is derived from th…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

Thiele2013 - Lymph node non germinal center cellsThe model of lymph node non germinal center cells metabolism is derived…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

Thiele2013 - Nasopharynx respiratory epithelial cellsThe model of nasopharynx respiratory epithelial cells metabolism is…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

Thiele2013 - Oral mucosa squamous epithelial cellsThe model of oral mucosa squamous epithelial cells metabolism is deriv…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

MODEL1310110029 @ v0.0.1

Thiele2013 - Ovary ovarian stroma cellsThe model of ovary ovarian stroma cells metabolism is derived from the community-…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

Thiele2013 - Pancreas exocrine glandular cellsThe model of pancreas exocrine glandular cells metabolism is derived from…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

Thiele2013 - Pancreas islets of LangerhansThe model of pancreas islets of Langerhans metabolism is derived from the comm…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

Thiele2013 - Parathyroid gland glandular cellsThe model of parathyroid gland glandular cells metabolism is derived from…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

MODEL1310110049 @ v0.0.1

Thiele2013 - Placenta decidual cellsThe model of placenta decidual cells metabolism is derived from the community-driven…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

Thiele2013 - Placenta trophoblastic cellsThe model of placenta trophoblastic cells metabolism is derived from the commun…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

MODEL1310110018 @ v0.0.1

Thiele2013 - Prostate glandular cellsThe model of prostate glandular cells metabolism is derived from the community-driv…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

MODEL1310110057 @ v0.0.1

Thiele2013 - Rectum glandular cellsThe model of rectum glandular cells metabolism is derived from the community-driven g…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

Thiele2013 - Salivary gland glandular cellsThe model of salivary gland glandular cells metabolism is derived from the co…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

Thiele2013 - Seminal vesicle glandular cellsThe model of seminal vesicle glandular cells metabolism is derived from the…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

MODEL1310110011 @ v0.0.1

Thiele2013 - Skeletal muscle myocytesThe model of skeletal muscle myocytes metabolism is derived from the community-driv…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

MODEL1310110003 @ v0.0.1

Thiele2013 - Skin epidermal cellsThe model of skin epidermal cells metabolism is derived from the community-driven globa…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

Thiele2013 - Small intestine glandular cellsThe model of small intestine glandular cells metabolism is derived from the…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

Thiele2013 - Smooth muscle smooth muscle cellsThe model of smooth muscle smooth muscle cells metabolism is derived from…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

MODEL1310110060 @ v0.0.1

Thiele2013 - Spleen cells in red pulpThe model of spleen cells in red pulp metabolism is derived from the community-driv…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

MODEL1310110002 @ v0.0.1

Thiele2013 - Spleen cells in white pulpThe model of spleen cells in white pulp metabolism is derived from the community-…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

Thiele2013 - Stomach lower glandular cellsThe model of stomach lower glandular cells metabolism is derived from the comm…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

Thiele2013 - Stomach upper glandular cellsThe model of stomach upper glandular cells metabolism is derived from the comm…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

Thiele2013 - Testis cells in seminiferus ductsThe model of testis cells in seminiferus ducts metabolism is derived from…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

MODEL1310110008 @ v0.0.1

Thiele2013 - Testis Leydig cellsThe model of testis Leydig cells metabolism is derived from the community-driven global…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

Thiele2013 - Thyroid gland glandular cellsThe model of thyroid gland glandular cells metabolism is derived from the comm…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

Thiele2013 - Tonsil germinal center cellsThe model of tonsil germinal center cells metabolism is derived from the commun…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

Thiele2013 - Tonsil non germinal center cellsThe model of tonsil non germinal center cells metabolism is derived from th…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

Thiele2013 - Tonsil squamous epithelial cellsThe model of tonsil squamous epithelial cells metabolism is derived from th…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

Thiele2013 - Urinary bladder urothelial cellsThe model of urinary bladder urothelial cells metabolism is derived from th…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

Thiele2013 - Uterus post menopause cells in endometrial stromaThe model of uterus post menopause cells in endometrial st…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

Thiele2013 - Uterus post menopause glandular cellsThe model of uterus post menopause glandular cells metabolism is deriv…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

Thiele2013 - Uterus pre menopause cells in endometrial stromaThe model of uterus pre menopause cells in endometrial stro…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

Thiele2013 - Uterus pre menopause glandular cellsThe model of uterus pre menopause glandular cells metabolism is derived…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

Thiele2013 - Vagina squamous epithelial cellsThe model of vagina squamous epithelial cells metabolism is derived from th…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

Thiele2013 - Vulva anal skin epidermal cellsThe model of vulva anal skin epidermal cells metabolism is derived from the…

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ∼2× more reactions and ∼1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. link: http://identifiers.org/pubmed/23455439

Parameters: none

States: none

Observables: none

MODEL7434234848 @ v0.0.1

This is the flux balance model from: **A fragile metabolic network adapted for cooperation in the symbiotic bacterium…

In silico analyses provide valuable insight into the biology of obligately intracellular pathogens and symbionts with small genomes. There is a particular opportunity to apply systems-level tools developed for the model bacterium Escherichia coli to study the evolution and function of symbiotic bacteria which are metabolically specialised to overproduce specific nutrients for their host and, remarkably, have a gene complement that is a subset of the E. coli genome.We have reconstructed and analysed the metabolic network of the gamma-proteobacterium Buchnera aphidicola (symbiont of the pea aphid) as a model for using systems-level approaches to discover key traits of symbionts with small genomes. The metabolic network is extremely fragile with > 90% of the reactions essential for viability in silico; and it is structured so that the bacterium cannot grow without producing the essential amino acid, histidine, which is released to the insect host. Further, the amount of essential amino acid produced by the bacterium in silico can be controlled by host supply of carbon and nitrogen substrates.This systems-level analysis predicts that the fragility of the bacterial metabolic network renders the symbiotic bacterium intolerant of drastic environmental fluctuations, whilst the coupling of histidine production to growth prevents the bacterium from exploiting host nutrients without reciprocating. These metabolic traits underpin the sustained nutritional contribution of B. aphidicola to the host and, together with the impact of host-derived substrates on the profile of nutrients released from the bacteria, point to a dominant role of the host in controlling the symbiosis. link: http://identifiers.org/pubmed/19232131

Parameters: none

States: none

Observables: none

BIOMD0000000082 @ v0.0.1

This model was created according to the paper *Inhibition of Adenylate Cyclase Is Mediated by the High Affinity Conforma…

The functional significance of high affinity agonist binding to receptors that interact with guanine nucleotide regulatory proteins has remained controversial. Preincubation of human platelet membranes with the full alpha 2-agonist UK 14,304 in the absence of GTP increases the potency of the agonist to inhibit adenylate cyclase in a pre-steady state (15-sec) assay. The EC50 after preincubation (6 +/- 1 nM) is within a factor of 2 of the high affinity Kd for [3H]UK 14,304 binding determined under identical conditions (2.7 +/- 0.1 nM). In contrast, in the usual steady state measurements (15 min) or in pre-steady state measurements without agonist preincubation, the EC50 values (74 +/- 1 and 207 +/- 8 nM, respectively) are near the low affinity Kd for [3H]UK 14,304 binding. Reduction of the GTP concentration in steady state adenylate cyclase assays also decreases the EC50 for UK 14,304 from 40 +/- 5 nM at 10 microM GTP to 14 +/- 5 nM with no added GTP. Both sets of experimental observations are accommodated by a complete kinetic model of inhibition in which the high affinity ternary complex of drug, receptor, and G protein leads to the response. Explicit rate parameters are included for agonist binding, receptor-G protein interactions, GTP binding, and hydrolysis. Despite the functional role of the high affinity state of the alpha 2-receptor in this model, the steady state EC50 for agonist-mediated inhibition correlates best with the Kd of low affinity agonist binding in the presence of high levels of GTP. Under conditions in which formation of the high affinity ternary complex is favored, the EC50 for responses approaches the high affinity Kd. link: http://identifiers.org/pubmed/2904647

Parameters:

Name Description
k5=0.05 Reaction: DRG_GTP => G_GTP + DR, Rate Law: cell*k5*DRG_GTP
k2=1.0E8; k8=0.1 Reaction: DR + G_GDP => DRG_GDP, Rate Law: cell*(k2*DR*G_GDP-k8*DRG_GDP)
k4=1.0E7; k10=0.1 Reaction: DRG + GTP => DRG_GTP, Rate Law: cell*(k4*DRG*GTP-k10*DRG_GTP)
k9=100000.0; k3=0.1 Reaction: DRG_GDP => GDP + DRG, Rate Law: cell*(k3*DRG_GDP-k9*DRG*GDP)
k6=0.1 Reaction: G_GTP => G_GDP, Rate Law: cell*k6*G_GTP
k1=5000000.0; k7=0.5 Reaction: agonist + Recptor => DR, Rate Law: cell*(k1*agonist*Recptor-k7*DR)

States:

Name Description
agonist [alpha-adrenergic agonist]
DR [alpha-adrenergic agonist; Alpha-2C adrenergic receptor; alpha-adrenergic agonist; Alpha-2A adrenergic receptor; alpha-adrenergic agonist; Alpha-2B adrenergic receptor]
DRG GDP [GDP; alpha-adrenergic agonist; Alpha-2A adrenergic receptor; heterotrimeric G-protein complex; GDP; alpha-adrenergic agonist; Alpha-2B adrenergic receptor; heterotrimeric G-protein complex; GDP; alpha-adrenergic agonist; Alpha-2C adrenergic receptor; heterotrimeric G-protein complex]
DRG [alpha-adrenergic agonist; Alpha-2B adrenergic receptor; heterotrimeric G-protein complex; alpha-adrenergic agonist; Alpha-2A adrenergic receptor; heterotrimeric G-protein complex; alpha-adrenergic agonist; Alpha-2C adrenergic receptor; heterotrimeric G-protein complex]
GDP [GDP]
DRG GTP [GTP; alpha-adrenergic agonist; Alpha-2A adrenergic receptor; heterotrimeric G-protein complex; GTP; alpha-adrenergic agonist; Alpha-2B adrenergic receptor; heterotrimeric G-protein complex; GTP; alpha-adrenergic agonist; Alpha-2C adrenergic receptor; heterotrimeric G-protein complex]
G GDP [GDP; heterotrimeric G-protein complex]
G GTP [GTP; heterotrimeric G-protein complex]
Recptor [Alpha-2A adrenergic receptor; Alpha-2B adrenergic receptor; Alpha-2C adrenergic receptor]
GTP [GTP]

Observables: none

BIOMD0000000080 @ v0.0.1

This model reproduces figure 5 and figure 4(B)of the paper, with Kinh represented by [G-GTP]. We arbitrarily chosed to s…

Activation and inhibition of adenylate cyclase in the presence of GTP, the natural guanine nucleotide regulator, are too fast to study by standard biochemical methods. In order to identify the rate-limiting steps in adenylate cyclase regulation, we measured the kinetics of stimulation and inhibition of the enzyme on a subsecond to second time scale using a novel rapid-mix quench technique. Even using our rapid-mix quench method, activation by PGE1 and forskolin was instantaneous (cAMP accumulation was linear between 0.5 and 30 s). In contrast, we found a lag period of 1.2-10 s for epinephrine-mediated inhibition. The length of the lag depended on the concentration of GTP and monovalent cations present. In the absence of NaCl, the rate constant for the onset of inhibition (kinh) increased only slightly with GTP concentration saturating at a value of 0.16 s-1 (t1/2 4.3 s) at 1 microM GTP. In the presence of 100 mM NaCl, kinh was strongly dependent on GTP concentration, reaching a maximum value of 0.57 s-1 (t1/2 1.2 s) at 100 microM GTP. Thus, activation of both Gi and Gs in intact platelet membranes is much faster (t1/2 less than 5 s) than previously reported for reconstituted systems. Also, the strong dependence of the rate of adenylate cyclase inhibition on GTP concentration implies that the rate-limiting step in inhibition is distal to GTP binding. The effect of NaCl to increase the maximal rate of inhibition is specific for sodium since KCl has no effect on kinh.(ABSTRACT TRUNCATED AT 250 WORDS) link: http://identifiers.org/pubmed/2574993

Parameters:

Name Description
k5=1.0 Reaction: DRG_GTP => G_GTP + DR, Rate Law: cell*k5*DRG_GTP
k7=10.0; k1=5000000.0 Reaction: D + R => DR, Rate Law: cell*(k1*D*R-k7*DR)
k2=1.0E8; k8=0.1 Reaction: DR + G_GDP => DRG_GDP, Rate Law: cell*(k2*DR*G_GDP-k8*DRG_GDP)
k6=2.0 Reaction: G_GTP => G_GDP, Rate Law: cell*k6*G_GTP
k3=5.0; k9=100000.0 Reaction: DRG_GDP => GDP + DRG, Rate Law: cell*(k3*DRG_GDP-k9*GDP*DRG)
k4=5000000.0; k10=55.0 Reaction: DRG + GTP => DRG_GTP, Rate Law: cell*(k4*DRG*GTP-k10*DRG_GTP)

States:

Name Description
DRG [alpha-adrenergic agonist; Alpha-2A adrenergic receptor; heterotrimeric G-protein complex; alpha-adrenergic agonist; Alpha-2B adrenergic receptor; heterotrimeric G-protein complex; alpha-adrenergic agonist; Alpha-2C adrenergic receptor; heterotrimeric G-protein complex]
G GTP [GTP; heterotrimeric G-protein complex]
DRG GDP [alpha-adrenergic agonist; GDP; Alpha-2A adrenergic receptor; heterotrimeric G-protein complex; alpha-adrenergic agonist; GDP; Alpha-2B adrenergic receptor; heterotrimeric G-protein complex; alpha-adrenergic agonist; GDP; Alpha-2C adrenergic receptor; heterotrimeric G-protein complex]
G GDP [GDP; heterotrimeric G-protein complex]
DRG GTP [alpha-adrenergic agonist; GTP; Alpha-2A adrenergic receptor; heterotrimeric G-protein complex; alpha-adrenergic agonist; GTP; Alpha-2B adrenergic receptor; heterotrimeric G-protein complex; alpha-adrenergic agonist; GTP; Alpha-2C adrenergic receptor; heterotrimeric G-protein complex]
DR [alpha-adrenergic agonist; Alpha-2A adrenergic receptor; alpha-adrenergic agonist; Alpha-2B adrenergic receptor; alpha-adrenergic agonist; Alpha-2C adrenergic receptor]
GDP [GDP]
D [alpha-adrenergic agonist]
R [Alpha-2A adrenergic receptor; Alpha-2B adrenergic receptor; Alpha-2C adrenergic receptor]
GTP [GTP]

Observables: none

This model describes a distributive, sequential system with n = 4, which is a simplified example of unlimited multistabi…

Reversible phosphorylation on serine, threonine and tyrosine is the most widely studied posttranslational modification of proteins. The number of phosphorylated sites on a protein (n) shows a significant increase from prokaryotes, with n </= 7 sites, to eukaryotes, with examples having n >/= 150 sites. Multisite phosphorylation has many roles and site conservation indicates that increasing numbers of sites cannot be due merely to promiscuous phosphorylation. A substrate with n sites has an exponential number (2^n) of phospho-forms and individual phospho-forms may have distinct biological effects. The distribution of these phospho-forms and how this distribution is regulated have remained unknown. Here we show that, when kinase and phosphatase act in opposition on a multisite substrate, the system can exhibit distinct stable phospho-form distributions at steady state and that the maximum number of such distributions increases with n. Whereas some stable distributions are focused on a single phospho-form, others are more diffuse, giving the phospho-proteome the potential to behave as a fluid regulatory network able to encode information and flexibly respond to varying demands. Such plasticity may underlie complex information processing in eukaryotic cells and suggests a functional advantage in having many sites. Our results follow from the unusual geometry of the steady-state phospho-form concentrations, which we show to constitute a rational algebraic curve, irrespective of n. We thereby reduce the complexity of calculating steady states from simulating 3 x 2^n differential equations to solving two algebraic equations, while treating parameters symbolically. We anticipate that these methods can be extended to systems with multiple substrates and multiple enzymes catalysing different modifications, as found in posttranslational modification 'codes' such as the histone code. Whereas simulations struggle with exponentially increasing molecular complexity, mathematical methods of the kind developed here can provide a new language in which to articulate the principles of cellular information processing. link: http://identifiers.org/pubmed/19536158

Parameters: none

States: none

Observables: none

BIOMD0000000260 @ v0.0.1

This a model from the article: Systems analysis of iron metabolism: the network of iron pools and fluxes Tiago JS L…

BACKGROUND: Every cell of the mammalian organism needs iron as trace element in numerous oxido-reductive processes as well as for transport and storage of oxygen. The very versatility of ionic iron makes it a toxic entity which can catalyze the production of radicals that damage vital membranous and macromolecular assemblies in the cell. The mammalian organism maintains therefore a complex regulatory network of iron uptake, excretion and intra-body distribution. Intracellular regulation in different cell types is intertwined with a global hormonal signalling structure. Iron deficiency as well as excess of iron are frequent and serious human disorders. They can affect every cell, but also the organism as a whole. RESULTS: Here, we present a kinematic model of the dynamic system of iron pools and fluxes. It is based on ferrokinetic data and chemical measurements in C57BL6 wild-type mice maintained on iron-deficient, iron-adequate, or iron-loaded diet. The tracer iron levels in major tissues and organs (16 compartment) were followed for 28 days. The evaluation resulted in a whole-body model of fractional clearance rates. The analysis permits calculation of absolute flux rates in the steady-state, of iron distribution into different organs, of tracer-accessible pool sizes and of residence times of iron in the different compartments in response to three states of iron-repletion induced by the dietary regime. CONCLUSIONS: This mathematical model presents a comprehensive physiological picture of mice under three different diets with varying iron contents. The quantitative results reflect systemic properties of iron metabolism: dynamic closedness, hierarchy of time scales, switch-over response and dynamics of iron storage in parenchymal organs. Therefore, we could assess which parameters will change under dietary perturbations and study in quantitative terms when those changes take place. link: http://identifiers.org/pubmed/20704761

Parameters:

Name Description
k1=0.137763703 Reaction: s5 => s1, Rate Law: s5*k1
k1=0.042900396 Reaction: s1 => s7, Rate Law: s1*k1
k1=0.445547231 Reaction: s1 => s13, Rate Law: s1*k1
k1=0.899045295 Reaction: s1 => s9, Rate Law: s1*k1
k1=0.134371419 Reaction: s1 => s11, Rate Law: s1*k1
k1=0.031742475 Reaction: s1 => s17, Rate Law: s1*k1
k1=0.192119917 Reaction: s12 => s1, Rate Law: s12*k1
k1=2.613229205 Reaction: s1 => s5, Rate Law: s1*k1
k1=0.125873837 Reaction: s16 => s1, Rate Law: s16*k1
k1=0.201360515 Reaction: s13 => s1, Rate Law: s13*k1
k1=1.067150955 Reaction: s2 => s3, Rate Law: s2*k1
k1=7.27706671 Reaction: s4 => s1, Rate Law: s4*k1
k1=0.093227796 Reaction: s14 => s1, Rate Law: s14*k1
k1=0.37 Reaction: s15 => s10, Rate Law: k1*s15
k1=0.304695409 Reaction: s1 => s12, Rate Law: s1*k1
k1=1.144130546 Reaction: s1 => s8, Rate Law: s1*k1
k1=0.043759386 Reaction: s8 => s10, Rate Law: s8*k1
k1=12.67031539 Reaction: s1 => s2, Rate Law: s1*k1
k1=0.42 Reaction: s7 => s1, Rate Law: k1*s7
k1=1.493333162 Reaction: s1 => s6, Rate Law: s1*k1
k1=0.355490081 Reaction: s9 => s10, Rate Law: s9*k1
k1=0.054570911 Reaction: s1 => s14, Rate Law: s1*k1
k1=0.044747636 Reaction: s1 => s16, Rate Law: s1*k1
k1=0.060942602 Reaction: s17 => s1, Rate Law: s17*k1
k1=0.100527605 Reaction: s2 => s4, Rate Law: s2*k1
k1=0.154446568 Reaction: s6 => s1, Rate Law: s6*k1
k1=0.061112865 Reaction: s3 => s4, Rate Law: s3*k1
k1=0.076683565 Reaction: s11 => s1, Rate Law: s11*k1
k1=0.121370929 Reaction: s1 => s15, Rate Law: s1*k1

States:

Name Description
s8 [integument; iron cation; Iron]
s1 [blood plasma; iron cation; Iron]
s5 [liver; iron cation; Iron]
s7 [duodenum; iron cation; Iron]
s14 [testis; iron cation; Iron]
s17 [brain; iron cation; Iron]
s13 [kidney; iron cation; Iron]
s12 [lung; iron cation; Iron]
s2 [bone marrow; iron cation; Iron]
s4 [spleen; iron cation; Iron]
s9 [intestine; iron cation; Iron]
s16 [adipose tissue; iron cation; Iron]
s10 [extraorganismal space; iron cation; Iron; iron atom]
s6 [skeletal muscle; iron cation; Iron]
s11 [heart; iron cation; Iron]
s15 [stomach; iron cation; Iron]
s3 [erythrocyte; iron cation; Iron]

Observables: none

BIOMD0000000259 @ v0.0.1

This a model from the article: Systems analysis of iron metabolism: the network of iron pools and fluxes Tiago JS L…

BACKGROUND: Every cell of the mammalian organism needs iron as trace element in numerous oxido-reductive processes as well as for transport and storage of oxygen. The very versatility of ionic iron makes it a toxic entity which can catalyze the production of radicals that damage vital membranous and macromolecular assemblies in the cell. The mammalian organism maintains therefore a complex regulatory network of iron uptake, excretion and intra-body distribution. Intracellular regulation in different cell types is intertwined with a global hormonal signalling structure. Iron deficiency as well as excess of iron are frequent and serious human disorders. They can affect every cell, but also the organism as a whole. RESULTS: Here, we present a kinematic model of the dynamic system of iron pools and fluxes. It is based on ferrokinetic data and chemical measurements in C57BL6 wild-type mice maintained on iron-deficient, iron-adequate, or iron-loaded diet. The tracer iron levels in major tissues and organs (16 compartment) were followed for 28 days. The evaluation resulted in a whole-body model of fractional clearance rates. The analysis permits calculation of absolute flux rates in the steady-state, of iron distribution into different organs, of tracer-accessible pool sizes and of residence times of iron in the different compartments in response to three states of iron-repletion induced by the dietary regime. CONCLUSIONS: This mathematical model presents a comprehensive physiological picture of mice under three different diets with varying iron contents. The quantitative results reflect systemic properties of iron metabolism: dynamic closedness, hierarchy of time scales, switch-over response and dynamics of iron storage in parenchymal organs. Therefore, we could assess which parameters will change under dietary perturbations and study in quantitative terms when those changes take place. link: http://identifiers.org/pubmed/20704761

Parameters:

Name Description
k1=0.05 Reaction: s14 => s1, Rate Law: s14*k1
k1=1.04 Reaction: s1 => s8, Rate Law: s1*k1
k1=0.03 Reaction: s1 => s17, Rate Law: s1*k1
k1=1.85 Reaction: s2 => s3, Rate Law: s2*k1
k1=0.96 Reaction: s1 => s6, Rate Law: s1*k1
k1=0.06 Reaction: s11 => s1, Rate Law: s11*k1
k1=0.3 Reaction: s9 => s10, Rate Law: s9*k1
k1=0.18 Reaction: s15 => s10, Rate Law: k1*s15
k1=0.98 Reaction: s1 => s9, Rate Law: s1*k1
k1=0.09 Reaction: s1 => s15, Rate Law: s1*k1
k1=0.42 Reaction: s1 => s13, Rate Law: s1*k1
k1=0.04 Reaction: s1 => s14, Rate Law: s1*k1
k1=0.17 Reaction: s7 => s1, Rate Law: k1*s7
k1=13.22 Reaction: s1 => s2, Rate Law: s1*k1
k1=0.79 Reaction: s1 => s12, Rate Law: s1*k1
k1=0.2 Reaction: s13 => s1, Rate Law: s13*k1
k1=0.25 Reaction: s5 => s1, Rate Law: s5*k1
k1=0.41 Reaction: s12 => s1, Rate Law: s12*k1
k1=0.11 Reaction: s1 => s11, Rate Law: s1*k1
k1=0.1 Reaction: s16 => s1, Rate Law: s16*k1
k1=0.02 Reaction: s17 => s1, Rate Law: s17*k1
k1=2.27 Reaction: s1 => s5, Rate Law: s1*k1
k1=0.56 Reaction: s2 => s4, Rate Law: s2*k1
k1=14.61 Reaction: s4 => s1, Rate Law: s4*k1

States:

Name Description
s8 [integument; iron cation; Iron]
s1 [blood plasma; iron cation; Iron]
s5 [liver; iron cation; Iron]
s7 [duodenum; iron cation; Iron]
s14 [testis; iron cation; Iron]
s17 [brain; iron cation; Iron]
s13 [kidney; iron cation; Iron]
s12 [lung; iron cation; Iron]
s2 [bone marrow; iron cation; Iron]
s4 [spleen; iron cation; Iron]
s9 [intestine; iron cation; Iron]
s16 [adipose tissue; iron cation; Iron]
s10 [extraorganismal space; iron cation; Iron]
s6 [skeletal muscle; iron cation; Iron]
s11 [heart; iron cation; Iron]
s15 [stomach; iron cation; Iron]
s3 [erythrocyte; iron cation; Iron]

Observables: none

BIOMD0000000261 @ v0.0.1

This a model from the article: Systems analysis of iron metabolism: the network of iron pools and fluxes Tiago JS L…

BACKGROUND: Every cell of the mammalian organism needs iron as trace element in numerous oxido-reductive processes as well as for transport and storage of oxygen. The very versatility of ionic iron makes it a toxic entity which can catalyze the production of radicals that damage vital membranous and macromolecular assemblies in the cell. The mammalian organism maintains therefore a complex regulatory network of iron uptake, excretion and intra-body distribution. Intracellular regulation in different cell types is intertwined with a global hormonal signalling structure. Iron deficiency as well as excess of iron are frequent and serious human disorders. They can affect every cell, but also the organism as a whole. RESULTS: Here, we present a kinematic model of the dynamic system of iron pools and fluxes. It is based on ferrokinetic data and chemical measurements in C57BL6 wild-type mice maintained on iron-deficient, iron-adequate, or iron-loaded diet. The tracer iron levels in major tissues and organs (16 compartment) were followed for 28 days. The evaluation resulted in a whole-body model of fractional clearance rates. The analysis permits calculation of absolute flux rates in the steady-state, of iron distribution into different organs, of tracer-accessible pool sizes and of residence times of iron in the different compartments in response to three states of iron-repletion induced by the dietary regime. CONCLUSIONS: This mathematical model presents a comprehensive physiological picture of mice under three different diets with varying iron contents. The quantitative results reflect systemic properties of iron metabolism: dynamic closedness, hierarchy of time scales, switch-over response and dynamics of iron storage in parenchymal organs. Therefore, we could assess which parameters will change under dietary perturbations and study in quantitative terms when those changes take place. link: http://identifiers.org/pubmed/20704761

Parameters:

Name Description
k1=0.24 Reaction: s7 => s1, Rate Law: k1*s7
k1=0.23 Reaction: s13 => s1, Rate Law: s13*k1
k1=0.086 Reaction: s12 => s1, Rate Law: s12*k1
k1=0.93 Reaction: s1 => s9, Rate Law: s1*k1
k1=0.63 Reaction: s1 => s12, Rate Law: s1*k1
k1=1.91 Reaction: s4 => s1, Rate Law: s4*k1
k1=0.099 Reaction: s16 => s1, Rate Law: s16*k1
k1=0.27 Reaction: s1 => s15, Rate Law: s1*k1
k1=0.043 Reaction: s1 => s14, Rate Law: s1*k1
k1=0.066 Reaction: s1 => s16, Rate Law: s1*k1
k1=0.038 Reaction: s1 => s7, Rate Law: s1*k1
k1=0.29 Reaction: s15 => s10, Rate Law: k1*s15
k1=0.22 Reaction: s9 => s10, Rate Law: s9*k1
k1=2.52 Reaction: s1 => s6, Rate Law: s1*k1
k1=5.25 Reaction: s1 => s5, Rate Law: s1*k1
k1=0.17 Reaction: s11 => s1, Rate Law: s11*k1
k1=0.14 Reaction: s6 => s1, Rate Law: s6*k1
k1=0.032 Reaction: s3 => s4, Rate Law: s3*k1
k1=0.021 Reaction: s1 => s17, Rate Law: s1*k1
k1=0.36 Reaction: s1 => s11, Rate Law: s1*k1
k1=0.028 Reaction: s17 => s1, Rate Law: s17*k1
k1=0.1 Reaction: s5 => s1, Rate Law: s5*k1
k1=0.067 Reaction: s14 => s1, Rate Law: s14*k1
k1=0.5 Reaction: s2 => s3, Rate Law: s2*k1
k1=0.046 Reaction: s2 => s4, Rate Law: s2*k1
k1=0.072 Reaction: s8 => s10, Rate Law: s8*k1
k1=1.33 Reaction: s1 => s8, Rate Law: s1*k1
k1=1.62 Reaction: s1 => s13, Rate Law: s1*k1
k1=6.92 Reaction: s1 => s2, Rate Law: s1*k1

States:

Name Description
s8 [integument; iron cation; Iron]
s1 [blood plasma; iron cation; Iron]
s5 [liver; iron cation; Iron]
s7 [duodenum; iron cation; Iron]
s14 [testis; iron cation; Iron]
s17 [brain; iron cation; Iron]
s13 [kidney; iron cation; Iron]
s12 [lung; iron cation; Iron]
s2 [bone marrow; iron cation; Iron]
s4 [iron cation; Iron; spleen]
s9 [intestine; iron cation; Iron]
s16 [adipose tissue; iron cation; Iron]
s10 [extraorganismal space; iron cation; Iron; iron atom]
s6 [skeletal muscle; iron cation; Iron]
s11 [heart; iron cation; Iron]
s15 [stomach; iron cation; Iron]
s3 [erythrocyte; iron cation; Iron]

Observables: none

MODEL1112150000 @ v0.0.1

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedic…

The study of phenotype transitions is important to understand progressive diseases, e.g., diabetes mellitus, metabolic syndrome, and cardiovascular diseases. A challenge remains to explain phenotype transitions in terms of adaptations in molecular components and interactions in underlying biological systems.Here, mathematical modeling is used to describe the different phenotypes by integrating experimental data on metabolic pools and fluxes. Subsequently, trajectories of parameter adaptations are identified that are essential for the phenotypical changes. These changes in parameters reflect progressive adaptations at the transcriptome and proteome level, which occur at larger timescales. The approach was employed to study the metabolic processes underlying liver X receptor induced hepatic steatosis. Model analysis predicts which molecular processes adapt in time after pharmacological activation of the liver X receptor. Our results show that hepatic triglyceride fluxes are increased and triglycerides are especially stored in cytosolic fractions, rather than in endoplasmic reticulum fractions. Furthermore, the model reveals several possible scenarios for adaptations in cholesterol metabolism. According to the analysis, the additional quantification of one cholesterol flux is sufficient to exclude many of these hypotheses.We propose a generic computational approach to analyze biological systems evolving through various phenotypes and to predict which molecular processes are responsible for the transition. For the case of liver X receptor induced hepatic steatosis the novel approach yields information about the redistribution of fluxes and pools of triglycerides and cholesterols that was not directly apparent from the experimental data. Model analysis provides guidance which specific molecular processes to study in more detail to obtain further understanding of the underlying biological system. link: http://identifiers.org/pubmed/22029623

Parameters: none

States: none

Observables: none

Tikidji-Hamburyan2018 - Rod phototransduction under strong illuminationThis model is described in the article: [Rods pr…

Rod and cone photoreceptors support vision across large light intensity ranges. Rods, active under dim illumination, are thought to saturate at higher (photopic) irradiances. The extent of rod saturation is not well defined; some studies report rod activity well into the photopic range. Using electrophysiological recordings from retina and dorsal lateral geniculate nucleus of cone-deficient and visually intact mice, we describe stimulus and physiological factors that influence photopic rod-driven responses. We find that rod contrast sensitivity is initially strongly reduced at high irradiances, but progressively recovers to allow responses to moderate contrast stimuli. Surprisingly, rods recover faster at higher light levels. A model of rod phototransduction suggests that phototransduction gain adjustments and bleaching adaptation underlie rod recovery. Consistently, exogenous chromophore reduces rod responses at bright background. Thus, bleaching adaptation renders mouse rods responsive to modest contrast at any irradiance. Paradoxically, raising irradiance across the photopic range increases the robustness of rod responses. link: http://identifiers.org/pubmed/29180667

Parameters: none

States: none

Observables: none

Tiveci2005 - Calcium dynamics in brain energy metabolism and Alzheimer's diseaseEncoded non-curated model. Issues: -…

Functional imaging techniques play a major role in the study of brain activation by monitoring the changes in blood flow and energy metabolism. In order to interpret functional neuroimaging data better, the existing mathematical models describing the links that may exist between electrical activity, energy metabolism and hemodynamics in literature are thoroughly analyzed for their advantages and disadvantages in terms of their prediction of available experimental data. Then, these models are combined within a single model that includes membrane ionic currents, glycolysis, mitochondrial activity, exchanges through the blood-brain barrier, as well as brain hemodynamics. Particular attention is paid to the transport and storage of calcium ions in neurons since calcium is not only an important molecule for signalling in neurons, but it is also essential for memory storage. Multiple efforts have underlined the importance of calcium dependent cellular processes in the biochemical characterization of Alzheimer's disease (AD), suggesting that abnormalities in calcium homeostasis might be involved in the pathophysiology of the disease. The ultimate goal of this study is to investigate the hypotheses about the physiological or biochemical changes in health and disease and to correlate them to measurable physiological parameters obtained from functional neuroimaging data as in the time course of blood oxygenation level dependent (BOLD) signal. When calcium dynamics are included in the model, both BOLD signal and metabolite concentration profiles are shown to exhibit temporal behaviour consistent with the experimental data found in literature. In the case of Alzheimer's disease, the effect of halved cerebral blood flow increase results in a negative BOLD signal implying suppressed neural activity. link: http://identifiers.org/pubmed/15833443

Parameters: none

States: none

Observables: none

The model was constructed to describe TLR4 induced NF-κB activation in native bone marrow-derived macrophages. It includ…

Signaling via Toll-like receptor 4 (TLR4) in macrophages constitutes an essential part of the innate immune response to bacterial infections. Detailed and quantified descriptions of TLR4 signal transduction would help to understand and exploit the first-line response of innate immune defense. To date, most mathematical modelling studies were performed on transformed cell lines. However, properties of primary macrophages differ significantly. We therefore studied TLR4-dependent activation of NF-κB transcription factor in bone marrow-derived and peritoneal primary macrophages. We demonstrate that the kinetics of NF-κB phosphorylation and nuclear translocation induced by a wide range of bacterial lipopolysaccharide (LPS) concentrations in primary macrophages is much faster than previously reported for macrophage cell lines. We used a comprehensive combination of experiments and mathematical modeling to understand the mechanisms of this rapid response. We found that elevated basal NF-κB in the nuclei of primary macrophages is a mechanism increasing native macrophage sensitivity and response speed to the infection. Such pre-activated state of macrophages accelerates the NF-κB translocation kinetics in response to low agonist concentrations. These findings enabled us to refine and construct a new model combining both NF-κB phosphorylation and translocation processes and predict the existence of a negative feedback loop inactivating phosphorylated NF-κB. link: http://identifiers.org/pubmed/30872589

Parameters: none

States: none

Observables: none

BIOMD0000000372 @ v0.0.1

This a model from the article: Modeling the insulin-glucose feedback system: the significance of pulsatile insulin s…

A mathematical model of the insulin-glucose feedback regulation in man is used to examine the effects of an oscillatory supply of insulin compared to a constant supply at the same average rate. We show that interactions between the oscillatory insulin supply and the receptor dynamics can be of minute significance only. It is possible, however, to interpret seemingly conflicting results of clinical studies in terms of their different experimental conditions with respect to the hepatic glucose release. If this release is operating near an upper limit, an oscillatory insulin supply will be more efficient in lowering the blood glucose level than a constant supply. If the insulin level is high enough for the hepatic release of glucose to nearly vanish, the opposite effect is observed. For insulin concentrations close to the point of inflection of the insulin-glucose dose-response curve an oscillatory and a constant insulin infusion produce similar effects. link: http://identifiers.org/pubmed/11082306

Parameters:

Name Description
td = 36.0 Reaction: x1 = 3/td*(Ip/1-x1), Rate Law: 3/td*(Ip/1-x1)
f4_Ii = 204.190214963962; f3_G = 1.234261665; Gin = 216.0; f2_G = 71.9863579104227; f5_x3 = 12.7950632297315 Reaction: G = Gin+f5_x3+(-(f2_G+f3_G*f4_Ii)), Rate Law: Gin+f5_x3+(-(f2_G+f3_G*f4_Ii))
Vi = 11.0; E = 0.2; Vp = 3.0; ti = 100.0 Reaction: Ii = E*(Ip/Vp-Ii/Vi)-Ii/ti, Rate Law: E*(Ip/Vp-Ii/Vi)-Ii/ti
Vi = 11.0; E = 0.2; f1_G = 15.174877041143; Vp = 3.0; tp = 6.0 Reaction: Ip = f1_G-(E*(Ip/Vp-Ii/Vi)+Ip/tp), Rate Law: f1_G-(E*(Ip/Vp-Ii/Vi)+Ip/tp)

States:

Name Description
x1 x1
x2 x2
Ip [Insulin]
G [glucose]
x3 x3
Ii [Insulin]

Observables: none

Tomida2003 - NFAT functions Calcium OscillationThis model is described in the article: [NFAT functions as a working mem…

Transcription by the nuclear factor of activated T cells (NFAT) is regulated by the frequency of Ca(2+) oscillation. However, why and how Ca(2+) oscillation regulates NFAT activity remain elusive. NFAT is dephosphorylated by Ca(2+)-dependent phosphatase calcineurin and translocates from the cytoplasm to the nucleus to initiate transcription. We analyzed the kinetics of dephosphorylation and translocation of NFAT. We show that Ca(2+)-dependent dephosphorylation proceeds rapidly, while the rephosphorylation and nuclear transport of NFAT proceed slowly. Therefore, after brief Ca(2+) stimulation, dephosphorylated NFAT has a lifetime of several minutes in the cytoplasm. Thus, Ca(2+) oscillation induces a build-up of dephosphorylated NFAT in the cytoplasm, allowing effective nuclear translocation, provided that the oscillation interval is shorter than the lifetime of dephosphorylated NFAT. We also show that Ca(2+) oscillation is more cost-effective in inducing the translocation of NFAT than continuous Ca(2+) signaling. Thus, the lifetime of dephosphorylated NFAT functions as a working memory of Ca(2+) signals and enables the control of NFAT nuclear translocation by the frequency of Ca(2+) oscillation at a reduced cost of Ca(2+) signaling. link: http://identifiers.org/pubmed/12881417

Parameters:

Name Description
k2 = 0.147 Reaction: NFAT_dephosphorylated => NFAT_phosphorylated, Rate Law: Jurkat_cell*k2*NFAT_dephosphorylated
k4 = 0.035 Reaction: NFAT_transported => NFAT_phosphorylated, Rate Law: Jurkat_cell*k4*NFAT_transported
k3 = 0.06 Reaction: NFAT_dephosphorylated => NFAT_transported, Rate Law: Jurkat_cell*k3*NFAT_dephosphorylated
ModelValue_17 = 1.0; ModelValue_13 = 3.0 Reaction: stimulus = piecewise(1, (time-floor(time/ModelValue_13)*ModelValue_13) < ModelValue_17, 0), Rate Law: missing
k1 = 0.359 Reaction: NFAT_phosphorylated => NFAT_dephosphorylated; stimulus, Rate Law: Jurkat_cell*k1*stimulus*NFAT_phosphorylated

States:

Name Description
NFAT transported [Nuclear factor of activated T-cells, cytoplasmic 3; nucleus; NFAT protein]
stimulus [Stimulus]
NFAT dephosphorylated [NFAT protein; Nuclear factor of activated T-cells, cytoplasmic 3]
NFAT phosphorylated [NFAT protein; Nuclear factor of activated T-cells, cytoplasmic 3]

Observables: none

Tomàs-Gamisans2016 - Genome-Scale Metabolic Model of Pichia pastoris (version 2)Note: This is iMT1026v2, an update of th…

Genome-scale metabolic models (GEMs) are tools that allow predicting a phenotype from a genotype under certain environmental conditions. GEMs have been developed in the last ten years for a broad range of organisms, and are used for multiple purposes such as discovering new properties of metabolic networks, predicting new targets for metabolic engineering, as well as optimizing the cultivation conditions for biochemicals or recombinant protein production. Pichia pastoris is one of the most widely used organisms for heterologous protein expression. There are different GEMs for this methylotrophic yeast of which the most relevant and complete in the published literature are iPP668, PpaMBEL1254 and iLC915. However, these three models differ regarding certain pathways, terminology for metabolites and reactions and annotations. Moreover, GEMs for some species are typically built based on the reconstructed models of related model organisms. In these cases, some organism-specific pathways could be missing or misrepresented.In order to provide an updated and more comprehensive GEM for P. pastoris, we have reconstructed and validated a consensus model integrating and merging all three existing models. In this step a comprehensive review and integration of the metabolic pathways included in each one of these three versions was performed. In addition, the resulting iMT1026 model includes a new description of some metabolic processes. Particularly new information described in recently published literature is included, mainly related to fatty acid and sphingolipid metabolism, glycosylation and cell energetics. Finally the reconstructed model was tested and validated, by comparing the results of the simulations with available empirical physiological datasets results obtained from a wide range of experimental conditions, such as different carbon sources, distinct oxygen availability conditions, as well as producing of two different recombinant proteins. In these simulations, the iMT1026 model has shown a better performance than the previous existing models. link: http://identifiers.org/pubmed/26812499

Parameters: none

States: none

Observables: none

A Model of β -Cell Mass, Insulin, and Glucose Kinetics: Pathways to Diabetes BRIANTOPP, KEITHPROMISLOW, GERDADEVRIES, RO…

Diabetes is a disease of the glucose regulatory system that is associated with increased morbidity and early mortality. The primary variables of this system are beta-cell mass, plasma insulin concentrations, and plasma glucose concentrations. Existing mathematical models of glucose regulation incorporate only glucose and/or insulin dynamics. Here we develop a novel model of beta -cell mass, insulin, and glucose dynamics, which consists of a system of three nonlinear ordinary differential equations, where glucose and insulin dynamics are fast relative to beta-cell mass dynamics. For normal parameter values, the model has two stable fixed points (representing physiological and pathological steady states), separated on a slow manifold by a saddle point. Mild hyperglycemia leads to the growth of the beta -cell mass (negative feedback) while extreme hyperglycemia leads to the reduction of the beta-cell mass (positive feedback). The model predicts that there are three pathways in prolonged hyperglycemia: (1) the physiological fixed point can be shifted to a hyperglycemic level (regulated hyperglycemia), (2) the physiological and saddle points can be eliminated (bifurcation), and (3) progressive defects in glucose and/or insulin dynamics can drive glucose levels up at a rate faster than the adaptation of the beta -cell mass which can drive glucose levels down (dynamical hyperglycemia). link: http://identifiers.org/pubmed/11013117

Parameters: none

States: none

Observables: none

BIOMD0000000341 @ v0.0.1

This model is from the article: A model of beta-cell mass, insulin, and glucose kinetics: pathways to diabetes. Top…

Diabetes is a disease of the glucose regulatory system that is associated with increased morbidity and early mortality. The primary variables of this system are beta-cell mass, plasma insulin concentrations, and plasma glucose concentrations. Existing mathematical models of glucose regulation incorporate only glucose and/or insulin dynamics. Here we develop a novel model of beta -cell mass, insulin, and glucose dynamics, which consists of a system of three nonlinear ordinary differential equations, where glucose and insulin dynamics are fast relative to beta-cell mass dynamics. For normal parameter values, the model has two stable fixed points (representing physiological and pathological steady states), separated on a slow manifold by a saddle point. Mild hyperglycemia leads to the growth of the beta -cell mass (negative feedback) while extreme hyperglycemia leads to the reduction of the beta-cell mass (positive feedback). The model predicts that there are three pathways in prolonged hyperglycemia: (1) the physiological fixed point can be shifted to a hyperglycemic level (regulated hyperglycemia), (2) the physiological and saddle points can be eliminated (bifurcation), and (3) progressive defects in glucose and/or insulin dynamics can drive glucose levels up at a rate faster than the adaptation of the beta -cell mass which can drive glucose levels down (dynamical hyperglycemia). link: http://identifiers.org/pubmed/11013117

Parameters:

Name Description
r1 = 8.4E-4; r2 = 2.4E-6; d0 = 0.06 Reaction: B = (((-d0)+r1*G)-r2*G^2)*B, Rate Law: (((-d0)+r1*G)-r2*G^2)*B
k = 432.0; sigma = 43.2; alpha = 20000.0 Reaction: I = B*sigma*G^2/(alpha+G^2)-k*I, Rate Law: B*sigma*G^2/(alpha+G^2)-k*I
R0 = 864.0; Eg0 = 1.44; si = 0.72 Reaction: G = R0-(Eg0+si*I)*G, Rate Law: R0-(Eg0+si*I)*G

States:

Name Description
B [pancreatic beta cell; pancreatic islet]
I [Insulin]
G [glucose; C00293]

Observables: none

This SBML file contains a contextualized GSMM of P. pastoris metabolism based on the most recent metabolic reconstructio…

Pichia pastoris is recognized as a biotechnological workhorse for recombinant protein expression. The metabolic performance of this microorganism depends on genetic makeup and culture conditions, amongst which the specific growth rate and oxygenation level are critical. Despite their importance, only their individual effects have been assessed so far, and thus their combined effects and metabolic consequences still remain to be elucidated. In this work, we present a comprehensive framework for revealing high-order (i.e., individual and combined) metabolic effects of the above parameters in glucose-limited continuous cultures of P. pastoris, using thaumatin production as a case study. Specifically, we employed a rational experimental design to calculate statistically significant metabolic effects from multiple chemostat data, which were later contextualized using a refined and highly predictive genome-scale metabolic model of this yeast under the simulated conditions. Our results revealed a negative effect of the oxygenation on the specific product formation rate (thaumatin), and a positive effect on the biomass yield. Notably, we identified a novel positive combined effect of both the specific growth rate and oxygenation level on the specific product formation rate. Finally, model predictions indicated an opposite relationship between the oxygenation level and the growth-associated maintenance energy (GAME) requirement, suggesting a linear GAME decrease of 0.56 mmol ATP/gDCW per each 1% increase in oxygenation level, which translated into a 44% higher metabolic cost under low oxygenation compared to high oxygenation. Overall, this work provides a systematic framework for mapping high-order metabolic effects of different culture parameters on the performance of a microbial cell factory. Particularly in this case, it provided valuable insights about optimal operational conditions for protein production in P. pastoris. link: http://identifiers.org/doi/10.1016/j.mec.2019.e00103

Parameters: none

States: none

Observables: none

MODEL1006230121 @ v0.0.1

This a model from the article: A thermodynamic model of the cardiac sarcoplasmic/endoplasmic Ca(2+) (SERCA) pump. Tr…

We present a biophysically based kinetic model of the cardiac SERCA pump that consolidates a range of experimental data into a consistent and thermodynamically constrained framework. The SERCA model consists of a number of sub-states with partial reactions that are sensitive to Ca(2+) and pH, and to the metabolites MgATP, MgADP, and Pi. Optimization of model parameters to fit experimental data favors a fully cooperative Ca(2+)-binding mechanism and predicts a Ca(2+)/H(+) counter-transport stoichiometry of 2. Moreover, the order of binding of the partial reactions, particularly the binding of MgATP, proves to be a strong determinant of the ability of the model to fit the data. A thermodynamic investigation of the model indicates that the binding of MgATP has a large inhibitory effect on the maximal reverse rate of the pump. The model is suitable for integrating into whole-cell models of cardiac electrophysiology and Ca(2+) dynamics to simulate the effects on the cell of compromised metabolism arising in ischemia and hypoxia. link: http://identifiers.org/pubmed/19254563

Parameters: none

States: none

Observables: none

MODEL1006230116 @ v0.0.1

This a model from the article: A metabolite-sensitive, thermodynamically constrained model of cardiac cross-bridge cyc…

We present a metabolically regulated model of cardiac active force generation with which we investigate the effects of ischemia on maximum force production. Our model, based on a model of cross-bridge kinetics that was developed by others, reproduces many of the observed effects of MgATP, MgADP, Pi, and H(+) on force development while retaining the force/length/Ca(2+) properties of the original model. We introduce three new parameters to account for the competitive binding of H(+) to the Ca(2+) binding site on troponin C and the binding of MgADP within the cross-bridge cycle. These parameters, along with the Pi and H(+) regulatory steps within the cross-bridge cycle, were constrained using data from the literature and validated using a range of metabolic and sinusoidal length perturbation protocols. The placement of the MgADP binding step between two strongly-bound and force-generating states leads to the emergence of an unexpected effect on the force-MgADP curve, where the trend of the relationship (positive or negative) depends on the concentrations of the other metabolites and [H(+)]. The model is used to investigate the sensitivity of maximum force production to changes in metabolite concentrations during the development of ischemia. link: http://identifiers.org/pubmed/20338848

Parameters: none

States: none

Observables: none

Traynard2016 - Mammalian cell cycle regulation - Logical ModelThis model is described in the article: [Logical model sp…

Understanding the temporal behaviour of biological regulatory networks requires the integration of molecular information into a formal model. However, the analysis of model dynamics faces a combinatorial explosion as the number of regulatory components and interactions increases.We use model-checking techniques to verify sophisticated dynamical properties resulting from the model regulatory structure in the absence of kinetic assumption. We demonstrate the power of this approach by analysing a logical model of the molecular network controlling mammalian cell cycle. This approach enables a systematic analysis of model properties, the delineation of model limitations, and the assessment of various refinements and extensions based on recent experimental observations. The resulting logical model accounts for the main irreversible transitions between cell cycle phases, the sequential activation of cyclins, and the inhibitory role of Skp2, and further emphasizes the multifunctional role for the cell cycle inhibitor Rb.The original and revised mammalian cell cycle models are available in the model repository associated with the public modelling software GINsim (http://ginsim.org/node/189)[email protected] data are available at Bioinformatics online. link: http://identifiers.org/pubmed/27587700

Parameters: none

States: none

Observables: none

This is a mathematical model of a growing tumor and its interaction with the immune system. The model consists of four p…

This paper concerns the optimal control of a mathematical model of a growing tumor and its interaction with the immune system. This model consists of four populations - tumor cells, dendritic cells (as an innate immune system), cytotoxic T cells, and helper T cells (as a specific immune system) - in the form of a system of ordinary differential equations. Some tumors present dendritic cell and such cells have a potential role in regulating the immune system. In this model, we assume that dendritic cells can activate cytotoxic T cells and, in turn, can clear out tumor cells. Furthermore, by adding controls as a treatment to the model, we minimize both the tumor cell population and the cost of treatment. We do this by applying the optimal control for this problem. First, Pontryagin's Principle is used to characterize the optimal control. Then, the optimal system is solved numerically using the Forward-Backward Runge- Kutta method. Finally, the effect of each treatment is investigated. The numerical results show that these controls are effective in reducing the number of tumor cells. link: http://identifiers.org/doi/10.1063/1.5062816

Parameters:

Name Description
e = 1.04E-8 Reaction: L_CD8_T_Cells =>, Rate Law: compartment*e*L_CD8_T_Cells
b_1__1 = 1.02E-9; a_1 = 0.431 Reaction: => T_Tumor_Cells, Rate Law: compartment*a_1*T_Tumor_Cells*(1-b_1__1*T_Tumor_Cells)
beta_3 = 2.0E-5 Reaction: H_CD4_T_Cells => ; L_CD8_T_Cells, Rate Law: compartment*beta_3*H_CD4_T_Cells*L_CD8_T_Cells
u_1 = 0.0 Reaction: T_Tumor_Cells =>, Rate Law: compartment*u_1*T_Tumor_Cells
alpha_2 = 8.0E-10 Reaction: L_CD8_T_Cells => ; T_Tumor_Cells, Rate Law: compartment*alpha_2*T_Tumor_Cells*L_CD8_T_Cells
alpha_1 = 4.2E-8 Reaction: T_Tumor_Cells => ; L_CD8_T_Cells, Rate Law: compartment*alpha_1*T_Tumor_Cells*L_CD8_T_Cells
b_3__1 = 5.0E-4; a_3 = 0.017 Reaction: => H_CD4_T_Cells, Rate Law: compartment*a_3*H_CD4_T_Cells*(1-b_3__1*H_CD4_T_Cells)
beta_2 = 2.0E-5 Reaction: D_Dendritic_Cells => ; L_CD8_T_Cells, Rate Law: compartment*beta_2*D_Dendritic_Cells*L_CD8_T_Cells
b_2__1 = 1.25E-5; a_2 = 0.234 Reaction: => D_Dendritic_Cells, Rate Law: compartment*a_2*D_Dendritic_Cells*(1-b_2__1*D_Dendritic_Cells)
u_2 = 0.0 Reaction: => D_Dendritic_Cells, Rate Law: compartment*u_2
beta_1 = 2.0E-5 Reaction: => L_CD8_T_Cells; D_Dendritic_Cells, H_CD4_T_Cells, Rate Law: compartment*beta_1*(D_Dendritic_Cells+H_CD4_T_Cells)*L_CD8_T_Cells

States:

Name Description
H CD4 T Cells [helper T cell]
D Dendritic Cells [dendritic cell]
L CD8 T Cells [cytotoxic T cell]
T Tumor Cells [neoplastic cell]

Observables: none

This model is from the article: Multiple light inputs to a simple clock circuit allow complex biological rhythms Tr…

Circadian clocks are biological timekeepers that allow living cells to time their activity in anticipation of predictable environmental changes. Detailed understanding of the circadian network of higher plants, such as Arabidopsis thaliana, is hampered by the high number of partially redundant genes. However, the picoeukaryotic alga Ostreococcus tauri, which was recently shown to possess a small number of non-redundant clock genes, presents an attractive alternative target for detailed modelling of circadian clocks in the green lineage. Based on extensive time-series data from in vivo reporter gene assays, we developed a model of the Ostreococcus clock as a feedback loop between the genes TOC1 and CCA1. The model reproduces the dynamics of the transcriptional and translational reporters over a range of photoperiods. Surprisingly, the model is also able to predict the transient behaviour of the clock when the light conditions are altered. Despite the apparent simplicity of the clock circuit, it displays considerable complexity in its response to changing light conditions. Systematic screening of the effects of altered day length revealed a complex relationship between phase and photoperiod, which is also captured by the model. The complex light response is shown to stem from circadian gating of light-dependent mechanisms. This study provides insights into the contributions of light inputs to the Ostreococcus clock. The model suggests that a high number of light-dependent reactions are important for flexible timing in a circadian clock with only one feedback loop. link: http://identifiers.org/pubmed/21219507

Parameters:

Name Description
D_luc = 0.182881217463259 Reaction: toc1luc_1 =>, Rate Law: compartment*D_luc*toc1luc_1
parameter_3 = 0.0; L_toc1 = 1.0E-4; H_toc1_cca1 = 2.07807738692343; R_toc1_acc = 0.231107032949407; R_toc1_cca1 = 1.08706126858966 Reaction: => luc_mrna; acc, cca1_n, Rate Law: compartment*parameter_3*(1+0*time)*(L_toc1+R_toc1_acc*acc)/(1+L_toc1+R_toc1_acc*acc+(R_toc1_cca1*cca1_n)^H_toc1_cca1)
D_cca1_d = 0.269380178154091; D_cca1_l = 0.424177877449438 Reaction: cca1_c =>, Rate Law: compartment*(1+0*time)*(ceil(sin(pi*time/12)/2)*D_cca1_l+(1-ceil(sin(pi*time/12)/2))*D_cca1_d)*cca1_c
H_cca1_toc1 = 2.5007062880634; R_cca1_toc1_2_d = 1.38563901682266; parameter_4 = 0.0; R_cca1_toc1_2_l = 3.27520292103832 Reaction: => cca1luc_mrna; toc1_2, Rate Law: compartment*parameter_4*(1+0*time)*(1+0*time)*(toc1_2*(ceil(sin(pi*time/12)/2)*R_cca1_toc1_2_l+(1-ceil(sin(pi*time/12)/2))*R_cca1_toc1_2_d))^H_cca1_toc1/((toc1_2*(ceil(sin(pi*time/12)/2)*R_cca1_toc1_2_l+(1-ceil(sin(pi*time/12)/2))*R_cca1_toc1_2_d))^H_cca1_toc1+1)
Di_cca1_cn = 10.0 Reaction: cca1_c => cca1_n, Rate Law: compartment*Di_cca1_cn*cca1_c
T_toc1 = 0.769970172977886 Reaction: => toc1luc_1; toc1luc_mrna, Rate Law: compartment*(1+0*time)*T_toc1*toc1luc_mrna
D_mrna_luc = 1.0 Reaction: luc_mrna =>, Rate Law: compartment*D_mrna_luc*luc_mrna
D_toc1_2_l = 0.461550559180802; D_toc1_2_d = 0.356613920551118 Reaction: toc1luc_2 =>, Rate Law: compartment*(1+0*time)*(ceil(sin(pi*time/12)/2)*D_toc1_2_l+(1-ceil(sin(pi*time/12)/2))*D_toc1_2_d)*toc1luc_2
T_cca1 = 4.90486610428652 Reaction: => cca1_c; cca1_mrna, Rate Law: compartment*(1+0*time)*T_cca1*cca1_mrna
H_cca1_toc1 = 2.5007062880634; parameter_5 = 0.0; R_cca1_toc1_2_d = 1.38563901682266; R_cca1_toc1_2_l = 3.27520292103832 Reaction: => luc_mrna; toc1_2, Rate Law: compartment*parameter_5*(1+0*time)*(toc1_2*(ceil(sin(pi*time/12)/2)*R_cca1_toc1_2_l+(1-ceil(sin(pi*time/12)/2))*R_cca1_toc1_2_d))^H_cca1_toc1/((toc1_2*(ceil(sin(pi*time/12)/2)*R_cca1_toc1_2_l+(1-ceil(sin(pi*time/12)/2))*R_cca1_toc1_2_d))^H_cca1_toc1+1)
D_mrna_cca1 = 1.33082080954527 Reaction: cca1_mrna =>, Rate Law: compartment*D_mrna_cca1*cca1_mrna
Di_toc1_12_l = 0.136490583368648; Di_toc1_12_d = 0.326619492089715 Reaction: toc1_1 => toc1_2, Rate Law: compartment*(ceil(sin(pi*time/12)/2)*Di_toc1_12_l+(1-ceil(sin(pi*time/12)/2))*Di_toc1_12_d)*toc1_1
acc_rate = 0.0820132250303287 Reaction: => acc, Rate Law: compartment*acc_rate*ceil(sin(pi*time/12)/2)
H_cca1_toc1 = 2.5007062880634; R_cca1_toc1_2_d = 1.38563901682266; parameter_2 = 0.0; effcopies_toc1_TOC8 = 1.0; R_cca1_toc1_2_l = 3.27520292103832 Reaction: => cca1_mrna; toc1_2, Rate Law: compartment*(1+0*time)*(0*time+(1+0*time)*((1+parameter_2*(effcopies_toc1_TOC8-1))*toc1_2*(ceil(sin(pi*time/12)/2)*R_cca1_toc1_2_l+(1-ceil(sin(pi*time/12)/2))*R_cca1_toc1_2_d))^H_cca1_toc1/(((1+parameter_2*(effcopies_toc1_TOC8-1))*toc1_2*(ceil(sin(pi*time/12)/2)*R_cca1_toc1_2_l+(1-ceil(sin(pi*time/12)/2))*R_cca1_toc1_2_d))^H_cca1_toc1+1))
parameter_1 = 1.0 Reaction: => luc; luc_mrna, Rate Law: compartment*(1+0*time)*parameter_1*luc_mrna
L_toc1 = 1.0E-4; effcopies_cca1_LHY7 = 1.13965755508623; H_toc1_cca1 = 2.07807738692343; R_toc1_acc = 0.231107032949407; R_toc1_cca1 = 1.08706126858966; parameter_4 = 0.0 Reaction: => toc1_mrna; acc, cca1_n, Rate Law: compartment*(1+0*time)*(0*time+(1+0*time)*(L_toc1+R_toc1_acc*acc)/(1+L_toc1+R_toc1_acc*acc+(R_toc1_cca1*(1+parameter_4*(effcopies_cca1_LHY7-1))*cca1_n)^H_toc1_cca1))
L_toc1 = 1.0E-4; parameter_2 = 0.0; H_toc1_cca1 = 2.07807738692343; R_toc1_acc = 0.231107032949407; R_toc1_cca1 = 1.08706126858966 Reaction: => toc1luc_mrna; acc, cca1_n, Rate Law: compartment*parameter_2*(1+0*time)*(1+0*time)*(L_toc1+R_toc1_acc*acc)/(1+L_toc1+R_toc1_acc*acc+(R_toc1_cca1*cca1_n)^H_toc1_cca1)
D_mrna_toc1 = 0.29213049778373 Reaction: toc1luc_mrna =>, Rate Law: compartment*D_mrna_toc1*toc1luc_mrna

States:

Name Description
cca1 n [Protein CCA1]
toc1 mrna [messenger RNA]
toc1luc mrna [messenger RNA]
cca1 c [Protein CCA1]
toc1 2 [Two-component response regulator-like APRR1]
cca1 mrna [messenger RNA]
cca1luc [Protein CCA1]
toc1 1 [Two-component response regulator-like APRR1]
toc1luc 1 [Two-component response regulator-like APRR1]
luc mrna [messenger RNA]
toc1luc 2 [Two-component response regulator-like APRR1]
luc [luciferin]
cca1luc mrna [messenger RNA]
acc acc

Observables: none

During the early development of Xenopus laevis embryos, the first mitotic cell cycle is long (∼85 min) and the subsequen…

During the early development of Xenopus laevis embryos, the first mitotic cell cycle is long (∼85 min) and the subsequent 11 cycles are short (∼30 min) and clock-like. Here we address the question of how the Cdk1 cell cycle oscillator changes between these two modes of operation. We found that the change can be attributed to an alteration in the balance between Wee1/Myt1 and Cdc25. The change in balance converts a circuit that acts like a positive-plus-negative feedback oscillator, with spikes of Cdk1 activation, to one that acts like a negative-feedback-only oscillator, with a shorter period and smoothly varying Cdk1 activity. Shortening the first cycle, by treating embryos with the Wee1A/Myt1 inhibitor PD0166285, resulted in a dramatic reduction in embryo viability, and restoring the length of the first cycle in inhibitor-treated embryos with low doses of cycloheximide partially rescued viability. Computations with an experimentally parameterized mathematical model show that modest changes in the Wee1/Cdc25 ratio can account for the observed qualitative changes in the cell cycle. The high ratio in the first cycle allows the period to be long and tunable, and decreasing the ratio in the subsequent cycles allows the oscillator to run at a maximal speed. Thus, the embryo rewires its feedback regulation to meet two different developmental requirements during early development. link: http://identifiers.org/pubmed/24523664

Parameters:

Name Description
ec50_plx = 60.0; n_plx = 5.0; k_plxon = 1.5 Reaction: => Plx1_active; Cyclin_B1_Cdk1_complex_phosphorylated, Plx1_total, Rate Law: nuclear*k_plxon/(1+(ec50_plx/Cyclin_B1_Cdk1_complex_phosphorylated)^n_plx)*(Plx1_total-Plx1_active)
k_cdk1_on = 0.0354; n_cdc25 = 11.0; r = 0.499999924670036; ec50_cdc25 = 30.0; p = 5.0 Reaction: Cyclin_B1_Cdk1_complex_unphosphorylated => Cyclin_B1_Cdk1_complex_phosphorylated, Rate Law: nuclear*1/r^(1/2)*k_cdk1_on*(1+p/(1+(ec50_cdc25/Cyclin_B1_Cdk1_complex_phosphorylated)^n_cdc25))*Cyclin_B1_Cdk1_complex_unphosphorylated
k_synth = 1.5 Reaction: => Cyclin_B1_Cdk1_complex_phosphorylated, Rate Law: nuclear*k_synth
k_cdk1_off = 0.0354; r = 0.499999924670036; n_wee1 = 3.5; p = 5.0; ec50_wee1 = 35.0 Reaction: Cyclin_B1_Cdk1_complex_phosphorylated => Cyclin_B1_Cdk1_complex_unphosphorylated, Rate Law: nuclear*r^(1/2)*k_cdk1_off*(1+p/((Cyclin_B1_Cdk1_complex_phosphorylated/ec50_wee1)^n_wee1+1))*Cyclin_B1_Cdk1_complex_phosphorylated
k_apc_off = 0.15 Reaction: APC_C_active =>, Rate Law: nuclear*k_apc_off*APC_C_active
k_dest = 0.4 Reaction: Cyclin_B1_Cdk1_complex_phosphorylated => ; APC_C_active, Rate Law: nuclear*k_dest*APC_C_active*Cyclin_B1_Cdk1_complex_phosphorylated
k_plx_off = 0.125 Reaction: Plx1_active =>, Rate Law: nuclear*k_plx_off*Plx1_active
n_apc = 4.0; k_apc_on = 1.5; ec50_apc = 0.5 Reaction: => APC_C_active; Plx1_active, APC_C_total, Rate Law: nuclear*k_apc_on/(1+(ec50_apc/Plx1_active)^n_apc)*(APC_C_total-APC_C_active)

States:

Name Description
Cyclin B1 Cdk1 complex total [Cyclin-dependent kinase 1-A; G2/mitotic-specific cyclin-B1; protein-containing complex]
Cyclin B1 Cdk1 complex unphosphorylated [G2/mitotic-specific cyclin-B1; Cyclin-dependent kinase 1-A; protein-containing complex]
Cyclin B1 Cdk1 complex phosphorylated [G2/mitotic-specific cyclin-B1; Cyclin-dependent kinase 1-A; protein-containing complex]
Plx1 active [Serine/threonine-protein kinase PLK1; active]
APC C active [Anaphase-promoting complex subunit 16; active]

Observables: none

BIOMD0000000437 @ v0.0.1

Tseng2012 - Circadian clock of N.crassaA comprehensive model of the circardian clock of fungal Neurospora crassa , whic…

Circadian clocks provide an internal measure of external time allowing organisms to anticipate and exploit predictable daily changes in the environment. Rhythms driven by circadian clocks have a temperature compensated periodicity of approximately 24 hours that persists in constant conditions and can be reset by environmental time cues. Computational modelling has aided our understanding of the molecular mechanisms of circadian clocks, nevertheless it remains a major challenge to integrate the large number of clock components and their interactions into a single, comprehensive model that is able to account for the full breadth of clock phenotypes. Here we present a comprehensive dynamic model of the Neurospora crassa circadian clock that incorporates its key components and their transcriptional and post-transcriptional regulation. The model accounts for a wide range of clock characteristics including: a periodicity of 21.6 hours, persistent oscillation in constant conditions, arrhythmicity in constant light, resetting by brief light pulses, and entrainment to full photoperiods. Crucial components influencing the period and amplitude of oscillations were identified by control analysis. Furthermore, simulations enabled us to propose a mechanism for temperature compensation, which is achieved by simultaneously increasing the translation of frq RNA and decreasing the nuclear import of FRQ protein. link: http://identifiers.org/pubmed/22496627

Parameters:

Name Description
k_hypoWCCc = 0.472 substance Reaction: WC1c + WC2c => hypoWCCc; WC1c, WC2c, Rate Law: WC1c*WC2c*k_hypoWCCc
kd_L_WCC = 6.0 substance Reaction: L_WCC => degraded_L_WCCCVVDn; L_WCC, Rate Law: L_WCC*kd_L_WCC
kd_active_hypoWCCn = 1.29 substance Reaction: active_hypoWCCn => degraded_active_hypoWCCn; active_hypoWCCn, Rate Law: active_hypoWCCn*kd_active_hypoWCCn
kact_L_WCC = 0.0 substance Reaction: hypoWCCn => L_WCC; hypoWCCn, Rate Law: kact_L_WCC*hypoWCCn
kd_WC2c = 0.085 substance Reaction: WC2c => degraded_WC2c; WC2c, Rate Law: WC2c*kd_WC2c
I_hypoFRQn_hyperWCCn = 12.0 substance; kmaxp_hypoWCCn = 0.6 substance; Kmp_hypoFRQn_hyperWCCn = 0.475 substance Reaction: hypoWCCn => hyperWCCn; hypoFRQn, hypoWCCn, hypoFRQn, Rate Law: kmaxp_hypoWCCn*hypoWCCn*hypoFRQn^I_hypoFRQn_hyperWCCn/(Kmp_hypoFRQn_hyperWCCn^I_hypoFRQn_hyperWCCn+hypoFRQn^I_hypoFRQn_hyperWCCn)
kd_hyperFRQn = 0.27 substance Reaction: hyperFRQn => degraded_hyperFFCn; hyperFRQn, Rate Law: hyperFRQn*kd_hyperFRQn
kd_WCCVVD = 0.75 substance Reaction: L_WCCVVDn => degraded_L_WCCCVVDn; L_WCCVVDn, Rate Law: L_WCCVVDn*kd_WCCVVD
kout_hyperWCCn = 0.29 substance Reaction: hyperWCCn => hyperWCCc; hyperWCCn, Rate Law: hyperWCCn*kout_hyperWCCn
kadd_vvd_light_mRNA = 800.0 substance Reaction: vvd_gene => vvd_mRNA; L_WCC, L_WCC, Rate Law: kadd_vvd_light_mRNA*L_WCC
kp_hypoFRQn = 0.1 substance Reaction: hypoFRQn => hyperFRQn; hypoFRQn, Rate Law: hypoFRQn*kp_hypoFRQn
kin_hypoFRQc = 0.1 substance Reaction: hypoFRQc => hypoFRQn; hypoFRQc, Rate Law: kin_hypoFRQc*hypoFRQc
kdfrq_hypoFRQc = 0.356 substance; kd_frq = 2.0 substance Reaction: frq_mRNA => degraded_frq_mRNA; hypoFRQc, frq_mRNA, hypoFRQc, Rate Law: frq_mRNA*(kd_frq+hypoFRQc*kdfrq_hypoFRQc)
kact_hypoWCCn = 0.15 substance Reaction: hypoWCCn => active_hypoWCCn; hypoWCCn, Rate Law: hypoWCCn*kact_hypoWCCn
kd_VVDn = 0.24 substance Reaction: VVDn => degraded_VVDn; VVDn, Rate Law: VVDn*kd_VVDn
k_hypoFRQc = 0.19 substance Reaction: frq_mRNA => hypoFRQc; frq_mRNA, Rate Law: frq_mRNA*k_hypoFRQc
k_WCCVVD = 20.0 substance Reaction: VVDn + L_WCC => L_WCCVVDn; VVDn, L_WCC, Rate Law: VVDn*L_WCC*k_WCCVVD
k_min_wc1 = 1.19 substance; kadd_wc1 = 1.2 substance; kadd_L_wc1 = 90.0 substance Reaction: wc1_gene => wc1_mRNA; active_hypoWCCn, L_WCC, active_hypoWCCn, L_WCC, Rate Law: k_min_wc1+kadd_wc1*active_hypoWCCn+kadd_L_wc1*L_WCC
kp_hypoWCCc = 0.3 substance Reaction: hypoWCCc => hyperWCCc; hypoWCCc, Rate Law: hypoWCCc*kp_hypoWCCc
kout_hyperFRQn = 0.3 substance Reaction: hyperFRQn => hyperFRQc; hyperFRQn, Rate Law: hyperFRQn*kout_hyperFRQn
k_WC2c = 1.0 substance Reaction: wc2_mRNA => WC2c; wc2_mRNA, Rate Law: wc2_mRNA*k_WC2c
kout_hypoFRQn = 0.1 substance Reaction: hypoFRQn => hypoFRQc; hypoFRQn, Rate Law: hypoFRQn*kout_hypoFRQn
k_VVDc = 0.68 substance Reaction: vvd_mRNA => VVDc; vvd_mRNA, Rate Law: k_VVDc*vvd_mRNA
kd_VVDc = 0.24 substance Reaction: VVDc => degraded_VVDc; VVDc, Rate Law: VVDc*kd_VVDc
kd_hyperWCCc = 0.05 substance Reaction: hyperWCCc => degraded_hyperWCCc; hyperWCCc, Rate Law: hyperWCCc*kd_hyperWCCc
k_WC1c = 0.226 substance Reaction: wc1_mRNA => WC1c; wc1_mRNA, Rate Law: k_WC1c*wc1_mRNA
kin_hypoWCCc = 0.3 substance Reaction: hypoWCCc => hypoWCCn; hypoWCCc, Rate Law: hypoWCCc*kin_hypoWCCc
kd_hyperFRQc = 0.27 substance Reaction: hyperFRQc => degraded_hyperFRQc; hyperFRQc, Rate Law: hyperFRQc*kd_hyperFRQc
kd_wc1 = 2.4 substance Reaction: wc1_mRNA => degraded_wc1_mRNA; wc1_mRNA, Rate Law: wc1_mRNA*kd_wc1
kd_WC1c = 0.135 substance Reaction: WC1c => degraded_WC1c; WC1c, Rate Law: WC1c*kd_WC1c
kd_hyperWCCn = 0.05 substance Reaction: hyperWCCn => degraded_hyperWCCn; hyperWCCn, Rate Law: hyperWCCn*kd_hyperWCCn
kd_vvd_mRNA = 6.2 substance Reaction: vvd_mRNA => degraded_vvd_mRNA; vvd_mRNA, Rate Law: kd_vvd_mRNA*vvd_mRNA
kdp_hyperWCCc = 0.3 substance Reaction: hyperWCCc => hypoWCCc; hyperWCCc, Rate Law: hyperWCCc*kdp_hyperWCCc
kd_wc2 = 2.5 substance Reaction: wc2_mRNA => degraded_wc2_mRNA; wc2_mRNA, Rate Law: wc2_mRNA*kd_wc2
kadd_wc2_transcription_hypoFRQn = 0.03 substance; kmax_wc2 = 1.6 substance; ki_wc2_transcription = 0.03 substance Reaction: wc2_gene => wc2_mRNA; hypoFRQn, hypoWCCn, hypoWCCn, hypoFRQn, Rate Law: kmax_wc2*1/(1+hypoWCCn*ki_wc2_transcription)+hypoFRQn*kadd_wc2_transcription_hypoFRQn
k_dis_WCCVVD = 1.8 substance Reaction: L_WCCVVDn => hypoWCCn + VVDn; L_WCCVVDn, Rate Law: L_WCCVVDn*k_dis_WCCVVD
kin_VVDc = 0.3 substance Reaction: VVDc => VVDn; VVDc, Rate Law: kin_VVDc*VVDc
kp_hypoFRQc = 0.1 substance Reaction: hypoFRQc => hyperFRQc; hypoFRQc, Rate Law: hypoFRQc*kp_hypoFRQc
kmax_frq = 7.3 substance; A_active_hypoWCCn_frq = 4.0 substance; kadd_light_frq = 320.0 substance; Km_frq = 0.1 substance Reaction: frq_gene => frq_mRNA; active_hypoWCCn, L_WCC, active_hypoWCCn, L_WCC, Rate Law: kmax_frq*active_hypoWCCn^A_active_hypoWCCn_frq/(Km_frq^A_active_hypoWCCn_frq+active_hypoWCCn^A_active_hypoWCCn_frq)+kadd_light_frq*L_WCC

States:

Name Description
total FRQc [Frequency clock protein]
s61 [White collar 1 protein; White collar 2 protein]
degraded L WCCCVVDn degraded_L_WCCCVVDn
T [temperature]
L WCC [White collar 1 protein; White collar 2 protein]
degraded WC2c degraded_WC2c
degraded VVDn degraded_VVDn
degraded active hypoWCCn degraded_active_hypoWCCn
hypoFRQc [Frequency clock protein]
frq gene frq_gene
c hypoFRQ to hyperFRQ c_hypoFRQ_to_hyperFRQ
Period Period
vvd mRNA vvd_mRNA
total WC1 [White collar 1 protein]
degraded frq mRNA degraded_frq_mRNA
degraded VVDc degraded_VVDc
VVDc VVDc
wc2 gene wc2_gene
degraded hyperFFCn degraded_hyperFFCn
L WCCVVDn [White collar 1 protein; White collar 2 protein]
degraded wc2 mRNA degraded_wc2_mRNA
wc2 mRNA wc2_mRNA
hypoFRQn [Frequency clock protein]
frq mRNA [messenger RNA; Frequency clock protein]
total hypoWCC [White collar 1 protein; White collar 2 protein]
wc1 mRNA wc1_mRNA
hyperWCCc [White collar 1 protein; White collar 2 protein]
total WCCn [White collar 1 protein; White collar 2 protein]
hyperFRQn [Frequency clock protein]
degraded hyperWCCc degraded_hyperWCCc
hyperWCCn [White collar 1 protein; White collar 2 protein]
total VVD total_VVD
degraded hyperFRQc degraded_hyperFRQc
time time
total hypo FRQ [Frequency clock protein]
degraded vvd mRNA degraded_vvd_mRNA
total WC2 [White collar 2 protein]
total hyperWCC [White collar 1 protein; White collar 2 protein]
total hyper FRQ [Frequency clock protein]
hypoWCCn [White collar 1 protein; White collar 2 protein]
n hypoFRQ to hyperFRQ n_hypoFRQ_to_hyperFRQ
hypoWCCc [White collar 1 protein; White collar 2 protein]
degraded hyperWCCn degraded_hyperWCCn
VVDn VVDn
active hypoWCCn active_hypoWCCn
hyperFRQc hyperFRQc
WC1c [White collar 1 protein]
degraded wc1 mRNA degraded_wc1_mRNA
vvd gene vvd_gene
degraded WC1c degraded_WC1c
total FRQ [Frequency clock protein]
wc1 gene wc1_gene
WC2c [White collar 2 protein]
total FRQn [Frequency clock protein]

Observables: none

This is a simple mathematical model describing the interactions of an advanced melanoma tumor with both the immune syste…

BACKGROUND:At present, immune checkpoint inhibitors, such as pembrolizumab, are widely used in the therapy of advanced non-resectable melanoma, as they induce more durable responses than other available treatments. However, the overall response rate does not exceed 50% and, considering the high costs and low life expectancy of nonresponding patients, there is a need to select potential responders before therapy. Our aim was to develop a new personalization algorithm which could be beneficial in the clinical setting for predicting time to disease progression under pembrolizumab treatment. METHODS:We developed a simple mathematical model for the interactions of an advanced melanoma tumor with both the immune system and the immunotherapy drug, pembrolizumab. We implemented the model in an algorithm which, in conjunction with clinical pretreatment data, enables prediction of the personal patient response to the drug. To develop the algorithm, we retrospectively collected clinical data of 54 patients with advanced melanoma, who had been treated by pembrolizumab, and correlated personal pretreatment measurements to the mathematical model parameters. Using the algorithm together with the longitudinal tumor burden of each patient, we identified the personal mathematical models, and simulated them to predict the patient's time to progression. We validated the prediction capacity of the algorithm by the Leave-One-Out cross-validation methodology. RESULTS:Among the analyzed clinical parameters, the baseline tumor load, the Breslow tumor thickness, and the status of nodular melanoma were significantly correlated with the activation rate of CD8+ T cells and the net tumor growth rate. Using the measurements of these correlates to personalize the mathematical model, we predicted the time to progression of individual patients (Cohen's κ = 0.489). Comparison of the predicted and the clinical time to progression in patients progressing during the follow-up period showed moderate accuracy (R2 = 0.505). CONCLUSIONS:Our results show for the first time that a relatively simple mathematical mechanistic model, implemented in a personalization algorithm, can be personalized by clinical data, evaluated before immunotherapy onset. The algorithm, currently yielding moderately accurate predictions of individual patients' response to pembrolizumab, can be improved by training on a larger number of patients. Algorithm validation by an independent clinical dataset will enable its use as a tool for treatment personalization. link: http://identifiers.org/pubmed/31590677

Parameters: none

States: none

Observables: none

This is a simple mathematical population model for pembrolizumab-treated advanced melanoma patients, used to predict teh…

Immune checkpoint inhibitors (ICI) are becoming widely used in the treatment of metastatic melanoma. However, the ability to predict the patient's benefit from these therapeutics remains an unmet clinical need. Mathematical models that predict melanoma patients' response to ICI can contribute to better informed clinical decisions. Here, we developed a simple mathematical population model for pembrolizumab-treated advanced melanoma patients, and analyzed the local and global dynamics of the system. Our results show that zero, one, or two steady states of the mathematical system exist in the phase plane, depending on the parameter values of individual patients. Without treatment, the simulated tumors grew uncontrollably. At increased efficacy of the immune system, e.g., due to immunotherapy, two steady states were found, one leading to uncontrollable tumor growth, and the other resulting in tumor size stabilization. Model analysis indicates that a sufficient increase in the activation of CD8+ T cells results in stable disease, whereas a significant reduction in T-cell exhaustion, another process contributing CD8+ T cell activity, temporarily reduces the tumor mass, but fails to control disease progression in the long run. Importantly, the initial tumor burden influences the response to treatment: small tumors respond better to treatment than larger tumors. In conclusion, our model suggests that disease progression and response to ICI depend on the ratio between activation and exhaustion rates of CD8+ T cells. The analysis of the model provides a foundation for the use of computational methods to personalize immunotherapy. link: http://identifiers.org/pubmed/31580835

Parameters:

Name Description
mu_a = 0.231 Reaction: A =>, Rate Law: compartment*mu_a*A
b = 92330.0; alpha_A = 2986.0 Reaction: => A; M, Rate Law: compartment*alpha_A*M/(M+b)
gamma_mel = 0.04496 Reaction: => M, Rate Law: compartment*gamma_mel*M
nu_mel = 0.1245; g = 6.01E7 Reaction: M => ; T, Rate Law: compartment*nu_mel*T*M/(M+g)
mu_e = 0.1777 Reaction: T =>, Rate Law: compartment*mu_e*T
alpha_e = 831.8 Reaction: => T; A, Rate Law: compartment*alpha_e*A

States:

Name Description
A [trans-3-Hydroxycinnamate]
T [CD8-Positive T-Lymphocyte]
M [C36873]

Observables: none

Mathematical model of malaria transmission between humans and mosquitoes.

Mathematical models have the capability to incorporate statistical data so that infectious diseases can be studied in-depth. In this article, we use mathematical modeling to study malaria through a combination of the Susceptible, Exposed, Infectious and Recovered (SEIR) Model for humans; Susceptible, Exposed and Infectious (SEI) Model for mosquitos; and the Four Stage Life Cycle Model of the mosquito. Due to the fact that malaria is spread to humans through the bite of a female mosquito that has been infected by the plasmodium parasite, the impacts of mosquitos are also studied in this paper using the SEI Model. Finally, the growth of the mosquito population is directly related to the spread of malaria, the Four Stage Life Cycle is incorporated to model the effects of climate change and interspecies competition within the mosquito life cycle stages of Egg, Larvae, and Pupae. The combination of these models are used to show the growth and spread of malaria. link: http://identifiers.org/doi/10.1109/SECON.2015.7132968

Parameters: none

States: none

Observables: none

BIOMD0000000922 @ v0.0.1

the growth of the mosquito population is directly related to the spread of malaria, the Four Stage Life Cycle is incorpo…

Mathematical models have the capability to incorporate statistical data so that infectious diseases can be studied in-depth. In this article, we use mathematical modeling to study malaria through a combination of the Susceptible, Exposed, Infectious and Recovered (SEIR) Model for humans; Susceptible, Exposed and Infectious (SEI) Model for mosquitos; and the Four Stage Life Cycle Model of the mosquito. Due to the fact that malaria is spread to humans through the bite of a female mosquito that has been infected by the plasmodium parasite, the impacts of mosquitos are also studied in this paper using the SEI Model. Finally, the growth of the mosquito population is directly related to the spread of malaria, the Four Stage Life Cycle is incorporated to model the effects of climate change and interspecies competition within the mosquito life cycle stages of Egg, Larvae, and Pupae. The combination of these models are used to show the growth and spread of malaria. link: http://identifiers.org/doi/10.1109/SECON.2015.7132968

Parameters:

Name Description
Te = 0.361; ep = 30.0; Me = 0.05; Ar = 20.0 Reaction: => Population_of_Eggs, Rate Law: compartment*(Ar*ep-Population_of_Eggs*(Te+Me))
Te = 0.361; Ml = 0.0501; K0 = 2.0E-4; Tl = 0.134 Reaction: => Population_of_Larvae; Population_of_Eggs, Rate Law: compartment*((Population_of_Eggs*Te-Population_of_Larvae*(Tl+Ml))-K0*Population_of_Larvae^2)
Tl = 0.134; Tp = 0.342; Mp = 0.0025 Reaction: => Population_of_Pupae; Population_of_Larvae, Rate Law: compartment*(Population_of_Larvae*Tl-Population_of_Pupae*(Tp+Mp))

States:

Name Description
Population of Pupae [MIRO_30000050]
Population of Eggs [MIRO_30000049]
Population of Larvae [MIRO_30000028]

Observables: none

MODEL1005200000 @ v0.0.1

This is a model with 20 cells - 20 cytplasms and 21 apoplasts - as described in the article: **Stochastic and determin…

Stochastic and asymptotic methods are powerful tools in developing multiscale systems biology models; however, little has been done in this context to compare the efficacy of these methods. The majority of current systems biology modelling research, including that of auxin transport, uses numerical simulations to study the behaviour of large systems of deterministic ordinary differential equations, with little consideration of alternative modelling frameworks.In this case study, we solve an auxin-transport model using analytical methods, deterministic numerical simulations and stochastic numerical simulations. Although the three approaches in general predict the same behaviour, the approaches provide different information that we use to gain distinct insights into the modelled biological system. We show in particular that the analytical approach readily provides straightforward mathematical expressions for the concentrations and transport speeds, while the stochastic simulations naturally provide information on the variability of the system.Our study provides a constructive comparison which highlights the advantages and disadvantages of each of the considered modelling approaches. This will prove helpful to researchers when weighing up which modelling approach to select. In addition, the paper goes some way to bridging the gap between these approaches, which in the future we hope will lead to integrative hybrid models. link: http://identifiers.org/pubmed/20346112

Parameters: none

States: none

Observables: none

In the field of cardiac drug efficacy and safety assessment, information on drug concentration in heart tissue is desira…

In the field of cardiac drug efficacy and safety assessment, information on drug concentration in heart tissue is desirable. Because measuring drug concentrations in human cardiac tissue is challenging in healthy volunteers, mathematical models are used to cope with such limitations. With a goal of predicting drug concentration in cardiac tissue, we have developed a whole-body PBPK model consisting of seventeen perfusion-limited compartments. The proposed PBPK heart model consisted of four compartments: the epicardium, midmyocardium, endocardium, and pericardial fluid, and accounted for cardiac metabolism using CYP450. The model was written in R. The plasma:tissues partition coefficients (Kp) were calculated in Simcyp Simulator. The model was fitted to the concentrations of amitriptyline in plasma and the heart. The estimated parameters were as follows: 0.80 for the absorption rate [h-1], 52.6 for Kprest, 0.01 for the blood flow through the pericardial fluid [L/h], and 0.78 for the P-parameter describing the diffusion between the pericardial fluid and epicardium [L/h]. The total cardiac clearance of amitriptyline was calculated as 0.316 L/h. Although the model needs further improvement, the results support its feasibility, and it is a first attempt to provide an active drug concentration in various locations within heart tissue using a PBPK approach. link: http://identifiers.org/pubmed/28051093

Parameters: none

States: none

Observables: none

Tymoshenko2015 - Genome scale metabolic model - ToxoNet1This model is described in the article: [Metabolic Needs and Ca…

Toxoplasma gondii is a human pathogen prevalent worldwide that poses a challenging and unmet need for novel treatment of toxoplasmosis. Using a semi-automated reconstruction algorithm, we reconstructed a genome-scale metabolic model, ToxoNet1. The reconstruction process and flux-balance analysis of the model offer a systematic overview of the metabolic capabilities of this parasite. Using ToxoNet1 we have identified significant gaps in the current knowledge of Toxoplasma metabolic pathways and have clarified its minimal nutritional requirements for replication. By probing the model via metabolic tasks, we have further defined sets of alternative precursors necessary for parasite growth. Within a human host cell environment, ToxoNet1 predicts a minimal set of 53 enzyme-coding genes and 76 reactions to be essential for parasite replication. Double-gene-essentiality analysis identified 20 pairs of genes for which simultaneous deletion is deleterious. To validate several predictions of ToxoNet1 we have performed experimental analyses of cytosolic acetyl-CoA biosynthesis. ATP-citrate lyase and acetyl-CoA synthase were localised and their corresponding genes disrupted, establishing that each of these enzymes is dispensable for the growth of T. gondii, however together they make a synthetic lethal pair. link: http://identifiers.org/pubmed/26001086

Parameters: none

States: none

Observables: none

BIOMD0000000006 @ v0.0.1

Tyson1991 - Cell Cycle 2 varMathematical model of the interactions of cdc2 and cyclin. Description taken from the origi…

The proteins cdc2 and cyclin form a heterodimer (maturation promoting factor) that controls the major events of the cell cycle. A mathematical model for the interactions of cdc2 and cyclin is constructed. Simulation and analysis of the model show that the control system can operate in three modes: as a steady state with high maturation promoting factor activity, as a spontaneous oscillator, or as an excitable switch. We associate the steady state with metaphase arrest in unfertilized eggs, the spontaneous oscillations with rapid division cycles in early embryos, and the excitable switch with growth-controlled division cycles typical of nonembryonic cells. link: http://identifiers.org/pubmed/1831270

Parameters:

Name Description
k4 = 180.0; k4prime = 0.018 Reaction: z => u, Rate Law: k4*z*(k4prime/k4+u^2)
k4 = 180.0; alpha = NaN; k6 = 1.0 Reaction: u = k4*(v-u)*(alpha+u^2)-k6*u, Rate Law: k4*(v-u)*(alpha+u^2)-k6*u
kappa = 0.015; k6 = 1.0 Reaction: v = kappa-k6*u, Rate Law: kappa-k6*u
k6 = 1.0 Reaction: u => EmptySet, Rate Law: k6*u
kappa = 0.015 Reaction: EmptySet => z, Rate Law: kappa

States:

Name Description
v [Cyclin-dependent kinase 1; MPF complex; cyclin-dependent protein serine/threonine kinase activity]
u [cyclin-dependent protein serine/threonine kinase activity; MPF complex]
z [Cyclin-dependent kinase 1]

Observables: none

BIOMD0000000005 @ v0.0.1

Tyson1991 - Cell Cycle 6 varMathematical model of the interactions of cdc2 and cyclin. This model is described in the a…

The proteins cdc2 and cyclin form a heterodimer (maturation promoting factor) that controls the major events of the cell cycle. A mathematical model for the interactions of cdc2 and cyclin is constructed. Simulation and analysis of the model show that the control system can operate in three modes: as a steady state with high maturation promoting factor activity, as a spontaneous oscillator, or as an excitable switch. We associate the steady state with metaphase arrest in unfertilized eggs, the spontaneous oscillations with rapid division cycles in early embryos, and the excitable switch with growth-controlled division cycles typical of nonembryonic cells. link: http://identifiers.org/pubmed/1831270

Parameters:

Name Description
k7=0.6 Reaction: YP => EmptySet, Rate Law: cell*k7*YP
k8notP=1000000.0 Reaction: C2 => CP, Rate Law: cell*C2*k8notP
k5notP=0.0 Reaction: M => pM, Rate Law: cell*k5notP*M
k1aa=0.015 Reaction: EmptySet => Y, Rate Law: cell*k1aa
k9=1000.0 Reaction: CP => C2, Rate Law: cell*CP*k9
k4=180.0; k4prime=0.018 Reaction: pM => M; CT, Rate Law: cell*pM*(k4prime+k4*(M/CT)^2)
k6=1.0 Reaction: M => C2 + YP, Rate Law: cell*k6*M
k2=0.0 Reaction: Y => EmptySet, Rate Law: cell*k2*Y
k3=200.0 Reaction: CP + Y => pM, Rate Law: cell*CP*k3*Y

States:

Name Description
Y [IPR006670]
YT [IPR006670]
YP [IPR006670]
CT [Cyclin-dependent kinase 1]
C2 [Cyclin-dependent kinase 1]
CP [Cyclin-dependent kinase 1]
M [Cyclin-dependent kinase 1; IPR006670]
pM [Cyclin-dependent kinase 1; IPR006670]

Observables: none

BIOMD0000000036 @ v0.0.1

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedic…

Many organisms display rhythms of physiology and behavior that are entrained to the 24-h cycle of light and darkness prevailing on Earth. Under constant conditions of illumination and temperature, these internal biological rhythms persist with a period close to 1 day ("circadian"), but it is usually not exactly 24h. Recent discoveries have uncovered stunning similarities among the molecular circuitries of circadian clocks in mice, fruit flies, and bread molds. A consensus picture is coming into focus around two proteins (called PER and TIM in fruit flies), which dimerize and then inhibit transcription of their own genes. Although this picture seems to confirm a venerable model of circadian rhythms based on time-delayed negative feedback, we suggest that just as crucial to the circadian oscillator is a positive feedback loop based on stabilization of PER upon dimerization. These ideas can be expressed in simple mathematical form (phase plane portraits), and the model accounts naturally for several hallmarks of circadian rhythms, including temperature compensation and the per(L) mutant phenotype. In addition, the model suggests how an endogenous circadian oscillator could have evolved from a more primitive, light-activated switch. link: http://identifiers.org/pubmed/20540926

Parameters:

Name Description
D=0.1 Reaction: M => EmptySet, Rate Law: D*M*CYTOPLASM
Vm=1.0; Pcrit=0.1; Keq=200.0 Reaction: EmptySet => M; P, Rate Law: CYTOPLASM*Vm/(1+(P*(1-2/(1+(1+8*Keq*P)^0.5))/(2*Pcrit))^2)
k1=10.0; k2=0.03; Keq=200.0; J=0.05 Reaction: P => EmptySet, Rate Law: CYTOPLASM*(k1*P*2/(1+(1+8*Keq*P)^0.5)+k2*P)/(J+P)
V=0.5 Reaction: EmptySet => P; M, Rate Law: V*M*CYTOPLASM

States:

Name Description
M M
P [Period circadian protein]

Observables: none

BIOMD0000000195 @ v0.0.1

This model describes the budding yeast cell cycle model used in fig 8 a in Regulation of the eukaryotic cell cycle: mo…

In recent years, molecular biologists have uncovered a wealth of information about the proteins controlling cell growth and division in eukaryotes. The regulatory system is so complex that it defies understanding by verbal arguments alone. Quantitative tools are necessary to probe reliably into the details of cell cycle control. To this end, we convert hypothetical molecular mechanisms into sets of nonlinear ordinary differential equations and use standard analytical and numerical methods to study their solutions. First, we present a simple model of the antagonistic interactions between cyclin-dependent kinases and the anaphase promoting complex, which shows how progress through the cell cycle can be thought of as irreversible transitions (Start and Finish) between two stable states (G1 and S-G2-M) of the regulatory system. Then we add new pieces to the "puzzle" until we obtain reasonable models of the control systems in yeast cells, frog eggs, and cultured mammalian cells. link: http://identifiers.org/pubmed/11371178

Parameters:

Name Description
k11 = 1.0 Reaction: => CKIt, Rate Law: k11
TF = NaN; k13 = 1.0 Reaction: => SK, Rate Law: k13*TF
J3 = 0.04; k3pp = 10.0; k3p = 1.0 Reaction: => Cdh1; Cdc20a, Rate Law: (k3p+k3pp*Cdc20a)*(1-Cdh1)/((J3+1)-Cdh1)
k2pp = 1.0 Reaction: CycBt => ; Cdh1, Rate Law: k2pp*Cdh1*CycBt
k8 = 0.5; J8 = 0.001 Reaction: Cdc20a => ; Mad, Rate Law: k8*Mad*Cdc20a/(J8+Cdc20a)
k14 = 1.0 Reaction: SK =>, Rate Law: k14*SK
k2ppp = 1.0 Reaction: CycBt => ; Cdc20a, Rate Law: k2ppp*Cdc20a*CycBt
mmax = 10.0; mu = 0.005 Reaction: => m, Rate Law: mu*m*(1-m/mmax)
n = 4.0; k5p = 0.005; J5 = 0.3; k5pp = 0.2 Reaction: => Cdc20t; CycB, m, Rate Law: k5p+k5pp*(CycB*m/J5)^n/(1+(CycB*m/J5)^n)
J4 = 0.04; k4p = 2.0; k4 = 35.0 Reaction: Cdh1 => ; SK, m, CycB, Rate Law: (k4p*SK*Cdh1+k4*m*CycB*Cdh1)/(J4+Cdh1)
k1 = 0.04 Reaction: => CycBt, Rate Law: k1
k12pp = 50.0 Reaction: CKIt => ; SK, Rate Law: k12pp*SK*CKIt
k2p = 0.04 Reaction: CycBt =>, Rate Law: k2p*CycBt
k9 = 0.1 Reaction: => IEP; m, CycB, Rate Law: k9*m*CycB*(1-IEP)
Keq = 1000.0 Reaction: CycB = CycBt-2*CycBt*CKIt/(CycBt+CKIt+1/Keq+((CycBt+CKIt+1/Keq)^2-4*CycBt*CKIt)^(1/2)), Rate Law: missing
k10 = 0.02 Reaction: IEP =>, Rate Law: k10*IEP
k7 = 1.0; J7 = 0.001 Reaction: => Cdc20a; Cdc20t, IEP, Rate Law: k7*IEP*(Cdc20t-Cdc20a)/((J7+Cdc20t)-Cdc20a)
k6 = 0.1 Reaction: Cdc20a =>, Rate Law: k6*Cdc20a
k12ppp = 100.0 Reaction: CKIt => ; m, CycB, Rate Law: k12ppp*m*CycB*CKIt
k12p = 0.2 Reaction: CKIt =>, Rate Law: k12p*CKIt

States:

Name Description
Mad [Spindle assembly checkpoint component MAD1]
Cdc20a [APC/C activator protein CDC20]
Cdc20t [APC/C activator protein CDC20]
CKIt [Protein SIC1]
m [cell growth]
Trimer [Cyclin-dependent kinase 1; Protein SIC1; IPR015454]
IEP IEP
CycB [Cyclin-dependent kinase 1; IPR015454]
CycBt [Cyclin-dependent kinase 1; IPR015454; cyclin-dependent protein kinase holoenzyme complex; Cyclin-dependent kinase 1; S-phase entry cyclin-6; S-phase entry cyclin-5; G2/mitotic-specific cyclin-4; G2/mitotic-specific cyclin-3; G2/mitotic-specific cyclin-2; G2/mitotic-specific cyclin-1]
SK [G1/S-specific cyclin CLN1; G1/S-specific cyclin CLN2; IPR014399]
Cdh1 [APC/C activator protein CDH1]

Observables: none

BIOMD0000000306 @ v0.0.1

This is an SBML implementation the model of the activator inhibitor oscillator (figure 2b) described in the article: **…

The physiological responses of cells to external and internal stimuli are governed by genes and proteins interacting in complex networks whose dynamical properties are impossible to understand by intuitive reasoning alone. Recent advances by theoretical biologists have demonstrated that molecular regulatory networks can be accurately modeled in mathematical terms. These models shed light on the design principles of biological control systems and make predictions that have been verified experimentally. link: http://identifiers.org/pubmed/12648679

Parameters:

Name Description
Et = 1.0 M Reaction: E = Et-Ep, Rate Law: missing
k4 = 1.0 M_per_s; Km4 = NaN M Reaction: Ep => E, Rate Law: env*k4*Ep/(Km4+Ep)
k4 = 1.0 M_per_s; J3 = 0.3 dimensionless; J4 = 0.3 dimensionless; k3 = 1.0 per_s; Et = 1.0 M Reaction: Ep = 2*k3*R*J4/((k4-k3*R)+J3*k4+J4*k3*R+(((k4-k3*R)+J3*k4+J4*k3*R)^2-4*(k4-k3*R)*k3*R*J4)^(1/2))*Et, Rate Law: missing
k2_prime = 1.0 per_M_per_s Reaction: R => ; X, Rate Law: env*k2_prime*R*X
k5 = 0.1 per_s Reaction: => X; R, Rate Law: env*k5*R
k6 = 0.075 per_s Reaction: X =>, Rate Law: env*k6*X
k1 = 1.0 per_s Reaction: => R; S, Rate Law: env*k1*S
k0 = 4.0 per_s Reaction: => R; Ep, Rate Law: env*k0*Ep
k2 = 1.0 per_s Reaction: R =>, Rate Law: env*k2*R
k3 = 1.0 per_s; Km3 = NaN M Reaction: E => Ep; R, Rate Law: env*k3*R*E/(Km3+E)

States:

Name Description
X X
R R
E [protein]
Ep [phosphorylated residue; Phosphoprotein]

Observables: none

BIOMD0000000311 @ v0.0.1

This is an SBML implementation the model of mutual activation (figure 1e) described in the article: **Sniffers, buzzers…

The physiological responses of cells to external and internal stimuli are governed by genes and proteins interacting in complex networks whose dynamical properties are impossible to understand by intuitive reasoning alone. Recent advances by theoretical biologists have demonstrated that molecular regulatory networks can be accurately modeled in mathematical terms. These models shed light on the design principles of biological control systems and make predictions that have been verified experimentally. link: http://identifiers.org/pubmed/12648679

Parameters:

Name Description
k0 = 0.4 per_s Reaction: => R; Ep, Rate Law: env*k0*Ep
k3 = 1.0 per_s; J3 = 0.05 M Reaction: E => Ep; R, Rate Law: env*k3*R*E/(J3+E)
J4 = 0.05 M; k4 = 0.2 M_per_s; J3 = 0.05 M; k3 = 1.0 per_s Reaction: Ep = 2*k3*R*J4/((k4-k3*R)+J3*k4+J4*k3*R+(((k4-k3*R)+J3*k4+J4*k3*R)^2-4*(k4-k3*R)*k3*R*J4)^(1/2)), Rate Law: missing
Et = 1.0 M Reaction: E = Et-Ep, Rate Law: missing
k1 = 0.01 per_s Reaction: => R; S, Rate Law: env*k1*S
k2 = 1.0 per_s Reaction: R =>, Rate Law: env*k2*R
J4 = 0.05 M; k4 = 0.2 M_per_s Reaction: Ep => E, Rate Law: env*k4*Ep/(J4+Ep)

States:

Name Description
E [protein]
R [kinase activity; protein]
Ep [phosphorylated residue; Phosphoprotein]

Observables: none

BIOMD0000000310 @ v0.0.1

This is an SBML implementation the model of mutual inhibition (figure 1f) described in the article: **Sniffers, buzzers…

The physiological responses of cells to external and internal stimuli are governed by genes and proteins interacting in complex networks whose dynamical properties are impossible to understand by intuitive reasoning alone. Recent advances by theoretical biologists have demonstrated that molecular regulatory networks can be accurately modeled in mathematical terms. These models shed light on the design principles of biological control systems and make predictions that have been verified experimentally. link: http://identifiers.org/pubmed/12648679

Parameters:

Name Description
k2_prime = 0.5 per_M_per_s Reaction: R => ; E, Rate Law: env*k2_prime*R*E
k3 = 0.2 M_per_s; Km3 = NaN M Reaction: Ep => E, Rate Law: env*k3*Ep/(Km3+Ep)
k4 = 1.0 per_s; J4 = 0.05 dimensionless; k3 = 0.2 M_per_s; Et = 1.0 M; J3 = 0.05 dimensionless Reaction: E = Et*2*k3*J4/((k4*R-k3)+J3*k4*R+J4*k3+(((k4*R-k3)+J3*k4*R+J4*k3)^2-4*(k4*R-k3)*k3*J4)^(1/2)), Rate Law: missing
Et = 1.0 M Reaction: Ep = Et-E, Rate Law: missing
k2 = 0.1 per_s Reaction: R =>, Rate Law: env*k2*R
Km4 = NaN M; k4 = 1.0 per_s Reaction: E => Ep; R, Rate Law: env*k4*R*E/(Km4+E)
k1 = 0.05 per_s Reaction: => R; S, Rate Law: env*k1*S
k0 = 0.0 M_per_s Reaction: => R, Rate Law: env*k0

States:

Name Description
E [protein]
R [protein; kinase activity]
Ep [Phosphoprotein; phosphorylated residue]

Observables: none

BIOMD0000000309 @ v0.0.1

This is an SBML implementation the model of homeostastis by negative feedback (figure 1g) described in the article: **S…

The physiological responses of cells to external and internal stimuli are governed by genes and proteins interacting in complex networks whose dynamical properties are impossible to understand by intuitive reasoning alone. Recent advances by theoretical biologists have demonstrated that molecular regulatory networks can be accurately modeled in mathematical terms. These models shed light on the design principles of biological control systems and make predictions that have been verified experimentally. link: http://identifiers.org/pubmed/12648679

Parameters:

Name Description
k2 = 1.0 per_M_per_s Reaction: R => ; S, Rate Law: env*k2*R*S
k3 = 0.5 M_per_s; Km3 = NaN M Reaction: Ep => E, Rate Law: env*k3*Ep/(Km3+Ep)
k0 = 1.0 per_s Reaction: => R; E, Rate Law: env*k0*E
k4 = 1.0 per_s; J3 = 0.01 dimensionless; J4 = 0.01 dimensionless; k3 = 0.5 M_per_s; Et = 1.0 M Reaction: E = Et*2*k3*J4/((k4*R-k3)+J3*k4*R+J4*k3+(((k4*R-k3)+J3*k4*R+J4*k3)^2-4*(k4*R-k3)*k3*J4)^(1/2)), Rate Law: missing
Et = 1.0 M Reaction: Ep = Et-E, Rate Law: missing
Km4 = NaN M; k4 = 1.0 per_s Reaction: E => Ep; R, Rate Law: env*k4*R*E/(Km4+E)

States:

Name Description
R [protein; kinase activity]
E [protein]
Ep [Phosphoprotein; phosphorylated residue]

Observables: none

BIOMD0000000308 @ v0.0.1

Originally created by libAntimony v1.4 (using libSBML 3.4.1) This is an SBML implementation the model of negative feed…

The physiological responses of cells to external and internal stimuli are governed by genes and proteins interacting in complex networks whose dynamical properties are impossible to understand by intuitive reasoning alone. Recent advances by theoretical biologists have demonstrated that molecular regulatory networks can be accurately modeled in mathematical terms. These models shed light on the design principles of biological control systems and make predictions that have been verified experimentally. link: http://identifiers.org/pubmed/12648679

Parameters:

Name Description
Yt = 1.0 M Reaction: Y = Yt-Yp, Rate Law: missing
k0 = 0.0 M_per_s; k1 = 1.0 per_s Reaction: => X; S, Rate Law: env*(k0+k1*S)
Yt = 1.0 M; k3 = 0.1 per_s; Km3 = 0.01 M Reaction: Y => Yp; X, Rate Law: env*k3*X*(Yt-Yp)/(Km3+(Yt-Yp))
Rt = 1.0 M; Km5 = 0.01 M; k5 = 0.1 per_s Reaction: R => Rp; Yp, Rate Law: env*k5*Yp*(Rt-Rp)/(Km5+(Rt-Rp))
k6 = 0.05 M_per_s; Km6 = 0.01 M Reaction: Rp => R, Rate Law: env*k6*Rp/(Km6+Rp)
k4 = 0.2 M_per_s; Km4 = 0.01 M Reaction: Yp => Y, Rate Law: env*k4*Yp/(Km4+Yp)
k2_prime = 10.0 per_M_per_s; k2 = 0.01 per_s Reaction: X => ; Rp, Rate Law: env*(k2+k2_prime*Rp)*X
Rt = 1.0 M Reaction: R = Rt-Rp, Rate Law: missing

States:

Name Description
Y [protein]
X [protein]
Yp [Phosphoprotein; phosphorylated residue; kinase activity]
R [protein]
Rp [Phosphoprotein; phosphorylated residue]

Observables: none

BIOMD0000000312 @ v0.0.1

This is an SBML implementation the model of the perfect adaptor (figure 1d) described in the article: **Sniffers, buzze…

The physiological responses of cells to external and internal stimuli are governed by genes and proteins interacting in complex networks whose dynamical properties are impossible to understand by intuitive reasoning alone. Recent advances by theoretical biologists have demonstrated that molecular regulatory networks can be accurately modeled in mathematical terms. These models shed light on the design principles of biological control systems and make predictions that have been verified experimentally. link: http://identifiers.org/pubmed/12648679

Parameters:

Name Description
k3 = 1.0 per_s Reaction: => X; S, Rate Law: env*k3*S
k2 = 2.0 per_M_per_s Reaction: R => ; X, Rate Law: env*k2*R*X
k1 = 2.0 per_s Reaction: => R; S, Rate Law: env*k1*S
tau = 4.0 s Reaction: S = 1*floor(time/tau), Rate Law: missing
k4 = 1.0 per_s Reaction: X =>, Rate Law: env*k4*X

States:

Name Description
S S
X [protein]
R [protein]

Observables: none

BIOMD0000000307 @ v0.0.1

This is an SBML implementation the model of the substrate depletion oscillator (figure 2c) described in the article: **…

The physiological responses of cells to external and internal stimuli are governed by genes and proteins interacting in complex networks whose dynamical properties are impossible to understand by intuitive reasoning alone. Recent advances by theoretical biologists have demonstrated that molecular regulatory networks can be accurately modeled in mathematical terms. These models shed light on the design principles of biological control systems and make predictions that have been verified experimentally. link: http://identifiers.org/pubmed/12648679

Parameters:

Name Description
Km4 = NaN M; k4 = 0.3 M_per_s Reaction: Ep => E, Rate Law: env*k4*Ep/(Km4+Ep)
Et = 1.0 M Reaction: E = Et-Ep, Rate Law: missing
k0 = 0.4 per_M_per_s; k0_prime = 0.01 per_s Reaction: X => R; Ep, Rate Law: env*(k0_prime+k0*Ep)*X
k3 = 1.0 per_s; Km3 = NaN M Reaction: E => Ep; R, Rate Law: env*k3*R*E/(Km3+E)
k1 = 1.0 per_s Reaction: => X; S, Rate Law: env*k1*S
k2 = 1.0 per_s Reaction: R =>, Rate Law: env*k2*R
J4 = 0.05 dimensionless; k3 = 1.0 per_s; k4 = 0.3 M_per_s; J3 = 0.05 dimensionless; Et = 1.0 M Reaction: Ep = 2*k3*R*J4/((k4-k3*R)+J3*k4+J4*k3*R+(((k4-k3*R)+J3*k4+J4*k3*R)^2-4*(k4-k3*R)*k3*R*J4)^(1/2))*Et, Rate Law: missing

States:

Name Description
X X
R R
E [protein]
Ep [Phosphoprotein; phosphorylated residue]

Observables: none

U


BIOMD0000000022 @ v0.0.1

Bruce Shapiro: Generated by Cellerator Version 1.0 update 3.0303 using Mathematica 4.1 for Microsoft Windows (June 13, 2…

A mechanism for generating circadian rhythms has been of major interest in recent years. After the discovery of per and tim, a model with a simple feedback loop involving per and tim has been proposed. However, it is recognized that the simple feedback model cannot account for phenotypes generated by various mutants. A recent report by Glossop, Lyons & Hardin [Science286, 766 (1999)] on Drosophila suggests involvement of another feedback loop by dClk that is interlocked with per-tim feedback loop. In order to examine whether interlocked feedback loops can be a basic mechanism for circadian rhythms, a mathematical model was created and examined. Through extensive simulation and mathematical analysis, it was revealed that the interlocked feedback model accounts for the observations that are not explained by the simple feedback model. Moreover, the interlocked feedback model has robust properties in oscillations. link: http://identifiers.org/pubmed/11403560

Parameters:

Name Description
D9=0.6; L9=0.2 Reaction: CCc => EmptySet, Rate Law: compartment_0000003*CCc*D9/(CCc+L9)
L2=0.2; D2=0.44 Reaction: Perc => EmptySet; species_0000013, Rate Law: compartment_0000003*D2*species_0000013*Perc/(L2+Perc)
L10=0.2; D10=0.3 Reaction: CCn => EmptySet, Rate Law: compartment_0000002*CCn*D10/(CCn+L10)
L4=0.2; D4=0.44 Reaction: Timc => EmptySet, Rate Law: compartment_0000003*D4*Timc/(L4+Timc)
s6=0.47 Reaction: EmptySet => Clkc; Clkm, Rate Law: compartment_0000003*Clkm*s6
v3=1.63; parameter_0000073=1.63 Reaction: species_0000012 + Clkc => CCc, Rate Law: compartment_0000003*(Clkc*v3*species_0000012-parameter_0000073*CCc)
T3=1.63; k3=2.0 Reaction: CCc => CCn, Rate Law: compartment_0000003*CCc*T3/(k3+CCc)
T1=1.73; k1=2.0 Reaction: PTc => PTn, Rate Law: compartment_0000003*PTc*T1/(k1+PTc)
L6=0.2; D6=0.29 Reaction: PTn => EmptySet, Rate Law: compartment_0000002*D6*PTn/(L6+PTn)
s2=0.48 Reaction: EmptySet => Perc; Perm, Rate Law: compartment_0000003*s2*Perm
T2=0.72; k2=2.0 Reaction: PTn => PTc, Rate Law: compartment_0000002*PTn*T2/(k2+PTn)
D7=0.54; L7=0.13 Reaction: Clkm => EmptySet, Rate Law: compartment_0000003*Clkm*D7/(Clkm+L7)
L3=0.3; D3=0.94 Reaction: Timm => EmptySet, Rate Law: compartment_0000003*D3*Timm/(L3+Timm)
L1=0.3; D1=0.94 Reaction: Perm => EmptySet, Rate Law: compartment_0000003*D1*Perm/(L1+Perm)
L8=0.2; D8=0.6 Reaction: Clkc => EmptySet, Rate Law: compartment_0000003*Clkc*D8/(Clkc+L8)
A2=0.45; a=1.0; r2=1.02; c2=0.0; B2=0.0; r=4.0; s3=1.45 Reaction: EmptySet => Timm; CCn, PTn, Rate Law: compartment_0000003*(c2+(B2+(CCn/A2)^a)*s3/(1+B2+(CCn/A2)^a+(PTn/r2)^r))
D5=0.44; L5=0.2 Reaction: PTc => EmptySet, Rate Law: compartment_0000003*D5*PTc/(L5+PTc)
a=1.0; r3=0.89; s5=1.63; B3=0.6; A3=0.8; r=4.0; c3=0.0 Reaction: EmptySet => Clkm; PTn, CCn, Rate Law: compartment_0000003*(c3+(B3+(PTn/A3)^a)*s5/(1+B3+(PTn/A3)^a+(CCn/r3)^r))
s4=0.48 Reaction: EmptySet => Timc; Timm, Rate Law: compartment_0000003*s4*Timm
parameter_0000072=1.45; v1=1.45 Reaction: Perc + Timc => PTc, Rate Law: compartment_0000003*(Perc*Timc*v1-parameter_0000072*PTc)
s1=1.45; a=1.0; A1=0.45; r=4.0; B1=0.0; r1=1.02; c1=0.0 Reaction: EmptySet => Perm; CCn, PTn, Rate Law: compartment_0000003*(c1+(B1+(CCn/A1)^a)*s1/(1+B1+(CCn/A1)^a+(PTn/r1)^r))
D0=0.012 Reaction: CCc => EmptySet, Rate Law: compartment_0000003*CCc*D0
T4=0.52; k4=2.0 Reaction: CCn => CCc, Rate Law: compartment_0000002*CCn*T4/(k4+CCn)

States:

Name Description
Perc [Period circadian protein]
CCn [Protein cycle; Circadian locomoter output cycles protein kaput]
Perm [messenger RNA; RNA]
Clkc [Circadian locomoter output cycles protein kaput]
CCc [Circadian locomoter output cycles protein kaput; Protein cycle]
PTc [Period circadian protein; Protein timeless]
Clkm [messenger RNA; RNA]
PTn [Period circadian protein; Protein timeless]
Timc [Protein timeless]
Timm [messenger RNA; RNA]
species 0000012 [Protein cycle]

Observables: none

MODEL1006230044 @ v0.0.1

This a model from the article: Critical study of and improvements in chromatographic methods for the analysis of type…

A mechanism for generating circadian rhythms has been of major interest in recent years. After the discovery of per and tim, a model with a simple feedback loop involving per and tim has been proposed. However, it is recognized that the simple feedback model cannot account for phenotypes generated by various mutants. A recent report by Glossop, Lyons & Hardin [Science286, 766 (1999)] on Drosophila suggests involvement of another feedback loop by dClk that is interlocked with per-tim feedback loop. In order to examine whether interlocked feedback loops can be a basic mechanism for circadian rhythms, a mathematical model was created and examined. Through extensive simulation and mathematical analysis, it was revealed that the interlocked feedback model accounts for the observations that are not explained by the simple feedback model. Moreover, the interlocked feedback model has robust properties in oscillations. link: http://identifiers.org/pubmed/11403560

Parameters: none

States: none

Observables: none

Uhlén2015 - Human tissue-based proteome metabolic network - adipose Human adipose tissue specific proteome metabolic net…

Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body. link: http://identifiers.org/pubmed/25613900

Parameters: none

States: none

Observables: none

Uhlén2015 - Human tissue-based proteome metabolic network - adrenal Human adrenal specific proteome metabolic network…

Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body. link: http://identifiers.org/pubmed/25613900

Parameters: none

States: none

Observables: none

Uhlén2015 - Human tissue-based proteome metabolic network - apendix Human apendix specific proteome metabolic network…

Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body. link: http://identifiers.org/pubmed/25613900

Parameters: none

States: none

Observables: none

Uhlén2015 - Human tissue-based proteome metabolic network - bone marrow Human bone marrow tissue specific proteome metab…

Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body. link: http://identifiers.org/pubmed/25613900

Parameters: none

States: none

Observables: none

Uhlén2015 - Human tissue-based proteome metabolic network - brain Human brain specific proteome metabolic network This…

Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body. link: http://identifiers.org/pubmed/25613900

Parameters: none

States: none

Observables: none

Uhlén2015 - Human tissue-based proteome metabolic network - colon Human colon specific proteome metabolic network This…

Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body. link: http://identifiers.org/pubmed/25613900

Parameters: none

States: none

Observables: none

Uhlén2015 - Human tissue-based proteome metabolic network - duodenum Human duodenum specific proteome metabolic network…

Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body. link: http://identifiers.org/pubmed/25613900

Parameters: none

States: none

Observables: none

Uhlén2015 - Human tissue-based proteome metabolic network - endometrium Human endometrium specific proteome metabolic ne…

Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body. link: http://identifiers.org/pubmed/25613900

Parameters: none

States: none

Observables: none

Uhlén2015 - Human tissue-based proteome metabolic network - esophagus Human esophages specific proteome metabolic networ…

Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body. link: http://identifiers.org/pubmed/25613900

Parameters: none

States: none

Observables: none

Uhlén2015 - Human tissue-based proteome metabolic network -fallopian Human fallopian tube specific proteome metabolic ne…

Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body. link: http://identifiers.org/pubmed/25613900

Parameters: none

States: none

Observables: none

This SBML representation of the Homo sapiens generic metabolic network is made available under the Creative Commons Attr…

Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body. link: http://identifiers.org/pubmed/25613900

Parameters: none

States: none

Observables: none

Uhlén2015 - Human tissue-based proteome metabolic network - heart Human heart specific proteome metabolic network This…

Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body. link: http://identifiers.org/pubmed/25613900

Parameters: none

States: none

Observables: none

Uhlén2015 - Human tissue-based proteome metabolic network - kidney Human kidney specific proteome metabolic network Th…

Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body. link: http://identifiers.org/pubmed/25613900

Parameters: none

States: none

Observables: none

Uhlén2015 - Human tissue-based proteome metabolic network - liver Human liver specific proteome metabolic network This…

Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body. link: http://identifiers.org/pubmed/25613900

Parameters: none

States: none

Observables: none

Uhlén2015 - Human tissue-based proteome metabolic network - lung Human lung specific proteome metabolic network This m…

Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body. link: http://identifiers.org/pubmed/25613900

Parameters: none

States: none

Observables: none

Uhlén2015 - Human tissue-based proteome metabolic network - lymph node Human lymph node specific proteome metabolic netw…

Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body. link: http://identifiers.org/pubmed/25613900

Parameters: none

States: none

Observables: none

Uhlén2015 - Human tissue-based proteome metabolic network - ovary Human ovary specific proteome metabolic network This…

Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body. link: http://identifiers.org/pubmed/25613900

Parameters: none

States: none

Observables: none

Uhlén2015 - Human tissue-based proteome metabolic network - pancreas Human pancreas specific proteome metabolic network…

Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body. link: http://identifiers.org/pubmed/25613900

Parameters: none

States: none

Observables: none

Uhlén2015 - Human tissue-based proteome metabolic network - placenta Human placenta specific proteome metabolic network…

Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body. link: http://identifiers.org/pubmed/25613900

Parameters: none

States: none

Observables: none

Uhlén2015 - Human tissue-based proteome metabolic network - prostate Human prostate specific proteome metabolic network…

Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body. link: http://identifiers.org/pubmed/25613900

Parameters: none

States: none

Observables: none

Uhlén2015 - Human tissue-based proteome metabolic network - rectum Human rectum specific proteome metabolic network Th…

Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body. link: http://identifiers.org/pubmed/25613900

Parameters: none

States: none

Observables: none

Uhlén2015 - Human tissue-based proteome metabolic network - salivary gland Human salivary gland specific proteome metabo…

Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body. link: http://identifiers.org/pubmed/25613900

Parameters: none

States: none

Observables: none

Uhlén2015 - Human tissue-based proteome metabolic network - skeletal Human skeletal tissue specific proteome metabolic n…

Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body. link: http://identifiers.org/pubmed/25613900

Parameters: none

States: none

Observables: none

Uhlén2015 - Human tissue-based proteome metabolic network - skin Human skin specific proteome metabolic network This m…

Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body. link: http://identifiers.org/pubmed/25613900

Parameters: none

States: none

Observables: none

Uhlén2015 - Human tissue-based proteome metabolic network - small intestine Human small intestine specific proteome meta…

Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body. link: http://identifiers.org/pubmed/25613900

Parameters: none

States: none

Observables: none

Uhlén2015 - Human tissue-based proteome metabolic network - smooth muscle Human smooth muscle tissue specific proteome m…

Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body. link: http://identifiers.org/pubmed/25613900

Parameters: none

States: none

Observables: none

Uhlén2015 - Human tissue-based proteome metabolic network - spleen Human spleen specific proteome metabolic network Th…

Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body. link: http://identifiers.org/pubmed/25613900

Parameters: none

States: none

Observables: none

Uhlén2015 - Human tissue-based proteome metabolic network - stomach Human stomach specific proteome metabolic network…

Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body. link: http://identifiers.org/pubmed/25613900

Parameters: none

States: none

Observables: none

Uhlén2015 - Human tissue-based proteome metabolic network - testis Human testis specific proteome metabolic network Th…

Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body. link: http://identifiers.org/pubmed/25613900

Parameters: none

States: none

Observables: none

Uhlén2015 - Human tissue-based proteome metabolic network - thyroid Human thyroid tissue specific proteome metabolic net…

Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body. link: http://identifiers.org/pubmed/25613900

Parameters: none

States: none

Observables: none

This SBML representation of the Homo sapiens generic metabolic network is made available under the Creative Commons Attr…

Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body. link: http://identifiers.org/pubmed/25613900

Parameters: none

States: none

Observables: none

Uhlén2015 - Human tissue-based proteome metabolic network - urinary Human urinary track specific proteome metabolic netw…

Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body. link: http://identifiers.org/pubmed/25613900

Parameters: none

States: none

Observables: none

BIOMD0000000205 @ v0.0.1

Model reproduces the various plots in the publication for "Control" concentrations. Model successfully tested on MathSBM…

Deregulations of EGFR endocytosis in EGFR-ERK signaling are known to cause cancers and developmental disorders. Mutations that impaired c-Cbl-EGFR association delay EGFR endocytosis and produce higher mitogenic signals in lung cancer. ROCK, an effector of small GTPase RhoA was shown to negatively regulate EGFR endocytosis via endophilin A1. A mathematical model was developed to study how RhoA and ROCK regulate EGFR endocytosis. Our study suggested that over-expressing RhoA as well as ROCK prolonged ERK activation partly by reducing EGFR endocytosis. Overall, our study hypothesized an alternative role of RhoA in tumorigenesis in addition to its regulation of cytoskeleton and cell motility. link: http://identifiers.org/pubmed/18505685

Parameters:

Name Description
k1=0.001 sec_1; k2=10.0 uM_1_s_1 Reaction: species_62 => species_63 + species_57, Rate Law: compartment_0*(k1*species_62-k2*species_63*species_57)
k1=0.2661 sec_1 Reaction: species_6 => species_3 + species_5, Rate Law: compartment_0*k1*species_6
k2=1.67 sec_1; k1=5.0 uM_1_s_1 Reaction: species_166 + species_26 => species_167, Rate Law: compartment_0*(k1*species_166*species_26-k2*species_167)
k1=2.9 sec_1 Reaction: species_29 => species_25 + species_30, Rate Law: compartment_0*k1*species_29
k1=0.27 sec_1 Reaction: species_42 => species_33 + species_41, Rate Law: compartment_0*k1*species_42
k1=4.481 sec_1; k2=0.3 uM_1_s_1 Reaction: species_9 => species_4 + species_10, Rate Law: compartment_0*(k1*species_9-k2*species_4*species_10)
k2=0.1 sec_1; k1=8.898 uM_1_s_1 Reaction: species_35 + species_15 => species_46, Rate Law: compartment_0*(k1*species_35*species_15-k2*species_46)
k1=100.0 uM_1_s_1; k2=0.0038 sec_1 Reaction: species_0 + species_1 => species_2, Rate Law: compartment_0*(k1*species_0*species_1-k2*species_2)
k2=0.9356 sec_1; k1=40.0 uM_1_s_1 Reaction: species_101 + species_94 => species_102, Rate Law: compartment_0*(k1*species_101*species_94-k2*species_102)
k1=3.0 uM_1_s_1; k2=0.033 sec_1 Reaction: species_30 + species_33 => species_34, Rate Law: compartment_0*(k1*species_30*species_33-k2*species_34)
k1=3.0 uM_1_s_1; k2=0.5 sec_1 Reaction: species_4 + species_131 => species_133, Rate Law: compartment_0*(k1*species_4*species_131-k2*species_133)
k1=1.205 sec_1 Reaction: species_190 => species_143 + species_82 + species_95, Rate Law: compartment_0*k1*species_190
k2=1.0 sec_1; k1=3.0 uM_1_s_1 Reaction: species_58 + species_57 => species_59, Rate Law: compartment_0*(k1*species_58*species_57-k2*species_59)
k1=10.0 sec_1 Reaction: species_103 => species_101 + species_95, Rate Law: compartment_0*k1*species_103
k1=3.0 uM_1_s_1; k2=0.05 sec_1 Reaction: species_4 + species_12 => species_20, Rate Law: compartment_0*(k1*species_4*species_12-k2*species_20)
k2=0.01 sec_1; k1=0.1 uM_1_s_1 Reaction: species_4 + species_44 => species_87, Rate Law: compartment_0*(k1*species_4*species_44-k2*species_87)
k1=25.0 sec_1 Reaction: species_56 => species_52 + species_57, Rate Law: compartment_0*k1*species_56
k1=2.845 uM_1_s_1; k2=0.96 sec_1 Reaction: species_95 + species_86 => species_104, Rate Law: compartment_0*(k1*species_95*species_86-k2*species_104)
k2=0.0214 sec_1; k1=10.0 uM_1_s_1 Reaction: species_57 + species_73 => species_74, Rate Law: compartment_0*(k1*species_57*species_73-k2*species_74)
k1=10.0 uM_1_s_1; k2=0.02 sec_1 Reaction: species_2 => species_3, Rate Law: compartment_0*(k1*species_2*species_2-k2*species_3)
k1=3.0 sec_1 Reaction: species_61 => species_62 + species_60, Rate Law: compartment_0*k1*species_61
k1=2.014 sec_1 Reaction: species_3 => species_4, Rate Law: compartment_0*k1*species_3
k1=0.058 sec_1 Reaction: species_39 => species_28 + species_38, Rate Law: compartment_0*k1*species_39
k2=0.5 sec_1; k1=1.667 uM_1_s_1 Reaction: species_133 + species_25 => species_166, Rate Law: compartment_0*(k1*species_133*species_25-k2*species_166)
k1=5.0 uM_1_s_1; k2=0.5 sec_1 Reaction: species_33 + species_41 => species_43, Rate Law: compartment_0*(k1*species_33*species_41-k2*species_43)
k2=0.6 sec_1; k1=90.0 uM_1_s_1 Reaction: species_4 + species_7 => species_8, Rate Law: compartment_0*(k1*species_4*species_7-k2*species_8)
k1=1.67 sec_1 Reaction: species_158 => species_151 + species_35, Rate Law: compartment_0*k1*species_158
k1=5.0 sec_1 Reaction: species_72 => species_71 + species_55, Rate Law: compartment_0*k1*species_72
k1=1.1 uM_1_s_1; k2=0.033 sec_1 Reaction: species_68 + species_69 => species_70, Rate Law: compartment_0*(k1*species_68*species_69-k2*species_70)
k1=2.661 sec_1 Reaction: species_92 => species_3 + species_12 + species_90, Rate Law: compartment_0*k1*species_92
k1=0.1002 sec_1 Reaction: species_182 => species_183, Rate Law: compartment_0*k1*species_182
k1=0.25 uM_1_s_1; k2=0.5 sec_1 Reaction: species_28 + species_38 => species_40, Rate Law: compartment_0*(k1*species_28*species_38-k2*species_40)
k1=3.14 uM_1_s_1; k2=0.2 sec_1 Reaction: species_4 + species_5 => species_6, Rate Law: compartment_0*(k1*species_4*species_5-k2*species_6)
k2=1.0 sec_1; k1=8.898 uM_1_s_1 Reaction: species_35 + species_68 => species_129, Rate Law: compartment_0*(k1*species_35*species_68-k2*species_129)
k1=17.0 sec_1 Reaction: species_57 => species_55, Rate Law: compartment_0*k1*species_57
k1=1.693 sec_1 Reaction: species_163 => species_164, Rate Law: compartment_0*k1*species_163
k1=0.426 sec_1 Reaction: species_46 => species_35 + species_4 + species_10 + species_12 + species_47, Rate Law: compartment_0*k1*species_46
k1=0.1298 sec_1 Reaction: species_84 => species_94, Rate Law: compartment_0*k1*species_84
k1=16.0 sec_1 Reaction: species_32 => species_30 + species_33, Rate Law: compartment_0*k1*species_32
k1=4.0 uM_1_s_1; k2=0.01833 sec_1 Reaction: species_25 + species_26 => species_27, Rate Law: compartment_0*(k1*species_25*species_26-k2*species_27)
k1=16.67 uM_1_s_1; k2=0.05 sec_1 Reaction: species_145 + species_86 => species_176, Rate Law: compartment_0*(k1*species_145*species_86-k2*species_176)
k2=5.0 uM_1_s_1; k1=1.67 sec_1 Reaction: species_165 => species_162 + species_30, Rate Law: compartment_0*(k1*species_165-k2*species_162*species_30)

States:

Name Description
species 70 [Rho-associated protein kinase 1; Phosphatidylinositol 3,4,5-trisphosphate 3-phosphatase and dual-specificity protein phosphatase PTEN]
species 27 [RAF proto-oncogene serine/threonine-protein kinase; Dual specificity mitogen-activated protein kinase kinase 1]
species 71 [Phosphatidylinositol 3,4,5-trisphosphate 3-phosphatase and dual-specificity protein phosphatase PTEN]
species 31 [Mitogen-activated protein kinase 1]
species 62 [1-phosphatidyl-1D-myo-inositol 3,4,5-trisphosphate; RAC-alpha serine/threonine-protein kinase]
species 149 [GTP; Receptor protein-tyrosine kinase; Pro-epidermal growth factor; SHC-transforming protein 2; Growth factor receptor-bound protein 2; Ras-related protein R-Ras2; Dual specificity mitogen-activated protein kinase kinase 1; Mitogen-activated protein kinase kinase kinase 1]
species 184 [Receptor protein-tyrosine kinase; Pro-epidermal growth factor; SHC-transforming protein 2; Growth factor receptor-bound protein 2; RAF proto-oncogene serine/threonine-protein kinase; Dual specificity mitogen-activated protein kinase kinase 1; Mitogen-activated protein kinase 1]
species 4 [Receptor protein-tyrosine kinase; Pro-epidermal growth factor]
species 28 [Dual specificity mitogen-activated protein kinase kinase 1]
species 59 [1-phosphatidyl-1D-myo-inositol 3,4,5-trisphosphate; RAC-alpha serine/threonine-protein kinase]
species 61 [1-phosphatidyl-1D-myo-inositol 3,4,5-trisphosphate; RAC-alpha serine/threonine-protein kinase; 3-phosphoinositide-dependent protein kinase 1]
species 34 [Dual specificity mitogen-activated protein kinase kinase 1; Mitogen-activated protein kinase 1]
species 29 [RAF proto-oncogene serine/threonine-protein kinase; Dual specificity mitogen-activated protein kinase kinase 1]
species 30 [Dual specificity mitogen-activated protein kinase kinase 1]
species 166 [Receptor protein-tyrosine kinase; Pro-epidermal growth factor; Growth factor receptor-bound protein 2; RAF proto-oncogene serine/threonine-protein kinase]
species 57 [1-phosphatidyl-1D-myo-inositol 3,4,5-trisphosphate]
species 5 [Tyrosine-protein phosphatase non-receptor type 11]
species 94 [Rho guanine nucleotide exchange factor 12]
species 186 [Receptor protein-tyrosine kinase; Pro-epidermal growth factor; SHC-transforming protein 2; Growth factor receptor-bound protein 2; RAF proto-oncogene serine/threonine-protein kinase; Dual specificity mitogen-activated protein kinase kinase 1; Mitogen-activated protein kinase 1]
species 2 [Receptor protein-tyrosine kinase; Pro-epidermal growth factor]
species 163 [Receptor protein-tyrosine kinase; Pro-epidermal growth factor; SHC-transforming protein 2; Growth factor receptor-bound protein 2; RAF proto-oncogene serine/threonine-protein kinase; Dual specificity mitogen-activated protein kinase kinase 1]
species 183 [Receptor protein-tyrosine kinase; Pro-epidermal growth factor; Growth factor receptor-bound protein 2; Dual specificity mitogen-activated protein kinase kinase 1; Mitogen-activated protein kinase 1]
species 74 [1-phosphatidyl-1D-myo-inositol 3,4,5-trisphosphate; Rho guanine nucleotide exchange factor 6]
species 33 [Mitogen-activated protein kinase 1]
species 145 [Receptor protein-tyrosine kinase; Pro-epidermal growth factor; Growth factor receptor-bound protein 2; RAF proto-oncogene serine/threonine-protein kinase; Dual specificity mitogen-activated protein kinase kinase 1; Mitogen-activated protein kinase kinase kinase 1]
species 146 [Receptor protein-tyrosine kinase; Pro-epidermal growth factor; Growth factor receptor-bound protein 2; RAF proto-oncogene serine/threonine-protein kinase; Dual specificity mitogen-activated protein kinase kinase 1; Mitogen-activated protein kinase kinase kinase 1]
species 148 [GTP; Receptor protein-tyrosine kinase; Pro-epidermal growth factor; SHC-transforming protein 2; Growth factor receptor-bound protein 2; Ras-related protein R-Ras2; Mitogen-activated protein kinase kinase kinase 1]
species 72 [1-phosphatidyl-1D-myo-inositol 3,4,5-trisphosphate; Phosphatidylinositol 3,4,5-trisphosphate 3-phosphatase and dual-specificity protein phosphatase PTEN]
species 147 [Receptor protein-tyrosine kinase; Pro-epidermal growth factor; Growth factor receptor-bound protein 2; RAF proto-oncogene serine/threonine-protein kinase; Dual specificity mitogen-activated protein kinase kinase 1; Mitogen-activated protein kinase kinase kinase 1]
species 73 [Rho guanine nucleotide exchange factor 6]
species 69 [Phosphatidylinositol 3,4,5-trisphosphate 3-phosphatase and dual-specificity protein phosphatase PTEN]
species 95 pRhoGAP
species 164 [Receptor protein-tyrosine kinase; Pro-epidermal growth factor; SHC-transforming protein 2; Growth factor receptor-bound protein 2; RAF proto-oncogene serine/threonine-protein kinase; Dual specificity mitogen-activated protein kinase kinase 1]
species 35 [Mitogen-activated protein kinase 1]
species 3 [Receptor protein-tyrosine kinase; Pro-epidermal growth factor]
species 56 [1-phosphatidyl-1D-myo-inositol 3,4-bisphosphate; Phosphoinositide 3-kinase regulatory subunit 5]
species 58 [RAC-alpha serine/threonine-protein kinase]
species 165 [Receptor protein-tyrosine kinase; Pro-epidermal growth factor; SHC-transforming protein 2; Growth factor receptor-bound protein 2; RAF proto-oncogene serine/threonine-protein kinase; Dual specificity mitogen-activated protein kinase kinase 1]
species 26 [Dual specificity mitogen-activated protein kinase kinase 1]

Observables: none

Mathematical Modeling, Analysis, and Simulation of Tumor Dynamics with Drug Interventions Pranav Unni 1 and Padmanabhan…

Over the last few decades, there have been significant developments in theoretical, experimental, and clinical approaches to understand the dynamics of cancer cells and their interactions with the immune system. These have led to the development of important methods for cancer therapy including virotherapy, immunotherapy, chemotherapy, targeted drug therapy, and many others. Along with this, there have also been some developments on analytical and computational models to help provide insights into clinical observations. This work develops a new mathematical model that combines important interactions between tumor cells and cells in the immune systems including natural killer cells, dendritic cells, and cytotoxic CD8+ T cells combined with drug delivery to these cell sites. These interactions are described via a system of ordinary differential equations that are solved numerically. A stability analysis of this model is also performed to determine conditions for tumor-free equilibrium to be stable. We also study the influence of proliferation rates and drug interventions in the dynamics of all the cells involved. Another contribution is the development of a novel parameter estimation methodology to determine optimal parameters in the model that can reproduce a given dataset. Our results seem to suggest that the model employed is a robust candidate for studying the dynamics of tumor cells and it helps to provide the dynamic interactions between the tumor cells, immune system, and drug-response systems. link: http://identifiers.org/pubmed/31687042

Parameters:

Name Description
f_1 = 1.0E-8; d_2 = 4.0E-6; g = 0.024; d_3 = 1.0E-4 Reaction: D => ; L, N, T, Rate Law: compartment*(((f_1*L+d_2*N)-d_3*T)*D-g*D)
s_1 = 13000.0; g_1 = 0.0; h_1 = 0.0 Reaction: => N; T, Rate Law: compartment*(s_1+g_1*N*T*T/(h_1+T*T))
i = 0.02; h = 3.42E-10; u = 1.8E-8 Reaction: L => ; T, N, Rate Law: compartment*(h*L*T+u*N*L*L+i*L)
c_1 = 3.5E-6; j = 1.0E-7; k = 1.0E-7 Reaction: T => ; N, D, L, Rate Law: compartment*(c_1*N+j*D+k*L)*T
d_1 = 1.0E-6; c_2 = 1.0E-7; e = 0.0412 Reaction: N => ; T, D, Rate Law: compartment*((c_2*T+d_1*D)*N+e*N)
s_2 = 480.0 Reaction: => D, Rate Law: compartment*s_2
a = 0.431; b = 2.17E-8 Reaction: => T, Rate Law: compartment*a*T*(1-b*T)
f_2 = 0.01; r_1 = 0.0 Reaction: => L; D, T, N, Rate Law: compartment*(f_2*D*T+r_1*N*T)

States:

Name Description
T [Tumor Mass]
N [natural killer cell]
D [dendritic cell]
L [T-lymphocyte]

Observables: none

V


BIOMD0000000231 @ v0.0.1

This a model from the article: A kinetic study of a ternary cycle between adenine nucleotides. Valero E, Varón R, Ga…

In the present paper, a kinetic study is made of the behavior of a moiety-conserved ternary cycle between the adenine nucleotides. The system contains the enzymes S-acetyl coenzyme A synthetase, adenylate kinase and pyruvate kinase, and converts ATP into AMP, then into ADP and finally back to ATP. L-Lactate dehydrogenase is added to the system to enable continuous monitoring of the progress of the reaction. The cycle cannot work when the only recycling substrate in the reaction medium is AMP. A mathematical model is proposed whose kinetic behavior has been analyzed both numerically by integration of the nonlinear differential equations describing the kinetics of the reactions involved, and analytically under steady-state conditions, with good agreement with the experimental results being obtained. The data obtained showed that there is a threshold value of the S-acetyl coenzyme A synthetase/adenylate kinase ratio, above which the cycle stops because all the recycling substrate has been accumulated as AMP, never reaching the steady state. In addition, the concept of adenylate energy charge has been applied to the system, obtaining the enabled values of the rate constants for a fixed adenylate energy charge value and vice versa. link: http://identifiers.org/pubmed/16884499

Parameters:

Name Description
Vmapp1 = 2.3; Kmapp1 = 700.0 Reaction: ATP => AMP, Rate Law: Vmapp1*ATP/(Kmapp1+ATP)
Vmapp3 = 65.0; Kmapp3 = 260.0 Reaction: ADP => ATP + Pyr, Rate Law: Vmapp3*ADP/(Kmapp3+ADP)
K = 71000.0; Km2AMP = 110.0; Km2ATP = 25.0; Vm2 = 170.0 Reaction: ATP + AMP => ADP, Rate Law: Vm2*ATP*AMP/(K+Km2ATP*AMP+Km2AMP*ATP+ATP*AMP)
k4 = 5.0 Reaction: Pyr + NADH => Lac, Rate Law: k4*Pyr

States:

Name Description
ATP [ATP; ATP]
NADH [NADH; NADH]
ADP [ADP; ADP]
Pyr [IPR001697; Pyruvate kinase]
AMP [AMP; AMP]
Lac [L-lactate dehydrogenase; IPR011304]

Observables: none

Valero2016 - Ascorbate-Glutathione cycle in chloroplasts under light/dark conditionsThis model is described in the artic…

Light/dark cycles are probably the most important environmental signals that regulate plant development. Light is essential for photosynthesis, but an excess, in combination with the unavoidable presence of atmospheric oxygen inside the chloroplast, leads to excessive reactive oxygen species production. Among the defense mechanisms that activate plants to cope with environmental stress situations, it is worth noting the ascorbate-glutathione cycle, a complex metabolic pathway in which a variety of photochemical, chemical and enzymatic steps are involved.We herein studied the dynamic behavior of this pathway under light/dark conditions and for several consecutive days. For this purpose, a mathematical model was developed including a variable electron source with a rate law proportional to the intensity of solar irradiance during the photoperiod, and which is continuously turned off at night and on again the next day. The model is defined by a nonlinear system of ordinary differential equations with an on/off time-dependent input, including a parameter to simulate the fact that the photoperiod length is not constant throughout the year, and which takes into account the particular experimental kinetics of each enzyme involved in the pathway. Unlike previous models, which have only provided steady-state solutions, the present model is able to simulate diurnal fluctuations in the metabolite concentrations, fluxes and enzymatic rates involved in the network.The obtained results are broadly consistent with experimental observations and highlight the key role played by ascorbate recycling for plants to adapt to their surrounding environment. This approach provides a new strategy to in vivo studies to analyze plant defense mechanisms against oxidative stress induced by external changes, which can also be extrapolated to other complex metabolic pathways to constitute a useful tool to the scientific community in general. link: http://identifiers.org/pubmed/26797294

Parameters:

Name Description
k6 = 720.0; k3 = 0.01; F13 = 0.0; vDHAR = 0.0; k3APX = 7560.0; k2APX = 180000.0; k1 = 1800.0; k5 = 0.0072; vMDAR = 0.0 Reaction: ASC = (((((vDHAR+k1*MDA^2+k3*DHA*GSH+F13)-k2APX*ASC*CoI)-k3APX*ASC*CoII)-k6*O2neg*ASC)-2*k5*H2O2*ASC)+2*vMDAR, Rate Law: (((((vDHAR+k1*MDA^2+k3*DHA*GSH+F13)-k2APX*ASC*CoI)-k3APX*ASC*CoII)-k6*O2neg*ASC)-2*k5*H2O2*ASC)+2*vMDAR
k3 = 0.01; vDHAR = 0.0; k1 = 1800.0 Reaction: DHA = ((-vDHAR)+k1*MDA^2)-k3*DHA*GSH, Rate Law: ((-vDHAR)+k1*MDA^2)-k3*DHA*GSH
vGR = 0.0; k3 = 0.01; vDHAR = 0.0; k4 = 2520.0 Reaction: GSH = 2*(((vGR-vDHAR)-k4*O2neg*GSH)-k3*DHA*GSH), Rate Law: 2*(((vGR-vDHAR)-k4*O2neg*GSH)-k3*DHA*GSH)
k1APX = 43200.0; k3APX = 7560.0; Metabolite_17 = 40.0; k5APX = 1.0 Reaction: APX = (-k1APX)*H2O2*APX+k3APX*ASC*CoII+k5APX*(((Metabolite_17-APX)-CoI)-CoII), Rate Law: (-k1APX)*H2O2*APX+k3APX*ASC*CoII+k5APX*(((Metabolite_17-APX)-CoI)-CoII)
vGR = 0.0; kN = 3.97846553950471E-12; F12 = 7.95693107900941E-10; vMDAR = 0.0 Reaction: NADPH = (((-vGR)-kN*NADPH)+F12*0.5)-vMDAR, Rate Law: (((-vGR)-kN*NADPH)+F12*0.5)-vMDAR
k4APX = 2520.0 Reaction: APXi = k4APX*H2O2*CoI, Rate Law: k4APX*H2O2*CoI
k4APX = 2520.0; k1APX = 43200.0; k2APX = 180000.0 Reaction: CoI = (k1APX*H2O2*APX-k2APX*ASC*CoI)-k4APX*H2O2*CoI, Rate Law: (k1APX*H2O2*APX-k2APX*ASC*CoI)-k4APX*H2O2*CoI
k6 = 720.0; F13 = 0.0; k3APX = 7560.0; k2APX = 180000.0; k1 = 1800.0; k5 = 0.0072; vMDAR = 0.0 Reaction: MDA = ((((k2APX*ASC*CoI+k3APX*ASC*CoII)-2*k1*MDA^2)+k6*O2neg*ASC+2*k5*H2O2*ASC)-F13)-2*vMDAR, Rate Law: ((((k2APX*ASC*CoI+k3APX*ASC*CoII)-2*k1*MDA^2)+k6*O2neg*ASC+2*k5*H2O2*ASC)-F13)-2*vMDAR
k3APX = 7560.0; k2APX = 180000.0 Reaction: CoII = k2APX*ASC*CoI-k3APX*ASC*CoII, Rate Law: k2APX*ASC*CoI-k3APX*ASC*CoII
k2 = 720.0; k6 = 720.0; vSOD = 0.0; F11 = 1.35629466714537E-10; k4 = 2520.0 Reaction: O2neg = ((((-2)*vSOD+F11)-2*k2*O2neg^2)-k6*O2neg*ASC)-k4*O2neg*GSH, Rate Law: ((((-2)*vSOD+F11)-2*k2*O2neg^2)-k6*O2neg*ASC)-k4*O2neg*GSH
k2 = 720.0; k6 = 720.0; vSOD = 0.0; k4APX = 2520.0; k1APX = 43200.0; k5 = 0.0072; k4 = 2520.0 Reaction: H2O2 = (((vSOD-k1APX*H2O2*APX)-k4APX*H2O2*CoI)+k2*O2neg^2+k6*O2neg*ASC+k4*O2neg*GSH)-k5*H2O2*ASC, Rate Law: (((vSOD-k1APX*H2O2*APX)-k4APX*H2O2*CoI)+k2*O2neg^2+k6*O2neg*ASC+k4*O2neg*GSH)-k5*H2O2*ASC

States:

Name Description
O2neg [oxide(2-)]
APXi [Probable L-ascorbate peroxidase 4; inactive]
CoI [oxidising agent]
CoII [oxidising agent]
GSSG [glutathione; oxidized]
NADPH [NADPH]
DHA [dehydroascorbic acid]
ASC [ascorbate]
H2O2 [hydrogen peroxide]
NADPplus [NADP(+)]
MDA [monodehydro-L-ascorbic acid]
GSH [glutathione]
APX [Probable L-ascorbate peroxidase 4]

Observables: none

MODEL1006230027 @ v0.0.1

This a model from the article: Adenine nucleotide-creatine-phosphate module in myocardial metabolic system explains fa…

Computational models of a large metabolic system can be assembled from modules that represent a biological function emerging from interaction of a small subset of molecules. A "skeleton model" is tested here for a module that regulates the first phase of dynamic adaptation of oxidative phosphorylation (OxPhos) to demand in heart muscle cells. The model contains only diffusion, mitochondrial outer membrane (MOM) permeation, and two isoforms of creatine kinase (CK), in cytosol and mitochondrial intermembrane space (IMS), respectively. The communication with two neighboring modules occurs via stimulation of mitochondrial ATP production by ADP and P(i) from the IMS and via time-varying cytosolic ATP hydrolysis during contraction. Assuming normal cytosolic diffusion and high MOM permeability for ADP, the response time of OxPhos (t(mito); generalized time constant) to steps in cardiac pacing rate is predicted to be 2.4 s. In contrast, with low MOM permeability, t(mito) is predicted to be 15 s. An optimized MOM permeability of 21 mum/s gives t(mito) = 3.7 s, in agreement with experiments on rabbit heart with blocked glycolytic ATP synthesis. The model correctly predicts a lower t(mito) if CK activity is reduced by 98%. Among others, the following predictions result from the model analysis: 1) CK activity buffers large ADP oscillations; 2) ATP production is pulsatile in beating heart, although it adapts slowly to demand with "time constant" approximately 14 heartbeats; 3) if the muscle isoform of CK is overexpressed, OxPhos reacts slower to changing workload; and 4) if mitochondrial CK is overexpressed, OxPhos reacts faster. link: http://identifiers.org/pubmed/17581855

Parameters: none

States: none

Observables: none

Vanee2010 - Genome-scale metabolic model of Cryptosporidium hominis (iNV213)This model is described in the article: [A…

The apicomplexan Cryptosporidium is a protozoan parasite of humans and other mammals. Cryptosporidium species cause acute gastroenteritis and diarrheal disease in healthy humans and animals, and cause life-threatening infection in immunocompromised individuals such as people with AIDS. The parasite has a one-host life cycle and commonly invades intestinal epithelial cells. The current genome annotation of C. hominis, the most serious human pathogen, predicts 3884 genes of which ca. 1581 have predicted functional annotations. Using a combination of bioinformatics analysis, biochemical evidence, and high-throughput data, we have constructed a genome-scale metabolic model of C. hominis. The model is comprised of 213 gene-associated enzymes involved in 540 reactions among the major metabolic pathways and provides a link between the genotype and the phenotype of the organism, making it possible to study and predict behavior based upon genome content. This model was also used to analyze the two life stages of the parasite by integrating the stage-specific proteomic data for oocyst and sporozoite stages. Overall, this model provides a computational framework to systematically study and analyze various functional behaviors of C. hominis with respect to its life cycle and pathogenicity. link: http://identifiers.org/pubmed/20491062

Parameters: none

States: none

Observables: none

This is corresponding to the model of yeast glycolysis "glucose upshift" condition described in the paper "Testing Bioch…

A decade ago, a team of biochemists including two of us, modeled yeast glycolysis and showed that one of the most studied biochemical pathways could not be quite understood in terms of the kinetic properties of the constituent enzymes as measured in cell extract. Moreover, when the same model was later applied to different experimental steady-state conditions, it often exhibited unrestrained metabolite accumulation.Here we resolve this issue by showing that the results of such ab initio modeling are improved substantially by (i) including appropriate allosteric regulation and (ii) measuring the enzyme kinetic parameters under conditions that resemble the intracellular environment. The following modifications proved crucial: (i) implementation of allosteric regulation of hexokinase and pyruvate kinase, (ii) implementation of V(max) values measured under conditions that resembled the yeast cytosol, and (iii) redetermination of the kinetic parameters of glyceraldehyde-3-phosphate dehydrogenase under physiological conditions.Model predictions and experiments were compared under five different conditions of yeast growth and starvation. When either the original model was used (which lacked important allosteric regulation), or the enzyme parameters were measured under conditions that were, as usual, optimal for high enzyme activity, fructose 1,6-bisphosphate and some other glycolytic intermediates tended to accumulate to unrealistically high concentrations. Combining all adjustments yielded an accurate correspondence between model and experiments for all five steady-state and dynamic conditions. This enhances our understanding of in vivo metabolism in terms of in vitro biochemistry. link: http://identifiers.org/pubmed/22570597

Parameters: none

States: none

Observables: none

vanEunen2013 - Network dynamics of fatty acid β-oxidation (steady-state model)Lipid metabolism plays an important role i…

Fatty-acid metabolism plays a key role in acquired and inborn metabolic diseases. To obtain insight into the network dynamics of fatty-acid β-oxidation, we constructed a detailed computational model of the pathway and subjected it to a fat overload condition. The model contains reversible and saturable enzyme-kinetic equations and experimentally determined parameters for rat-liver enzymes. It was validated by adding palmitoyl CoA or palmitoyl carnitine to isolated rat-liver mitochondria: without refitting of measured parameters, the model correctly predicted the β-oxidation flux as well as the time profiles of most acyl-carnitine concentrations. Subsequently, we simulated the condition of obesity by increasing the palmitoyl-CoA concentration. At a high concentration of palmitoyl CoA the β-oxidation became overloaded: the flux dropped and metabolites accumulated. This behavior originated from the competition between acyl CoAs of different chain lengths for a set of acyl-CoA dehydrogenases with overlapping substrate specificity. This effectively induced competitive feedforward inhibition and thereby led to accumulation of CoA-ester intermediates and depletion of free CoA (CoASH). The mitochondrial [NAD⁺]/[NADH] ratio modulated the sensitivity to substrate overload, revealing a tight interplay between regulation of β-oxidation and mitochondrial respiration. link: http://identifiers.org/pubmed/23966849

Parameters:

Name Description
KmvlcadC14AcylCoAMAT = 4.0 uM; KmvlcadC14EnoylCoAMAT = 1.08 uM; KmvlcadFAD = 0.12 uM; KmvlcadFADH = 24.2 uM; KmvlcadC12AcylCoAMAT = 2.7 uM; sfvlcadC14=0.42 dimensionless; KmvlcadC12EnoylCoAMAT = 1.08 uM; Vvlcad = 0.008 uM per min per mgProtein; KmvlcadC16EnoylCoAMAT = 1.08 uM; KmvlcadC16AcylCoAMAT = 6.5 uM; Keqvlcad = 6.0 dimensionless Reaction: C14AcylCoAMAT => C14EnoylCoAMAT + FADHMAT; C16AcylCoAMAT, C12AcylCoAMAT, FADtMAT, C16EnoylCoAMAT, C12EnoylCoAMAT, C14AcylCoAMAT, C16AcylCoAMAT, C12AcylCoAMAT, FADtMAT, C14EnoylCoAMAT, C16EnoylCoAMAT, C12EnoylCoAMAT, FADHMAT, C14AcylCoAMAT, C16AcylCoAMAT, C12AcylCoAMAT, FADtMAT, C14EnoylCoAMAT, C16EnoylCoAMAT, C12EnoylCoAMAT, FADHMAT, Rate Law: sfvlcadC14*Vvlcad*(C14AcylCoAMAT*(FADtMAT-FADHMAT)/(KmvlcadC14AcylCoAMAT*KmvlcadFAD)-C14EnoylCoAMAT*FADHMAT/(KmvlcadC14AcylCoAMAT*KmvlcadFAD*Keqvlcad))/((1+C14AcylCoAMAT/KmvlcadC14AcylCoAMAT+C14EnoylCoAMAT/KmvlcadC14EnoylCoAMAT+C16AcylCoAMAT/KmvlcadC16AcylCoAMAT+C16EnoylCoAMAT/KmvlcadC16EnoylCoAMAT+C12AcylCoAMAT/KmvlcadC12AcylCoAMAT+C12EnoylCoAMAT/KmvlcadC12EnoylCoAMAT)*(1+(FADtMAT-FADHMAT)/KmvlcadFAD+FADHMAT/KmvlcadFADH))
KmlcadC8AcylCoAMAT = 123.0 uM; Vlcad = 0.01 uM per min per mgProtein; KmlcadC14EnoylCoAMAT = 1.08 uM; KmlcadFAD = 0.12 uM; KmlcadFADH = 24.2 uM; sflcadC10=0.75 dimensionless; KmlcadC12EnoylCoAMAT = 1.08 uM; KmlcadC8EnoylCoAMAT = 1.08 uM; Keqlcad = 6.0 dimensionless; KmlcadC16AcylCoAMAT = 2.5 uM; KmlcadC12AcylCoAMAT = 9.0 uM; KmlcadC10AcylCoAMAT = 24.3 uM; KmlcadC10EnoylCoAMAT = 1.08 uM; KmlcadC14AcylCoAMAT = 7.4 uM; KmlcadC16EnoylCoAMAT = 1.08 uM Reaction: C10AcylCoAMAT => C10EnoylCoAMAT + FADHMAT; C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C8AcylCoAMAT, FADtMAT, C16EnoylCoAMAT, C14EnoylCoAMAT, C12EnoylCoAMAT, C8EnoylCoAMAT, C10AcylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C8AcylCoAMAT, FADtMAT, C10EnoylCoAMAT, C16EnoylCoAMAT, C14EnoylCoAMAT, C12EnoylCoAMAT, C8EnoylCoAMAT, FADHMAT, C10AcylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C8AcylCoAMAT, FADtMAT, C10EnoylCoAMAT, C16EnoylCoAMAT, C14EnoylCoAMAT, C12EnoylCoAMAT, C8EnoylCoAMAT, FADHMAT, Rate Law: sflcadC10*Vlcad*(C10AcylCoAMAT*(FADtMAT-FADHMAT)/(KmlcadC10AcylCoAMAT*KmlcadFAD)-C10EnoylCoAMAT*FADHMAT/(KmlcadC10AcylCoAMAT*KmlcadFAD*Keqlcad))/((1+C10AcylCoAMAT/KmlcadC10AcylCoAMAT+C10EnoylCoAMAT/KmlcadC10EnoylCoAMAT+C16AcylCoAMAT/KmlcadC16AcylCoAMAT+C16EnoylCoAMAT/KmlcadC16EnoylCoAMAT+C14AcylCoAMAT/KmlcadC14AcylCoAMAT+C14EnoylCoAMAT/KmlcadC14EnoylCoAMAT+C12AcylCoAMAT/KmlcadC12AcylCoAMAT+C12EnoylCoAMAT/KmlcadC12EnoylCoAMAT+C8AcylCoAMAT/KmlcadC8AcylCoAMAT+C8EnoylCoAMAT/KmlcadC8EnoylCoAMAT)*(1+(FADtMAT-FADHMAT)/KmlcadFAD+FADHMAT/KmlcadFADH))
KmmcadC12AcylCoAMAT = 5.7 uM; KmmcadC8AcylCoAMAT = 4.0 uM; KmmcadC4AcylCoAMAT = 135.0 uM; KmmcadC10AcylCoAMAT = 5.4 uM; KmmcadFAD = 0.12 uM; KmmcadC10EnoylCoAMAT = 1.08 uM; KmmcadC4EnoylCoAMAT = 1.08 uM; Vmcad = 0.081 uM per min per mgProtein; KmmcadC6AcylCoAMAT = 9.4 uM; sfmcadC10=0.8 dimensionless; KmmcadC6EnoylCoAMAT = 1.08 uM; KmmcadFADH = 24.2 uM; KmmcadC12EnoylCoAMAT = 1.08 uM; KmmcadC8EnoylCoAMAT = 1.08 uM; Keqmcad = 6.0 dimensionless Reaction: C10AcylCoAMAT => C10EnoylCoAMAT + FADHMAT; C12AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, FADtMAT, C12EnoylCoAMAT, C8EnoylCoAMAT, C6EnoylCoAMAT, C4EnoylCoAMAT, C10AcylCoAMAT, C12AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, FADtMAT, C10EnoylCoAMAT, C12EnoylCoAMAT, C8EnoylCoAMAT, C6EnoylCoAMAT, C4EnoylCoAMAT, FADHMAT, C10AcylCoAMAT, C12AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, FADtMAT, C10EnoylCoAMAT, C12EnoylCoAMAT, C8EnoylCoAMAT, C6EnoylCoAMAT, C4EnoylCoAMAT, FADHMAT, Rate Law: sfmcadC10*Vmcad*(C10AcylCoAMAT*(FADtMAT-FADHMAT)/(KmmcadC10AcylCoAMAT*KmmcadFAD)-C10EnoylCoAMAT*FADHMAT/(KmmcadC10AcylCoAMAT*KmmcadFAD*Keqmcad))/((1+C10AcylCoAMAT/KmmcadC10AcylCoAMAT+C10EnoylCoAMAT/KmmcadC10EnoylCoAMAT+C12AcylCoAMAT/KmmcadC12AcylCoAMAT+C12EnoylCoAMAT/KmmcadC12EnoylCoAMAT+C8AcylCoAMAT/KmmcadC8AcylCoAMAT+C8EnoylCoAMAT/KmmcadC8EnoylCoAMAT+C6AcylCoAMAT/KmmcadC6AcylCoAMAT+C6EnoylCoAMAT/KmmcadC6EnoylCoAMAT+C4AcylCoAMAT/KmmcadC4AcylCoAMAT+C4EnoylCoAMAT/KmmcadC4EnoylCoAMAT)*(1+(FADtMAT-FADHMAT)/KmmcadFAD+FADHMAT/KmmcadFADH))
sfmckatC10=0.65 dimensionless; KmmckatC14AcylCoAMAT = 13.83 uM; KmmckatC10KetoacylCoAMAT = 2.1 uM; KmmckatCoAMAT = 26.6 uM; KmmckatC6KetoacylCoAMAT = 6.7 uM; Keqmckat = 1051.0 dimensionless; KmmckatC12KetoacylCoAMAT = 1.3 uM; KmmckatC8KetoacylCoAMAT = 3.2 uM; KmmckatC4AcetoacylCoAMAT = 12.4 uM; KmmckatC16KetoacylCoAMAT = 1.1 uM; KmmckatC12AcylCoAMAT = 13.83 uM; KmmckatC8AcylCoAMAT = 13.83 uM; KmmckatC10AcylCoAMAT = 13.83 uM; KmmckatC6AcylCoAMAT = 13.83 uM; Vmckat = 0.377 uM per min per mgProtein; KmmckatC16AcylCoAMAT = 13.83 uM; KmmckatC14KetoacylCoAMAT = 1.2 uM; KmmckatC4AcylCoAMAT = 13.83 uM; KmmckatAcetylCoAMAT = 30.0 uM Reaction: C10KetoacylCoAMAT => C8AcylCoAMAT + AcetylCoAMAT; C16KetoacylCoAMAT, C14KetoacylCoAMAT, C12KetoacylCoAMAT, C8KetoacylCoAMAT, C6KetoacylCoAMAT, C4AcetoacylCoAMAT, CoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, C10KetoacylCoAMAT, C16KetoacylCoAMAT, C14KetoacylCoAMAT, C12KetoacylCoAMAT, C8KetoacylCoAMAT, C6KetoacylCoAMAT, C4AcetoacylCoAMAT, CoAMAT, C8AcylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, AcetylCoAMAT, C10KetoacylCoAMAT, C16KetoacylCoAMAT, C14KetoacylCoAMAT, C12KetoacylCoAMAT, C8KetoacylCoAMAT, C6KetoacylCoAMAT, C4AcetoacylCoAMAT, CoAMAT, C8AcylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, AcetylCoAMAT, Rate Law: sfmckatC10*Vmckat*(C10KetoacylCoAMAT*CoAMAT/(KmmckatC10KetoacylCoAMAT*KmmckatCoAMAT)-C8AcylCoAMAT*AcetylCoAMAT/(KmmckatC10KetoacylCoAMAT*KmmckatCoAMAT*Keqmckat))/((1+C10KetoacylCoAMAT/KmmckatC10KetoacylCoAMAT+C8AcylCoAMAT/KmmckatC8AcylCoAMAT+C16KetoacylCoAMAT/KmmckatC16KetoacylCoAMAT+C16AcylCoAMAT/KmmckatC16AcylCoAMAT+C14KetoacylCoAMAT/KmmckatC14KetoacylCoAMAT+C14AcylCoAMAT/KmmckatC14AcylCoAMAT+C12KetoacylCoAMAT/KmmckatC12KetoacylCoAMAT+C12AcylCoAMAT/KmmckatC12AcylCoAMAT+C8KetoacylCoAMAT/KmmckatC8KetoacylCoAMAT+C10AcylCoAMAT/KmmckatC10AcylCoAMAT+C6KetoacylCoAMAT/KmmckatC6KetoacylCoAMAT+C6AcylCoAMAT/KmmckatC6AcylCoAMAT+C4AcetoacylCoAMAT/KmmckatC4AcetoacylCoAMAT+C4AcylCoAMAT/KmmckatC4AcylCoAMAT+AcetylCoAMAT/KmmckatAcetylCoAMAT)*(1+CoAMAT/KmmckatCoAMAT+AcetylCoAMAT/KmmckatAcetylCoAMAT))
KmscadFAD = 0.12 uM; KmscadC4EnoylCoAMAT = 1.08 uM; sfscadC4=1.0 dimensionless; Vscad = 0.081 uM per min per mgProtein; KmscadFADH = 24.2 uM; KmscadC4AcylCoAMAT = 10.7 uM; KmscadC6AcylCoAMAT = 285.0 uM; KmscadC6EnoylCoAMAT = 1.08 uM; Keqscad = 6.0 dimensionless Reaction: C4AcylCoAMAT => C4EnoylCoAMAT + FADHMAT; C6AcylCoAMAT, FADtMAT, C6EnoylCoAMAT, C4AcylCoAMAT, C6AcylCoAMAT, FADtMAT, C4EnoylCoAMAT, C6EnoylCoAMAT, FADHMAT, C4AcylCoAMAT, C6AcylCoAMAT, FADtMAT, C4EnoylCoAMAT, C6EnoylCoAMAT, FADHMAT, Rate Law: sfscadC4*Vscad*(C4AcylCoAMAT*(FADtMAT-FADHMAT)/(KmscadC4AcylCoAMAT*KmscadFAD)-C4EnoylCoAMAT*FADHMAT/(KmscadC4AcylCoAMAT*KmscadFAD*Keqscad))/((1+C4AcylCoAMAT/KmscadC4AcylCoAMAT+C4EnoylCoAMAT/KmscadC4EnoylCoAMAT+C6AcylCoAMAT/KmscadC6AcylCoAMAT+C6EnoylCoAMAT/KmscadC6EnoylCoAMAT)*(1+(FADtMAT-FADHMAT)/KmscadFAD+FADHMAT/KmscadFADH))
K1acesink=70.0 uM; Ksacesink=6000000.0 l per min per mgProtein Reaction: AcetylCoAMAT => ; AcetylCoAMAT, AcetylCoAMAT, Rate Law: Ksacesink*(AcetylCoAMAT-K1acesink)
KmcactC6AcylCarMAT=15.0 uM; Vfcact = 0.42 uM per min per mgProtein; KmcactCarCYT = 130.0 uM; KmcactC6AcylCarCYT=15.0 uM; KmcactCarMAT = 130.0 uM; Keqcact = 1.0 dimensionless; KicactCarCYT = 200.0 uM; KicactC6AcylCarCYT=56.0 uM; Vrcact = 0.42 uM per min per mgProtein Reaction: C6AcylCarCYT => C6AcylCarMAT; CarMAT, CarCYT, C6AcylCarCYT, CarMAT, C6AcylCarMAT, CarCYT, C6AcylCarCYT, CarMAT, C6AcylCarMAT, CarCYT, Rate Law: Vfcact*(C6AcylCarCYT*CarMAT-C6AcylCarMAT*CarCYT/Keqcact)/(C6AcylCarCYT*CarMAT+KmcactCarMAT*C6AcylCarCYT+KmcactC6AcylCarCYT*CarMAT*(1+CarCYT/KicactCarCYT)+Vfcact/(Vrcact*Keqcact)*(KmcactCarCYT*C6AcylCarMAT*(1+C6AcylCarCYT/KicactC6AcylCarCYT)+CarCYT*(KmcactC6AcylCarMAT+C6AcylCarMAT)))
KmmckatC14AcylCoAMAT = 13.83 uM; KmmckatC10KetoacylCoAMAT = 2.1 uM; sfmckatC6=1.0 dimensionless; KmmckatCoAMAT = 26.6 uM; KmmckatC6KetoacylCoAMAT = 6.7 uM; Keqmckat = 1051.0 dimensionless; KmmckatC12KetoacylCoAMAT = 1.3 uM; KmmckatC8KetoacylCoAMAT = 3.2 uM; KmmckatC4AcetoacylCoAMAT = 12.4 uM; KmmckatC16KetoacylCoAMAT = 1.1 uM; KmmckatC12AcylCoAMAT = 13.83 uM; KmmckatC8AcylCoAMAT = 13.83 uM; KmmckatC10AcylCoAMAT = 13.83 uM; KmmckatC6AcylCoAMAT = 13.83 uM; Vmckat = 0.377 uM per min per mgProtein; KmmckatC16AcylCoAMAT = 13.83 uM; KmmckatC14KetoacylCoAMAT = 1.2 uM; KmmckatC4AcylCoAMAT = 13.83 uM; KmmckatAcetylCoAMAT = 30.0 uM Reaction: C6KetoacylCoAMAT => C4AcylCoAMAT + AcetylCoAMAT; C16KetoacylCoAMAT, C14KetoacylCoAMAT, C12KetoacylCoAMAT, C10KetoacylCoAMAT, C8KetoacylCoAMAT, C4AcetoacylCoAMAT, CoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C6KetoacylCoAMAT, C16KetoacylCoAMAT, C14KetoacylCoAMAT, C12KetoacylCoAMAT, C10KetoacylCoAMAT, C8KetoacylCoAMAT, C4AcetoacylCoAMAT, CoAMAT, C4AcylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, AcetylCoAMAT, C6KetoacylCoAMAT, C16KetoacylCoAMAT, C14KetoacylCoAMAT, C12KetoacylCoAMAT, C10KetoacylCoAMAT, C8KetoacylCoAMAT, C4AcetoacylCoAMAT, CoAMAT, C4AcylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, AcetylCoAMAT, Rate Law: sfmckatC6*Vmckat*(C6KetoacylCoAMAT*CoAMAT/(KmmckatC6KetoacylCoAMAT*KmmckatCoAMAT)-C4AcylCoAMAT*AcetylCoAMAT/(KmmckatC6KetoacylCoAMAT*KmmckatCoAMAT*Keqmckat))/((1+C6KetoacylCoAMAT/KmmckatC6KetoacylCoAMAT+C4AcylCoAMAT/KmmckatC4AcylCoAMAT+C16KetoacylCoAMAT/KmmckatC16KetoacylCoAMAT+C16AcylCoAMAT/KmmckatC16AcylCoAMAT+C14KetoacylCoAMAT/KmmckatC14KetoacylCoAMAT+C14AcylCoAMAT/KmmckatC14AcylCoAMAT+C12KetoacylCoAMAT/KmmckatC12KetoacylCoAMAT+C12AcylCoAMAT/KmmckatC12AcylCoAMAT+C10KetoacylCoAMAT/KmmckatC10KetoacylCoAMAT+C10AcylCoAMAT/KmmckatC10AcylCoAMAT+C8KetoacylCoAMAT/KmmckatC8KetoacylCoAMAT+C8AcylCoAMAT/KmmckatC8AcylCoAMAT+C4AcetoacylCoAMAT/KmmckatC4AcetoacylCoAMAT+C6AcylCoAMAT/KmmckatC6AcylCoAMAT+AcetylCoAMAT/KmmckatAcetylCoAMAT)*(1+CoAMAT/KmmckatCoAMAT+AcetylCoAMAT/KmmckatAcetylCoAMAT))
KmcrotC16EnoylCoAMAT = 150.0 uM; KmcrotC12EnoylCoAMAT = 25.0 uM; KicrotC4AcetoacylCoA = 1.6 uM; KmcrotC4EnoylCoAMAT = 40.0 uM; Vcrot = 3.6 uM per min per mgProtein; Keqcrot = 3.13 dimensionless; KmcrotC14EnoylCoAMAT = 100.0 uM; KmcrotC8EnoylCoAMAT = 25.0 uM; KmcrotC10HydroxyacylCoAMAT = 45.0 uM; KmcrotC8HydroxyacylCoAMAT = 45.0 uM; KmcrotC6EnoylCoAMAT = 25.0 uM; KmcrotC10EnoylCoAMAT = 25.0 uM; KmcrotC14HydroxyacylCoAMAT = 45.0 uM; sfcrotC14=0.2 dimensionless; KmcrotC4HydroxyacylCoAMAT = 45.0 uM; KmcrotC16HydroxyacylCoAMAT = 45.0 uM; KmcrotC12HydroxyacylCoAMAT = 45.0 uM; KmcrotC6HydroxyacylCoAMAT = 45.0 uM Reaction: C14EnoylCoAMAT => C14HydroxyacylCoAMAT; C16EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, C6EnoylCoAMAT, C4EnoylCoAMAT, C16HydroxyacylCoAMAT, C12HydroxyacylCoAMAT, C10HydroxyacylCoAMAT, C8HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, C4HydroxyacylCoAMAT, C4AcetoacylCoAMAT, C14EnoylCoAMAT, C16EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, C6EnoylCoAMAT, C4EnoylCoAMAT, C14HydroxyacylCoAMAT, C16HydroxyacylCoAMAT, C12HydroxyacylCoAMAT, C10HydroxyacylCoAMAT, C8HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, C4HydroxyacylCoAMAT, C4AcetoacylCoAMAT, C14EnoylCoAMAT, C16EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, C6EnoylCoAMAT, C4EnoylCoAMAT, C14HydroxyacylCoAMAT, C16HydroxyacylCoAMAT, C12HydroxyacylCoAMAT, C10HydroxyacylCoAMAT, C8HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, C4HydroxyacylCoAMAT, C4AcetoacylCoAMAT, Rate Law: sfcrotC14*Vcrot*(C14EnoylCoAMAT/KmcrotC14EnoylCoAMAT-C14HydroxyacylCoAMAT/(KmcrotC14EnoylCoAMAT*Keqcrot))/(1+C14EnoylCoAMAT/KmcrotC14EnoylCoAMAT+C14HydroxyacylCoAMAT/KmcrotC14HydroxyacylCoAMAT+C16EnoylCoAMAT/KmcrotC16EnoylCoAMAT+C16HydroxyacylCoAMAT/KmcrotC16HydroxyacylCoAMAT+C12EnoylCoAMAT/KmcrotC12EnoylCoAMAT+C12HydroxyacylCoAMAT/KmcrotC12HydroxyacylCoAMAT+C10EnoylCoAMAT/KmcrotC10EnoylCoAMAT+C10HydroxyacylCoAMAT/KmcrotC10HydroxyacylCoAMAT+C8EnoylCoAMAT/KmcrotC8EnoylCoAMAT+C8HydroxyacylCoAMAT/KmcrotC8HydroxyacylCoAMAT+C6EnoylCoAMAT/KmcrotC6EnoylCoAMAT+C6HydroxyacylCoAMAT/KmcrotC6HydroxyacylCoAMAT+C4EnoylCoAMAT/KmcrotC4EnoylCoAMAT+C4HydroxyacylCoAMAT/KmcrotC4HydroxyacylCoAMAT+C4AcetoacylCoAMAT/KicrotC4AcetoacylCoA)
Vfcact = 0.42 uM per min per mgProtein; KmcactCarCYT = 130.0 uM; KmcactC10AcylCarCYT=15.0 uM; KicactC10AcylCarCYT=56.0 uM; KmcactC10AcylCarMAT=15.0 uM; KmcactCarMAT = 130.0 uM; Keqcact = 1.0 dimensionless; KicactCarCYT = 200.0 uM; Vrcact = 0.42 uM per min per mgProtein Reaction: C10AcylCarCYT => C10AcylCarMAT; CarMAT, CarCYT, C10AcylCarCYT, CarMAT, C10AcylCarMAT, CarCYT, C10AcylCarCYT, CarMAT, C10AcylCarMAT, CarCYT, Rate Law: Vfcact*(C10AcylCarCYT*CarMAT-C10AcylCarMAT*CarCYT/Keqcact)/(C10AcylCarCYT*CarMAT+KmcactCarMAT*C10AcylCarCYT+KmcactC10AcylCarCYT*CarMAT*(1+CarCYT/KicactCarCYT)+Vfcact/(Vrcact*Keqcact)*(KmcactCarCYT*C10AcylCarMAT*(1+C10AcylCarCYT/KicactC10AcylCarCYT)+CarCYT*(KmcactC10AcylCarMAT+C10AcylCarMAT)))
KmmtpC14EnoylCoAMAT = 25.0 uM; KmmtpC8EnoylCoAMAT = 25.0 uM; KmmtpC16AcylCoAMAT = 13.83 uM; KmmtpCoAMAT = 30.0 uM; KmmtpC16EnoylCoAMAT = 25.0 uM; KmmtpC12EnoylCoAMAT = 25.0 uM; KmmtpAcetylCoAMAT = 30.0 uM; Vmtp = 2.84 uM per min per mgProtein; KmmtpC12AcylCoAMAT = 13.83 uM; KmmtpC8AcylCoAMAT = 13.83 uM; KmmtpC14AcylCoAMAT = 13.83 uM; KmmtpC6AcylCoAMAT = 13.83 uM; KmmtpC10EnoylCoAMAT = 25.0 uM; Keqmtp = 0.71 dimensionless; KicrotC4AcetoacylCoA = 1.6 uM; KmmtpNADMAT = 60.0 uM; KmmtpNADHMAT = 50.0 uM; KmmtpC10AcylCoAMAT = 13.83 uM; sfmtpC14=0.9 dimensionless Reaction: C14EnoylCoAMAT => C12AcylCoAMAT + AcetylCoAMAT + NADHMAT; C16EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, NADtMAT, CoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcetoacylCoAMAT, C14EnoylCoAMAT, C16EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, NADtMAT, CoAMAT, C12AcylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, NADHMAT, AcetylCoAMAT, C4AcetoacylCoAMAT, C14EnoylCoAMAT, C16EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, NADtMAT, CoAMAT, C12AcylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, NADHMAT, AcetylCoAMAT, C4AcetoacylCoAMAT, Rate Law: sfmtpC14*Vmtp*(C14EnoylCoAMAT*(NADtMAT-NADHMAT)*CoAMAT/(KmmtpC14EnoylCoAMAT*KmmtpNADMAT*KmmtpCoAMAT)-C12AcylCoAMAT*NADHMAT*AcetylCoAMAT/(KmmtpC14EnoylCoAMAT*KmmtpNADMAT*KmmtpCoAMAT*Keqmtp))/((1+C14EnoylCoAMAT/KmmtpC14EnoylCoAMAT+C12AcylCoAMAT/KmmtpC12AcylCoAMAT+C16EnoylCoAMAT/KmmtpC16EnoylCoAMAT+C16AcylCoAMAT/KmmtpC16AcylCoAMAT+C12EnoylCoAMAT/KmmtpC12EnoylCoAMAT+C14AcylCoAMAT/KmmtpC14AcylCoAMAT+C10EnoylCoAMAT/KmmtpC10EnoylCoAMAT+C10AcylCoAMAT/KmmtpC10AcylCoAMAT+C8EnoylCoAMAT/KmmtpC8EnoylCoAMAT+C8AcylCoAMAT/KmmtpC8AcylCoAMAT+C6AcylCoAMAT/KmmtpC6AcylCoAMAT+C4AcetoacylCoAMAT/KicrotC4AcetoacylCoA)*(1+(NADtMAT-NADHMAT)/KmmtpNADMAT+NADHMAT/KmmtpNADHMAT)*(1+CoAMAT/KmmtpCoAMAT+AcetylCoAMAT/KmmtpAcetylCoAMAT))
Vfcact = 0.42 uM per min per mgProtein; KmcactCarCYT = 130.0 uM; KmcactCarMAT = 130.0 uM; Keqcact = 1.0 dimensionless; KicactCarCYT = 200.0 uM; KmcactC12AcylCarMAT=15.0 uM; KicactC12AcylCarCYT=56.0 uM; Vrcact = 0.42 uM per min per mgProtein; KmcactC12AcylCarCYT=15.0 uM Reaction: C12AcylCarCYT => C12AcylCarMAT; CarMAT, CarCYT, C12AcylCarCYT, CarMAT, C12AcylCarMAT, CarCYT, C12AcylCarCYT, CarMAT, C12AcylCarMAT, CarCYT, Rate Law: Vfcact*(C12AcylCarCYT*CarMAT-C12AcylCarMAT*CarCYT/Keqcact)/(C12AcylCarCYT*CarMAT+KmcactCarMAT*C12AcylCarCYT+KmcactC12AcylCarCYT*CarMAT*(1+CarCYT/KicactCarCYT)+Vfcact/(Vrcact*Keqcact)*(KmcactCarCYT*C12AcylCarMAT*(1+C12AcylCarCYT/KicactC12AcylCarCYT)+CarCYT*(KmcactC12AcylCarMAT+C12AcylCarMAT)))
KmmschadC16KetoacylCoAMAT = 1.4 uM; KmmschadC16HydroxyacylCoAMAT = 1.5 uM; Keqmschad = 2.17E-4 dimensionless; KmmschadC14HydroxyacylCoAMAT = 1.8 uM; Vmschad = 1.0 uM per min per mgProtein; KmmschadC6HydroxyacylCoAMAT = 28.6 uM; KmmschadC12KetoacylCoAMAT = 1.6 uM; KmmschadC4HydroxyacylCoAMAT = 69.9 uM; KmmschadC14KetoacylCoAMAT = 1.4 uM; KmmschadC12HydroxyacylCoAMAT = 3.7 uM; KmmschadC6KetoacylCoAMAT = 5.8 uM; KmmschadNADMAT = 58.5 uM; KmmschadC10HydroxyacylCoAMAT = 8.8 uM; KmmschadC8HydroxyacylCoAMAT = 16.3 uM; KmmschadC4AcetoacylCoAMAT = 16.9 uM; KmmschadC10KetoacylCoAMAT = 2.3 uM; KmmschadNADHMAT = 5.4 uM; sfmschadC16=0.6 dimensionless; KmmschadC8KetoacylCoAMAT = 4.1 uM Reaction: C16HydroxyacylCoAMAT => C16KetoacylCoAMAT + NADHMAT; C14HydroxyacylCoAMAT, C12HydroxyacylCoAMAT, C10HydroxyacylCoAMAT, C8HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, C4HydroxyacylCoAMAT, NADtMAT, C14KetoacylCoAMAT, C12KetoacylCoAMAT, C10KetoacylCoAMAT, C8KetoacylCoAMAT, C6KetoacylCoAMAT, C4AcetoacylCoAMAT, C16HydroxyacylCoAMAT, C14HydroxyacylCoAMAT, C12HydroxyacylCoAMAT, C10HydroxyacylCoAMAT, C8HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, C4HydroxyacylCoAMAT, NADtMAT, C16KetoacylCoAMAT, C14KetoacylCoAMAT, C12KetoacylCoAMAT, C10KetoacylCoAMAT, C8KetoacylCoAMAT, C6KetoacylCoAMAT, C4AcetoacylCoAMAT, NADHMAT, C16HydroxyacylCoAMAT, C14HydroxyacylCoAMAT, C12HydroxyacylCoAMAT, C10HydroxyacylCoAMAT, C8HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, C4HydroxyacylCoAMAT, NADtMAT, C16KetoacylCoAMAT, C14KetoacylCoAMAT, C12KetoacylCoAMAT, C10KetoacylCoAMAT, C8KetoacylCoAMAT, C6KetoacylCoAMAT, C4AcetoacylCoAMAT, NADHMAT, Rate Law: sfmschadC16*Vmschad*(C16HydroxyacylCoAMAT*(NADtMAT-NADHMAT)/(KmmschadC16HydroxyacylCoAMAT*KmmschadNADMAT)-C16KetoacylCoAMAT*NADHMAT/(KmmschadC16HydroxyacylCoAMAT*KmmschadNADMAT*Keqmschad))/((1+C16HydroxyacylCoAMAT/KmmschadC16HydroxyacylCoAMAT+C16KetoacylCoAMAT/KmmschadC16KetoacylCoAMAT+C14HydroxyacylCoAMAT/KmmschadC14HydroxyacylCoAMAT+C14KetoacylCoAMAT/KmmschadC14KetoacylCoAMAT+C12HydroxyacylCoAMAT/KmmschadC12HydroxyacylCoAMAT+C12KetoacylCoAMAT/KmmschadC12KetoacylCoAMAT+C10HydroxyacylCoAMAT/KmmschadC10HydroxyacylCoAMAT+C10KetoacylCoAMAT/KmmschadC10KetoacylCoAMAT+C8HydroxyacylCoAMAT/KmmschadC8HydroxyacylCoAMAT+C8KetoacylCoAMAT/KmmschadC8KetoacylCoAMAT+C6HydroxyacylCoAMAT/KmmschadC6HydroxyacylCoAMAT+C6KetoacylCoAMAT/KmmschadC6KetoacylCoAMAT+C4HydroxyacylCoAMAT/KmmschadC4HydroxyacylCoAMAT+C4AcetoacylCoAMAT/KmmschadC4AcetoacylCoAMAT)*(1+(NADtMAT-NADHMAT)/KmmschadNADMAT+NADHMAT/KmmschadNADHMAT))
Kmcpt2C10AcylCarMAT = 51.0 uM; Kmcpt2C14AcylCarMAT = 51.0 uM; Kmcpt2CoAMAT = 30.0 uM; Kmcpt2C6AcylCarMAT = 51.0 uM; Kmcpt2C14AcylCoAMAT = 38.0 uM; Keqcpt2 = 2.22 dimensionless; Kmcpt2C8AcylCoAMAT = 38.0 uM; Kmcpt2C6AcylCoAMAT = 1000.0 uM; Kmcpt2C16AcylCarMAT = 51.0 uM; Kmcpt2C12AcylCarMAT = 51.0 uM; Kmcpt2C12AcylCoAMAT = 38.0 uM; Vcpt2 = 0.391 uM per min per mgProtein; Kmcpt2C8AcylCarMAT = 51.0 uM; Kmcpt2CarMAT = 350.0 uM; sfcpt2C10=0.95 dimensionless; Kmcpt2C16AcylCoAMAT = 38.0 uM; Kmcpt2C4AcylCoAMAT = 1000000.0 uM; Kmcpt2C10AcylCoAMAT = 38.0 uM; Kmcpt2C4AcylCarMAT = 51.0 uM Reaction: C10AcylCarMAT => C10AcylCoAMAT; C16AcylCarMAT, C14AcylCarMAT, C12AcylCarMAT, C8AcylCarMAT, C6AcylCarMAT, C4AcylCarMAT, CoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, CarMAT, C10AcylCarMAT, C16AcylCarMAT, C14AcylCarMAT, C12AcylCarMAT, C8AcylCarMAT, C6AcylCarMAT, C4AcylCarMAT, CoAMAT, C10AcylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, CarMAT, C10AcylCarMAT, C16AcylCarMAT, C14AcylCarMAT, C12AcylCarMAT, C8AcylCarMAT, C6AcylCarMAT, C4AcylCarMAT, CoAMAT, C10AcylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, CarMAT, Rate Law: sfcpt2C10*Vcpt2*(C10AcylCarMAT*CoAMAT/(Kmcpt2C10AcylCarMAT*Kmcpt2CoAMAT)-C10AcylCoAMAT*CarMAT/(Kmcpt2C10AcylCarMAT*Kmcpt2CoAMAT*Keqcpt2))/((1+C10AcylCarMAT/Kmcpt2C10AcylCarMAT+C10AcylCoAMAT/Kmcpt2C10AcylCoAMAT+C16AcylCarMAT/Kmcpt2C16AcylCarMAT+C16AcylCoAMAT/Kmcpt2C16AcylCoAMAT+C14AcylCarMAT/Kmcpt2C14AcylCarMAT+C14AcylCoAMAT/Kmcpt2C14AcylCoAMAT+C12AcylCarMAT/Kmcpt2C12AcylCarMAT+C12AcylCoAMAT/Kmcpt2C12AcylCoAMAT+C8AcylCarMAT/Kmcpt2C8AcylCarMAT+C8AcylCoAMAT/Kmcpt2C8AcylCoAMAT+C6AcylCarMAT/Kmcpt2C6AcylCarMAT+C6AcylCoAMAT/Kmcpt2C6AcylCoAMAT+C4AcylCarMAT/Kmcpt2C4AcylCarMAT+C4AcylCoAMAT/Kmcpt2C4AcylCoAMAT)*(1+CoAMAT/Kmcpt2CoAMAT+CarMAT/Kmcpt2CarMAT))
KmcrotC16EnoylCoAMAT = 150.0 uM; KmcrotC12EnoylCoAMAT = 25.0 uM; KicrotC4AcetoacylCoA = 1.6 uM; KmcrotC4EnoylCoAMAT = 40.0 uM; Vcrot = 3.6 uM per min per mgProtein; Keqcrot = 3.13 dimensionless; KmcrotC14EnoylCoAMAT = 100.0 uM; KmcrotC8EnoylCoAMAT = 25.0 uM; KmcrotC10HydroxyacylCoAMAT = 45.0 uM; KmcrotC8HydroxyacylCoAMAT = 45.0 uM; KmcrotC6EnoylCoAMAT = 25.0 uM; KmcrotC10EnoylCoAMAT = 25.0 uM; KmcrotC14HydroxyacylCoAMAT = 45.0 uM; KmcrotC4HydroxyacylCoAMAT = 45.0 uM; sfcrotC4=1.0 dimensionless; KmcrotC16HydroxyacylCoAMAT = 45.0 uM; KmcrotC12HydroxyacylCoAMAT = 45.0 uM; KmcrotC6HydroxyacylCoAMAT = 45.0 uM Reaction: C4EnoylCoAMAT => C4HydroxyacylCoAMAT; C16EnoylCoAMAT, C14EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, C6EnoylCoAMAT, C16HydroxyacylCoAMAT, C14HydroxyacylCoAMAT, C12HydroxyacylCoAMAT, C10HydroxyacylCoAMAT, C8HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, C4AcetoacylCoAMAT, C4EnoylCoAMAT, C16EnoylCoAMAT, C14EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, C6EnoylCoAMAT, C4HydroxyacylCoAMAT, C16HydroxyacylCoAMAT, C14HydroxyacylCoAMAT, C12HydroxyacylCoAMAT, C10HydroxyacylCoAMAT, C8HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, C4AcetoacylCoAMAT, C4EnoylCoAMAT, C16EnoylCoAMAT, C14EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, C6EnoylCoAMAT, C4HydroxyacylCoAMAT, C16HydroxyacylCoAMAT, C14HydroxyacylCoAMAT, C12HydroxyacylCoAMAT, C10HydroxyacylCoAMAT, C8HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, C4AcetoacylCoAMAT, Rate Law: sfcrotC4*Vcrot*(C4EnoylCoAMAT/KmcrotC4EnoylCoAMAT-C4HydroxyacylCoAMAT/(KmcrotC4EnoylCoAMAT*Keqcrot))/(1+C4EnoylCoAMAT/KmcrotC4EnoylCoAMAT+C4HydroxyacylCoAMAT/KmcrotC4HydroxyacylCoAMAT+C16EnoylCoAMAT/KmcrotC16EnoylCoAMAT+C16HydroxyacylCoAMAT/KmcrotC16HydroxyacylCoAMAT+C14EnoylCoAMAT/KmcrotC14EnoylCoAMAT+C14HydroxyacylCoAMAT/KmcrotC14HydroxyacylCoAMAT+C12EnoylCoAMAT/KmcrotC12EnoylCoAMAT+C12HydroxyacylCoAMAT/KmcrotC12HydroxyacylCoAMAT+C10EnoylCoAMAT/KmcrotC10EnoylCoAMAT+C10HydroxyacylCoAMAT/KmcrotC10HydroxyacylCoAMAT+C8EnoylCoAMAT/KmcrotC8EnoylCoAMAT+C8HydroxyacylCoAMAT/KmcrotC8HydroxyacylCoAMAT+C6EnoylCoAMAT/KmcrotC6EnoylCoAMAT+C6HydroxyacylCoAMAT/KmcrotC6HydroxyacylCoAMAT+C4AcetoacylCoAMAT/KicrotC4AcetoacylCoA)
KmmschadC16KetoacylCoAMAT = 1.4 uM; Keqmschad = 2.17E-4 dimensionless; KmmschadC16HydroxyacylCoAMAT = 1.5 uM; KmmschadC14HydroxyacylCoAMAT = 1.8 uM; Vmschad = 1.0 uM per min per mgProtein; KmmschadC6HydroxyacylCoAMAT = 28.6 uM; KmmschadC12KetoacylCoAMAT = 1.6 uM; KmmschadC4HydroxyacylCoAMAT = 69.9 uM; KmmschadC14KetoacylCoAMAT = 1.4 uM; KmmschadC12HydroxyacylCoAMAT = 3.7 uM; KmmschadC6KetoacylCoAMAT = 5.8 uM; KmmschadC10HydroxyacylCoAMAT = 8.8 uM; KmmschadNADMAT = 58.5 uM; sfmschadC10=0.64 dimensionless; KmmschadC8HydroxyacylCoAMAT = 16.3 uM; KmmschadC4AcetoacylCoAMAT = 16.9 uM; KmmschadC10KetoacylCoAMAT = 2.3 uM; KmmschadNADHMAT = 5.4 uM; KmmschadC8KetoacylCoAMAT = 4.1 uM Reaction: C10HydroxyacylCoAMAT => C10KetoacylCoAMAT + NADHMAT; C16HydroxyacylCoAMAT, C14HydroxyacylCoAMAT, C12HydroxyacylCoAMAT, C8HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, C4HydroxyacylCoAMAT, NADtMAT, C16KetoacylCoAMAT, C14KetoacylCoAMAT, C12KetoacylCoAMAT, C8KetoacylCoAMAT, C6KetoacylCoAMAT, C4AcetoacylCoAMAT, C10HydroxyacylCoAMAT, C16HydroxyacylCoAMAT, C14HydroxyacylCoAMAT, C12HydroxyacylCoAMAT, C8HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, C4HydroxyacylCoAMAT, NADtMAT, C10KetoacylCoAMAT, C16KetoacylCoAMAT, C14KetoacylCoAMAT, C12KetoacylCoAMAT, C8KetoacylCoAMAT, C6KetoacylCoAMAT, C4AcetoacylCoAMAT, NADHMAT, C10HydroxyacylCoAMAT, C16HydroxyacylCoAMAT, C14HydroxyacylCoAMAT, C12HydroxyacylCoAMAT, C8HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, C4HydroxyacylCoAMAT, NADtMAT, C10KetoacylCoAMAT, C16KetoacylCoAMAT, C14KetoacylCoAMAT, C12KetoacylCoAMAT, C8KetoacylCoAMAT, C6KetoacylCoAMAT, C4AcetoacylCoAMAT, NADHMAT, Rate Law: sfmschadC10*Vmschad*(C10HydroxyacylCoAMAT*(NADtMAT-NADHMAT)/(KmmschadC10HydroxyacylCoAMAT*KmmschadNADMAT)-C10KetoacylCoAMAT*NADHMAT/(KmmschadC10HydroxyacylCoAMAT*KmmschadNADMAT*Keqmschad))/((1+C10HydroxyacylCoAMAT/KmmschadC10HydroxyacylCoAMAT+C10KetoacylCoAMAT/KmmschadC10KetoacylCoAMAT+C16HydroxyacylCoAMAT/KmmschadC16HydroxyacylCoAMAT+C16KetoacylCoAMAT/KmmschadC16KetoacylCoAMAT+C14HydroxyacylCoAMAT/KmmschadC14HydroxyacylCoAMAT+C14KetoacylCoAMAT/KmmschadC14KetoacylCoAMAT+C12HydroxyacylCoAMAT/KmmschadC12HydroxyacylCoAMAT+C12KetoacylCoAMAT/KmmschadC12KetoacylCoAMAT+C8HydroxyacylCoAMAT/KmmschadC8HydroxyacylCoAMAT+C8KetoacylCoAMAT/KmmschadC8KetoacylCoAMAT+C6HydroxyacylCoAMAT/KmmschadC6HydroxyacylCoAMAT+C6KetoacylCoAMAT/KmmschadC6KetoacylCoAMAT+C4HydroxyacylCoAMAT/KmmschadC4HydroxyacylCoAMAT+C4AcetoacylCoAMAT/KmmschadC4AcetoacylCoAMAT)*(1+(NADtMAT-NADHMAT)/KmmschadNADMAT+NADHMAT/KmmschadNADHMAT))
K1fadhsink=0.46 uM; Ksfadhsink=6000000.0 l per min per mgProtein Reaction: FADHMAT => ; FADHMAT, FADHMAT, Rate Law: Ksfadhsink*(FADHMAT-K1fadhsink)
KmlcadC8AcylCoAMAT = 123.0 uM; Vlcad = 0.01 uM per min per mgProtein; KmlcadC14EnoylCoAMAT = 1.08 uM; KmlcadFAD = 0.12 uM; KmlcadFADH = 24.2 uM; KmlcadC12EnoylCoAMAT = 1.08 uM; KmlcadC8EnoylCoAMAT = 1.08 uM; sflcadC8=0.4 dimensionless; Keqlcad = 6.0 dimensionless; KmlcadC16AcylCoAMAT = 2.5 uM; KmlcadC12AcylCoAMAT = 9.0 uM; KmlcadC10AcylCoAMAT = 24.3 uM; KmlcadC10EnoylCoAMAT = 1.08 uM; KmlcadC14AcylCoAMAT = 7.4 uM; KmlcadC16EnoylCoAMAT = 1.08 uM Reaction: C8AcylCoAMAT => C8EnoylCoAMAT + FADHMAT; C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, FADtMAT, C16EnoylCoAMAT, C14EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C8AcylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, FADtMAT, C8EnoylCoAMAT, C16EnoylCoAMAT, C14EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, FADHMAT, C8AcylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, FADtMAT, C8EnoylCoAMAT, C16EnoylCoAMAT, C14EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, FADHMAT, Rate Law: sflcadC8*Vlcad*(C8AcylCoAMAT*(FADtMAT-FADHMAT)/(KmlcadC8AcylCoAMAT*KmlcadFAD)-C8EnoylCoAMAT*FADHMAT/(KmlcadC8AcylCoAMAT*KmlcadFAD*Keqlcad))/((1+C8AcylCoAMAT/KmlcadC8AcylCoAMAT+C8EnoylCoAMAT/KmlcadC8EnoylCoAMAT+C16AcylCoAMAT/KmlcadC16AcylCoAMAT+C16EnoylCoAMAT/KmlcadC16EnoylCoAMAT+C14AcylCoAMAT/KmlcadC14AcylCoAMAT+C14EnoylCoAMAT/KmlcadC14EnoylCoAMAT+C12AcylCoAMAT/KmlcadC12AcylCoAMAT+C12EnoylCoAMAT/KmlcadC12EnoylCoAMAT+C10AcylCoAMAT/KmlcadC10AcylCoAMAT+C10EnoylCoAMAT/KmlcadC10EnoylCoAMAT)*(1+(FADtMAT-FADHMAT)/KmlcadFAD+FADHMAT/KmlcadFADH))
KmmschadC16KetoacylCoAMAT = 1.4 uM; Keqmschad = 2.17E-4 dimensionless; KmmschadC16HydroxyacylCoAMAT = 1.5 uM; KmmschadC14HydroxyacylCoAMAT = 1.8 uM; Vmschad = 1.0 uM per min per mgProtein; KmmschadC6HydroxyacylCoAMAT = 28.6 uM; KmmschadC4HydroxyacylCoAMAT = 69.9 uM; KmmschadC12KetoacylCoAMAT = 1.6 uM; KmmschadC14KetoacylCoAMAT = 1.4 uM; KmmschadC12HydroxyacylCoAMAT = 3.7 uM; KmmschadC6KetoacylCoAMAT = 5.8 uM; KmmschadNADMAT = 58.5 uM; KmmschadC10HydroxyacylCoAMAT = 8.8 uM; sfmschadC4=0.67 dimensionless; KmmschadC8HydroxyacylCoAMAT = 16.3 uM; KmmschadC4AcetoacylCoAMAT = 16.9 uM; KmmschadC10KetoacylCoAMAT = 2.3 uM; KmmschadNADHMAT = 5.4 uM; KmmschadC8KetoacylCoAMAT = 4.1 uM Reaction: C4HydroxyacylCoAMAT => C4AcetoacylCoAMAT + NADHMAT; C16HydroxyacylCoAMAT, C14HydroxyacylCoAMAT, C12HydroxyacylCoAMAT, C10HydroxyacylCoAMAT, C8HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, NADtMAT, C16KetoacylCoAMAT, C14KetoacylCoAMAT, C12KetoacylCoAMAT, C10KetoacylCoAMAT, C8KetoacylCoAMAT, C6KetoacylCoAMAT, C4HydroxyacylCoAMAT, C16HydroxyacylCoAMAT, C14HydroxyacylCoAMAT, C12HydroxyacylCoAMAT, C10HydroxyacylCoAMAT, C8HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, NADtMAT, C4AcetoacylCoAMAT, C16KetoacylCoAMAT, C14KetoacylCoAMAT, C12KetoacylCoAMAT, C10KetoacylCoAMAT, C8KetoacylCoAMAT, C6KetoacylCoAMAT, NADHMAT, C4HydroxyacylCoAMAT, C16HydroxyacylCoAMAT, C14HydroxyacylCoAMAT, C12HydroxyacylCoAMAT, C10HydroxyacylCoAMAT, C8HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, NADtMAT, C4AcetoacylCoAMAT, C16KetoacylCoAMAT, C14KetoacylCoAMAT, C12KetoacylCoAMAT, C10KetoacylCoAMAT, C8KetoacylCoAMAT, C6KetoacylCoAMAT, NADHMAT, Rate Law: sfmschadC4*Vmschad*(C4HydroxyacylCoAMAT*(NADtMAT-NADHMAT)/(KmmschadC4HydroxyacylCoAMAT*KmmschadNADMAT)-C4AcetoacylCoAMAT*NADHMAT/(KmmschadC4HydroxyacylCoAMAT*KmmschadNADMAT*Keqmschad))/((1+C4HydroxyacylCoAMAT/KmmschadC4HydroxyacylCoAMAT+C4AcetoacylCoAMAT/KmmschadC4AcetoacylCoAMAT+C16HydroxyacylCoAMAT/KmmschadC16HydroxyacylCoAMAT+C16KetoacylCoAMAT/KmmschadC16KetoacylCoAMAT+C14HydroxyacylCoAMAT/KmmschadC14HydroxyacylCoAMAT+C14KetoacylCoAMAT/KmmschadC14KetoacylCoAMAT+C12HydroxyacylCoAMAT/KmmschadC12HydroxyacylCoAMAT+C12KetoacylCoAMAT/KmmschadC12KetoacylCoAMAT+C10HydroxyacylCoAMAT/KmmschadC10HydroxyacylCoAMAT+C10KetoacylCoAMAT/KmmschadC10KetoacylCoAMAT+C8HydroxyacylCoAMAT/KmmschadC8HydroxyacylCoAMAT+C8KetoacylCoAMAT/KmmschadC8KetoacylCoAMAT+C6HydroxyacylCoAMAT/KmmschadC6HydroxyacylCoAMAT+C6KetoacylCoAMAT/KmmschadC6KetoacylCoAMAT)*(1+(NADtMAT-NADHMAT)/KmmschadNADMAT+NADHMAT/KmmschadNADHMAT))
KmmckatC14AcylCoAMAT = 13.83 uM; KmmckatC10KetoacylCoAMAT = 2.1 uM; sfmckatC16=0.0 dimensionless; KmmckatCoAMAT = 26.6 uM; KmmckatC6KetoacylCoAMAT = 6.7 uM; Keqmckat = 1051.0 dimensionless; KmmckatC12KetoacylCoAMAT = 1.3 uM; KmmckatC8KetoacylCoAMAT = 3.2 uM; KmmckatC4AcetoacylCoAMAT = 12.4 uM; KmmckatC16KetoacylCoAMAT = 1.1 uM; KmmckatC12AcylCoAMAT = 13.83 uM; KmmckatC8AcylCoAMAT = 13.83 uM; KmmckatC10AcylCoAMAT = 13.83 uM; KmmckatC6AcylCoAMAT = 13.83 uM; Vmckat = 0.377 uM per min per mgProtein; KmmckatC16AcylCoAMAT = 13.83 uM; KmmckatC14KetoacylCoAMAT = 1.2 uM; KmmckatC4AcylCoAMAT = 13.83 uM; KmmckatAcetylCoAMAT = 30.0 uM Reaction: C16KetoacylCoAMAT => C14AcylCoAMAT + AcetylCoAMAT; C14KetoacylCoAMAT, C12KetoacylCoAMAT, C10KetoacylCoAMAT, C8KetoacylCoAMAT, C6KetoacylCoAMAT, C4AcetoacylCoAMAT, CoAMAT, C16AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, C16KetoacylCoAMAT, C14KetoacylCoAMAT, C12KetoacylCoAMAT, C10KetoacylCoAMAT, C8KetoacylCoAMAT, C6KetoacylCoAMAT, C4AcetoacylCoAMAT, CoAMAT, C14AcylCoAMAT, C16AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, AcetylCoAMAT, C16KetoacylCoAMAT, C14KetoacylCoAMAT, C12KetoacylCoAMAT, C10KetoacylCoAMAT, C8KetoacylCoAMAT, C6KetoacylCoAMAT, C4AcetoacylCoAMAT, CoAMAT, C14AcylCoAMAT, C16AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, AcetylCoAMAT, Rate Law: sfmckatC16*Vmckat*(C16KetoacylCoAMAT*CoAMAT/(KmmckatC16KetoacylCoAMAT*KmmckatCoAMAT)-C14AcylCoAMAT*AcetylCoAMAT/(KmmckatC16KetoacylCoAMAT*KmmckatCoAMAT*Keqmckat))/((1+C16KetoacylCoAMAT/KmmckatC16KetoacylCoAMAT+C14AcylCoAMAT/KmmckatC14AcylCoAMAT+C14KetoacylCoAMAT/KmmckatC14KetoacylCoAMAT+C16AcylCoAMAT/KmmckatC16AcylCoAMAT+C12KetoacylCoAMAT/KmmckatC12KetoacylCoAMAT+C12AcylCoAMAT/KmmckatC12AcylCoAMAT+C10KetoacylCoAMAT/KmmckatC10KetoacylCoAMAT+C10AcylCoAMAT/KmmckatC10AcylCoAMAT+C8KetoacylCoAMAT/KmmckatC8KetoacylCoAMAT+C8AcylCoAMAT/KmmckatC8AcylCoAMAT+C6KetoacylCoAMAT/KmmckatC6KetoacylCoAMAT+C6AcylCoAMAT/KmmckatC6AcylCoAMAT+C4AcetoacylCoAMAT/KmmckatC4AcetoacylCoAMAT+C4AcylCoAMAT/KmmckatC4AcylCoAMAT+AcetylCoAMAT/KmmckatAcetylCoAMAT)*(1+CoAMAT/KmmckatCoAMAT+AcetylCoAMAT/KmmckatAcetylCoAMAT))
KmvlcadC14AcylCoAMAT = 4.0 uM; KmvlcadC14EnoylCoAMAT = 1.08 uM; KmvlcadFAD = 0.12 uM; KmvlcadFADH = 24.2 uM; KmvlcadC12AcylCoAMAT = 2.7 uM; KmvlcadC12EnoylCoAMAT = 1.08 uM; Vvlcad = 0.008 uM per min per mgProtein; KmvlcadC16EnoylCoAMAT = 1.08 uM; sfvlcadC12=0.11 dimensionless; KmvlcadC16AcylCoAMAT = 6.5 uM; Keqvlcad = 6.0 dimensionless Reaction: C12AcylCoAMAT => C12EnoylCoAMAT + FADHMAT; C16AcylCoAMAT, C14AcylCoAMAT, FADtMAT, C16EnoylCoAMAT, C14EnoylCoAMAT, C12AcylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, FADtMAT, C12EnoylCoAMAT, C16EnoylCoAMAT, C14EnoylCoAMAT, FADHMAT, C12AcylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, FADtMAT, C12EnoylCoAMAT, C16EnoylCoAMAT, C14EnoylCoAMAT, FADHMAT, Rate Law: sfvlcadC12*Vvlcad*(C12AcylCoAMAT*(FADtMAT-FADHMAT)/(KmvlcadC12AcylCoAMAT*KmvlcadFAD)-C12EnoylCoAMAT*FADHMAT/(KmvlcadC12AcylCoAMAT*KmvlcadFAD*Keqvlcad))/((1+C12AcylCoAMAT/KmvlcadC12AcylCoAMAT+C12EnoylCoAMAT/KmvlcadC12EnoylCoAMAT+C16AcylCoAMAT/KmvlcadC16AcylCoAMAT+C16EnoylCoAMAT/KmvlcadC16EnoylCoAMAT+C14AcylCoAMAT/KmvlcadC14AcylCoAMAT+C14EnoylCoAMAT/KmvlcadC14EnoylCoAMAT)*(1+(FADtMAT-FADHMAT)/KmvlcadFAD+FADHMAT/KmvlcadFADH))
Kmcpt2C14AcylCarMAT = 51.0 uM; Kmcpt2C10AcylCarMAT = 51.0 uM; Kmcpt2CoAMAT = 30.0 uM; Kmcpt2C6AcylCarMAT = 51.0 uM; Kmcpt2C14AcylCoAMAT = 38.0 uM; sfcpt2C8=0.35 dimensionless; Keqcpt2 = 2.22 dimensionless; Kmcpt2C8AcylCoAMAT = 38.0 uM; Kmcpt2C6AcylCoAMAT = 1000.0 uM; Kmcpt2C16AcylCarMAT = 51.0 uM; Kmcpt2C12AcylCarMAT = 51.0 uM; Kmcpt2C12AcylCoAMAT = 38.0 uM; Vcpt2 = 0.391 uM per min per mgProtein; Kmcpt2C8AcylCarMAT = 51.0 uM; Kmcpt2CarMAT = 350.0 uM; Kmcpt2C16AcylCoAMAT = 38.0 uM; Kmcpt2C4AcylCoAMAT = 1000000.0 uM; Kmcpt2C10AcylCoAMAT = 38.0 uM; Kmcpt2C4AcylCarMAT = 51.0 uM Reaction: C8AcylCarMAT => C8AcylCoAMAT; C16AcylCarMAT, C14AcylCarMAT, C12AcylCarMAT, C10AcylCarMAT, C6AcylCarMAT, C4AcylCarMAT, CoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, CarMAT, C8AcylCarMAT, C16AcylCarMAT, C14AcylCarMAT, C12AcylCarMAT, C10AcylCarMAT, C6AcylCarMAT, C4AcylCarMAT, CoAMAT, C8AcylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, CarMAT, C8AcylCarMAT, C16AcylCarMAT, C14AcylCarMAT, C12AcylCarMAT, C10AcylCarMAT, C6AcylCarMAT, C4AcylCarMAT, CoAMAT, C8AcylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, CarMAT, Rate Law: sfcpt2C8*Vcpt2*(C8AcylCarMAT*CoAMAT/(Kmcpt2C8AcylCarMAT*Kmcpt2CoAMAT)-C8AcylCoAMAT*CarMAT/(Kmcpt2C8AcylCarMAT*Kmcpt2CoAMAT*Keqcpt2))/((1+C8AcylCarMAT/Kmcpt2C8AcylCarMAT+C8AcylCoAMAT/Kmcpt2C8AcylCoAMAT+C16AcylCarMAT/Kmcpt2C16AcylCarMAT+C16AcylCoAMAT/Kmcpt2C16AcylCoAMAT+C14AcylCarMAT/Kmcpt2C14AcylCarMAT+C14AcylCoAMAT/Kmcpt2C14AcylCoAMAT+C12AcylCarMAT/Kmcpt2C12AcylCarMAT+C12AcylCoAMAT/Kmcpt2C12AcylCoAMAT+C10AcylCarMAT/Kmcpt2C10AcylCarMAT+C10AcylCoAMAT/Kmcpt2C10AcylCoAMAT+C6AcylCarMAT/Kmcpt2C6AcylCarMAT+C6AcylCoAMAT/Kmcpt2C6AcylCoAMAT+C4AcylCarMAT/Kmcpt2C4AcylCarMAT+C4AcylCoAMAT/Kmcpt2C4AcylCoAMAT)*(1+CoAMAT/Kmcpt2CoAMAT+CarMAT/Kmcpt2CarMAT))
KmcrotC16EnoylCoAMAT = 150.0 uM; KmcrotC12EnoylCoAMAT = 25.0 uM; KicrotC4AcetoacylCoA = 1.6 uM; KmcrotC4EnoylCoAMAT = 40.0 uM; sfcrotC16=0.13 dimensionless; Vcrot = 3.6 uM per min per mgProtein; Keqcrot = 3.13 dimensionless; KmcrotC14EnoylCoAMAT = 100.0 uM; KmcrotC8EnoylCoAMAT = 25.0 uM; KmcrotC10HydroxyacylCoAMAT = 45.0 uM; KmcrotC8HydroxyacylCoAMAT = 45.0 uM; KmcrotC6EnoylCoAMAT = 25.0 uM; KmcrotC10EnoylCoAMAT = 25.0 uM; KmcrotC14HydroxyacylCoAMAT = 45.0 uM; KmcrotC4HydroxyacylCoAMAT = 45.0 uM; KmcrotC16HydroxyacylCoAMAT = 45.0 uM; KmcrotC12HydroxyacylCoAMAT = 45.0 uM; KmcrotC6HydroxyacylCoAMAT = 45.0 uM Reaction: C16EnoylCoAMAT => C16HydroxyacylCoAMAT; C14EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, C6EnoylCoAMAT, C4EnoylCoAMAT, C14HydroxyacylCoAMAT, C12HydroxyacylCoAMAT, C10HydroxyacylCoAMAT, C8HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, C4HydroxyacylCoAMAT, C4AcetoacylCoAMAT, C16EnoylCoAMAT, C14EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, C6EnoylCoAMAT, C4EnoylCoAMAT, C16HydroxyacylCoAMAT, C14HydroxyacylCoAMAT, C12HydroxyacylCoAMAT, C10HydroxyacylCoAMAT, C8HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, C4HydroxyacylCoAMAT, C4AcetoacylCoAMAT, C16EnoylCoAMAT, C14EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, C6EnoylCoAMAT, C4EnoylCoAMAT, C16HydroxyacylCoAMAT, C14HydroxyacylCoAMAT, C12HydroxyacylCoAMAT, C10HydroxyacylCoAMAT, C8HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, C4HydroxyacylCoAMAT, C4AcetoacylCoAMAT, Rate Law: sfcrotC16*Vcrot*(C16EnoylCoAMAT/KmcrotC16EnoylCoAMAT-C16HydroxyacylCoAMAT/(KmcrotC16EnoylCoAMAT*Keqcrot))/(1+C16EnoylCoAMAT/KmcrotC16EnoylCoAMAT+C16HydroxyacylCoAMAT/KmcrotC16HydroxyacylCoAMAT+C14EnoylCoAMAT/KmcrotC14EnoylCoAMAT+C14HydroxyacylCoAMAT/KmcrotC14HydroxyacylCoAMAT+C12EnoylCoAMAT/KmcrotC12EnoylCoAMAT+C12HydroxyacylCoAMAT/KmcrotC12HydroxyacylCoAMAT+C10EnoylCoAMAT/KmcrotC10EnoylCoAMAT+C10HydroxyacylCoAMAT/KmcrotC10HydroxyacylCoAMAT+C8EnoylCoAMAT/KmcrotC8EnoylCoAMAT+C8HydroxyacylCoAMAT/KmcrotC8HydroxyacylCoAMAT+C6EnoylCoAMAT/KmcrotC6EnoylCoAMAT+C6HydroxyacylCoAMAT/KmcrotC6HydroxyacylCoAMAT+C4EnoylCoAMAT/KmcrotC4EnoylCoAMAT+C4HydroxyacylCoAMAT/KmcrotC4HydroxyacylCoAMAT+C4AcetoacylCoAMAT/KicrotC4AcetoacylCoA)
KmvlcadC14AcylCoAMAT = 4.0 uM; KmvlcadC14EnoylCoAMAT = 1.08 uM; KmvlcadFAD = 0.12 uM; KmvlcadFADH = 24.2 uM; KmvlcadC12AcylCoAMAT = 2.7 uM; KmvlcadC12EnoylCoAMAT = 1.08 uM; Vvlcad = 0.008 uM per min per mgProtein; KmvlcadC16EnoylCoAMAT = 1.08 uM; KmvlcadC16AcylCoAMAT = 6.5 uM; sfvlcadC16=1.0 dimensionless; Keqvlcad = 6.0 dimensionless Reaction: C16AcylCoAMAT => C16EnoylCoAMAT + FADHMAT; C14AcylCoAMAT, C12AcylCoAMAT, FADtMAT, C14EnoylCoAMAT, C12EnoylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, FADtMAT, C16EnoylCoAMAT, C14EnoylCoAMAT, C12EnoylCoAMAT, FADHMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, FADtMAT, C16EnoylCoAMAT, C14EnoylCoAMAT, C12EnoylCoAMAT, FADHMAT, Rate Law: sfvlcadC16*Vvlcad*(C16AcylCoAMAT*(FADtMAT-FADHMAT)/(KmvlcadC16AcylCoAMAT*KmvlcadFAD)-C16EnoylCoAMAT*FADHMAT/(KmvlcadC16AcylCoAMAT*KmvlcadFAD*Keqvlcad))/((1+C16AcylCoAMAT/KmvlcadC16AcylCoAMAT+C16EnoylCoAMAT/KmvlcadC16EnoylCoAMAT+C14AcylCoAMAT/KmvlcadC14AcylCoAMAT+C14EnoylCoAMAT/KmvlcadC14EnoylCoAMAT+C12AcylCoAMAT/KmvlcadC12AcylCoAMAT+C12EnoylCoAMAT/KmvlcadC12EnoylCoAMAT)*(1+(FADtMAT-FADHMAT)/KmvlcadFAD+FADHMAT/KmvlcadFADH))
KmlcadC8AcylCoAMAT = 123.0 uM; Vlcad = 0.01 uM per min per mgProtein; KmlcadC14EnoylCoAMAT = 1.08 uM; KmlcadFAD = 0.12 uM; KmlcadFADH = 24.2 uM; KmlcadC12EnoylCoAMAT = 1.08 uM; KmlcadC8EnoylCoAMAT = 1.08 uM; Keqlcad = 6.0 dimensionless; KmlcadC16AcylCoAMAT = 2.5 uM; sflcadC12=0.9 dimensionless; KmlcadC12AcylCoAMAT = 9.0 uM; KmlcadC10AcylCoAMAT = 24.3 uM; KmlcadC10EnoylCoAMAT = 1.08 uM; KmlcadC14AcylCoAMAT = 7.4 uM; KmlcadC16EnoylCoAMAT = 1.08 uM Reaction: C12AcylCoAMAT => C12EnoylCoAMAT + FADHMAT; C16AcylCoAMAT, C14AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, FADtMAT, C14EnoylCoAMAT, C16EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, C12AcylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, FADtMAT, C14EnoylCoAMAT, C16EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, FADHMAT, C12AcylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, FADtMAT, C14EnoylCoAMAT, C16EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, FADHMAT, Rate Law: sflcadC12*Vlcad*(C12AcylCoAMAT*(FADtMAT-FADHMAT)/(KmlcadC12AcylCoAMAT*KmlcadFAD)-C14EnoylCoAMAT*FADHMAT/(KmlcadC12AcylCoAMAT*KmlcadFAD*Keqlcad))/((1+C12AcylCoAMAT/KmlcadC12AcylCoAMAT+C14EnoylCoAMAT/KmlcadC12EnoylCoAMAT+C16AcylCoAMAT/KmlcadC16AcylCoAMAT+C16EnoylCoAMAT/KmlcadC16EnoylCoAMAT+C14AcylCoAMAT/KmlcadC14AcylCoAMAT+C14EnoylCoAMAT/KmlcadC14EnoylCoAMAT+C10AcylCoAMAT/KmlcadC10AcylCoAMAT+C10EnoylCoAMAT/KmlcadC10EnoylCoAMAT+C8AcylCoAMAT/KmlcadC8AcylCoAMAT+C8EnoylCoAMAT/KmlcadC8EnoylCoAMAT)*(1+(FADtMAT-FADHMAT)/KmlcadFAD+FADHMAT/KmlcadFADH))
Kmcpt2C14AcylCarMAT = 51.0 uM; Kmcpt2C10AcylCarMAT = 51.0 uM; Kmcpt2CoAMAT = 30.0 uM; Kmcpt2C6AcylCarMAT = 51.0 uM; Kmcpt2C14AcylCoAMAT = 38.0 uM; Keqcpt2 = 2.22 dimensionless; Kmcpt2C8AcylCoAMAT = 38.0 uM; Kmcpt2C6AcylCoAMAT = 1000.0 uM; Kmcpt2C12AcylCarMAT = 51.0 uM; Kmcpt2C16AcylCarMAT = 51.0 uM; Kmcpt2C12AcylCoAMAT = 38.0 uM; Vcpt2 = 0.391 uM per min per mgProtein; Kmcpt2C8AcylCarMAT = 51.0 uM; Kmcpt2CarMAT = 350.0 uM; Kmcpt2C16AcylCoAMAT = 38.0 uM; Kmcpt2C4AcylCoAMAT = 1000000.0 uM; Kmcpt2C10AcylCoAMAT = 38.0 uM; sfcpt2C12=0.95 dimensionless; Kmcpt2C4AcylCarMAT = 51.0 uM Reaction: C12AcylCarMAT => C12AcylCoAMAT; C16AcylCarMAT, C14AcylCarMAT, C10AcylCarMAT, C8AcylCarMAT, C6AcylCarMAT, C4AcylCarMAT, CoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, CarMAT, C12AcylCarMAT, C16AcylCarMAT, C14AcylCarMAT, C10AcylCarMAT, C8AcylCarMAT, C6AcylCarMAT, C4AcylCarMAT, CoAMAT, C12AcylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, CarMAT, C12AcylCarMAT, C16AcylCarMAT, C14AcylCarMAT, C10AcylCarMAT, C8AcylCarMAT, C6AcylCarMAT, C4AcylCarMAT, CoAMAT, C12AcylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, CarMAT, Rate Law: sfcpt2C12*Vcpt2*(C12AcylCarMAT*CoAMAT/(Kmcpt2C12AcylCarMAT*Kmcpt2CoAMAT)-C12AcylCoAMAT*CarMAT/(Kmcpt2C12AcylCarMAT*Kmcpt2CoAMAT*Keqcpt2))/((1+C12AcylCarMAT/Kmcpt2C12AcylCarMAT+C12AcylCoAMAT/Kmcpt2C12AcylCoAMAT+C16AcylCarMAT/Kmcpt2C16AcylCarMAT+C16AcylCoAMAT/Kmcpt2C16AcylCoAMAT+C14AcylCarMAT/Kmcpt2C14AcylCarMAT+C14AcylCoAMAT/Kmcpt2C14AcylCoAMAT+C10AcylCarMAT/Kmcpt2C10AcylCarMAT+C10AcylCoAMAT/Kmcpt2C10AcylCoAMAT+C8AcylCarMAT/Kmcpt2C8AcylCarMAT+C8AcylCoAMAT/Kmcpt2C8AcylCoAMAT+C6AcylCarMAT/Kmcpt2C6AcylCarMAT+C6AcylCoAMAT/Kmcpt2C6AcylCoAMAT+C4AcylCarMAT/Kmcpt2C4AcylCarMAT+C4AcylCoAMAT/Kmcpt2C4AcylCoAMAT)*(1+CoAMAT/Kmcpt2CoAMAT+CarMAT/Kmcpt2CarMAT))
KmmtpC14EnoylCoAMAT = 25.0 uM; KmmtpC8EnoylCoAMAT = 25.0 uM; KmmtpC16AcylCoAMAT = 13.83 uM; KmmtpCoAMAT = 30.0 uM; KmmtpC16EnoylCoAMAT = 25.0 uM; KmmtpC12EnoylCoAMAT = 25.0 uM; KmmtpAcetylCoAMAT = 30.0 uM; Vmtp = 2.84 uM per min per mgProtein; KmmtpC8AcylCoAMAT = 13.83 uM; KmmtpC12AcylCoAMAT = 13.83 uM; KmmtpC14AcylCoAMAT = 13.83 uM; KmmtpC6AcylCoAMAT = 13.83 uM; KmmtpC10EnoylCoAMAT = 25.0 uM; Keqmtp = 0.71 dimensionless; KicrotC4AcetoacylCoA = 1.6 uM; KmmtpNADMAT = 60.0 uM; KmmtpNADHMAT = 50.0 uM; KmmtpC10AcylCoAMAT = 13.83 uM; sfmtpC10=0.73 dimensionless Reaction: C10EnoylCoAMAT => C8AcylCoAMAT + AcetylCoAMAT + NADHMAT; C16EnoylCoAMAT, C14EnoylCoAMAT, C12EnoylCoAMAT, C8EnoylCoAMAT, NADtMAT, CoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C6AcylCoAMAT, C4AcetoacylCoAMAT, C10EnoylCoAMAT, C16EnoylCoAMAT, C14EnoylCoAMAT, C12EnoylCoAMAT, C8EnoylCoAMAT, NADtMAT, CoAMAT, C8AcylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C6AcylCoAMAT, NADHMAT, AcetylCoAMAT, C4AcetoacylCoAMAT, C10EnoylCoAMAT, C16EnoylCoAMAT, C14EnoylCoAMAT, C12EnoylCoAMAT, C8EnoylCoAMAT, NADtMAT, CoAMAT, C8AcylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C6AcylCoAMAT, NADHMAT, AcetylCoAMAT, C4AcetoacylCoAMAT, Rate Law: sfmtpC10*Vmtp*(C10EnoylCoAMAT*(NADtMAT-NADHMAT)*CoAMAT/(KmmtpC10EnoylCoAMAT*KmmtpNADMAT*KmmtpCoAMAT)-C8AcylCoAMAT*NADHMAT*AcetylCoAMAT/(KmmtpC10EnoylCoAMAT*KmmtpNADMAT*KmmtpCoAMAT*Keqmtp))/((1+C10EnoylCoAMAT/KmmtpC10EnoylCoAMAT+C8AcylCoAMAT/KmmtpC8AcylCoAMAT+C16EnoylCoAMAT/KmmtpC16EnoylCoAMAT+C16AcylCoAMAT/KmmtpC16AcylCoAMAT+C14EnoylCoAMAT/KmmtpC14EnoylCoAMAT+C14AcylCoAMAT/KmmtpC14AcylCoAMAT+C12EnoylCoAMAT/KmmtpC12EnoylCoAMAT+C12AcylCoAMAT/KmmtpC12AcylCoAMAT+C8EnoylCoAMAT/KmmtpC8EnoylCoAMAT+C10AcylCoAMAT/KmmtpC10AcylCoAMAT+C6AcylCoAMAT/KmmtpC6AcylCoAMAT+C4AcetoacylCoAMAT/KicrotC4AcetoacylCoA)*(1+(NADtMAT-NADHMAT)/KmmtpNADMAT+NADHMAT/KmmtpNADHMAT)*(1+CoAMAT/KmmtpCoAMAT+AcetylCoAMAT/KmmtpAcetylCoAMAT))
KmcrotC16EnoylCoAMAT = 150.0 uM; KmcrotC12EnoylCoAMAT = 25.0 uM; KicrotC4AcetoacylCoA = 1.6 uM; KmcrotC4EnoylCoAMAT = 40.0 uM; Vcrot = 3.6 uM per min per mgProtein; Keqcrot = 3.13 dimensionless; KmcrotC8EnoylCoAMAT = 25.0 uM; KmcrotC14EnoylCoAMAT = 100.0 uM; KmcrotC10HydroxyacylCoAMAT = 45.0 uM; KmcrotC8HydroxyacylCoAMAT = 45.0 uM; KmcrotC6EnoylCoAMAT = 25.0 uM; KmcrotC10EnoylCoAMAT = 25.0 uM; KmcrotC14HydroxyacylCoAMAT = 45.0 uM; KmcrotC4HydroxyacylCoAMAT = 45.0 uM; KmcrotC16HydroxyacylCoAMAT = 45.0 uM; KmcrotC12HydroxyacylCoAMAT = 45.0 uM; sfcrotC8=0.58 dimensionless; KmcrotC6HydroxyacylCoAMAT = 45.0 uM Reaction: C8EnoylCoAMAT => C8HydroxyacylCoAMAT; C16EnoylCoAMAT, C14EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C6EnoylCoAMAT, C4EnoylCoAMAT, C16HydroxyacylCoAMAT, C14HydroxyacylCoAMAT, C12HydroxyacylCoAMAT, C10HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, C4HydroxyacylCoAMAT, C4AcetoacylCoAMAT, C8EnoylCoAMAT, C16EnoylCoAMAT, C14EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C6EnoylCoAMAT, C4EnoylCoAMAT, C8HydroxyacylCoAMAT, C16HydroxyacylCoAMAT, C14HydroxyacylCoAMAT, C12HydroxyacylCoAMAT, C10HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, C4HydroxyacylCoAMAT, C4AcetoacylCoAMAT, C8EnoylCoAMAT, C16EnoylCoAMAT, C14EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C6EnoylCoAMAT, C4EnoylCoAMAT, C8HydroxyacylCoAMAT, C16HydroxyacylCoAMAT, C14HydroxyacylCoAMAT, C12HydroxyacylCoAMAT, C10HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, C4HydroxyacylCoAMAT, C4AcetoacylCoAMAT, Rate Law: sfcrotC8*Vcrot*(C8EnoylCoAMAT/KmcrotC8EnoylCoAMAT-C8HydroxyacylCoAMAT/(KmcrotC8EnoylCoAMAT*Keqcrot))/(1+C8EnoylCoAMAT/KmcrotC8EnoylCoAMAT+C8HydroxyacylCoAMAT/KmcrotC8HydroxyacylCoAMAT+C16EnoylCoAMAT/KmcrotC16EnoylCoAMAT+C16HydroxyacylCoAMAT/KmcrotC16HydroxyacylCoAMAT+C14EnoylCoAMAT/KmcrotC14EnoylCoAMAT+C14HydroxyacylCoAMAT/KmcrotC14HydroxyacylCoAMAT+C12EnoylCoAMAT/KmcrotC12EnoylCoAMAT+C12HydroxyacylCoAMAT/KmcrotC12HydroxyacylCoAMAT+C10EnoylCoAMAT/KmcrotC10EnoylCoAMAT+C10HydroxyacylCoAMAT/KmcrotC10HydroxyacylCoAMAT+C6EnoylCoAMAT/KmcrotC6EnoylCoAMAT+C6HydroxyacylCoAMAT/KmcrotC6HydroxyacylCoAMAT+C4EnoylCoAMAT/KmcrotC4EnoylCoAMAT+C4HydroxyacylCoAMAT/KmcrotC4HydroxyacylCoAMAT+C4AcetoacylCoAMAT/KicrotC4AcetoacylCoA)
KmmcadC8AcylCoAMAT = 4.0 uM; KmmcadC12AcylCoAMAT = 5.7 uM; KmmcadC4AcylCoAMAT = 135.0 uM; KmmcadC10AcylCoAMAT = 5.4 uM; KmmcadFAD = 0.12 uM; KmmcadC10EnoylCoAMAT = 1.08 uM; KmmcadC4EnoylCoAMAT = 1.08 uM; Vmcad = 0.081 uM per min per mgProtein; KmmcadC6AcylCoAMAT = 9.4 uM; KmmcadC6EnoylCoAMAT = 1.08 uM; KmmcadFADH = 24.2 uM; sfmcadC8=0.87 dimensionless; KmmcadC8EnoylCoAMAT = 1.08 uM; KmmcadC12EnoylCoAMAT = 1.08 uM; Keqmcad = 6.0 dimensionless Reaction: C8AcylCoAMAT => C8EnoylCoAMAT + FADHMAT; C12AcylCoAMAT, C10AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, FADtMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C6EnoylCoAMAT, C4EnoylCoAMAT, C8AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, FADtMAT, C8EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C6EnoylCoAMAT, C4EnoylCoAMAT, FADHMAT, C8AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, FADtMAT, C8EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C6EnoylCoAMAT, C4EnoylCoAMAT, FADHMAT, Rate Law: sfmcadC8*Vmcad*(C8AcylCoAMAT*(FADtMAT-FADHMAT)/(KmmcadC8AcylCoAMAT*KmmcadFAD)-C8EnoylCoAMAT*FADHMAT/(KmmcadC8AcylCoAMAT*KmmcadFAD*Keqmcad))/((1+C8AcylCoAMAT/KmmcadC8AcylCoAMAT+C8EnoylCoAMAT/KmmcadC8EnoylCoAMAT+C12AcylCoAMAT/KmmcadC12AcylCoAMAT+C12EnoylCoAMAT/KmmcadC12EnoylCoAMAT+C10AcylCoAMAT/KmmcadC10AcylCoAMAT+C10EnoylCoAMAT/KmmcadC10EnoylCoAMAT+C6AcylCoAMAT/KmmcadC6AcylCoAMAT+C6EnoylCoAMAT/KmmcadC6EnoylCoAMAT+C4AcylCoAMAT/KmmcadC4AcylCoAMAT+C4EnoylCoAMAT/KmmcadC4EnoylCoAMAT)*(1+(FADtMAT-FADHMAT)/KmmcadFAD+FADHMAT/KmmcadFADH))
KmmckatC14AcylCoAMAT = 13.83 uM; KmmckatC10KetoacylCoAMAT = 2.1 uM; KmmckatCoAMAT = 26.6 uM; KmmckatC6KetoacylCoAMAT = 6.7 uM; Keqmckat = 1051.0 dimensionless; KmmckatC12KetoacylCoAMAT = 1.3 uM; KmmckatC8KetoacylCoAMAT = 3.2 uM; KmmckatC4AcetoacylCoAMAT = 12.4 uM; KmmckatC16KetoacylCoAMAT = 1.1 uM; KmmckatC12AcylCoAMAT = 13.83 uM; KmmckatC8AcylCoAMAT = 13.83 uM; KmmckatC10AcylCoAMAT = 13.83 uM; KmmckatC6AcylCoAMAT = 13.83 uM; Vmckat = 0.377 uM per min per mgProtein; sfmckatC4=0.49 dimensionless; KmmckatC16AcylCoAMAT = 13.83 uM; KmmckatC14KetoacylCoAMAT = 1.2 uM; KmmckatC4AcylCoAMAT = 13.83 uM; KmmckatAcetylCoAMAT = 30.0 uM Reaction: C4AcetoacylCoAMAT => AcetylCoAMAT; C16KetoacylCoAMAT, C14KetoacylCoAMAT, C12KetoacylCoAMAT, C10KetoacylCoAMAT, C8KetoacylCoAMAT, C6KetoacylCoAMAT, CoAMAT, C4AcylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcetoacylCoAMAT, C16KetoacylCoAMAT, C14KetoacylCoAMAT, C12KetoacylCoAMAT, C10KetoacylCoAMAT, C8KetoacylCoAMAT, C6KetoacylCoAMAT, CoAMAT, C4AcylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, AcetylCoAMAT, C4AcetoacylCoAMAT, C16KetoacylCoAMAT, C14KetoacylCoAMAT, C12KetoacylCoAMAT, C10KetoacylCoAMAT, C8KetoacylCoAMAT, C6KetoacylCoAMAT, CoAMAT, C4AcylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, AcetylCoAMAT, Rate Law: sfmckatC4*Vmckat*(C4AcetoacylCoAMAT*CoAMAT/(KmmckatC4AcetoacylCoAMAT*KmmckatCoAMAT)-AcetylCoAMAT*AcetylCoAMAT/(KmmckatC4AcetoacylCoAMAT*KmmckatCoAMAT*Keqmckat))/((1+C4AcetoacylCoAMAT/KmmckatC4AcetoacylCoAMAT+C4AcylCoAMAT/KmmckatC4AcylCoAMAT+C16KetoacylCoAMAT/KmmckatC16KetoacylCoAMAT+C16AcylCoAMAT/KmmckatC16AcylCoAMAT+C14KetoacylCoAMAT/KmmckatC14KetoacylCoAMAT+C14AcylCoAMAT/KmmckatC14AcylCoAMAT+C12KetoacylCoAMAT/KmmckatC12KetoacylCoAMAT+C12AcylCoAMAT/KmmckatC12AcylCoAMAT+C10KetoacylCoAMAT/KmmckatC10KetoacylCoAMAT+C10AcylCoAMAT/KmmckatC10AcylCoAMAT+C8KetoacylCoAMAT/KmmckatC8KetoacylCoAMAT+C8AcylCoAMAT/KmmckatC8AcylCoAMAT+C6KetoacylCoAMAT/KmmckatC6KetoacylCoAMAT+C6AcylCoAMAT/KmmckatC6AcylCoAMAT+AcetylCoAMAT/KmmckatAcetylCoAMAT)*(1+CoAMAT/KmmckatCoAMAT+AcetylCoAMAT/KmmckatAcetylCoAMAT))
KmcactC8AcylCarMAT=15.0 uM; Vfcact = 0.42 uM per min per mgProtein; KmcactCarCYT = 130.0 uM; KicactC8AcylCarCYT=56.0 uM; KmcactCarMAT = 130.0 uM; KmcactC8AcylCarCYT=15.0 uM; Keqcact = 1.0 dimensionless; KicactCarCYT = 200.0 uM; Vrcact = 0.42 uM per min per mgProtein Reaction: C8AcylCarCYT => C8AcylCarMAT; CarMAT, CarCYT, C8AcylCarCYT, CarMAT, C8AcylCarMAT, CarCYT, C8AcylCarCYT, CarMAT, C8AcylCarMAT, CarCYT, Rate Law: Vfcact*(C8AcylCarCYT*CarMAT-C8AcylCarMAT*CarCYT/Keqcact)/(C8AcylCarCYT*CarMAT+KmcactCarMAT*C8AcylCarCYT+KmcactC8AcylCarCYT*CarMAT*(1+CarCYT/KicactCarCYT)+Vfcact/(Vrcact*Keqcact)*(KmcactCarCYT*C8AcylCarMAT*(1+C8AcylCarCYT/KicactC8AcylCarCYT)+CarCYT*(KmcactC8AcylCarMAT+C8AcylCarMAT)))
KmmcadC12AcylCoAMAT = 5.7 uM; KmmcadC8AcylCoAMAT = 4.0 uM; KmmcadC4AcylCoAMAT = 135.0 uM; KmmcadC10AcylCoAMAT = 5.4 uM; KmmcadFAD = 0.12 uM; KmmcadC10EnoylCoAMAT = 1.08 uM; KmmcadC4EnoylCoAMAT = 1.08 uM; Vmcad = 0.081 uM per min per mgProtein; KmmcadC6AcylCoAMAT = 9.4 uM; KmmcadC6EnoylCoAMAT = 1.08 uM; sfmcadC12=0.38 dimensionless; KmmcadFADH = 24.2 uM; KmmcadC12EnoylCoAMAT = 1.08 uM; KmmcadC8EnoylCoAMAT = 1.08 uM; Keqmcad = 6.0 dimensionless Reaction: C12AcylCoAMAT => C12EnoylCoAMAT + FADHMAT; C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, FADtMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, C6EnoylCoAMAT, C4EnoylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, FADtMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, C6EnoylCoAMAT, C4EnoylCoAMAT, FADHMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, FADtMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, C6EnoylCoAMAT, C4EnoylCoAMAT, FADHMAT, Rate Law: sfmcadC12*Vmcad*(C12AcylCoAMAT*(FADtMAT-FADHMAT)/(KmmcadC12AcylCoAMAT*KmmcadFAD)-C12EnoylCoAMAT*FADHMAT/(KmmcadC12AcylCoAMAT*KmmcadFAD*Keqmcad))/((1+C12AcylCoAMAT/KmmcadC12AcylCoAMAT+C12EnoylCoAMAT/KmmcadC12EnoylCoAMAT+C10AcylCoAMAT/KmmcadC10AcylCoAMAT+C10EnoylCoAMAT/KmmcadC10EnoylCoAMAT+C8AcylCoAMAT/KmmcadC8AcylCoAMAT+C8EnoylCoAMAT/KmmcadC8EnoylCoAMAT+C6AcylCoAMAT/KmmcadC6AcylCoAMAT+C6EnoylCoAMAT/KmmcadC6EnoylCoAMAT+C4AcylCoAMAT/KmmcadC4AcylCoAMAT+C4EnoylCoAMAT/KmmcadC4EnoylCoAMAT)*(1+(FADtMAT-FADHMAT)/KmmcadFAD+FADHMAT/KmmcadFADH))
KmmckatC14AcylCoAMAT = 13.83 uM; KmmckatC10KetoacylCoAMAT = 2.1 uM; KmmckatCoAMAT = 26.6 uM; KmmckatC6KetoacylCoAMAT = 6.7 uM; Keqmckat = 1051.0 dimensionless; KmmckatC12KetoacylCoAMAT = 1.3 uM; KmmckatC8KetoacylCoAMAT = 3.2 uM; KmmckatC4AcetoacylCoAMAT = 12.4 uM; KmmckatC16KetoacylCoAMAT = 1.1 uM; KmmckatC12AcylCoAMAT = 13.83 uM; sfmckatC14=0.2 dimensionless; KmmckatC8AcylCoAMAT = 13.83 uM; KmmckatC10AcylCoAMAT = 13.83 uM; KmmckatC6AcylCoAMAT = 13.83 uM; Vmckat = 0.377 uM per min per mgProtein; KmmckatC16AcylCoAMAT = 13.83 uM; KmmckatC14KetoacylCoAMAT = 1.2 uM; KmmckatC4AcylCoAMAT = 13.83 uM; KmmckatAcetylCoAMAT = 30.0 uM Reaction: C14KetoacylCoAMAT => C12AcylCoAMAT + AcetylCoAMAT; C16KetoacylCoAMAT, C12KetoacylCoAMAT, C10KetoacylCoAMAT, C8KetoacylCoAMAT, C6KetoacylCoAMAT, C4AcetoacylCoAMAT, CoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, C14KetoacylCoAMAT, C16KetoacylCoAMAT, C12KetoacylCoAMAT, C10KetoacylCoAMAT, C8KetoacylCoAMAT, C6KetoacylCoAMAT, C4AcetoacylCoAMAT, CoAMAT, C12AcylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, AcetylCoAMAT, C14KetoacylCoAMAT, C16KetoacylCoAMAT, C12KetoacylCoAMAT, C10KetoacylCoAMAT, C8KetoacylCoAMAT, C6KetoacylCoAMAT, C4AcetoacylCoAMAT, CoAMAT, C12AcylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, AcetylCoAMAT, Rate Law: sfmckatC14*Vmckat*(C14KetoacylCoAMAT*CoAMAT/(KmmckatC14KetoacylCoAMAT*KmmckatCoAMAT)-C12AcylCoAMAT*AcetylCoAMAT/(KmmckatC14KetoacylCoAMAT*KmmckatCoAMAT*Keqmckat))/((1+C14KetoacylCoAMAT/KmmckatC14KetoacylCoAMAT+C12AcylCoAMAT/KmmckatC12AcylCoAMAT+C16KetoacylCoAMAT/KmmckatC16KetoacylCoAMAT+C16AcylCoAMAT/KmmckatC16AcylCoAMAT+C12KetoacylCoAMAT/KmmckatC12KetoacylCoAMAT+C14AcylCoAMAT/KmmckatC14AcylCoAMAT+C10KetoacylCoAMAT/KmmckatC10KetoacylCoAMAT+C10AcylCoAMAT/KmmckatC10AcylCoAMAT+C8KetoacylCoAMAT/KmmckatC8KetoacylCoAMAT+C8AcylCoAMAT/KmmckatC8AcylCoAMAT+C6KetoacylCoAMAT/KmmckatC6KetoacylCoAMAT+C6AcylCoAMAT/KmmckatC6AcylCoAMAT+C4AcetoacylCoAMAT/KmmckatC4AcetoacylCoAMAT+C4AcylCoAMAT/KmmckatC4AcylCoAMAT+AcetylCoAMAT/KmmckatAcetylCoAMAT)*(1+CoAMAT/KmmckatCoAMAT+AcetylCoAMAT/KmmckatAcetylCoAMAT))
KmmschadC16KetoacylCoAMAT = 1.4 uM; KmmschadC14HydroxyacylCoAMAT = 1.8 uM; Keqmschad = 2.17E-4 dimensionless; KmmschadC16HydroxyacylCoAMAT = 1.5 uM; Vmschad = 1.0 uM per min per mgProtein; KmmschadC6HydroxyacylCoAMAT = 28.6 uM; KmmschadC12KetoacylCoAMAT = 1.6 uM; KmmschadC4HydroxyacylCoAMAT = 69.9 uM; KmmschadC14KetoacylCoAMAT = 1.4 uM; KmmschadC12HydroxyacylCoAMAT = 3.7 uM; KmmschadC6KetoacylCoAMAT = 5.8 uM; KmmschadNADMAT = 58.5 uM; KmmschadC10HydroxyacylCoAMAT = 8.8 uM; sfmschadC14=0.5 dimensionless; KmmschadC8HydroxyacylCoAMAT = 16.3 uM; KmmschadC4AcetoacylCoAMAT = 16.9 uM; KmmschadC10KetoacylCoAMAT = 2.3 uM; KmmschadNADHMAT = 5.4 uM; KmmschadC8KetoacylCoAMAT = 4.1 uM Reaction: C14HydroxyacylCoAMAT => C14KetoacylCoAMAT + NADHMAT; C16HydroxyacylCoAMAT, C12HydroxyacylCoAMAT, C10HydroxyacylCoAMAT, C8HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, C4HydroxyacylCoAMAT, NADtMAT, C16KetoacylCoAMAT, C12KetoacylCoAMAT, C10KetoacylCoAMAT, C8KetoacylCoAMAT, C6KetoacylCoAMAT, C4AcetoacylCoAMAT, C14HydroxyacylCoAMAT, C16HydroxyacylCoAMAT, C12HydroxyacylCoAMAT, C10HydroxyacylCoAMAT, C8HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, C4HydroxyacylCoAMAT, NADtMAT, C14KetoacylCoAMAT, C16KetoacylCoAMAT, C12KetoacylCoAMAT, C10KetoacylCoAMAT, C8KetoacylCoAMAT, C6KetoacylCoAMAT, C4AcetoacylCoAMAT, NADHMAT, C14HydroxyacylCoAMAT, C16HydroxyacylCoAMAT, C12HydroxyacylCoAMAT, C10HydroxyacylCoAMAT, C8HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, C4HydroxyacylCoAMAT, NADtMAT, C14KetoacylCoAMAT, C16KetoacylCoAMAT, C12KetoacylCoAMAT, C10KetoacylCoAMAT, C8KetoacylCoAMAT, C6KetoacylCoAMAT, C4AcetoacylCoAMAT, NADHMAT, Rate Law: sfmschadC14*Vmschad*(C14HydroxyacylCoAMAT*(NADtMAT-NADHMAT)/(KmmschadC14HydroxyacylCoAMAT*KmmschadNADMAT)-C14KetoacylCoAMAT*NADHMAT/(KmmschadC14HydroxyacylCoAMAT*KmmschadNADMAT*Keqmschad))/((1+C14HydroxyacylCoAMAT/KmmschadC14HydroxyacylCoAMAT+C14KetoacylCoAMAT/KmmschadC14KetoacylCoAMAT+C16HydroxyacylCoAMAT/KmmschadC16HydroxyacylCoAMAT+C16KetoacylCoAMAT/KmmschadC16KetoacylCoAMAT+C12HydroxyacylCoAMAT/KmmschadC12HydroxyacylCoAMAT+C12KetoacylCoAMAT/KmmschadC12KetoacylCoAMAT+C10HydroxyacylCoAMAT/KmmschadC10HydroxyacylCoAMAT+C10KetoacylCoAMAT/KmmschadC10KetoacylCoAMAT+C8HydroxyacylCoAMAT/KmmschadC8HydroxyacylCoAMAT+C8KetoacylCoAMAT/KmmschadC8KetoacylCoAMAT+C6HydroxyacylCoAMAT/KmmschadC6HydroxyacylCoAMAT+C6KetoacylCoAMAT/KmmschadC6KetoacylCoAMAT+C4HydroxyacylCoAMAT/KmmschadC4HydroxyacylCoAMAT+C4AcetoacylCoAMAT/KmmschadC4AcetoacylCoAMAT)*(1+(NADtMAT-NADHMAT)/KmmschadNADMAT+NADHMAT/KmmschadNADHMAT))
KmcactC16AcylCarMAT=15.0 uM; Vfcact = 0.42 uM per min per mgProtein; KmcactCarCYT = 130.0 uM; KicactC16AcylCarCYT=56.0 uM; KmcactCarMAT = 130.0 uM; KmcactC16AcylCarCYT=15.0 uM; Keqcact = 1.0 dimensionless; KicactCarCYT = 200.0 uM; Vrcact = 0.42 uM per min per mgProtein Reaction: C16AcylCarCYT => C16AcylCarMAT; CarMAT, CarCYT, C16AcylCarCYT, CarMAT, C16AcylCarMAT, CarCYT, C16AcylCarCYT, CarMAT, C16AcylCarMAT, CarCYT, Rate Law: Vfcact*(C16AcylCarCYT*CarMAT-C16AcylCarMAT*CarCYT/Keqcact)/(C16AcylCarCYT*CarMAT+KmcactCarMAT*C16AcylCarCYT+KmcactC16AcylCarCYT*CarMAT*(1+CarCYT/KicactCarCYT)+Vfcact/(Vrcact*Keqcact)*(KmcactCarCYT*C16AcylCarMAT*(1+C16AcylCarCYT/KicactC16AcylCarCYT)+CarCYT*(KmcactC16AcylCarMAT+C16AcylCarMAT)))
KmlcadC8AcylCoAMAT = 123.0 uM; sflcadC14=1.0 dimensionless; Vlcad = 0.01 uM per min per mgProtein; KmlcadC14EnoylCoAMAT = 1.08 uM; KmlcadFAD = 0.12 uM; KmlcadFADH = 24.2 uM; KmlcadC12EnoylCoAMAT = 1.08 uM; KmlcadC8EnoylCoAMAT = 1.08 uM; Keqlcad = 6.0 dimensionless; KmlcadC16AcylCoAMAT = 2.5 uM; KmlcadC12AcylCoAMAT = 9.0 uM; KmlcadC10AcylCoAMAT = 24.3 uM; KmlcadC10EnoylCoAMAT = 1.08 uM; KmlcadC14AcylCoAMAT = 7.4 uM; KmlcadC16EnoylCoAMAT = 1.08 uM Reaction: C14AcylCoAMAT => C14EnoylCoAMAT + FADHMAT; C16AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, FADtMAT, C16EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, C14AcylCoAMAT, C16AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, FADtMAT, C14EnoylCoAMAT, C16EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, FADHMAT, C14AcylCoAMAT, C16AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, FADtMAT, C14EnoylCoAMAT, C16EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, FADHMAT, Rate Law: sflcadC14*Vlcad*(C14AcylCoAMAT*(FADtMAT-FADHMAT)/(KmlcadC14AcylCoAMAT*KmlcadFAD)-C14EnoylCoAMAT*FADHMAT/(KmlcadC14AcylCoAMAT*KmlcadFAD*Keqlcad))/((1+C14AcylCoAMAT/KmlcadC14AcylCoAMAT+C14EnoylCoAMAT/KmlcadC14EnoylCoAMAT+C16AcylCoAMAT/KmlcadC16AcylCoAMAT+C16EnoylCoAMAT/KmlcadC16EnoylCoAMAT+C12AcylCoAMAT/KmlcadC12AcylCoAMAT+C12EnoylCoAMAT/KmlcadC12EnoylCoAMAT+C10AcylCoAMAT/KmlcadC10AcylCoAMAT+C10EnoylCoAMAT/KmlcadC10EnoylCoAMAT+C8AcylCoAMAT/KmlcadC8AcylCoAMAT+C8EnoylCoAMAT/KmlcadC8EnoylCoAMAT)*(1+(FADtMAT-FADHMAT)/KmlcadFAD+FADHMAT/KmlcadFADH))
KmlcadC8AcylCoAMAT = 123.0 uM; Vlcad = 0.01 uM per min per mgProtein; KmlcadC14EnoylCoAMAT = 1.08 uM; KmlcadFAD = 0.12 uM; KmlcadFADH = 24.2 uM; KmlcadC12EnoylCoAMAT = 1.08 uM; KmlcadC8EnoylCoAMAT = 1.08 uM; sflcadC16=0.9 dimensionless; KmlcadC16AcylCoAMAT = 2.5 uM; Keqlcad = 6.0 dimensionless; KmlcadC12AcylCoAMAT = 9.0 uM; KmlcadC10AcylCoAMAT = 24.3 uM; KmlcadC10EnoylCoAMAT = 1.08 uM; KmlcadC14AcylCoAMAT = 7.4 uM; KmlcadC16EnoylCoAMAT = 1.08 uM Reaction: C16AcylCoAMAT => C16EnoylCoAMAT + FADHMAT; C14AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, FADtMAT, C14EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, FADtMAT, C16EnoylCoAMAT, C14EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, FADHMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, FADtMAT, C16EnoylCoAMAT, C14EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, FADHMAT, Rate Law: sflcadC16*Vlcad*(C16AcylCoAMAT*(FADtMAT-FADHMAT)/(KmlcadC16AcylCoAMAT*KmlcadFAD)-C16EnoylCoAMAT*FADHMAT/(KmlcadC16AcylCoAMAT*KmlcadFAD*Keqlcad))/((1+C16AcylCoAMAT/KmlcadC16AcylCoAMAT+C16EnoylCoAMAT/KmlcadC16EnoylCoAMAT+C14AcylCoAMAT/KmlcadC14AcylCoAMAT+C14EnoylCoAMAT/KmlcadC14EnoylCoAMAT+C12AcylCoAMAT/KmlcadC12AcylCoAMAT+C12EnoylCoAMAT/KmlcadC12EnoylCoAMAT+C10AcylCoAMAT/KmlcadC10AcylCoAMAT+C10EnoylCoAMAT/KmlcadC10EnoylCoAMAT+C8AcylCoAMAT/KmlcadC8AcylCoAMAT+C8EnoylCoAMAT/KmlcadC8EnoylCoAMAT)*(1+(FADtMAT-FADHMAT)/KmlcadFAD+FADHMAT/KmlcadFADH))
KmcrotC16EnoylCoAMAT = 150.0 uM; KmcrotC12EnoylCoAMAT = 25.0 uM; KicrotC4AcetoacylCoA = 1.6 uM; KmcrotC4EnoylCoAMAT = 40.0 uM; Vcrot = 3.6 uM per min per mgProtein; Keqcrot = 3.13 dimensionless; KmcrotC14EnoylCoAMAT = 100.0 uM; KmcrotC8EnoylCoAMAT = 25.0 uM; KmcrotC10HydroxyacylCoAMAT = 45.0 uM; KmcrotC8HydroxyacylCoAMAT = 45.0 uM; KmcrotC6EnoylCoAMAT = 25.0 uM; sfcrotC12=0.25 dimensionless; KmcrotC10EnoylCoAMAT = 25.0 uM; KmcrotC14HydroxyacylCoAMAT = 45.0 uM; KmcrotC4HydroxyacylCoAMAT = 45.0 uM; KmcrotC12HydroxyacylCoAMAT = 45.0 uM; KmcrotC16HydroxyacylCoAMAT = 45.0 uM; KmcrotC6HydroxyacylCoAMAT = 45.0 uM Reaction: C12EnoylCoAMAT => C12HydroxyacylCoAMAT; C16EnoylCoAMAT, C14EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, C6EnoylCoAMAT, C4EnoylCoAMAT, C16HydroxyacylCoAMAT, C14HydroxyacylCoAMAT, C10HydroxyacylCoAMAT, C8HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, C4HydroxyacylCoAMAT, C4AcetoacylCoAMAT, C12EnoylCoAMAT, C16EnoylCoAMAT, C14EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, C6EnoylCoAMAT, C4EnoylCoAMAT, C12HydroxyacylCoAMAT, C16HydroxyacylCoAMAT, C14HydroxyacylCoAMAT, C10HydroxyacylCoAMAT, C8HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, C4HydroxyacylCoAMAT, C4AcetoacylCoAMAT, C12EnoylCoAMAT, C16EnoylCoAMAT, C14EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, C6EnoylCoAMAT, C4EnoylCoAMAT, C12HydroxyacylCoAMAT, C16HydroxyacylCoAMAT, C14HydroxyacylCoAMAT, C10HydroxyacylCoAMAT, C8HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, C4HydroxyacylCoAMAT, C4AcetoacylCoAMAT, Rate Law: sfcrotC12*Vcrot*(C12EnoylCoAMAT/KmcrotC12EnoylCoAMAT-C12HydroxyacylCoAMAT/(KmcrotC12EnoylCoAMAT*Keqcrot))/(1+C12EnoylCoAMAT/KmcrotC12EnoylCoAMAT+C12HydroxyacylCoAMAT/KmcrotC12HydroxyacylCoAMAT+C16EnoylCoAMAT/KmcrotC16EnoylCoAMAT+C16HydroxyacylCoAMAT/KmcrotC16HydroxyacylCoAMAT+C14EnoylCoAMAT/KmcrotC14EnoylCoAMAT+C14HydroxyacylCoAMAT/KmcrotC14HydroxyacylCoAMAT+C10EnoylCoAMAT/KmcrotC10EnoylCoAMAT+C10HydroxyacylCoAMAT/KmcrotC10HydroxyacylCoAMAT+C8EnoylCoAMAT/KmcrotC8EnoylCoAMAT+C8HydroxyacylCoAMAT/KmcrotC8HydroxyacylCoAMAT+C6EnoylCoAMAT/KmcrotC6EnoylCoAMAT+C6HydroxyacylCoAMAT/KmcrotC6HydroxyacylCoAMAT+C4EnoylCoAMAT/KmcrotC4EnoylCoAMAT+C4HydroxyacylCoAMAT/KmcrotC4HydroxyacylCoAMAT+C4AcetoacylCoAMAT/KicrotC4AcetoacylCoA)
sfmtpC8=0.34 dimensionless; KmmtpC14EnoylCoAMAT = 25.0 uM; KmmtpC8EnoylCoAMAT = 25.0 uM; KmmtpC16AcylCoAMAT = 13.83 uM; KmmtpCoAMAT = 30.0 uM; KmmtpC16EnoylCoAMAT = 25.0 uM; KmmtpC12EnoylCoAMAT = 25.0 uM; KmmtpAcetylCoAMAT = 30.0 uM; Vmtp = 2.84 uM per min per mgProtein; KmmtpC12AcylCoAMAT = 13.83 uM; KmmtpC8AcylCoAMAT = 13.83 uM; KmmtpC6AcylCoAMAT = 13.83 uM; KmmtpC14AcylCoAMAT = 13.83 uM; KmmtpC10EnoylCoAMAT = 25.0 uM; Keqmtp = 0.71 dimensionless; KicrotC4AcetoacylCoA = 1.6 uM; KmmtpNADMAT = 60.0 uM; KmmtpNADHMAT = 50.0 uM; KmmtpC10AcylCoAMAT = 13.83 uM Reaction: C8EnoylCoAMAT => C6AcylCoAMAT + AcetylCoAMAT + NADHMAT; C16EnoylCoAMAT, C14EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, NADtMAT, CoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C4AcetoacylCoAMAT, C8EnoylCoAMAT, C16EnoylCoAMAT, C14EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, NADtMAT, CoAMAT, C6AcylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, NADHMAT, AcetylCoAMAT, C4AcetoacylCoAMAT, C8EnoylCoAMAT, C16EnoylCoAMAT, C14EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, NADtMAT, CoAMAT, C6AcylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, NADHMAT, AcetylCoAMAT, C4AcetoacylCoAMAT, Rate Law: sfmtpC8*Vmtp*(C8EnoylCoAMAT*(NADtMAT-NADHMAT)*CoAMAT/(KmmtpC8EnoylCoAMAT*KmmtpNADMAT*KmmtpCoAMAT)-C6AcylCoAMAT*NADHMAT*AcetylCoAMAT/(KmmtpC8EnoylCoAMAT*KmmtpNADMAT*KmmtpCoAMAT*Keqmtp))/((1+C8EnoylCoAMAT/KmmtpC8EnoylCoAMAT+C6AcylCoAMAT/KmmtpC6AcylCoAMAT+C16EnoylCoAMAT/KmmtpC16EnoylCoAMAT+C16AcylCoAMAT/KmmtpC16AcylCoAMAT+C14EnoylCoAMAT/KmmtpC14EnoylCoAMAT+C14AcylCoAMAT/KmmtpC14AcylCoAMAT+C12EnoylCoAMAT/KmmtpC12EnoylCoAMAT+C12AcylCoAMAT/KmmtpC12AcylCoAMAT+C10EnoylCoAMAT/KmmtpC10EnoylCoAMAT+C10AcylCoAMAT/KmmtpC10AcylCoAMAT+C8AcylCoAMAT/KmmtpC8AcylCoAMAT+C4AcetoacylCoAMAT/KicrotC4AcetoacylCoA)*(1+(NADtMAT-NADHMAT)/KmmtpNADMAT+NADHMAT/KmmtpNADHMAT)*(1+CoAMAT/KmmtpCoAMAT+AcetylCoAMAT/KmmtpAcetylCoAMAT))
KmmschadC16KetoacylCoAMAT = 1.4 uM; Keqmschad = 2.17E-4 dimensionless; KmmschadC16HydroxyacylCoAMAT = 1.5 uM; KmmschadC14HydroxyacylCoAMAT = 1.8 uM; Vmschad = 1.0 uM per min per mgProtein; KmmschadC6HydroxyacylCoAMAT = 28.6 uM; KmmschadC12KetoacylCoAMAT = 1.6 uM; KmmschadC4HydroxyacylCoAMAT = 69.9 uM; KmmschadC14KetoacylCoAMAT = 1.4 uM; KmmschadC12HydroxyacylCoAMAT = 3.7 uM; KmmschadC6KetoacylCoAMAT = 5.8 uM; KmmschadNADMAT = 58.5 uM; KmmschadC10HydroxyacylCoAMAT = 8.8 uM; KmmschadC8HydroxyacylCoAMAT = 16.3 uM; KmmschadC4AcetoacylCoAMAT = 16.9 uM; KmmschadC10KetoacylCoAMAT = 2.3 uM; KmmschadNADHMAT = 5.4 uM; sfmschadC12=0.43 dimensionless; KmmschadC8KetoacylCoAMAT = 4.1 uM Reaction: C12HydroxyacylCoAMAT => C12KetoacylCoAMAT + NADHMAT; C16HydroxyacylCoAMAT, C14HydroxyacylCoAMAT, C10HydroxyacylCoAMAT, C8HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, C4HydroxyacylCoAMAT, NADtMAT, C16KetoacylCoAMAT, C14KetoacylCoAMAT, C10KetoacylCoAMAT, C8KetoacylCoAMAT, C6KetoacylCoAMAT, C4AcetoacylCoAMAT, C12HydroxyacylCoAMAT, C16HydroxyacylCoAMAT, C14HydroxyacylCoAMAT, C10HydroxyacylCoAMAT, C8HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, C4HydroxyacylCoAMAT, NADtMAT, C12KetoacylCoAMAT, C16KetoacylCoAMAT, C14KetoacylCoAMAT, C10KetoacylCoAMAT, C8KetoacylCoAMAT, C6KetoacylCoAMAT, C4AcetoacylCoAMAT, NADHMAT, C12HydroxyacylCoAMAT, C16HydroxyacylCoAMAT, C14HydroxyacylCoAMAT, C10HydroxyacylCoAMAT, C8HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, C4HydroxyacylCoAMAT, NADtMAT, C12KetoacylCoAMAT, C16KetoacylCoAMAT, C14KetoacylCoAMAT, C10KetoacylCoAMAT, C8KetoacylCoAMAT, C6KetoacylCoAMAT, C4AcetoacylCoAMAT, NADHMAT, Rate Law: sfmschadC12*Vmschad*(C12HydroxyacylCoAMAT*(NADtMAT-NADHMAT)/(KmmschadC12HydroxyacylCoAMAT*KmmschadNADMAT)-C12KetoacylCoAMAT*NADHMAT/(KmmschadC12HydroxyacylCoAMAT*KmmschadNADMAT*Keqmschad))/((1+C12HydroxyacylCoAMAT/KmmschadC12HydroxyacylCoAMAT+C12KetoacylCoAMAT/KmmschadC12KetoacylCoAMAT+C16HydroxyacylCoAMAT/KmmschadC16HydroxyacylCoAMAT+C16KetoacylCoAMAT/KmmschadC16KetoacylCoAMAT+C14HydroxyacylCoAMAT/KmmschadC14HydroxyacylCoAMAT+C14KetoacylCoAMAT/KmmschadC14KetoacylCoAMAT+C10HydroxyacylCoAMAT/KmmschadC10HydroxyacylCoAMAT+C10KetoacylCoAMAT/KmmschadC10KetoacylCoAMAT+C8HydroxyacylCoAMAT/KmmschadC8HydroxyacylCoAMAT+C8KetoacylCoAMAT/KmmschadC8KetoacylCoAMAT+C6HydroxyacylCoAMAT/KmmschadC6HydroxyacylCoAMAT+C6KetoacylCoAMAT/KmmschadC6KetoacylCoAMAT+C4HydroxyacylCoAMAT/KmmschadC4HydroxyacylCoAMAT+C4AcetoacylCoAMAT/KmmschadC4AcetoacylCoAMAT)*(1+(NADtMAT-NADHMAT)/KmmschadNADMAT+NADHMAT/KmmschadNADHMAT))
KmmtpC14EnoylCoAMAT = 25.0 uM; KmmtpC8EnoylCoAMAT = 25.0 uM; KmmtpC16AcylCoAMAT = 13.83 uM; KmmtpCoAMAT = 30.0 uM; KmmtpC16EnoylCoAMAT = 25.0 uM; KmmtpC12EnoylCoAMAT = 25.0 uM; KmmtpAcetylCoAMAT = 30.0 uM; Vmtp = 2.84 uM per min per mgProtein; KmmtpC12AcylCoAMAT = 13.83 uM; KmmtpC8AcylCoAMAT = 13.83 uM; KmmtpC14AcylCoAMAT = 13.83 uM; KmmtpC6AcylCoAMAT = 13.83 uM; sfmtpC12=0.81 dimensionless; KmmtpC10EnoylCoAMAT = 25.0 uM; Keqmtp = 0.71 dimensionless; KicrotC4AcetoacylCoA = 1.6 uM; KmmtpNADMAT = 60.0 uM; KmmtpNADHMAT = 50.0 uM; KmmtpC10AcylCoAMAT = 13.83 uM Reaction: C12EnoylCoAMAT => C10AcylCoAMAT + AcetylCoAMAT + NADHMAT; C16EnoylCoAMAT, C14EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, NADtMAT, CoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcetoacylCoAMAT, C12EnoylCoAMAT, C16EnoylCoAMAT, C14EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, NADtMAT, CoAMAT, C10AcylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, NADHMAT, AcetylCoAMAT, C4AcetoacylCoAMAT, C12EnoylCoAMAT, C16EnoylCoAMAT, C14EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, NADtMAT, CoAMAT, C10AcylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, NADHMAT, AcetylCoAMAT, C4AcetoacylCoAMAT, Rate Law: sfmtpC12*Vmtp*(C12EnoylCoAMAT*(NADtMAT-NADHMAT)*CoAMAT/(KmmtpC12EnoylCoAMAT*KmmtpNADMAT*KmmtpCoAMAT)-C10AcylCoAMAT*NADHMAT*AcetylCoAMAT/(KmmtpC12EnoylCoAMAT*KmmtpNADMAT*KmmtpCoAMAT*Keqmtp))/((1+C12EnoylCoAMAT/KmmtpC12EnoylCoAMAT+C10AcylCoAMAT/KmmtpC10AcylCoAMAT+C16EnoylCoAMAT/KmmtpC16EnoylCoAMAT+C16AcylCoAMAT/KmmtpC16AcylCoAMAT+C14EnoylCoAMAT/KmmtpC14EnoylCoAMAT+C14AcylCoAMAT/KmmtpC14AcylCoAMAT+C10EnoylCoAMAT/KmmtpC10EnoylCoAMAT+C12AcylCoAMAT/KmmtpC12AcylCoAMAT+C8EnoylCoAMAT/KmmtpC8EnoylCoAMAT+C8AcylCoAMAT/KmmtpC8AcylCoAMAT+C6AcylCoAMAT/KmmtpC6AcylCoAMAT+C4AcetoacylCoAMAT/KicrotC4AcetoacylCoA)*(1+(NADtMAT-NADHMAT)/KmmtpNADMAT+NADHMAT/KmmtpNADHMAT)*(1+CoAMAT/KmmtpCoAMAT+AcetylCoAMAT/KmmtpAcetylCoAMAT))
KmmcadC12AcylCoAMAT = 5.7 uM; KmmcadC8AcylCoAMAT = 4.0 uM; KmmcadC4AcylCoAMAT = 135.0 uM; KmmcadC10AcylCoAMAT = 5.4 uM; KmmcadFAD = 0.12 uM; KmmcadC10EnoylCoAMAT = 1.08 uM; KmmcadC4EnoylCoAMAT = 1.08 uM; Vmcad = 0.081 uM per min per mgProtein; KmmcadC6AcylCoAMAT = 9.4 uM; KmmcadC6EnoylCoAMAT = 1.08 uM; KmmcadFADH = 24.2 uM; KmmcadC12EnoylCoAMAT = 1.08 uM; KmmcadC8EnoylCoAMAT = 1.08 uM; sfmcadC4=0.12 dimensionless; Keqmcad = 6.0 dimensionless Reaction: C4AcylCoAMAT => C4EnoylCoAMAT + FADHMAT; C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, FADtMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, C6EnoylCoAMAT, C4AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, FADtMAT, C4EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, C6EnoylCoAMAT, FADHMAT, C4AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, FADtMAT, C4EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, C6EnoylCoAMAT, FADHMAT, Rate Law: sfmcadC4*Vmcad*(C4AcylCoAMAT*(FADtMAT-FADHMAT)/(KmmcadC4AcylCoAMAT*KmmcadFAD)-C4EnoylCoAMAT*FADHMAT/(KmmcadC4AcylCoAMAT*KmmcadFAD*Keqmcad))/((1+C4AcylCoAMAT/KmmcadC4AcylCoAMAT+C4EnoylCoAMAT/KmmcadC4EnoylCoAMAT+C12AcylCoAMAT/KmmcadC12AcylCoAMAT+C12EnoylCoAMAT/KmmcadC12EnoylCoAMAT+C10AcylCoAMAT/KmmcadC10AcylCoAMAT+C10EnoylCoAMAT/KmmcadC10EnoylCoAMAT+C8AcylCoAMAT/KmmcadC8AcylCoAMAT+C8EnoylCoAMAT/KmmcadC8EnoylCoAMAT+C6AcylCoAMAT/KmmcadC6AcylCoAMAT+C6EnoylCoAMAT/KmmcadC6EnoylCoAMAT)*(1+(FADtMAT-FADHMAT)/KmmcadFAD+FADHMAT/KmmcadFADH))
Kmcpt2C14AcylCarMAT = 51.0 uM; Kmcpt2C10AcylCarMAT = 51.0 uM; Kmcpt2CoAMAT = 30.0 uM; Kmcpt2C6AcylCarMAT = 51.0 uM; Kmcpt2C14AcylCoAMAT = 38.0 uM; sfcpt2C14=1.0 dimensionless; Keqcpt2 = 2.22 dimensionless; Kmcpt2C8AcylCoAMAT = 38.0 uM; Kmcpt2C6AcylCoAMAT = 1000.0 uM; Kmcpt2C16AcylCarMAT = 51.0 uM; Kmcpt2C12AcylCarMAT = 51.0 uM; Kmcpt2C12AcylCoAMAT = 38.0 uM; Vcpt2 = 0.391 uM per min per mgProtein; Kmcpt2C8AcylCarMAT = 51.0 uM; Kmcpt2CarMAT = 350.0 uM; Kmcpt2C16AcylCoAMAT = 38.0 uM; Kmcpt2C4AcylCoAMAT = 1000000.0 uM; Kmcpt2C10AcylCoAMAT = 38.0 uM Reaction: C14AcylCarMAT => C14AcylCoAMAT; C16AcylCarMAT, C12AcylCarMAT, C10AcylCarMAT, C8AcylCarMAT, C6AcylCarMAT, C4AcylCarMAT, CoAMAT, C16AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, CarMAT, C14AcylCarMAT, C16AcylCarMAT, C12AcylCarMAT, C10AcylCarMAT, C8AcylCarMAT, C6AcylCarMAT, C4AcylCarMAT, CoAMAT, C14AcylCoAMAT, C16AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, CarMAT, C14AcylCarMAT, C16AcylCarMAT, C12AcylCarMAT, C10AcylCarMAT, C8AcylCarMAT, C6AcylCarMAT, C4AcylCarMAT, CoAMAT, C14AcylCoAMAT, C16AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, CarMAT, Rate Law: sfcpt2C14*Vcpt2*(C14AcylCarMAT*CoAMAT/(Kmcpt2C14AcylCarMAT*Kmcpt2CoAMAT)-C14AcylCoAMAT*CarMAT/(Kmcpt2C14AcylCarMAT*Kmcpt2CoAMAT*Keqcpt2))/((1+C14AcylCarMAT/Kmcpt2C14AcylCarMAT+C14AcylCoAMAT/Kmcpt2C14AcylCoAMAT+C16AcylCarMAT/Kmcpt2C16AcylCarMAT+C16AcylCoAMAT/Kmcpt2C16AcylCoAMAT+C12AcylCarMAT/Kmcpt2C12AcylCarMAT+C12AcylCoAMAT/Kmcpt2C12AcylCoAMAT+C10AcylCarMAT/Kmcpt2C10AcylCarMAT+C10AcylCoAMAT/Kmcpt2C10AcylCoAMAT+C8AcylCarMAT/Kmcpt2C8AcylCarMAT+C8AcylCoAMAT/Kmcpt2C8AcylCoAMAT+C6AcylCarMAT/Kmcpt2C6AcylCarMAT+C6AcylCoAMAT/Kmcpt2C6AcylCoAMAT+C4AcylCarMAT/Kmcpt2C4AcylCoAMAT+C4AcylCoAMAT/Kmcpt2C4AcylCoAMAT)*(1+CoAMAT/Kmcpt2CoAMAT+CarMAT/Kmcpt2CarMAT))
KmmckatC14AcylCoAMAT = 13.83 uM; KmmckatC10KetoacylCoAMAT = 2.1 uM; KmmckatCoAMAT = 26.6 uM; KmmckatC6KetoacylCoAMAT = 6.7 uM; KmmckatC12KetoacylCoAMAT = 1.3 uM; Keqmckat = 1051.0 dimensionless; KmmckatC8KetoacylCoAMAT = 3.2 uM; KmmckatC4AcetoacylCoAMAT = 12.4 uM; KmmckatC16KetoacylCoAMAT = 1.1 uM; KmmckatC12AcylCoAMAT = 13.83 uM; sfmckatC12=0.38 dimensionless; KmmckatC8AcylCoAMAT = 13.83 uM; KmmckatC10AcylCoAMAT = 13.83 uM; KmmckatC6AcylCoAMAT = 13.83 uM; Vmckat = 0.377 uM per min per mgProtein; KmmckatC16AcylCoAMAT = 13.83 uM; KmmckatC14KetoacylCoAMAT = 1.2 uM; KmmckatC4AcylCoAMAT = 13.83 uM; KmmckatAcetylCoAMAT = 30.0 uM Reaction: C12KetoacylCoAMAT => C10AcylCoAMAT + AcetylCoAMAT; C16KetoacylCoAMAT, C14KetoacylCoAMAT, C10KetoacylCoAMAT, C8KetoacylCoAMAT, C6KetoacylCoAMAT, C4AcetoacylCoAMAT, CoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, C12KetoacylCoAMAT, C16KetoacylCoAMAT, C14KetoacylCoAMAT, C10KetoacylCoAMAT, C8KetoacylCoAMAT, C6KetoacylCoAMAT, C4AcetoacylCoAMAT, CoAMAT, C10AcylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, AcetylCoAMAT, C12KetoacylCoAMAT, C16KetoacylCoAMAT, C14KetoacylCoAMAT, C10KetoacylCoAMAT, C8KetoacylCoAMAT, C6KetoacylCoAMAT, C4AcetoacylCoAMAT, CoAMAT, C10AcylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, AcetylCoAMAT, Rate Law: sfmckatC12*Vmckat*(C12KetoacylCoAMAT*CoAMAT/(KmmckatC12KetoacylCoAMAT*KmmckatCoAMAT)-C10AcylCoAMAT*AcetylCoAMAT/(KmmckatC12KetoacylCoAMAT*KmmckatCoAMAT*Keqmckat))/((1+C12KetoacylCoAMAT/KmmckatC12KetoacylCoAMAT+C10AcylCoAMAT/KmmckatC10AcylCoAMAT+C16KetoacylCoAMAT/KmmckatC16KetoacylCoAMAT+C16AcylCoAMAT/KmmckatC16AcylCoAMAT+C14KetoacylCoAMAT/KmmckatC14KetoacylCoAMAT+C14AcylCoAMAT/KmmckatC14AcylCoAMAT+C10KetoacylCoAMAT/KmmckatC10KetoacylCoAMAT+C12AcylCoAMAT/KmmckatC12AcylCoAMAT+C8KetoacylCoAMAT/KmmckatC8KetoacylCoAMAT+C8AcylCoAMAT/KmmckatC8AcylCoAMAT+C6KetoacylCoAMAT/KmmckatC6KetoacylCoAMAT+C6AcylCoAMAT/KmmckatC6AcylCoAMAT+C4AcetoacylCoAMAT/KmmckatC4AcetoacylCoAMAT+C4AcylCoAMAT/KmmckatC4AcylCoAMAT+AcetylCoAMAT/KmmckatAcetylCoAMAT)*(1+CoAMAT/KmmckatCoAMAT+AcetylCoAMAT/KmmckatAcetylCoAMAT))
KmcactC14AcylCarMAT=15.0 uM; KmcactC14AcylCarCYT=15.0 uM; Vfcact = 0.42 uM per min per mgProtein; KicactC14AcylCarCYT=56.0 uM; KmcactCarCYT = 130.0 uM; KmcactCarMAT = 130.0 uM; Keqcact = 1.0 dimensionless; KicactCarCYT = 200.0 uM; Vrcact = 0.42 uM per min per mgProtein Reaction: C14AcylCarCYT => C14AcylCarMAT; CarMAT, CarCYT, C14AcylCarCYT, CarMAT, C14AcylCarMAT, CarCYT, C14AcylCarCYT, CarMAT, C14AcylCarMAT, CarCYT, Rate Law: Vfcact*(C14AcylCarCYT*CarMAT-C14AcylCarMAT*CarCYT/Keqcact)/(C14AcylCarCYT*CarMAT+KmcactCarMAT*C14AcylCarCYT+KmcactC14AcylCarCYT*CarMAT*(1+CarCYT/KicactCarCYT)+Vfcact/(Vrcact*Keqcact)*(KmcactCarCYT*C14AcylCarMAT*(1+C14AcylCarCYT/KicactC14AcylCarCYT)+CarCYT*(KmcactC14AcylCarMAT+C14AcylCarMAT)))
KmscadFAD = 0.12 uM; KmscadC4EnoylCoAMAT = 1.08 uM; Vscad = 0.081 uM per min per mgProtein; KmscadFADH = 24.2 uM; KmscadC4AcylCoAMAT = 10.7 uM; KmscadC6AcylCoAMAT = 285.0 uM; KmscadC6EnoylCoAMAT = 1.08 uM; sfscadC6=0.3 dimensionless; Keqscad = 6.0 dimensionless Reaction: C6AcylCoAMAT => C6EnoylCoAMAT + FADHMAT; C4AcylCoAMAT, FADtMAT, C4EnoylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, FADtMAT, C6EnoylCoAMAT, C4EnoylCoAMAT, FADHMAT, C6AcylCoAMAT, C4AcylCoAMAT, FADtMAT, C6EnoylCoAMAT, C4EnoylCoAMAT, FADHMAT, Rate Law: sfscadC6*Vscad*(C6AcylCoAMAT*(FADtMAT-FADHMAT)/(KmscadC6AcylCoAMAT*KmscadFAD)-C6EnoylCoAMAT*FADHMAT/(KmscadC6AcylCoAMAT*KmscadFAD*Keqscad))/((1+C6AcylCoAMAT/KmscadC6AcylCoAMAT+C6EnoylCoAMAT/KmscadC6EnoylCoAMAT+C4AcylCoAMAT/KmscadC4AcylCoAMAT+C4EnoylCoAMAT/KmscadC4EnoylCoAMAT)*(1+(FADtMAT-FADHMAT)/KmscadFAD+FADHMAT/KmscadFADH))
KmmschadC16KetoacylCoAMAT = 1.4 uM; Keqmschad = 2.17E-4 dimensionless; KmmschadC16HydroxyacylCoAMAT = 1.5 uM; KmmschadC14HydroxyacylCoAMAT = 1.8 uM; Vmschad = 1.0 uM per min per mgProtein; KmmschadC6HydroxyacylCoAMAT = 28.6 uM; KmmschadC12KetoacylCoAMAT = 1.6 uM; KmmschadC4HydroxyacylCoAMAT = 69.9 uM; KmmschadC14KetoacylCoAMAT = 1.4 uM; KmmschadC12HydroxyacylCoAMAT = 3.7 uM; KmmschadC6KetoacylCoAMAT = 5.8 uM; KmmschadNADMAT = 58.5 uM; KmmschadC10HydroxyacylCoAMAT = 8.8 uM; KmmschadC8HydroxyacylCoAMAT = 16.3 uM; KmmschadC4AcetoacylCoAMAT = 16.9 uM; KmmschadC10KetoacylCoAMAT = 2.3 uM; KmmschadNADHMAT = 5.4 uM; sfmschadC8=0.89 dimensionless; KmmschadC8KetoacylCoAMAT = 4.1 uM Reaction: C8HydroxyacylCoAMAT => C8KetoacylCoAMAT + NADHMAT; C16HydroxyacylCoAMAT, C14HydroxyacylCoAMAT, C12HydroxyacylCoAMAT, C10HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, C4HydroxyacylCoAMAT, NADtMAT, C16KetoacylCoAMAT, C14KetoacylCoAMAT, C12KetoacylCoAMAT, C10KetoacylCoAMAT, C6KetoacylCoAMAT, C4AcetoacylCoAMAT, C8HydroxyacylCoAMAT, C16HydroxyacylCoAMAT, C14HydroxyacylCoAMAT, C12HydroxyacylCoAMAT, C10HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, C4HydroxyacylCoAMAT, NADtMAT, C8KetoacylCoAMAT, C16KetoacylCoAMAT, C14KetoacylCoAMAT, C12KetoacylCoAMAT, C10KetoacylCoAMAT, C6KetoacylCoAMAT, C4AcetoacylCoAMAT, NADHMAT, C8HydroxyacylCoAMAT, C16HydroxyacylCoAMAT, C14HydroxyacylCoAMAT, C12HydroxyacylCoAMAT, C10HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, C4HydroxyacylCoAMAT, NADtMAT, C8KetoacylCoAMAT, C16KetoacylCoAMAT, C14KetoacylCoAMAT, C12KetoacylCoAMAT, C10KetoacylCoAMAT, C6KetoacylCoAMAT, C4AcetoacylCoAMAT, NADHMAT, Rate Law: sfmschadC8*Vmschad*(C8HydroxyacylCoAMAT*(NADtMAT-NADHMAT)/(KmmschadC8HydroxyacylCoAMAT*KmmschadNADMAT)-C8KetoacylCoAMAT*NADHMAT/(KmmschadC8HydroxyacylCoAMAT*KmmschadNADMAT*Keqmschad))/((1+C8HydroxyacylCoAMAT/KmmschadC8HydroxyacylCoAMAT+C8KetoacylCoAMAT/KmmschadC8KetoacylCoAMAT+C16HydroxyacylCoAMAT/KmmschadC16HydroxyacylCoAMAT+C16KetoacylCoAMAT/KmmschadC16KetoacylCoAMAT+C14HydroxyacylCoAMAT/KmmschadC14HydroxyacylCoAMAT+C14KetoacylCoAMAT/KmmschadC14KetoacylCoAMAT+C12HydroxyacylCoAMAT/KmmschadC12HydroxyacylCoAMAT+C12KetoacylCoAMAT/KmmschadC12KetoacylCoAMAT+C10HydroxyacylCoAMAT/KmmschadC10HydroxyacylCoAMAT+C10KetoacylCoAMAT/KmmschadC10KetoacylCoAMAT+C6HydroxyacylCoAMAT/KmmschadC6HydroxyacylCoAMAT+C6KetoacylCoAMAT/KmmschadC6KetoacylCoAMAT+C4HydroxyacylCoAMAT/KmmschadC4HydroxyacylCoAMAT+C4AcetoacylCoAMAT/KmmschadC4AcetoacylCoAMAT)*(1+(NADtMAT-NADHMAT)/KmmschadNADMAT+NADHMAT/KmmschadNADHMAT))
KmcrotC16EnoylCoAMAT = 150.0 uM; KmcrotC12EnoylCoAMAT = 25.0 uM; KicrotC4AcetoacylCoA = 1.6 uM; KmcrotC4EnoylCoAMAT = 40.0 uM; Vcrot = 3.6 uM per min per mgProtein; Keqcrot = 3.13 dimensionless; KmcrotC14EnoylCoAMAT = 100.0 uM; KmcrotC8EnoylCoAMAT = 25.0 uM; KmcrotC10HydroxyacylCoAMAT = 45.0 uM; KmcrotC8HydroxyacylCoAMAT = 45.0 uM; KmcrotC6EnoylCoAMAT = 25.0 uM; KmcrotC10EnoylCoAMAT = 25.0 uM; sfcrotC10=0.33 dimensionless; KmcrotC14HydroxyacylCoAMAT = 45.0 uM; KmcrotC4HydroxyacylCoAMAT = 45.0 uM; KmcrotC16HydroxyacylCoAMAT = 45.0 uM; KmcrotC12HydroxyacylCoAMAT = 45.0 uM; KmcrotC6HydroxyacylCoAMAT = 45.0 uM Reaction: C10EnoylCoAMAT => C10HydroxyacylCoAMAT; C16EnoylCoAMAT, C14EnoylCoAMAT, C12EnoylCoAMAT, C8EnoylCoAMAT, C6EnoylCoAMAT, C4EnoylCoAMAT, C16HydroxyacylCoAMAT, C14HydroxyacylCoAMAT, C12HydroxyacylCoAMAT, C8HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, C4HydroxyacylCoAMAT, C4AcetoacylCoAMAT, C10EnoylCoAMAT, C16EnoylCoAMAT, C14EnoylCoAMAT, C12EnoylCoAMAT, C8EnoylCoAMAT, C6EnoylCoAMAT, C4EnoylCoAMAT, C10HydroxyacylCoAMAT, C16HydroxyacylCoAMAT, C14HydroxyacylCoAMAT, C12HydroxyacylCoAMAT, C8HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, C4HydroxyacylCoAMAT, C4AcetoacylCoAMAT, C10EnoylCoAMAT, C16EnoylCoAMAT, C14EnoylCoAMAT, C12EnoylCoAMAT, C8EnoylCoAMAT, C6EnoylCoAMAT, C4EnoylCoAMAT, C10HydroxyacylCoAMAT, C16HydroxyacylCoAMAT, C14HydroxyacylCoAMAT, C12HydroxyacylCoAMAT, C8HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, C4HydroxyacylCoAMAT, C4AcetoacylCoAMAT, Rate Law: sfcrotC10*Vcrot*(C10EnoylCoAMAT/KmcrotC10EnoylCoAMAT-C10HydroxyacylCoAMAT/(KmcrotC10EnoylCoAMAT*Keqcrot))/(1+C10EnoylCoAMAT/KmcrotC10EnoylCoAMAT+C10HydroxyacylCoAMAT/KmcrotC10HydroxyacylCoAMAT+C16EnoylCoAMAT/KmcrotC16EnoylCoAMAT+C16HydroxyacylCoAMAT/KmcrotC16HydroxyacylCoAMAT+C14EnoylCoAMAT/KmcrotC14EnoylCoAMAT+C14HydroxyacylCoAMAT/KmcrotC14HydroxyacylCoAMAT+C12EnoylCoAMAT/KmcrotC12EnoylCoAMAT+C12HydroxyacylCoAMAT/KmcrotC12HydroxyacylCoAMAT+C8EnoylCoAMAT/KmcrotC8EnoylCoAMAT+C8HydroxyacylCoAMAT/KmcrotC8HydroxyacylCoAMAT+C6EnoylCoAMAT/KmcrotC6EnoylCoAMAT+C6HydroxyacylCoAMAT/KmcrotC6HydroxyacylCoAMAT+C4EnoylCoAMAT/KmcrotC4EnoylCoAMAT+C4HydroxyacylCoAMAT/KmcrotC4HydroxyacylCoAMAT+C4AcetoacylCoAMAT/KicrotC4AcetoacylCoA)
Kmcpt2C14AcylCarMAT = 51.0 uM; Kmcpt2C10AcylCarMAT = 51.0 uM; Kmcpt2CoAMAT = 30.0 uM; Kmcpt2C6AcylCarMAT = 51.0 uM; Kmcpt2C14AcylCoAMAT = 38.0 uM; Keqcpt2 = 2.22 dimensionless; sfcpt2C16=0.85 dimensionless; Kmcpt2C8AcylCoAMAT = 38.0 uM; Kmcpt2C6AcylCoAMAT = 1000.0 uM; Kmcpt2C16AcylCarMAT = 51.0 uM; Kmcpt2C12AcylCarMAT = 51.0 uM; Kmcpt2C12AcylCoAMAT = 38.0 uM; Vcpt2 = 0.391 uM per min per mgProtein; Kmcpt2C8AcylCarMAT = 51.0 uM; Kmcpt2CarMAT = 350.0 uM; Kmcpt2C16AcylCoAMAT = 38.0 uM; Kmcpt2C4AcylCoAMAT = 1000000.0 uM; Kmcpt2C10AcylCoAMAT = 38.0 uM; Kmcpt2C4AcylCarMAT = 51.0 uM Reaction: C16AcylCarMAT => C16AcylCoAMAT; C14AcylCarMAT, C12AcylCarMAT, C10AcylCarMAT, C8AcylCarMAT, C6AcylCarMAT, C4AcylCarMAT, CoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, CarMAT, C16AcylCarMAT, C14AcylCarMAT, C12AcylCarMAT, C10AcylCarMAT, C8AcylCarMAT, C6AcylCarMAT, C4AcylCarMAT, CoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, CarMAT, C16AcylCarMAT, C14AcylCarMAT, C12AcylCarMAT, C10AcylCarMAT, C8AcylCarMAT, C6AcylCarMAT, C4AcylCarMAT, CoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, CarMAT, Rate Law: sfcpt2C16*Vcpt2*(C16AcylCarMAT*CoAMAT/(Kmcpt2C16AcylCarMAT*Kmcpt2CoAMAT)-C16AcylCoAMAT*CarMAT/(Kmcpt2C16AcylCarMAT*Kmcpt2CoAMAT*Keqcpt2))/((1+C16AcylCarMAT/Kmcpt2C16AcylCarMAT+C16AcylCoAMAT/Kmcpt2C16AcylCoAMAT+C14AcylCarMAT/Kmcpt2C14AcylCarMAT+C14AcylCoAMAT/Kmcpt2C14AcylCoAMAT+C12AcylCarMAT/Kmcpt2C12AcylCarMAT+C12AcylCoAMAT/Kmcpt2C12AcylCoAMAT+C10AcylCarMAT/Kmcpt2C10AcylCarMAT+C10AcylCoAMAT/Kmcpt2C10AcylCoAMAT+C8AcylCarMAT/Kmcpt2C8AcylCarMAT+C8AcylCoAMAT/Kmcpt2C8AcylCoAMAT+C6AcylCarMAT/Kmcpt2C6AcylCarMAT+C6AcylCoAMAT/Kmcpt2C6AcylCoAMAT+C4AcylCarMAT/Kmcpt2C4AcylCarMAT+C4AcylCoAMAT/Kmcpt2C4AcylCoAMAT)*(1+CoAMAT/Kmcpt2CoAMAT+CarMAT/Kmcpt2CarMAT))
KmmtpC14EnoylCoAMAT = 25.0 uM; KmmtpC8EnoylCoAMAT = 25.0 uM; KmmtpC16AcylCoAMAT = 13.83 uM; KmmtpCoAMAT = 30.0 uM; KmmtpC16EnoylCoAMAT = 25.0 uM; KmmtpC12EnoylCoAMAT = 25.0 uM; KmmtpAcetylCoAMAT = 30.0 uM; Vmtp = 2.84 uM per min per mgProtein; KmmtpC12AcylCoAMAT = 13.83 uM; KmmtpC8AcylCoAMAT = 13.83 uM; KmmtpC14AcylCoAMAT = 13.83 uM; KmmtpC6AcylCoAMAT = 13.83 uM; KmmtpC10EnoylCoAMAT = 25.0 uM; Keqmtp = 0.71 dimensionless; KicrotC4AcetoacylCoA = 1.6 uM; KmmtpNADMAT = 60.0 uM; KmmtpNADHMAT = 50.0 uM; KmmtpC10AcylCoAMAT = 13.83 uM; sfmtpC16=1.0 dimensionless Reaction: C16EnoylCoAMAT => C14AcylCoAMAT + AcetylCoAMAT + NADHMAT; C14EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, NADtMAT, CoAMAT, C16AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcetoacylCoAMAT, C16EnoylCoAMAT, C14EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, NADtMAT, CoAMAT, C14AcylCoAMAT, C16AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, NADHMAT, AcetylCoAMAT, C4AcetoacylCoAMAT, C16EnoylCoAMAT, C14EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, NADtMAT, CoAMAT, C14AcylCoAMAT, C16AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, NADHMAT, AcetylCoAMAT, C4AcetoacylCoAMAT, Rate Law: sfmtpC16*Vmtp*(C16EnoylCoAMAT*(NADtMAT-NADHMAT)*CoAMAT/(KmmtpC16EnoylCoAMAT*KmmtpNADMAT*KmmtpCoAMAT)-C14AcylCoAMAT*NADHMAT*AcetylCoAMAT/(KmmtpC16EnoylCoAMAT*KmmtpNADMAT*KmmtpCoAMAT*Keqmtp))/((1+C16EnoylCoAMAT/KmmtpC16EnoylCoAMAT+C14AcylCoAMAT/KmmtpC14AcylCoAMAT+C14EnoylCoAMAT/KmmtpC14EnoylCoAMAT+C16AcylCoAMAT/KmmtpC16AcylCoAMAT+C12EnoylCoAMAT/KmmtpC12EnoylCoAMAT+C12AcylCoAMAT/KmmtpC12AcylCoAMAT+C10EnoylCoAMAT/KmmtpC10EnoylCoAMAT+C10AcylCoAMAT/KmmtpC10AcylCoAMAT+C8EnoylCoAMAT/KmmtpC8EnoylCoAMAT+C8AcylCoAMAT/KmmtpC8AcylCoAMAT+C6AcylCoAMAT/KmmtpC6AcylCoAMAT+C4AcetoacylCoAMAT/KicrotC4AcetoacylCoA)*(1+(NADtMAT-NADHMAT)/KmmtpNADMAT+NADHMAT/KmmtpNADHMAT)*(1+CoAMAT/KmmtpCoAMAT+AcetylCoAMAT/KmmtpAcetylCoAMAT))

States:

Name Description
C12AcylCarCYT [O-acylcarnitine]
C8HydroxyacylCoAMAT [hydroxy fatty acyl-CoA]
C14KetoacylCoAMAT [fatty acyl-CoA]
C10AcylCarCYT [O-acylcarnitine]
C10EnoylCoAMAT [cis-2-enoyl-CoA]
C12HydroxyacylCoAMAT [hydroxy fatty acyl-CoA]
C14EnoylCoAMAT [cis-2-enoyl-CoA]
C12AcylCoAMAT [fatty acyl-CoA]
C8KetoacylCoAMAT [fatty acyl-CoA]
C6AcylCarMAT [O-acylcarnitine]
C10AcylCarMAT [O-acylcarnitine]
C4HydroxyacylCoAMAT [hydroxy fatty acyl-CoA]
C14HydroxyacylCoAMAT [hydroxy fatty acyl-CoA]
C8AcylCoAMAT [fatty acyl-CoA]
C4AcylCoAMAT [fatty acyl-CoA]
C4EnoylCoAMAT [cis-2-enoyl-CoA]
C12KetoacylCoAMAT [fatty acyl-CoA]
C16HydroxyacylCoAMAT [hydroxy fatty acyl-CoA]
NADHMAT [NADH]
C6AcylCarCYT [O-acylcarnitine]
C8AcylCarCYT [O-acylcarnitine]
C14AcylCarCYT [O-acylcarnitine]
C10KetoacylCoAMAT [fatty acyl-CoA]
C16AcylCarMAT [O-acylcarnitine]
C10AcylCoAMAT [fatty acyl-CoA]
C16EnoylCoAMAT [cis-2-enoyl-CoA]
FADHMAT [FADH(.)]
C14AcylCoAMAT [fatty acyl-CoA]
AcetylCoAMAT [acetyl-CoA]
C16AcylCoAMAT [palmitoyl-CoA; fatty acyl-CoA]
C16KetoacylCoAMAT [fatty acyl-CoA]
C12EnoylCoAMAT [cis-2-enoyl-CoA]
C8AcylCarMAT [O-acylcarnitine]
C10HydroxyacylCoAMAT [hydroxy fatty acyl-CoA]
C12AcylCarMAT [O-acylcarnitine]
C4AcetoacylCoAMAT [acetoacetyl-CoA]
C8EnoylCoAMAT [cis-2-enoyl-CoA]

Observables: none

vanEunen2013 - Network dynamics of fatty acid β-oxidation (time-course model)Lipid metabolism plays an important role in…

Fatty-acid metabolism plays a key role in acquired and inborn metabolic diseases. To obtain insight into the network dynamics of fatty-acid β-oxidation, we constructed a detailed computational model of the pathway and subjected it to a fat overload condition. The model contains reversible and saturable enzyme-kinetic equations and experimentally determined parameters for rat-liver enzymes. It was validated by adding palmitoyl CoA or palmitoyl carnitine to isolated rat-liver mitochondria: without refitting of measured parameters, the model correctly predicted the β-oxidation flux as well as the time profiles of most acyl-carnitine concentrations. Subsequently, we simulated the condition of obesity by increasing the palmitoyl-CoA concentration. At a high concentration of palmitoyl CoA the β-oxidation became overloaded: the flux dropped and metabolites accumulated. This behavior originated from the competition between acyl CoAs of different chain lengths for a set of acyl-CoA dehydrogenases with overlapping substrate specificity. This effectively induced competitive feedforward inhibition and thereby led to accumulation of CoA-ester intermediates and depletion of free CoA (CoASH). The mitochondrial [NAD⁺]/[NADH] ratio modulated the sensitivity to substrate overload, revealing a tight interplay between regulation of β-oxidation and mitochondrial respiration. link: http://identifiers.org/pubmed/23966849

Parameters:

Name Description
KmvlcadC14AcylCoAMAT = 4.0 uM; KmvlcadC14EnoylCoAMAT = 1.08 uM; KmvlcadFAD = 0.12 uM; KmvlcadFADH = 24.2 uM; KmvlcadC12AcylCoAMAT = 2.7 uM; sfvlcadC14=0.42 dimensionless; KmvlcadC12EnoylCoAMAT = 1.08 uM; Vvlcad = 0.008 uM per min per mgProtein; KmvlcadC16EnoylCoAMAT = 1.08 uM; KmvlcadC16AcylCoAMAT = 6.5 uM; Keqvlcad = 6.0 dimensionless Reaction: C14AcylCoAMAT => C14EnoylCoAMAT + FADHMAT; C16AcylCoAMAT, C12AcylCoAMAT, FADtMAT, C16EnoylCoAMAT, C12EnoylCoAMAT, C14AcylCoAMAT, C16AcylCoAMAT, C12AcylCoAMAT, FADtMAT, C14EnoylCoAMAT, C16EnoylCoAMAT, C12EnoylCoAMAT, FADHMAT, Rate Law: sfvlcadC14*Vvlcad*(C14AcylCoAMAT*(FADtMAT-FADHMAT)/(KmvlcadC14AcylCoAMAT*KmvlcadFAD)-C14EnoylCoAMAT*FADHMAT/(KmvlcadC14AcylCoAMAT*KmvlcadFAD*Keqvlcad))/((1+C14AcylCoAMAT/KmvlcadC14AcylCoAMAT+C14EnoylCoAMAT/KmvlcadC14EnoylCoAMAT+C16AcylCoAMAT/KmvlcadC16AcylCoAMAT+C16EnoylCoAMAT/KmvlcadC16EnoylCoAMAT+C12AcylCoAMAT/KmvlcadC12AcylCoAMAT+C12EnoylCoAMAT/KmvlcadC12EnoylCoAMAT)*(1+(FADtMAT-FADHMAT)/KmvlcadFAD+FADHMAT/KmvlcadFADH))
KmlcadC8AcylCoAMAT = 123.0 uM; Vlcad = 0.01 uM per min per mgProtein; KmlcadC14EnoylCoAMAT = 1.08 uM; KmlcadFAD = 0.12 uM; KmlcadFADH = 24.2 uM; sflcadC10=0.75 dimensionless; KmlcadC12EnoylCoAMAT = 1.08 uM; KmlcadC8EnoylCoAMAT = 1.08 uM; Keqlcad = 6.0 dimensionless; KmlcadC16AcylCoAMAT = 2.5 uM; KmlcadC12AcylCoAMAT = 9.0 uM; KmlcadC10AcylCoAMAT = 24.3 uM; KmlcadC10EnoylCoAMAT = 1.08 uM; KmlcadC14AcylCoAMAT = 7.4 uM; KmlcadC16EnoylCoAMAT = 1.08 uM Reaction: C10AcylCoAMAT => C10EnoylCoAMAT + FADHMAT; C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C8AcylCoAMAT, FADtMAT, C16EnoylCoAMAT, C14EnoylCoAMAT, C12EnoylCoAMAT, C8EnoylCoAMAT, C10AcylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C8AcylCoAMAT, FADtMAT, C10EnoylCoAMAT, C16EnoylCoAMAT, C14EnoylCoAMAT, C12EnoylCoAMAT, C8EnoylCoAMAT, FADHMAT, Rate Law: sflcadC10*Vlcad*(C10AcylCoAMAT*(FADtMAT-FADHMAT)/(KmlcadC10AcylCoAMAT*KmlcadFAD)-C10EnoylCoAMAT*FADHMAT/(KmlcadC10AcylCoAMAT*KmlcadFAD*Keqlcad))/((1+C10AcylCoAMAT/KmlcadC10AcylCoAMAT+C10EnoylCoAMAT/KmlcadC10EnoylCoAMAT+C16AcylCoAMAT/KmlcadC16AcylCoAMAT+C16EnoylCoAMAT/KmlcadC16EnoylCoAMAT+C14AcylCoAMAT/KmlcadC14AcylCoAMAT+C14EnoylCoAMAT/KmlcadC14EnoylCoAMAT+C12AcylCoAMAT/KmlcadC12AcylCoAMAT+C12EnoylCoAMAT/KmlcadC12EnoylCoAMAT+C8AcylCoAMAT/KmlcadC8AcylCoAMAT+C8EnoylCoAMAT/KmlcadC8EnoylCoAMAT)*(1+(FADtMAT-FADHMAT)/KmlcadFAD+FADHMAT/KmlcadFADH))
KmmcadC12AcylCoAMAT = 5.7 uM; KmmcadC8AcylCoAMAT = 4.0 uM; KmmcadC4AcylCoAMAT = 135.0 uM; KmmcadC10AcylCoAMAT = 5.4 uM; KmmcadFAD = 0.12 uM; KmmcadC10EnoylCoAMAT = 1.08 uM; KmmcadC4EnoylCoAMAT = 1.08 uM; Vmcad = 0.081 uM per min per mgProtein; KmmcadC6AcylCoAMAT = 9.4 uM; sfmcadC10=0.8 dimensionless; KmmcadC6EnoylCoAMAT = 1.08 uM; KmmcadFADH = 24.2 uM; KmmcadC12EnoylCoAMAT = 1.08 uM; KmmcadC8EnoylCoAMAT = 1.08 uM; Keqmcad = 6.0 dimensionless Reaction: C10AcylCoAMAT => C10EnoylCoAMAT + FADHMAT; C12AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, FADtMAT, C12EnoylCoAMAT, C8EnoylCoAMAT, C6EnoylCoAMAT, C4EnoylCoAMAT, C10AcylCoAMAT, C12AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, FADtMAT, C10EnoylCoAMAT, C12EnoylCoAMAT, C8EnoylCoAMAT, C6EnoylCoAMAT, C4EnoylCoAMAT, FADHMAT, Rate Law: sfmcadC10*Vmcad*(C10AcylCoAMAT*(FADtMAT-FADHMAT)/(KmmcadC10AcylCoAMAT*KmmcadFAD)-C10EnoylCoAMAT*FADHMAT/(KmmcadC10AcylCoAMAT*KmmcadFAD*Keqmcad))/((1+C10AcylCoAMAT/KmmcadC10AcylCoAMAT+C10EnoylCoAMAT/KmmcadC10EnoylCoAMAT+C12AcylCoAMAT/KmmcadC12AcylCoAMAT+C12EnoylCoAMAT/KmmcadC12EnoylCoAMAT+C8AcylCoAMAT/KmmcadC8AcylCoAMAT+C8EnoylCoAMAT/KmmcadC8EnoylCoAMAT+C6AcylCoAMAT/KmmcadC6AcylCoAMAT+C6EnoylCoAMAT/KmmcadC6EnoylCoAMAT+C4AcylCoAMAT/KmmcadC4AcylCoAMAT+C4EnoylCoAMAT/KmmcadC4EnoylCoAMAT)*(1+(FADtMAT-FADHMAT)/KmmcadFAD+FADHMAT/KmmcadFADH))
sfmckatC10=0.65 dimensionless; KmmckatC14AcylCoAMAT = 13.83 uM; KmmckatC10KetoacylCoAMAT = 2.1 uM; KmmckatCoAMAT = 26.6 uM; KmmckatC6KetoacylCoAMAT = 6.7 uM; Keqmckat = 1051.0 dimensionless; KmmckatC12KetoacylCoAMAT = 1.3 uM; KmmckatC8KetoacylCoAMAT = 3.2 uM; KmmckatC4AcetoacylCoAMAT = 12.4 uM; KmmckatC16KetoacylCoAMAT = 1.1 uM; KmmckatC12AcylCoAMAT = 13.83 uM; KmmckatC8AcylCoAMAT = 13.83 uM; KmmckatC10AcylCoAMAT = 13.83 uM; KmmckatC6AcylCoAMAT = 13.83 uM; Vmckat = 0.377 uM per min per mgProtein; KmmckatC16AcylCoAMAT = 13.83 uM; KmmckatC14KetoacylCoAMAT = 1.2 uM; KmmckatC4AcylCoAMAT = 13.83 uM; KmmckatAcetylCoAMAT = 30.0 uM Reaction: C10KetoacylCoAMAT => C8AcylCoAMAT + AcetylCoAMAT; C16KetoacylCoAMAT, C14KetoacylCoAMAT, C12KetoacylCoAMAT, C8KetoacylCoAMAT, C6KetoacylCoAMAT, C4AcetoacylCoAMAT, CoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, C10KetoacylCoAMAT, C16KetoacylCoAMAT, C14KetoacylCoAMAT, C12KetoacylCoAMAT, C8KetoacylCoAMAT, C6KetoacylCoAMAT, C4AcetoacylCoAMAT, CoAMAT, C8AcylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, AcetylCoAMAT, Rate Law: sfmckatC10*Vmckat*(C10KetoacylCoAMAT*CoAMAT/(KmmckatC10KetoacylCoAMAT*KmmckatCoAMAT)-C8AcylCoAMAT*AcetylCoAMAT/(KmmckatC10KetoacylCoAMAT*KmmckatCoAMAT*Keqmckat))/((1+C10KetoacylCoAMAT/KmmckatC10KetoacylCoAMAT+C8AcylCoAMAT/KmmckatC8AcylCoAMAT+C16KetoacylCoAMAT/KmmckatC16KetoacylCoAMAT+C16AcylCoAMAT/KmmckatC16AcylCoAMAT+C14KetoacylCoAMAT/KmmckatC14KetoacylCoAMAT+C14AcylCoAMAT/KmmckatC14AcylCoAMAT+C12KetoacylCoAMAT/KmmckatC12KetoacylCoAMAT+C12AcylCoAMAT/KmmckatC12AcylCoAMAT+C8KetoacylCoAMAT/KmmckatC8KetoacylCoAMAT+C10AcylCoAMAT/KmmckatC10AcylCoAMAT+C6KetoacylCoAMAT/KmmckatC6KetoacylCoAMAT+C6AcylCoAMAT/KmmckatC6AcylCoAMAT+C4AcetoacylCoAMAT/KmmckatC4AcetoacylCoAMAT+C4AcylCoAMAT/KmmckatC4AcylCoAMAT+AcetylCoAMAT/KmmckatAcetylCoAMAT)*(1+CoAMAT/KmmckatCoAMAT+AcetylCoAMAT/KmmckatAcetylCoAMAT))
KmscadFAD = 0.12 uM; KmscadC4EnoylCoAMAT = 1.08 uM; sfscadC4=1.0 dimensionless; Vscad = 0.081 uM per min per mgProtein; KmscadFADH = 24.2 uM; KmscadC4AcylCoAMAT = 10.7 uM; KmscadC6AcylCoAMAT = 285.0 uM; KmscadC6EnoylCoAMAT = 1.08 uM; Keqscad = 6.0 dimensionless Reaction: C4AcylCoAMAT => C4EnoylCoAMAT + FADHMAT; C6AcylCoAMAT, FADtMAT, C6EnoylCoAMAT, C4AcylCoAMAT, C6AcylCoAMAT, FADtMAT, C4EnoylCoAMAT, C6EnoylCoAMAT, FADHMAT, Rate Law: sfscadC4*Vscad*(C4AcylCoAMAT*(FADtMAT-FADHMAT)/(KmscadC4AcylCoAMAT*KmscadFAD)-C4EnoylCoAMAT*FADHMAT/(KmscadC4AcylCoAMAT*KmscadFAD*Keqscad))/((1+C4AcylCoAMAT/KmscadC4AcylCoAMAT+C4EnoylCoAMAT/KmscadC4EnoylCoAMAT+C6AcylCoAMAT/KmscadC6AcylCoAMAT+C6EnoylCoAMAT/KmscadC6EnoylCoAMAT)*(1+(FADtMAT-FADHMAT)/KmscadFAD+FADHMAT/KmscadFADH))
KmcactC6AcylCarMAT=15.0 uM; Vfcact = 0.42 uM per min per mgProtein; KmcactCarCYT = 130.0 uM; KmcactC6AcylCarCYT=15.0 uM; KmcactCarMAT = 130.0 uM; Keqcact = 1.0 dimensionless; KicactCarCYT = 200.0 uM; KicactC6AcylCarCYT=56.0 uM; Vrcact = 0.42 uM per min per mgProtein Reaction: C6AcylCarCYT => C6AcylCarMAT; CarMAT, CarCYT, C6AcylCarCYT, CarMAT, C6AcylCarMAT, CarCYT, Rate Law: Vfcact*(C6AcylCarCYT*CarMAT-C6AcylCarMAT*CarCYT/Keqcact)/(C6AcylCarCYT*CarMAT+KmcactCarMAT*C6AcylCarCYT+KmcactC6AcylCarCYT*CarMAT*(1+CarCYT/KicactCarCYT)+Vfcact/(Vrcact*Keqcact)*(KmcactCarCYT*C6AcylCarMAT*(1+C6AcylCarCYT/KicactC6AcylCarCYT)+CarCYT*(KmcactC6AcylCarMAT+C6AcylCarMAT)))
KmcrotC16EnoylCoAMAT = 150.0 uM; KmcrotC12EnoylCoAMAT = 25.0 uM; KicrotC4AcetoacylCoA = 1.6 uM; KmcrotC4EnoylCoAMAT = 40.0 uM; Vcrot = 3.6 uM per min per mgProtein; Keqcrot = 3.13 dimensionless; KmcrotC14EnoylCoAMAT = 100.0 uM; KmcrotC8EnoylCoAMAT = 25.0 uM; KmcrotC10HydroxyacylCoAMAT = 45.0 uM; KmcrotC8HydroxyacylCoAMAT = 45.0 uM; KmcrotC6EnoylCoAMAT = 25.0 uM; KmcrotC10EnoylCoAMAT = 25.0 uM; KmcrotC14HydroxyacylCoAMAT = 45.0 uM; sfcrotC14=0.2 dimensionless; KmcrotC4HydroxyacylCoAMAT = 45.0 uM; KmcrotC16HydroxyacylCoAMAT = 45.0 uM; KmcrotC12HydroxyacylCoAMAT = 45.0 uM; KmcrotC6HydroxyacylCoAMAT = 45.0 uM Reaction: C14EnoylCoAMAT => C14HydroxyacylCoAMAT; C16EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, C6EnoylCoAMAT, C4EnoylCoAMAT, C16HydroxyacylCoAMAT, C12HydroxyacylCoAMAT, C10HydroxyacylCoAMAT, C8HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, C4HydroxyacylCoAMAT, C4AcetoacylCoAMAT, C14EnoylCoAMAT, C16EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, C6EnoylCoAMAT, C4EnoylCoAMAT, C14HydroxyacylCoAMAT, C16HydroxyacylCoAMAT, C12HydroxyacylCoAMAT, C10HydroxyacylCoAMAT, C8HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, C4HydroxyacylCoAMAT, C4AcetoacylCoAMAT, Rate Law: sfcrotC14*Vcrot*(C14EnoylCoAMAT/KmcrotC14EnoylCoAMAT-C14HydroxyacylCoAMAT/(KmcrotC14EnoylCoAMAT*Keqcrot))/(1+C14EnoylCoAMAT/KmcrotC14EnoylCoAMAT+C14HydroxyacylCoAMAT/KmcrotC14HydroxyacylCoAMAT+C16EnoylCoAMAT/KmcrotC16EnoylCoAMAT+C16HydroxyacylCoAMAT/KmcrotC16HydroxyacylCoAMAT+C12EnoylCoAMAT/KmcrotC12EnoylCoAMAT+C12HydroxyacylCoAMAT/KmcrotC12HydroxyacylCoAMAT+C10EnoylCoAMAT/KmcrotC10EnoylCoAMAT+C10HydroxyacylCoAMAT/KmcrotC10HydroxyacylCoAMAT+C8EnoylCoAMAT/KmcrotC8EnoylCoAMAT+C8HydroxyacylCoAMAT/KmcrotC8HydroxyacylCoAMAT+C6EnoylCoAMAT/KmcrotC6EnoylCoAMAT+C6HydroxyacylCoAMAT/KmcrotC6HydroxyacylCoAMAT+C4EnoylCoAMAT/KmcrotC4EnoylCoAMAT+C4HydroxyacylCoAMAT/KmcrotC4HydroxyacylCoAMAT+C4AcetoacylCoAMAT/KicrotC4AcetoacylCoA)
KmmtpC14EnoylCoAMAT = 25.0 uM; KmmtpC8EnoylCoAMAT = 25.0 uM; KmmtpC16AcylCoAMAT = 13.83 uM; KmmtpCoAMAT = 30.0 uM; KmmtpC16EnoylCoAMAT = 25.0 uM; KmmtpC12EnoylCoAMAT = 25.0 uM; KmmtpAcetylCoAMAT = 30.0 uM; Vmtp = 2.84 uM per min per mgProtein; KmmtpC12AcylCoAMAT = 13.83 uM; KmmtpC8AcylCoAMAT = 13.83 uM; KmmtpC14AcylCoAMAT = 13.83 uM; KmmtpC6AcylCoAMAT = 13.83 uM; KmmtpC10EnoylCoAMAT = 25.0 uM; Keqmtp = 0.71 dimensionless; KicrotC4AcetoacylCoA = 1.6 uM; KmmtpNADMAT = 60.0 uM; KmmtpNADHMAT = 50.0 uM; KmmtpC10AcylCoAMAT = 13.83 uM; sfmtpC14=0.9 dimensionless Reaction: C14EnoylCoAMAT => C12AcylCoAMAT + AcetylCoAMAT + NADHMAT; C16EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, NADtMAT, CoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcetoacylCoAMAT, C14EnoylCoAMAT, C16EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, NADtMAT, CoAMAT, C12AcylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, NADHMAT, AcetylCoAMAT, C4AcetoacylCoAMAT, Rate Law: sfmtpC14*Vmtp*(C14EnoylCoAMAT*(NADtMAT-NADHMAT)*CoAMAT/(KmmtpC14EnoylCoAMAT*KmmtpNADMAT*KmmtpCoAMAT)-C12AcylCoAMAT*NADHMAT*AcetylCoAMAT/(KmmtpC14EnoylCoAMAT*KmmtpNADMAT*KmmtpCoAMAT*Keqmtp))/((1+C14EnoylCoAMAT/KmmtpC14EnoylCoAMAT+C12AcylCoAMAT/KmmtpC12AcylCoAMAT+C16EnoylCoAMAT/KmmtpC16EnoylCoAMAT+C16AcylCoAMAT/KmmtpC16AcylCoAMAT+C12EnoylCoAMAT/KmmtpC12EnoylCoAMAT+C14AcylCoAMAT/KmmtpC14AcylCoAMAT+C10EnoylCoAMAT/KmmtpC10EnoylCoAMAT+C10AcylCoAMAT/KmmtpC10AcylCoAMAT+C8EnoylCoAMAT/KmmtpC8EnoylCoAMAT+C8AcylCoAMAT/KmmtpC8AcylCoAMAT+C6AcylCoAMAT/KmmtpC6AcylCoAMAT+C4AcetoacylCoAMAT/KicrotC4AcetoacylCoA)*(1+(NADtMAT-NADHMAT)/KmmtpNADMAT+NADHMAT/KmmtpNADHMAT)*(1+CoAMAT/KmmtpCoAMAT+AcetylCoAMAT/KmmtpAcetylCoAMAT))
Vfcact = 0.42 uM per min per mgProtein; KmcactCarCYT = 130.0 uM; KmcactCarMAT = 130.0 uM; Keqcact = 1.0 dimensionless; KicactCarCYT = 200.0 uM; KmcactC12AcylCarMAT=15.0 uM; KicactC12AcylCarCYT=56.0 uM; Vrcact = 0.42 uM per min per mgProtein; KmcactC12AcylCarCYT=15.0 uM Reaction: C12AcylCarCYT => C12AcylCarMAT; CarMAT, CarCYT, C12AcylCarCYT, CarMAT, C12AcylCarMAT, CarCYT, Rate Law: Vfcact*(C12AcylCarCYT*CarMAT-C12AcylCarMAT*CarCYT/Keqcact)/(C12AcylCarCYT*CarMAT+KmcactCarMAT*C12AcylCarCYT+KmcactC12AcylCarCYT*CarMAT*(1+CarCYT/KicactCarCYT)+Vfcact/(Vrcact*Keqcact)*(KmcactCarCYT*C12AcylCarMAT*(1+C12AcylCarCYT/KicactC12AcylCarCYT)+CarCYT*(KmcactC12AcylCarMAT+C12AcylCarMAT)))
KmmschadC16KetoacylCoAMAT = 1.4 uM; KmmschadC16HydroxyacylCoAMAT = 1.5 uM; Keqmschad = 2.17E-4 dimensionless; KmmschadC14HydroxyacylCoAMAT = 1.8 uM; Vmschad = 1.0 uM per min per mgProtein; KmmschadC6HydroxyacylCoAMAT = 28.6 uM; KmmschadC12KetoacylCoAMAT = 1.6 uM; KmmschadC4HydroxyacylCoAMAT = 69.9 uM; KmmschadC14KetoacylCoAMAT = 1.4 uM; KmmschadC12HydroxyacylCoAMAT = 3.7 uM; KmmschadC6KetoacylCoAMAT = 5.8 uM; KmmschadNADMAT = 58.5 uM; KmmschadC10HydroxyacylCoAMAT = 8.8 uM; KmmschadC8HydroxyacylCoAMAT = 16.3 uM; KmmschadC4AcetoacylCoAMAT = 16.9 uM; KmmschadC10KetoacylCoAMAT = 2.3 uM; KmmschadNADHMAT = 5.4 uM; sfmschadC16=0.6 dimensionless; KmmschadC8KetoacylCoAMAT = 4.1 uM Reaction: C16HydroxyacylCoAMAT => C16KetoacylCoAMAT + NADHMAT; C14HydroxyacylCoAMAT, C12HydroxyacylCoAMAT, C10HydroxyacylCoAMAT, C8HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, C4HydroxyacylCoAMAT, NADtMAT, C14KetoacylCoAMAT, C12KetoacylCoAMAT, C10KetoacylCoAMAT, C8KetoacylCoAMAT, C6KetoacylCoAMAT, C4AcetoacylCoAMAT, C16HydroxyacylCoAMAT, C14HydroxyacylCoAMAT, C12HydroxyacylCoAMAT, C10HydroxyacylCoAMAT, C8HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, C4HydroxyacylCoAMAT, NADtMAT, C16KetoacylCoAMAT, C14KetoacylCoAMAT, C12KetoacylCoAMAT, C10KetoacylCoAMAT, C8KetoacylCoAMAT, C6KetoacylCoAMAT, C4AcetoacylCoAMAT, NADHMAT, Rate Law: sfmschadC16*Vmschad*(C16HydroxyacylCoAMAT*(NADtMAT-NADHMAT)/(KmmschadC16HydroxyacylCoAMAT*KmmschadNADMAT)-C16KetoacylCoAMAT*NADHMAT/(KmmschadC16HydroxyacylCoAMAT*KmmschadNADMAT*Keqmschad))/((1+C16HydroxyacylCoAMAT/KmmschadC16HydroxyacylCoAMAT+C16KetoacylCoAMAT/KmmschadC16KetoacylCoAMAT+C14HydroxyacylCoAMAT/KmmschadC14HydroxyacylCoAMAT+C14KetoacylCoAMAT/KmmschadC14KetoacylCoAMAT+C12HydroxyacylCoAMAT/KmmschadC12HydroxyacylCoAMAT+C12KetoacylCoAMAT/KmmschadC12KetoacylCoAMAT+C10HydroxyacylCoAMAT/KmmschadC10HydroxyacylCoAMAT+C10KetoacylCoAMAT/KmmschadC10KetoacylCoAMAT+C8HydroxyacylCoAMAT/KmmschadC8HydroxyacylCoAMAT+C8KetoacylCoAMAT/KmmschadC8KetoacylCoAMAT+C6HydroxyacylCoAMAT/KmmschadC6HydroxyacylCoAMAT+C6KetoacylCoAMAT/KmmschadC6KetoacylCoAMAT+C4HydroxyacylCoAMAT/KmmschadC4HydroxyacylCoAMAT+C4AcetoacylCoAMAT/KmmschadC4AcetoacylCoAMAT)*(1+(NADtMAT-NADHMAT)/KmmschadNADMAT+NADHMAT/KmmschadNADHMAT))
Kmcpt2C10AcylCarMAT = 51.0 uM; Kmcpt2C14AcylCarMAT = 51.0 uM; Kmcpt2CoAMAT = 30.0 uM; Kmcpt2C6AcylCarMAT = 51.0 uM; Kmcpt2C14AcylCoAMAT = 38.0 uM; Keqcpt2 = 2.22 dimensionless; Kmcpt2C8AcylCoAMAT = 38.0 uM; Kmcpt2C6AcylCoAMAT = 1000.0 uM; Kmcpt2C16AcylCarMAT = 51.0 uM; Kmcpt2C12AcylCarMAT = 51.0 uM; Kmcpt2C12AcylCoAMAT = 38.0 uM; Vcpt2 = 0.391 uM per min per mgProtein; Kmcpt2C8AcylCarMAT = 51.0 uM; Kmcpt2CarMAT = 350.0 uM; sfcpt2C10=0.95 dimensionless; Kmcpt2C16AcylCoAMAT = 38.0 uM; Kmcpt2C4AcylCoAMAT = 1000000.0 uM; Kmcpt2C10AcylCoAMAT = 38.0 uM; Kmcpt2C4AcylCarMAT = 51.0 uM Reaction: C10AcylCarMAT => C10AcylCoAMAT; C16AcylCarMAT, C14AcylCarMAT, C12AcylCarMAT, C8AcylCarMAT, C6AcylCarMAT, C4AcylCarMAT, CoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, CarMAT, C10AcylCarMAT, C16AcylCarMAT, C14AcylCarMAT, C12AcylCarMAT, C8AcylCarMAT, C6AcylCarMAT, C4AcylCarMAT, CoAMAT, C10AcylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, CarMAT, Rate Law: sfcpt2C10*Vcpt2*(C10AcylCarMAT*CoAMAT/(Kmcpt2C10AcylCarMAT*Kmcpt2CoAMAT)-C10AcylCoAMAT*CarMAT/(Kmcpt2C10AcylCarMAT*Kmcpt2CoAMAT*Keqcpt2))/((1+C10AcylCarMAT/Kmcpt2C10AcylCarMAT+C10AcylCoAMAT/Kmcpt2C10AcylCoAMAT+C16AcylCarMAT/Kmcpt2C16AcylCarMAT+C16AcylCoAMAT/Kmcpt2C16AcylCoAMAT+C14AcylCarMAT/Kmcpt2C14AcylCarMAT+C14AcylCoAMAT/Kmcpt2C14AcylCoAMAT+C12AcylCarMAT/Kmcpt2C12AcylCarMAT+C12AcylCoAMAT/Kmcpt2C12AcylCoAMAT+C8AcylCarMAT/Kmcpt2C8AcylCarMAT+C8AcylCoAMAT/Kmcpt2C8AcylCoAMAT+C6AcylCarMAT/Kmcpt2C6AcylCarMAT+C6AcylCoAMAT/Kmcpt2C6AcylCoAMAT+C4AcylCarMAT/Kmcpt2C4AcylCarMAT+C4AcylCoAMAT/Kmcpt2C4AcylCoAMAT)*(1+CoAMAT/Kmcpt2CoAMAT+CarMAT/Kmcpt2CarMAT))
Kmcpt2C14AcylCarMAT = 51.0 uM; Kmcpt2C10AcylCarMAT = 51.0 uM; Kmcpt2C6AcylCarMAT = 51.0 uM; Kmcpt2CoAMAT = 30.0 uM; Kmcpt2C14AcylCoAMAT = 38.0 uM; Keqcpt2 = 2.22 dimensionless; Kmcpt2C8AcylCoAMAT = 38.0 uM; Kmcpt2C6AcylCoAMAT = 1000.0 uM; Kmcpt2C16AcylCarMAT = 51.0 uM; Kmcpt2C12AcylCarMAT = 51.0 uM; sfcpt2C6=0.15 dimensionless; Kmcpt2C12AcylCoAMAT = 38.0 uM; Vcpt2 = 0.391 uM per min per mgProtein; Kmcpt2C8AcylCarMAT = 51.0 uM; Kmcpt2CarMAT = 350.0 uM; Kmcpt2C16AcylCoAMAT = 38.0 uM; Kmcpt2C4AcylCoAMAT = 1000000.0 uM; Kmcpt2C10AcylCoAMAT = 38.0 uM; Kmcpt2C4AcylCarMAT = 51.0 uM Reaction: C6AcylCarMAT => C6AcylCoAMAT; C16AcylCarMAT, C14AcylCarMAT, C12AcylCarMAT, C10AcylCarMAT, C8AcylCarMAT, C4AcylCarMAT, CoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C4AcylCoAMAT, CarMAT, C6AcylCarMAT, C16AcylCarMAT, C14AcylCarMAT, C12AcylCarMAT, C10AcylCarMAT, C8AcylCarMAT, C4AcylCarMAT, CoAMAT, C6AcylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C4AcylCoAMAT, CarMAT, Rate Law: sfcpt2C6*Vcpt2*(C6AcylCarMAT*CoAMAT/(Kmcpt2C6AcylCarMAT*Kmcpt2CoAMAT)-C6AcylCoAMAT*CarMAT/(Kmcpt2C6AcylCarMAT*Kmcpt2CoAMAT*Keqcpt2))/((1+C6AcylCarMAT/Kmcpt2C6AcylCarMAT+C6AcylCoAMAT/Kmcpt2C6AcylCoAMAT+C16AcylCarMAT/Kmcpt2C16AcylCarMAT+C16AcylCoAMAT/Kmcpt2C16AcylCoAMAT+C14AcylCarMAT/Kmcpt2C14AcylCarMAT+C14AcylCoAMAT/Kmcpt2C14AcylCoAMAT+C12AcylCarMAT/Kmcpt2C12AcylCarMAT+C12AcylCoAMAT/Kmcpt2C12AcylCoAMAT+C10AcylCarMAT/Kmcpt2C10AcylCarMAT+C10AcylCoAMAT/Kmcpt2C10AcylCoAMAT+C8AcylCarMAT/Kmcpt2C8AcylCarMAT+C8AcylCoAMAT/Kmcpt2C8AcylCoAMAT+C4AcylCarMAT/Kmcpt2C4AcylCarMAT+C4AcylCoAMAT/Kmcpt2C4AcylCoAMAT)*(1+CoAMAT/Kmcpt2CoAMAT+CarMAT/Kmcpt2CarMAT))
KmcrotC16EnoylCoAMAT = 150.0 uM; KmcrotC12EnoylCoAMAT = 25.0 uM; KicrotC4AcetoacylCoA = 1.6 uM; KmcrotC4EnoylCoAMAT = 40.0 uM; Vcrot = 3.6 uM per min per mgProtein; Keqcrot = 3.13 dimensionless; KmcrotC14EnoylCoAMAT = 100.0 uM; KmcrotC8EnoylCoAMAT = 25.0 uM; KmcrotC10HydroxyacylCoAMAT = 45.0 uM; KmcrotC8HydroxyacylCoAMAT = 45.0 uM; KmcrotC6EnoylCoAMAT = 25.0 uM; KmcrotC10EnoylCoAMAT = 25.0 uM; KmcrotC14HydroxyacylCoAMAT = 45.0 uM; KmcrotC4HydroxyacylCoAMAT = 45.0 uM; sfcrotC4=1.0 dimensionless; KmcrotC16HydroxyacylCoAMAT = 45.0 uM; KmcrotC12HydroxyacylCoAMAT = 45.0 uM; KmcrotC6HydroxyacylCoAMAT = 45.0 uM Reaction: C4EnoylCoAMAT => C4HydroxyacylCoAMAT; C16EnoylCoAMAT, C14EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, C6EnoylCoAMAT, C16HydroxyacylCoAMAT, C14HydroxyacylCoAMAT, C12HydroxyacylCoAMAT, C10HydroxyacylCoAMAT, C8HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, C4AcetoacylCoAMAT, C4EnoylCoAMAT, C16EnoylCoAMAT, C14EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, C6EnoylCoAMAT, C4HydroxyacylCoAMAT, C16HydroxyacylCoAMAT, C14HydroxyacylCoAMAT, C12HydroxyacylCoAMAT, C10HydroxyacylCoAMAT, C8HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, C4AcetoacylCoAMAT, Rate Law: sfcrotC4*Vcrot*(C4EnoylCoAMAT/KmcrotC4EnoylCoAMAT-C4HydroxyacylCoAMAT/(KmcrotC4EnoylCoAMAT*Keqcrot))/(1+C4EnoylCoAMAT/KmcrotC4EnoylCoAMAT+C4HydroxyacylCoAMAT/KmcrotC4HydroxyacylCoAMAT+C16EnoylCoAMAT/KmcrotC16EnoylCoAMAT+C16HydroxyacylCoAMAT/KmcrotC16HydroxyacylCoAMAT+C14EnoylCoAMAT/KmcrotC14EnoylCoAMAT+C14HydroxyacylCoAMAT/KmcrotC14HydroxyacylCoAMAT+C12EnoylCoAMAT/KmcrotC12EnoylCoAMAT+C12HydroxyacylCoAMAT/KmcrotC12HydroxyacylCoAMAT+C10EnoylCoAMAT/KmcrotC10EnoylCoAMAT+C10HydroxyacylCoAMAT/KmcrotC10HydroxyacylCoAMAT+C8EnoylCoAMAT/KmcrotC8EnoylCoAMAT+C8HydroxyacylCoAMAT/KmcrotC8HydroxyacylCoAMAT+C6EnoylCoAMAT/KmcrotC6EnoylCoAMAT+C6HydroxyacylCoAMAT/KmcrotC6HydroxyacylCoAMAT+C4AcetoacylCoAMAT/KicrotC4AcetoacylCoA)
KmmschadC16KetoacylCoAMAT = 1.4 uM; Keqmschad = 2.17E-4 dimensionless; KmmschadC16HydroxyacylCoAMAT = 1.5 uM; KmmschadC14HydroxyacylCoAMAT = 1.8 uM; Vmschad = 1.0 uM per min per mgProtein; KmmschadC6HydroxyacylCoAMAT = 28.6 uM; KmmschadC12KetoacylCoAMAT = 1.6 uM; KmmschadC4HydroxyacylCoAMAT = 69.9 uM; KmmschadC14KetoacylCoAMAT = 1.4 uM; KmmschadC12HydroxyacylCoAMAT = 3.7 uM; KmmschadC6KetoacylCoAMAT = 5.8 uM; KmmschadC10HydroxyacylCoAMAT = 8.8 uM; KmmschadNADMAT = 58.5 uM; sfmschadC10=0.64 dimensionless; KmmschadC8HydroxyacylCoAMAT = 16.3 uM; KmmschadC4AcetoacylCoAMAT = 16.9 uM; KmmschadC10KetoacylCoAMAT = 2.3 uM; KmmschadNADHMAT = 5.4 uM; KmmschadC8KetoacylCoAMAT = 4.1 uM Reaction: C10HydroxyacylCoAMAT => C10KetoacylCoAMAT + NADHMAT; C16HydroxyacylCoAMAT, C14HydroxyacylCoAMAT, C12HydroxyacylCoAMAT, C8HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, C4HydroxyacylCoAMAT, NADtMAT, C16KetoacylCoAMAT, C14KetoacylCoAMAT, C12KetoacylCoAMAT, C8KetoacylCoAMAT, C6KetoacylCoAMAT, C4AcetoacylCoAMAT, C10HydroxyacylCoAMAT, C16HydroxyacylCoAMAT, C14HydroxyacylCoAMAT, C12HydroxyacylCoAMAT, C8HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, C4HydroxyacylCoAMAT, NADtMAT, C10KetoacylCoAMAT, C16KetoacylCoAMAT, C14KetoacylCoAMAT, C12KetoacylCoAMAT, C8KetoacylCoAMAT, C6KetoacylCoAMAT, C4AcetoacylCoAMAT, NADHMAT, Rate Law: sfmschadC10*Vmschad*(C10HydroxyacylCoAMAT*(NADtMAT-NADHMAT)/(KmmschadC10HydroxyacylCoAMAT*KmmschadNADMAT)-C10KetoacylCoAMAT*NADHMAT/(KmmschadC10HydroxyacylCoAMAT*KmmschadNADMAT*Keqmschad))/((1+C10HydroxyacylCoAMAT/KmmschadC10HydroxyacylCoAMAT+C10KetoacylCoAMAT/KmmschadC10KetoacylCoAMAT+C16HydroxyacylCoAMAT/KmmschadC16HydroxyacylCoAMAT+C16KetoacylCoAMAT/KmmschadC16KetoacylCoAMAT+C14HydroxyacylCoAMAT/KmmschadC14HydroxyacylCoAMAT+C14KetoacylCoAMAT/KmmschadC14KetoacylCoAMAT+C12HydroxyacylCoAMAT/KmmschadC12HydroxyacylCoAMAT+C12KetoacylCoAMAT/KmmschadC12KetoacylCoAMAT+C8HydroxyacylCoAMAT/KmmschadC8HydroxyacylCoAMAT+C8KetoacylCoAMAT/KmmschadC8KetoacylCoAMAT+C6HydroxyacylCoAMAT/KmmschadC6HydroxyacylCoAMAT+C6KetoacylCoAMAT/KmmschadC6KetoacylCoAMAT+C4HydroxyacylCoAMAT/KmmschadC4HydroxyacylCoAMAT+C4AcetoacylCoAMAT/KmmschadC4AcetoacylCoAMAT)*(1+(NADtMAT-NADHMAT)/KmmschadNADMAT+NADHMAT/KmmschadNADHMAT))
Kmcpt1C16AcylCoACYT=13.8 uM; Kmcpt1CarCYT=125.0 uM; ncpt1=2.4799 dimensionless; Kicpt1MalCoACYT=9.1 uM; Kmcpt1CoACYT=40.7 uM; Vcpt1=0.012 uM per min per mgProtein; sfcpt1C16=1.0 dimensionless; Keqcpt1=0.45 dimensionless; Kmcpt1C16AcylCarCYT=136.0 uM Reaction: => C16AcylCarCYT; C16AcylCoACYT, CarCYT, CoACYT, MalCoACYT, C16AcylCoACYT, CarCYT, C16AcylCarCYT, CoACYT, MalCoACYT, Rate Law: sfcpt1C16*Vcpt1*(C16AcylCoACYT*CarCYT/(Kmcpt1C16AcylCoACYT*Kmcpt1CarCYT)-C16AcylCarCYT*CoACYT/(Kmcpt1C16AcylCoACYT*Kmcpt1CarCYT*Keqcpt1))/((1+C16AcylCoACYT/Kmcpt1C16AcylCoACYT+C16AcylCarCYT/Kmcpt1C16AcylCarCYT+(MalCoACYT/Kicpt1MalCoACYT)^ncpt1)*(1+CarCYT/Kmcpt1CarCYT+CoACYT/Kmcpt1CoACYT))
KmmschadC16KetoacylCoAMAT = 1.4 uM; Keqmschad = 2.17E-4 dimensionless; KmmschadC16HydroxyacylCoAMAT = 1.5 uM; KmmschadC14HydroxyacylCoAMAT = 1.8 uM; Vmschad = 1.0 uM per min per mgProtein; KmmschadC6HydroxyacylCoAMAT = 28.6 uM; KmmschadC4HydroxyacylCoAMAT = 69.9 uM; KmmschadC12KetoacylCoAMAT = 1.6 uM; KmmschadC14KetoacylCoAMAT = 1.4 uM; KmmschadC12HydroxyacylCoAMAT = 3.7 uM; KmmschadC6KetoacylCoAMAT = 5.8 uM; KmmschadNADMAT = 58.5 uM; KmmschadC10HydroxyacylCoAMAT = 8.8 uM; sfmschadC4=0.67 dimensionless; KmmschadC8HydroxyacylCoAMAT = 16.3 uM; KmmschadC4AcetoacylCoAMAT = 16.9 uM; KmmschadC10KetoacylCoAMAT = 2.3 uM; KmmschadNADHMAT = 5.4 uM; KmmschadC8KetoacylCoAMAT = 4.1 uM Reaction: C4HydroxyacylCoAMAT => C4AcetoacylCoAMAT + NADHMAT; C16HydroxyacylCoAMAT, C14HydroxyacylCoAMAT, C12HydroxyacylCoAMAT, C10HydroxyacylCoAMAT, C8HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, NADtMAT, C16KetoacylCoAMAT, C14KetoacylCoAMAT, C12KetoacylCoAMAT, C10KetoacylCoAMAT, C8KetoacylCoAMAT, C6KetoacylCoAMAT, C4HydroxyacylCoAMAT, C16HydroxyacylCoAMAT, C14HydroxyacylCoAMAT, C12HydroxyacylCoAMAT, C10HydroxyacylCoAMAT, C8HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, NADtMAT, C4AcetoacylCoAMAT, C16KetoacylCoAMAT, C14KetoacylCoAMAT, C12KetoacylCoAMAT, C10KetoacylCoAMAT, C8KetoacylCoAMAT, C6KetoacylCoAMAT, NADHMAT, Rate Law: sfmschadC4*Vmschad*(C4HydroxyacylCoAMAT*(NADtMAT-NADHMAT)/(KmmschadC4HydroxyacylCoAMAT*KmmschadNADMAT)-C4AcetoacylCoAMAT*NADHMAT/(KmmschadC4HydroxyacylCoAMAT*KmmschadNADMAT*Keqmschad))/((1+C4HydroxyacylCoAMAT/KmmschadC4HydroxyacylCoAMAT+C4AcetoacylCoAMAT/KmmschadC4AcetoacylCoAMAT+C16HydroxyacylCoAMAT/KmmschadC16HydroxyacylCoAMAT+C16KetoacylCoAMAT/KmmschadC16KetoacylCoAMAT+C14HydroxyacylCoAMAT/KmmschadC14HydroxyacylCoAMAT+C14KetoacylCoAMAT/KmmschadC14KetoacylCoAMAT+C12HydroxyacylCoAMAT/KmmschadC12HydroxyacylCoAMAT+C12KetoacylCoAMAT/KmmschadC12KetoacylCoAMAT+C10HydroxyacylCoAMAT/KmmschadC10HydroxyacylCoAMAT+C10KetoacylCoAMAT/KmmschadC10KetoacylCoAMAT+C8HydroxyacylCoAMAT/KmmschadC8HydroxyacylCoAMAT+C8KetoacylCoAMAT/KmmschadC8KetoacylCoAMAT+C6HydroxyacylCoAMAT/KmmschadC6HydroxyacylCoAMAT+C6KetoacylCoAMAT/KmmschadC6KetoacylCoAMAT)*(1+(NADtMAT-NADHMAT)/KmmschadNADMAT+NADHMAT/KmmschadNADHMAT))
K1fadhsink=0.46 uM; Ksfadhsink=6000000.0 l per min per mgProtein Reaction: FADHMAT => ; FADHMAT, Rate Law: Ksfadhsink*(FADHMAT-K1fadhsink)
KmlcadC8AcylCoAMAT = 123.0 uM; Vlcad = 0.01 uM per min per mgProtein; KmlcadC14EnoylCoAMAT = 1.08 uM; KmlcadFAD = 0.12 uM; KmlcadFADH = 24.2 uM; KmlcadC12EnoylCoAMAT = 1.08 uM; KmlcadC8EnoylCoAMAT = 1.08 uM; sflcadC8=0.4 dimensionless; Keqlcad = 6.0 dimensionless; KmlcadC16AcylCoAMAT = 2.5 uM; KmlcadC12AcylCoAMAT = 9.0 uM; KmlcadC10AcylCoAMAT = 24.3 uM; KmlcadC10EnoylCoAMAT = 1.08 uM; KmlcadC14AcylCoAMAT = 7.4 uM; KmlcadC16EnoylCoAMAT = 1.08 uM Reaction: C8AcylCoAMAT => C8EnoylCoAMAT + FADHMAT; C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, FADtMAT, C16EnoylCoAMAT, C14EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C8AcylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, FADtMAT, C8EnoylCoAMAT, C16EnoylCoAMAT, C14EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, FADHMAT, Rate Law: sflcadC8*Vlcad*(C8AcylCoAMAT*(FADtMAT-FADHMAT)/(KmlcadC8AcylCoAMAT*KmlcadFAD)-C8EnoylCoAMAT*FADHMAT/(KmlcadC8AcylCoAMAT*KmlcadFAD*Keqlcad))/((1+C8AcylCoAMAT/KmlcadC8AcylCoAMAT+C8EnoylCoAMAT/KmlcadC8EnoylCoAMAT+C16AcylCoAMAT/KmlcadC16AcylCoAMAT+C16EnoylCoAMAT/KmlcadC16EnoylCoAMAT+C14AcylCoAMAT/KmlcadC14AcylCoAMAT+C14EnoylCoAMAT/KmlcadC14EnoylCoAMAT+C12AcylCoAMAT/KmlcadC12AcylCoAMAT+C12EnoylCoAMAT/KmlcadC12EnoylCoAMAT+C10AcylCoAMAT/KmlcadC10AcylCoAMAT+C10EnoylCoAMAT/KmlcadC10EnoylCoAMAT)*(1+(FADtMAT-FADHMAT)/KmlcadFAD+FADHMAT/KmlcadFADH))
KmmckatC14AcylCoAMAT = 13.83 uM; KmmckatC10KetoacylCoAMAT = 2.1 uM; sfmckatC16=0.0 dimensionless; KmmckatCoAMAT = 26.6 uM; KmmckatC6KetoacylCoAMAT = 6.7 uM; Keqmckat = 1051.0 dimensionless; KmmckatC12KetoacylCoAMAT = 1.3 uM; KmmckatC8KetoacylCoAMAT = 3.2 uM; KmmckatC4AcetoacylCoAMAT = 12.4 uM; KmmckatC16KetoacylCoAMAT = 1.1 uM; KmmckatC12AcylCoAMAT = 13.83 uM; KmmckatC8AcylCoAMAT = 13.83 uM; KmmckatC10AcylCoAMAT = 13.83 uM; KmmckatC6AcylCoAMAT = 13.83 uM; Vmckat = 0.377 uM per min per mgProtein; KmmckatC16AcylCoAMAT = 13.83 uM; KmmckatC14KetoacylCoAMAT = 1.2 uM; KmmckatC4AcylCoAMAT = 13.83 uM; KmmckatAcetylCoAMAT = 30.0 uM Reaction: C16KetoacylCoAMAT => C14AcylCoAMAT + AcetylCoAMAT; C14KetoacylCoAMAT, C12KetoacylCoAMAT, C10KetoacylCoAMAT, C8KetoacylCoAMAT, C6KetoacylCoAMAT, C4AcetoacylCoAMAT, CoAMAT, C16AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, C16KetoacylCoAMAT, C14KetoacylCoAMAT, C12KetoacylCoAMAT, C10KetoacylCoAMAT, C8KetoacylCoAMAT, C6KetoacylCoAMAT, C4AcetoacylCoAMAT, CoAMAT, C14AcylCoAMAT, C16AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, AcetylCoAMAT, Rate Law: sfmckatC16*Vmckat*(C16KetoacylCoAMAT*CoAMAT/(KmmckatC16KetoacylCoAMAT*KmmckatCoAMAT)-C14AcylCoAMAT*AcetylCoAMAT/(KmmckatC16KetoacylCoAMAT*KmmckatCoAMAT*Keqmckat))/((1+C16KetoacylCoAMAT/KmmckatC16KetoacylCoAMAT+C14AcylCoAMAT/KmmckatC14AcylCoAMAT+C14KetoacylCoAMAT/KmmckatC14KetoacylCoAMAT+C16AcylCoAMAT/KmmckatC16AcylCoAMAT+C12KetoacylCoAMAT/KmmckatC12KetoacylCoAMAT+C12AcylCoAMAT/KmmckatC12AcylCoAMAT+C10KetoacylCoAMAT/KmmckatC10KetoacylCoAMAT+C10AcylCoAMAT/KmmckatC10AcylCoAMAT+C8KetoacylCoAMAT/KmmckatC8KetoacylCoAMAT+C8AcylCoAMAT/KmmckatC8AcylCoAMAT+C6KetoacylCoAMAT/KmmckatC6KetoacylCoAMAT+C6AcylCoAMAT/KmmckatC6AcylCoAMAT+C4AcetoacylCoAMAT/KmmckatC4AcetoacylCoAMAT+C4AcylCoAMAT/KmmckatC4AcylCoAMAT+AcetylCoAMAT/KmmckatAcetylCoAMAT)*(1+CoAMAT/KmmckatCoAMAT+AcetylCoAMAT/KmmckatAcetylCoAMAT))
KmvlcadC14AcylCoAMAT = 4.0 uM; KmvlcadC14EnoylCoAMAT = 1.08 uM; KmvlcadFAD = 0.12 uM; KmvlcadFADH = 24.2 uM; KmvlcadC12AcylCoAMAT = 2.7 uM; KmvlcadC12EnoylCoAMAT = 1.08 uM; Vvlcad = 0.008 uM per min per mgProtein; KmvlcadC16EnoylCoAMAT = 1.08 uM; sfvlcadC12=0.11 dimensionless; KmvlcadC16AcylCoAMAT = 6.5 uM; Keqvlcad = 6.0 dimensionless Reaction: C12AcylCoAMAT => C12EnoylCoAMAT + FADHMAT; C16AcylCoAMAT, C14AcylCoAMAT, FADtMAT, C16EnoylCoAMAT, C14EnoylCoAMAT, C12AcylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, FADtMAT, C12EnoylCoAMAT, C16EnoylCoAMAT, C14EnoylCoAMAT, FADHMAT, Rate Law: sfvlcadC12*Vvlcad*(C12AcylCoAMAT*(FADtMAT-FADHMAT)/(KmvlcadC12AcylCoAMAT*KmvlcadFAD)-C12EnoylCoAMAT*FADHMAT/(KmvlcadC12AcylCoAMAT*KmvlcadFAD*Keqvlcad))/((1+C12AcylCoAMAT/KmvlcadC12AcylCoAMAT+C12EnoylCoAMAT/KmvlcadC12EnoylCoAMAT+C16AcylCoAMAT/KmvlcadC16AcylCoAMAT+C16EnoylCoAMAT/KmvlcadC16EnoylCoAMAT+C14AcylCoAMAT/KmvlcadC14AcylCoAMAT+C14EnoylCoAMAT/KmvlcadC14EnoylCoAMAT)*(1+(FADtMAT-FADHMAT)/KmvlcadFAD+FADHMAT/KmvlcadFADH))
Kmcpt2C14AcylCarMAT = 51.0 uM; Kmcpt2C10AcylCarMAT = 51.0 uM; Kmcpt2CoAMAT = 30.0 uM; Kmcpt2C6AcylCarMAT = 51.0 uM; Kmcpt2C14AcylCoAMAT = 38.0 uM; sfcpt2C8=0.35 dimensionless; Keqcpt2 = 2.22 dimensionless; Kmcpt2C8AcylCoAMAT = 38.0 uM; Kmcpt2C6AcylCoAMAT = 1000.0 uM; Kmcpt2C16AcylCarMAT = 51.0 uM; Kmcpt2C12AcylCarMAT = 51.0 uM; Kmcpt2C12AcylCoAMAT = 38.0 uM; Vcpt2 = 0.391 uM per min per mgProtein; Kmcpt2C8AcylCarMAT = 51.0 uM; Kmcpt2CarMAT = 350.0 uM; Kmcpt2C16AcylCoAMAT = 38.0 uM; Kmcpt2C4AcylCoAMAT = 1000000.0 uM; Kmcpt2C10AcylCoAMAT = 38.0 uM; Kmcpt2C4AcylCarMAT = 51.0 uM Reaction: C8AcylCarMAT => C8AcylCoAMAT; C16AcylCarMAT, C14AcylCarMAT, C12AcylCarMAT, C10AcylCarMAT, C6AcylCarMAT, C4AcylCarMAT, CoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, CarMAT, C8AcylCarMAT, C16AcylCarMAT, C14AcylCarMAT, C12AcylCarMAT, C10AcylCarMAT, C6AcylCarMAT, C4AcylCarMAT, CoAMAT, C8AcylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, CarMAT, Rate Law: sfcpt2C8*Vcpt2*(C8AcylCarMAT*CoAMAT/(Kmcpt2C8AcylCarMAT*Kmcpt2CoAMAT)-C8AcylCoAMAT*CarMAT/(Kmcpt2C8AcylCarMAT*Kmcpt2CoAMAT*Keqcpt2))/((1+C8AcylCarMAT/Kmcpt2C8AcylCarMAT+C8AcylCoAMAT/Kmcpt2C8AcylCoAMAT+C16AcylCarMAT/Kmcpt2C16AcylCarMAT+C16AcylCoAMAT/Kmcpt2C16AcylCoAMAT+C14AcylCarMAT/Kmcpt2C14AcylCarMAT+C14AcylCoAMAT/Kmcpt2C14AcylCoAMAT+C12AcylCarMAT/Kmcpt2C12AcylCarMAT+C12AcylCoAMAT/Kmcpt2C12AcylCoAMAT+C10AcylCarMAT/Kmcpt2C10AcylCarMAT+C10AcylCoAMAT/Kmcpt2C10AcylCoAMAT+C6AcylCarMAT/Kmcpt2C6AcylCarMAT+C6AcylCoAMAT/Kmcpt2C6AcylCoAMAT+C4AcylCarMAT/Kmcpt2C4AcylCarMAT+C4AcylCoAMAT/Kmcpt2C4AcylCoAMAT)*(1+CoAMAT/Kmcpt2CoAMAT+CarMAT/Kmcpt2CarMAT))
KmcrotC16EnoylCoAMAT = 150.0 uM; KmcrotC12EnoylCoAMAT = 25.0 uM; KicrotC4AcetoacylCoA = 1.6 uM; KmcrotC4EnoylCoAMAT = 40.0 uM; sfcrotC16=0.13 dimensionless; Vcrot = 3.6 uM per min per mgProtein; Keqcrot = 3.13 dimensionless; KmcrotC14EnoylCoAMAT = 100.0 uM; KmcrotC8EnoylCoAMAT = 25.0 uM; KmcrotC10HydroxyacylCoAMAT = 45.0 uM; KmcrotC8HydroxyacylCoAMAT = 45.0 uM; KmcrotC6EnoylCoAMAT = 25.0 uM; KmcrotC10EnoylCoAMAT = 25.0 uM; KmcrotC14HydroxyacylCoAMAT = 45.0 uM; KmcrotC4HydroxyacylCoAMAT = 45.0 uM; KmcrotC16HydroxyacylCoAMAT = 45.0 uM; KmcrotC12HydroxyacylCoAMAT = 45.0 uM; KmcrotC6HydroxyacylCoAMAT = 45.0 uM Reaction: C16EnoylCoAMAT => C16HydroxyacylCoAMAT; C14EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, C6EnoylCoAMAT, C4EnoylCoAMAT, C14HydroxyacylCoAMAT, C12HydroxyacylCoAMAT, C10HydroxyacylCoAMAT, C8HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, C4HydroxyacylCoAMAT, C4AcetoacylCoAMAT, C16EnoylCoAMAT, C14EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, C6EnoylCoAMAT, C4EnoylCoAMAT, C16HydroxyacylCoAMAT, C14HydroxyacylCoAMAT, C12HydroxyacylCoAMAT, C10HydroxyacylCoAMAT, C8HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, C4HydroxyacylCoAMAT, C4AcetoacylCoAMAT, Rate Law: sfcrotC16*Vcrot*(C16EnoylCoAMAT/KmcrotC16EnoylCoAMAT-C16HydroxyacylCoAMAT/(KmcrotC16EnoylCoAMAT*Keqcrot))/(1+C16EnoylCoAMAT/KmcrotC16EnoylCoAMAT+C16HydroxyacylCoAMAT/KmcrotC16HydroxyacylCoAMAT+C14EnoylCoAMAT/KmcrotC14EnoylCoAMAT+C14HydroxyacylCoAMAT/KmcrotC14HydroxyacylCoAMAT+C12EnoylCoAMAT/KmcrotC12EnoylCoAMAT+C12HydroxyacylCoAMAT/KmcrotC12HydroxyacylCoAMAT+C10EnoylCoAMAT/KmcrotC10EnoylCoAMAT+C10HydroxyacylCoAMAT/KmcrotC10HydroxyacylCoAMAT+C8EnoylCoAMAT/KmcrotC8EnoylCoAMAT+C8HydroxyacylCoAMAT/KmcrotC8HydroxyacylCoAMAT+C6EnoylCoAMAT/KmcrotC6EnoylCoAMAT+C6HydroxyacylCoAMAT/KmcrotC6HydroxyacylCoAMAT+C4EnoylCoAMAT/KmcrotC4EnoylCoAMAT+C4HydroxyacylCoAMAT/KmcrotC4HydroxyacylCoAMAT+C4AcetoacylCoAMAT/KicrotC4AcetoacylCoA)
KmvlcadC14AcylCoAMAT = 4.0 uM; KmvlcadC14EnoylCoAMAT = 1.08 uM; KmvlcadFAD = 0.12 uM; KmvlcadFADH = 24.2 uM; KmvlcadC12AcylCoAMAT = 2.7 uM; KmvlcadC12EnoylCoAMAT = 1.08 uM; Vvlcad = 0.008 uM per min per mgProtein; KmvlcadC16EnoylCoAMAT = 1.08 uM; KmvlcadC16AcylCoAMAT = 6.5 uM; sfvlcadC16=1.0 dimensionless; Keqvlcad = 6.0 dimensionless Reaction: C16AcylCoAMAT => C16EnoylCoAMAT + FADHMAT; C14AcylCoAMAT, C12AcylCoAMAT, FADtMAT, C14EnoylCoAMAT, C12EnoylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, FADtMAT, C16EnoylCoAMAT, C14EnoylCoAMAT, C12EnoylCoAMAT, FADHMAT, Rate Law: sfvlcadC16*Vvlcad*(C16AcylCoAMAT*(FADtMAT-FADHMAT)/(KmvlcadC16AcylCoAMAT*KmvlcadFAD)-C16EnoylCoAMAT*FADHMAT/(KmvlcadC16AcylCoAMAT*KmvlcadFAD*Keqvlcad))/((1+C16AcylCoAMAT/KmvlcadC16AcylCoAMAT+C16EnoylCoAMAT/KmvlcadC16EnoylCoAMAT+C14AcylCoAMAT/KmvlcadC14AcylCoAMAT+C14EnoylCoAMAT/KmvlcadC14EnoylCoAMAT+C12AcylCoAMAT/KmvlcadC12AcylCoAMAT+C12EnoylCoAMAT/KmvlcadC12EnoylCoAMAT)*(1+(FADtMAT-FADHMAT)/KmvlcadFAD+FADHMAT/KmvlcadFADH))
KmlcadC8AcylCoAMAT = 123.0 uM; Vlcad = 0.01 uM per min per mgProtein; KmlcadC14EnoylCoAMAT = 1.08 uM; KmlcadFAD = 0.12 uM; KmlcadFADH = 24.2 uM; KmlcadC12EnoylCoAMAT = 1.08 uM; KmlcadC8EnoylCoAMAT = 1.08 uM; Keqlcad = 6.0 dimensionless; KmlcadC16AcylCoAMAT = 2.5 uM; sflcadC12=0.9 dimensionless; KmlcadC12AcylCoAMAT = 9.0 uM; KmlcadC10AcylCoAMAT = 24.3 uM; KmlcadC10EnoylCoAMAT = 1.08 uM; KmlcadC14AcylCoAMAT = 7.4 uM; KmlcadC16EnoylCoAMAT = 1.08 uM Reaction: C12AcylCoAMAT => C12EnoylCoAMAT + FADHMAT; C16AcylCoAMAT, C14AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, FADtMAT, C14EnoylCoAMAT, C16EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, C12AcylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, FADtMAT, C14EnoylCoAMAT, C16EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, FADHMAT, Rate Law: sflcadC12*Vlcad*(C12AcylCoAMAT*(FADtMAT-FADHMAT)/(KmlcadC12AcylCoAMAT*KmlcadFAD)-C14EnoylCoAMAT*FADHMAT/(KmlcadC12AcylCoAMAT*KmlcadFAD*Keqlcad))/((1+C12AcylCoAMAT/KmlcadC12AcylCoAMAT+C14EnoylCoAMAT/KmlcadC12EnoylCoAMAT+C16AcylCoAMAT/KmlcadC16AcylCoAMAT+C16EnoylCoAMAT/KmlcadC16EnoylCoAMAT+C14AcylCoAMAT/KmlcadC14AcylCoAMAT+C14EnoylCoAMAT/KmlcadC14EnoylCoAMAT+C10AcylCoAMAT/KmlcadC10AcylCoAMAT+C10EnoylCoAMAT/KmlcadC10EnoylCoAMAT+C8AcylCoAMAT/KmlcadC8AcylCoAMAT+C8EnoylCoAMAT/KmlcadC8EnoylCoAMAT)*(1+(FADtMAT-FADHMAT)/KmlcadFAD+FADHMAT/KmlcadFADH))
Kmcpt2C14AcylCarMAT = 51.0 uM; Kmcpt2C10AcylCarMAT = 51.0 uM; Kmcpt2CoAMAT = 30.0 uM; Kmcpt2C6AcylCarMAT = 51.0 uM; Kmcpt2C14AcylCoAMAT = 38.0 uM; Keqcpt2 = 2.22 dimensionless; Kmcpt2C8AcylCoAMAT = 38.0 uM; Kmcpt2C6AcylCoAMAT = 1000.0 uM; Kmcpt2C12AcylCarMAT = 51.0 uM; Kmcpt2C16AcylCarMAT = 51.0 uM; Kmcpt2C12AcylCoAMAT = 38.0 uM; Vcpt2 = 0.391 uM per min per mgProtein; Kmcpt2C8AcylCarMAT = 51.0 uM; Kmcpt2CarMAT = 350.0 uM; Kmcpt2C16AcylCoAMAT = 38.0 uM; Kmcpt2C4AcylCoAMAT = 1000000.0 uM; Kmcpt2C10AcylCoAMAT = 38.0 uM; sfcpt2C12=0.95 dimensionless; Kmcpt2C4AcylCarMAT = 51.0 uM Reaction: C12AcylCarMAT => C12AcylCoAMAT; C16AcylCarMAT, C14AcylCarMAT, C10AcylCarMAT, C8AcylCarMAT, C6AcylCarMAT, C4AcylCarMAT, CoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, CarMAT, C12AcylCarMAT, C16AcylCarMAT, C14AcylCarMAT, C10AcylCarMAT, C8AcylCarMAT, C6AcylCarMAT, C4AcylCarMAT, CoAMAT, C12AcylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, CarMAT, Rate Law: sfcpt2C12*Vcpt2*(C12AcylCarMAT*CoAMAT/(Kmcpt2C12AcylCarMAT*Kmcpt2CoAMAT)-C12AcylCoAMAT*CarMAT/(Kmcpt2C12AcylCarMAT*Kmcpt2CoAMAT*Keqcpt2))/((1+C12AcylCarMAT/Kmcpt2C12AcylCarMAT+C12AcylCoAMAT/Kmcpt2C12AcylCoAMAT+C16AcylCarMAT/Kmcpt2C16AcylCarMAT+C16AcylCoAMAT/Kmcpt2C16AcylCoAMAT+C14AcylCarMAT/Kmcpt2C14AcylCarMAT+C14AcylCoAMAT/Kmcpt2C14AcylCoAMAT+C10AcylCarMAT/Kmcpt2C10AcylCarMAT+C10AcylCoAMAT/Kmcpt2C10AcylCoAMAT+C8AcylCarMAT/Kmcpt2C8AcylCarMAT+C8AcylCoAMAT/Kmcpt2C8AcylCoAMAT+C6AcylCarMAT/Kmcpt2C6AcylCarMAT+C6AcylCoAMAT/Kmcpt2C6AcylCoAMAT+C4AcylCarMAT/Kmcpt2C4AcylCarMAT+C4AcylCoAMAT/Kmcpt2C4AcylCoAMAT)*(1+CoAMAT/Kmcpt2CoAMAT+CarMAT/Kmcpt2CarMAT))
KmmtpC14EnoylCoAMAT = 25.0 uM; KmmtpC8EnoylCoAMAT = 25.0 uM; KmmtpC16AcylCoAMAT = 13.83 uM; KmmtpCoAMAT = 30.0 uM; KmmtpC16EnoylCoAMAT = 25.0 uM; KmmtpC12EnoylCoAMAT = 25.0 uM; KmmtpAcetylCoAMAT = 30.0 uM; Vmtp = 2.84 uM per min per mgProtein; KmmtpC8AcylCoAMAT = 13.83 uM; KmmtpC12AcylCoAMAT = 13.83 uM; KmmtpC14AcylCoAMAT = 13.83 uM; KmmtpC6AcylCoAMAT = 13.83 uM; KmmtpC10EnoylCoAMAT = 25.0 uM; Keqmtp = 0.71 dimensionless; KicrotC4AcetoacylCoA = 1.6 uM; KmmtpNADMAT = 60.0 uM; KmmtpNADHMAT = 50.0 uM; KmmtpC10AcylCoAMAT = 13.83 uM; sfmtpC10=0.73 dimensionless Reaction: C10EnoylCoAMAT => C8AcylCoAMAT + AcetylCoAMAT + NADHMAT; C16EnoylCoAMAT, C14EnoylCoAMAT, C12EnoylCoAMAT, C8EnoylCoAMAT, NADtMAT, CoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C6AcylCoAMAT, C4AcetoacylCoAMAT, C10EnoylCoAMAT, C16EnoylCoAMAT, C14EnoylCoAMAT, C12EnoylCoAMAT, C8EnoylCoAMAT, NADtMAT, CoAMAT, C8AcylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C6AcylCoAMAT, NADHMAT, AcetylCoAMAT, C4AcetoacylCoAMAT, Rate Law: sfmtpC10*Vmtp*(C10EnoylCoAMAT*(NADtMAT-NADHMAT)*CoAMAT/(KmmtpC10EnoylCoAMAT*KmmtpNADMAT*KmmtpCoAMAT)-C8AcylCoAMAT*NADHMAT*AcetylCoAMAT/(KmmtpC10EnoylCoAMAT*KmmtpNADMAT*KmmtpCoAMAT*Keqmtp))/((1+C10EnoylCoAMAT/KmmtpC10EnoylCoAMAT+C8AcylCoAMAT/KmmtpC8AcylCoAMAT+C16EnoylCoAMAT/KmmtpC16EnoylCoAMAT+C16AcylCoAMAT/KmmtpC16AcylCoAMAT+C14EnoylCoAMAT/KmmtpC14EnoylCoAMAT+C14AcylCoAMAT/KmmtpC14AcylCoAMAT+C12EnoylCoAMAT/KmmtpC12EnoylCoAMAT+C12AcylCoAMAT/KmmtpC12AcylCoAMAT+C8EnoylCoAMAT/KmmtpC8EnoylCoAMAT+C10AcylCoAMAT/KmmtpC10AcylCoAMAT+C6AcylCoAMAT/KmmtpC6AcylCoAMAT+C4AcetoacylCoAMAT/KicrotC4AcetoacylCoA)*(1+(NADtMAT-NADHMAT)/KmmtpNADMAT+NADHMAT/KmmtpNADHMAT)*(1+CoAMAT/KmmtpCoAMAT+AcetylCoAMAT/KmmtpAcetylCoAMAT))
KmcrotC16EnoylCoAMAT = 150.0 uM; KmcrotC12EnoylCoAMAT = 25.0 uM; KicrotC4AcetoacylCoA = 1.6 uM; KmcrotC4EnoylCoAMAT = 40.0 uM; Vcrot = 3.6 uM per min per mgProtein; Keqcrot = 3.13 dimensionless; KmcrotC8EnoylCoAMAT = 25.0 uM; KmcrotC14EnoylCoAMAT = 100.0 uM; KmcrotC10HydroxyacylCoAMAT = 45.0 uM; KmcrotC8HydroxyacylCoAMAT = 45.0 uM; KmcrotC6EnoylCoAMAT = 25.0 uM; KmcrotC10EnoylCoAMAT = 25.0 uM; KmcrotC14HydroxyacylCoAMAT = 45.0 uM; KmcrotC4HydroxyacylCoAMAT = 45.0 uM; KmcrotC16HydroxyacylCoAMAT = 45.0 uM; KmcrotC12HydroxyacylCoAMAT = 45.0 uM; sfcrotC8=0.58 dimensionless; KmcrotC6HydroxyacylCoAMAT = 45.0 uM Reaction: C8EnoylCoAMAT => C8HydroxyacylCoAMAT; C16EnoylCoAMAT, C14EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C6EnoylCoAMAT, C4EnoylCoAMAT, C16HydroxyacylCoAMAT, C14HydroxyacylCoAMAT, C12HydroxyacylCoAMAT, C10HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, C4HydroxyacylCoAMAT, C4AcetoacylCoAMAT, C8EnoylCoAMAT, C16EnoylCoAMAT, C14EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C6EnoylCoAMAT, C4EnoylCoAMAT, C8HydroxyacylCoAMAT, C16HydroxyacylCoAMAT, C14HydroxyacylCoAMAT, C12HydroxyacylCoAMAT, C10HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, C4HydroxyacylCoAMAT, C4AcetoacylCoAMAT, Rate Law: sfcrotC8*Vcrot*(C8EnoylCoAMAT/KmcrotC8EnoylCoAMAT-C8HydroxyacylCoAMAT/(KmcrotC8EnoylCoAMAT*Keqcrot))/(1+C8EnoylCoAMAT/KmcrotC8EnoylCoAMAT+C8HydroxyacylCoAMAT/KmcrotC8HydroxyacylCoAMAT+C16EnoylCoAMAT/KmcrotC16EnoylCoAMAT+C16HydroxyacylCoAMAT/KmcrotC16HydroxyacylCoAMAT+C14EnoylCoAMAT/KmcrotC14EnoylCoAMAT+C14HydroxyacylCoAMAT/KmcrotC14HydroxyacylCoAMAT+C12EnoylCoAMAT/KmcrotC12EnoylCoAMAT+C12HydroxyacylCoAMAT/KmcrotC12HydroxyacylCoAMAT+C10EnoylCoAMAT/KmcrotC10EnoylCoAMAT+C10HydroxyacylCoAMAT/KmcrotC10HydroxyacylCoAMAT+C6EnoylCoAMAT/KmcrotC6EnoylCoAMAT+C6HydroxyacylCoAMAT/KmcrotC6HydroxyacylCoAMAT+C4EnoylCoAMAT/KmcrotC4EnoylCoAMAT+C4HydroxyacylCoAMAT/KmcrotC4HydroxyacylCoAMAT+C4AcetoacylCoAMAT/KicrotC4AcetoacylCoA)
KmcrotC16EnoylCoAMAT = 150.0 uM; KmcrotC12EnoylCoAMAT = 25.0 uM; KicrotC4AcetoacylCoA = 1.6 uM; KmcrotC4EnoylCoAMAT = 40.0 uM; Vcrot = 3.6 uM per min per mgProtein; Keqcrot = 3.13 dimensionless; KmcrotC14EnoylCoAMAT = 100.0 uM; KmcrotC8EnoylCoAMAT = 25.0 uM; KmcrotC10HydroxyacylCoAMAT = 45.0 uM; KmcrotC8HydroxyacylCoAMAT = 45.0 uM; KmcrotC6EnoylCoAMAT = 25.0 uM; KmcrotC10EnoylCoAMAT = 25.0 uM; KmcrotC14HydroxyacylCoAMAT = 45.0 uM; KmcrotC4HydroxyacylCoAMAT = 45.0 uM; KmcrotC16HydroxyacylCoAMAT = 45.0 uM; KmcrotC12HydroxyacylCoAMAT = 45.0 uM; KmcrotC6HydroxyacylCoAMAT = 45.0 uM; sfcrotC6=0.83 dimensionless Reaction: C6EnoylCoAMAT => C6HydroxyacylCoAMAT; C16EnoylCoAMAT, C14EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, C4EnoylCoAMAT, C16HydroxyacylCoAMAT, C14HydroxyacylCoAMAT, C12HydroxyacylCoAMAT, C10HydroxyacylCoAMAT, C8HydroxyacylCoAMAT, C4HydroxyacylCoAMAT, C4AcetoacylCoAMAT, C6EnoylCoAMAT, C16EnoylCoAMAT, C14EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, C4EnoylCoAMAT, C6HydroxyacylCoAMAT, C16HydroxyacylCoAMAT, C14HydroxyacylCoAMAT, C12HydroxyacylCoAMAT, C10HydroxyacylCoAMAT, C8HydroxyacylCoAMAT, C4HydroxyacylCoAMAT, C4AcetoacylCoAMAT, Rate Law: sfcrotC6*Vcrot*(C6EnoylCoAMAT/KmcrotC6EnoylCoAMAT-C6HydroxyacylCoAMAT/(KmcrotC6EnoylCoAMAT*Keqcrot))/(1+C6EnoylCoAMAT/KmcrotC6EnoylCoAMAT+C6HydroxyacylCoAMAT/KmcrotC6HydroxyacylCoAMAT+C16EnoylCoAMAT/KmcrotC16EnoylCoAMAT+C16HydroxyacylCoAMAT/KmcrotC16HydroxyacylCoAMAT+C14EnoylCoAMAT/KmcrotC14EnoylCoAMAT+C14HydroxyacylCoAMAT/KmcrotC14HydroxyacylCoAMAT+C12EnoylCoAMAT/KmcrotC12EnoylCoAMAT+C12HydroxyacylCoAMAT/KmcrotC12HydroxyacylCoAMAT+C10EnoylCoAMAT/KmcrotC10EnoylCoAMAT+C10HydroxyacylCoAMAT/KmcrotC10HydroxyacylCoAMAT+C8EnoylCoAMAT/KmcrotC8EnoylCoAMAT+C8HydroxyacylCoAMAT/KmcrotC8HydroxyacylCoAMAT+C4EnoylCoAMAT/KmcrotC4EnoylCoAMAT+C4HydroxyacylCoAMAT/KmcrotC4HydroxyacylCoAMAT+C4AcetoacylCoAMAT/KicrotC4AcetoacylCoA)
sfmcadC6=1.0 dimensionless; KmmcadC12AcylCoAMAT = 5.7 uM; KmmcadC8AcylCoAMAT = 4.0 uM; KmmcadC4AcylCoAMAT = 135.0 uM; KmmcadC10AcylCoAMAT = 5.4 uM; KmmcadFAD = 0.12 uM; KmmcadC10EnoylCoAMAT = 1.08 uM; KmmcadC4EnoylCoAMAT = 1.08 uM; Vmcad = 0.081 uM per min per mgProtein; KmmcadC6AcylCoAMAT = 9.4 uM; KmmcadC6EnoylCoAMAT = 1.08 uM; KmmcadFADH = 24.2 uM; KmmcadC12EnoylCoAMAT = 1.08 uM; KmmcadC8EnoylCoAMAT = 1.08 uM; Keqmcad = 6.0 dimensionless Reaction: C6AcylCoAMAT => C6EnoylCoAMAT + FADHMAT; C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C4AcylCoAMAT, FADtMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, C4EnoylCoAMAT, C6AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C4AcylCoAMAT, FADtMAT, C6EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, C4EnoylCoAMAT, FADHMAT, Rate Law: sfmcadC6*Vmcad*(C6AcylCoAMAT*(FADtMAT-FADHMAT)/(KmmcadC6AcylCoAMAT*KmmcadFAD)-C6EnoylCoAMAT*FADHMAT/(KmmcadC6AcylCoAMAT*KmmcadFAD*Keqmcad))/((1+C6AcylCoAMAT/KmmcadC6AcylCoAMAT+C6EnoylCoAMAT/KmmcadC6EnoylCoAMAT+C12AcylCoAMAT/KmmcadC12AcylCoAMAT+C12EnoylCoAMAT/KmmcadC12EnoylCoAMAT+C10AcylCoAMAT/KmmcadC10AcylCoAMAT+C10EnoylCoAMAT/KmmcadC10EnoylCoAMAT+C8AcylCoAMAT/KmmcadC8AcylCoAMAT+C8EnoylCoAMAT/KmmcadC8EnoylCoAMAT+C4AcylCoAMAT/KmmcadC4AcylCoAMAT+C4EnoylCoAMAT/KmmcadC4EnoylCoAMAT)*(1+(FADtMAT-FADHMAT)/KmmcadFAD+FADHMAT/KmmcadFADH))
KmmcadC8AcylCoAMAT = 4.0 uM; KmmcadC12AcylCoAMAT = 5.7 uM; KmmcadC4AcylCoAMAT = 135.0 uM; KmmcadC10AcylCoAMAT = 5.4 uM; KmmcadFAD = 0.12 uM; KmmcadC10EnoylCoAMAT = 1.08 uM; KmmcadC4EnoylCoAMAT = 1.08 uM; Vmcad = 0.081 uM per min per mgProtein; KmmcadC6AcylCoAMAT = 9.4 uM; KmmcadC6EnoylCoAMAT = 1.08 uM; KmmcadFADH = 24.2 uM; sfmcadC8=0.87 dimensionless; KmmcadC8EnoylCoAMAT = 1.08 uM; KmmcadC12EnoylCoAMAT = 1.08 uM; Keqmcad = 6.0 dimensionless Reaction: C8AcylCoAMAT => C8EnoylCoAMAT + FADHMAT; C12AcylCoAMAT, C10AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, FADtMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C6EnoylCoAMAT, C4EnoylCoAMAT, C8AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, FADtMAT, C8EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C6EnoylCoAMAT, C4EnoylCoAMAT, FADHMAT, Rate Law: sfmcadC8*Vmcad*(C8AcylCoAMAT*(FADtMAT-FADHMAT)/(KmmcadC8AcylCoAMAT*KmmcadFAD)-C8EnoylCoAMAT*FADHMAT/(KmmcadC8AcylCoAMAT*KmmcadFAD*Keqmcad))/((1+C8AcylCoAMAT/KmmcadC8AcylCoAMAT+C8EnoylCoAMAT/KmmcadC8EnoylCoAMAT+C12AcylCoAMAT/KmmcadC12AcylCoAMAT+C12EnoylCoAMAT/KmmcadC12EnoylCoAMAT+C10AcylCoAMAT/KmmcadC10AcylCoAMAT+C10EnoylCoAMAT/KmmcadC10EnoylCoAMAT+C6AcylCoAMAT/KmmcadC6AcylCoAMAT+C6EnoylCoAMAT/KmmcadC6EnoylCoAMAT+C4AcylCoAMAT/KmmcadC4AcylCoAMAT+C4EnoylCoAMAT/KmmcadC4EnoylCoAMAT)*(1+(FADtMAT-FADHMAT)/KmmcadFAD+FADHMAT/KmmcadFADH))
KmmckatC14AcylCoAMAT = 13.83 uM; KmmckatC10KetoacylCoAMAT = 2.1 uM; KmmckatCoAMAT = 26.6 uM; KmmckatC6KetoacylCoAMAT = 6.7 uM; Keqmckat = 1051.0 dimensionless; KmmckatC12KetoacylCoAMAT = 1.3 uM; KmmckatC8KetoacylCoAMAT = 3.2 uM; KmmckatC4AcetoacylCoAMAT = 12.4 uM; KmmckatC16KetoacylCoAMAT = 1.1 uM; KmmckatC12AcylCoAMAT = 13.83 uM; KmmckatC8AcylCoAMAT = 13.83 uM; KmmckatC10AcylCoAMAT = 13.83 uM; KmmckatC6AcylCoAMAT = 13.83 uM; Vmckat = 0.377 uM per min per mgProtein; sfmckatC4=0.49 dimensionless; KmmckatC16AcylCoAMAT = 13.83 uM; KmmckatC14KetoacylCoAMAT = 1.2 uM; KmmckatC4AcylCoAMAT = 13.83 uM; KmmckatAcetylCoAMAT = 30.0 uM Reaction: C4AcetoacylCoAMAT => AcetylCoAMAT; C16KetoacylCoAMAT, C14KetoacylCoAMAT, C12KetoacylCoAMAT, C10KetoacylCoAMAT, C8KetoacylCoAMAT, C6KetoacylCoAMAT, CoAMAT, C4AcylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcetoacylCoAMAT, C16KetoacylCoAMAT, C14KetoacylCoAMAT, C12KetoacylCoAMAT, C10KetoacylCoAMAT, C8KetoacylCoAMAT, C6KetoacylCoAMAT, CoAMAT, C4AcylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, AcetylCoAMAT, Rate Law: sfmckatC4*Vmckat*(C4AcetoacylCoAMAT*CoAMAT/(KmmckatC4AcetoacylCoAMAT*KmmckatCoAMAT)-AcetylCoAMAT*AcetylCoAMAT/(KmmckatC4AcetoacylCoAMAT*KmmckatCoAMAT*Keqmckat))/((1+C4AcetoacylCoAMAT/KmmckatC4AcetoacylCoAMAT+C4AcylCoAMAT/KmmckatC4AcylCoAMAT+C16KetoacylCoAMAT/KmmckatC16KetoacylCoAMAT+C16AcylCoAMAT/KmmckatC16AcylCoAMAT+C14KetoacylCoAMAT/KmmckatC14KetoacylCoAMAT+C14AcylCoAMAT/KmmckatC14AcylCoAMAT+C12KetoacylCoAMAT/KmmckatC12KetoacylCoAMAT+C12AcylCoAMAT/KmmckatC12AcylCoAMAT+C10KetoacylCoAMAT/KmmckatC10KetoacylCoAMAT+C10AcylCoAMAT/KmmckatC10AcylCoAMAT+C8KetoacylCoAMAT/KmmckatC8KetoacylCoAMAT+C8AcylCoAMAT/KmmckatC8AcylCoAMAT+C6KetoacylCoAMAT/KmmckatC6KetoacylCoAMAT+C6AcylCoAMAT/KmmckatC6AcylCoAMAT+AcetylCoAMAT/KmmckatAcetylCoAMAT)*(1+CoAMAT/KmmckatCoAMAT+AcetylCoAMAT/KmmckatAcetylCoAMAT))
KmcactC8AcylCarMAT=15.0 uM; Vfcact = 0.42 uM per min per mgProtein; KmcactCarCYT = 130.0 uM; KicactC8AcylCarCYT=56.0 uM; KmcactCarMAT = 130.0 uM; KmcactC8AcylCarCYT=15.0 uM; Keqcact = 1.0 dimensionless; KicactCarCYT = 200.0 uM; Vrcact = 0.42 uM per min per mgProtein Reaction: C8AcylCarCYT => C8AcylCarMAT; CarMAT, CarCYT, C8AcylCarCYT, CarMAT, C8AcylCarMAT, CarCYT, Rate Law: Vfcact*(C8AcylCarCYT*CarMAT-C8AcylCarMAT*CarCYT/Keqcact)/(C8AcylCarCYT*CarMAT+KmcactCarMAT*C8AcylCarCYT+KmcactC8AcylCarCYT*CarMAT*(1+CarCYT/KicactCarCYT)+Vfcact/(Vrcact*Keqcact)*(KmcactCarCYT*C8AcylCarMAT*(1+C8AcylCarCYT/KicactC8AcylCarCYT)+CarCYT*(KmcactC8AcylCarMAT+C8AcylCarMAT)))
KmmcadC12AcylCoAMAT = 5.7 uM; KmmcadC8AcylCoAMAT = 4.0 uM; KmmcadC4AcylCoAMAT = 135.0 uM; KmmcadC10AcylCoAMAT = 5.4 uM; KmmcadFAD = 0.12 uM; KmmcadC10EnoylCoAMAT = 1.08 uM; KmmcadC4EnoylCoAMAT = 1.08 uM; Vmcad = 0.081 uM per min per mgProtein; KmmcadC6AcylCoAMAT = 9.4 uM; KmmcadC6EnoylCoAMAT = 1.08 uM; sfmcadC12=0.38 dimensionless; KmmcadFADH = 24.2 uM; KmmcadC12EnoylCoAMAT = 1.08 uM; KmmcadC8EnoylCoAMAT = 1.08 uM; Keqmcad = 6.0 dimensionless Reaction: C12AcylCoAMAT => C12EnoylCoAMAT + FADHMAT; C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, FADtMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, C6EnoylCoAMAT, C4EnoylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, FADtMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, C6EnoylCoAMAT, C4EnoylCoAMAT, FADHMAT, Rate Law: sfmcadC12*Vmcad*(C12AcylCoAMAT*(FADtMAT-FADHMAT)/(KmmcadC12AcylCoAMAT*KmmcadFAD)-C12EnoylCoAMAT*FADHMAT/(KmmcadC12AcylCoAMAT*KmmcadFAD*Keqmcad))/((1+C12AcylCoAMAT/KmmcadC12AcylCoAMAT+C12EnoylCoAMAT/KmmcadC12EnoylCoAMAT+C10AcylCoAMAT/KmmcadC10AcylCoAMAT+C10EnoylCoAMAT/KmmcadC10EnoylCoAMAT+C8AcylCoAMAT/KmmcadC8AcylCoAMAT+C8EnoylCoAMAT/KmmcadC8EnoylCoAMAT+C6AcylCoAMAT/KmmcadC6AcylCoAMAT+C6EnoylCoAMAT/KmmcadC6EnoylCoAMAT+C4AcylCoAMAT/KmmcadC4AcylCoAMAT+C4EnoylCoAMAT/KmmcadC4EnoylCoAMAT)*(1+(FADtMAT-FADHMAT)/KmmcadFAD+FADHMAT/KmmcadFADH))
KmmckatC14AcylCoAMAT = 13.83 uM; KmmckatC10KetoacylCoAMAT = 2.1 uM; KmmckatCoAMAT = 26.6 uM; KmmckatC6KetoacylCoAMAT = 6.7 uM; Keqmckat = 1051.0 dimensionless; KmmckatC12KetoacylCoAMAT = 1.3 uM; KmmckatC8KetoacylCoAMAT = 3.2 uM; KmmckatC4AcetoacylCoAMAT = 12.4 uM; KmmckatC16KetoacylCoAMAT = 1.1 uM; KmmckatC12AcylCoAMAT = 13.83 uM; sfmckatC14=0.2 dimensionless; KmmckatC8AcylCoAMAT = 13.83 uM; KmmckatC10AcylCoAMAT = 13.83 uM; KmmckatC6AcylCoAMAT = 13.83 uM; Vmckat = 0.377 uM per min per mgProtein; KmmckatC16AcylCoAMAT = 13.83 uM; KmmckatC14KetoacylCoAMAT = 1.2 uM; KmmckatC4AcylCoAMAT = 13.83 uM; KmmckatAcetylCoAMAT = 30.0 uM Reaction: C14KetoacylCoAMAT => C12AcylCoAMAT + AcetylCoAMAT; C16KetoacylCoAMAT, C12KetoacylCoAMAT, C10KetoacylCoAMAT, C8KetoacylCoAMAT, C6KetoacylCoAMAT, C4AcetoacylCoAMAT, CoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, C14KetoacylCoAMAT, C16KetoacylCoAMAT, C12KetoacylCoAMAT, C10KetoacylCoAMAT, C8KetoacylCoAMAT, C6KetoacylCoAMAT, C4AcetoacylCoAMAT, CoAMAT, C12AcylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, AcetylCoAMAT, Rate Law: sfmckatC14*Vmckat*(C14KetoacylCoAMAT*CoAMAT/(KmmckatC14KetoacylCoAMAT*KmmckatCoAMAT)-C12AcylCoAMAT*AcetylCoAMAT/(KmmckatC14KetoacylCoAMAT*KmmckatCoAMAT*Keqmckat))/((1+C14KetoacylCoAMAT/KmmckatC14KetoacylCoAMAT+C12AcylCoAMAT/KmmckatC12AcylCoAMAT+C16KetoacylCoAMAT/KmmckatC16KetoacylCoAMAT+C16AcylCoAMAT/KmmckatC16AcylCoAMAT+C12KetoacylCoAMAT/KmmckatC12KetoacylCoAMAT+C14AcylCoAMAT/KmmckatC14AcylCoAMAT+C10KetoacylCoAMAT/KmmckatC10KetoacylCoAMAT+C10AcylCoAMAT/KmmckatC10AcylCoAMAT+C8KetoacylCoAMAT/KmmckatC8KetoacylCoAMAT+C8AcylCoAMAT/KmmckatC8AcylCoAMAT+C6KetoacylCoAMAT/KmmckatC6KetoacylCoAMAT+C6AcylCoAMAT/KmmckatC6AcylCoAMAT+C4AcetoacylCoAMAT/KmmckatC4AcetoacylCoAMAT+C4AcylCoAMAT/KmmckatC4AcylCoAMAT+AcetylCoAMAT/KmmckatAcetylCoAMAT)*(1+CoAMAT/KmmckatCoAMAT+AcetylCoAMAT/KmmckatAcetylCoAMAT))
KmmschadC16KetoacylCoAMAT = 1.4 uM; KmmschadC14HydroxyacylCoAMAT = 1.8 uM; Keqmschad = 2.17E-4 dimensionless; KmmschadC16HydroxyacylCoAMAT = 1.5 uM; Vmschad = 1.0 uM per min per mgProtein; KmmschadC6HydroxyacylCoAMAT = 28.6 uM; KmmschadC12KetoacylCoAMAT = 1.6 uM; KmmschadC4HydroxyacylCoAMAT = 69.9 uM; KmmschadC14KetoacylCoAMAT = 1.4 uM; KmmschadC12HydroxyacylCoAMAT = 3.7 uM; KmmschadC6KetoacylCoAMAT = 5.8 uM; KmmschadNADMAT = 58.5 uM; KmmschadC10HydroxyacylCoAMAT = 8.8 uM; sfmschadC14=0.5 dimensionless; KmmschadC8HydroxyacylCoAMAT = 16.3 uM; KmmschadC4AcetoacylCoAMAT = 16.9 uM; KmmschadC10KetoacylCoAMAT = 2.3 uM; KmmschadNADHMAT = 5.4 uM; KmmschadC8KetoacylCoAMAT = 4.1 uM Reaction: C14HydroxyacylCoAMAT => C14KetoacylCoAMAT + NADHMAT; C16HydroxyacylCoAMAT, C12HydroxyacylCoAMAT, C10HydroxyacylCoAMAT, C8HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, C4HydroxyacylCoAMAT, NADtMAT, C16KetoacylCoAMAT, C12KetoacylCoAMAT, C10KetoacylCoAMAT, C8KetoacylCoAMAT, C6KetoacylCoAMAT, C4AcetoacylCoAMAT, C14HydroxyacylCoAMAT, C16HydroxyacylCoAMAT, C12HydroxyacylCoAMAT, C10HydroxyacylCoAMAT, C8HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, C4HydroxyacylCoAMAT, NADtMAT, C14KetoacylCoAMAT, C16KetoacylCoAMAT, C12KetoacylCoAMAT, C10KetoacylCoAMAT, C8KetoacylCoAMAT, C6KetoacylCoAMAT, C4AcetoacylCoAMAT, NADHMAT, Rate Law: sfmschadC14*Vmschad*(C14HydroxyacylCoAMAT*(NADtMAT-NADHMAT)/(KmmschadC14HydroxyacylCoAMAT*KmmschadNADMAT)-C14KetoacylCoAMAT*NADHMAT/(KmmschadC14HydroxyacylCoAMAT*KmmschadNADMAT*Keqmschad))/((1+C14HydroxyacylCoAMAT/KmmschadC14HydroxyacylCoAMAT+C14KetoacylCoAMAT/KmmschadC14KetoacylCoAMAT+C16HydroxyacylCoAMAT/KmmschadC16HydroxyacylCoAMAT+C16KetoacylCoAMAT/KmmschadC16KetoacylCoAMAT+C12HydroxyacylCoAMAT/KmmschadC12HydroxyacylCoAMAT+C12KetoacylCoAMAT/KmmschadC12KetoacylCoAMAT+C10HydroxyacylCoAMAT/KmmschadC10HydroxyacylCoAMAT+C10KetoacylCoAMAT/KmmschadC10KetoacylCoAMAT+C8HydroxyacylCoAMAT/KmmschadC8HydroxyacylCoAMAT+C8KetoacylCoAMAT/KmmschadC8KetoacylCoAMAT+C6HydroxyacylCoAMAT/KmmschadC6HydroxyacylCoAMAT+C6KetoacylCoAMAT/KmmschadC6KetoacylCoAMAT+C4HydroxyacylCoAMAT/KmmschadC4HydroxyacylCoAMAT+C4AcetoacylCoAMAT/KmmschadC4AcetoacylCoAMAT)*(1+(NADtMAT-NADHMAT)/KmmschadNADMAT+NADHMAT/KmmschadNADHMAT))
KmcactC16AcylCarMAT=15.0 uM; Vfcact = 0.42 uM per min per mgProtein; KmcactCarCYT = 130.0 uM; KicactC16AcylCarCYT=56.0 uM; KmcactCarMAT = 130.0 uM; KmcactC16AcylCarCYT=15.0 uM; Keqcact = 1.0 dimensionless; KicactCarCYT = 200.0 uM; Vrcact = 0.42 uM per min per mgProtein Reaction: C16AcylCarCYT => C16AcylCarMAT; CarMAT, CarCYT, C16AcylCarCYT, CarMAT, C16AcylCarMAT, CarCYT, Rate Law: Vfcact*(C16AcylCarCYT*CarMAT-C16AcylCarMAT*CarCYT/Keqcact)/(C16AcylCarCYT*CarMAT+KmcactCarMAT*C16AcylCarCYT+KmcactC16AcylCarCYT*CarMAT*(1+CarCYT/KicactCarCYT)+Vfcact/(Vrcact*Keqcact)*(KmcactCarCYT*C16AcylCarMAT*(1+C16AcylCarCYT/KicactC16AcylCarCYT)+CarCYT*(KmcactC16AcylCarMAT+C16AcylCarMAT)))
KmlcadC8AcylCoAMAT = 123.0 uM; sflcadC14=1.0 dimensionless; Vlcad = 0.01 uM per min per mgProtein; KmlcadC14EnoylCoAMAT = 1.08 uM; KmlcadFAD = 0.12 uM; KmlcadFADH = 24.2 uM; KmlcadC12EnoylCoAMAT = 1.08 uM; KmlcadC8EnoylCoAMAT = 1.08 uM; Keqlcad = 6.0 dimensionless; KmlcadC16AcylCoAMAT = 2.5 uM; KmlcadC12AcylCoAMAT = 9.0 uM; KmlcadC10AcylCoAMAT = 24.3 uM; KmlcadC10EnoylCoAMAT = 1.08 uM; KmlcadC14AcylCoAMAT = 7.4 uM; KmlcadC16EnoylCoAMAT = 1.08 uM Reaction: C14AcylCoAMAT => C14EnoylCoAMAT + FADHMAT; C16AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, FADtMAT, C16EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, C14AcylCoAMAT, C16AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, FADtMAT, C14EnoylCoAMAT, C16EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, FADHMAT, Rate Law: sflcadC14*Vlcad*(C14AcylCoAMAT*(FADtMAT-FADHMAT)/(KmlcadC14AcylCoAMAT*KmlcadFAD)-C14EnoylCoAMAT*FADHMAT/(KmlcadC14AcylCoAMAT*KmlcadFAD*Keqlcad))/((1+C14AcylCoAMAT/KmlcadC14AcylCoAMAT+C14EnoylCoAMAT/KmlcadC14EnoylCoAMAT+C16AcylCoAMAT/KmlcadC16AcylCoAMAT+C16EnoylCoAMAT/KmlcadC16EnoylCoAMAT+C12AcylCoAMAT/KmlcadC12AcylCoAMAT+C12EnoylCoAMAT/KmlcadC12EnoylCoAMAT+C10AcylCoAMAT/KmlcadC10AcylCoAMAT+C10EnoylCoAMAT/KmlcadC10EnoylCoAMAT+C8AcylCoAMAT/KmlcadC8AcylCoAMAT+C8EnoylCoAMAT/KmlcadC8EnoylCoAMAT)*(1+(FADtMAT-FADHMAT)/KmlcadFAD+FADHMAT/KmlcadFADH))
KmcrotC16EnoylCoAMAT = 150.0 uM; KmcrotC12EnoylCoAMAT = 25.0 uM; KicrotC4AcetoacylCoA = 1.6 uM; KmcrotC4EnoylCoAMAT = 40.0 uM; Vcrot = 3.6 uM per min per mgProtein; Keqcrot = 3.13 dimensionless; KmcrotC14EnoylCoAMAT = 100.0 uM; KmcrotC8EnoylCoAMAT = 25.0 uM; KmcrotC10HydroxyacylCoAMAT = 45.0 uM; KmcrotC8HydroxyacylCoAMAT = 45.0 uM; KmcrotC6EnoylCoAMAT = 25.0 uM; sfcrotC12=0.25 dimensionless; KmcrotC10EnoylCoAMAT = 25.0 uM; KmcrotC14HydroxyacylCoAMAT = 45.0 uM; KmcrotC4HydroxyacylCoAMAT = 45.0 uM; KmcrotC12HydroxyacylCoAMAT = 45.0 uM; KmcrotC16HydroxyacylCoAMAT = 45.0 uM; KmcrotC6HydroxyacylCoAMAT = 45.0 uM Reaction: C12EnoylCoAMAT => C12HydroxyacylCoAMAT; C16EnoylCoAMAT, C14EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, C6EnoylCoAMAT, C4EnoylCoAMAT, C16HydroxyacylCoAMAT, C14HydroxyacylCoAMAT, C10HydroxyacylCoAMAT, C8HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, C4HydroxyacylCoAMAT, C4AcetoacylCoAMAT, C12EnoylCoAMAT, C16EnoylCoAMAT, C14EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, C6EnoylCoAMAT, C4EnoylCoAMAT, C12HydroxyacylCoAMAT, C16HydroxyacylCoAMAT, C14HydroxyacylCoAMAT, C10HydroxyacylCoAMAT, C8HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, C4HydroxyacylCoAMAT, C4AcetoacylCoAMAT, Rate Law: sfcrotC12*Vcrot*(C12EnoylCoAMAT/KmcrotC12EnoylCoAMAT-C12HydroxyacylCoAMAT/(KmcrotC12EnoylCoAMAT*Keqcrot))/(1+C12EnoylCoAMAT/KmcrotC12EnoylCoAMAT+C12HydroxyacylCoAMAT/KmcrotC12HydroxyacylCoAMAT+C16EnoylCoAMAT/KmcrotC16EnoylCoAMAT+C16HydroxyacylCoAMAT/KmcrotC16HydroxyacylCoAMAT+C14EnoylCoAMAT/KmcrotC14EnoylCoAMAT+C14HydroxyacylCoAMAT/KmcrotC14HydroxyacylCoAMAT+C10EnoylCoAMAT/KmcrotC10EnoylCoAMAT+C10HydroxyacylCoAMAT/KmcrotC10HydroxyacylCoAMAT+C8EnoylCoAMAT/KmcrotC8EnoylCoAMAT+C8HydroxyacylCoAMAT/KmcrotC8HydroxyacylCoAMAT+C6EnoylCoAMAT/KmcrotC6EnoylCoAMAT+C6HydroxyacylCoAMAT/KmcrotC6HydroxyacylCoAMAT+C4EnoylCoAMAT/KmcrotC4EnoylCoAMAT+C4HydroxyacylCoAMAT/KmcrotC4HydroxyacylCoAMAT+C4AcetoacylCoAMAT/KicrotC4AcetoacylCoA)
KmlcadC8AcylCoAMAT = 123.0 uM; Vlcad = 0.01 uM per min per mgProtein; KmlcadC14EnoylCoAMAT = 1.08 uM; KmlcadFAD = 0.12 uM; KmlcadFADH = 24.2 uM; KmlcadC12EnoylCoAMAT = 1.08 uM; KmlcadC8EnoylCoAMAT = 1.08 uM; sflcadC16=0.9 dimensionless; KmlcadC16AcylCoAMAT = 2.5 uM; Keqlcad = 6.0 dimensionless; KmlcadC12AcylCoAMAT = 9.0 uM; KmlcadC10AcylCoAMAT = 24.3 uM; KmlcadC10EnoylCoAMAT = 1.08 uM; KmlcadC14AcylCoAMAT = 7.4 uM; KmlcadC16EnoylCoAMAT = 1.08 uM Reaction: C16AcylCoAMAT => C16EnoylCoAMAT + FADHMAT; C14AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, FADtMAT, C14EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, FADtMAT, C16EnoylCoAMAT, C14EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, FADHMAT, Rate Law: sflcadC16*Vlcad*(C16AcylCoAMAT*(FADtMAT-FADHMAT)/(KmlcadC16AcylCoAMAT*KmlcadFAD)-C16EnoylCoAMAT*FADHMAT/(KmlcadC16AcylCoAMAT*KmlcadFAD*Keqlcad))/((1+C16AcylCoAMAT/KmlcadC16AcylCoAMAT+C16EnoylCoAMAT/KmlcadC16EnoylCoAMAT+C14AcylCoAMAT/KmlcadC14AcylCoAMAT+C14EnoylCoAMAT/KmlcadC14EnoylCoAMAT+C12AcylCoAMAT/KmlcadC12AcylCoAMAT+C12EnoylCoAMAT/KmlcadC12EnoylCoAMAT+C10AcylCoAMAT/KmlcadC10AcylCoAMAT+C10EnoylCoAMAT/KmlcadC10EnoylCoAMAT+C8AcylCoAMAT/KmlcadC8AcylCoAMAT+C8EnoylCoAMAT/KmlcadC8EnoylCoAMAT)*(1+(FADtMAT-FADHMAT)/KmlcadFAD+FADHMAT/KmlcadFADH))
sfmtpC8=0.34 dimensionless; KmmtpC14EnoylCoAMAT = 25.0 uM; KmmtpC8EnoylCoAMAT = 25.0 uM; KmmtpC16AcylCoAMAT = 13.83 uM; KmmtpCoAMAT = 30.0 uM; KmmtpC16EnoylCoAMAT = 25.0 uM; KmmtpC12EnoylCoAMAT = 25.0 uM; KmmtpAcetylCoAMAT = 30.0 uM; Vmtp = 2.84 uM per min per mgProtein; KmmtpC12AcylCoAMAT = 13.83 uM; KmmtpC8AcylCoAMAT = 13.83 uM; KmmtpC6AcylCoAMAT = 13.83 uM; KmmtpC14AcylCoAMAT = 13.83 uM; KmmtpC10EnoylCoAMAT = 25.0 uM; Keqmtp = 0.71 dimensionless; KicrotC4AcetoacylCoA = 1.6 uM; KmmtpNADMAT = 60.0 uM; KmmtpNADHMAT = 50.0 uM; KmmtpC10AcylCoAMAT = 13.83 uM Reaction: C8EnoylCoAMAT => C6AcylCoAMAT + AcetylCoAMAT + NADHMAT; C16EnoylCoAMAT, C14EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, NADtMAT, CoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C4AcetoacylCoAMAT, C8EnoylCoAMAT, C16EnoylCoAMAT, C14EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, NADtMAT, CoAMAT, C6AcylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, NADHMAT, AcetylCoAMAT, C4AcetoacylCoAMAT, Rate Law: sfmtpC8*Vmtp*(C8EnoylCoAMAT*(NADtMAT-NADHMAT)*CoAMAT/(KmmtpC8EnoylCoAMAT*KmmtpNADMAT*KmmtpCoAMAT)-C6AcylCoAMAT*NADHMAT*AcetylCoAMAT/(KmmtpC8EnoylCoAMAT*KmmtpNADMAT*KmmtpCoAMAT*Keqmtp))/((1+C8EnoylCoAMAT/KmmtpC8EnoylCoAMAT+C6AcylCoAMAT/KmmtpC6AcylCoAMAT+C16EnoylCoAMAT/KmmtpC16EnoylCoAMAT+C16AcylCoAMAT/KmmtpC16AcylCoAMAT+C14EnoylCoAMAT/KmmtpC14EnoylCoAMAT+C14AcylCoAMAT/KmmtpC14AcylCoAMAT+C12EnoylCoAMAT/KmmtpC12EnoylCoAMAT+C12AcylCoAMAT/KmmtpC12AcylCoAMAT+C10EnoylCoAMAT/KmmtpC10EnoylCoAMAT+C10AcylCoAMAT/KmmtpC10AcylCoAMAT+C8AcylCoAMAT/KmmtpC8AcylCoAMAT+C4AcetoacylCoAMAT/KicrotC4AcetoacylCoA)*(1+(NADtMAT-NADHMAT)/KmmtpNADMAT+NADHMAT/KmmtpNADHMAT)*(1+CoAMAT/KmmtpCoAMAT+AcetylCoAMAT/KmmtpAcetylCoAMAT))
KmmschadC16KetoacylCoAMAT = 1.4 uM; Keqmschad = 2.17E-4 dimensionless; KmmschadC16HydroxyacylCoAMAT = 1.5 uM; KmmschadC14HydroxyacylCoAMAT = 1.8 uM; Vmschad = 1.0 uM per min per mgProtein; KmmschadC6HydroxyacylCoAMAT = 28.6 uM; KmmschadC12KetoacylCoAMAT = 1.6 uM; KmmschadC4HydroxyacylCoAMAT = 69.9 uM; KmmschadC14KetoacylCoAMAT = 1.4 uM; KmmschadC12HydroxyacylCoAMAT = 3.7 uM; KmmschadC6KetoacylCoAMAT = 5.8 uM; KmmschadNADMAT = 58.5 uM; KmmschadC10HydroxyacylCoAMAT = 8.8 uM; KmmschadC8HydroxyacylCoAMAT = 16.3 uM; KmmschadC4AcetoacylCoAMAT = 16.9 uM; KmmschadC10KetoacylCoAMAT = 2.3 uM; KmmschadNADHMAT = 5.4 uM; sfmschadC12=0.43 dimensionless; KmmschadC8KetoacylCoAMAT = 4.1 uM Reaction: C12HydroxyacylCoAMAT => C12KetoacylCoAMAT + NADHMAT; C16HydroxyacylCoAMAT, C14HydroxyacylCoAMAT, C10HydroxyacylCoAMAT, C8HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, C4HydroxyacylCoAMAT, NADtMAT, C16KetoacylCoAMAT, C14KetoacylCoAMAT, C10KetoacylCoAMAT, C8KetoacylCoAMAT, C6KetoacylCoAMAT, C4AcetoacylCoAMAT, C12HydroxyacylCoAMAT, C16HydroxyacylCoAMAT, C14HydroxyacylCoAMAT, C10HydroxyacylCoAMAT, C8HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, C4HydroxyacylCoAMAT, NADtMAT, C12KetoacylCoAMAT, C16KetoacylCoAMAT, C14KetoacylCoAMAT, C10KetoacylCoAMAT, C8KetoacylCoAMAT, C6KetoacylCoAMAT, C4AcetoacylCoAMAT, NADHMAT, Rate Law: sfmschadC12*Vmschad*(C12HydroxyacylCoAMAT*(NADtMAT-NADHMAT)/(KmmschadC12HydroxyacylCoAMAT*KmmschadNADMAT)-C12KetoacylCoAMAT*NADHMAT/(KmmschadC12HydroxyacylCoAMAT*KmmschadNADMAT*Keqmschad))/((1+C12HydroxyacylCoAMAT/KmmschadC12HydroxyacylCoAMAT+C12KetoacylCoAMAT/KmmschadC12KetoacylCoAMAT+C16HydroxyacylCoAMAT/KmmschadC16HydroxyacylCoAMAT+C16KetoacylCoAMAT/KmmschadC16KetoacylCoAMAT+C14HydroxyacylCoAMAT/KmmschadC14HydroxyacylCoAMAT+C14KetoacylCoAMAT/KmmschadC14KetoacylCoAMAT+C10HydroxyacylCoAMAT/KmmschadC10HydroxyacylCoAMAT+C10KetoacylCoAMAT/KmmschadC10KetoacylCoAMAT+C8HydroxyacylCoAMAT/KmmschadC8HydroxyacylCoAMAT+C8KetoacylCoAMAT/KmmschadC8KetoacylCoAMAT+C6HydroxyacylCoAMAT/KmmschadC6HydroxyacylCoAMAT+C6KetoacylCoAMAT/KmmschadC6KetoacylCoAMAT+C4HydroxyacylCoAMAT/KmmschadC4HydroxyacylCoAMAT+C4AcetoacylCoAMAT/KmmschadC4AcetoacylCoAMAT)*(1+(NADtMAT-NADHMAT)/KmmschadNADMAT+NADHMAT/KmmschadNADHMAT))
KmmckatC14AcylCoAMAT = 13.83 uM; KmmckatC10KetoacylCoAMAT = 2.1 uM; KmmckatCoAMAT = 26.6 uM; KmmckatC6KetoacylCoAMAT = 6.7 uM; KmmckatC8KetoacylCoAMAT = 3.2 uM; Keqmckat = 1051.0 dimensionless; KmmckatC12KetoacylCoAMAT = 1.3 uM; KmmckatC4AcetoacylCoAMAT = 12.4 uM; KmmckatC16KetoacylCoAMAT = 1.1 uM; KmmckatC12AcylCoAMAT = 13.83 uM; sfmckatC8=0.81 dimensionless; KmmckatC8AcylCoAMAT = 13.83 uM; KmmckatC10AcylCoAMAT = 13.83 uM; KmmckatC6AcylCoAMAT = 13.83 uM; Vmckat = 0.377 uM per min per mgProtein; KmmckatC16AcylCoAMAT = 13.83 uM; KmmckatC14KetoacylCoAMAT = 1.2 uM; KmmckatC4AcylCoAMAT = 13.83 uM; KmmckatAcetylCoAMAT = 30.0 uM Reaction: C8KetoacylCoAMAT => C6AcylCoAMAT + AcetylCoAMAT; C16KetoacylCoAMAT, C14KetoacylCoAMAT, C12KetoacylCoAMAT, C10KetoacylCoAMAT, C6KetoacylCoAMAT, C4AcetoacylCoAMAT, CoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C4AcylCoAMAT, C8KetoacylCoAMAT, C16KetoacylCoAMAT, C14KetoacylCoAMAT, C12KetoacylCoAMAT, C10KetoacylCoAMAT, C6KetoacylCoAMAT, C4AcetoacylCoAMAT, CoAMAT, C6AcylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C4AcylCoAMAT, AcetylCoAMAT, Rate Law: sfmckatC8*Vmckat*(C8KetoacylCoAMAT*CoAMAT/(KmmckatC8KetoacylCoAMAT*KmmckatCoAMAT)-C6AcylCoAMAT*AcetylCoAMAT/(KmmckatC8KetoacylCoAMAT*KmmckatCoAMAT*Keqmckat))/((1+C8KetoacylCoAMAT/KmmckatC8KetoacylCoAMAT+C6AcylCoAMAT/KmmckatC6AcylCoAMAT+C16KetoacylCoAMAT/KmmckatC16KetoacylCoAMAT+C16AcylCoAMAT/KmmckatC16AcylCoAMAT+C14KetoacylCoAMAT/KmmckatC14KetoacylCoAMAT+C14AcylCoAMAT/KmmckatC14AcylCoAMAT+C12KetoacylCoAMAT/KmmckatC12KetoacylCoAMAT+C12AcylCoAMAT/KmmckatC12AcylCoAMAT+C10KetoacylCoAMAT/KmmckatC10KetoacylCoAMAT+C10AcylCoAMAT/KmmckatC10AcylCoAMAT+C6KetoacylCoAMAT/KmmckatC6KetoacylCoAMAT+C8AcylCoAMAT/KmmckatC8AcylCoAMAT+C4AcetoacylCoAMAT/KmmckatC4AcetoacylCoAMAT+C4AcylCoAMAT/KmmckatC4AcylCoAMAT+AcetylCoAMAT/KmmckatAcetylCoAMAT)*(1+CoAMAT/KmmckatCoAMAT+AcetylCoAMAT/KmmckatAcetylCoAMAT))
KmmtpC14EnoylCoAMAT = 25.0 uM; KmmtpC8EnoylCoAMAT = 25.0 uM; KmmtpC16AcylCoAMAT = 13.83 uM; KmmtpCoAMAT = 30.0 uM; KmmtpC16EnoylCoAMAT = 25.0 uM; KmmtpC12EnoylCoAMAT = 25.0 uM; KmmtpAcetylCoAMAT = 30.0 uM; Vmtp = 2.84 uM per min per mgProtein; KmmtpC12AcylCoAMAT = 13.83 uM; KmmtpC8AcylCoAMAT = 13.83 uM; KmmtpC14AcylCoAMAT = 13.83 uM; KmmtpC6AcylCoAMAT = 13.83 uM; sfmtpC12=0.81 dimensionless; KmmtpC10EnoylCoAMAT = 25.0 uM; Keqmtp = 0.71 dimensionless; KicrotC4AcetoacylCoA = 1.6 uM; KmmtpNADMAT = 60.0 uM; KmmtpNADHMAT = 50.0 uM; KmmtpC10AcylCoAMAT = 13.83 uM Reaction: C12EnoylCoAMAT => C10AcylCoAMAT + AcetylCoAMAT + NADHMAT; C16EnoylCoAMAT, C14EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, NADtMAT, CoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcetoacylCoAMAT, C12EnoylCoAMAT, C16EnoylCoAMAT, C14EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, NADtMAT, CoAMAT, C10AcylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, NADHMAT, AcetylCoAMAT, C4AcetoacylCoAMAT, Rate Law: sfmtpC12*Vmtp*(C12EnoylCoAMAT*(NADtMAT-NADHMAT)*CoAMAT/(KmmtpC12EnoylCoAMAT*KmmtpNADMAT*KmmtpCoAMAT)-C10AcylCoAMAT*NADHMAT*AcetylCoAMAT/(KmmtpC12EnoylCoAMAT*KmmtpNADMAT*KmmtpCoAMAT*Keqmtp))/((1+C12EnoylCoAMAT/KmmtpC12EnoylCoAMAT+C10AcylCoAMAT/KmmtpC10AcylCoAMAT+C16EnoylCoAMAT/KmmtpC16EnoylCoAMAT+C16AcylCoAMAT/KmmtpC16AcylCoAMAT+C14EnoylCoAMAT/KmmtpC14EnoylCoAMAT+C14AcylCoAMAT/KmmtpC14AcylCoAMAT+C10EnoylCoAMAT/KmmtpC10EnoylCoAMAT+C12AcylCoAMAT/KmmtpC12AcylCoAMAT+C8EnoylCoAMAT/KmmtpC8EnoylCoAMAT+C8AcylCoAMAT/KmmtpC8AcylCoAMAT+C6AcylCoAMAT/KmmtpC6AcylCoAMAT+C4AcetoacylCoAMAT/KicrotC4AcetoacylCoA)*(1+(NADtMAT-NADHMAT)/KmmtpNADMAT+NADHMAT/KmmtpNADHMAT)*(1+CoAMAT/KmmtpCoAMAT+AcetylCoAMAT/KmmtpAcetylCoAMAT))
Kmcpt2C14AcylCarMAT = 51.0 uM; Kmcpt2C10AcylCarMAT = 51.0 uM; Kmcpt2CoAMAT = 30.0 uM; Kmcpt2C6AcylCarMAT = 51.0 uM; Kmcpt2C14AcylCoAMAT = 38.0 uM; sfcpt2C14=1.0 dimensionless; Keqcpt2 = 2.22 dimensionless; Kmcpt2C8AcylCoAMAT = 38.0 uM; Kmcpt2C6AcylCoAMAT = 1000.0 uM; Kmcpt2C16AcylCarMAT = 51.0 uM; Kmcpt2C12AcylCarMAT = 51.0 uM; Kmcpt2C12AcylCoAMAT = 38.0 uM; Vcpt2 = 0.391 uM per min per mgProtein; Kmcpt2C8AcylCarMAT = 51.0 uM; Kmcpt2CarMAT = 350.0 uM; Kmcpt2C16AcylCoAMAT = 38.0 uM; Kmcpt2C4AcylCoAMAT = 1000000.0 uM; Kmcpt2C10AcylCoAMAT = 38.0 uM Reaction: C14AcylCarMAT => C14AcylCoAMAT; C16AcylCarMAT, C12AcylCarMAT, C10AcylCarMAT, C8AcylCarMAT, C6AcylCarMAT, C4AcylCarMAT, CoAMAT, C16AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, CarMAT, C14AcylCarMAT, C16AcylCarMAT, C12AcylCarMAT, C10AcylCarMAT, C8AcylCarMAT, C6AcylCarMAT, C4AcylCarMAT, CoAMAT, C14AcylCoAMAT, C16AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, CarMAT, Rate Law: sfcpt2C14*Vcpt2*(C14AcylCarMAT*CoAMAT/(Kmcpt2C14AcylCarMAT*Kmcpt2CoAMAT)-C14AcylCoAMAT*CarMAT/(Kmcpt2C14AcylCarMAT*Kmcpt2CoAMAT*Keqcpt2))/((1+C14AcylCarMAT/Kmcpt2C14AcylCarMAT+C14AcylCoAMAT/Kmcpt2C14AcylCoAMAT+C16AcylCarMAT/Kmcpt2C16AcylCarMAT+C16AcylCoAMAT/Kmcpt2C16AcylCoAMAT+C12AcylCarMAT/Kmcpt2C12AcylCarMAT+C12AcylCoAMAT/Kmcpt2C12AcylCoAMAT+C10AcylCarMAT/Kmcpt2C10AcylCarMAT+C10AcylCoAMAT/Kmcpt2C10AcylCoAMAT+C8AcylCarMAT/Kmcpt2C8AcylCarMAT+C8AcylCoAMAT/Kmcpt2C8AcylCoAMAT+C6AcylCarMAT/Kmcpt2C6AcylCarMAT+C6AcylCoAMAT/Kmcpt2C6AcylCoAMAT+C4AcylCarMAT/Kmcpt2C4AcylCoAMAT+C4AcylCoAMAT/Kmcpt2C4AcylCoAMAT)*(1+CoAMAT/Kmcpt2CoAMAT+CarMAT/Kmcpt2CarMAT))
KmmckatC14AcylCoAMAT = 13.83 uM; KmmckatC10KetoacylCoAMAT = 2.1 uM; KmmckatCoAMAT = 26.6 uM; KmmckatC6KetoacylCoAMAT = 6.7 uM; KmmckatC12KetoacylCoAMAT = 1.3 uM; Keqmckat = 1051.0 dimensionless; KmmckatC8KetoacylCoAMAT = 3.2 uM; KmmckatC4AcetoacylCoAMAT = 12.4 uM; KmmckatC16KetoacylCoAMAT = 1.1 uM; KmmckatC12AcylCoAMAT = 13.83 uM; sfmckatC12=0.38 dimensionless; KmmckatC8AcylCoAMAT = 13.83 uM; KmmckatC10AcylCoAMAT = 13.83 uM; KmmckatC6AcylCoAMAT = 13.83 uM; Vmckat = 0.377 uM per min per mgProtein; KmmckatC16AcylCoAMAT = 13.83 uM; KmmckatC14KetoacylCoAMAT = 1.2 uM; KmmckatC4AcylCoAMAT = 13.83 uM; KmmckatAcetylCoAMAT = 30.0 uM Reaction: C12KetoacylCoAMAT => C10AcylCoAMAT + AcetylCoAMAT; C16KetoacylCoAMAT, C14KetoacylCoAMAT, C10KetoacylCoAMAT, C8KetoacylCoAMAT, C6KetoacylCoAMAT, C4AcetoacylCoAMAT, CoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, C12KetoacylCoAMAT, C16KetoacylCoAMAT, C14KetoacylCoAMAT, C10KetoacylCoAMAT, C8KetoacylCoAMAT, C6KetoacylCoAMAT, C4AcetoacylCoAMAT, CoAMAT, C10AcylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, AcetylCoAMAT, Rate Law: sfmckatC12*Vmckat*(C12KetoacylCoAMAT*CoAMAT/(KmmckatC12KetoacylCoAMAT*KmmckatCoAMAT)-C10AcylCoAMAT*AcetylCoAMAT/(KmmckatC12KetoacylCoAMAT*KmmckatCoAMAT*Keqmckat))/((1+C12KetoacylCoAMAT/KmmckatC12KetoacylCoAMAT+C10AcylCoAMAT/KmmckatC10AcylCoAMAT+C16KetoacylCoAMAT/KmmckatC16KetoacylCoAMAT+C16AcylCoAMAT/KmmckatC16AcylCoAMAT+C14KetoacylCoAMAT/KmmckatC14KetoacylCoAMAT+C14AcylCoAMAT/KmmckatC14AcylCoAMAT+C10KetoacylCoAMAT/KmmckatC10KetoacylCoAMAT+C12AcylCoAMAT/KmmckatC12AcylCoAMAT+C8KetoacylCoAMAT/KmmckatC8KetoacylCoAMAT+C8AcylCoAMAT/KmmckatC8AcylCoAMAT+C6KetoacylCoAMAT/KmmckatC6KetoacylCoAMAT+C6AcylCoAMAT/KmmckatC6AcylCoAMAT+C4AcetoacylCoAMAT/KmmckatC4AcetoacylCoAMAT+C4AcylCoAMAT/KmmckatC4AcylCoAMAT+AcetylCoAMAT/KmmckatAcetylCoAMAT)*(1+CoAMAT/KmmckatCoAMAT+AcetylCoAMAT/KmmckatAcetylCoAMAT))
KmmschadC16KetoacylCoAMAT = 1.4 uM; Keqmschad = 2.17E-4 dimensionless; KmmschadC16HydroxyacylCoAMAT = 1.5 uM; KmmschadC14HydroxyacylCoAMAT = 1.8 uM; sfmschadC6=1.0 dimensionless; Vmschad = 1.0 uM per min per mgProtein; KmmschadC6HydroxyacylCoAMAT = 28.6 uM; KmmschadC12KetoacylCoAMAT = 1.6 uM; KmmschadC4HydroxyacylCoAMAT = 69.9 uM; KmmschadC14KetoacylCoAMAT = 1.4 uM; KmmschadC12HydroxyacylCoAMAT = 3.7 uM; KmmschadC6KetoacylCoAMAT = 5.8 uM; KmmschadNADMAT = 58.5 uM; KmmschadC10HydroxyacylCoAMAT = 8.8 uM; KmmschadC8HydroxyacylCoAMAT = 16.3 uM; KmmschadC4AcetoacylCoAMAT = 16.9 uM; KmmschadC10KetoacylCoAMAT = 2.3 uM; KmmschadNADHMAT = 5.4 uM; KmmschadC8KetoacylCoAMAT = 4.1 uM Reaction: C6HydroxyacylCoAMAT => C6KetoacylCoAMAT + NADHMAT; C16HydroxyacylCoAMAT, C14HydroxyacylCoAMAT, C12HydroxyacylCoAMAT, C10HydroxyacylCoAMAT, C8HydroxyacylCoAMAT, C4HydroxyacylCoAMAT, NADtMAT, C16KetoacylCoAMAT, C14KetoacylCoAMAT, C12KetoacylCoAMAT, C10KetoacylCoAMAT, C8KetoacylCoAMAT, C4AcetoacylCoAMAT, C6HydroxyacylCoAMAT, C16HydroxyacylCoAMAT, C14HydroxyacylCoAMAT, C12HydroxyacylCoAMAT, C10HydroxyacylCoAMAT, C8HydroxyacylCoAMAT, C4HydroxyacylCoAMAT, NADtMAT, C6KetoacylCoAMAT, C16KetoacylCoAMAT, C14KetoacylCoAMAT, C12KetoacylCoAMAT, C10KetoacylCoAMAT, C8KetoacylCoAMAT, C4AcetoacylCoAMAT, NADHMAT, Rate Law: sfmschadC6*Vmschad*(C6HydroxyacylCoAMAT*(NADtMAT-NADHMAT)/(KmmschadC6HydroxyacylCoAMAT*KmmschadNADMAT)-C6KetoacylCoAMAT*NADHMAT/(KmmschadC6HydroxyacylCoAMAT*KmmschadNADMAT*Keqmschad))/((1+C6HydroxyacylCoAMAT/KmmschadC6HydroxyacylCoAMAT+C6KetoacylCoAMAT/KmmschadC6KetoacylCoAMAT+C16HydroxyacylCoAMAT/KmmschadC16HydroxyacylCoAMAT+C16KetoacylCoAMAT/KmmschadC16KetoacylCoAMAT+C14HydroxyacylCoAMAT/KmmschadC14HydroxyacylCoAMAT+C14KetoacylCoAMAT/KmmschadC14KetoacylCoAMAT+C12HydroxyacylCoAMAT/KmmschadC12HydroxyacylCoAMAT+C12KetoacylCoAMAT/KmmschadC12KetoacylCoAMAT+C10HydroxyacylCoAMAT/KmmschadC10HydroxyacylCoAMAT+C10KetoacylCoAMAT/KmmschadC10KetoacylCoAMAT+C8HydroxyacylCoAMAT/KmmschadC8HydroxyacylCoAMAT+C8KetoacylCoAMAT/KmmschadC8KetoacylCoAMAT+C4HydroxyacylCoAMAT/KmmschadC4HydroxyacylCoAMAT+C4AcetoacylCoAMAT/KmmschadC4AcetoacylCoAMAT)*(1+(NADtMAT-NADHMAT)/KmmschadNADMAT+NADHMAT/KmmschadNADHMAT))
KmscadFAD = 0.12 uM; KmscadC4EnoylCoAMAT = 1.08 uM; Vscad = 0.081 uM per min per mgProtein; KmscadFADH = 24.2 uM; KmscadC4AcylCoAMAT = 10.7 uM; KmscadC6AcylCoAMAT = 285.0 uM; KmscadC6EnoylCoAMAT = 1.08 uM; sfscadC6=0.3 dimensionless; Keqscad = 6.0 dimensionless Reaction: C6AcylCoAMAT => C6EnoylCoAMAT + FADHMAT; C4AcylCoAMAT, FADtMAT, C4EnoylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, FADtMAT, C6EnoylCoAMAT, C4EnoylCoAMAT, FADHMAT, Rate Law: sfscadC6*Vscad*(C6AcylCoAMAT*(FADtMAT-FADHMAT)/(KmscadC6AcylCoAMAT*KmscadFAD)-C6EnoylCoAMAT*FADHMAT/(KmscadC6AcylCoAMAT*KmscadFAD*Keqscad))/((1+C6AcylCoAMAT/KmscadC6AcylCoAMAT+C6EnoylCoAMAT/KmscadC6EnoylCoAMAT+C4AcylCoAMAT/KmscadC4AcylCoAMAT+C4EnoylCoAMAT/KmscadC4EnoylCoAMAT)*(1+(FADtMAT-FADHMAT)/KmscadFAD+FADHMAT/KmscadFADH))
KmmschadC16KetoacylCoAMAT = 1.4 uM; Keqmschad = 2.17E-4 dimensionless; KmmschadC16HydroxyacylCoAMAT = 1.5 uM; KmmschadC14HydroxyacylCoAMAT = 1.8 uM; Vmschad = 1.0 uM per min per mgProtein; KmmschadC6HydroxyacylCoAMAT = 28.6 uM; KmmschadC12KetoacylCoAMAT = 1.6 uM; KmmschadC4HydroxyacylCoAMAT = 69.9 uM; KmmschadC14KetoacylCoAMAT = 1.4 uM; KmmschadC12HydroxyacylCoAMAT = 3.7 uM; KmmschadC6KetoacylCoAMAT = 5.8 uM; KmmschadNADMAT = 58.5 uM; KmmschadC10HydroxyacylCoAMAT = 8.8 uM; KmmschadC8HydroxyacylCoAMAT = 16.3 uM; KmmschadC4AcetoacylCoAMAT = 16.9 uM; KmmschadC10KetoacylCoAMAT = 2.3 uM; KmmschadNADHMAT = 5.4 uM; sfmschadC8=0.89 dimensionless; KmmschadC8KetoacylCoAMAT = 4.1 uM Reaction: C8HydroxyacylCoAMAT => C8KetoacylCoAMAT + NADHMAT; C16HydroxyacylCoAMAT, C14HydroxyacylCoAMAT, C12HydroxyacylCoAMAT, C10HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, C4HydroxyacylCoAMAT, NADtMAT, C16KetoacylCoAMAT, C14KetoacylCoAMAT, C12KetoacylCoAMAT, C10KetoacylCoAMAT, C6KetoacylCoAMAT, C4AcetoacylCoAMAT, C8HydroxyacylCoAMAT, C16HydroxyacylCoAMAT, C14HydroxyacylCoAMAT, C12HydroxyacylCoAMAT, C10HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, C4HydroxyacylCoAMAT, NADtMAT, C8KetoacylCoAMAT, C16KetoacylCoAMAT, C14KetoacylCoAMAT, C12KetoacylCoAMAT, C10KetoacylCoAMAT, C6KetoacylCoAMAT, C4AcetoacylCoAMAT, NADHMAT, Rate Law: sfmschadC8*Vmschad*(C8HydroxyacylCoAMAT*(NADtMAT-NADHMAT)/(KmmschadC8HydroxyacylCoAMAT*KmmschadNADMAT)-C8KetoacylCoAMAT*NADHMAT/(KmmschadC8HydroxyacylCoAMAT*KmmschadNADMAT*Keqmschad))/((1+C8HydroxyacylCoAMAT/KmmschadC8HydroxyacylCoAMAT+C8KetoacylCoAMAT/KmmschadC8KetoacylCoAMAT+C16HydroxyacylCoAMAT/KmmschadC16HydroxyacylCoAMAT+C16KetoacylCoAMAT/KmmschadC16KetoacylCoAMAT+C14HydroxyacylCoAMAT/KmmschadC14HydroxyacylCoAMAT+C14KetoacylCoAMAT/KmmschadC14KetoacylCoAMAT+C12HydroxyacylCoAMAT/KmmschadC12HydroxyacylCoAMAT+C12KetoacylCoAMAT/KmmschadC12KetoacylCoAMAT+C10HydroxyacylCoAMAT/KmmschadC10HydroxyacylCoAMAT+C10KetoacylCoAMAT/KmmschadC10KetoacylCoAMAT+C6HydroxyacylCoAMAT/KmmschadC6HydroxyacylCoAMAT+C6KetoacylCoAMAT/KmmschadC6KetoacylCoAMAT+C4HydroxyacylCoAMAT/KmmschadC4HydroxyacylCoAMAT+C4AcetoacylCoAMAT/KmmschadC4AcetoacylCoAMAT)*(1+(NADtMAT-NADHMAT)/KmmschadNADMAT+NADHMAT/KmmschadNADHMAT))
KmcrotC16EnoylCoAMAT = 150.0 uM; KmcrotC12EnoylCoAMAT = 25.0 uM; KicrotC4AcetoacylCoA = 1.6 uM; KmcrotC4EnoylCoAMAT = 40.0 uM; Vcrot = 3.6 uM per min per mgProtein; Keqcrot = 3.13 dimensionless; KmcrotC14EnoylCoAMAT = 100.0 uM; KmcrotC8EnoylCoAMAT = 25.0 uM; KmcrotC10HydroxyacylCoAMAT = 45.0 uM; KmcrotC8HydroxyacylCoAMAT = 45.0 uM; KmcrotC6EnoylCoAMAT = 25.0 uM; KmcrotC10EnoylCoAMAT = 25.0 uM; sfcrotC10=0.33 dimensionless; KmcrotC14HydroxyacylCoAMAT = 45.0 uM; KmcrotC4HydroxyacylCoAMAT = 45.0 uM; KmcrotC16HydroxyacylCoAMAT = 45.0 uM; KmcrotC12HydroxyacylCoAMAT = 45.0 uM; KmcrotC6HydroxyacylCoAMAT = 45.0 uM Reaction: C10EnoylCoAMAT => C10HydroxyacylCoAMAT; C16EnoylCoAMAT, C14EnoylCoAMAT, C12EnoylCoAMAT, C8EnoylCoAMAT, C6EnoylCoAMAT, C4EnoylCoAMAT, C16HydroxyacylCoAMAT, C14HydroxyacylCoAMAT, C12HydroxyacylCoAMAT, C8HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, C4HydroxyacylCoAMAT, C4AcetoacylCoAMAT, C10EnoylCoAMAT, C16EnoylCoAMAT, C14EnoylCoAMAT, C12EnoylCoAMAT, C8EnoylCoAMAT, C6EnoylCoAMAT, C4EnoylCoAMAT, C10HydroxyacylCoAMAT, C16HydroxyacylCoAMAT, C14HydroxyacylCoAMAT, C12HydroxyacylCoAMAT, C8HydroxyacylCoAMAT, C6HydroxyacylCoAMAT, C4HydroxyacylCoAMAT, C4AcetoacylCoAMAT, Rate Law: sfcrotC10*Vcrot*(C10EnoylCoAMAT/KmcrotC10EnoylCoAMAT-C10HydroxyacylCoAMAT/(KmcrotC10EnoylCoAMAT*Keqcrot))/(1+C10EnoylCoAMAT/KmcrotC10EnoylCoAMAT+C10HydroxyacylCoAMAT/KmcrotC10HydroxyacylCoAMAT+C16EnoylCoAMAT/KmcrotC16EnoylCoAMAT+C16HydroxyacylCoAMAT/KmcrotC16HydroxyacylCoAMAT+C14EnoylCoAMAT/KmcrotC14EnoylCoAMAT+C14HydroxyacylCoAMAT/KmcrotC14HydroxyacylCoAMAT+C12EnoylCoAMAT/KmcrotC12EnoylCoAMAT+C12HydroxyacylCoAMAT/KmcrotC12HydroxyacylCoAMAT+C8EnoylCoAMAT/KmcrotC8EnoylCoAMAT+C8HydroxyacylCoAMAT/KmcrotC8HydroxyacylCoAMAT+C6EnoylCoAMAT/KmcrotC6EnoylCoAMAT+C6HydroxyacylCoAMAT/KmcrotC6HydroxyacylCoAMAT+C4EnoylCoAMAT/KmcrotC4EnoylCoAMAT+C4HydroxyacylCoAMAT/KmcrotC4HydroxyacylCoAMAT+C4AcetoacylCoAMAT/KicrotC4AcetoacylCoA)
Kmcpt2C14AcylCarMAT = 51.0 uM; Kmcpt2C10AcylCarMAT = 51.0 uM; Kmcpt2CoAMAT = 30.0 uM; Kmcpt2C6AcylCarMAT = 51.0 uM; Kmcpt2C14AcylCoAMAT = 38.0 uM; Keqcpt2 = 2.22 dimensionless; sfcpt2C16=0.85 dimensionless; Kmcpt2C8AcylCoAMAT = 38.0 uM; Kmcpt2C6AcylCoAMAT = 1000.0 uM; Kmcpt2C16AcylCarMAT = 51.0 uM; Kmcpt2C12AcylCarMAT = 51.0 uM; Kmcpt2C12AcylCoAMAT = 38.0 uM; Vcpt2 = 0.391 uM per min per mgProtein; Kmcpt2C8AcylCarMAT = 51.0 uM; Kmcpt2CarMAT = 350.0 uM; Kmcpt2C16AcylCoAMAT = 38.0 uM; Kmcpt2C4AcylCoAMAT = 1000000.0 uM; Kmcpt2C10AcylCoAMAT = 38.0 uM; Kmcpt2C4AcylCarMAT = 51.0 uM Reaction: C16AcylCarMAT => C16AcylCoAMAT; C14AcylCarMAT, C12AcylCarMAT, C10AcylCarMAT, C8AcylCarMAT, C6AcylCarMAT, C4AcylCarMAT, CoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, CarMAT, C16AcylCarMAT, C14AcylCarMAT, C12AcylCarMAT, C10AcylCarMAT, C8AcylCarMAT, C6AcylCarMAT, C4AcylCarMAT, CoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, CarMAT, Rate Law: sfcpt2C16*Vcpt2*(C16AcylCarMAT*CoAMAT/(Kmcpt2C16AcylCarMAT*Kmcpt2CoAMAT)-C16AcylCoAMAT*CarMAT/(Kmcpt2C16AcylCarMAT*Kmcpt2CoAMAT*Keqcpt2))/((1+C16AcylCarMAT/Kmcpt2C16AcylCarMAT+C16AcylCoAMAT/Kmcpt2C16AcylCoAMAT+C14AcylCarMAT/Kmcpt2C14AcylCarMAT+C14AcylCoAMAT/Kmcpt2C14AcylCoAMAT+C12AcylCarMAT/Kmcpt2C12AcylCarMAT+C12AcylCoAMAT/Kmcpt2C12AcylCoAMAT+C10AcylCarMAT/Kmcpt2C10AcylCarMAT+C10AcylCoAMAT/Kmcpt2C10AcylCoAMAT+C8AcylCarMAT/Kmcpt2C8AcylCarMAT+C8AcylCoAMAT/Kmcpt2C8AcylCoAMAT+C6AcylCarMAT/Kmcpt2C6AcylCarMAT+C6AcylCoAMAT/Kmcpt2C6AcylCoAMAT+C4AcylCarMAT/Kmcpt2C4AcylCarMAT+C4AcylCoAMAT/Kmcpt2C4AcylCoAMAT)*(1+CoAMAT/Kmcpt2CoAMAT+CarMAT/Kmcpt2CarMAT))
KmmtpC14EnoylCoAMAT = 25.0 uM; KmmtpC8EnoylCoAMAT = 25.0 uM; KmmtpC16AcylCoAMAT = 13.83 uM; KmmtpCoAMAT = 30.0 uM; KmmtpC16EnoylCoAMAT = 25.0 uM; KmmtpC12EnoylCoAMAT = 25.0 uM; KmmtpAcetylCoAMAT = 30.0 uM; Vmtp = 2.84 uM per min per mgProtein; KmmtpC12AcylCoAMAT = 13.83 uM; KmmtpC8AcylCoAMAT = 13.83 uM; KmmtpC14AcylCoAMAT = 13.83 uM; KmmtpC6AcylCoAMAT = 13.83 uM; KmmtpC10EnoylCoAMAT = 25.0 uM; Keqmtp = 0.71 dimensionless; KicrotC4AcetoacylCoA = 1.6 uM; KmmtpNADMAT = 60.0 uM; KmmtpNADHMAT = 50.0 uM; KmmtpC10AcylCoAMAT = 13.83 uM; sfmtpC16=1.0 dimensionless Reaction: C16EnoylCoAMAT => C14AcylCoAMAT + AcetylCoAMAT + NADHMAT; C14EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, NADtMAT, CoAMAT, C16AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcetoacylCoAMAT, C16EnoylCoAMAT, C14EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, NADtMAT, CoAMAT, C14AcylCoAMAT, C16AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, NADHMAT, AcetylCoAMAT, C4AcetoacylCoAMAT, Rate Law: sfmtpC16*Vmtp*(C16EnoylCoAMAT*(NADtMAT-NADHMAT)*CoAMAT/(KmmtpC16EnoylCoAMAT*KmmtpNADMAT*KmmtpCoAMAT)-C14AcylCoAMAT*NADHMAT*AcetylCoAMAT/(KmmtpC16EnoylCoAMAT*KmmtpNADMAT*KmmtpCoAMAT*Keqmtp))/((1+C16EnoylCoAMAT/KmmtpC16EnoylCoAMAT+C14AcylCoAMAT/KmmtpC14AcylCoAMAT+C14EnoylCoAMAT/KmmtpC14EnoylCoAMAT+C16AcylCoAMAT/KmmtpC16AcylCoAMAT+C12EnoylCoAMAT/KmmtpC12EnoylCoAMAT+C12AcylCoAMAT/KmmtpC12AcylCoAMAT+C10EnoylCoAMAT/KmmtpC10EnoylCoAMAT+C10AcylCoAMAT/KmmtpC10AcylCoAMAT+C8EnoylCoAMAT/KmmtpC8EnoylCoAMAT+C8AcylCoAMAT/KmmtpC8AcylCoAMAT+C6AcylCoAMAT/KmmtpC6AcylCoAMAT+C4AcetoacylCoAMAT/KicrotC4AcetoacylCoA)*(1+(NADtMAT-NADHMAT)/KmmtpNADMAT+NADHMAT/KmmtpNADHMAT)*(1+CoAMAT/KmmtpCoAMAT+AcetylCoAMAT/KmmtpAcetylCoAMAT))

States:

Name Description
C12AcylCarCYT [O-acylcarnitine]
C8HydroxyacylCoAMAT [hydroxy fatty acyl-CoA]
C14KetoacylCoAMAT [fatty acyl-CoA]
C14EnoylCoAMAT [cis-2-enoyl-CoA]
C10EnoylCoAMAT [cis-2-enoyl-CoA]
C16AcylCarCYT [O-acylcarnitine]
C12AcylCoAMAT [fatty acyl-CoA]
C8KetoacylCoAMAT [fatty acyl-CoA]
C4HydroxyacylCoAMAT [hydroxy fatty acyl-CoA]
C14HydroxyacylCoAMAT [hydroxy fatty acyl-CoA]
C6AcylCoAMAT [fatty acyl-CoA]
C6AcylCarMAT [O-acylcarnitine]
C8AcylCoAMAT [fatty acyl-CoA]
C16AcylCarMAT [O-acylcarnitine]
C16AcylCoACYT [palmitoyl-CoA; fatty acyl-CoA]
NADHMAT [NADH]
C8AcylCarCYT [O-acylcarnitine]
C10KetoacylCoAMAT [fatty acyl-CoA]
C6AcylCarCYT [O-acylcarnitine]
C16EnoylCoAMAT [cis-2-enoyl-CoA]
C6HydroxyacylCoAMAT [hydroxy fatty acyl-CoA]
C10AcylCoAMAT [fatty acyl-CoA]
C16HydroxyacylCoAMAT [hydroxy fatty acyl-CoA]
FADHMAT [FADH(.)]
C14AcylCoAMAT [myristoyl-CoA; fatty acyl-CoA]
AcetylCoAMAT [acetyl-CoA]
C6KetoacylCoAMAT [fatty acyl-CoA]
C16AcylCoAMAT [fatty acyl-CoA; palmitoyl-CoA]
CoAMAT [coenzyme A]
C6EnoylCoAMAT [cis-2-enoyl-CoA]
C12EnoylCoAMAT [cis-2-enoyl-CoA]
C10HydroxyacylCoAMAT [hydroxy fatty acyl-CoA]
C12AcylCarMAT [O-acylcarnitine]
C8AcylCarMAT [O-acylcarnitine]
C4AcetoacylCoAMAT [fatty acyl-CoA]
C8EnoylCoAMAT [cis-2-enoyl-CoA]

Observables: none

Thalassiosira pseudonana CCMP 1335, acclimated to 200 umol photons m-2 s-1 (iTps1432_HL). doi: https://doi.org/10.1101/…

Diatoms are unicellular photosynthetic algae known to secrete organic matter that fuels secondary production in the ocean, though our knowledge of how their physiology impacts the character of dissolved organic matter remains limited. Like all photosynthetic organisms, their use of light for energy and reducing power creates the challenge of avoiding cellular damage. To better understand the interplay between redox balance and organic matter secretion, we reconstructed a genome-scale metabolic model of Thalassiosira pseudonana strain CCMP 1335, a model for diatom molecular biology and physiology, with a 60-year history of studies. The model simulates the metabolic activities of 1,432 genes via a network of 2,792 metabolites produced through 6,079 reactions distributed across six subcellular compartments. Growth was simulated under different steady-state light conditions (5-200 µmol photons m-2 s-1) and in a batch culture progressing from exponential growth to nitrate-limitation and nitrogen-starvation. We used the model to examine the dissipation of reductants generated through light-dependent processes and found that when available, nitrate assimilation is an important means of dissipating reductants in the plastid; under nitrate-limiting conditions, sulfate assimilation plays a similar role. The use of either nitrate or sulfate uptake to balance redox reactions leads to the secretion of distinct organic nitrogen and sulfur compounds. Such compounds can be accessed by bacteria in the surface ocean. The model of the diatom Thalassiosira pseudonana provides a mechanistic explanation for the production of ecologically and climatologically relevant compounds that may serve as the basis for intricate, cross-kingdom microbial networks. Diatom metabolism has an important influence on global biogeochemistry; metabolic models of marine microorganisms link genes to ecosystems and may be key to integrating molecular data with models of ocean biogeochemistry. link: http://identifiers.org/doi/10.1101/2020.10.26.355578

Parameters: none

States: none

Observables: none

Thalassiosira pseudonana CCMP 1335, acclimated to 5 umol photons m-2 s-1 (iTps1432_LL). doi: https://doi.org/10.1101/20…

Diatoms are unicellular photosynthetic algae known to secrete organic matter that fuels secondary production in the ocean, though our knowledge of how their physiology impacts the character of dissolved organic matter remains limited. Like all photosynthetic organisms, their use of light for energy and reducing power creates the challenge of avoiding cellular damage. To better understand the interplay between redox balance and organic matter secretion, we reconstructed a genome-scale metabolic model of Thalassiosira pseudonana strain CCMP 1335, a model for diatom molecular biology and physiology, with a 60-year history of studies. The model simulates the metabolic activities of 1,432 genes via a network of 2,792 metabolites produced through 6,079 reactions distributed across six subcellular compartments. Growth was simulated under different steady-state light conditions (5-200 µmol photons m-2 s-1) and in a batch culture progressing from exponential growth to nitrate-limitation and nitrogen-starvation. We used the model to examine the dissipation of reductants generated through light-dependent processes and found that when available, nitrate assimilation is an important means of dissipating reductants in the plastid; under nitrate-limiting conditions, sulfate assimilation plays a similar role. The use of either nitrate or sulfate uptake to balance redox reactions leads to the secretion of distinct organic nitrogen and sulfur compounds. Such compounds can be accessed by bacteria in the surface ocean. The model of the diatom Thalassiosira pseudonana provides a mechanistic explanation for the production of ecologically and climatologically relevant compounds that may serve as the basis for intricate, cross-kingdom microbial networks. Diatom metabolism has an important influence on global biogeochemistry; metabolic models of marine microorganisms link genes to ecosystems and may be key to integrating molecular data with models of ocean biogeochemistry. link: http://identifiers.org/doi/10.1101/2020.10.26.355578

Parameters: none

States: none

Observables: none

Thalassiosira pseudonana CCMP 1335, acclimated to 60 umol photons m-2 s-1 (iTps1432_ML). doi: https://doi.org/10.1101/2…

Diatoms are unicellular photosynthetic algae known to secrete organic matter that fuels secondary production in the ocean, though our knowledge of how their physiology impacts the character of dissolved organic matter remains limited. Like all photosynthetic organisms, their use of light for energy and reducing power creates the challenge of avoiding cellular damage. To better understand the interplay between redox balance and organic matter secretion, we reconstructed a genome-scale metabolic model of Thalassiosira pseudonana strain CCMP 1335, a model for diatom molecular biology and physiology, with a 60-year history of studies. The model simulates the metabolic activities of 1,432 genes via a network of 2,792 metabolites produced through 6,079 reactions distributed across six subcellular compartments. Growth was simulated under different steady-state light conditions (5-200 µmol photons m-2 s-1) and in a batch culture progressing from exponential growth to nitrate-limitation and nitrogen-starvation. We used the model to examine the dissipation of reductants generated through light-dependent processes and found that when available, nitrate assimilation is an important means of dissipating reductants in the plastid; under nitrate-limiting conditions, sulfate assimilation plays a similar role. The use of either nitrate or sulfate uptake to balance redox reactions leads to the secretion of distinct organic nitrogen and sulfur compounds. Such compounds can be accessed by bacteria in the surface ocean. The model of the diatom Thalassiosira pseudonana provides a mechanistic explanation for the production of ecologically and climatologically relevant compounds that may serve as the basis for intricate, cross-kingdom microbial networks. Diatom metabolism has an important influence on global biogeochemistry; metabolic models of marine microorganisms link genes to ecosystems and may be key to integrating molecular data with models of ocean biogeochemistry. link: http://identifiers.org/doi/10.1101/2020.10.26.355578

Parameters: none

States: none

Observables: none

This is a mathematical describing the effect that DEP domain-containing mTOR-interacting protein (DEPTOR) has on the mam…

The mechanistic Target of Rapamycin (mTOR) signalling network is an evolutionarily conserved network that controls key cellular processes, including cell growth and metabolism. Consisting of the major kinase complexes mTOR Complex 1 and 2 (mTORC1/2), the mTOR network harbours complex interactions and feedback loops. The DEP domain-containing mTOR-interacting protein (DEPTOR) was recently identified as an endogenous inhibitor of both mTORC1 and 2 through direct interactions, and is in turn degraded by mTORC1/2, adding an extra layer of complexity to the mTOR network. Yet, the dynamic properties of the DEPTOR-mTOR network and the roles of DEPTOR in coordinating mTORC1/2 activation dynamics have not been characterised. Using computational modelling, systems analysis and dynamic simulations we show that DEPTOR confers remarkably rich and complex dynamic behaviours to mTOR signalling, including abrupt, bistable switches, oscillations and co-existing bistable/oscillatory responses. Transitions between these distinct modes of behaviour are enabled by modulating DEPTOR expression alone. We characterise the governing conditions for the observed dynamics by elucidating the network in its vast multi-dimensional parameter space, and develop strategies to identify core network design motifs underlying these dynamics. Our findings provide new systems-level insights into the complexity of mTOR signalling contributed by DEPTOR. link: http://identifiers.org/pubmed/29330362

Parameters:

Name Description
Km16 = 50.0; V16 = 1.0 Reaction: iIRS => IRS, Rate Law: compartment*V16*iIRS/(Km16+iIRS)
kd18 = 0.0 Reaction: pDEPTOR =>, Rate Law: compartment*kd18*pDEPTOR
Km8 = 1.0; V8 = 6.0 Reaction: pmTORC1 => mTORC1, Rate Law: compartment*V8*pmTORC1/(Km8+pmTORC1)
ks17 = 0.0 Reaction: => DEPTOR, Rate Law: compartment*ks17
Km6 = 34.0; V6 = 2.0 Reaction: pAkt => Akt, Rate Law: compartment*V6*pAkt/(Km6+pAkt)
V4 = 1.0; Km4 = 50.0 Reaction: pIRS => IRS, Rate Law: compartment*V4*pIRS/(Km4+pIRS)
Km2 = 35.0; V2 = 1.0 Reaction: pIR => IR, Rate Law: compartment*V2*pIR/(Km2+pIR)
k9c = 0.3; Km9 = 160.0 Reaction: mTORC2 => pmTORC2; pIR, Rate Law: compartment*k9c*mTORC2*pIR/(Km9+mTORC2)
Km12 = 7.0; V12 = 4.0 Reaction: pDEPTOR => DEPTOR, Rate Law: compartment*V12*pDEPTOR/(Km12+pDEPTOR)
Km3 = 50.0; k3c = 0.1 Reaction: IRS => pIRS; pIR, Rate Law: compartment*k3c*IRS*pIR/(Km3+IRS)
V10 = 3.0; Km10 = 7.0 Reaction: pmTORC2 => mTORC2, Rate Law: compartment*V10*pmTORC2/(Km10+pmTORC2)
k7c = 0.1; Km7 = 2.0 Reaction: mTORC1 => pmTORC1; pAkt, Rate Law: compartment*k7c*mTORC1*pAkt/(Km7+mTORC1)
k13r = 0.006; k13f = 0.001 Reaction: mTORC1 + DEPTOR => mTORC1_DEPTOR, Rate Law: compartment*(k13f*mTORC1*DEPTOR-k13r*mTORC1_DEPTOR)
k15c = 0.1; Km15 = 50.0 Reaction: IRS => iIRS; pmTORC1, Rate Law: compartment*k15c*IRS*pmTORC1/(Km15+IRS)
k5ca = 0.05; k5cb = 1.5; Km5a = 7.0; Km5b = 4.0 Reaction: Akt => pAkt; pIRS, pmTORC2, Rate Law: compartment*(k5ca*pIRS*Akt/(Km5a+Akt)+k5cb*pmTORC2*Akt/(Km5b+Akt))
k14f = 0.007; k14r = 0.006 Reaction: mTORC2 + DEPTOR => mTORC2_DEPTOR, Rate Law: compartment*(k14f*mTORC2*DEPTOR-k14r*mTORC2_DEPTOR)
V1 = 1.0; Km1 = 95.0 Reaction: IR => pIR, Rate Law: compartment*V1*IR/(Km1+IR)
Km11b = 11.0; k11ca = 0.1; Km11a = 120.0; k11cb = 0.13 Reaction: DEPTOR => pDEPTOR; pmTORC1, pmTORC2, Rate Law: compartment*(k11ca*pmTORC1*DEPTOR/(Km11a+pDEPTOR)+k11cb*pmTORC2*DEPTOR/(Km11b+DEPTOR))

States:

Name Description
mTORC2 DEPTOR [DEP Domain-Containing mTOR-Interacting Protein; mTORC2]
mTORC2 [mTORC2]
IRS [Insulin Receptor Substrate 1]
Akt [AKT kinase]
pIR [insulin receptor]
pmTORC1 [mTORC1]
iIRS [Insulin Receptor Substrate 1]
mTORC1 [mTORC1]
pmTORC2 [mTORC2]
pIRS [Insulin Receptor Substrate 1]
pDEPTOR [DEP Domain-Containing mTOR-Interacting Protein]
pAkt [AKT kinase]
IR [insulin receptor]
DEPTOR [DEP Domain-Containing mTOR-Interacting Protein]
mTORC1 DEPTOR [mTORC1; DEP Domain-Containing mTOR-Interacting Protein]

Observables: none

BIOMD0000000246 @ v0.0.1

This the single cell model from the article: A multiscale model to investigate circadian rhythmicity of pacemaker neur…

The suprachiasmatic nucleus (SCN) of the hypothalamus is a multicellular system that drives daily rhythms in mammalian behavior and physiology. Although the gene regulatory network that produces daily oscillations within individual neurons is well characterized, less is known about the electrophysiology of the SCN cells and how firing rate correlates with circadian gene expression. We developed a firing rate code model to incorporate known electrophysiological properties of SCN pacemaker cells, including circadian dependent changes in membrane voltage and ion conductances. Calcium dynamics were included in the model as the putative link between electrical firing and gene expression. Individual ion currents exhibited oscillatory patterns matching experimental data both in current levels and phase relationships. VIP and GABA neurotransmitters, which encode synaptic signals across the SCN, were found to play critical roles in daily oscillations of membrane excitability and gene expression. Blocking various mechanisms of intracellular calcium accumulation by simulated pharmacological agents (nimodipine, IP3- and ryanodine-blockers) reproduced experimentally observed trends in firing rate dynamics and core-clock gene transcription. The intracellular calcium concentration was shown to regulate diverse circadian processes such as firing frequency, gene expression and system periodicity. The model predicted a direct relationship between firing frequency and gene expression amplitudes, demonstrated the importance of intracellular pathways for single cell behavior and provided a novel multiscale framework which captured characteristics of the SCN at both the electrophysiological and gene regulatory levels. link: http://identifiers.org/pubmed/20300645

Parameters:

Name Description
k1 = 0.45 per_h; k2 = 0.2 per_h Reaction: PC_C => PC_N, Rate Law: cytoplasm*k1*PC_C-nucleus*k2*PC_N
vP = 1.0 nM_per_h; v_K = NaN nM_per_h; K_2_CB = 0.01 nM; K_1_CB = 0.01 nM; WT = 1.0 dimensionless Reaction: => CB, Rate Law: cytoplasm*(v_K*(1-CB)/((K_1_CB+1)-CB)-vP*CB/(K_2_CB+CB))/WT
V1_C = 0.6 nM_per_h; K_dp = 0.1 nM; V2_C = 0.1 nM_per_h; K_p = 0.1 nM Reaction: C_C => C_CP, Rate Law: cytoplasm*(V1_C*C_C/(K_p+C_C)-V2_C*C_CP/(K_dp+C_CP))
k3 = 0.4 per_nM_per_h; k4 = 0.2 per_h Reaction: P_C + C_C => PC_C, Rate Law: cytoplasm*(k3*P_C*C_C-k4*PC_C)
v_mC = 1.0 nM_per_h; kd_mC = 0.01 per_h; K_mC = 0.4 nM Reaction: M_C =>, Rate Law: cytoplasm*(v_mC*M_C/(K_mC+M_C)+kd_mC*M_C)
Kd = 0.3 nM; v_dPC = 0.7 per_nM_per_h; kd_n = 0.01 per_h Reaction: P_CP =>, Rate Law: cytoplasm*(v_dPC*P_CP/(Kd+P_CP)+kd_n*P_CP)
Kd = 0.3 nM; v_dCC = 0.7 nM_per_h; kd_n = 0.01 per_h Reaction: C_CP =>, Rate Law: cytoplasm*(v_dCC*C_CP/(Kd+C_CP)+kd_n*C_CP)
m_BN = 2.0 dimensionless; K_IB = 2.2 nM; v_sB = 1.0 nM_per_h Reaction: => M_B; B_N, Rate Law: cytoplasm*v_sB*K_IB^m_BN/(K_IB^m_BN+B_N^m_BN)
n_BN = 4.0 dimensionless; v_sC = 1.1 nM_per_h; K_sC = 0.6 nM Reaction: => M_C; B_N, Rate Law: cytoplasm*v_sC*B_N^n_BN/(K_sC^n_BN+B_N^n_BN)
n_BN = 4.0 dimensionless; K_C = 0.15 nM; v_sP0 = 1.0 nM_per_h; K_AP = 0.6 nM; C_T = 1.6 nM_per_h Reaction: => M_P; CB, B_N, Rate Law: cytoplasm*(v_sP0+C_T*CB/(K_C+CB))*B_N^n_BN/(K_AP^n_BN+B_N^n_BN)
K_VIP = 15.0; v_VIP = 0.5 nM_per_h; n_VIP = 1.9 dimensionless; f_r = NaN Hz Reaction: => VIP, Rate Law: cytoplasm*v_VIP*f_r^n_VIP/(K_VIP+f_r^n_VIP)
K_dp = 0.1 nM; V2_P = 0.3 nM_per_h; K_p = 0.1 nM; V1_P = NaN nM_per_h Reaction: P_C => P_CP, Rate Law: cytoplasm*(V1_P*P_C/(K_p+P_C)-V2_P*P_CP/(K_dp+P_CP))
n_M3 = 6.0 dimensionless; p_A = 4.2 dimensionless; K_R_Ca = 3.0 uM; K_A = 0.67 uM; V_M3 = 400.0 uM_per_h Reaction: Ca_store => Ca_in, Rate Law: 1000*store*V_M3*Ca_store^n_M3/(K_R_Ca^n_M3+Ca_store^n_M3)*Ca_in^p_A/(K_A^p_A+Ca_in^p_A)
theta_Na = NaN milliVolt Reaction: Na_in = Na_ex/theta_Na, Rate Law: missing
kd_nc = 0.12 per_h Reaction: C_C =>, Rate Law: cytoplasm*kd_nc*C_C
k5 = 0.4; k6 = 0.2 Reaction: B_C => B_N, Rate Law: cytoplasm*k5*B_C-nucleus*k6*B_N
Kd = 0.3 nM; vd_IN = 0.8 nM_per_h; kd_n = 0.01 per_h Reaction: I_N =>, Rate Law: nucleus*(vd_IN*I_N/(Kd+I_N)+kd_n*I_N)
K_dp = 0.1 nM; V4_B = 0.2 nM_per_h; V3_B = 0.5 nM_per_h; K_p = 0.1 nM Reaction: B_N => B_NP, Rate Law: nucleus*(V3_B*B_N/(K_p+B_N)-V4_B*B_NP/(K_dp+B_NP))
v_kk = 3.3 per_uM_per_h; K_kk = 0.02 nM; n_kCa = 2.0 dimensionless; n_kk = 0.1 dimensionless Reaction: Ca_in => ; C_C, Rate Law: 1000*cytoplasm*v_kk*C_C^n_kk/(K_kk+C_C^n_kk)*Ca_in^n_kCa
K_vo = 4.5 nM; n_vo = 4.5 dimensionless; v_vo = 0.09 uM_per_h Reaction: => Ca_in; B_C, Rate Law: 1000*cytoplasm*v_vo*B_C^n_vo/(K_vo+B_C^n_vo)
k7 = 0.5 per_nM_per_h; k8 = 0.1 per_h Reaction: B_N + PC_N => I_N, Rate Law: cytoplasm*(k7*B_N*PC_N-k8*I_N)
Kd = 0.3 nM; vd_BC = 0.5 nM_per_h; kd_n = 0.01 per_h Reaction: B_CP =>, Rate Law: cytoplasm*(vd_BC*B_CP/(Kd+B_CP)+kd_n*B_CP)
theta_K = NaN milliVolt Reaction: K_in = K_ex/theta_K, Rate Law: missing
V_M2 = 149.5 uM_per_h; n_M2 = 2.2 dimensionless; K_2 = 5.0 uM Reaction: Ca_in => Ca_store, Rate Law: 1000*cytoplasm*V_M2*Ca_in^n_M2/(K_2^n_M2+Ca_in^n_M2)
v_mP = 1.1 nM_per_h; K_mP = 0.31 nM; kd_mP = 0.01 per_h Reaction: M_P =>, Rate Law: cytoplasm*(v_mP*M_P/(K_mP+M_P)+kd_mP*M_P)
Kd = 0.3 nM; vd_PCC = 0.7 nM_per_h; kd_n = 0.01 per_h Reaction: PC_CP =>, Rate Law: cytoplasm*(vd_PCC*PC_CP/(Kd+PC_CP)+kd_n*PC_CP)
V1_B = 0.5 nM_per_h; K_dp = 0.1 nM; V2_B = 0.1 nM_per_h; K_p = 0.1 nM Reaction: B_C => B_CP, Rate Law: cytoplasm*(V1_B*B_C/(K_p+B_C)-V2_B*B_CP/(K_dp+B_CP))
V3_PC = NaN nM_per_h; V4_PC = 0.1 nM_per_h; K_dp = 0.1 nM; K_p = 0.1 nM Reaction: PC_N => PC_NP, Rate Law: nucleus*(V3_PC*PC_N/(K_p+PC_N)-V4_PC*PC_NP/(K_dp+PC_NP))
kd_n = 0.01 per_h Reaction: B_N =>, Rate Law: nucleus*kd_n*B_N
kd_mB = 0.01 per_h; K_mB = 0.4 nM; v_mB = 0.8 nM_per_h Reaction: M_B =>, Rate Law: cytoplasm*(v_mB*M_B/(K_mB+M_B)+kd_mB*M_B)
n_dVIP = 0.2 dimensionless; k_dVIP = 0.5 Reaction: VIP =>, Rate Law: cytoplasm*k_dVIP*VIP^n_dVIP
ks_P = 0.6 per_h Reaction: => P_C; M_P, Rate Law: cytoplasm*ks_P*M_P
V1_PC = NaN nM_per_h; K_dp = 0.1 nM; K_p = 0.1 nM; V2_PC = 0.1 nM_per_h Reaction: PC_C => PC_CP, Rate Law: cytoplasm*(V1_PC*PC_C/(K_p+PC_C)-V2_PC*PC_CP/(K_dp+PC_CP))
k_f = 0.001 per_h Reaction: Ca_store => Ca_in, Rate Law: 1000*store*k_f*Ca_store
ks_C = 1.6 per_h Reaction: => C_C; M_C, Rate Law: cytoplasm*ks_C*M_C
beta_IP3 = 0.5 dimensionless; V_M1 = 3.0E-4 uM_per_h Reaction: => Ca_in, Rate Law: 1000*cytoplasm*V_M1*beta_IP3
v_GABA = 19.0 nM; K_GABA = 3.0 nM Reaction: GABA = GABA_o+v_GABA*VIP/(K_GABA+VIP), Rate Law: missing
ksB = 0.12 Reaction: => B_C; M_B, Rate Law: cytoplasm*ksB*M_B
Kd = 0.3 nM; vd_BN = 0.6 nM_per_h; kd_n = 0.01 per_h Reaction: B_NP =>, Rate Law: nucleus*(vd_BN*B_NP/(Kd+B_NP)+kd_n*B_NP)
Kd = 0.3 nM; vd_PCN = 0.7 nM_per_h; kd_n = 0.01 per_h Reaction: PC_NP =>, Rate Law: nucleus*(vd_PCN*PC_NP/(Kd+PC_NP)+kd_n*PC_NP)

States:

Name Description
Na in [CHEBI_9175; Sodium cation]
M B [Aryl hydrocarbon receptor nuclear translocator-like protein 1; messenger RNA]
B N [Aryl hydrocarbon receptor nuclear translocator-like protein 1]
C C [Cryptochrome-2; Cryptochrome-1]
GABA [4-Aminobutanoate; gamma-aminobutyric acid]
PC NP [Cryptochrome-1; Period circadian protein homolog 3; Cryptochrome-1; Period circadian protein homolog 2; Cryptochrome-1; Period circadian protein homolog 1]
B C [Aryl hydrocarbon receptor nuclear translocator-like protein 1]
C CP [Cryptochrome-2; Cryptochrome-1; Phosphoprotein]
Ca store [calcium(2+); Calcium cation]
K in [potassium(1+); Potassium cation]
P CP [Period circadian protein homolog 3; Period circadian protein homolog 2; Period circadian protein homolog 1]
B CP [Phosphoprotein; Aryl hydrocarbon receptor nuclear translocator-like protein 1]
CB [Cyclic AMP-responsive element-binding protein 1]
M C [Cryptochrome-2; Cryptochrome-1; messenger RNA]
VIP [Vasoactive intestinal polypeptide receptor 1]
M P [Period circadian protein homolog 3; Period circadian protein homolog 2; Period circadian protein homolog 1; messenger RNA]
PC C [Cryptochrome-1; Period circadian protein homolog 3; Cryptochrome-1; Period circadian protein homolog 1]
PC CP [Phosphoprotein; Cryptochrome-1; Period circadian protein homolog 1; Cryptochrome-1; Period circadian protein homolog 3]
Ca in [calcium(2+); Calcium cation]
I N I_N
P C [Period circadian protein homolog 3; Period circadian protein homolog 2; Period circadian protein homolog 1]
B NP [Aryl hydrocarbon receptor nuclear translocator-like protein 1]
PC N [Cryptochrome-2; Period circadian protein homolog 3; Cryptochrome-1; Period circadian protein homolog 1]

Observables: none

MODEL1004070001 @ v0.0.1

Model as described in: In vivo dynamics of the pentose phosphate pathway in Saccharomyces cerevisiae Vaseghi S, Baum…

The in vivo dynamics of the pentose phosphate pathway has been studied with transient experiments in continuous culture of Saccharomyces cerevisiae. Rapid sampling was performed with a special sampling device after disturbing the steady state with a pulse of glucose. The time span of observation was 120 s after the pulse. During this short time period the dynamic effect of protein biosynthesis can be neglected. The metabolites of interest (glucose 6-phosphate, NADP, NADPH, 6-phosphogluconate, and MgATP2-) we determined with enzymatic assays and HPLC. The experimental observations were then used for the identification of kinetic rate equations and parameters under in vivo conditions. In accordance with results from in vitro studies the in vivo diagnosis supports an ordered Bi-Bi mechanism with noncompetitive inhibition by MgATP2- for the enzyme glucose-6-phosphate dehydrogenase. In the case of 6-phosphogluconate dehydrogenase an ordered Bi-Ter mechanism with a competitive inhibition by MgATP2- has been found. Because the MgATP2- concentration decreases abruptly after the pulse of glucose the inhibitory effect vanishes and the flux through the pentose phosphate pathway increases. This regulation phenomenon guarantees the balance of fluxes through glycolysis and pentose phosphate pathway during the dynamic time period. link: http://identifiers.org/pubmed/10935926

Parameters: none

States: none

Observables: none

Vazquez2014 - Chemical inhibition from amyloid protein aggregation kineticsThis model is described in the article: [Mod…

BACKGROUNDS: The process of amyloid proteins aggregation causes several human neuropathologies. In some cases, e.g. fibrillar deposits of insulin, the problems are generated in the processes of production and purification of protein and in the pump devices or injectable preparations for diabetics. Experimental kinetics and adequate modelling of chemical inhibition from amyloid aggregation are of practical importance in order to study the viable processing, formulation and storage as well as to predict and optimize the best conditions to reduce the effect of protein nucleation. RESULTS: In this manuscript, experimental data of insulin, Aβ42 amyloid protein and apomyoglobin fibrillation from recent bibliography were selected to evaluate the capability of a bivariate sigmoid equation to model them. The mathematical functions (logistic combined with Weibull equation) were used in reparameterized form and the effect of inhibitor concentrations on kinetic parameters from logistic equation were perfectly defined and explained. The surfaces of data were accurately described by proposed model and the presented analysis characterized the inhibitory influence on the protein aggregation by several chemicals. Discrimination between true and apparent inhibitors was also confirmed by the bivariate equation. EGCG for insulin (working at pH = 7.4/T = 37°C) and taiwaniaflavone for Aβ42 were the compounds studied that shown the greatest inhibition capacity. CONCLUSIONS: An accurate, simple and effective model to investigate the inhibition of chemicals on amyloid protein aggregation has been developed. The equation could be useful for the clear quantification of inhibitor potential of chemicals and rigorous comparison among them. link: http://identifiers.org/pubmed/24572069

Parameters:

Name Description
lambda = 3.0; C = 1.0; mlambda = 2.0; alambda = 2.0; klambda = 1.0 Reaction: Lambda = lambda*(1+klambda*(1-exp((-ln(2))*(C/mlambda)^alambda))), Rate Law: missing
kx = 1.0; C = 1.0; ax = 2.0; mx = 5.0; xm = 1.0 Reaction: Xm = xm*(1-kx*(1-exp((-ln(2))*(C/mx)^ax))), Rate Law: missing
av = 2.0; C = 1.0; kv = 1.0; mv = 4.0; vm = 0.25 Reaction: Vm = vm*(1-kv*(1-exp((-ln(2))*(C/mv)^av))), Rate Law: missing

States:

Name Description
Xm Xm
X [amyloid plaque]
Lambda Lambda
Vm Vm

Observables: none

BIOMD0000000240 @ v0.0.1

This a model from the article: Transient heterogeneity in extracellular protease production by Bacillus subtilis. V…

The most sophisticated survival strategy Bacillus subtilis employs is the differentiation of a subpopulation of cells into highly resistant endospores. To examine the expression patterns of non-sporulating cells within heterogeneous populations, we used buoyant density centrifugation to separate vegetative cells from endospore-containing cells and compared the transcriptome profiles of both subpopulations. This demonstrated the differential expression of various regulons. Subsequent single-cell analyses using promoter-gfp fusions confirmed our microarray results. Surprisingly, only part of the vegetative subpopulation highly and transiently expresses genes encoding the extracellular proteases Bpr (bacillopeptidase) and AprE (subtilisin), both of which are under the control of the DegU transcriptional regulator. As these proteases and their degradation products freely diffuse within the liquid growth medium, all cells within the clonal population are expected to benefit from their activities, suggesting that B. subtilis employs cooperative or even altruistic behavior. To unravel the mechanisms by which protease production heterogeneity within the non-sporulating subpopulation is established, we performed a series of genetic experiments combined with mathematical modeling. Simulations with our model yield valuable insights into how population heterogeneity may arise by the relatively long and variable response times within the DegU autoactivating pathway. link: http://identifiers.org/pubmed/18414485

Parameters:

Name Description
ksyn1 = 0.04 Reaction: => DegU; mDegU, Rate Law: ksyn1*mDegU*univ
ka = 0.025 Reaction: DegUP => Dim, Rate Law: ka*DegUP^2
kdeg = 4.0E-4 Reaction: DegU =>, Rate Law: kdeg*DegU*univ
kdegm = 0.01 Reaction: mAprE =>, Rate Law: kdegm*mAprE
kdephos = NaN Reaction: DegUP => DegU, Rate Law: kdephos*DegUP
kd = 0.1 Reaction: Dim => DegUP, Rate Law: kd*Dim
kphos = NaN Reaction: DegU => DegUP, Rate Law: kphos*DegU
ksyn = 0.04 Reaction: => AprE; mAprE, Rate Law: ksyn*mAprE*univ
Kr1 = 7.0; R = 7.0; Kdim = 12.0; Iro = 0.02; Kr = 7.0; Irmax = 0.4 Reaction: => mAprE; Dim, Rate Law: Kr1/(R+Kr1)*(Iro*(Dim*univ/Kdim+1)/(1+Dim*univ/Kdim+(Dim*univ)^2/Kdim^2+R/Kr)+Irmax*(Dim*univ)^2/(Kdim^2*(1+Dim*univ/Kdim+(Dim*univ)^2/Kdim^2+R/Kr)))
K = 7.0; Imax = 0.048; Io = 0.004 Reaction: => mDegU; Dim, Rate Law: Io*K/(Dim*univ+K)+Imax*Dim*univ/(Dim*univ+K)

States:

Name Description
Dim [protein homodimerization activity]
DegU [Transcriptional regulatory protein DegU]
mAprE [Subtilisin E]
AprE [Subtilisin E]
DegUP [Transcriptional regulatory protein DegU]
mDegU [Transcriptional regulatory protein DegU]

Observables: none

efa201208

Increasing antibiotic resistance in pathogenic bacteria necessitates the development of new medication strategies. Interfering with the metabolic network of the pathogen can provide novel drug targets but simultaneously requires a deeper and more detailed organism-specific understanding of the metabolism, which is often surprisingly sparse. In light of this, we reconstructed a genome-scale metabolic model of the pathogen Enterococcus faecalis V583. The manually curated metabolic network comprises 642 metabolites and 706 reactions. We experimentally determined metabolic profiles of E. faecalis grown in chemically defined medium in an anaerobic chemostat setup at different dilution rates and calculated the net uptake and product fluxes to constrain the model. We computed growth-associated energy and maintenance parameters and studied flux distributions through the metabolic network. Amino acid auxotrophies were identified experimentally for model validation and revealed seven essential amino acids. In addition, the important metabolic hub of glutamine/glutamate was altered by constructing a glutamine synthetase knockout mutant. The metabolic profile showed a slight shift in the fermentation pattern toward ethanol production and increased uptake rates of multiple amino acids, especially l-glutamine and l-glutamate. The model was used to understand the altered flux distributions in the mutant and provided an explanation for the experimentally observed redirection of the metabolic flux. We further highlighted the importance of gene-regulatory effects on the redirection of the metabolic fluxes upon perturbation. The genome-scale metabolic model presented here includes gene-protein-reaction associations, allowing a further use for biotechnological applications, for studying essential genes, proteins, or reactions, and the search for novel drug targets. link: http://identifiers.org/pubmed/25527553

Parameters: none

States: none

Observables: none

MODEL7888000034 @ v0.0.1

This is the model described in the article: A biochemical model of matrix metalloproteinase 9 activation and inhibitio…

Matrix metalloproteinases (MMPs) are a class of extracellular and membrane-bound proteases involved in an array of physiological processes, including angiogenesis. We present a detailed computational model of MMP9 activation and inhibition. Our model is validated to existing biochemical experimental data. We determine kinetic rate constants for the processes of MMP9 activation by MMP3, MMP10, MMP13, and trypsin; inhibition by the tissue inhibitors of metalloproteinases (TIMPs) 1 and 2; and MMP9 deactivation. This computational approach allows us to investigate discrepancies in our understanding of the interaction of MMP9 with TIMP1. Specifically, we find that inhibition due to a single binding event cannot describe MMP9 inhibition by TIMP1. Temporally accurate biphasic inhibition requires either an additional isomerization step or a second lower affinity isoform of MMP9. We also theoretically characterize the MMP3/TIMP2/pro-MMP9 and MMP3/TIMP1/pro-MMP9 systems. We speculate that these systems differ significantly in their time scales of activation and inhibition such that MMP9 is able to temporarily overshoot its final equilibrium value in the latter. Our numerical simulations suggest that the ability of pro-MMP9 to complex TIMP1 increases this overshoot. In all, our analysis serves as a summary of existing kinetic data for MMP9 and a foundation for future models utilizing MMP9 or other MMPs under physiologically well defined microenvironments. link: http://identifiers.org/pubmed/17848556

Parameters: none

States: none

Observables: none

Venkatraman2011 - PLS-UPA behaviour in the presence of substrate competitionThe posibility of ultrasensitivity and bista…

Plasmin (PLS) and urokinase-type plasminogen activator (UPA) are ubiquitous proteases that regulate the extracellular environment. Although they are secreted in inactive forms, they can activate each other through proteolytic cleavage. This mutual interplay creates the potential for complex dynamics, which we investigated using mathematical modeling and in vitro experiments. We constructed ordinary differential equations to model the conversion of precursor plasminogen into active PLS, and precursor urokinase (scUPA) into active urokinase (tcUPA). Although neither PLS nor UPA exhibits allosteric cooperativity, modeling showed that cooperativity occurred at the system level because of substrate competition. Computational simulations and bifurcation analysis predicted that the system would be bistable over a range of parameters for cooperativity and positive feedback. Cell-free experiments with recombinant proteins tested key predictions of the model. PLS activation in response to scUPA stimulus was found to be cooperative in vitro. Finally, bistability was demonstrated in vitro by the presence of two significantly different steady-state levels of PLS activation for the same levels of stimulus. We conclude that ultrasensitive, bistable activation of UPA-PLS is possible in the presence of substrate competition. An ultrasensitive threshold for activation of PLS and UPA would have ramifications for normal and disease processes, including angiogenesis, metastasis, wound healing, and fibrosis. link: http://identifiers.org/pubmed/22004735

Parameters:

Name Description
parameter_1 = 0.084 Reaction: species_2 => ; species_2, Rate Law: compartment_1*parameter_1*species_2
k1=0.9 Reaction: species_4 + species_1 => species_2 + species_4; species_4, species_1, Rate Law: compartment_1*k1*species_4*species_1
k1=0.035 Reaction: species_3 + species_1 => species_2 + species_3; species_3, species_1, Rate Law: compartment_1*k1*species_3*species_1
parameter_13 = 2.0; parameter_8=40.0 Reaction: species_2 + species_3 => species_4 + species_2; species_2, species_3, Rate Law: compartment_1*parameter_8*species_2^parameter_13*species_3
v=0.01 Reaction: => species_5, Rate Law: compartment_1*v
v=0.0032 Reaction: => species_3, Rate Law: compartment_1*v
k1=0.02 Reaction: species_6 => species_2; species_6, Rate Law: compartment_1*k1*species_6
parameter_2 = 0.032 Reaction: species_5 => ; species_5, Rate Law: compartment_1*parameter_2*species_5
k2=0.016; k1=0.0 Reaction: species_2 + species_5 => species_6; species_2, species_5, species_6, Rate Law: compartment_1*(k1*species_2*species_5-k2*species_6)

States:

Name Description
species 2 [Plasminogen]
species 6 [Plasminogen; 10370340]
species 3 [Urokinase-type plasminogen activator]
species 1 [Plasminogen]
species 4 [Urokinase-type plasminogen activator]
species 5 [10370340]

Observables: none

Venkatraman2012 - Interplay between PLS and TSP1 in TGF-β1 activationThe interplay between PLS (Plasmin) and TSP1 (Throm…

Transforming growth factor-β1 (TGF-β1) is a potent regulator of extracellular matrix production, wound healing, differentiation, and immune response, and is implicated in the progression of fibrotic diseases and cancer. Extracellular activation of TGF-β1 from its latent form provides spatiotemporal control over TGF-β1 signaling, but the current understanding of TGF-β1 activation does not emphasize cross talk between activators. Plasmin (PLS) and thrombospondin-1 (TSP1) have been studied individually as activators of TGF-β1, and in this work we used a systems-level approach with mathematical modeling and in vitro experiments to study the interplay between PLS and TSP1 in TGF-β1 activation. Simulations and steady-state analysis predicted a switch-like bistable transition between two levels of active TGF-β1, with an inverse correlation between PLS and TSP1. In particular, the model predicted that increasing PLS breaks a TSP1-TGF-β1 positive feedback loop and causes an unexpected net decrease in TGF-β1 activation. To test these predictions in vitro, we treated rat hepatocytes and hepatic stellate cells with PLS, which caused proteolytic cleavage of TSP1 and decreased activation of TGF-β1. The TGF-β1 activation levels showed a cooperative dose response, and a test of hysteresis in the cocultured cells validated that TGF-β1 activation is bistable. We conclude that switch-like behavior arises from natural competition between two distinct modes of TGF-β1 activation: a TSP1-mediated mode of high activation and a PLS-mediated mode of low activation. This switch suggests an explanation for the unexpected effects of the plasminogen activation system on TGF-β1 in fibrotic diseases in vivo, as well as novel prognostic and therapeutic approaches for diseases with TGF-β dysregulation. link: http://identifiers.org/pubmed/23009856

Parameters:

Name Description
parameter_2 = 0.35 Reaction: species_2 + species_3 => species_4 + species_2; species_2, species_3, Rate Law: compartment_1*parameter_2*species_2*species_3
parameter_7 = 0.35 Reaction: species_6 => species_6 + species_7; species_6, Rate Law: compartment_1*parameter_7*species_6
parameter_9 = 17.5; parameter_10 = 0.0245 Reaction: species_7 + species_2 => species_9; species_7, species_2, species_9, Rate Law: compartment_1*(parameter_9*species_7*species_2-parameter_10*species_9)
parameter_8 = 1.05 Reaction: species_6 => species_6 + species_8; species_6, Rate Law: compartment_1*parameter_8*species_6
parameter_17 = 0.0035; parameter_16 = 0.07 Reaction: species_8 + species_3 => species_13; species_8, species_3, species_13, Rate Law: compartment_1*(parameter_16*species_8*species_3-parameter_17*species_13)
parameter_1 = 0.035 Reaction: species_3 + species_1 => species_2 + species_3; species_3, species_1, Rate Law: compartment_1*parameter_1*species_3*species_1
parameter_20 = 0.0525 Reaction: species_2 => ; species_2, Rate Law: compartment_1*parameter_20*species_2
parameter_22 = 0.0035 Reaction: => species_3, Rate Law: compartment_1*parameter_22
parameter_14 = 0.035; parameter_15 = 0.0035 Reaction: species_8 + species_4 => species_12; species_8, species_4, species_12, Rate Law: compartment_1*(parameter_14*species_8*species_4-parameter_15*species_12)
parameter_18 = 24.5 Reaction: species_9 => ; species_9, Rate Law: compartment_1*parameter_18*species_9
parameter_23 = 0.035 Reaction: => species_1, Rate Law: compartment_1*parameter_23
parameter_21 = 0.0175 Reaction: species_7 => ; species_7, Rate Law: compartment_1*parameter_21*species_7
parameter_19 = 0.21 Reaction: species_6 => ; species_6, Rate Law: compartment_1*parameter_19*species_6
parameter_6 = 0.005 Reaction: species_5 => species_6; species_5, Rate Law: compartment_1*parameter_6*species_5
parameter_4 = 0.035 Reaction: species_2 + species_5 => species_6 + species_2; species_2, species_5, Rate Law: compartment_1*parameter_4*species_2*species_5
parameter_5 = 24.5 Reaction: species_7 + species_5 => species_6; species_7, species_5, Rate Law: compartment_1*parameter_5*species_7*species_5
parameter_3 = 1.4 Reaction: species_4 + species_1 => species_2 + species_4; species_4, species_1, Rate Law: compartment_1*parameter_3*species_4*species_1
parameter_11 = 0.35 Reaction: species_9 => species_2; species_9, Rate Law: compartment_1*parameter_11*species_9
parameter_12 = 24.5; parameter_13 = 0.0105 Reaction: species_10 + species_2 => species_11; species_10, species_2, species_11, Rate Law: compartment_1*(parameter_12*species_10*species_2-parameter_13*species_11)

States:

Name Description
species 9 [Plasminogen; Thrombospondin 1]
species 2 [Plasminogen]
species 6 [Transforming growth factor beta-1]
species 10 [Alpha-2-macroglobulin]
species 11 [Plasminogen; Alpha-2-macroglobulin]
species 1 [Plasminogen]
species 4 [Urokinase-type plasminogen activator; active]
species 3 [Urokinase-type plasminogen activator; inactive]
species 8 [Plasminogen activator inhibitor 1]
species 12 [Plasminogen activator inhibitor 1; Urokinase-type plasminogen activator; active]
species 7 [Thrombospondin 1]
species 5 [Transforming growth factor beta-1; inactive]
species 13 [Urokinase-type plasminogen activator; Plasminogen activator inhibitor 1; inactive]

Observables: none

Verlingue2016 - Signalling pathway that control S-phase entry and geroconversion - Boolean ModelThis model is described…

Altered molecular responses to insulin and growth factors (GF) are responsible for late-life shortening diseases such as type-2 diabetes mellitus (T2DM) and cancers. We have built a network of the signaling pathways that control S-phase entry and a specific type of senescence called geroconversion. We have translated this network into a Boolean model to study possible cell phenotype outcomes under diverse molecular signaling conditions. In the context of insulin resistance, the model was able to reproduce the variations of the senescence level observed in tissues related to T2DM's main morbidity and mortality. Furthermore, by calibrating the pharmacodynamics of mTOR inhibitors, we have been able to reproduce the dose-dependent effect of rapamycin on liver degeneration and lifespan expansion in wild-type and HER2-neu mice. Using the model, we have finally performed an in silico prospective screen of the risk-benefit ratio of rapamycin dosage for healthy lifespan expansion strategies. We present here a comprehensive prognostic and predictive systems biology tool for human aging. link: http://identifiers.org/pubmed/27613445

Parameters: none

States: none

Observables: none

Verma2016 - Ca(2+) Signal Propagation Along Hepatocyte CordsThis model is described in the article: [Computational Mode…

The purpose of this study is to model the dynamics of lobular Ca(2+) wave propagation induced by an extracellular stimulus, and to analyze the effect of spatially systematic variations in cell-intrinsic signaling parameters on sinusoidal Ca(2+) response.We developed a computational model of lobular scale Ca(2+) signaling that accounts for receptor- mediated initiation of cell-intrinsic Ca(2+) signal in hepatocytes and its propagation to neighboring hepatocytes through gap junction-mediated molecular exchange.Analysis of the simulations showed that a pericentral-to-periportal spatial gradient in hormone sensitivity and/or rates of IP3 synthesis underlies the Ca(2+) wave propagation. We simulated specific cases corresponding to localized disruptions in the graded pattern of these parameters along a hepatic sinusoid. Simulations incorporating locally altered parameters exhibited Ca(2+) waves that do not propagate throughout the hepatic plate. Increased gap junction coupling restored normal Ca(2+) wave propagation when hepatocytes with low Ca(2+) signaling ability were localized in the midlobular or the pericentral region.Multiple spatial patterns in intracellular signaling parameters can lead to Ca(2+) wave propagation that is consistent with the experimentally observed spatial patterns of Ca(2+) dynamics. Based on simulations and analysis, we predict that increased gap junction-mediated intercellular coupling can induce robust Ca(2+) signals in otherwise poorly responsive hepatocytes, at least partly restoring the sinusoidally oriented Ca (2+) waves.Our bottom-up model of agonist-evoked spatial Ca(2+) patterns can be integrated with detailed descriptions of liver histology to study Ca(2+) regulation at the tissue level. link: http://identifiers.org/pubmed/27076052

Parameters:

Name Description
k_ip315=0.8; H = 1.8E-4; k3 = 1.0; kcat = 0.45 Reaction: => IP3_15; CaI_15, r_15, Rate Law: cytosol15*k_ip315*H*r_15*(1+(-k3*1/(CaI_15+k3)))*1/(kcat+r_15)
k_r3 = 1.928571 Reaction: => r_3, Rate Law: cytosol3*k_r3
k_r6 = 1.785714; k_d = 0.34; H = 1.8E-4; k_HR = 1.0 Reaction: r_6 =>, Rate Law: cytosol6*(k_d*r_6+k_HR*H*r_6+k_r6*r_6)
k_r7 = 1.571429; k_d = 0.34; H = 1.8E-4; k_HR = 1.0 Reaction: r_7 =>, Rate Law: cytosol7*(k_d*r_7+k_HR*H*r_7+k_r7*r_7)
D = 1.6 Reaction: IP3_4 =>, Rate Law: cytosol4*0.5*D*IP3_4
v=1.28571 Reaction: => r_15, Rate Law: cytosol15*v
G = 0.9 Reaction: IP3_3 => IP3_2, Rate Law: G*(IP3_3+(-IP3_2))*cytosol3
k_ip34=0.857143; H = 1.8E-4; k3 = 1.0; kcat = 0.45 Reaction: => IP3_4; CaI_4, r_4, Rate Law: cytosol4*k_ip34*H*r_4*(1+(-k3*1/(CaI_4+k3)))*1/(kcat+r_4)
E = 1.0 Reaction: => g_3; CaI_3, Rate Law: cytosol3*E*CaI_3^4*(1+(-g_3))
H = 1.8E-4; k3 = 1.0; k_ip311=0.714286; kcat = 0.45 Reaction: => IP3_11; CaI_11, r_11, Rate Law: cytosol11*k_ip311*H*r_11*(1+(-k3*1/(CaI_11+k3)))*1/(kcat+r_11)
k_d = 0.34; H = 1.8E-4; k_r12 = 2.0; k_HR = 1.0 Reaction: r_12 =>, Rate Law: cytosol12*(k_d*r_12+k_HR*H*r_12+k_r12*r_12)
k_r13 = 1.214286 Reaction: => r_13, Rate Law: cytosol13*k_r13
k_d = 0.34; H = 1.8E-4; k_r9 = 1.142857; k_HR = 1.0 Reaction: r_9 =>, Rate Law: cytosol9*(k_d*r_9+k_HR*H*r_9+k_r9*r_9)
B = 0.082; k2 = 0.15 Reaction: CaI_3 => CaT_3, Rate Law: B*CaI_3^2*1/(k2^2+CaI_3^2)*cytosol3
k_r14 = 1.071429 Reaction: => r_14, Rate Law: cytosol14*k_r14
k_d = 0.34; H = 1.8E-4; k_r4 = 1.357143; k_HR = 1.0 Reaction: r_4 =>, Rate Law: cytosol4*(k_d*r_4+k_HR*H*r_4+k_r4*r_4)
k_ip314=0.7; H = 1.8E-4; k3 = 1.0; kcat = 0.45 Reaction: => IP3_14; CaI_14, r_14, Rate Law: cytosol14*k_ip314*H*r_14*(1+(-k3*1/(CaI_14+k3)))*1/(kcat+r_14)
k_r13 = 1.214286; k_d = 0.34; H = 1.8E-4; k_HR = 1.0 Reaction: r_13 =>, Rate Law: cytosol13*(k_d*r_13+k_HR*H*r_13+k_r13*r_13)
k_r10 = 1.642857 Reaction: => r_10, Rate Law: cytosol10*k_r10
k_r15=1.28571; k_d = 0.34; H = 1.8E-4; k_HR = 1.0 Reaction: r_15 =>, Rate Law: cytosol15*(k_d*r_15+k_HR*H*r_15+k_r15*r_15)
k_r8 = 1.714286; k_d = 0.34; H = 1.8E-4; k_HR = 1.0 Reaction: r_8 =>, Rate Law: cytosol8*(k_d*r_8+k_HR*H*r_8+k_r8*r_8)
k_r14 = 1.071429; k_d = 0.34; H = 1.8E-4; k_HR = 1.0 Reaction: r_14 =>, Rate Law: cytosol14*(k_d*r_14+k_HR*H*r_14+k_r14*r_14)
k_r7 = 1.571429 Reaction: => r_7, Rate Law: cytosol7*k_r7
k_ip37=0.742857; H = 1.8E-4; k3 = 1.0; kcat = 0.45 Reaction: => IP3_7; CaI_7, r_7, Rate Law: cytosol7*k_ip37*H*r_7*(1+(-k3*1/(CaI_7+k3)))*1/(kcat+r_7)
L = 1.5E-4; A = 0.2; k1 = 0.5 Reaction: CaT_10 => CaI_10; g_10, IP3_10, Rate Law: (1+(-g_10))*(A*(0.5*IP3_10)^4*1/(k1+0.5*IP3_10)^4+L)*(CaT_10+(-CaI_10))*store10
k_ip38=0.828571; H = 1.8E-4; k3 = 1.0; kcat = 0.45 Reaction: => IP3_8; CaI_8, r_8, Rate Law: cytosol8*k_ip38*H*r_8*(1+(-k3*1/(CaI_8+k3)))*1/(kcat+r_8)
k_d = 0.34; H = 1.8E-4; k_r10 = 1.642857; k_HR = 1.0 Reaction: r_10 =>, Rate Law: cytosol10*(k_d*r_10+k_HR*H*r_10+k_r10*r_10)
k_r8 = 1.714286 Reaction: => r_8, Rate Law: cytosol8*k_r8
k_ip35=0.842857; H = 1.8E-4; k3 = 1.0; kcat = 0.45 Reaction: => IP3_5; CaI_5, r_5, Rate Law: cytosol5*k_ip35*H*r_5*(1+(-k3*1/(CaI_5+k3)))*1/(kcat+r_5)
k_d = 0.34; k_r11 = 1.428571; H = 1.8E-4; k_HR = 1.0 Reaction: r_11 =>, Rate Law: cytosol11*(k_d*r_11+k_HR*H*r_11+k_r11*r_11)
F = 0.01 Reaction: g_3 => ; CaI_3, Rate Law: cytosol3*F

States:

Name Description
CaI 12 cytosolic Ca2+_CaI_12
CaI 7 cytosolic Ca2+_CaI_7
r 10 total receptor levels _r_10
CaI 1 [Calcium cation]
r 3 total receptor levels _r_3
r 9 total receptor levels _r_9
g 5 total IP3R_g_5
IP3 1 cytosolic IP3_IP3_1
IP3 5 cytosolic IP3_IP3_5
g 3 total IP3R_g_3
CaT 4 total intracellular store Ca2+ content_CaT_4
CaI 15 cytosolic Ca2+_CaI_15
CaT 3 total intracellular store Ca2+ content_CaT_3
CaT 12 total intracellular store Ca2+ content_CaT_12
CaI 5 cytosolic Ca2+_CaI_5
IP3 6 cytosolic IP3_IP3_6
r 12 total receptor levels _r_12
g 6 total IP3R_g_6
r 11 total receptor levels _r_11
g 12 total IP3R_g_12
CaI 13 cytosolic Ca2+_CaI_13
CaT 5 total intracellular store Ca2+ content_CaT_5
CaI 6 cytosolic Ca2+_CaI_6
r 15 total receptor levels _r_15
IP3 4 cytosolic IP3_IP3_4
CaT 7 total intracellular store Ca2+ content_CaT_7
CaT 11 total intracellular store Ca2+ content_CaT_11
g 4 total IP3R_g_4
CaT 15 total intracellular store Ca2+ content_CaT_15
CaI 10 cytosolic Ca2+_CaI_10
g 11 total IP3R_g_11
r 7 total receptor levels_ r_7
CaI 4 cytosolic Ca2+_CaI_4
r 13 total receptor levels _r_13
IP3 15 cytosolic IP3_IP3_15
g 9 total IP3R_g_9
g 14 total IP3R_g_14
CaT 14 total intracellular store Ca2+ content_CaT_14
IP3 9 cytosolic IP3_IP3_9
CaT 13 total intracellular store Ca2+ content_CaT_13
CaI 9 cytosolic Ca2+_CaI_9
IP3 7 cytosolic IP3_IP3_7
IP3 11 cytosolic IP3_IP3_11
r 14 total receptor levels _r_14
g 7 total IP3R_g_7
r 4 total receptor levels _r_4
g 15 total IP3R_g_15
CaT 6 total intracellular store Ca2+ content_CaT_6
CaT 9 total intracellular store Ca2+ content_CaT_9
IP3 14 cytosolic IP3_IP3_14
g 8 total IP3R_g_8
IP3 13 cytosolic IP3_IP3_13
CaI 8 cytosolic Ca2+_CaI_8
IP3 8 cytosolic IP3_IP3_8
IP3 3 cytosolic IP3_IP3_3
r 6 total receptor levels _r_6
r 8 total receptor levels _r_8
CaI 14 cytosolic Ca2+_CaI_14
g 13 total IP3R_g_13
CaT 8 total intracellular store Ca2+ content_CaT_8

Observables: none

This is a COPASI version of the HIV/HPV coinfection model submitted to PLoS One. Title: Modeling the mechanisms by whic…

Human immunodeficiency virus (HIV)-infected patients are at an increased risk of co-infection with human papilloma virus (HPV), and subsequent malignancies such as oral cancer. To determine the role of HIV-associated immune suppression on HPV persistence and pathogenesis, and to investigate the mechanisms underlying the modulation of HPV infection and oral cancer by HIV, we developed a mathematical model of HIV/HPV co-infection. Our model captures known immunological and molecular features such as impaired HPV-specific effector T helper 1 (Th1) cell responses, and enhanced HPV infection due to HIV. We used the model to determine HPV prognosis in the presence of HIV infection, and identified conditions under which HIV infection alters HPV persistence in the oral mucosa system. The model predicts that conditions leading to HPV persistence during HIV/HPV co-infection are the permissive immune environment created by HIV and molecular interactions between the two viruses. The model also determines when HPV infection continues to persist in the short run in a co-infected patient undergoing antiretroviral therapy. Lastly, the model predicts that, under efficacious antiretroviral treatment, HPV infections will decrease in the long run due to the restoration of CD4+ T cell numbers and protective immune responses. link: http://identifiers.org/doi/10.1371/journal.pone.0168133

Parameters:

Name Description
mu = 0.048; k2=1000.0 Reaction: => s14; s13, Rate Law: default*Production_of_HPV_due_to_HPV_self_proliferating_1(k2, mu, s13)
k1=1.0 Reaction: s3 =>, Rate Law: default*k1*s3
beta = 1.97002141327623E-7; e_rt = 0.0 Reaction: s4 => s3; s2, Rate Law: default*Rate_Law_for_production_of_HIV_infected_cells_1(beta, e_rt, s2, s4)
k1=0.05 Reaction: s14 =>, Rate Law: default*k1*s14
epi = 0.5; r=0.1 Reaction: s13 => s13; s13, Rate Law: default*Proliferation_of_HPV_self_proliferating(epi, r, s13)
c1 = 23.0 Reaction: s2 =>, Rate Law: default*c1*s2
k1=1000.0; mu = 0.048 Reaction: => s14; s12, Rate Law: default*Rate_Law_for_Production_of_HPV_due_to_HPVinfected_1(k1, mu, s12)
s = 4864.02569593148 Reaction: => s4, Rate Law: default*Constant_flux__irreversible(s)
e_pi = 0.0; delta=1.0; N1 = 467.0 Reaction: => s2; s3, Rate Law: default*Rate_Law_for_Production_of_HIV_virion_1(N1, delta, e_pi, s3)
b=3.5E-5; omega = 0.001 Reaction: s16 => ; s13, s4, Rate Law: default*Logistic_term_for_Effector_cells_1(b, omega, s13, s16, s4)
N2=10000.0; psi=0.0067; phi=1000000.0; p=2.0833E-5 Reaction: s12 => s12; s14, s2, Rate Law: default*Productionof_HPV_infected_cells(N2, p, phi, psi, s12, s14, s2)
d = 0.01 Reaction: s4 =>, Rate Law: default*d*s4
k1=0.048 Reaction: s13 =>, Rate Law: default*k1*s13
epi = 0.5 Reaction: s12 => s13, Rate Law: default*epi*s12
mu = 0.048 Reaction: s12 =>, Rate Law: default*mu*s12
a=0.01 Reaction: s13 => ; s16, Rate Law: default*Death_of_HPV_self_proliferating_cells_due_to_effector_cells(a, s13, s16)
omega = 0.001 Reaction: s16 => s16; s13, Rate Law: default*Rate_Law_for_Production_of_Effector_cell_1_1(omega, s13, s16)

States:

Name Description
s14 [0004510]
s13 [Epithelial Cell]
s12 [Epithelial Cell]
s2 [Human Immunodeficiency Virus]
s4 [C97350]
s3 [C97350]
s16 [C12543]

Observables: none

This model is from the article: The auxin signalling network translates dynamic input into robust patterning at the sh…

The plant hormone auxin is thought to provide positional information for patterning during development. It is still unclear, however, precisely how auxin is distributed across tissues and how the hormone is sensed in space and time. The control of gene expression in response to auxin involves a complex network of over 50 potentially interacting transcriptional activators and repressors, the auxin response factors (ARFs) and Aux/IAAs. Here, we perform a large-scale analysis of the Aux/IAA-ARF pathway in the shoot apex of Arabidopsis, where dynamic auxin-based patterning controls organogenesis. A comprehensive expression map and full interactome uncovered an unexpectedly simple distribution and structure of this pathway in the shoot apex. A mathematical model of the Aux/IAA-ARF network predicted a strong buffering capacity along with spatial differences in auxin sensitivity. We then tested and confirmed these predictions using a novel auxin signalling sensor that reports input into the signalling pathway, in conjunction with the published DR5 transcriptional output reporter. Our results provide evidence that the auxin signalling network is essential to create robust patterns at the shoot apex. link: http://identifiers.org/pubmed/21734647

Parameters:

Name Description
d_A = 0.003 Reaction: A =>, Rate Law: d_A*A
d_r = 0.007 Reaction: R =>, Rate Law: d_r*R
pi_A = 1.0 Reaction: => A, Rate Law: pi_A
d_IA = 0.003 Reaction: D_IA =>, Rate Law: d_IA*D_IA
d_II = 0.003 Reaction: D_II =>, Rate Law: d_II*D_II
pi_I = 1.0 Reaction: => I; R, Rate Law: pi_I*R
w_A = 10.0; f_c = 10.0; B_d = 100.0; f_A = 10.0; K_IA = 10.0; w_I = 10.0; k_Am = 10.0; w_D = 10.0 Reaction: => R; A, D_IA, I, Rate Law: (1+f_c/B_d*A*(1+w_A*f_A*A/B_d))/(1+A/B_d*(1+w_A*A/B_d)+w_I*A*I/(K_IA*B_d)+w_D*D_IA/B_d+k_Am)
K_aux = 1.0; d_I = 0.05; gamma_I = 10.0 Reaction: I => ; aux, Rate Law: gamma_I*d_I*K_aux*aux/(K_aux*aux+1)*I
kprime_II = 10.0; k_II = 1.0 Reaction: I + I => D_II, Rate Law: k_II*I*I-kprime_II*D_II
k_IA = 1.0; kprime_IA = 10.0 Reaction: A + I => D_IA, Rate Law: k_IA*I*A-kprime_IA*D_IA

States:

Name Description
I [Auxin-responsive protein IAA1]
A [Auxin response factor 2]
aux [Auxin transporter protein 1]
D II [Auxin-responsive protein IAA1]
R [messenger RNA]
D IA [Auxin-responsive protein IAA1; Auxin response factor 2]

Observables: none

This model is from the article: The auxin signalling network translates dynamic input into robust patterning at the sh…

The plant hormone auxin is thought to provide positional information for patterning during development. It is still unclear, however, precisely how auxin is distributed across tissues and how the hormone is sensed in space and time. The control of gene expression in response to auxin involves a complex network of over 50 potentially interacting transcriptional activators and repressors, the auxin response factors (ARFs) and Aux/IAAs. Here, we perform a large-scale analysis of the Aux/IAA-ARF pathway in the shoot apex of Arabidopsis, where dynamic auxin-based patterning controls organogenesis. A comprehensive expression map and full interactome uncovered an unexpectedly simple distribution and structure of this pathway in the shoot apex. A mathematical model of the Aux/IAA-ARF network predicted a strong buffering capacity along with spatial differences in auxin sensitivity. We then tested and confirmed these predictions using a novel auxin signalling sensor that reports input into the signalling pathway, in conjunction with the published DR5 transcriptional output reporter. Our results provide evidence that the auxin signalling network is essential to create robust patterns at the shoot apex. link: http://identifiers.org/pubmed/21734647

Parameters:

Name Description
d_A = 0.003 Reaction: A =>, Rate Law: d_A*A
d_r = 0.007 Reaction: R =>, Rate Law: d_r*R
pi_A = 1.0 Reaction: => A, Rate Law: pi_A
d_IA = 0.003 Reaction: D_IA =>, Rate Law: d_IA*D_IA
d_II = 0.003 Reaction: D_II =>, Rate Law: d_II*D_II
pi_I = 1.0 Reaction: => I; R, Rate Law: pi_I*R
w_A = 10.0; f_c = 10.0; B_d = 100.0; f_A = 10.0; K_IA = 10.0; w_I = 10.0; k_Am = 10.0; w_D = 10.0 Reaction: => R; A, D_IA, I, Rate Law: (1+f_c/B_d*A*(1+w_A*f_A*A/B_d))/(1+A/B_d*(1+w_A*A/B_d)+w_I*A*I/(K_IA*B_d)+w_D*D_IA/B_d+k_Am)
K_aux = 1.0; d_I = 0.05; gamma_I = 10.0 Reaction: I => ; aux, Rate Law: gamma_I*d_I*K_aux*aux/(K_aux*aux+1)*I
kprime_II = 10.0; k_II = 1.0 Reaction: I + I => D_II, Rate Law: k_II*I*I-kprime_II*D_II
k_IA = 1.0; kprime_IA = 10.0 Reaction: A + I => D_IA, Rate Law: k_IA*I*A-kprime_IA*D_IA

States:

Name Description
I [Auxin-responsive protein IAA1]
A [Auxin response factor 2]
aux [Auxin transporter protein 1]
D II [Auxin-responsive protein IAA1]
R [messenger RNA]
D IA [Auxin response factor 2; Auxin-responsive protein IAA1]

Observables: none

MODEL2003170002 @ v0.0.1

model allows us to infer proliferation rates and cell cycle phase durations from complex experimental 5-ethynyl-2'-deoxy…

Cell proliferation is the common characteristic of all biological systems. The immune system insures the maintenance of body integrity on the basis of a continuous production of diversified T lymphocytes in the thymus. This involves processes of proliferation, differentiation, selection, death and migration of lymphocytes to peripheral tissues, where proliferation also occurs upon antigen recognition. Quantification of cell proliferation dynamics requires specific experimental methods and mathematical modelling. Here, we assess the impact of genetics and aging on the immune system by investigating the dynamics of proliferation of T lymphocytes across their differentiation through thymus and spleen in mice. Our investigation is based on single-cell multicolour flow cytometry analysis revealing the active incorporation of a thymidine analogue during S phase after pulse-chase-pulse experiments in vivo, versus cell DNA content. A generic mathematical model of state transition simulates through Ordinary Differential Equations (ODEs) the evolution of single cell behaviour during various durations of labelling. It allows us to fit our data, to deduce proliferation rates and estimate cell cycle durations in sub-populations. Our model is simple and flexible and is validated with other durations of pulse/chase experiments. Our results reveal that T cell proliferation is highly heterogeneous but with a specific "signature" that depends upon genetic origins, is specific to cell differentiation stages in thymus and spleen and is altered with age. In conclusion, our model allows us to infer proliferation rates and cell cycle phase durations from complex experimental 5-ethynyl-2'-deoxyuridine (EdU) data, revealing T cell proliferation heterogeneity and specific signatures. link: http://identifiers.org/pubmed/28288157

Parameters: none

States: none

Observables: none

This is a mathematical conductance-based model of the bursting activity in external tufted (ET) cells of the olfactory b…

We introduce a novel detailed conductance-based model of the bursting activity in external tufted (ET) cells of the olfactory bulb. We investigate the mechanisms underlying their bursting, and make experimentally-testable predictions. The ionic currents included in the model are specific to ET cells, and their kinetic and other parameters are based on experimental recordings. We validate the model by showing that its bursting characteristics under various conditions (e.g. blocking various currents) are consistent with experimental observations. Further, we identify the bifurcation structure and dynamics that explain bursting behavior. This analysis allows us to make predictions of the response of the cell to current pulses at different burst phases. We find that depolarizing (but not hyperpolarizing) inputs received during the interburst interval can advance burst timing, creating the substrate for synchronization by excitatory connections. It has been hypothesized that such synchronization among the ET cells within one glomerulus might help coordinate the glomerular output. Next we investigate model parameter sensitivity and identify parameters that play the most prominent role in controlling each burst characteristic, such as the burst frequency and duration. Finally, the response of the cell to periodic inputs is examined, reflecting the sniffing-modulated input that these cell receive in vivo. We find that individual cells can be better entrained by inputs with higher, rather than lower, frequencies than the intrinsic bursting frequency of the cell. Nevertheless, a heterogeneous population of ET cells (as may be found in a glomerulus) is able to produce reliable periodic population responses even at lower input frequencies. link: http://identifiers.org/pubmed/30290156

Parameters:

Name Description
hNaP_tau = 483.668077978459; hNaP_inf = 0.373251468077049 Reaction: => hNaP, Rate Law: compartment*(hNaP_inf-hNaP)/hNaP_tau
nHVK_tau = 1000.00007479349; nHVK_inf = 2.244842984971E-5 Reaction: => nHVK, Rate Law: compartment*(nHVK_inf-nHVK)/nHVK_tau
tau_Ca = 8.0; Ca0 = 2.0E-5 Reaction: => Ca, Rate Law: compartment*(Ca0-Ca)/tau_Ca
hH_tau = 7.13025057731447; hH_inf = 0.155405252349385 Reaction: => hH, Rate Law: compartment*(hH_inf-hH)/hH_tau
hLVA_tau = 329.955639297499; hLVA_inf = 0.333222156222541 Reaction: => hLVA, Rate Law: compartment*(hLVA_inf-hLVA)/hLVA_tau
mLVA_tau = 17.5876479384678; mLVA_inf = 0.0509254933768459 Reaction: => mLVA, Rate Law: compartment*(mLVA_inf-mLVA)/mLVA_tau
IBK = 31.678826380681; INa = -0.012838612439222; IHVA = -0.585636135043006; INaP = -6.09894732028694; C = 21.0; ILVA = -1.31979373465397; IK = 0.00668833084914886; IHVK = 0.0878809960822716; IL = 27.7286628129031; IH = -51.7633892852534 Reaction: V =>, Rate Law: compartment*(INa+IK+ILVA+IH+INaP+IL+IHVA+IBK+IHVK)/C
d = 1.0; Ca_buffer = 0.5; F = 96485.0; IHVA = -0.585636135043006; ILVA = -1.31979373465397; Ca_z = 2.0 Reaction: Ca =>, Rate Law: compartment*Ca_buffer*10*(ILVA+IHVA)/(Ca_z*F*d)
nK_Inf = 0.0560848507623637; nK_tau = 4.60171012895541 Reaction: => nK, Rate Law: compartment*(nK_Inf-nK)/nK_tau
mBK_inf = 2.00990799551082E-5; mBK_tau = 219.103190338819 Reaction: => mBK, Rate Law: compartment*(mBK_inf-mBK)/mBK_tau

States:

Name Description
hNaP [Electrical Current; C830]
nHVK [C765; Electrical Current]
mLVA [Electrical Current; C331]
V [SBO:0000259]
hLVA [C331; Electrical Current]
Ca [C331]
hH [Electrical Current]
nK [Electrical Current; C765]
mBK [C765; Electrical Current]

Observables: none

"Modeling the Role of Peroxisome Proliferator-Activated Receptor γ and MicroRNA-146 in Mucosal Immune Responses to Clost…

Clostridium difficile is an anaerobic bacterium that has re-emerged as a facultative pathogen and can cause nosocomial diarrhea, colitis or even death. Peroxisome proliferator-activated receptor (PPAR) γ has been implicated in the prevention of inflammation in autoimmune and infectious diseases; however, its role in the immunoregulatory mechanisms modulating host responses to C. difficile and its toxins remains largely unknown. To characterize the role of PPARγ in C. difficile-associated disease (CDAD), immunity and gut pathology, we used a mouse model of C. difficile infection in wild-type and T cell-specific PPARγ null mice. The loss of PPARγ in T cells increased disease activity and colonic inflammatory lesions following C. difficile infection. Colonic expression of IL-17 was upregulated and IL-10 downregulated in colons of T cell-specific PPARγ null mice. Also, both the loss of PPARγ in T cells and C. difficile infection favored Th17 responses in spleen and colonic lamina propria of mice with CDAD. MicroRNA (miRNA)-sequencing analysis and RT-PCR validation indicated that miR-146b was significantly overexpressed and nuclear receptor co-activator 4 (NCOA4) suppressed in colons of C. difficile-infected mice. We next developed a computational model that predicts the upregulation of miR-146b, downregulation of the PPARγ co-activator NCOA4, and PPARγ, leading to upregulation of IL-17. Oral treatment of C. difficile-infected mice with the PPARγ agonist pioglitazone ameliorated colitis and suppressed pro-inflammatory gene expression. In conclusion, our data indicates that miRNA-146b and PPARγ activation may be implicated in the regulation of Th17 responses and colitis in C. difficile-infected mice. link: http://identifiers.org/pubmed/23071818

Parameters: none

States: none

Observables: none

BIOMD0000000035 @ v0.0.1

# # Minimal Model for Circadian Oscillations CitationVilar JMG, Kueh HY, Barkai N, Leibler S, (2002) . M…

A wide range of organisms use circadian clocks to keep internal sense of daily time and regulate their behavior accordingly. Most of these clocks use intracellular genetic networks based on positive and negative regulatory elements. The integration of these "circuits" at the cellular level imposes strong constraints on their functioning and design. Here, we study a recently proposed model [Barkai, N. & Leibler, S. (2000) Nature (London), 403, 267-268] that incorporates just the essential elements found experimentally. We show that this type of oscillator is driven mainly by two elements: the concentration of a repressor protein and the dynamics of an activator protein forming an inactive complex with the repressor. Thus, the clock does not need to rely on mRNA dynamics to oscillate, which makes it especially resistant to fluctuations. Oscillations can be present even when the time average of the number of mRNA molecules goes below one. Under some conditions, this oscillator is not only resistant to but, paradoxically, also enhanced by the intrinsic biochemical noise. link: http://identifiers.org/pubmed/11972055

Parameters:

Name Description
alphaAp=500.0 Reaction: DAp => DAp + MA, Rate Law: DAp*alphaAp
alphaA=50.0 Reaction: DA => DA + MA, Rate Law: DA*alphaA
alphaR=0.01 Reaction: DR => DR + MR, Rate Law: DR*alphaR
deltaR=0.2 Reaction: R => EmptySet, Rate Law: R*deltaR
betaR=5.0 Reaction: MR => MR + R, Rate Law: MR*betaR
deltaMR=0.5 Reaction: MR => EmptySet, Rate Law: MR*deltaMR
betaA=50.0 Reaction: MA => A + MA, Rate Law: MA*betaA
gammaA=1.0 Reaction: A + DA => DAp, Rate Law: A*DA*gammaA
alphaRp=50.0 Reaction: DRp => DRp + MR, Rate Law: DRp*alphaRp
thetaR=100.0 Reaction: DRp => A + DR, Rate Law: DRp*thetaR
deltaMA=10.0 Reaction: MA => EmptySet, Rate Law: MA*deltaMA
gammaC=2.0 Reaction: A + R => C, Rate Law: A*R*gammaC
thetaA=50.0 Reaction: DAp => A + DA, Rate Law: DAp*thetaA
gammaR=1.0 Reaction: A + DR => DRp, Rate Law: A*DR*gammaR
deltaA=1.0 Reaction: A => EmptySet, Rate Law: A*deltaA

States:

Name Description
DR DR
A [protein]
MR [messenger RNA]
C [protein]
DRp DRp
MA [messenger RNA]
DA DA
R [protein]
DAp DAp

Observables: none

BIOMD0000000101 @ v0.0.1

The model reproduces Fig 5A of the paper. The ligand concentration is increased from 3E-5 to 0.01 at time t=2500 to ensu…

The TGF-beta pathway plays a central role in tissue homeostasis and morphogenesis. It transduces a variety of extracellular signals into intracellular transcriptional responses that control a plethora of cellular processes, including cell growth, apoptosis, and differentiation. We use computational modeling to show that coupling of signaling with receptor trafficking results in a highly versatile signal-processing unit, able to sense by itself absolute levels of ligand, temporal changes in ligand concentration, and ratios of multiple ligands. This coupling controls whether the response of the receptor module is transient or permanent and whether or not different signaling channels behave independently of each other. Our computational approach unifies seemingly disparate experimental observations and suggests specific changes in receptor trafficking patterns that can lead to phenotypes that favor tumor progression. link: http://identifiers.org/pubmed/16446785

Parameters:

Name Description
klid = 0.25 Reaction: lRIRII =>, Rate Law: klid*lRIRII
ki = 0.3333333333333 Reaction: lRIRII => lRIRII_endo, Rate Law: ki*lRIRII
pRI = 8.0 Reaction: => RI, Rate Law: pRI
ka = 1.0; ligand = 3.0E-5 Reaction: RII + RI => lRIRII, Rate Law: ka*ligand*RI*RII
pRII = 4.0 Reaction: => RII, Rate Law: pRII
kcd = 0.0277777778 Reaction: RII =>, Rate Law: kcd*RII
kr = 0.0333333333333333 Reaction: RII_endo => RII, Rate Law: kr*RII_endo

States:

Name Description
RI endo [TGF-beta receptor type-1]
lRIRII [Transforming growth factor beta-1; TGF-beta receptor type-1; TGF-beta receptor type-2]
RII [TGF-beta receptor type-2]
lRIRII endo [Transforming growth factor beta-1; TGF-beta receptor type-1; TGF-beta receptor type-2]
RI [TGF-beta receptor type-1]
RII endo [TGF-beta receptor type-2]

Observables: none

Genome-scale metabolic model of Phaeodactylum tricornutum

Diatoms are prominent marine microalgae, interesting not only from an ecological point of view, but also for their possible use in biotechnology applications. They can be cultivated in phototrophic conditions, using sunlight as the sole energy source. Some diatoms, however, can also grow in a mixotrophic mode, wherein both light and external reduced carbon contribute to biomass accumulation. In this study, we investigated the consequences of mixotrophy on the growth and metabolism of the pennate diatom Phaeodactylum tricornutum, using glycerol as the source of reduced carbon. Transcriptomics, metabolomics, metabolic modelling and physiological data combine to indicate that glycerol affects the central-carbon, carbon-storage and lipid metabolism of the diatom. In particular, provision of glycerol mimics typical responses of nitrogen limitation on lipid metabolism at the level of triacylglycerol accumulation and fatty acid composition. The presence of glycerol, despite provoking features reminiscent of nutrient limitation, neither diminishes photosynthetic activity nor cell growth, revealing essential aspects of the metabolic flexibility of these microalgae and suggesting possible biotechnological applications of mixotrophy. link: http://identifiers.org/doi/10.1098/rstb.2016.0404

Parameters: none

States: none

Observables: none

This a model from the article: Dynamics of muscle glycogenolysis modeled with pH time course computation and pH-depend…

Cellular metabolites are moieties defined by their specific binding constants to H+, Mg2+, and K+ or anions without ligands. As a consequence, every biochemical reaction in the cytoplasm has an associated proton stoichiometry that is generally noninteger- and pH-dependent. Therefore, with metabolic flux, pH is altered in a medium with finite buffer capacity. Apparent equilibrium constants and maximum enzyme velocities, which are functions of pH, are also altered. We augmented an earlier mathematical model of skeletal muscle glycogenolysis with pH-dependent enzyme kinetics and reaction equilibria to compute the time course of pH changes. Analysis shows that kinetics and final equilibrium states of the closed system are highly constrained by the pH-dependent parameters. This kinetic model of glycogenolysis, coupled to creatine kinase and adenylate kinase, simulated published experiments made with a cell-free enzyme mixture to reconstitute the network and to synthesize PCr and lactate in vitro. Using the enzyme kinetic and thermodynamic data in the literature, the simulations required minimal adjustments of parameters to describe the data. These results show that incorporation of appropriate physical chemistry of the reactions with accurate kinetic modeling gives a reasonable simulation of experimental data and is necessary for a physically correct representation of the metabolic network. The approach is general for modeling metabolic networks beyond the specific pathway and conditions presented here. link: http://identifiers.org/pubmed/16617075

Parameters: none

States: none

Observables: none

This a model from the article: Dynamics of muscle glycogenolysis modeled with pH time course computation and pH-depend…

Cellular metabolites are moieties defined by their specific binding constants to H+, Mg2+, and K+ or anions without ligands. As a consequence, every biochemical reaction in the cytoplasm has an associated proton stoichiometry that is generally noninteger- and pH-dependent. Therefore, with metabolic flux, pH is altered in a medium with finite buffer capacity. Apparent equilibrium constants and maximum enzyme velocities, which are functions of pH, are also altered. We augmented an earlier mathematical model of skeletal muscle glycogenolysis with pH-dependent enzyme kinetics and reaction equilibria to compute the time course of pH changes. Analysis shows that kinetics and final equilibrium states of the closed system are highly constrained by the pH-dependent parameters. This kinetic model of glycogenolysis, coupled to creatine kinase and adenylate kinase, simulated published experiments made with a cell-free enzyme mixture to reconstitute the network and to synthesize PCr and lactate in vitro. Using the enzyme kinetic and thermodynamic data in the literature, the simulations required minimal adjustments of parameters to describe the data. These results show that incorporation of appropriate physical chemistry of the reactions with accurate kinetic modeling gives a reasonable simulation of experimental data and is necessary for a physically correct representation of the metabolic network. The approach is general for modeling metabolic networks beyond the specific pathway and conditions presented here. link: http://identifiers.org/pubmed/16617075

Parameters: none

States: none

Observables: none

This a model from the article: Dynamics of muscle glycogenolysis modeled with pH time course computation and pH-depend…

Cellular metabolites are moieties defined by their specific binding constants to H+, Mg2+, and K+ or anions without ligands. As a consequence, every biochemical reaction in the cytoplasm has an associated proton stoichiometry that is generally noninteger- and pH-dependent. Therefore, with metabolic flux, pH is altered in a medium with finite buffer capacity. Apparent equilibrium constants and maximum enzyme velocities, which are functions of pH, are also altered. We augmented an earlier mathematical model of skeletal muscle glycogenolysis with pH-dependent enzyme kinetics and reaction equilibria to compute the time course of pH changes. Analysis shows that kinetics and final equilibrium states of the closed system are highly constrained by the pH-dependent parameters. This kinetic model of glycogenolysis, coupled to creatine kinase and adenylate kinase, simulated published experiments made with a cell-free enzyme mixture to reconstitute the network and to synthesize PCr and lactate in vitro. Using the enzyme kinetic and thermodynamic data in the literature, the simulations required minimal adjustments of parameters to describe the data. These results show that incorporation of appropriate physical chemistry of the reactions with accurate kinetic modeling gives a reasonable simulation of experimental data and is necessary for a physically correct representation of the metabolic network. The approach is general for modeling metabolic networks beyond the specific pathway and conditions presented here. link: http://identifiers.org/pubmed/16617075

Parameters: none

States: none

Observables: none

MODEL1006230100 @ v0.0.1

This a model from the article: Common phenotype of resting mouse extensor digitorum longus and soleus muscles: equal A…

Rates of ATPase and glycolysis are several times faster in actively contracting mouse extensor digitorum longus muscle (EDL) than soleus (SOL), but we find these rates are not distinguishable at rest. We used a transient anoxic perturbation of steady state energy balance to decrease phosphocreatine (PCr) reversibly and to measure the rates of ATPase and of lactate production without muscle activation or contraction. The rate of glycolytic ATP synthesis is less than the ATPase rate, accounting for the continual PCr decrease during anoxia in both muscles. We fitted a mathematical model validated with properties of enzymes and solutes measured in vitro and appropriate for the transient perturbation of these muscles to experimental data to test whether the model accounts for the results. Simulations showed equal rates of ATPase and lactate production in both muscles. ATPase controls glycolytic flux by feedback from its products. Adenylate kinase function is critical because a rise in [AMP] is necessary to activate glycogen phosphorylase. ATPase is the primary source of H+ production. The sum of contributions of the 13 reactions of the glycogenolytic and glycolytic network to total proton load is negligible. The stoichiometry of lactate and H+ production is near unity. These results identify a default state of energy metabolism for resting muscle in which there is no difference in the metabolic phenotype of EDL and SOL. Therefore, additional control mechanisms, involving higher ATPase flux and [Ca2+], must exist to explain the well-known difference in glycolytic rates in fast-twitch and slow-twitch muscles in actively contracting muscle. link: http://identifiers.org/pubmed/20308252

Parameters: none

States: none

Observables: none

This a model from the article: Common phenotype of resting mouse extensor digitorum longus and soleus muscles: equal A…

Rates of ATPase and glycolysis are several times faster in actively contracting mouse extensor digitorum longus muscle (EDL) than soleus (SOL), but we find these rates are not distinguishable at rest. We used a transient anoxic perturbation of steady state energy balance to decrease phosphocreatine (PCr) reversibly and to measure the rates of ATPase and of lactate production without muscle activation or contraction. The rate of glycolytic ATP synthesis is less than the ATPase rate, accounting for the continual PCr decrease during anoxia in both muscles. We fitted a mathematical model validated with properties of enzymes and solutes measured in vitro and appropriate for the transient perturbation of these muscles to experimental data to test whether the model accounts for the results. Simulations showed equal rates of ATPase and lactate production in both muscles. ATPase controls glycolytic flux by feedback from its products. Adenylate kinase function is critical because a rise in [AMP] is necessary to activate glycogen phosphorylase. ATPase is the primary source of H+ production. The sum of contributions of the 13 reactions of the glycogenolytic and glycolytic network to total proton load is negligible. The stoichiometry of lactate and H+ production is near unity. These results identify a default state of energy metabolism for resting muscle in which there is no difference in the metabolic phenotype of EDL and SOL. Therefore, additional control mechanisms, involving higher ATPase flux and [Ca2+], must exist to explain the well-known difference in glycolytic rates in fast-twitch and slow-twitch muscles in actively contracting muscle. link: http://identifiers.org/pubmed/20308252

Parameters: none

States: none

Observables: none

BIOMD0000000370 @ v0.0.1

This model is from the article: Computational modelling of mitotic exit in budding yeast: the role of separase and Cdc…

The operating principles of complex regulatory networks are best understood with the help of mathematical modelling rather than by intuitive reasoning. Hereby, we study the dynamics of the mitotic exit (ME) control system in budding yeast by further developing the Queralt's model. A comprehensive systems view of the network regulating ME is provided based on classical experiments in the literature. In this picture, Cdc20-APC is a critical node controlling both cyclin (Clb2 and Clb5) and phosphatase (Cdc14) branches of the regulatory network. On the basis of experimental situations ranging from single to quintuple mutants, the kinetic parameters of the network are estimated. Numerical analysis of the model quantifies the dependence of ME control on the proteolytic and non-proteolytic functions of separase. We show that the requirement of the non-proteolytic function of separase for ME depends on cyclin-dependent kinase activity. The model is also used for the systematic analysis of the recently discovered Cdc14 endocycles. The significance of Cdc14 endocycles in eukaryotic cell cycle control is discussed as well. link: http://identifiers.org/pubmed/21288956

Parameters:

Name Description
kssic_1 = 0.2; Vdsic_1 = NaN; kssic_2 = 0.004 Reaction: Sic1T_1 = (kssic_2+kssic_1*Swi5_1)-Vdsic_1*Sic1T_1, Rate Law: (kssic_2+kssic_1*Swi5_1)-Vdsic_1*Sic1T_1
Clb2nd_1 = 0.0 Reaction: Clb2_2 = (Clb2T_1+Clb2nd_1)-Trim2_1, Rate Law: missing
Vd_1 = NaN; Vp_1 = NaN; Net1T_1 = 1.0 Reaction: Net1dep_1 = Vd_1*(Net1T_1-Net1dep_1)-Vp_1*Net1dep_1, Rate Law: Vd_1*(Net1T_1-Net1dep_1)-Vp_1*Net1dep_1
Viswi_1 = NaN; Vaswi_1 = NaN; Swi5T_1 = 1.0; Jswi_1 = 0.1 Reaction: Swi5_1 = Vaswi_1*(Swi5T_1-Swi5_1)/((Jswi_1+Swi5T_1)-Swi5_1)-Viswi_1*Swi5_1/(Jswi_1+Swi5_1), Rate Law: Vaswi_1*(Swi5T_1-Swi5_1)/((Jswi_1+Swi5T_1)-Swi5_1)-Viswi_1*Swi5_1/(Jswi_1+Swi5_1)
kp_1 = 2.0; ldnet_1 = 1.0; Vd_1 = NaN; Vp_1 = NaN; lanet_1 = 500.0; Net1T_1 = 1.0 Reaction: RENTp_1 = (((Vp_1*(RENT_1-RENTp_1)-Vd_1*RENTp_1)+lanet_1*(((Net1T_1-Net1dep_1)-Net1pp_1)-RENTp_1)*Cdc14n_1)-ldnet_1*RENTp_1)-kp_1*Polo_1*RENTp_1, Rate Law: (((Vp_1*(RENT_1-RENTp_1)-Vd_1*RENTp_1)+lanet_1*(((Net1T_1-Net1dep_1)-Net1pp_1)-RENTp_1)*Cdc14n_1)-ldnet_1*RENTp_1)-kp_1*Polo_1*RENTp_1
kimbf_1 = 0.5; Jmbf_1 = 0.01; kambf_1 = 0.1; kimbf_3 = 0.5 Reaction: MBF_1 = kambf_1*(1-MBF_1)/((Jmbf_1+1)-MBF_1)-(kimbf_1*Clb2_2+kimbf_3*Clb5_1)*MBF_1/(Jmbf_1+MBF_1), Rate Law: kambf_1*(1-MBF_1)/((Jmbf_1+1)-MBF_1)-(kimbf_1*Clb2_2+kimbf_3*Clb5_1)*MBF_1/(Jmbf_1+MBF_1)
kdsic2_1 = 0.1; V2_1 = NaN; Vdsic_1 = NaN; kasic2_1 = 40.0 Reaction: Trim2_1 = kasic2_1*Clb2_2*Sic1_1-(kdsic2_1+V2_1+Vdsic_1)*Trim2_1, Rate Law: kasic2_1*Clb2_2*Sic1_1-(kdsic2_1+V2_1+Vdsic_1)*Trim2_1
ksclb5_1 = 0.01; V6_1 = NaN; ksclb5_2 = 0.002 Reaction: Clb5T_1 = (ksclb5_2+ksclb5_1*MBF_1)-V6_1*Clb5T_1, Rate Law: (ksclb5_2+ksclb5_1*MBF_1)-V6_1*Clb5T_1
katem_2 = 0.6; kitem_3 = 0.1; kitem_1 = 20.0; kitem_2 = 1.0; Jtem1_1 = 0.005; katem_1 = 0.0 Reaction: Tem1_1 = (katem_1+katem_2*Polo_1)*(1-Tem1_1)/((Jtem1_1+1)-Tem1_1)-(kitem_3+kitem_2/(1+kitem_1*Esp1_1))/(Jtem1_1+Tem1_1)*Tem1_1, Rate Law: (katem_1+katem_2*Polo_1)*(1-Tem1_1)/((Jtem1_1+1)-Tem1_1)-(kitem_3+kitem_2/(1+kitem_1*Esp1_1))/(Jtem1_1+Tem1_1)*Tem1_1
ksclb2_1 = 0.015; V2_1 = NaN; ksclb2_2 = 0.005 Reaction: Clb2T_1 = (ksclb2_1+ksclb2_2*Mcm_1)-V2_1*Clb2T_1, Rate Law: (ksclb2_1+ksclb2_2*Mcm_1)-V2_1*Clb2T_1
kd20_1 = 0.1; ks20_2 = 0.001; ks20_1 = 0.05; kd20_2 = 1.0 Reaction: Cdc20_1 = (ks20_2+ks20_1*Mcm_1)-(kd20_1+kd20_2*Cdh1_1)*Cdc20_1, Rate Law: (ks20_2+ks20_1*Mcm_1)-(kd20_1+kd20_2*Cdh1_1)*Cdc20_1
kapolo_1 = 0.0; kipolo_1 = 0.1; kapolo_2 = 1.0; kdpolo_1 = 0.05; kdpolo_2 = 0.5; Jpolo_1 = 0.1 Reaction: Polo_1 = ((kapolo_1+kapolo_2*Clb2_2)*(PoloT_1-Polo_1)/((Jpolo_1+PoloT_1)-Polo_1)-kipolo_1*Polo_1/(Jpolo_1+Polo_1))-(kdpolo_1+kdpolo_2*Cdh1_1)*Polo_1, Rate Law: ((kapolo_1+kapolo_2*Clb2_2)*(PoloT_1-Polo_1)/((Jpolo_1+PoloT_1)-Polo_1)-kipolo_1*Polo_1/(Jpolo_1+Polo_1))-(kdpolo_1+kdpolo_2*Cdh1_1)*Polo_1
Jcdh_1 = 0.01; Vicdh_1 = NaN; Vacdh_1 = NaN Reaction: Cdh1_1 = Vacdh_1*(1-Cdh1_1)/((Jcdh_1+1)-Cdh1_1)-Vicdh_1*Cdh1_1/(Jcdh_1+Cdh1_1), Rate Law: Vacdh_1*(1-Cdh1_1)/((Jcdh_1+1)-Cdh1_1)-Vicdh_1*Cdh1_1/(Jcdh_1+Cdh1_1)
kp_1 = 2.0; ldnet_1 = 1.0; lanet_1 = 500.0; kimp_1 = 1.0; Vexp_1 = NaN; Net1T_1 = 1.0 Reaction: Cdc14n_1 = (((kp_1*Polo_1*RENTp_1-lanet_1*((Net1T_1-Net1pp_1)-RENT_1)*Cdc14n_1)+ldnet_1*RENT_1)-Vexp_1*Cdc14n_1)+kimp_1*Cdc14c_1, Rate Law: (((kp_1*Polo_1*RENTp_1-lanet_1*((Net1T_1-Net1pp_1)-RENT_1)*Cdc14n_1)+ldnet_1*RENT_1)-Vexp_1*Cdc14n_1)+kimp_1*Cdc14c_1
kdcln_1 = 0.25; kscln_1 = 0.1; kscln_2 = 0.01 Reaction: Cln_1 = (kscln_2+kscln_1*MBF_1)-kdcln_1*Cln_1, Rate Law: (kscln_2+kscln_1*MBF_1)-kdcln_1*Cln_1
Net1T_1 = 1.0 Reaction: Net1_2 = ((Net1T_1-Net1p_1)-RENT_1)-Net1pp_1, Rate Law: missing
ksmcm_1 = 1.0; Jmcm_1 = 0.01; kdmcm_1 = 0.25; ksmcm_3 = 0.01 Reaction: Mcm_1 = (ksmcm_3+ksmcm_1*Clb2_2)*(1-Mcm_1)/((Jmcm_1+1)-Mcm_1)-kdmcm_1*Mcm_1/(Jmcm_1+Mcm_1), Rate Law: (ksmcm_3+ksmcm_1*Clb2_2)*(1-Mcm_1)/((Jmcm_1+1)-Mcm_1)-kdmcm_1*Mcm_1/(Jmcm_1+Mcm_1)
Jcdc15_1 = 1.0; ldmen_1 = 0.1; kitem_3 = 0.1; kic15_2 = 0.2; kitem_2 = 1.0; lamen_1 = 100.0; kic15_1 = 0.03; Jtem1_1 = 0.005 Reaction: MEN_1 = ((lamen_1*(Tem1_1-MEN_1)*(Cdc15_1-MEN_1)-ldmen_1*MEN_1)-(kitem_3+kitem_2/(1+kitem_3*Esp1_1))/(Jtem1_1+Tem1_1)*MEN_1)-(kic15_1+kic15_2*Clb2_2)/(Jcdc15_1+Cdc15_1)*MEN_1, Rate Law: ((lamen_1*(Tem1_1-MEN_1)*(Cdc15_1-MEN_1)-ldmen_1*MEN_1)-(kitem_3+kitem_2/(1+kitem_3*Esp1_1))/(Jtem1_1+Tem1_1)*MEN_1)-(kic15_1+kic15_2*Clb2_2)/(Jcdc15_1+Cdc15_1)*MEN_1
lapds_1 = 500.0; ldpds_1 = 1.0; kdesp_1 = 0.004; kdpds_2 = 2.0; kdpds_1 = 0.01 Reaction: Esp1b_1 = lapds_1*Pds1_1*Esp1_1-(ldpds_1+kdesp_1+kdpds_1+kdpds_2*Cdc20_1)*Esp1b_1, Rate Law: lapds_1*Pds1_1*Esp1_1-(ldpds_1+kdesp_1+kdpds_1+kdpds_2*Cdc20_1)*Esp1b_1
kp_1 = 2.0; Vd_1 = NaN; Net1T_1 = 1.0 Reaction: Net1pp_1 = kp_1*Polo_1*((Net1T_1-Net1dep_1)-Net1pp_1)-Vd_1*Net1pp_1, Rate Law: kp_1*Polo_1*((Net1T_1-Net1dep_1)-Net1pp_1)-Vd_1*Net1pp_1
kp_1 = 2.0; ldnet_1 = 1.0; lanet_1 = 500.0; Net1T_1 = 1.0 Reaction: RENT_1 = (lanet_1*((Net1T_1-Net1pp_1)-RENT_1)*Cdc14n_1-ldnet_1*RENT_1)-kp_1*Polo_1*RENTp_1, Rate Law: (lanet_1*((Net1T_1-Net1pp_1)-RENT_1)*Cdc14n_1-ldnet_1*RENT_1)-kp_1*Polo_1*RENTp_1
kasic5_1 = 10.0; kdsic5_1 = 0.1; V6_1 = NaN; Vdsic_1 = NaN Reaction: Trim5_1 = kasic5_1*Clb5_1*Sic1_1-(kdsic5_1+V6_1+Vdsic_1)*Trim5_1, Rate Law: kasic5_1*Clb5_1*Sic1_1-(kdsic5_1+V6_1+Vdsic_1)*Trim5_1
kdesp_1 = 0.004; ksesp_1 = 0.001 Reaction: Esp1T_1 = ksesp_1-kdesp_1*Esp1T_1, Rate Law: ksesp_1-kdesp_1*Esp1T_1
kspds_1 = 0.01; kdpds_2 = 2.0; kspds_2 = 0.006; kdpds_1 = 0.01 Reaction: Pds1T_1 = (kspds_2+kspds_1*MBF_1)-(kdpds_1+kdpds_2*Cdc20_1)*Pds1T_1, Rate Law: (kspds_2+kspds_1*MBF_1)-(kdpds_1+kdpds_2*Cdc20_1)*Pds1T_1
Cdc14T_1 = 0.5 Reaction: Cdc14c_1 = (Cdc14T_1-Cdc14n_1)-RENT_1, Rate Law: missing
Jcdc15_1 = 1.0; kac15_2 = 0.5; kic15_2 = 0.2; kac15_1 = 0.03; kic15_1 = 0.03 Reaction: Cdc15_1 = (kac15_1+kac15_2*Cdc14c_1)*(1-Cdc15_1)/((Jcdc15_1+1)-Cdc15_1)-(kic15_1+kic15_2*Clb2_2)*Cdc15_1/(Jcdc15_1+Cdc15_1), Rate Law: (kac15_1+kac15_2*Cdc14c_1)*(1-Cdc15_1)/((Jcdc15_1+1)-Cdc15_1)-(kic15_1+kic15_2*Clb2_2)*Cdc15_1/(Jcdc15_1+Cdc15_1)
kspolo_1 = 0.05; kdpolo_1 = 0.05; kdpolo_2 = 0.5; kspolo_2 = 0.001 Reaction: PoloT_1 = (kspolo_2+kspolo_1*Mcm_1)-(kdpolo_1+kdpolo_2*Cdh1_1)*PoloT_1, Rate Law: (kspolo_2+kspolo_1*Mcm_1)-(kdpolo_1+kdpolo_2*Cdh1_1)*PoloT_1

States:

Name Description
Pds1 1 [Securin]
Sic1 1 [Protein SIC1]
Esp1T 1 [Separin]
Cdc15 1 [Cell division control protein 15]
Esp1 1 [Separin]
Cdh1 1 [APC/C activator protein CDH1]
MEN 1 [Cell division control protein 15; Protein TEM1]
Trim5 1 [Cyclin-dependent kinase 1; S-phase entry cyclin-5; Protein SIC1]
Esp1b 1 [Separin]
Net1dep 1 [Nucleolar protein NET1]
Cdc14n 1 [Tyrosine-protein phosphatase CDC14]
Tem1 1 [Protein TEM1]
Pds1T 1 [Securin]
PoloT 1 [Cell cycle serine/threonine-protein kinase CDC5/MSD2]
Net1pp 1 [Nucleolar protein NET1; Phosphoprotein]
MBF 1 [Multiprotein-bridging factor 1]
Swi5 1 [Transcriptional factor SWI5]
RENTp 1 [Phosphoprotein; Tyrosine-protein phosphatase CDC14; Nucleolar protein NET1]
Cdc14c 1 [Tyrosine-protein phosphatase CDC14]
Clb5T 1 [S-phase entry cyclin-5]
Sic1T 1 [Protein SIC1]
Trim2 1 [Cyclin-dependent kinase 1; G2/mitotic-specific cyclin-2; Protein SIC1]
Clb2 2 [G2/mitotic-specific cyclin-2]
Polo 1 [Cell cycle serine/threonine-protein kinase CDC5/MSD2]
Cln 1 [G1/S-specific cyclin CLN2]
Net1 2 [Nucleolar protein NET1]
Cdc20 1 [APC/C activator protein CDC20]
Mcm 1 [Nuclear division defective protein 1; Fork head protein homolog 2; Pheromone receptor transcription factor]
Clb2T 1 [G2/mitotic-specific cyclin-2]
Clb5 1 [S-phase entry cyclin-5]
RENT 1 [Tyrosine-protein phosphatase CDC14; Nucleolar protein NET1]

Observables: none

BIOMD0000000266 @ v0.0.1

Voit2003 - Trehalose CycleThis model is described in the article: [Biochemical and genomic regulation of the trehalose…

The physiological hallmark of heat-shock response in yeast is a rapid, enormous increase in the concentration of trehalose. Normally found in growing yeast cells and other organisms only as traces, trehalose becomes a crucial protector of proteins and membranes against a variety of stresses, including heat, cold, starvation, desiccation, osmotic or oxidative stress, and exposure to toxicants. Trehalose is produced from glucose 6-phosphate and uridine diphosphate glucose in a two-step process, and recycled to glucose by trehalases. Even though the trehalose cycle consists of only a few metabolites and enzymatic steps, its regulatory structure and operation are surprisingly complex. The article begins with a review of experimental observations on the regulation of the trehalose cycle in yeast and proposes a canonical model for its analysis. The first part of this analysis demonstrates the benefits of the various regulatory features by means of controlled comparisons with models of otherwise equivalent pathways lacking these features. The second part elucidates the significance of the expression pattern of the trehalose cycle genes in response to heat shock. Interestingly, the genes contributing to trehalose formation are up-regulated to very different degrees, and even the trehalose degrading trehalases show drastically increased activity during heat-shock response. Again using the method of controlled comparisons, the model provides rationale for the observed pattern of gene expression and reveals benefits of the counterintuitive trehalase up-regulation. link: http://identifiers.org/pubmed/12782117

Parameters:

Name Description
flux_X1_in = NaN mM per minute; flux_X1_out = NaN mM per minute Reaction: X1 = flux_X1_in-flux_X1_out, Rate Law: flux_X1_in-flux_X1_out
flux_X5_out = NaN mM per minute; flux_X5_in = NaN mM per minute Reaction: X5 = flux_X5_in-flux_X5_out, Rate Law: flux_X5_in-flux_X5_out
flux_X3_in = NaN mM per minute; flux_X3_out = NaN mM per minute Reaction: X3 = flux_X3_in-flux_X3_out, Rate Law: flux_X3_in-flux_X3_out
flux_X6_in = NaN mM per minute; flux_X6_out = NaN mM per minute Reaction: X6 = flux_X6_in-flux_X6_out, Rate Law: flux_X6_in-flux_X6_out
flux_X4_out = NaN mM per minute; flux_X4_in = NaN mM per minute Reaction: X4 = flux_X4_in-flux_X4_out, Rate Law: flux_X4_in-flux_X4_out
flux_X2_in = NaN mM per minute; flux_X2_out = NaN mM per minute Reaction: X2 = flux_X2_in-flux_X2_out, Rate Law: flux_X2_in-flux_X2_out
flux_X7_in = NaN mM per minute; flux_X7_out = NaN mM per minute Reaction: X7 = flux_X7_in-flux_X7_out, Rate Law: flux_X7_in-flux_X7_out

States:

Name Description
X3 [D-glucopyranose 1-phosphate]
X7 [alpha,alpha-trehalose]
X4 [UDP-D-glucose]
X5 [glycogen]
X2 [alpha-D-glucose 6-phosphate]
X1 [alpha-D-glucose]
X6 [alpha,alpha-trehalose 6-phosphate]

Observables: none

BIOMD0000000931 @ v0.0.1

Fertility critically depends on the gonadotropin-releasing hormone (GnRH) pulse generator, a neural construct comprised…

Fertility critically depends on the gonadotropin-releasing hormone (GnRH) pulse generator, a neural construct comprised of hypothalamic neurons coexpressing kisspeptin, neurokoinin-B and dynorphin. Here, using mathematical modeling and in vivo optogenetics we reveal for the first time how this neural construct initiates and sustains the appropriate ultradian frequency essential for reproduction. Prompted by mathematical modeling, we show experimentally using female estrous mice that robust pulsatile release of luteinizing hormone, a proxy for GnRH, emerges abruptly as we increase the basal activity of the neuronal network using continuous low-frequency optogenetic stimulation. Further increase in basal activity markedly increases pulse frequency and eventually leads to pulse termination. Additional model predictions that pulsatile dynamics emerge from nonlinear positive and negative feedback interactions mediated through neurokinin-B and dynorphin signaling respectively are confirmed neuropharmacologically. Our results shed light on the long-elusive GnRH pulse generator offering new horizons for reproductive health and wellbeing.SIGNIFICANCE STATEMENT The gonadotropin-releasing hormone (GnRH) pulse generator controls the pulsatile secretion of the gonadotropic hormones LH and FSH and is critical for fertility. The hypothalamic arcuate kisspeptin neurons are thought to represent the GnRH pulse generator, since their oscillatory activity is coincident with LH pulses in the blood; a proxy for GnRH pulses. However, the mechanisms underlying GnRH pulse generation remain elusive. We developed a mathematical model of the kisspeptin neuronal network and confirmed its predictions experimentally, showing how LH secretion is frequency-modulated as we increase the basal activity of the arcuate kisspeptin neurons in vivo using continuous optogenetic stimulation. Our model provides a quantitative framework for understanding the reproductive neuroendocrine system and opens new horizons for fertility regulation. link: http://identifiers.org/pubmed/31645462

Parameters:

Name Description
d_N = 1.0 Reaction: N =>, Rate Law: compartment*d_N*N
f_N = 0.302353418222975 Reaction: => N, Rate Law: compartment*f_N
f_v = 3016.26432543932 Reaction: => v, Rate Law: compartment*f_v
d_D = 0.25 Reaction: D =>, Rate Law: compartment*d_D*D
f_D = 0.439705882352941 Reaction: => D, Rate Law: compartment*f_D
d_v = 10.0 Reaction: v =>, Rate Law: compartment*d_v*v

States:

Name Description
v v
N N
D D

Observables: none

Vongsangnak2008 - Genome-scale metabolic network of Aspergillus oryzae (iWV1314)This model is described in the article:…

BACKGROUND: Since ancient times the filamentous fungus Aspergillus oryzae has been used in the fermentation industry for the production of fermented sauces and the production of industrial enzymes. Recently, the genome sequence of A. oryzae with 12,074 annotated genes was released but the number of hypothetical proteins accounted for more than 50% of the annotated genes. Considering the industrial importance of this fungus, it is therefore valuable to improve the annotation and further integrate genomic information with biochemical and physiological information available for this microorganism and other related fungi. Here we proposed the gene prediction by construction of an A. oryzae Expressed Sequence Tag (EST) library, sequencing and assembly. We enhanced the function assignment by our developed annotation strategy. The resulting better annotation was used to reconstruct the metabolic network leading to a genome scale metabolic model of A. oryzae. RESULTS: Our assembled EST sequences we identified 1,046 newly predicted genes in the A. oryzae genome. Furthermore, it was possible to assign putative protein functions to 398 of the newly predicted genes. Noteworthy, our annotation strategy resulted in assignment of new putative functions to 1,469 hypothetical proteins already present in the A. oryzae genome database. Using the substantially improved annotated genome we reconstructed the metabolic network of A. oryzae. This network contains 729 enzymes, 1,314 enzyme-encoding genes, 1,073 metabolites and 1,846 (1,053 unique) biochemical reactions. The metabolic reactions are compartmentalized into the cytosol, the mitochondria, the peroxisome and the extracellular space. Transport steps between the compartments and the extracellular space represent 281 reactions, of which 161 are unique. The metabolic model was validated and shown to correctly describe the phenotypic behavior of A. oryzae grown on different carbon sources. CONCLUSION: A much enhanced annotation of the A. oryzae genome was performed and a genome-scale metabolic model of A. oryzae was reconstructed. The model accurately predicted the growth and biomass yield on different carbon sources. The model serves as an important resource for gaining further insight into our understanding of A. oryzae physiology. link: http://identifiers.org/pubmed/18500999

Parameters: none

States: none

Observables: none

This SBML representation of the Homo sapiens myocyte metabolic network is made available under the Creative Commons Attr…

Skeletal myocytes are metabolically active and susceptible to insulin resistance and are thus implicated in type 2 diabetes (T2D). This complex disease involves systemic metabolic changes, and their elucidation at the systems level requires genome-wide data and biological networks. Genome-scale metabolic models (GEMs) provide a network context for the integration of high-throughput data. We generated myocyte-specific RNA-sequencing data and investigated their correlation with proteome data. These data were then used to reconstruct a comprehensive myocyte GEM. Next, we performed a meta-analysis of six studies comparing muscle transcription in T2D versus healthy subjects. Transcriptional changes were mapped on the myocyte GEM, revealing extensive transcriptional regulation in T2D, particularly around pyruvate oxidation, branched-chain amino acid catabolism, and tetrahydrofolate metabolism, connected through the downregulated dihydrolipoamide dehydrogenase. Strikingly, the gene signature underlying this metabolic regulation successfully classifies the disease state of individual samples, suggesting that regulation of these pathways is a ubiquitous feature of myocytes in response to T2D. link: http://identifiers.org/pubmed/25937284

Parameters: none

States: none

Observables: none

W


BIOMD0000000338 @ v0.0.1

This model is from the article: A comprehensive model for the humoral coagulation network in humans. Wajima T, Is…

Coagulation is an important process in hemostasis and comprises a complicated interaction of multiple enzymes and proteins. We have developed a mechanistic quantitative model of the coagulation network. The model accurately describes the time courses of coagulation factors following in vivo activation as well as in vitro blood coagulation tests of prothrombin time (PT, often reported as international normalized ratio (INR)) and activated partial thromboplastin time (aPTT). The model predicts the concentration-time and time-effect profiles of warfarin, heparins, and vitamin K in humans. The model can be applied to predict the time courses of coagulation kinetics in clinical situations (e.g., hemophilia) and for biomarker identification during drug development. The model developed in this study is the first quantitative description of the comprehensive coagulation network. link: http://identifiers.org/pubmed/19516255

Parameters:

Name Description
v=20000.0; k=0.5 Reaction: Fg => F; IIa, Rate Law: compartment_1*v*Fg*IIa/(k+IIa)
VKH20 = 0.1; II0 = 1394.4; d_II = 0.01 Reaction: => II; VKH2, Rate Law: compartment_1*d_II*II0*VKH2/VKH20
c=0.5 Reaction: VIIa_TF + Xa_TFPI => VIIa_TF_Xa_TFPI, Rate Law: compartment_1*VIIa_TF*Xa_TFPI/c
k1=0.05 Reaction: F => FDP, Rate Law: compartment_1*k1*F
v=50000.0; k=1.0E-6 Reaction: VIII => VIIIa; IIa, Rate Law: compartment_1*v*VIII*IIa/(k+IIa)
k=1.0; v=1.0 Reaction: XF => D; APC_PS, Rate Law: compartment_1*v*XF*APC_PS/(k+APC_PS)
v=7.0; k=10.0 Reaction: F => XF; XIIIa, Rate Law: compartment_1*v*F*XIIIa/(k+XIIIa)
d_Pg = 0.05 Reaction: Pg =>, Rate Law: compartment_1*d_Pg*Pg
PC0 = 60.0; VKH20 = 0.1; d_PC = 0.05 Reaction: => PC; VKH2, Rate Law: compartment_1*d_PC*PC0*VKH2/VKH20
c46 = 0.85 Reaction: IXa + ATIII_Heparin => IXa_ATIII_Heparin, Rate Law: compartment_1*IXa*ATIII_Heparin/c46
c45 = 0.85 Reaction: Xa + ATIII_Heparin => Xa_ATIII_Heparin, Rate Law: compartment_1*Xa*ATIII_Heparin/c45
k=1.0; v=7.0 Reaction: XIII => XIIIa; IIa, Rate Law: compartment_1*v*XIII*IIa/(k+IIa)
d_Tmod = 0.05; Tmod0 = 50.0 Reaction: => Tmod, Rate Law: compartment_1*Tmod0*d_Tmod
k1=0.7 Reaction: VII_TF =>, Rate Law: compartment_1*k1*VII_TF
Fg0 = 8945.5; d_Fg = 0.032 Reaction: => Fg, Rate Law: compartment_1*Fg0*d_Fg
k1=20.0 Reaction: Va =>, Rate Law: compartment_1*k1*Va
d_XIII = 0.0036 Reaction: XIII =>, Rate Law: compartment_1*d_XIII*XIII
k=1.0; v=70.0 Reaction: VII_TF => VIIa_TF; Xa, Rate Law: compartment_1*v*VII_TF*Xa/(k+Xa)
k1=0.69 Reaction: XIIIa =>, Rate Law: compartment_1*k1*XIIIa
VitaminK_k12 = 0.0587; VitaminK_k21_Vc = 5.08333333333333E-4 Reaction: VK => VK_p, Rate Law: compartment_1*(VitaminK_k12*VK-VitaminK_k21_Vc*VK_p)
v=10.0; k=10.0 Reaction: XI => XIa; IIa, Rate Law: compartment_1*v*XI*IIa/(k+IIa)
k=500.0; v=9.0 Reaction: II => IIa; Xa, Rate Law: compartment_1*v*II*Xa/(k+Xa)
k1=0.1 Reaction: D =>, Rate Law: compartment_1*k1*D
v=100.0; k=10.0 Reaction: II => IIa; Va_Xa, Rate Law: compartment_1*v*II*Va_Xa/(k+Va_Xa)
Pg0 = 2154.3; d_Pg = 0.05 Reaction: => Pg, Rate Law: compartment_1*Pg0*d_Pg
d_PC = 0.05 Reaction: PC =>, Rate Law: compartment_1*d_PC*PC
k=500.0; v=500.0 Reaction: Fg => FDP; P, Rate Law: compartment_1*v*Fg*P/(k+P)
c=0.1 Reaction: VII + TF => VII_TF, Rate Law: compartment_1*VII*TF/c
v=7.0; k=5000.0 Reaction: Pg => P; IIa, Rate Law: compartment_1*v*Pg*IIa/(k+IIa)
v=7.0; k=100.0 Reaction: XF => D; P, Rate Law: compartment_1*v*XF*P/(k+P)
k=1.0; v=50.0 Reaction: VIIIa => ; APC_PS, Rate Law: compartment_1*v*VIIIa*APC_PS/(k+APC_PS)
c=0.01 Reaction: IXa + VIIIa => IXa_VIIIa, Rate Law: compartment_1*IXa*VIIIa/c
k1=20.4 Reaction: APC =>, Rate Law: compartment_1*k1*APC
Warfarin_ka = 1.0 Reaction: A_warf = (-Warfarin_ka)*A_warf, Rate Law: (-Warfarin_ka)*A_warf
d_II = 0.01 Reaction: II =>, Rate Law: compartment_1*d_II*II
d_VIII = 0.058; VIII0 = 0.7 Reaction: => VIII, Rate Law: compartment_1*VIII0*d_VIII
k=1.0; v=1000.0 Reaction: VII_TF => VIIa_TF; TF, Rate Law: compartment_1*v*VII_TF*TF/(k+TF)
k=1.0; v=2.0 Reaction: Pg => P; APC_PS, Rate Law: compartment_1*v*Pg*APC_PS/(k+APC_PS)
c44 = 0.119718309859155 Reaction: IIa + ATIII_Heparin => IIa_ATIII_Heparin, Rate Law: compartment_1*IIa*ATIII_Heparin/c44
d_Fg = 0.032 Reaction: Fg => FDP, Rate Law: compartment_1*d_Fg*Fg
d_IX = 0.029; VKH20 = 0.1; IX0 = 89.6 Reaction: => IX; VKH2, Rate Law: compartment_1*d_IX*IX0*VKH2/VKH20
k1=67.4 Reaction: IIa => TAT, Rate Law: compartment_1*k1*IIa
Heparin_ke = 0.693 Reaction: IIa_ATIII_Heparin =>, Rate Law: compartment_1*Heparin_ke*IIa_ATIII_Heparin
d_Tmod = 0.05 Reaction: Tmod =>, Rate Law: compartment_1*d_Tmod*Tmod
d_XII = 0.012 Reaction: XII =>, Rate Law: compartment_1*d_XII*XII
d_IX = 0.029 Reaction: IX =>, Rate Law: compartment_1*d_IX*IX
k1=0.2 Reaction: TAT =>, Rate Law: compartment_1*k1*TAT
k=10000.0; v=5.0 Reaction: Pg => P; F, Rate Law: compartment_1*v*Pg*F/(k+F)
XIII0 = 70.3; d_XIII = 0.0036 Reaction: => XIII, Rate Law: compartment_1*XIII0*d_XIII

States:

Name Description
II [Prothrombin]
VIIa TF Xa TFPI [Tissue factor; Coagulation factor X; Coagulation factor VII; Tissue factor pathway inhibitor]
VIII [Coagulation factor VIII]
P [Plasminogen]
TFPI [Tissue factor pathway inhibitor]
A warf [warfarin]
XIII [Coagulation factor XIII A chain; Coagulation factor XIII B chain]
APC PS [Vitamin K-dependent protein C; Vitamin K-dependent protein S]
DP DP
IIa Tmod [Thrombomodulin; Prothrombin]
Xa ATIII Heparin [heparin; Coagulation factor X; Antithrombin-III]
PC [Vitamin K-dependent protein C]
XF [Fibrinogen alpha chain; Fibrinogen gamma chain; Fibrinogen beta chain]
IIa ATIII Heparin [heparin; Antithrombin-III; Prothrombin]
TF [Tissue factor]
VII TF [Tissue factor; Coagulation factor VII]
ATIII Heparin [heparin; Antithrombin-III]
XII [Coagulation factor XII]
XIa [Coagulation factor XI]
Fg [Fibrinogen alpha chain; Fibrinogen gamma chain; Fibrinogen beta chain]
Tmod [Thrombomodulin]
VK p [vitamin K]
D D
VIIIa [Coagulation factor VIII]
Va [Coagulation factor V]
IIa [Prothrombin]
Xa TFPI [Coagulation factor X; Tissue factor pathway inhibitor]
XIIIa [Coagulation factor XIII A chain]
APC [Vitamin K-dependent protein C]
Pg [Plasminogen]
IXa [Coagulation factor IX]
TAT [Prothrombin; Antithrombin-III]
IXa ATIII Heparin [heparin; Coagulation factor IX; Antithrombin-III]
VIIa TF [Tissue factor; Coagulation factor VII]
K [Plasma kallikrein]
F [Fibrinogen alpha chain; Fibrinogen gamma chain; Fibrinogen beta chain]
IX [Coagulation factor IX]

Observables: none

BIOMD0000000339 @ v0.0.1

This model is from the article: A comprehensive model for the humoral coagulation network in humans. Wajima T, Is…

Coagulation is an important process in hemostasis and comprises a complicated interaction of multiple enzymes and proteins. We have developed a mechanistic quantitative model of the coagulation network. The model accurately describes the time courses of coagulation factors following in vivo activation as well as in vitro blood coagulation tests of prothrombin time (PT, often reported as international normalized ratio (INR)) and activated partial thromboplastin time (aPTT). The model predicts the concentration-time and time-effect profiles of warfarin, heparins, and vitamin K in humans. The model can be applied to predict the time courses of coagulation kinetics in clinical situations (e.g., hemophilia) and for biomarker identification during drug development. The model developed in this study is the first quantitative description of the comprehensive coagulation network. link: http://identifiers.org/pubmed/19516255

Parameters:

Name Description
c=0.5 Reaction: VIIa_TF + Xa_TFPI => VIIa_TF_Xa_TFPI, Rate Law: compartment_1*VIIa_TF*Xa_TFPI/c
k1=0.05 Reaction: F => FDP, Rate Law: compartment_1*k1*F
v=1.0E-9; k=10.0 Reaction: X => Xa; VIIa, Rate Law: compartment_1*v*X*VIIa/(k+VIIa)
v=50000.0; k=1.0E-6 Reaction: VIII => VIIIa; IIa, Rate Law: compartment_1*v*VIII*IIa/(k+IIa)
k=1.0; v=1.0 Reaction: XF => D; APC_PS, Rate Law: compartment_1*v*XF*APC_PS/(k+APC_PS)
v=7.0; k=10.0 Reaction: F => FDP; P, Rate Law: compartment_1*v*F*P/(k+P)
d_Pg = 0.05 Reaction: Pg =>, Rate Law: compartment_1*d_Pg*Pg
PC0 = 60.0; VKH20 = 0.1; d_PC = 0.05 Reaction: => PC; VKH2, Rate Law: compartment_1*d_PC*PC0*VKH2/VKH20
c46 = 0.85 Reaction: IXa + ATIII_Heparin => IXa_ATIII_Heparin, Rate Law: compartment_1*IXa*ATIII_Heparin/c46
v=0.1; k=10.0 Reaction: VII => VIIa; IIa, Rate Law: compartment_1*v*VII*IIa/(k+IIa)
k=1.0; v=7.0 Reaction: XI => XIa; XIIa, Rate Law: compartment_1*v*XI*XIIa/(k+XIIa)
v=1.0; k=10.0 Reaction: VII => VIIa; Xa, Rate Law: compartment_1*v*VII*Xa/(k+Xa)
v=0.2; k=10.0 Reaction: VII => VIIa; IXa, Rate Law: compartment_1*v*VII*IXa/(k+IXa)
d_Tmod = 0.05; Tmod0 = 50.0 Reaction: => Tmod, Rate Law: compartment_1*Tmod0*d_Tmod
d_VIII = 0.058 Reaction: VIII =>, Rate Law: compartment_1*d_VIII*VIII
Fg0 = 8945.5; d_Fg = 0.032 Reaction: => Fg, Rate Law: compartment_1*Fg0*d_Fg
k1=20.0 Reaction: VIIIa =>, Rate Law: compartment_1*k1*VIIIa
d_XIII = 0.0036 Reaction: XIII =>, Rate Law: compartment_1*d_XIII*XIII
k=1.0; v=70.0 Reaction: IX => IXa; VIIa_TF, Rate Law: compartment_1*v*IX*VIIa_TF/(k+VIIa_TF)
k=0.1; v=2.0 Reaction: X => Xa; IXa_VIIIa, Rate Law: compartment_1*v*X*IXa_VIIIa/(k+IXa_VIIIa)
v=10.0; k=10.0 Reaction: XI => XIa; IIa, Rate Law: compartment_1*v*XI*IIa/(k+IIa)
k1=0.1 Reaction: D =>, Rate Law: compartment_1*k1*D
k=500.0; v=9.0 Reaction: II => IIa; Xa, Rate Law: compartment_1*v*II*Xa/(k+Xa)
v=100.0; k=10.0 Reaction: II => IIa; Va_Xa, Rate Law: compartment_1*v*II*Va_Xa/(k+Va_Xa)
Pg0 = 2154.3; d_Pg = 0.05 Reaction: => Pg, Rate Law: compartment_1*Pg0*d_Pg
v=0.02; k=10.0 Reaction: X => Xa; IXa, Rate Law: compartment_1*v*X*IXa/(k+IXa)
d_PC = 0.05 Reaction: PC =>, Rate Law: compartment_1*d_PC*PC
k=500.0; v=500.0 Reaction: Fg => FDP; P, Rate Law: compartment_1*v*Fg*P/(k+P)
c=0.1 Reaction: VII + TF => VII_TF, Rate Law: compartment_1*VII*TF/c
v=7.0; k=5000.0 Reaction: Pg => P; IIa, Rate Law: compartment_1*v*Pg*IIa/(k+IIa)
v=7.0; k=100.0 Reaction: XF => D; P, Rate Law: compartment_1*v*XF*P/(k+P)
k=1.0; v=50.0 Reaction: VIIIa => ; APC_PS, Rate Law: compartment_1*v*VIIIa*APC_PS/(k+APC_PS)
c=0.01 Reaction: IXa + VIIIa => IXa_VIIIa, Rate Law: compartment_1*IXa*VIIIa/c
k1=20.4 Reaction: APC =>, Rate Law: compartment_1*k1*APC
Warfarin_ka = 1.0 Reaction: A_warf = (-Warfarin_ka)*A_warf, Rate Law: (-Warfarin_ka)*A_warf
d_VIII = 0.058; VIII0 = 0.7 Reaction: => VIII, Rate Law: compartment_1*VIII0*d_VIII
c44 = 0.119718309859155 Reaction: IIa + ATIII_Heparin => IIa_ATIII_Heparin, Rate Law: compartment_1*IIa*ATIII_Heparin/c44
k=1.0; v=2.0 Reaction: Pg => P; APC_PS, Rate Law: compartment_1*v*Pg*APC_PS/(k+APC_PS)
d_Fg = 0.032 Reaction: Fg => FDP, Rate Law: compartment_1*d_Fg*Fg
d_IX = 0.029; VKH20 = 0.1; IX0 = 89.6 Reaction: => IX; VKH2, Rate Law: compartment_1*d_IX*IX0*VKH2/VKH20
d_XI = 0.1; XI0 = 30.6 Reaction: => XI, Rate Law: compartment_1*XI0*d_XI
d_VII = 0.12 Reaction: VII =>, Rate Law: compartment_1*d_VII*VII
k1=67.4 Reaction: IIa => TAT, Rate Law: compartment_1*k1*IIa
d_Tmod = 0.05 Reaction: Tmod =>, Rate Law: compartment_1*d_Tmod*Tmod
v=900.0; k=200.0 Reaction: X => Xa; VIIa_TF, Rate Law: compartment_1*v*X*VIIa_TF/(k+VIIa_TF)
VII0 = 10.0; VKH20 = 0.1; d_VII = 0.12 Reaction: => VII; VKH2, Rate Law: compartment_1*d_VII*VII0*VKH2/VKH20
d_X = 0.018; VKH20 = 0.1; X0 = 174.3 Reaction: => X; VKH2, Rate Law: compartment_1*d_X*X0*VKH2/VKH20
d_X = 0.018 Reaction: X =>, Rate Law: compartment_1*d_X*X
d_IX = 0.029 Reaction: IX =>, Rate Law: compartment_1*d_IX*IX
k1=0.2 Reaction: TAT =>, Rate Law: compartment_1*k1*TAT
k=10000.0; v=5.0 Reaction: Pg => P; F, Rate Law: compartment_1*v*Pg*F/(k+F)
XIII0 = 70.3; d_XIII = 0.0036 Reaction: => XIII, Rate Law: compartment_1*XIII0*d_XIII

States:

Name Description
VIIa TF Xa TFPI [Tissue factor; Coagulation factor X; Coagulation factor VII; Tissue factor pathway inhibitor]
VIII [Coagulation factor VIII]
P [Plasminogen]
A warf [warfarin]
APC PS [Vitamin K-dependent protein C; Vitamin K-dependent protein S]
XIII [Coagulation factor XIII A chain; Coagulation factor XIII B chain]
Xa [Coagulation factor X]
FDP [Fibrinogen alpha chain; Fibrinogen gamma chain; Fibrinogen beta chain]
DP DP
IIa Tmod [Thrombomodulin; Prothrombin]
PC [Vitamin K-dependent protein C]
XF [Fibrinogen alpha chain; Fibrinogen gamma chain; Fibrinogen beta chain]
IIa ATIII Heparin [heparin; Antithrombin-III; Prothrombin]
TF [Tissue factor]
XIa [Coagulation factor XI]
Fg [Fibrinogen alpha chain; Fibrinogen gamma chain; Fibrinogen beta chain]
Tmod [Thrombomodulin]
X [Coagulation factor X]
D D
VIIIa [Coagulation factor VIII]
IIa [Prothrombin]
VIIa [Coagulation factor VII]
XI [Coagulation factor XI]
XIIa [Coagulation factor XII]
APC [Vitamin K-dependent protein C]
TAT [Prothrombin; Antithrombin-III]
IXa [Coagulation factor IX]
Pg [Plasminogen]
IXa ATIII Heparin [heparin; Coagulation factor IX; Antithrombin-III]
VII [Coagulation factor VII]
IX [Coagulation factor IX]

Observables: none

This model is from the article: A comprehensive model for the humoral coagulation network in humans. Wajima T, Is…

Coagulation is an important process in hemostasis and comprises a complicated interaction of multiple enzymes and proteins. We have developed a mechanistic quantitative model of the coagulation network. The model accurately describes the time courses of coagulation factors following in vivo activation as well as in vitro blood coagulation tests of prothrombin time (PT, often reported as international normalized ratio (INR)) and activated partial thromboplastin time (aPTT). The model predicts the concentration-time and time-effect profiles of warfarin, heparins, and vitamin K in humans. The model can be applied to predict the time courses of coagulation kinetics in clinical situations (e.g., hemophilia) and for biomarker identification during drug development. The model developed in this study is the first quantitative description of the comprehensive coagulation network. link: http://identifiers.org/pubmed/19516255

Parameters:

Name Description
v=20000.0; k=0.5 Reaction: Fg => F; IIa, Rate Law: compartment_1*v*Fg*IIa/(k+IIa)
VKH20 = 0.1; II0 = 1394.4; d_II = 0.01 Reaction: => II; VKH2, Rate Law: compartment_1*d_II*II0*VKH2/VKH20
c=0.5 Reaction: VIIa_TF + Xa_TFPI => VIIa_TF_Xa_TFPI, Rate Law: compartment_1*VIIa_TF*Xa_TFPI/c
k1=0.05 Reaction: F => FDP, Rate Law: compartment_1*k1*F
v=50000.0; k=10.0 Reaction: V => Va; IIa, Rate Law: compartment_1*v*V*IIa/(k+IIa)
v=1.0E-9; k=10.0 Reaction: X => Xa; VIIa, Rate Law: compartment_1*v*X*VIIa/(k+VIIa)
v=50000.0; k=1.0E-6 Reaction: VIII => VIIIa; IIa, Rate Law: compartment_1*v*VIII*IIa/(k+IIa)
v=7.0; k=10.0 Reaction: IX => IXa; XIa, Rate Law: compartment_1*v*IX*XIa/(k+XIa)
c46 = 0.85 Reaction: IXa + ATIII_Heparin => IXa_ATIII_Heparin, Rate Law: compartment_1*IXa*ATIII_Heparin/c46
v=0.1; k=10.0 Reaction: VII => VIIa; IIa, Rate Law: compartment_1*v*VII*IIa/(k+IIa)
c45 = 0.85 Reaction: Xa + ATIII_Heparin => Xa_ATIII_Heparin, Rate Law: compartment_1*Xa*ATIII_Heparin/c45
k=1.0; v=7.0 Reaction: XIII => XIIIa; IIa, Rate Law: compartment_1*v*XIII*IIa/(k+IIa)
v=1.0; k=10.0 Reaction: VII => VIIa; Xa, Rate Law: compartment_1*v*VII*Xa/(k+Xa)
v=0.2; k=10.0 Reaction: VII => VIIa; IXa, Rate Law: compartment_1*v*VII*IXa/(k+IXa)
d_VIII = 0.058 Reaction: VIII =>, Rate Law: compartment_1*d_VIII*VIII
Fg0 = 8945.5; d_Fg = 0.032 Reaction: => Fg, Rate Law: compartment_1*Fg0*d_Fg
k1=20.0 Reaction: VIIa_TF_Xa_TFPI =>, Rate Law: compartment_1*k1*VIIa_TF_Xa_TFPI
k=1.0; v=70.0 Reaction: IX => IXa; VIIa_TF, Rate Law: compartment_1*v*IX*VIIa_TF/(k+VIIa_TF)
k=0.1; v=2.0 Reaction: X => Xa; IXa_VIIIa, Rate Law: compartment_1*v*X*IXa_VIIIa/(k+IXa_VIIIa)
v=10.0; k=10.0 Reaction: XI => XIa; IIa, Rate Law: compartment_1*v*XI*IIa/(k+IIa)
k1=0.1 Reaction: D =>, Rate Law: compartment_1*k1*D
k=500.0; v=9.0 Reaction: II => IIa; Xa, Rate Law: compartment_1*v*II*Xa/(k+Xa)
v=100.0; k=10.0 Reaction: II => IIa; Va_Xa, Rate Law: compartment_1*v*II*Va_Xa/(k+Va_Xa)
v=0.02; k=10.0 Reaction: X => Xa; IXa, Rate Law: compartment_1*v*X*IXa/(k+IXa)
k=500.0; v=500.0 Reaction: Fg => FDP; P, Rate Law: compartment_1*v*Fg*P/(k+P)
c=0.1 Reaction: VII + TF => VII_TF, Rate Law: compartment_1*VII*TF/c
heparin_ke = 0.693 Reaction: IIa_ATIII_Heparin =>, Rate Law: compartment_1*heparin_ke*IIa_ATIII_Heparin
v=7.0; k=5000.0 Reaction: Pg => P; IIa, Rate Law: compartment_1*v*Pg*IIa/(k+IIa)
v=7.0; k=100.0 Reaction: XF => D; P, Rate Law: compartment_1*v*XF*P/(k+P)
k1=3.5 Reaction: FDP =>, Rate Law: compartment_1*k1*FDP
k=1.0; v=50.0 Reaction: VIIIa => ; APC_PS, Rate Law: compartment_1*v*VIIIa*APC_PS/(k+APC_PS)
c=0.01 Reaction: IXa + VIIIa => IXa_VIIIa, Rate Law: compartment_1*IXa*VIIIa/c
d_VIII = 0.058; VIII0 = 0.7 Reaction: => VIII, Rate Law: compartment_1*VIII0*d_VIII
d_II = 0.01 Reaction: II =>, Rate Law: compartment_1*d_II*II
c44 = 0.119718309859155 Reaction: IIa + ATIII_Heparin => IIa_ATIII_Heparin, Rate Law: compartment_1*IIa*ATIII_Heparin/c44
d_IX = 0.029; VKH20 = 0.1; IX0 = 89.6 Reaction: => IX; VKH2, Rate Law: compartment_1*d_IX*IX0*VKH2/VKH20
d_XI = 0.1; XI0 = 30.6 Reaction: => XI, Rate Law: compartment_1*XI0*d_XI
d_V = 0.043; V0 = 26.7 Reaction: => V, Rate Law: compartment_1*V0*d_V
d_Fg = 0.032 Reaction: Fg => FDP, Rate Law: compartment_1*d_Fg*Fg
d_VII = 0.12 Reaction: VII =>, Rate Law: compartment_1*d_VII*VII
k1=67.4 Reaction: IIa => TAT, Rate Law: compartment_1*k1*IIa
v=900.0; k=200.0 Reaction: X => Xa; VIIa_TF, Rate Law: compartment_1*v*X*VIIa_TF/(k+VIIa_TF)
vitaminK_k21_Vc = 5.08333333333333E-4; vitaminK_k12 = 0.0587 Reaction: VK => VK_p, Rate Law: compartment_1*(vitaminK_k12*VK-vitaminK_k21_Vc*VK_p)
VII0 = 10.0; VKH20 = 0.1; d_VII = 0.12 Reaction: => VII; VKH2, Rate Law: compartment_1*d_VII*VII0*VKH2/VKH20
d_X = 0.018; VKH20 = 0.1; X0 = 174.3 Reaction: => X; VKH2, Rate Law: compartment_1*d_X*X0*VKH2/VKH20
warfarin_ka = 1.0 Reaction: A_warf = (-warfarin_ka)*A_warf, Rate Law: (-warfarin_ka)*A_warf
d_X = 0.018 Reaction: X =>, Rate Law: compartment_1*d_X*X
Pk0 = 450.0; d_Pk = 0.05 Reaction: => Pk, Rate Law: compartment_1*Pk0*d_Pk
d_IX = 0.029 Reaction: IX =>, Rate Law: compartment_1*d_IX*IX
d_Pk = 0.05 Reaction: Pk =>, Rate Law: compartment_1*d_Pk*Pk
d_V = 0.043 Reaction: V =>, Rate Law: compartment_1*d_V*V

States:

Name Description
F [Fibrinogen alpha chain; Fibrinogen gamma chain; Fibrinogen beta chain]
VIIa TF Xa TFPI [Tissue factor; Coagulation factor X; Coagulation factor VII; Tissue factor pathway inhibitor]
VIII [Coagulation factor VIII]
P [Plasminogen]
V [Coagulation factor V]
A warf [warfarin]
APC PS [Vitamin K-dependent protein C; Vitamin K-dependent protein S]
Xa [Coagulation factor X]
Pk [Plasma kallikrein]
FDP [Fibrinogen alpha chain; Fibrinogen gamma chain; Fibrinogen beta chain]
DP DP
IIa ATIII Heparin [heparin; Antithrombin-III; Prothrombin]
XIa [Coagulation factor XI]
Fg [Fibrinogen alpha chain; Fibrinogen gamma chain; Fibrinogen beta chain]
X [Coagulation factor X]
VK p [vitamin K]
D D
VIIIa [Coagulation factor VIII]
Va [Coagulation factor V]
IIa [Prothrombin]
VIIa [Coagulation factor VII]
XIIIa [Coagulation factor XIII A chain]
XI [Coagulation factor XI]
XIIa [Coagulation factor XII]
IXa ATIII Heparin [heparin; Coagulation factor IX; Antithrombin-III]
IXa [Coagulation factor IX]
VII [Coagulation factor VII]
II [Prothrombin]
IX [Coagulation factor IX]
IXa VIIIa [Coagulation factor IX; Coagulation factor VIII]

Observables: none

Walsh2014 - Inhibition kinetics of DAPT on APP CleavageThis model is described in the article: [Are improper kinetic mo…

Reproducibility of biological data is a significant problem in research today. One potential contributor to this, which has received little attention, is the over complication of enzyme kinetic inhibition models. The over complication of inhibitory models stems from the common use of the inhibitory term (1 + [I]/Ki ), an equilibrium binding term that does not distinguish between inhibitor binding and inhibitory effect. Since its initial appearance in the literature, around a century ago, the perceived mechanistic methods used in its production have spurred countless inhibitory equations. These equations are overly complex and are seldom compared to each other, which has destroyed their usefulness resulting in the proliferation and regulatory acceptance of simpler models such as IC50s for drug characterization. However, empirical analysis of inhibitory data recognizing the clear distinctions between inhibitor binding and inhibitory effect can produce simple logical inhibition models. In contrast to the common divergent practice of generating new inhibitory models for every inhibitory situation that presents itself. The empirical approach to inhibition modeling presented here is broadly applicable allowing easy comparison and rational analysis of drug interactions. To demonstrate this, a simple kinetic model of DAPT, a compound that both activates and inhibits γ-s