SBMLBioModels: A - C

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A


Model does not simulate

In order for the cell's genome to be passed intact from one generation to the next, the events of the cell cycle (DNA replication, mitosis, cell division) must be executed in the correct order, despite the considerable molecular noise inherent in any protein-based regulatory system residing in the small confines of a eukaryotic cell. To assess the effects of molecular fluctuations on cell-cycle progression in budding yeast cells, we have constructed a new model of the regulation of Cln- and Clb-dependent kinases, based on multisite phosphorylation of their target proteins and on positive and negative feedback loops involving the kinases themselves. To account for the significant role of noise in the transcription and translation steps of gene expression, the model includes mRNAs as well as proteins. The model equations are simulated deterministically and stochastically to reveal the bistable switching behavior on which proper cell-cycle progression depends and to show that this behavior is robust to the level of molecular noise expected in yeast-sized cells (approximately 50 fL volume). The model gives a quantitatively accurate account of the variability observed in the G1-S transition in budding yeast, which is governed by an underlying sizer+timer control system. link: http://identifiers.org/pubmed/20739927

Parameters: none

States: none

Observables: none

Requires input from Chen2004(BIOMD0000000056) model but did not work correctly even after integrating both models.

Eukaryotic cell proliferation involves DNA replication, a tightly regulated process mediated by a multitude of protein factors. In budding yeast, the initiation of replication is facilitated by the heterohexameric origin recognition complex (ORC). ORC binds to specific origins of replication and then serves as a scaffold for the recruitment of other factors such as Cdt1, Cdc6, the Mcm2-7 complex, Cdc45 and the Dbf4-Cdc7 kinase complex. While many of the mechanisms controlling these associations are well documented, mathematical models are needed to explore the network's dynamic behaviour. We have developed an ordinary differential equation-based model of the protein-protein interaction network describing replication initiation.The model was validated against quantified levels of protein factors over a range of cell cycle timepoints. Using chromatin extracts from synchronized Saccharomyces cerevisiae cell cultures, we were able to monitor the in vivo fluctuations of several of the aforementioned proteins, with additional data obtained from the literature. The model behaviour conforms to perturbation trials previously reported in the literature, and accurately predicts the results of our own knockdown experiments. Furthermore, we successfully incorporated our replication initiation model into an established model of the entire yeast cell cycle, thus providing a comprehensive description of these processes.This study establishes a robust model of the processes driving DNA replication initiation. The model was validated against observed cell concentrations of the driving factors, and characterizes the interactions between factors implicated in eukaryotic DNA replication. Finally, this model can serve as a guide in efforts to generate a comprehensive model of the mammalian cell cycle in order to explore cancer-related phenotypes. link: http://identifiers.org/pubmed/22738223

Parameters: none

States: none

Observables: none

This is a mathematical model comprised of a simple system of four ordinary differential equations that account for the t…

CD4 T cells play a fundamental role in the adaptive immune response including the stimulation of cytotoxic lymphocytes (CTLs). Human immunodeficiency virus (HIV) which infects and kills CD4 T cells causes progressive failure of the immune system. However, HIV particles are also reproduced by the infected CD4 T cells. Therefore, during HIV infection, infected and healthy CD4 T cells act in opposition to each other, reproducing virus particles and activating and stimulating cellular immune responses, respectively. In this investigation, we develop and analyze a simple system of four ordinary differential equations that accounts for these two opposing roles of CD4 T cells. The model illustrates the importance of the CTL immune response during the asymptomatic stage of HIV infection. In addition, the solution behavior exhibits the two stages of infection, asymptomatic and final AIDS stages. In the model, a weak immune response results in a short asymptomatic stage and faster development of AIDS, whereas a strong immune response illustrates the long asymptomatic stage. A model with a latent stage for infected CD4 T cells is also investigated and compared numerically with the original model. The model shows that strong stimulation of CTLs by CD4 T cells is necessary to prevent progression to the AIDS stage. link: http://identifiers.org/doi/10.1016/j.apm.2019.05.028

Parameters:

Name Description
e = 0.001; C_ast = 1000.0 Reaction: => F_CTL; C_Uninfected_CD4, V_Virus, Rate Law: compartment*e*C_Uninfected_CD4*V_Virus*F_CTL/(C_ast+F_CTL)
a = 1.0 Reaction: I_Infected_CD4 =>, Rate Law: compartment*a*I_Infected_CD4
lambda = 0.01; C_ast = 1000.0 Reaction: => C_Uninfected_CD4, Rate Law: compartment*lambda*C_ast
rho = 1.0 Reaction: I_Infected_CD4 => ; F_CTL, Rate Law: compartment*rho*F_CTL*I_Infected_CD4
b = 0.1 Reaction: F_CTL =>, Rate Law: compartment*b*F_CTL
k = 23.0 Reaction: V_Virus =>, Rate Law: compartment*k*V_Virus
beta = 5.75E-5 Reaction: C_Uninfected_CD4 => I_Infected_CD4; V_Virus, Rate Law: compartment*beta*C_Uninfected_CD4*V_Virus
lambda = 0.01 Reaction: C_Uninfected_CD4 =>, Rate Law: compartment*lambda*C_Uninfected_CD4
a = 1.0; N = 2000.0 Reaction: => V_Virus; I_Infected_CD4, Rate Law: compartment*a*N*I_Infected_CD4

States:

Name Description
I Infected CD4 [CD4-Positive T-Lymphocyte; infected cell]
V Virus [Human Immunodeficiency Virus]
F CTL [cytotoxic T cell]
C Uninfected CD4 [CD4-Positive T-Lymphocyte; EFO:0001460]

Observables: none

This model is from the article: Parallel adaptive feedback enhances reliability of the Ca2+ signaling system. Abell…

Despite large cell-to-cell variations in the concentrations of individual signaling proteins, cells transmit signals correctly. This phenomenon raises the question of what signaling systems do to prevent a predicted high failure rate. Here we combine quantitative modeling, RNA interference, and targeted selective reaction monitoring (SRM) mass spectrometry, and we show for the ubiquitous and fundamental calcium signaling system that cells monitor cytosolic and endoplasmic reticulum (ER) Ca(2+) levels and adjust in parallel the concentrations of the store-operated Ca(2+) influx mediator stromal interaction molecule (STIM), the plasma membrane Ca(2+) pump plasma membrane Ca-ATPase (PMCA), and the ER Ca(2+) pump sarco/ER Ca(2+)-ATPase (SERCA). Model calculations show that this combined parallel regulation in protein expression levels effectively stabilizes basal cytosolic and ER Ca(2+) levels and preserves receptor signaling. Our results demonstrate that, rather than directly controlling the relative level of signaling proteins in a forward regulation strategy, cells prevent transmission failure by sensing the state of the signaling pathway and using multiple parallel adaptive feedbacks. link: http://identifiers.org/pubmed/21844332

Parameters:

Name Description
R = 1.0 Reaction: => IP3; CaI, Rate Law: R*CaI
k2 = 0.175 Reaction: CaI => CaS; mw0ebc76ad_49d7_4845_8f88_04d443fbe7f3, Rate Law: mw0ebc76ad_49d7_4845_8f88_04d443fbe7f3*CaI^2/(CaI^2+k2^2)
D = 2.0 Reaction: IP3 =>, Rate Law: D*IP3
mw219cf65d_18cc_4f7e_ab5a_5b87cda6fc43 = 0.005 Reaction: mw013a7c64_a9ec_483c_b3b8_ed658337ee95 => CaI, Rate Law: mw219cf65d_18cc_4f7e_ab5a_5b87cda6fc43*mw013a7c64_a9ec_483c_b3b8_ed658337ee95/(mw013a7c64_a9ec_483c_b3b8_ed658337ee95+0.01)
mwfbff577a_4e9c_40fe_8777_eb0ceade28c9 = 1.0E-6 Reaction: mwaf195932_a72c_4552_8cf2_b349b15d39c4 =>, Rate Law: mwaf195932_a72c_4552_8cf2_b349b15d39c4*mwfbff577a_4e9c_40fe_8777_eb0ceade28c9
mwfbff577a_4e9c_40fe_8777_eb0ceade28c9 = 1.0E-6; mwd3b36919_202a_4fed_a3c8_1a3a60594404 = 8.0; mw004dcb62_da5f_41c7_a7bd_033574894f48 = 0.02 Reaction: => mw7cb2644a_384a_4bbb_93fd_fd686e01d7cb; CaS, CaI, Rate Law: 1/(mwd3b36919_202a_4fed_a3c8_1a3a60594404*mwd3b36919_202a_4fed_a3c8_1a3a60594404)*mw004dcb62_da5f_41c7_a7bd_033574894f48*mwfbff577a_4e9c_40fe_8777_eb0ceade28c9*((mwd3b36919_202a_4fed_a3c8_1a3a60594404-1)*2^2+CaS^2)/CaS^2*((mwd3b36919_202a_4fed_a3c8_1a3a60594404-1)*0.05^2+CaI^2)/CaI^2
mwfbff577a_4e9c_40fe_8777_eb0ceade28c9 = 1.0E-6; mwd21d3f76_d133_4053_8e44_02a538657e0a = 0.013; mwd3b36919_202a_4fed_a3c8_1a3a60594404 = 8.0 Reaction: => mwaf195932_a72c_4552_8cf2_b349b15d39c4; CaI, Rate Law: mwd3b36919_202a_4fed_a3c8_1a3a60594404*mwfbff577a_4e9c_40fe_8777_eb0ceade28c9*mwd21d3f76_d133_4053_8e44_02a538657e0a*CaI^4/((mwd3b36919_202a_4fed_a3c8_1a3a60594404-1)*0.05^4+CaI^4)
mw92b257b7_00af_4fd6_a11b_8e4655a4ba65 = 0.175; L = 0.01; mw78dd80b8_e003_4c62_81d1_547d001767af = 0.13; A = 3.0; mwd8bf5d8f_ad00_4119_bde1_91015ef2cd7c = 0.03 Reaction: CaS => CaI; g, IP3, Rate Law: (1-mwd8bf5d8f_ad00_4119_bde1_91015ef2cd7c)*(L+(1-g)*A*IP3^2/(IP3^2+mw92b257b7_00af_4fd6_a11b_8e4655a4ba65^2)*CaI^2/(CaI^2+mw78dd80b8_e003_4c62_81d1_547d001767af^2))*CaS
F = 0.018 Reaction: g =>, Rate Law: F*g
mw3a93c3a6_623a_44fe_84e9_a47823defd1f = 0.2 Reaction: CaI => ; mwaf195932_a72c_4552_8cf2_b349b15d39c4, Rate Law: mwaf195932_a72c_4552_8cf2_b349b15d39c4*CaI^2/(CaI^2+mw3a93c3a6_623a_44fe_84e9_a47823defd1f^2)
mwfbff577a_4e9c_40fe_8777_eb0ceade28c9 = 1.0E-6; mwd3b36919_202a_4fed_a3c8_1a3a60594404 = 8.0; B = 0.266 Reaction: => mw0ebc76ad_49d7_4845_8f88_04d443fbe7f3; CaS, Rate Law: 1/mwd3b36919_202a_4fed_a3c8_1a3a60594404*B*mwfbff577a_4e9c_40fe_8777_eb0ceade28c9*((mwd3b36919_202a_4fed_a3c8_1a3a60594404-1)*2^4+CaS^4)/CaS^4
mwf998b218_be11_4aa4_81ae_41141861fb42 = 1.0; E = 5.0 Reaction: => g; CaI, Rate Law: E*CaI^4/(CaI^4+mwf998b218_be11_4aa4_81ae_41141861fb42^4)*(1-g)
mw0ad64e84_bb75_4be4_a9c3_2d4741b0f45f = 0.0346; mwfe8e89cf_3c67_4dd5_939e_b4cfee2e0778 = 1.0 Reaction: => CaI; mw7cb2644a_384a_4bbb_93fd_fd686e01d7cb, CaS, Rate Law: mw7cb2644a_384a_4bbb_93fd_fd686e01d7cb*(mw0ad64e84_bb75_4be4_a9c3_2d4741b0f45f+mwfe8e89cf_3c67_4dd5_939e_b4cfee2e0778^8/(CaS^8+mwfe8e89cf_3c67_4dd5_939e_b4cfee2e0778^8))
mwa3072851_e3e4_4767_ac41_49fa7c0de7a7 = 0.03; mwe3841c25_6042_49c2_9feb_90cbf6751167 = 0.6 Reaction: CaI => mw013a7c64_a9ec_483c_b3b8_ed658337ee95, Rate Law: mwa3072851_e3e4_4767_ac41_49fa7c0de7a7*CaI^4/(CaI^4+mwe3841c25_6042_49c2_9feb_90cbf6751167^4)

States:

Name Description
IP3 [1D-myo-inositol 1,4,5-trisphosphate; N-(6-Aminohexanoyl)-6-aminohexanoate]
mw0ebc76ad 49d7 4845 8f88 04d443fbe7f3 [Calcium-transporting ATPase sarcoplasmic/endoplasmic reticulum type]
g [calcium channel inhibitor activity]
CaI [calcium(2+); Calcium cation]
mw7cb2644a 384a 4bbb 93fd fd686e01d7cb [Stromal interaction molecule homolog]
CaS [calcium(2+); Calcium cation]
mwaf195932 a72c 4552 8cf2 b349b15d39c4 [Calcium-transporting ATPase]
mw013a7c64 a9ec 483c b3b8 ed658337ee95 [calcium(2+); Calcium cation]

Observables: none

This model is from the article: Parallel adaptive feedback enhances reliability of the Ca2+ signaling system. Abell…

Despite large cell-to-cell variations in the concentrations of individual signaling proteins, cells transmit signals correctly. This phenomenon raises the question of what signaling systems do to prevent a predicted high failure rate. Here we combine quantitative modeling, RNA interference, and targeted selective reaction monitoring (SRM) mass spectrometry, and we show for the ubiquitous and fundamental calcium signaling system that cells monitor cytosolic and endoplasmic reticulum (ER) Ca(2+) levels and adjust in parallel the concentrations of the store-operated Ca(2+) influx mediator stromal interaction molecule (STIM), the plasma membrane Ca(2+) pump plasma membrane Ca-ATPase (PMCA), and the ER Ca(2+) pump sarco/ER Ca(2+)-ATPase (SERCA). Model calculations show that this combined parallel regulation in protein expression levels effectively stabilizes basal cytosolic and ER Ca(2+) levels and preserves receptor signaling. Our results demonstrate that, rather than directly controlling the relative level of signaling proteins in a forward regulation strategy, cells prevent transmission failure by sensing the state of the signaling pathway and using multiple parallel adaptive feedbacks. link: http://identifiers.org/pubmed/21844332

Parameters:

Name Description
R = 1.0 Reaction: => IP3; CaI, Rate Law: R*CaI
F = 0.018 Reaction: g =>, Rate Law: F*g
k2 = 0.175; B = 0.266 Reaction: CaI => CaS, Rate Law: B*CaI^2/(CaI^2+k2^2)
mw886be93a_22c7_4966_a1fa_113afd832ae3 = 0.03; mwc8d6bdb5_59d4_43fa_b96d_7426f4857e0d = 0.6 Reaction: CaI => CaM, Rate Law: mw886be93a_22c7_4966_a1fa_113afd832ae3*CaI^4/(CaI^4+mwc8d6bdb5_59d4_43fa_b96d_7426f4857e0d^4)
PMleak = 0.0346; kSTIM = 1.0; mw004dcb62_da5f_41c7_a7bd_033574894f48 = 0.02 Reaction: => CaI; CaS, Rate Law: mw004dcb62_da5f_41c7_a7bd_033574894f48*(PMleak+kSTIM^8/(CaS^8+kSTIM^8))
mwd21d3f76_d133_4053_8e44_02a538657e0a = 0.013; mw3a93c3a6_623a_44fe_84e9_a47823defd1f = 0.2 Reaction: CaI =>, Rate Law: mwd21d3f76_d133_4053_8e44_02a538657e0a*CaI^2/(CaI^2+mw3a93c3a6_623a_44fe_84e9_a47823defd1f^2)
mwd90ce3ea_f8d5_4f0a_8093_e39a2d3dbf33 = 0.005 Reaction: CaM => CaI, Rate Law: mwd90ce3ea_f8d5_4f0a_8093_e39a2d3dbf33*CaM/(CaM+0.01)
D = 2.0 Reaction: IP3 =>, Rate Law: D*IP3
L = 0.01; mw78dd80b8_e003_4c62_81d1_547d001767af = 0.13; A = 3.0; kIP3R = 0.175; mwc714c217_c8fd_4024_912c_681cd6931f59 = 0.03 Reaction: CaS => CaI; g, IP3, Rate Law: (1-mwc714c217_c8fd_4024_912c_681cd6931f59)*(L+(1-g)*A*IP3^2/(IP3^2+kIP3R^2)*CaI^2/(CaI^2+mw78dd80b8_e003_4c62_81d1_547d001767af^2))*CaS
mwf998b218_be11_4aa4_81ae_41141861fb42 = 1.0; E = 5.0 Reaction: => g; CaI, Rate Law: E*CaI^4/(CaI^4+mwf998b218_be11_4aa4_81ae_41141861fb42^4)*(1-g)

States:

Name Description
IP3 [1D-myo-inositol 1,4,5-trisphosphate; N-(6-Aminohexanoyl)-6-aminohexanoate]
g [calcium channel inhibitor activity]
CaI [calcium(2+); Calcium cation]
CaS [calcium(2+); Calcium cation]
CaM [calcium(2+); Calcium cation]

Observables: none

BIOMD0000000757 @ v0.0.1

The paper describes a model of glioblastoma. Created by COPASI 4.25 (Build 207) This model is described in the art…

Despite improvements in cancer therapy and treatments, tumor recurrence is a common event in cancer patients. One explanation of recurrence is that cancer therapy focuses on treatment of tumor cells and does not eradicate cancer stem cells (CSCs). CSCs are postulated to behave similar to normal stem cells in that their role is to maintain homeostasis. That is, when the population of tumor cells is reduced or depleted by treatment, CSCs will repopulate the tumor, causing recurrence. In this paper, we study the application of the CSC Hypothesis to the treatment of glioblastoma multiforme by immunotherapy. We extend the work of Kogan et al. (2008) to incorporate the dynamics of CSCs, prove the existence of a recurrence state, and provide an analysis of possible cancerous states and their dependence on treatment levels. link: http://identifiers.org/pubmed/27022405

Parameters:

Name Description
abs = 5.75E-6 1/h Reaction: => TGFb; CancerStemCell, Rate Law: tumor_microenvironment*abs*CancerStemCell
hs = 5.0E8 1; asb = 0.69 1; as = 0.012 1/h; esb = 10000.0 1 Reaction: CancerStemCell => ; MHC1, TGFb, CytotoxicTcell, Rate Law: tumor_microenvironment*as*MHC1/(MHC1+esb)*(asb+esb*(1-asb)/(TGFb+esb))*CytotoxicTcell*CancerStemCell/(hs+CancerStemCell)
am1y = 2.88 1/h; em1y = 338000.0 1 Reaction: => MHC1; IFNy, Rate Law: tumor_microenvironment*am1y*IFNy/(IFNy+em1y)
gb = 63945.0 1/h Reaction: => TGFb, Rate Law: tumor_microenvironment*gb
ub = 7.0 1/h Reaction: TGFb =>, Rate Law: tumor_microenvironment*ub*TGFb
uy = 0.102 1/h Reaction: IFNy =>, Rate Law: tumor_microenvironment*uy*IFNy
ayc = 1.02E-4 1/h Reaction: => IFNy; CytotoxicTcell, Rate Law: tumor_microenvironment*ayc*CytotoxicTcell
r1 = 0.001 1/h; k1 = 1.0E8 1 Reaction: => Tumor, Rate Law: tumor_microenvironment*r1*Tumor*(1-Tumor/k1)
uc = 0.007 1/h Reaction: CytotoxicTcell =>, Rate Law: tumor_microenvironment*uc*CytotoxicTcell
um1 = 0.0144 1/h Reaction: MHC1 =>, Rate Law: tumor_microenvironment*um1*MHC1
atb = 0.69 1; at = 0.12 1/h; ht = 5.0E8 1; etb = 10000.0 1 Reaction: Tumor => ; MHC1, TGFb, CytotoxicTcell, Rate Law: tumor_microenvironment*at*MHC1/(MHC1+etb)*(atb+etb*(1-atb)/(TGFb+etb))*CytotoxicTcell*Tumor/(ht+Tumor)
um2 = 0.0144 1/h Reaction: MHC2 =>, Rate Law: tumor_microenvironment*um2*MHC2
k2 = 1.0E7 1; r2 = 0.1 1/h Reaction: => CancerStemCell, Rate Law: tumor_microenvironment*r2*CancerStemCell*(1-CancerStemCell/k2)
abt = 5.75E-6 1/h Reaction: => TGFb; Tumor, Rate Law: tumor_microenvironment*abt*Tumor
N = 0.0 1/h Reaction: => CytotoxicTcell, Rate Law: tumor_microenvironment*N
am2y = 8660.0 1/h; em2y = 1420.0 1; am2b = 0.012 1; em2b = 100000.0 1 Reaction: => MHC2; IFNy, TGFb, Rate Law: tumor_microenvironment*am2y*IFNy/(IFNy+em2y)*(em2b*(1-am2b)/(TGFb+em2b)+am2b)
gm1 = 1.44 1/h Reaction: => MHC1, Rate Law: tumor_microenvironment*gm1
k1 = 1.0E8 1; k2 = 1.0E7 1; ra = 0.006 1/h Reaction: CancerStemCell => Tumor, Rate Law: tumor_microenvironment*ra*Tumor/k1*CancerStemCell/k2*(k1-Tumor)

States:

Name Description
CytotoxicTcell [cytotoxic T cell]
Tumor [malignant cell]
TGFb [Transforming growth factor beta-1]
CTL Plot [cytotoxic T cell]
MHC1 [MHC Class I Protein]
CSC Plot [cancer stem cell]
CancerStemCell [cancer stem cell]
IFNy [Interferon gamma]
MHC2 [MHC Class II Protein]
Tumor Plot [malignant cell]

Observables: none

Not many models of mammalian cell cycle system exist due to its complexity. Some models are too complex and hard to unde…

Not many models of mammalian cell cycle system exist due to its complexity. Some models are too complex and hard to understand, while some others are too simple and not comprehensive enough. Moreover, some essential aspects, such as the response of G1-S and G2-M checkpoints to DNA damage as well as the growth factor signalling, have not been investigated from a systems point of view in current mammalian cell cycle models. To address these issues, we bring a holistic perspective to cell cycle by mathematically modelling it as a complex system consisting of important sub-systems that interact with each other. This retains the functionality of the system and provides a clearer interpretation to the processes within it while reducing the complexity in comprehending these processes. To achieve this, we first update a published ODE mathematical model of cell cycle with current knowledge. Then the part of the mathematical model relevant to each sub-system is shown separately in conjunction with a diagram of the sub-system as part of this representation. The model sub-systems are Growth Factor, DNA damage, G1-S, and G2-M checkpoint signalling. To further simplify the model and better explore the function of sub-systems, they are further divided into modules. Here we also add important new modules of: chk-related rapid cell cycle arrest, p53 modules expanded to seamlessly integrate with the rapid arrest module, Tyrosine phosphatase modules that activate CycCdk complexes and play a crucial role in rapid and delay arrest at both G1-S and G2-M, Tyrosine Kinase module that is important for inactivating nuclear transport of CycBcdk1 through Wee1 to resist M phase entry, Plk1-Related module that is crucial in activating Tyrosine phosphatases and inactivating Tyrosine kinase, and APC-Related module to show steps in CycB degradation. This multi-level systems approach incorporating all known aspects of cell cycle allowed us to (i) study, through dynamic simulation of an ODE model, comprehensive details of cell cycle dynamics under normal and DNA damage conditions revealing the role and value of the added new modules and elements, (ii) assess, through a global sensitivity analysis, the most influential sub-systems, modules and parameters on system response, such as G1-S and G2-M transitions, and (iii) probe deeply into the relationship between DNA damage and cell cycle progression and test the biological evidence that G1-S is relatively inefficient in arresting damaged cells compared to G2-M checkpoint. To perform sensitivity analysis, Self-Organizing Map with Correlation Coefficient Analysis (SOMCCA) is developed which shows that Growth Factor and G1-S Checkpoint sub-systems and 13 parameters in the modules within them are crucial for G1-S and G2-M transitions. To study the relative efficiency of DNA damage checkpoints, a Checkpoint Efficiency Evaluator (CEE) is developed based on perturbation studies and statistical Type II error. Accordingly, cell cycle is about 96% efficient in arresting damaged cells with G2-M checkpoint being more efficient than G1-S. Further, both checkpoint systems are near perfect (98.6%) in passing healthy cells. Thus this study has shown the efficacy of the proposed systems approach to gain a better understanding of different aspects of mammalian cell cycle system separately and as an integrated system that will also be useful in investigating targeted therapy in future cancer treatments. link: http://identifiers.org/pubmed/28647496

Parameters: none

States: none

Observables: none

MODEL9087988095 @ v0.0.1

This model is taken from the <a href = "http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&dopt=Abstra…

The kinetics of binding L-arginine and three alternative substrates (homoarginine, N-methylarginine, and N-hydroxyarginine) to neuronal nitric oxide synthase (nNOS) were characterized by conventional and stopped-flow spectroscopy. Because binding these substrates has only a small effect on the light absorbance spectrum of tetrahydrobiopterin-saturated nNOS, their binding was monitored by following displacement of imidazole, which displays a significant change in Soret absorbance from 427 to 398 nm. Rates of spectral change upon mixing Im-nNOS with increasing amounts of substrates were obtained and found to be monophasic in all cases. For each substrate, a plot of the apparent rate versus substrate concentration showed saturation at the higher concentrations. K(-)(1), k(2), k(-)(2), and the apparent dissociation constant were derived for each substrate from the kinetic data. The dissociation constants mostly agreed with those calculated from equilibrium spectral data obtained by titrating Im-nNOS with each substrate. We conclude that nNOS follows a two-step, reversible mechanism of substrate binding in which there is a rapid equilibrium between Im-nNOS and the substrate S followed by a slower isomerization process to generate nNOS'-S: Im-nNOS + S if Im-nNOS-S if nNOS'-S + Im. All four substrates followed this general mechanism, but differences in their kinetic values were significant and may contribute to their varying capacities to support NO synthesis. link: http://identifiers.org/pubmed/10493814

Parameters: none

States: none

Observables: none

MODEL9087766308 @ v0.0.1

This model was taken from the <a href = "http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&dopt=Abstr…

The kinetics of binding L-arginine and three alternative substrates (homoarginine, N-methylarginine, and N-hydroxyarginine) to neuronal nitric oxide synthase (nNOS) were characterized by conventional and stopped-flow spectroscopy. Because binding these substrates has only a small effect on the light absorbance spectrum of tetrahydrobiopterin-saturated nNOS, their binding was monitored by following displacement of imidazole, which displays a significant change in Soret absorbance from 427 to 398 nm. Rates of spectral change upon mixing Im-nNOS with increasing amounts of substrates were obtained and found to be monophasic in all cases. For each substrate, a plot of the apparent rate versus substrate concentration showed saturation at the higher concentrations. K(-)(1), k(2), k(-)(2), and the apparent dissociation constant were derived for each substrate from the kinetic data. The dissociation constants mostly agreed with those calculated from equilibrium spectral data obtained by titrating Im-nNOS with each substrate. We conclude that nNOS follows a two-step, reversible mechanism of substrate binding in which there is a rapid equilibrium between Im-nNOS and the substrate S followed by a slower isomerization process to generate nNOS'-S: Im-nNOS + S if Im-nNOS-S if nNOS'-S + Im. All four substrates followed this general mechanism, but differences in their kinetic values were significant and may contribute to their varying capacities to support NO synthesis. link: http://identifiers.org/pubmed/10493814

Parameters: none

States: none

Observables: none

MODEL9088169066 @ v0.0.1

This model is taken from the <a href = "http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=retrieve&db=pubmed&list_uids=7…

The kinetics of binding L-arginine and three alternative substrates (homoarginine, N-methylarginine, and N-hydroxyarginine) to neuronal nitric oxide synthase (nNOS) were characterized by conventional and stopped-flow spectroscopy. Because binding these substrates has only a small effect on the light absorbance spectrum of tetrahydrobiopterin-saturated nNOS, their binding was monitored by following displacement of imidazole, which displays a significant change in Soret absorbance from 427 to 398 nm. Rates of spectral change upon mixing Im-nNOS with increasing amounts of substrates were obtained and found to be monophasic in all cases. For each substrate, a plot of the apparent rate versus substrate concentration showed saturation at the higher concentrations. K(-)(1), k(2), k(-)(2), and the apparent dissociation constant were derived for each substrate from the kinetic data. The dissociation constants mostly agreed with those calculated from equilibrium spectral data obtained by titrating Im-nNOS with each substrate. We conclude that nNOS follows a two-step, reversible mechanism of substrate binding in which there is a rapid equilibrium between Im-nNOS and the substrate S followed by a slower isomerization process to generate nNOS'-S: Im-nNOS + S if Im-nNOS-S if nNOS'-S + Im. All four substrates followed this general mechanism, but differences in their kinetic values were significant and may contribute to their varying capacities to support NO synthesis. link: http://identifiers.org/pubmed/10493814

Parameters: none

States: none

Observables: none

MODEL9088294310 @ v0.0.1

This model is taken from the <a href = "http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&dopt=Abstra…

The kinetics of binding L-arginine and three alternative substrates (homoarginine, N-methylarginine, and N-hydroxyarginine) to neuronal nitric oxide synthase (nNOS) were characterized by conventional and stopped-flow spectroscopy. Because binding these substrates has only a small effect on the light absorbance spectrum of tetrahydrobiopterin-saturated nNOS, their binding was monitored by following displacement of imidazole, which displays a significant change in Soret absorbance from 427 to 398 nm. Rates of spectral change upon mixing Im-nNOS with increasing amounts of substrates were obtained and found to be monophasic in all cases. For each substrate, a plot of the apparent rate versus substrate concentration showed saturation at the higher concentrations. K(-)(1), k(2), k(-)(2), and the apparent dissociation constant were derived for each substrate from the kinetic data. The dissociation constants mostly agreed with those calculated from equilibrium spectral data obtained by titrating Im-nNOS with each substrate. We conclude that nNOS follows a two-step, reversible mechanism of substrate binding in which there is a rapid equilibrium between Im-nNOS and the substrate S followed by a slower isomerization process to generate nNOS'-S: Im-nNOS + S if Im-nNOS-S if nNOS'-S + Im. All four substrates followed this general mechanism, but differences in their kinetic values were significant and may contribute to their varying capacities to support NO synthesis. link: http://identifiers.org/pubmed/10493814

Parameters: none

States: none

Observables: none

AbuOun2009 - Genome-scale metabolic network of Salmonella typhimurium (iMA945)This model is described in the article: […

Salmonella are closely related to commensal Escherichia coli but have gained virulence factors enabling them to behave as enteric pathogens. Less well studied are the similarities and differences that exist between the metabolic properties of these organisms that may contribute toward niche adaptation of Salmonella pathogens. To address this, we have constructed a genome scale Salmonella metabolic model (iMA945). The model comprises 945 open reading frames or genes, 1964 reactions, and 1036 metabolites. There was significant overlap with genes present in E. coli MG1655 model iAF1260. In silico growth predictions were simulated using the model on different carbon, nitrogen, phosphorous, and sulfur sources. These were compared with substrate utilization data gathered from high throughput phenotyping microarrays revealing good agreement. Of the compounds tested, the majority were utilizable by both Salmonella and E. coli. Nevertheless a number of differences were identified both between Salmonella and E. coli and also within the Salmonella strains included. These differences provide valuable insight into differences between a commensal and a closely related pathogen and within different pathogenic strains opening new avenues for future explorations. link: http://identifiers.org/pubmed/19690172

Parameters: none

States: none

Observables: none

Achcar2012 - Glycolysis in bloodstream form T. bruceiKinetic models of metabolism require quantitative knowledge of deta…

Kinetic models of metabolism require detailed knowledge of kinetic parameters. However, due to measurement errors or lack of data this knowledge is often uncertain. The model of glycolysis in the parasitic protozoan Trypanosoma brucei is a particularly well analysed example of a quantitative metabolic model, but so far it has been studied with a fixed set of parameters only. Here we evaluate the effect of parameter uncertainty. In order to define probability distributions for each parameter, information about the experimental sources and confidence intervals for all parameters were collected. We created a wiki-based website dedicated to the detailed documentation of this information: the SilicoTryp wiki (http://silicotryp.ibls.gla.ac.uk/wiki/Glycolysis). Using information collected in the wiki, we then assigned probability distributions to all parameters of the model. This allowed us to sample sets of alternative models, accurately representing our degree of uncertainty. Some properties of the model, such as the repartition of the glycolytic flux between the glycerol and pyruvate producing branches, are robust to these uncertainties. However, our analysis also allowed us to identify fragilities of the model leading to the accumulation of 3-phosphoglycerate and/or pyruvate. The analysis of the control coefficients revealed the importance of taking into account the uncertainties about the parameters, as the ranking of the reactions can be greatly affected. This work will now form the basis for a comprehensive Bayesian analysis and extension of the model considering alternative topologies. link: http://identifiers.org/pubmed/22379410

Parameters:

Name Description
GlyT_g_k=9000.0 Reaction: Gly_g => Gly_c; Gly_g, Gly_c, Rate Law: GlyT_g_k*Gly_g-GlyT_g_k*Gly_c
_3PGAT_g_k=250.0 Reaction: _3PGA_g => _3PGA_c; _3PGA_g, _3PGA_c, Rate Law: _3PGAT_g_k*_3PGA_g-_3PGAT_g_k*_3PGA_c
GDA_g_k=600.0 Reaction: Gly3P_g + DHAP_c => Gly3P_c + DHAP_g; Gly3P_g, DHAP_c, Gly3P_c, DHAP_g, Rate Law: Gly3P_g*DHAP_c*GDA_g_k-Gly3P_c*DHAP_g*GDA_g_k
GPO_c_Vmax=368.0; GPO_c_KmGly3P=1.7 Reaction: Gly3P_c => DHAP_c; Gly3P_c, Rate Law: GPO_c_Vmax*Gly3P_c/(GPO_c_KmGly3P*(1+Gly3P_c/GPO_c_KmGly3P))
ALD_g_KmDHAP=0.015; ALD_g_KiGA3P=0.098; ALD_g_KmGA3P=0.067; ALD_g_Vmax=560.0; ALD_g_KmFru16BP=0.009; ALD_g_KiADP=1.51; ALD_g_KiAMP=3.65; ALD_g_Keq=0.093; ALD_g_KiATP=0.68 Reaction: Fru16BP_g => GA3P_g + DHAP_g; ATP_g, ADP_g, AMP_g, Fru16BP_g, GA3P_g, DHAP_g, ATP_g, ADP_g, AMP_g, Rate Law: Fru16BP_g*ALD_g_Vmax*(1-GA3P_g*DHAP_g/(Fru16BP_g*ALD_g_Keq))/(ALD_g_KmFru16BP*(1+ATP_g/ALD_g_KiATP+ADP_g/ALD_g_KiADP+AMP_g/ALD_g_KiAMP)*(1+GA3P_g/ALD_g_KmGA3P+DHAP_g/ALD_g_KmDHAP+Fru16BP_g/(ALD_g_KmFru16BP*(1+ATP_g/ALD_g_KiATP+ADP_g/ALD_g_KiADP+AMP_g/ALD_g_KiAMP))+GA3P_g*DHAP_g/(ALD_g_KmGA3P*ALD_g_KmDHAP)+Fru16BP_g*GA3P_g/(ALD_g_KmFru16BP*ALD_g_KiGA3P*(1+ATP_g/ALD_g_KiATP+ADP_g/ALD_g_KiADP+AMP_g/ALD_g_KiAMP))))
GK_g_Keq=8.0E-4; GK_g_KmATP=0.24; GK_g_KmGly=0.44; GK_g_KmADP=0.56; GK_g_KmGly3P=3.83; GK_g_Vmax=200.0 Reaction: Gly3P_g + ADP_g => Gly_g + ATP_g; Gly3P_g, ADP_g, Gly_g, ATP_g, Rate Law: GK_g_Vmax*Gly3P_g*ADP_g*(1-Gly_g*ATP_g/(GK_g_Keq*Gly3P_g*ADP_g))/(GK_g_KmGly3P*GK_g_KmADP*(1+ADP_g/GK_g_KmADP+ATP_g/GK_g_KmATP)*(1+Gly3P_g/GK_g_KmGly3P+Gly_g/GK_g_KmGly))
ENO_c_Keq=6.7; ENO_c_Vmax=598.0; ENO_c_Km2PGA=0.054; ENO_c_KmPEP=0.24 Reaction: _2PGA_c => PEP_c; _2PGA_c, PEP_c, Rate Law: ENO_c_Vmax*_2PGA_c*(1-PEP_c/(ENO_c_Keq*_2PGA_c))/(ENO_c_Km2PGA*(1+_2PGA_c/ENO_c_Km2PGA+PEP_c/ENO_c_KmPEP))
ATPu_c_k=50.0 Reaction: ATP_c => ADP_c; ATP_c, ADP_c, Rate Law: ATP_c*ATPu_c_k/ADP_c
TPI_g_Keq=0.045; TPI_g_KmDHAP=1.2; TPI_g_KmGA3P=0.25; TPI_g_Vmax=999.3 Reaction: DHAP_g => GA3P_g; DHAP_g, GA3P_g, Rate Law: TPI_g_Vmax*DHAP_g*(1-GA3P_g/(TPI_g_Keq*DHAP_g))/(TPI_g_KmDHAP*(1+DHAP_g/TPI_g_KmDHAP+GA3P_g/TPI_g_KmGA3P))
PyrT_c_KmPyr=1.96; PyrT_c_Vmax=200.0 Reaction: Pyr_c => Pyr_e; Pyr_c, Rate Law: PyrT_c_Vmax*Pyr_c/(PyrT_c_KmPyr*(1+Pyr_c/PyrT_c_KmPyr))
GlcT_c_KmGlc=1.0; GlcT_c_alpha=0.75; GlcT_c_Vmax=108.9 Reaction: Glc_e => Glc_c; Glc_e, Glc_c, Rate Law: GlcT_c_Vmax*(Glc_e-Glc_c)/(Glc_e+Glc_c+GlcT_c_KmGlc+Glc_e*Glc_c*GlcT_c_alpha/GlcT_c_KmGlc)
PGI_g_Keq=0.3; PGI_g_Vmax=1305.0; PGI_g_KmGlc6P=0.4; PGI_g_KmFru6P=0.12 Reaction: Glc6P_g => Fru6P_g; Glc6P_g, Fru6P_g, Rate Law: PGI_g_Vmax*Glc6P_g*(1-Fru6P_g/(PGI_g_Keq*Glc6P_g))/(PGI_g_KmGlc6P*(1+Glc6P_g/PGI_g_KmGlc6P+Fru6P_g/PGI_g_KmFru6P))
GlcT_g_k=250000.0 Reaction: Glc_c => Glc_g; Glc_c, Glc_g, Rate Law: GlcT_g_k*Glc_c-GlcT_g_k*Glc_g
HXK_g_KmGlc6P=12.0; HXK_g_KmADP=0.126; HXK_g_Vmax=1929.0; HXK_g_KmATP=0.116; HXK_g_KmGlc=0.1 Reaction: ATP_g + Glc_g => Glc6P_g + ADP_g; ATP_g, Glc_g, Glc6P_g, ADP_g, Rate Law: ATP_g*Glc_g*HXK_g_Vmax/(HXK_g_KmGlc*HXK_g_KmATP*(1+Glc_g/HXK_g_KmGlc+Glc6P_g/HXK_g_KmGlc6P)*(1+ATP_g/HXK_g_KmATP+ADP_g/HXK_g_KmADP))
PGK_g_Km13BPGA=0.003; PGK_g_Vmax=2862.0; PGK_g_Keq=3332.0; PGK_g_KmADP=0.1; PGK_g_Km3PGA=1.62; PGK_g_KmATP=0.29 Reaction: _13BPGA_g + ADP_g => _3PGA_g + ATP_g; _13BPGA_g, ADP_g, _3PGA_g, ATP_g, Rate Law: PGK_g_Vmax*_13BPGA_g*ADP_g*(1-_3PGA_g*ATP_g/(PGK_g_Keq*_13BPGA_g*ADP_g))/(PGK_g_Km13BPGA*PGK_g_KmADP*(1+ADP_g/PGK_g_KmADP+ATP_g/PGK_g_KmATP)*(1+_13BPGA_g/PGK_g_Km13BPGA+_3PGA_g/PGK_g_Km3PGA))
PYK_c_KiADP=0.64; PYK_c_Vmax=1020.0; PYK_c_KmADP=0.114; PYK_c_KiATP=0.57; PYK_c_KmPEP=0.34; PYK_c_n=2.5 Reaction: PEP_c + ADP_c => Pyr_c + ATP_c; ADP_c, PEP_c, ATP_c, Rate Law: ADP_c*PYK_c_Vmax*(PEP_c/(PYK_c_KmPEP*(1+ADP_c/PYK_c_KiADP+ATP_c/PYK_c_KiATP)))^PYK_c_n/(PYK_c_KmADP*(1+ADP_c/PYK_c_KmADP)*(1+(PEP_c/(PYK_c_KmPEP*(1+ADP_c/PYK_c_KiADP+ATP_c/PYK_c_KiATP)))^PYK_c_n))
G3PDH_g_KmDHAP=0.1; G3PDH_g_KmNAD=0.4; G3PDH_g_Vmax=465.0; G3PDH_g_Keq=2857.0; G3PDH_g_KmNADH=0.01; G3PDH_g_KmGly3P=2.0 Reaction: NADH_g + DHAP_g => Gly3P_g + NAD_g; DHAP_g, NADH_g, Gly3P_g, NAD_g, Rate Law: G3PDH_g_Vmax*DHAP_g*NADH_g*(1-Gly3P_g*NAD_g/(G3PDH_g_Keq*DHAP_g*NADH_g))/(G3PDH_g_KmDHAP*G3PDH_g_KmNADH*(1+NADH_g/G3PDH_g_KmNADH+NAD_g/G3PDH_g_KmNAD)*(1+DHAP_g/G3PDH_g_KmDHAP+Gly3P_g/G3PDH_g_KmGly3P))
GAPDH_g_Vmax=720.9; GAPDH_g_Km13BPGA=0.1; GAPDH_g_KmNAD=0.45; GAPDH_g_KmNADH=0.02; GAPDH_g_KmGA3P=0.15; GAPDH_g_Keq=0.044 Reaction: GA3P_g + NAD_g + Pi_g => NADH_g + _13BPGA_g; GA3P_g, NAD_g, _13BPGA_g, NADH_g, Rate Law: GAPDH_g_Vmax*GA3P_g*NAD_g*(1-_13BPGA_g*NADH_g/(GAPDH_g_Keq*GA3P_g*NAD_g))/(GAPDH_g_KmGA3P*GAPDH_g_KmNAD*(1+NAD_g/GAPDH_g_KmNAD+NADH_g/GAPDH_g_KmNADH)*(1+GA3P_g/GAPDH_g_KmGA3P+_13BPGA_g/GAPDH_g_Km13BPGA))
GlyT_c_KmGly=0.17; GlyT_c_Vmax=85.0; GlyT_c_k=9.0 Reaction: Gly_c => Gly_e; Gly_c, Gly_e, Rate Law: GlyT_c_k*(Gly_c-Gly_e)+GlyT_c_Vmax*(Gly_c-Gly_e)/(GlyT_c_KmGly*(1+Gly_c/GlyT_c_KmGly)*(1+Gly_e/GlyT_c_KmGly))
AK_g_k2=1000.0; AK_g_k1=442.0 Reaction: ADP_g => AMP_g + ATP_g; ADP_g, AMP_g, ATP_g, Rate Law: AK_g_k1*ADP_g^2-AMP_g*ATP_g*AK_g_k2
PGAM_c_Km3PGA=0.15; PGAM_c_Vmax=225.0; PGAM_c_Km2PGA=0.16; PGAM_c_Keq=0.185 Reaction: _3PGA_c => _2PGA_c; _3PGA_c, _2PGA_c, Rate Law: PGAM_c_Vmax*_3PGA_c*(1-_2PGA_c/(PGAM_c_Keq*_3PGA_c))/(PGAM_c_Km3PGA*(1+_3PGA_c/PGAM_c_Km3PGA+_2PGA_c/PGAM_c_Km2PGA))
AK_c_k1=442.0; AK_c_k2=1000.0 Reaction: ADP_c => AMP_c + ATP_c; ADP_c, AMP_c, ATP_c, Rate Law: AK_c_k1*ADP_c^2-AMP_c*ATP_c*AK_c_k2
PFK_g_Vmax=1708.0; PFK_g_KmFru6P=0.82; PFK_g_Ki2=10.7; PFK_g_KmATP=0.026; PFK_g_Ki1=15.8 Reaction: ATP_g + Fru6P_g => Fru16BP_g + ADP_g; ATP_g, Fru6P_g, Fru16BP_g, Rate Law: ATP_g*Fru6P_g*PFK_g_Vmax*PFK_g_Ki1/(PFK_g_KmFru6P*PFK_g_KmATP*(1+ATP_g/PFK_g_KmATP)*(Fru16BP_g+PFK_g_Ki1)*(1+Fru6P_g/PFK_g_KmFru6P+Fru16BP_g/PFK_g_Ki2))

States:

Name Description
Fru6P g [keto-D-fructose 6-phosphate]
PEP c [phosphoenolpyruvic acid]
2PGA c [59]
Gly3P c [glyceraldehyde 3-phosphate]
ATP g [ATP]
Gly c [glycerol]
Fru16BP g [alpha-D-fructofuranose 1,6-bisphosphate]
GA3P g [glyceraldehyde 3-phosphate]
Glc c [glucose]
3PGA c [3-phospho-D-glyceric acid]
Glc g [glucose]
Glc e [glucose]
Pi g [phosphate(3-)]
Glc6P g [alpha-D-glucose 6-phosphate]
ATP c [ATP]
Pyr e [pyruvic acid]
DHAP c [glycerone phosphate(2-)]
ADP g [ADP]
13BPGA g [683]
DHAP g [glycerone phosphate(2-)]
Gly e [glycerol]
NAD g [NAD]
Gly3P g [glyceraldehyde 3-phosphate]
ADP c [ADP]
AMP g [AMP]
NADH g [NADH]
Pyr c [pyruvic acid]
3PGA g [3-phospho-D-glyceric acid]
Gly g [glycerol]
AMP c [AMP]

Observables: none

MODEL1808150002 @ v0.0.1

Mathematical model of blood coagulation. Reused Hockin et al. 2002 model. Simulation of thrombin inhibitors: Hirudin, Hi…

Thrombotic disorders can lead to uncontrolled thrombin generation and clot formation within the circulatory system leading to vascular thrombosis. Direct inhibitors of thrombin have been developed and tested in clinical trials for the treatment of a variety of these thrombotic disorders. The bleeding complications observed during these trials have raised questions about their clinical use. The development of a computer-based model of coagulation using the kinetic rates of individual reactions and concentrations of the constituents involved in each reaction within blood has made it possible to study coagulation pathologies in silico. We present an extension of our initial model of coagulation to include several specific thrombin inhibitors. Using this model we have studied the effect of a variety of inhibitors on thrombin generation and compared these results with the clinically observed data. The data suggest that numerical models will be useful in predicting the effectiveness of inhibitors of coagulation. link: http://identifiers.org/pubmed/12871372

Parameters: none

States: none

Observables: none

As per BIO0000000089.xml but including a functional light.

Time-dependent light input is an important feature of computational models of the circadian clock. However, publicly available models encoded in standard representations such as the Systems Biology Markup Language (SBML) either do not encode this input or use different mechanisms to do so, which hinders reproducibility of published results as well as model reuse. The authors describe here a numerically continuous function suitable for use in SBML for models of circadian rhythms forced by periodic light-dark cycles. The Input Signal Step Function (ISSF) is broadly applicable to encoding experimental manipulations, such as drug treatments, temperature changes, or inducible transgene expression, which may be transient, periodic, or mixed. It is highly configurable and is able to reproduce a wide range of waveforms. The authors have implemented this function in SBML and demonstrated its ability to modify the behavior of publicly available models to accurately reproduce published results. The implementation of ISSF allows standard simulation software to reproduce specialized circadian protocols, such as the phase-response curve. To facilitate the reuse of this function in public models, the authors have developed software to configure its behavior without any specialist knowledge of SBML. A community-standard approach to represent the inputs that entrain circadian clock models could particularly facilitate research in chronobiology. link: http://identifiers.org/pubmed/22855577

Parameters:

Name Description
cyclePeriod = 24.0; q4 = 2.4514; photoPeriod = 12.0; lightOffset = 0.0; twilightPeriod = 0.0416666667; lightAmplitude = 1.0; phase = 0.0 Reaction: => cAm; cPn, cPn, Rate Law: (((lightOffset+0.5*lightAmplitude*(1+tanh(cyclePeriod*((t+phase)/cyclePeriod-floor(floor(t+phase)/cyclePeriod))/twilightPeriod)))-0.5*lightAmplitude*(1+tanh((cyclePeriod*((t+phase)/cyclePeriod-floor(floor(t+phase)/cyclePeriod))-photoPeriod)/twilightPeriod)))+0.5*lightAmplitude*(1+tanh((cyclePeriod*((t+phase)/cyclePeriod-floor(floor(t+phase)/cyclePeriod))-cyclePeriod)/twilightPeriod)))*q4*cPn*compartment
r3 = 0.3166; r4 = 2.1509 Reaction: cTc => cTn; cTn, cTc, Rate Law: compartment*((-r4)*cTn+r3*cTc)
m3 = 3.6888; k3 = 1.2765 Reaction: cLn => ; cLn, Rate Law: compartment*m3*cLn/(k3+cLn)
p2 = 4.324 Reaction: => cTc; cTm, cTm, Rate Law: p2*compartment*cTm
m18 = 0.0156; k16 = 0.6104 Reaction: cAn => ; cAn, Rate Law: compartment*m18*cAn/(k16+cAn)
r8 = 0.2002; r7 = 2.2123 Reaction: cYc => cYn; cYc, cYn, Rate Law: compartment*(r7*cYc-r8*cYn)
r6 = 3.3017; r5 = 1.0352 Reaction: cXc => cXn; cXc, cXn, Rate Law: compartment*(r5*cXc-r6*cXn)
cyclePeriod = 24.0; g5 = 1.178; g6 = 0.0645; lightOffset = 0.0; twilightPeriod = 0.0416666667; q2 = 2.4017; f = 1.0237; photoPeriod = 12.0; n5 = 0.1649; e = 3.6064; n4 = 0.0857; lightAmplitude = 1.0; phase = 0.0 Reaction: => cYm; cTn, cLn, cPn, cPn, cTn, cLn, Rate Law: compartment*((((lightOffset+0.5*lightAmplitude*(1+tanh(cyclePeriod*((t+phase)/cyclePeriod-floor(floor(t+phase)/cyclePeriod))/twilightPeriod)))-0.5*lightAmplitude*(1+tanh((cyclePeriod*((t+phase)/cyclePeriod-floor(floor(t+phase)/cyclePeriod))-photoPeriod)/twilightPeriod)))+0.5*lightAmplitude*(1+tanh((cyclePeriod*((t+phase)/cyclePeriod-floor(floor(t+phase)/cyclePeriod))-cyclePeriod)/twilightPeriod)))*q2*cPn+((((lightOffset+0.5*lightAmplitude*(1+tanh(cyclePeriod*((t+phase)/cyclePeriod-floor(floor(t+phase)/cyclePeriod))/twilightPeriod)))-0.5*lightAmplitude*(1+tanh((cyclePeriod*((t+phase)/cyclePeriod-floor(floor(t+phase)/cyclePeriod))-photoPeriod)/twilightPeriod)))+0.5*lightAmplitude*(1+tanh((cyclePeriod*((t+phase)/cyclePeriod-floor(floor(t+phase)/cyclePeriod))-cyclePeriod)/twilightPeriod)))*n4+n5)*g5^e/(g5^e+cTn^e))*g6^f/(g6^f+cLn^f)
r9 = 0.2528; r10 = 0.2212 Reaction: cAc => cAn; cAc, cAn, Rate Law: compartment*(r9*cAc-r10*cAn)
p1 = 0.8295 Reaction: => cLc; cLm, cLm, Rate Law: compartment*p1*cLm
k15 = 0.0703; m17 = 4.4505 Reaction: cAc => ; cAc, Rate Law: compartment*m17*cAc/(k15+cAc)
k2 = 1.5644; m2 = 20.44 Reaction: cLc => ; cLc, Rate Law: compartment*m2*cLc/(k2+cLc)
cyclePeriod = 24.0; photoPeriod = 12.0; q3 = 1.0; lightOffset = 0.0; twilightPeriod = 0.0416666667; lightAmplitude = 1.0; phase = 0.0 Reaction: cPn => ; cPn, Rate Law: (((lightOffset+0.5*lightAmplitude*(1+tanh(cyclePeriod*((t+phase)/cyclePeriod-floor(floor(t+phase)/cyclePeriod))/twilightPeriod)))-0.5*lightAmplitude*(1+tanh((cyclePeriod*((t+phase)/cyclePeriod-floor(floor(t+phase)/cyclePeriod))-photoPeriod)/twilightPeriod)))+0.5*lightAmplitude*(1+tanh((cyclePeriod*((t+phase)/cyclePeriod-floor(floor(t+phase)/cyclePeriod))-cyclePeriod)/twilightPeriod)))*q3*cPn*compartment
m9 = 10.1132; k7 = 6.5585 Reaction: cXm => ; cXm, Rate Law: compartment*m9*cXm/(k7+cXm)
m8 = 4.0424; k6 = 0.4033 Reaction: cTn => ; cTn, Rate Law: m8*compartment*cTn/(k6+cTn)
k9 = 17.1111; m11 = 3.3442 Reaction: cXn => ; cXn, Rate Law: compartment*m11*cXn/(k9+cXn)
k4 = 2.5734; m4 = 3.8231 Reaction: cTm => ; cTm, Rate Law: compartment*m4*cTm/(k4+cTm)
p4 = 0.2485 Reaction: => cYc; cYm, cYm, Rate Law: compartment*p4*cYm
k1 = 2.392; m1 = 1.999 Reaction: cLm => ; cLm, Rate Law: compartment*m1*cLm/(k1+cLm)
r1 = 16.8363; r2 = 0.1687 Reaction: cLc => cLn; cLc, cLn, Rate Law: compartment*(r1*cLc-r2*cLn)
b = 1.0258; n2 = 3.0087; g3 = 0.2658; g2 = 0.0368; c = 1.0258 Reaction: => cTm; cYn, cLn, cYn, cLn, Rate Law: compartment*n2*cYn^b/(g2^b+cYn^b)*g3^c/(g3^c+cLn^c)
cyclePeriod = 24.0; p5 = 0.5; photoPeriod = 12.0; lightOffset = 0.0; twilightPeriod = 0.0416666667; lightAmplitude = 1.0; phase = 0.0 Reaction: => cPn, Rate Law: (1-(((lightOffset+0.5*lightAmplitude*(1+tanh(cyclePeriod*((t+phase)/cyclePeriod-floor(floor(t+phase)/cyclePeriod))/twilightPeriod)))-0.5*lightAmplitude*(1+tanh((cyclePeriod*((t+phase)/cyclePeriod-floor(floor(t+phase)/cyclePeriod))-photoPeriod)/twilightPeriod)))+0.5*lightAmplitude*(1+tanh((cyclePeriod*((t+phase)/cyclePeriod-floor(floor(t+phase)/cyclePeriod))-cyclePeriod)/twilightPeriod))))*p5*compartment
p3 = 2.147 Reaction: => cXc; cXm, cXm, Rate Law: compartment*p3*cXm
m14 = 0.6114; k12 = 1.8066 Reaction: cYn => ; cYn, Rate Law: compartment*m14*cYn/(k12+cYn)
p6 = 0.2907 Reaction: => cAc; cAm, cAm, Rate Law: compartment*p6*cAm
m6 = 3.1741; k5 = 2.7454 Reaction: cTc => ; cTc, Rate Law: m6*compartment*cTc/(k5+cTc)
m5 = 0.0013; cyclePeriod = 24.0; k5 = 2.7454; photoPeriod = 12.0; lightOffset = 0.0; twilightPeriod = 0.0416666667; lightAmplitude = 1.0; phase = 0.0 Reaction: cTc => ; cTc, Rate Law: compartment*(1-(((lightOffset+0.5*lightAmplitude*(1+tanh(cyclePeriod*((t+phase)/cyclePeriod-floor(floor(t+phase)/cyclePeriod))/twilightPeriod)))-0.5*lightAmplitude*(1+tanh((cyclePeriod*((t+phase)/cyclePeriod-floor(floor(t+phase)/cyclePeriod))-photoPeriod)/twilightPeriod)))+0.5*lightAmplitude*(1+tanh((cyclePeriod*((t+phase)/cyclePeriod-floor(floor(t+phase)/cyclePeriod))-cyclePeriod)/twilightPeriod))))*m5*cTc/(k5+cTc)
n3 = 0.2431; d = 1.4422; g4 = 0.5388 Reaction: => cXm; cTn, cTn, Rate Law: compartment*n3*cTn^d/(g4^d+cTn^d)
alpha = 4.0; n1 = 7.8142; g1 = 3.1383; cyclePeriod = 24.0; g0 = 1.0; photoPeriod = 12.0; q1 = 4.1954; lightOffset = 0.0; a = 1.2479; twilightPeriod = 0.0416666667; lightAmplitude = 1.0; n0 = 0.05; phase = 0.0 Reaction: => cLm; cAn, cXn, cPn, cAn, cPn, cXn, Rate Law: compartment*g0^alpha/(g0^alpha+cAn^alpha)*((((lightOffset+0.5*lightAmplitude*(1+tanh(cyclePeriod*((t+phase)/cyclePeriod-floor(floor(t+phase)/cyclePeriod))/twilightPeriod)))-0.5*lightAmplitude*(1+tanh((cyclePeriod*((t+phase)/cyclePeriod-floor(floor(t+phase)/cyclePeriod))-photoPeriod)/twilightPeriod)))+0.5*lightAmplitude*(1+tanh((cyclePeriod*((t+phase)/cyclePeriod-floor(floor(t+phase)/cyclePeriod))-cyclePeriod)/twilightPeriod)))*(q1*cPn+n0)+n1*cXn^a/(g1^a+cXn^a))
k13 = 1.2; m15 = 1.2 Reaction: cPn => ; cPn, Rate Law: compartment*m15*cPn/(k13+cPn)
cyclePeriod = 24.0; photoPeriod = 12.0; lightOffset = 0.0; k6 = 0.4033; twilightPeriod = 0.0416666667; lightAmplitude = 1.0; phase = 0.0; m7 = 0.0492 Reaction: cTn => ; cTn, Rate Law: compartment*(1-(((lightOffset+0.5*lightAmplitude*(1+tanh(cyclePeriod*((t+phase)/cyclePeriod-floor(floor(t+phase)/cyclePeriod))/twilightPeriod)))-0.5*lightAmplitude*(1+tanh((cyclePeriod*((t+phase)/cyclePeriod-floor(floor(t+phase)/cyclePeriod))-photoPeriod)/twilightPeriod)))+0.5*lightAmplitude*(1+tanh((cyclePeriod*((t+phase)/cyclePeriod-floor(floor(t+phase)/cyclePeriod))-cyclePeriod)/twilightPeriod))))*m7*cTn/(k6+cTn)
k11 = 1.8258; m13 = 0.1347 Reaction: cYc => ; cYc, Rate Law: compartment*m13*cYc/(k11+cYc)
n6 = 8.0706; g7 = 4.0E-4; g = 1.0258 Reaction: => cAm; cLn, cLn, Rate Law: compartment*n6*cLn^g/(g7^g+cLn^g)
k10 = 1.7303; m12 = 4.297 Reaction: cYm => ; cYm, Rate Law: compartment*m12*cYm/(k10+cYm)
m16 = 12.2398; k14 = 10.3617 Reaction: cAm => ; cAm, Rate Law: compartment*m16*cAm/(k14+cAm)
m10 = 0.2179; k8 = 0.6632 Reaction: cXc => ; cXc, Rate Law: compartment*m10*cXc/(k8+cXc)

States:

Name Description
cXm [messenger RNA]
cTc [Two-component response regulator-like APRR1]
cTn [Two-component response regulator-like APRR1]
cXc X protein in cytoplasm
cAc [Two-component response regulator-like APRR9; Two-component response regulator-like APRR7]
cXn X protein in nucleus
cYm [messenger RNA]
cLn [Protein LHY]
cPn light sensitive protein P
cYn [Protein GIGANTEA]
cYc [Protein GIGANTEA]
cAn [Two-component response regulator-like APRR9; Two-component response regulator-like APRR7]
cLc [Protein LHY]
cLm [messenger RNA]
cAm [messenger RNA]
cTm [messenger RNA]

Observables: none

This is a mathematical model of phenylalanine metabolism in plants as influenced by shikimate, with specific evidence of…

In higher plants, the amino acid phenylalanine is a substrate of both primary and secondary metabolic pathways. The primary pathway that consumes phenylalanine, protein biosynthesis, is essential for the viability of all cells. Meanwhile, the secondary pathways are not necessary for the survival of individual cells, but benefit of the plant as a whole. Here we focus on the monolignol pathway, a secondary metabolic pathway in the cytosol that rapidly consumes phenylalanine to produce the precursors of lignin during wood formation. In planta monolignol biosynthesis involves a series of seemingly redundant steps wherein shikimate, a precursor of phenylalanine synthesized in the plastid, is transiently ligated to the main substrate of the pathway. However, shikimate is not catalytically involved in the reactions of the monolignol pathway, and is only needed for pathway enzymes to recognize their main substrates. After some steps the shikimate moiety is removed unaltered, and the main substrate continues along the pathway. It has been suggested that this portion of the monolignol pathway fulfills a regulatory role in the following way. Low phenylalanine concentrations (viz. availability) correlate with low shikimate concentrations. When shikimate concentratios are low, flux into the monolignol pathway will be limited by means of the steps requiring shikimate. Thus, when the concentration of phenylalanine is low it will be reserved for protein biosynthesis. Here we employ a theoretical approach to test this hypothesis. Simplified versions of plant phenylalanine metabolism are modelled as systems of ordinary differential equations. Our analysis shows that the seemingly redundant steps can be sufficient for the prioritization of protein biosynthesis over the monolignol pathway when the availability of phenylalanine is low, depending on system parameters. Thus, the phenylalanine precursor shikimate may signal low phenylalanine availability to secondary pathways. Because our models have been abstracted from plant phenylalanine metabolism, this mechanism of metabolic signalling, which we call the Precursor Shutoff Valve (PSV), may also be present in other biochemical networks comprised of two pathways that share a common substrate. link: http://identifiers.org/pubmed/30412698

Parameters:

Name Description
a_2_minus = 1.5; K_2_minus = 100.0; a_2_plus = 2.0; b2r = 0.0; K_2_plus = 100.0; b2f = 0.0 Reaction: X_1 => X_3, Rate Law: compartment*(a_2_plus*X_1/(K_2_plus*(1+b2f*X_3)+X_1)-a_2_minus*X_3/(K_2_minus*(1+b2r*X_1)+X_3))
a_1 = 100.0; b = 1.0; K_1 = 0.1 Reaction: X_1 => X_2, Rate Law: compartment*a_1*X_1/(K_1*(1+b*X_2)+X_1)
K_3_2 = 1.0; K_3_3 = 0.1; a_3 = 75.0 Reaction: X_2 + X_3 => X_4, Rate Law: compartment*a_3*X_2*X_3/((K_3_2+X_2)*(K_3_3+X_3))
a_5 = 5.0; K_5 = 1.0 Reaction: X_2 =>, Rate Law: compartment*a_5*X_2/(K_5+X_2)
a_0 = 25.0 Reaction: => X_1, Rate Law: compartment*a_0
K_4 = 1.0; a_4 = 75.0 Reaction: X_4 => X_3, Rate Law: compartment*a_4*X_4/(K_4+X_4)

States:

Name Description
X 1 [shikimate; GO:0009536]
X 4 [CHEBI:91005]
X 3 [shikimate; cytosol]
X 2 [phenylalanine]

Observables: none

Modelling tumor growth with immune response and drug using ordinary differential equations Mohd Rashid Admon, Normah Ma…

This is a mathematical study about tumor growth from a different perspective, with the aim of predicting and/or controlling the disease. The focus is on the effect and interaction of tumor cell with immune and drug. This paper presents a mathematical model of immune response and a cycle phase specific drug using a system of ordinary differential equations. Stability analysis is used to produce stability regions for various values of certain parameters during mitosis. The stability region of the graph shows that the curve splits the tumor decay and growth regions in the absence of immune response. However, when immune response is present, the tumor growth region is decreased. When drugs are considered in the system, the stability region remains unchanged as the system with the presence of immune response but the population of tumor cells at interphase and metaphase is reduced with percentage differences of 1.27 and 1.53 respectively. The combination of immunity and drug to fight cancer provides a better method to reduce tumor population compared to immunity alone. link: http://identifiers.org/doi/10.11113/jt.v79.9791

Parameters:

Name Description
c2 = 0.085; d1 = 0.29; k3 = 0.0; c4 = 0.085; k4 = 0.061 Reaction: I => ; Tm, u, Ti, Rate Law: compartment*(c2*I*Ti+c4*Tm*I+d1*I+k3*(1-exp((-k4)*u))*I)
a4 = 0.8 Reaction: => Ti; Tm, Rate Law: compartment*2*a4*Tm
gamma = 0.0 Reaction: u =>, Rate Law: compartment*gamma*u
k = 0.029; n = 3.0; p = 0.1; alpha = 0.2 Reaction: => I; Ti, Tm, Rate Law: compartment*(k+p*I*(Ti+Tm)^n/(alpha+(Ti+Tm)^n))
a1 = 1.0; c1 = 0.9; d2 = 0.11 Reaction: Ti => ; I, Rate Law: compartment*((c1*I+d2)*Ti+a1*Ti)
a1 = 1.0 Reaction: => Tm; Ti, Rate Law: compartment*a1*Ti
c3 = 0.9; k1 = 0.0; a4 = 0.8; k2 = 0.57; d3 = 0.4 Reaction: Tm => ; I, u, Rate Law: compartment*(d3*Tm+a4*Tm+c3*Tm*I+k1*(-exp((-k2)*u))*Tm)

States:

Name Description
Ti [Neoplastic Cell]
I [immune response]
u u
Tm [Neoplastic Cell]

Observables: none

Modelling combined virotherapy and immunotherapy:strengthening the antitumour immune response mediated byIL-12 and GM-CS…

Combined virotherapy and immunotherapy has been emergingas a promising and effective cancer treatment for some time.Intratumoural injections of an oncolytic virus instigate an immunereaction in the host, resulting in an influx of immune cells tothe tumour site. Through combining an oncolytic viral vector withimmunostimulatory cytokines an additional antitumour immuneresponse can be initiated, whereby immune cells induce apoptosisin both uninfected and virus infected tumour cells. We developa mathematical model to reproduce the experimental results fortumour growth under treatment with an oncolytic adenovirus co-expressing the immunostimulatory cytokines interleukin 12 (IL-12)and granulocyte-monocyte colony stimulating factor (GM-CSF). Byexploring heterogeneity in the immune cell stimulation by thetreatment, we find a subset of the parameter space for the immunecell induced apoptosis rate, in which the treatment will be lesseffective in a short time period. Therefore, we believe the bivariatenature of treatment outcome, whereby tumours are either completelyeradicated or grow unbounded, can be explained by heterogeneity inthis immune characteristic. Furthermore, the model highlights theapparent presence of negative feedback in the helper T cell and APCstimulation dynamics, when IL-12 and GM-CSF are co-expressed asopposed to individually expressed by the viral vector. link: http://identifiers.org/doi/10.1080/23737867.2018.1438216

Parameters: none

States: none

Observables: none

Abstract: The cancer stem cell hypothesis has gained currency in recent times but concerns remain about its scientific…

The cancer stem cell hypothesis has gained currency in recent times but concerns remain about its scientific foundations because of significant gaps that exist between research findings and comprehensive knowledge about cancer stem cells (CSCs). In this light, a mathematical model that considers hematopoietic dynamics in the diseased state of the bone marrow and peripheral blood is proposed and used to address findings about CSCs. The ensuing model, resulting from a modification and refinement of a recent model, develops out of the position that mathematical models of CSC development, that are few at this time, are needed to provide insightful underpinnings for biomedical findings about CSCs as the CSC idea gains traction. Accordingly, the mathematical challenges brought on by the model that mirror general challenges in dealing with nonlinear phenomena are discussed and placed in context. The proposed model describes the logical occurrence of discrete time delays, that by themselves present mathematical challenges, in the evolving cell populations under consideration. Under the challenging circumstances, the steady state properties of the model system of delay differential equations are obtained, analyzed, and the resulting mathematical predictions arising therefrom are interpreted and placed within the framework of findings regarding CSCs. Simulations of the model are carried out by considering various parameter scenarios that reflect different experimental situations involving disease evolution in human hosts. Model analyses and simulations suggest that the emergence of the cancer stem cell population alongside other malignant cells engenders higher dimensions of complexity in the evolution of malignancy in the bone marrow and peripheral blood at the expense of healthy hematopoietic development. The model predicts the evolution of an aberrant environment in which the malignant population particularly in the bone marrow shows tendencies of reaching an uncontrollable equilibrium state. Essentially, the model shows that a structural relationship exists between CSCs and non-stem malignant cells that confers on CSCs the role of temporally enhancing and stimulating the expansion of non-stem malignant cells while also benefitting from increases in their own population and these CSCs may be the main protagonists that drive the ultimate evolution of the uncontrollable equilibrium state of such malignant cells and these may have implications for treatment. link: http://identifiers.org/pubmed/30296448

Parameters: none

States: none

Observables: none

BIOMD0000000169 @ v0.0.1

A detailed model mechanism for the G1/S transition in the mammalian cell cycle is presented and analysed by computer sim…

The model reproduces the time profiles of p27, E2F and aE/cdk2 as depicted in Figure 5 c of the paper. Model was simulated on MathSBML.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.

To cite BioModels Database, please use:

Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

Parameters:

Name Description
k17_1 = 3.5 Reaction: Y6_1 + Y10_1 =>, Rate Law: k17_1*Y6_1*Y10_1
k6_1 = 0.018 Reaction: => Y6_1, Rate Law: k6_1
k18_1 = 1.0E-5 Reaction: => Y4_1, Rate Law: k18_1*Y4_1
k4_1 = 1.0E-6 Reaction: => Y4_1, Rate Law: k4_1
k8_1 = 2.0 Reaction: Y7_1 => ; Y1_1, Rate Law: k8_1*Y7_1*Y1_1
kminus4_1 = 0.016 Reaction: Y4_1 =>, Rate Law: kminus4_1*Y4_1
kminus6_1 = 5.0 Reaction: Y6_1 =>, Rate Law: kminus6_1*Y6_1
K10_1 = 0.035 Reaction: Y8_1 => Y1_1 + Y7_1, Rate Law: K10_1*Y8_1
k20_1 = 0.01 Reaction: Y9_1 => Y7_1 + Y6_1, Rate Law: k20_1*Y9_1
k25_1 = 0.01; k25p_1 = 0.02 Reaction: => Y10_1; Y5_1, Rate Law: k25_1/(1+k25p_1*Y5_1)
k2_1 = 0.1 Reaction: Y2_1 => Y1_1, Rate Law: k2_1*Y1_1*Y2_1
k28_1 = 0.01 Reaction: Y5_1 =>, Rate Law: k28_1*Y5_1
k1pp_1 = 0.5; k1_1 = 0.1; k1p_1 = 0.5 Reaction: Y3_1 => Y4_1 + Y11_1; Y3_1, Y1_1, Y6_1, Y9_1, Rate Law: k1p_1*Y6_1*Y3_1+k1pp_1*Y9_1*Y3_1+k1_1*Y1_1*Y3_1
k7_1 = 1.0E-5 Reaction: => Y7_1, Rate Law: k7_1
k19_1 = 0.05 Reaction: Y7_1 + Y6_1 => Y9_1, Rate Law: k19_1*Y7_1*Y6_1
k29_1 = 0.001 Reaction: Y11_1 => Y5_1, Rate Law: k29_1*Y11_1
kminus1_1 = 0.001 Reaction: Y5_1 + Y4_1 => Y3_1, Rate Law: kminus1_1*Y5_1*Y4_1
k5_1 = 0.02 Reaction: Y2_1 =>, Rate Law: k5_1*Y2_1
k22_1 = 0.001 Reaction: Y7_1 =>, Rate Law: k22_1*Y7_1
k21_1 = 0.1 Reaction: Y1_1 =>, Rate Law: k21_1*Y1_1*Y1_1
k24_1 = 0.1 Reaction: Y10_1 =>, Rate Law: k24_1*Y10_1
k26_1 = 0.01; k26p_1 = 0.1 Reaction: => Y5_1; Y10_1, Rate Law: k26_1/(1+k26p_1*Y10_1)
k23_1 = 0.2 Reaction: => Y10_1, Rate Law: k23_1
kminus2_1 = 1.0 Reaction: Y1_1 => Y2_1, Rate Law: kminus2_1*Y1_1
k3p_1 = 0.0; k3_1 = 1.42 Reaction: => Y2_1; Y4_1, Rate Law: k3_1*Y4_1+k3p_1
k9_1 = 2.0 Reaction: Y1_1 + Y7_1 => Y8_1, Rate Law: k9_1*Y1_1*Y7_1
k27_1 = 0.01 Reaction: => Y5_1, Rate Law: k27_1

States:

Name Description
Y6 1 [Cyclin-dependent kinase 4; IPR015451]
Y7 1 [Cyclin-dependent kinase inhibitor 1B]
Y2 1 [G1/S-specific cyclin-E2; Cyclin-dependent kinase 2; G1/S-specific cyclin-E1; Cyclin-dependent kinase 2]
Y3 1 [IPR015652; IPR015633]
Y5 1 [IPR015652]
Y8 1 [G1/S-specific cyclin-E1; Cyclin-dependent kinase 2; Cyclin-dependent kinase inhibitor 1B; G1/S-specific cyclin-E2; Cyclin-dependent kinase 2; Cyclin-dependent kinase inhibitor 1B]
Y4 1 [IPR015633]
Y10 1 [Cyclin-dependent kinase inhibitor 2A]
Y1 1 [Cyclin-dependent kinase 2; G1/S-specific cyclin-E2; G1/S-specific cyclin-E1; Cyclin-dependent kinase 2]
Y11 1 [Phosphoprotein; IPR015652]
Y9 1 [Cyclin-dependent kinase inhibitor 1B; Cyclin-dependent kinase 4; IPR015451]

Observables: none

Aguilera 2014 - HIV latency. Interaction between HIV proteins and immune responseThis model is described in the article:…

HIV infection leads to two cell fates, the viral productive state or viral latency (a reversible non-productive state). HIV latency is relevant because infected active CD4+ T-lymphocytes can reach a resting memory state in which the provirus remains silent for long periods of time. Despite experimental and theoretical efforts, the causal molecular mechanisms responsible for HIV latency are only partially understood. Studies have determined that HIV latency is influenced by the innate immune response carried out by cell restriction factors that inhibit the postintegration steps in the virus replication cycle. In this study, we present a mathematical study that combines deterministic and stochastic approaches to analyze the interactions between HIV proteins and the innate immune response. Using wide ranges of parameter values, we observed the following: (1) a phenomenological description of the viral productive and latent cell phenotypes is obtained by bistable and bimodal dynamics, (2) biochemical noise reduces the probability that an infected cell adopts the latent state, (3) the effects of the innate immune response enhance the HIV latency state, (4) the conditions of the cell before infection affect the latent phenotype, i.e., the existing expression of cell restriction factors propitiates HIV latency, and existing expression of HIV proteins reduces HIV latency. link: http://identifiers.org/pubmed/24997239

Parameters:

Name Description
k1=6.85E-5 Reaction: V => ; V, Rate Law: compartment*k1*V
v=0.00134 Reaction: => V, Rate Law: compartment*v
k1=0.0295 Reaction: V + C => C; V, C, Rate Law: compartment*k1*V*C
k1=5.01E-5 Reaction: C => ; C, Rate Law: compartment*k1*C
Vmax=0.134; Km=380.0 Reaction: V => V; V, Rate Law: compartment*Vmax*V/(Km+V)
k1=0.927 Reaction: V + C => V; V, C, Rate Law: compartment*k1*V*C
v=0.07 Reaction: => C, Rate Law: compartment*v

States:

Name Description
C C
V [structural constituent of virion; DNA viral genome]

Observables: none

Model destription: The model describes the stochastic dynamics of two variables, protein and mRNA of a gene with constit…

Background: Mathematical models are used to gain an integrative understanding of biochemical processes and networks. Commonly the models are based on deterministic ordinary differential equations. When molecular counts are low, stochastic formalisms like Monte Carlo simulations are more appropriate and well established. However, compared to the wealth of computational methods used to fit and analyze deterministic models, there is only little available to quantify the exactness of the fit of stochastic models compared to experimental data or to analyze different aspects of the modeling results. Results: Here, we developed a method to fit stochastic simulations to experimental high-throughput data, meaning data that exhibits distributions. The method uses a comparison of the probability density functions that are computed based on Monte Carlo simulations and the experimental data. Multiple parameter values are iteratively evaluated using optimization routines. The method improves its performance by selecting parameters values after comparing the similitude between the deterministic stability of the system and the modes in the experimental data distribution. As a case study we fitted a model of the IRF7 gene expression circuit to time-course experimental data obtained by flow cytometry. IRF7 shows bimodal dynamics upon IFN stimulation. This dynamics occurs due to the switching between active and basal states of the IRF7 promoter. However, the exact molecular mechanisms responsible for the bimodality of IRF7 is not fully understood. Conclusions: Our results allow us to conclude that the activation of the IRF7 promoter by the combination of IRF7 and ISGF3 is sufficient to explain the observed bimodal dynamics. link: http://identifiers.org/doi/10.1186/s12918-017-0406-4

Parameters: none

States: none

Observables: none

MODEL1608100001 @ v0.0.1

Model destription: The model describes the dynamics of murine IRF7 gene expression upon IFN stimulation. The present mo…

Background: Mathematical models are used to gain an integrative understanding of biochemical processes and networks. Commonly the models are based on deterministic ordinary differential equations. When molecular counts are low, stochastic formalisms like Monte Carlo simulations are more appropriate and well established. However, compared to the wealth of computational methods used to fit and analyze deterministic models, there is only little available to quantify the exactness of the fit of stochastic models compared to experimental data or to analyze different aspects of the modeling results. Results: Here, we developed a method to fit stochastic simulations to experimental high-throughput data, meaning data that exhibits distributions. The method uses a comparison of the probability density functions that are computed based on Monte Carlo simulations and the experimental data. Multiple parameter values are iteratively evaluated using optimization routines. The method improves its performance by selecting parameters values after comparing the similitude between the deterministic stability of the system and the modes in the experimental data distribution. As a case study we fitted a model of the IRF7 gene expression circuit to time-course experimental data obtained by flow cytometry. IRF7 shows bimodal dynamics upon IFN stimulation. This dynamics occurs due to the switching between active and basal states of the IRF7 promoter. However, the exact molecular mechanisms responsible for the bimodality of IRF7 is not fully understood. Conclusions: Our results allow us to conclude that the activation of the IRF7 promoter by the combination of IRF7 and ISGF3 is sufficient to explain the observed bimodal dynamics. link: http://identifiers.org/doi/10.1186/s12918-017-0406-4

Parameters: none

States: none

Observables: none

Ahmad2017 - Genome-scale metabolic model (iGT736) of Geobacillus thermoglucosidasius (C56-YS93)This model is described i…

Rice straw is a major crop residue which is burnt in many countries, creating significant air pollution. Thus, alternative routes for disposal of rice straw are needed. Biotechnological treatment of rice straw hydrolysate has potential to convert this agriculture waste into valuable biofuel(s) and platform chemicals. Geobacillus thermoglucosidasius is a thermophile with properties specially suited for use as a biocatalyst in lignocellulosic bioprocesses, such as high optimal temperature and tolerance to high levels of ethanol. However, the capabilities of Geobacillus thermoglucosidasius to utilize sugars in rice straw hydrolysate for making bioethanol and other platform chemicals have not been fully explored. In this work, we have created a genome scale metabolic model (denoted iGT736) of the organism containing 736 gene products, 1159 reactions and 1163 metabolites. The model was validated both by purely theoretical approaches and by comparing the behaviour of the model to previously published experimental results. The model was then used to determine the yields of a variety of platform chemicals from glucose and xylose — two primary sugars in rice straw hydrolysate. A comparison with results from a model of Escherichia coli shows that Geobacillus thermoglucosidasius is capable of producing a wider range of products, and that for the products also produced by Escherichia coli, the yields are comparable. We also discuss strategies to utilise arabinose, a minor component of rice straw hydrolysate, and propose additional reactions to lead to the synthesis of xylitol, not currently produced by Geobacillus thermoglucosidasius. Our results provide additional motivation for the current exploration of the industrial potential of Geobacillus thermoglucosidasius and we make our model publicly available to aid the development of metabolic engineering strategies for this organism. link: http://identifiers.org/doi/10.1016/j.jbiotec.2017.03.031

Parameters: none

States: none

Observables: none

MODEL3883569319 @ v0.0.1

This model is described in the article: Reconstruction and Validation of RefRec: A Global Model for the Yeast Molecula…

Molecular interaction networks establish all cell biological processes. The networks are under intensive research that is facilitated by new high-throughput measurement techniques for the detection, quantification, and characterization of molecules and their physical interactions. For the common model organism yeast Saccharomyces cerevisiae, public databases store a significant part of the accumulated information and, on the way to better understanding of the cellular processes, there is a need to integrate this information into a consistent reconstruction of the molecular interaction network. This work presents and validates RefRec, the most comprehensive molecular interaction network reconstruction currently available for yeast. The reconstruction integrates protein synthesis pathways, a metabolic network, and a protein-protein interaction network from major biological databases. The core of the reconstruction is based on a reference object approach in which genes, transcripts, and proteins are identified using their primary sequences. This enables their unambiguous identification and non-redundant integration. The obtained total number of different molecular species and their connecting interactions is approximately 67,000. In order to demonstrate the capacity of RefRec for functional predictions, it was used for simulating the gene knockout damage propagation in the molecular interaction network in approximately 590,000 experimentally validated mutant strains. Based on the simulation results, a statistical classifier was subsequently able to correctly predict the viability of most of the strains. The results also showed that the usage of different types of molecular species in the reconstruction is important for accurate phenotype prediction. In general, the findings demonstrate the benefits of global reconstructions of molecular interaction networks. With all the molecular species and their physical interactions explicitly modeled, our reconstruction is able to serve as a valuable resource in additional analyses involving objects from multiple molecular -omes. For that purpose, RefRec is freely available in the Systems Biology Markup Language format. link: http://identifiers.org/pubmed/20498836

Parameters: none

States: none

Observables: none

MODEL9089914876 @ v0.0.1

This model is taken from <a href = "http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&dopt=AbstractPl…

Stimulus reinforcement strengthens learning. Intervals between reinforcement affect both the kind of learning that occurs and the amount of learning. Stimuli spaced by a few minutes result in more effective learning than when massed together. There are several synaptic correlates of repeated stimuli, such as different kinds of plasticity and the amplitude of synaptic change. Here we study the role of signalling pathways in the synapse on this selectivity for spaced stimuli. Using the in vitro hippocampal slice technique we monitored long-term potentiation (LTP) amplitude in CA1 for repeated 100-Hz, 1-s tetani. We observe the highest LTP levels when the inter-tetanus interval is 5-10 min. We tested biochemical activity in the slice following the same stimuli, and found that extracellular signal-regulated kinase type II (ERKII) but not CaMKII exhibits a peak at about 10 min. When calcium influx into the slice is buffered using AM-ester calcium dyes, amplitude of the physiological and biochemical response is reduced, but the timing is not shifted. We have previously used computer simulations of synaptic signalling to predict such temporal tuning from signalling pathways. In the current study we consider feedback and feedforward models that exhibit temporal tuning consistent with our experiments. We find that a model incorporating post-stimulus build-up of PKM zeta acting upstream of mitogen-activated protein kinase is sufficient to explain the observed temporal tuning. On the basis of these combined experimental and modelling results we propose that the dynamics of PKM activation and ERKII signalling may provide a mechanism for functionally important forms of synaptic pattern selectivity. link: http://identifiers.org/pubmed/15548210

Parameters: none

States: none

Observables: none

MODEL9089538076 @ v0.0.1

This model is based on <a href = "http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&dopt=AbstractPlus…

Stimulus reinforcement strengthens learning. Intervals between reinforcement affect both the kind of learning that occurs and the amount of learning. Stimuli spaced by a few minutes result in more effective learning than when massed together. There are several synaptic correlates of repeated stimuli, such as different kinds of plasticity and the amplitude of synaptic change. Here we study the role of signalling pathways in the synapse on this selectivity for spaced stimuli. Using the in vitro hippocampal slice technique we monitored long-term potentiation (LTP) amplitude in CA1 for repeated 100-Hz, 1-s tetani. We observe the highest LTP levels when the inter-tetanus interval is 5-10 min. We tested biochemical activity in the slice following the same stimuli, and found that extracellular signal-regulated kinase type II (ERKII) but not CaMKII exhibits a peak at about 10 min. When calcium influx into the slice is buffered using AM-ester calcium dyes, amplitude of the physiological and biochemical response is reduced, but the timing is not shifted. We have previously used computer simulations of synaptic signalling to predict such temporal tuning from signalling pathways. In the current study we consider feedback and feedforward models that exhibit temporal tuning consistent with our experiments. We find that a model incorporating post-stimulus build-up of PKM zeta acting upstream of mitogen-activated protein kinase is sufficient to explain the observed temporal tuning. On the basis of these combined experimental and modelling results we propose that the dynamics of PKM activation and ERKII signalling may provide a mechanism for functionally important forms of synaptic pattern selectivity. link: http://identifiers.org/pubmed/15548210

Parameters: none

States: none

Observables: none

MODEL9089491423 @ v0.0.1

This model is taken from the <a href = http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&dopt=Abstrac…

Stimulus reinforcement strengthens learning. Intervals between reinforcement affect both the kind of learning that occurs and the amount of learning. Stimuli spaced by a few minutes result in more effective learning than when massed together. There are several synaptic correlates of repeated stimuli, such as different kinds of plasticity and the amplitude of synaptic change. Here we study the role of signalling pathways in the synapse on this selectivity for spaced stimuli. Using the in vitro hippocampal slice technique we monitored long-term potentiation (LTP) amplitude in CA1 for repeated 100-Hz, 1-s tetani. We observe the highest LTP levels when the inter-tetanus interval is 5-10 min. We tested biochemical activity in the slice following the same stimuli, and found that extracellular signal-regulated kinase type II (ERKII) but not CaMKII exhibits a peak at about 10 min. When calcium influx into the slice is buffered using AM-ester calcium dyes, amplitude of the physiological and biochemical response is reduced, but the timing is not shifted. We have previously used computer simulations of synaptic signalling to predict such temporal tuning from signalling pathways. In the current study we consider feedback and feedforward models that exhibit temporal tuning consistent with our experiments. We find that a model incorporating post-stimulus build-up of PKM zeta acting upstream of mitogen-activated protein kinase is sufficient to explain the observed temporal tuning. On the basis of these combined experimental and modelling results we propose that the dynamics of PKM activation and ERKII signalling may provide a mechanism for functionally important forms of synaptic pattern selectivity. link: http://identifiers.org/pubmed/15548210

Parameters: none

States: none

Observables: none

MODEL9147091146 @ v0.0.1

This is a model of ERKII signaling which is bistable due to feedback. The feedback occurs through ERKII phosphorylation…

Strong inputs to neurons trigger complex biochemical events leading to synaptic plasticity. These biochemical events occur at many spatial scales, ranging from submicron dendritic spines to signals that propagate hundreds of microns from dendrites to the nucleus. ERKII is an important signaling molecule that is involved in many aspects of plasticity, including local excitability, communication with the nucleus, and control of local protein synthesis. We observed that ERKII activation spreads long distances in apical dendrites of stimulated hippocampal CA1 pyramidal neurons. We combined experiments and models to show that this >100 mum spread was too large to be explained by biochemical reaction-diffusion effects. We show that two modes of calcium entry along the dendrite contribute to the extensive activation of ERKII. We predict the occurrence of feedback between biophysical events resulting in calcium entry, and biochemical events resulting in ERKII activation. This feedback causes a switch-like propagation of ERKII activation, coupled with enhanced electrical excitability, along the apical dendrite. We propose that this propagating switch forms zones on dendrites in which plasticity is facilitated. link: http://identifiers.org/pubmed/19404460

Parameters: none

States: none

Observables: none

MODEL9147232940 @ v0.0.1

This is a non-bistable model of ERKII signaling that also incorporates PKM synthesis triggered by Ca influx. It is a sim…

Strong inputs to neurons trigger complex biochemical events leading to synaptic plasticity. These biochemical events occur at many spatial scales, ranging from submicron dendritic spines to signals that propagate hundreds of microns from dendrites to the nucleus. ERKII is an important signaling molecule that is involved in many aspects of plasticity, including local excitability, communication with the nucleus, and control of local protein synthesis. We observed that ERKII activation spreads long distances in apical dendrites of stimulated hippocampal CA1 pyramidal neurons. We combined experiments and models to show that this >100 mum spread was too large to be explained by biochemical reaction-diffusion effects. We show that two modes of calcium entry along the dendrite contribute to the extensive activation of ERKII. We predict the occurrence of feedback between biophysical events resulting in calcium entry, and biochemical events resulting in ERKII activation. This feedback causes a switch-like propagation of ERKII activation, coupled with enhanced electrical excitability, along the apical dendrite. We propose that this propagating switch forms zones on dendrites in which plasticity is facilitated. link: http://identifiers.org/pubmed/19404460

Parameters: none

States: none

Observables: none

BIOMD0000000295 @ v0.0.1

This a model from the article: Isoform switching facilitates period control in the Neurospora crassa circadian clock.…

A striking and defining feature of circadian clocks is the small variation in period over a physiological range of temperatures. This is referred to as temperature compensation, although recent work has suggested that the variation observed is a specific, adaptive control of period. Moreover, given that many biological rate constants have a Q(10) of around 2, it is remarkable that such clocks remain rhythmic under significant temperature changes. We introduce a new mathematical model for the Neurospora crassa circadian network incorporating experimental work showing that temperature alters the balance of translation between a short and long form of the FREQUENCY (FRQ) protein. This is used to discuss period control and functionality for the Neurospora system. The model reproduces a broad range of key experimental data on temperature dependence and rhythmicity, both in wild-type and mutant strains. We present a simple mechanism utilising the presence of the FRQ isoforms (isoform switching) by which period control could have evolved, and argue that this regulatory structure may also increase the temperature range where the clock is robustly rhythmic. link: http://identifiers.org/pubmed/18277380

Parameters:

Name Description
k1np = 0.272306464006464; k2np = 0.295420749525813 Reaction: FCp => FNp, Rate Law: k1np*FCp-k2np*FNp
vm = 0.885376326739544; km = 0.0846004096489894 Reaction: MF =>, Rate Law: vm*MF/(km+MF)
ks = 0.313846476998244 Reaction: => FC; MF, Rate Law: ks*MF
k1n = 0.222636680929471; k2n = 0.331484503209686 Reaction: FC => FN, Rate Law: k1n*FC-k2n*FN
vs = 1.2236333742524; dawn = 6.0; n = 6.3958; dusk = 18.0; amp = 0.0; ki = 5.04543346939346 Reaction: => MF; FN, FNp, Rate Law: (vs+amp*(1+tanh(2*((time-24*floor(time/24))-dawn)))*(1-tanh(2*((time-24*floor(time/24))-dusk)))/4)*ki^n/(ki^n+(FN+FNp)^n)
ksp = 0.294840169149965 Reaction: => FCp; MF, Rate Law: ksp*MF
vdp = 0.139750313979272 Reaction: FCp =>, Rate Law: vdp*FCp
vd = 0.161111487362275 Reaction: FC =>, Rate Law: vd*FC

States:

Name Description
MF [Frequency clock protein]
FN [Frequency clock protein]
FNp [Frequency clock protein]
FC [Frequency clock protein]
FCp [Frequency clock protein]

Observables: none

BIOMD0000000214 @ v0.0.1

This model 2 described in the supplement of the article below. It is parameterized for the WT at 24°C. To reproduce figu…

A striking and defining feature of circadian clocks is the small variation in period over a physiological range of temperatures. This is referred to as temperature compensation, although recent work has suggested that the variation observed is a specific, adaptive control of period. Moreover, given that many biological rate constants have a Q(10) of around 2, it is remarkable that such clocks remain rhythmic under significant temperature changes. We introduce a new mathematical model for the Neurospora crassa circadian network incorporating experimental work showing that temperature alters the balance of translation between a short and long form of the FREQUENCY (FRQ) protein. This is used to discuss period control and functionality for the Neurospora system. The model reproduces a broad range of key experimental data on temperature dependence and rhythmicity, both in wild-type and mutant strains. We present a simple mechanism utilising the presence of the FRQ isoforms (isoform switching) by which period control could have evolved, and argue that this regulatory structure may also increase the temperature range where the clock is robustly rhythmic. link: http://identifiers.org/pubmed/18277380

Parameters:

Name Description
b9 = 81.10381; d4 = 3.36641 Reaction: PW =>, Rate Law: d4*PW/(PW+b9)
b8 = 0.11167; d3 = 0.50309 Reaction: MW =>, Rate Law: d3*MW/(MW+b8)
a3 = 0.2834 Reaction: => E1F; MF, Rate Law: a3*MF
f1p = 0.09588 Reaction: E2Fp => PFp, Rate Law: f1p*E2Fp
f1 = 0.09292 Reaction: E1F => E2F, Rate Law: f1*E1F
a5 = 0.02917; a4 = 0.46227; k = 2.18234; b7 = 2.96739 Reaction: => MW; PWL, Rate Law: a4+a5*PWL^k/(PWL^k+b7^k)
gam1 = 0.34603 Reaction: E1F =>, Rate Law: gam1*E1F
a3p = 0.34578 Reaction: => E1Fp; MF, Rate Law: a3p*MF
b10 = 93.03664; d5 = 0.41085 Reaction: PWL =>, Rate Law: d5*PWL/(PWL+b10)
gam1p = 0.40119 Reaction: E1Fp =>, Rate Law: gam1p*E1Fp
a7 = 3.02856; a6 = 0.20695 Reaction: => E1W; MW, PF, PFp, Rate Law: (a6+a7*(PF+PFp))*MW
r1 = 2.71574; dawn = 12.0; dusk = 24.0; r2 = 35.40005; amp = 0.0 Reaction: PW => PWL, Rate Law: r1*amp*PW*(1+tanh(24*((time-24*floor(time/24))-dawn)))*(1-tanh(24*((time-24*floor(time/24))-dusk)))/4-r2*PWL
d2p = 0.18191 Reaction: PFp =>, Rate Law: d2p*PFp
gam2 = 2.8E-4 Reaction: E1W =>, Rate Law: gam2*E1W
b5 = 0.13056; d1 = 1.43549 Reaction: MF =>, Rate Law: d1*MF/(MF+b5)
b3 = 0.08039; b1 = 0.00209; b2 = 2.13476; a1 = 24.9736; n = 1.02419; a2 = 3.59797; b4 = 0.45859; m = 1.34979 Reaction: => MF; PF, PFp, PW, PWL, Rate Law: a1*PWL^n/((1+(PF+PFp)/b1)*(PWL^n+b2^n))+a2*PW^m/((1+(PF+PFp)/b3)*(PW^m+b4^m))
d2 = 0.21251 Reaction: PF =>, Rate Law: d2*PF
f2 = 0.14979 Reaction: E1W => E2W, Rate Law: f2*E1W

States:

Name Description
E1Fp [Frequency clock protein]
Frq tot [Frequency clock protein]
E1W [White collar 1 protein]
PFp [Frequency clock protein]
MW [messenger RNA; RNA; White collar 1 protein]
PW [White collar 1 protein]
E2Fp [Frequency clock protein]
PF [Frequency clock protein]
E2F [Frequency clock protein]
WC1 tot [White collar 1 protein]
MF [messenger RNA; RNA; Frequency clock protein]
E1F [Frequency clock protein]
sFrq tot [Frequency clock protein]
E2W [White collar 1 protein]
lFrq tot [Frequency clock protein]
PWL [White collar 1 protein]

Observables: none

BIOMD0000000805 @ v0.0.1

The paper describes a model of pH control in tumor. Created by COPASI 4.26 (Build 213) This model is described in…

Non-invasive measurements of pH have shown that both tumour and normal cells have intracellular pH (pHi) that lies on the alkaline side of neutrality (7.1-7.2). However, extracellular pH (pHe) is reported to be more acidic in some tumours compared to normal tissues. Many cellular processes and therapeutic agents are known to be tightly pH dependent which makes the study of intracellular pH regulation of paramount importance. We develop a mathematical model that examines the role of various membrane-based ion transporters in tumour pH regulation, in particular, with a focus on the interplay between lactate and H(+) ions and whether the lactate/H(+) symporter activity is sufficient to give rise to the observed reversed pH gradient that is seen is some tumours. Using linear stability analysis and numerical methods, we are able to gain a clear understanding of the relationship between lactate and H(+) ions. We extend this analysis using perturbation techniques to specifically examine a rapid change in H(+)-ion concentrations relative to variations in lactate. We then perform a parameter sensitivity analysis to explore solution robustness to parameter variations. An important result from our study is that a reversed pH gradient is possible in our system but for unrealistic parameter estimates-pointing to the possible involvement of other mechanisms in cellular pH gradient reversal, for example acidic vesicles, lysosomes, golgi and endosomes. link: http://identifiers.org/pubmed/23340437

Parameters:

Name Description
lh = 0.017174 1 Reaction: He => Hi, Rate Law: tme*(lh*He-lh*Hi)
fg = 0.2823 1; p = 14000.0 1; v = 1.49968483550237 1; vv = 0.5 1 Reaction: => Hi, Rate Law: tme*p*2*fg/(Hi+1)*piecewise(1, v > vv, 0)
k3 = 5.4316 1; p = 14000.0 1 Reaction: Hi + Li => He + Le, Rate Law: tme*k3*p*(Hi*Li-He*Le)
k1=1.0 Reaction: Li =>, Rate Law: tme*k1*Li
f1 = 17174.0 1 Reaction: Hi => He, Rate Law: tme*f1*(Hi-He)*piecewise(1, Hi > He, 0)
d1 = 7999.6 1 Reaction: => Hi, Rate Law: tme*d1
v = 1.49968483550237 1; p1 = 20095.0 1 Reaction: He =>, Rate Law: tme*p1*v*He
v = 1.49968483550237 1; v=1.0 Reaction: => Li, Rate Law: tme*v
v = 1.49968483550237 1; p2 = 0.2857 1 Reaction: Le =>, Rate Law: tme*p2*v*Le
fg = 0.2823 1; v = 1.49968483550237 1; vv = 0.5 1 Reaction: => Li; Hi, Rate Law: tme*2*fg/(Hi+1)*piecewise(1, v > vv, 0)

States:

Name Description
Hi [pH]
Li [lactate]
Le [lactate]
He [pH]

Observables: none

Alam2010 - Genome-scale metabolic network of Streptomyces coelicolorThis model is described in the article: [Metabolic…

The transition from exponential to stationary phase in Streptomyces coelicolor is accompanied by a major metabolic switch and results in a strong activation of secondary metabolism. Here we have explored the underlying reorganization of the metabolome by combining computational predictions based on constraint-based modeling and detailed transcriptomics time course observations.We reconstructed the stoichiometric matrix of S. coelicolor, including the major antibiotic biosynthesis pathways, and performed flux balance analysis to predict flux changes that occur when the cell switches from biomass to antibiotic production. We defined the model input based on observed fermenter culture data and used a dynamically varying objective function to represent the metabolic switch. The predicted fluxes of many genes show highly significant correlation to the time series of the corresponding gene expression data. Individual mispredictions identify novel links between antibiotic production and primary metabolism.Our results show the usefulness of constraint-based modeling for providing a detailed interpretation of time course gene expression data. link: http://identifiers.org/pubmed/20338070

Parameters: none

States: none

Observables: none

BIOMD0000000220 @ v0.0.1

This the model used in the article: Quantitative analysis of pathways controlling extrinsic apoptosis in single cells.…

Apoptosis in response to TRAIL or TNF requires the activation of initiator caspases, which then activate the effector caspases that dismantle cells and cause death. However, little is known about the dynamics and regulatory logic linking initiators and effectors. Using a combination of live-cell reporters, flow cytometry, and immunoblotting, we find that initiator caspases are active during the long and variable delay that precedes mitochondrial outer membrane permeabilization (MOMP) and effector caspase activation. When combined with a mathematical model of core apoptosis pathways, experimental perturbation of regulatory links between initiator and effector caspases reveals that XIAP and proteasome-dependent degradation of effector caspases are important in restraining activity during the pre-MOMP delay. We identify conditions in which restraint is impaired, creating a physiologically indeterminate state of partial cell death with the potential to generate genomic instability. Together, these findings provide a quantitative picture of caspase regulatory networks and their failure modes. link: http://identifiers.org/pubmed/18406323

Parameters:

Name Description
kc8 = 0.1 Reaction: XIAP_C3 => C3_Ub + XIAP, Rate Law: cell*XIAP_C3*kc8
kc3 = 1.0 Reaction: R_hash_pC8 => C8 + R_hash, Rate Law: cell*R_hash_pC8*kc3
k_2 = 0.001; k2 = 1.0E-6 Reaction: R_hash + flip => flip_R_hash, Rate Law: cell*(R_hash*flip*k2-flip_R_hash*k_2)
k28 = 7.0E-6; k_28 = 0.001 Reaction: XIAP + Smac => Smac_XIAP, Rate Law: cell*(XIAP*Smac*k28-Smac_XIAP*k_28)
k19 = 1.0E-6; v = 0.07; k_19 = 0.001 Reaction: Bax4 + M => Bax4_M, Rate Law: mitochondrion*(Bax4*M*k19/v-Bax4_M*k_19)
v = 0.07; k16 = 1.0E-6; k_16 = 0.001 Reaction: Bcl2 + Bax2 => Bax2_Bcl2, Rate Law: mitochondrion*(Bcl2*Bax2*k16/v-Bax2_Bcl2*k_16)
v = 0.07; k_21 = 0.001; k21 = 2.0E-6 Reaction: M_hash + Smacm => M_hash_Smacm, Rate Law: mitochondrion*(M_hash*Smacm*k21/v-M_hash_Smacm*k_21)
k_20 = 0.001; v = 0.07; k20 = 2.0E-6 Reaction: M_hash + CytoCm => M_hash_CytoCm, Rate Law: mitochondrion*(M_hash*CytoCm*k20/v-M_hash_CytoCm*k_20)
kc10 = 1.0 Reaction: C8_Bid => tBid + C8, Rate Law: cell*C8_Bid*kc10
k10 = 1.0E-7; k_10 = 0.001 Reaction: C8 + Bid => C8_Bid, Rate Law: cell*(C8*Bid*k10-C8_Bid*k_10)
k_27 = 0.001; k27 = 2.0E-6 Reaction: XIAP + Apop => Apop_XIAP, Rate Law: cell*(XIAP*Apop*k27-Apop_XIAP*k_27)
kc19 = 1.0 Reaction: Bax4_M => M_hash, Rate Law: mitochondrion*Bax4_M*kc19
kc1 = 1.0E-5 Reaction: L_R => R_hash, Rate Law: cell*L_R*kc1
v = 0.07; k_18 = 0.001; k18 = 1.0E-6 Reaction: Bcl2 + Bax4 => Bax4_Bcl2, Rate Law: mitochondrion*(Bcl2*Bax4*k18/v-Bax4_Bcl2*k_18)
k_13 = 0.01; k13 = 0.01 Reaction: Bax_hash => Baxm, Rate Law: cell*(Bax_hash*k13-Baxm*k_13)
k6 = 1.0E-6; k_6 = 0.001 Reaction: C3 + pC6 => C3_pC6, Rate Law: cell*(C3*pC6*k6-C3_pC6*k_6)
k9 = 1.0E-6; k_9 = 0.01 Reaction: PARP + C3 => PARP_C3, Rate Law: cell*(PARP*C3*k9-PARP_C3*k_9)
k25 = 5.0E-9; k_25 = 0.001 Reaction: pC3 + Apop => pC3_Apop, Rate Law: cell*(pC3*Apop*k25-pC3_Apop*k_25)
kc6 = 1.0 Reaction: C3_pC6 => C3 + C6, Rate Law: cell*C3_pC6*kc6
kc23 = 1.0 Reaction: CytoC_Apaf => CytoC + Apaf_hash, Rate Law: cell*CytoC_Apaf*kc23
kc21 = 10.0 Reaction: M_hash_Smacm => M_hash + Smacr, Rate Law: mitochondrion*M_hash_Smacm*kc21
kc25 = 1.0 Reaction: pC3_Apop => C3 + Apop, Rate Law: cell*pC3_Apop*kc25
k_4 = 0.001; k4 = 1.0E-6 Reaction: C8 + BAR => BAR_C8, Rate Law: cell*(C8*BAR*k4-BAR_C8*k_4)
k_24 = 0.001; k24 = 5.0E-8 Reaction: Apaf_hash + pC9 => Apop, Rate Law: cell*(Apaf_hash*pC9*k24-Apop*k_24)
k3 = 1.0E-6; k_3 = 0.001 Reaction: R_hash + pC8 => R_hash_pC8, Rate Law: cell*(R_hash*pC8*k3-R_hash_pC8*k_3)
k5 = 1.0E-7; k_5 = 0.001 Reaction: pC3 + C8 => C8_pC3, Rate Law: cell*(pC3*C8*k5-C8_pC3*k_5)
k12 = 1.0E-7; k_12 = 0.001 Reaction: tBid + Bax => Bax_tBid, Rate Law: cell*(tBid*Bax*k12-Bax_tBid*k_12)
k26 = 0.01; k_26 = 0.01 Reaction: Smacr => Smac, Rate Law: cell*(Smacr*k26-Smac*k_26)
k22 = 0.01; k_22 = 0.01 Reaction: CytoCr => CytoC, Rate Law: cell*(CytoCr*k22-CytoC*k_22)
v = 0.07; k14 = 1.0E-6; k_14 = 0.001 Reaction: Baxm + Bcl2 => Baxm_Bcl2, Rate Law: mitochondrion*(Baxm*Bcl2*k14/v-Baxm_Bcl2*k_14)
kc12 = 1.0 Reaction: Bax_tBid => tBid + Bax_hash, Rate Law: cell*Bax_tBid*kc12
k23 = 5.0E-7; k_23 = 0.001 Reaction: CytoC + Apaf => CytoC_Apaf, Rate Law: cell*(CytoC*Apaf*k23-CytoC_Apaf*k_23)
v = 0.07; k_15 = 0.001; k15 = 1.0E-6 Reaction: Baxm + Baxm => Bax2, Rate Law: mitochondrion*(Baxm*Baxm*k15/v-Bax2*k_15)
k_8 = 0.001; k8 = 2.0E-6 Reaction: C3 + XIAP => XIAP_C3, Rate Law: cell*(C3*XIAP*k8-XIAP_C3*k_8)
v = 0.07; k17 = 1.0E-6; k_17 = 0.001 Reaction: Bax2 + Bax2 => Bax4, Rate Law: mitochondrion*(Bax2*Bax2*k17/v-Bax4*k_17)
k_1 = 0.001; k1 = 4.0E-7 Reaction: L + R => L_R, Rate Law: cell*(L*R*k1-L_R*k_1)
kc5 = 1.0 Reaction: C8_pC3 => C8 + C3, Rate Law: cell*C8_pC3*kc5
kc20 = 10.0 Reaction: M_hash_CytoCm => CytoCr + M_hash, Rate Law: mitochondrion*M_hash_CytoCm*kc20
kc9 = 1.0 Reaction: PARP_C3 => CPARP + C3, Rate Law: cell*PARP_C3*kc9
k_7 = 0.001; k7 = 3.0E-8 Reaction: C6 + pC8 => C6_pC8, Rate Law: cell*(C6*pC8*k7-C6_pC8*k_7)
k_11 = 0.001; k11 = 1.0E-6 Reaction: tBid + Bcl2c => Bcl2c_tBid, Rate Law: cell*(tBid*Bcl2c*k11-Bcl2c_tBid*k_11)
kc7 = 1.0 Reaction: C6_pC8 => C8 + C6, Rate Law: cell*C6_pC8*kc7

States:

Name Description
flip R hash flip:R#
XIAP [E3 ubiquitin-protein ligase XIAP; XIAP [cytosol]]
Bax2 Bcl2 [Apoptosis regulator BAX; Apoptosis regulator Bcl-2]
PARP C3 PARP:C3
flip [CASP8 and FADD-like apoptosis regulator; CFLAR(1-376) [cytosol]; 603599]
CytoC [Cytochrome c]
M hash Smacm M#:Smac_m
Smac XIAP Smac:XIAP
pC6 [Caspase-6]
pC3 [Caspase-3]
L [Tumor necrosis factor ligand superfamily member 10; TNFSF10 [extracellular region]]
C8 pC3 C8:pC3
C3 pC6 C3:pC6
M M
PARP [Poly [ADP-ribose] polymerase 1]
XIAP C3 XIAP:C3
C8 Bid C8:Bid
pC3 Apop pC3:Apop
Bcl2 [Apoptosis regulator Bcl-2]
Bax2 [Apoptosis regulator BAX]
M hash CytoCm M#:CytoC_m
R hash [Tumor necrosis factor receptor superfamily member 10B]
L R [REACT_5556; Tumor necrosis factor ligand superfamily member 10; Tumor necrosis factor receptor superfamily member 10B]
Baxm Bcl2 [Apoptosis regulator Bcl-2; Apoptosis regulator BAX]
tBid [BH3-interacting domain death agonist; REACT_385]
CPARP [Poly [ADP-ribose] polymerase 1]
CytoC Apaf CytoC:Apaf
Bid [BH3-interacting domain death agonist; BID(1-195) [cytosol]; 601997]
C3 casp3
pC9 [Caspase-9]
Bax [Apoptosis regulator BAX]
Apaf [Apoptotic protease-activating factor 1]
Baxm [Apoptosis regulator BAX]
C8 [Caspase-8 dimer [cytosol]]
Bcl2c [Apoptosis regulator Bcl-2; BCL2 [mitochondrial outer membrane]]
Bax4 [Apoptosis regulator BAX]
Smacm [Diablo homolog, mitochondrial; DIABLO [mitochondrial intermembrane space]]
BAR BAR
Bax tBid Bax:tBid
Smacr [Diablo homolog, mitochondrial; DIABLO [cytosol]]
Bax hash [Apoptosis regulator BAX]
pC8 [Caspase-8; CASP8(1-479) [cytosol]]
Apop [Cytochrome c; Apoptotic protease-activating factor 1; Caspase-9; Cytochrome C:Apaf-1:ATP:Procaspase-9 [cytosol]; apoptosome]
C6 casp6
CytoCr [Cytochrome c; CYCS [mitochondrial intermembrane space]]
C3 Ub [Caspase-3; Ubiquitin-60S ribosomal protein L40]
Apaf hash Apaf#
Bax4 M [Apoptosis regulator BAX]
R hash pC8 R#:pC8
M hash M#
BAR C8 BAR:C8
R [Tumor necrosis factor receptor superfamily member 10B; TNFRSF10B [plasma membrane]]
Smac [Diablo homolog, mitochondrial; DIABLO [cytosol]]
C6 pC8 C6:pC8

Observables: none

BIOMD0000000211 @ v0.0.1

This model is from the article: Experimental and in silico analyses of glycolytic flux control in bloodstream form T…

A mathematical model of glycolysis in bloodstream form Trypanosoma brucei was developed previously on the basis of all available enzyme kinetic data (Bakker, B. M., Michels, P. A. M., Opperdoes, F. R., and Westerhoff, H. V. (1997) J. Biol. Chem. 272, 3207-3215). The model predicted correctly the fluxes and cellular metabolite concentrations as measured in non-growing trypanosomes and the major contribution to the flux control exerted by the plasma membrane glucose transporter. Surprisingly, a large overcapacity was predicted for hexokinase (HXK), phosphofructokinase (PFK), and pyruvate kinase (PYK). Here, we present our further analysis of the control of glycolytic flux in bloodstream form T. brucei. First, the model was optimized and extended with recent information about the kinetics of enzymes and their activities as measured in lysates of in vitro cultured growing trypanosomes. Second, the concentrations of five glycolytic enzymes (HXK, PFK, phosphoglycerate mutase, enolase, and PYK) in trypanosomes were changed by RNA interference. The effects of the knockdown of these enzymes on the growth, activities, and levels of various enzymes and glycolytic flux were studied and compared with model predictions. Data thus obtained support the conclusion from the in silico analysis that HXK, PFK, and PYK are in excess, albeit less than predicted. Interestingly, depletion of PFK and enolase had an effect on the activity (but not, or to a lesser extent, expression) of some other glycolytic enzymes. Enzymes located both in the glycosomes (the peroxisome-like organelles harboring the first seven enzymes of the glycolytic pathway of trypanosomes) and in the cytosol were affected. These data suggest the existence of novel regulatory mechanisms operating in trypanosome glycolysis. link: http://identifiers.org/pubmed/15955817

Parameters:

Name Description
Km=1.96; V=200.0 Reaction: species_1 => species_26, Rate Law: V*species_1/(Km+species_1)
Kms=0.27; Vf=225.0; Vr=495.0; Kmp=0.11; RaPGAM = 1.0 Reaction: species_7 => species_5, Rate Law: RaPGAM*compartment_1*(Vf*species_7/Kms-Vr*species_5/Kmp)/(1+species_7/Kms+species_5/Kmp)
KGAP_v7=0.15; KNAD_v7=0.45; r_v7=0.67; Vmax_v7=720.9; KBPGA13_v7=0.1; KNADH_v7=0.02 Reaction: species_18 + species_19 => species_21 + species_20, Rate Law: compartment_2*Vmax_v7*(species_18/KGAP_v7*species_19/KNAD_v7-r_v7*species_21/KBPGA13_v7*species_20/KNADH_v7)/((1+species_18/KGAP_v7+species_21/KBPGA13_v7)*(1+species_19/KNAD_v7+species_20/KNADH_v7))
KADP_v12=0.114; Vmax_v12=1020.0; RaPYK = 1.0; PK_n=2.5 Reaction: species_4 + species_2 => species_1 + species_3, Rate Law: RaPYK*compartment_1*Vmax_v12*(species_4/(0.34*(1+species_3/0.57+species_2/0.64)))^PK_n*species_2/KADP_v12/((1+(species_4/(0.34*(1+species_3/0.57+species_2/0.64)))^PK_n)*(1+species_2/KADP_v12))
k=50.0 Reaction: species_3 => species_2, Rate Law: compartment_1*k*species_3/species_2
Ki1Fru16BP_v4=15.8; Vmax_v4=1708.0; KATPg_v4=0.026; RaPFK = 1.0; Ki2Fru16BP_v4=10.7; KFru6P_v4=0.82 Reaction: species_15 + species_11 => species_16 + species_12, Rate Law: RaPFK*compartment_2*Vmax_v4*Ki1Fru16BP_v4/(Ki1Fru16BP_v4+species_16)*species_15/KFru6P_v4*species_11/KATPg_v4/((1+species_15/KFru6P_v4+species_16/Ki2Fru16BP_v4)*(1+species_11/KATPg_v4))
Vr=394.68; RaENO = 1.0; Vf=598.0; Kms=0.054; Kmp=0.24 Reaction: species_5 => species_4, Rate Law: RaENO*compartment_1*(Vf*species_5/Kms-Vr*species_4/Kmp)/(1+species_5/Kms+species_4/Kmp)
KGlycerol_v14=0.44; KATPg_v14=0.24; KGly3Pg_v14=3.83; Vmax_v14=200.0; r_v14=60.86; KADPg_v14=0.56 Reaction: species_22 + species_12 => species_24 + species_11, Rate Law: compartment_2*Vmax_v14*(species_22/KGly3Pg_v14*species_12/KADPg_v14-r_v14*species_24/KGlycerol_v14*species_11/KATPg_v14)/((1+species_22/KGly3Pg_v14+species_24/KGlycerol_v14)*(1+species_12/KADPg_v14+species_11/KATPg_v14))
V=368.0; Km=1.7 Reaction: species_9 => species_8, Rate Law: compartment_1*V*species_9/(Km+species_9)
k=1000000.0; keqak=0.442 Reaction: species_11 + species_13 => species_12, Rate Law: compartment_2*k*(species_11*species_13-keqak*species_12*species_12)
r_v11=0.47; KBPGA13_v11=0.003; KADPg_v11=0.1; KATPg_v11=0.29; Vmax_v11=2862.0; KPGA3_v11=1.62 Reaction: species_21 + species_12 => species_23 + species_11, Rate Law: compartment_2*Vmax_v11*(species_21/KBPGA13_v11*species_12/KADPg_v11-r_v11*species_23/KPGA3_v11*species_11/KATPg_v11)/((1+species_21/KBPGA13_v11+species_23/KPGA3_v11)*(1+species_12/KADPg_v11+species_11/KATPg_v11))
Kmp=0.25; Vf=999.3; Vr=5696.01; Kms=1.2 Reaction: species_17 => species_18, Rate Law: compartment_2*(Vf*species_17/Kms-Vr*species_18/Kmp)/(1+species_17/Kms+species_18/Kmp)
k2=1000000.0; k1=1000000.0 Reaction: species_22 + species_8 => species_9 + species_17, Rate Law: k1*species_22*species_8-k2*species_9*species_17
KGAP_v5=0.067; Keq_v5=0.069; Vmax_v5=560.0; KGAPi_v5=0.098; r_v5=1.19 Reaction: species_16 => species_17 + species_18; species_11, species_12, species_13, Rate Law: compartment_2*Vmax_v5*(species_16-species_18*species_17/Keq_v5)/(0.009*(1+species_11/0.68+species_12/1.51+species_13/3.65)+species_16+species_18*0.015*(1+species_11/0.68+species_12/1.51+species_13/3.65)/Keq_v5*1/r_v5+species_17*KGAP_v5/Keq_v5*1/r_v5+species_16*species_18/KGAPi_v5+species_18*species_17/Keq_v5*1/r_v5)
KATPg_v2=0.116; KGlcInt_v2=0.1; RaHXK = 1.0; KGlc6P_v2=12.0; Vmax_v2=1929.0; KADPg_v2=0.126 Reaction: species_10 + species_11 => species_14 + species_12, Rate Law: RaHXK*compartment_2*Vmax_v2*species_10/KGlcInt_v2*species_11/KATPg_v2/((1+species_11/KATPg_v2+species_12/KADPg_v2)*(1+species_10/KGlcInt_v2+species_14/KGlc6P_v2))
Vf=1305.0; Vr=1305.0; Kms=0.4; Kmp=0.12 Reaction: species_14 => species_15, Rate Law: compartment_2*(Vf*species_14/Kms-Vr*species_15/Kmp)/(1+species_14/Kms+species_15/Kmp)
KGlc=1.0; Alpha_v1=0.75; Vmax_v1=108.9 Reaction: species_25 => species_10, Rate Law: Vmax_v1*(species_25-species_10)/(KGlc+species_25+species_10+Alpha_v1*species_25*species_10/KGlc)
KGly3Pg_v8=2.0; KDHAPg_v8=0.1; KNAD_v8=0.4; KNADH_v8=0.01; r_v8=0.28; Vmax_v8=465.0 Reaction: species_17 + species_20 => species_19 + species_22, Rate Law: compartment_2*Vmax_v8*(species_17/KDHAPg_v8*species_20/KNADH_v8-r_v8*species_19/KNAD_v8*species_22/KGly3Pg_v8)/((1+species_17/KDHAPg_v8+species_22/KGly3Pg_v8)*(1+species_20/KNADH_v8+species_19/KNAD_v8))

States:

Name Description
species 9 [sn-glycerol 3-phosphate; sn-Glycerol 3-phosphate; 3393; 57-03-4]
species 27 [glycerol; Glycerol; 3416; B00032; 56-81-5]
species 1 [pyruvic acid; Pyruvate; 3324; B00006; 127-17-3]
species 20 [NADH; NADH; 3306]
species 18 [D-glyceraldehyde 3-phosphate; D-Glyceraldehyde 3-phosphate; 3418; 591-57-1]
species 16 [beta-D-fructofuranose 1,6-bisphosphate; beta-D-Fructose 1,6-bisphosphate; 7752]
species 4 [Phosphoenolpyruvate; 3374; B00019; 138-08-9; phosphoenolpyruvate]
species 21 [3-phospho-D-glyceroyl dihydrogen phosphate; 3-Phospho-D-glyceroyl phosphate; 3535; 38168-82-0]
species 8 [dihydroxyacetone phosphate; Glycerone phosphate; 3411; B00029]
species 17 [dihydroxyacetone phosphate; Glycerone phosphate; 3411; B00029]
species 12 [ADP; ADP; 3310; 20398-34-9]
species 25 [D-glucopyranose; D-Glucose]
species 5 [2-phospho-D-glyceric acid; 2-Phospho-D-glycerate; 3904]
species 15 [beta-D-fructofuranose 6-phosphate; beta-D-Fructose 6-phosphate; 7723]
species 2 [ADP; ADP; 3310; 20398-34-9]
species 6 [AMP; AMP; 3322; 61-19-8]
species 19 [NAD(+); NAD+; 3305; 53-84-9]
species 10 [D-glucopyranose; D-Glucose; 3587]
species 11 [ATP; ATP; 3304; 56-65-5]
species 24 [glycerol; Glycerol; 3416; B00032; 56-81-5]
species 14 [D-glucopyranose 6-phosphate; alpha-D-Glucose 6-phosphate; 3937]
species 22 [sn-glycerol 3-phosphate; sn-Glycerol 3-phosphate; 3393; 57-03-4]
species 3 [ATP; ATP; 3304; 56-65-5]
species 23 [3-phospho-D-glyceric acid; 3-Phospho-D-glycerate; 3497]
species 7 [dihydroxyacetone phosphate; 3-phospho-D-glyceric acid; 3-Phospho-D-glycerate; 3497]
species 26 [pyruvic acid; Pyruvate; 3324; B00006; 127-17-3]
species 13 [AMP; AMP; 3322; 61-19-8]

Observables: none

BIOMD0000000289 @ v0.0.1

This is system 1, the model with linear antigen uptake by pAPCs, described in the article: Self-tolerance and Autoimmun…

The class of immunosuppressive lymphocytes known as regulatory T cells (Tregs) has been identified as a key component in preventing autoimmune diseases. Although Tregs have been incorporated previously in mathematical models of autoimmunity, we take a novel approach which emphasizes the importance of professional antigen presenting cells (pAPCs). We examine three possible mechanisms of Treg action (each in isolation) through ordinary differential equation (ODE) models. The immune response against a particular autoantigen is suppressed both by Tregs specific for that antigen and by Tregs of arbitrary specificities, through their action on either maturing or already mature pAPCs or on autoreactive effector T cells. In this deterministic approach, we find that qualitative long-term behaviour is predicted by the basic reproductive ratio R(0) for each system. When R(0)<1, only the trivial equilibrium exists and is stable; when R(0)>1, this equilibrium loses its stability and a stable non-trivial equilibrium appears. We interpret the absence of self-damaging populations at the trivial equilibrium to imply a state of self-tolerance, and their presence at the non-trivial equilibrium to imply a state of chronic autoimmunity. Irrespective of mechanism, our model predicts that Tregs specific for the autoantigen in question play no role in the system's qualitative long-term behaviour, but have quantitative effects that could potentially reduce an autoimmune response to sub-clinical levels. Our results also suggest an important role for Tregs of arbitrary specificities in modulating the qualitative outcome. A stochastic treatment of the same model demonstrates that the probability of developing a chronic autoimmune response increases with the initial exposure to self antigen or autoreactive effector T cells. The three different mechanisms we consider, while leading to a number of similar predictions, also exhibit key differences in both transient dynamics (ODE approach) and the probability of chronic autoimmunity (stochastic approach). link: http://identifiers.org/pubmed/20195912

Parameters:

Name Description
v = 0.0025 per_day Reaction: G =>, Rate Law: v*G
gamma = 2000.0 per_day Reaction: => G; E, Rate Law: gamma*E
muR = 0.25 per_day Reaction: R =>, Rate Law: muR*R
b1 = 0.25 per_day Reaction: A =>, Rate Law: b1*A
muA = 0.25 per_day Reaction: A =>, Rate Law: muA*A
muG = 5.0 per_day Reaction: G =>, Rate Law: muG*G
beta = 200.0 per_day Reaction: => R; A, Rate Law: beta*A
lambdaE = 1000.0 per_day Reaction: => E; A, Rate Law: lambdaE*A
sigma1 = 3.0E-6 per_day_per_item Reaction: A => ; R, Rate Law: sigma1*A*R
muE = 0.25 per_day Reaction: E =>, Rate Law: muE*E
f = 1.0E-4 dimensionless; v = 0.0025 per_day Reaction: A_im => A; G, Rate Law: f*v*G
pi1 = 0.016 per_day_per_item Reaction: => R; A, E, Rate Law: pi1*E*A

States:

Name Description
A [professional antigen presenting cell]
G [antigen]
E [effector T cell]
A im [defensive cell]
R [natural T-regulatory cell]

Observables: none

BIOMD0000000290 @ v0.0.1

This is system 2, the model with Michelis Menten type antigen uptake by pAPCs, described in the article: Self-toleranc…

The class of immunosuppressive lymphocytes known as regulatory T cells (Tregs) has been identified as a key component in preventing autoimmune diseases. Although Tregs have been incorporated previously in mathematical models of autoimmunity, we take a novel approach which emphasizes the importance of professional antigen presenting cells (pAPCs). We examine three possible mechanisms of Treg action (each in isolation) through ordinary differential equation (ODE) models. The immune response against a particular autoantigen is suppressed both by Tregs specific for that antigen and by Tregs of arbitrary specificities, through their action on either maturing or already mature pAPCs or on autoreactive effector T cells. In this deterministic approach, we find that qualitative long-term behaviour is predicted by the basic reproductive ratio R(0) for each system. When R(0)<1, only the trivial equilibrium exists and is stable; when R(0)>1, this equilibrium loses its stability and a stable non-trivial equilibrium appears. We interpret the absence of self-damaging populations at the trivial equilibrium to imply a state of self-tolerance, and their presence at the non-trivial equilibrium to imply a state of chronic autoimmunity. Irrespective of mechanism, our model predicts that Tregs specific for the autoantigen in question play no role in the system's qualitative long-term behaviour, but have quantitative effects that could potentially reduce an autoimmune response to sub-clinical levels. Our results also suggest an important role for Tregs of arbitrary specificities in modulating the qualitative outcome. A stochastic treatment of the same model demonstrates that the probability of developing a chronic autoimmune response increases with the initial exposure to self antigen or autoreactive effector T cells. The three different mechanisms we consider, while leading to a number of similar predictions, also exhibit key differences in both transient dynamics (ODE approach) and the probability of chronic autoimmunity (stochastic approach). link: http://identifiers.org/pubmed/20195912

Parameters:

Name Description
v_max = 125000.0 items_per_day; k = 5.0E7 number Reaction: G =>, Rate Law: v_max/(k+G)*G
gamma = 2000.0 per_day Reaction: => G; E, Rate Law: gamma*E
muR = 0.25 per_day Reaction: R =>, Rate Law: muR*R
b1 = 0.25 per_day Reaction: A =>, Rate Law: b1*A
muG = 5.0 per_day Reaction: G =>, Rate Law: muG*G
muA = 0.25 per_day Reaction: A =>, Rate Law: muA*A
beta = 200.0 per_day Reaction: => R; A, Rate Law: beta*A
lambdaE = 1000.0 per_day Reaction: => E; A, Rate Law: lambdaE*A
sigma1 = 3.0E-6 per_day_per_item Reaction: A => ; R, Rate Law: sigma1*A*R
muE = 0.25 per_day Reaction: E =>, Rate Law: muE*E
f = 1.0E-4 dimensionless; v_max = 125000.0 items_per_day; k = 5.0E7 number Reaction: A_im => A; G, Rate Law: f*v_max/(k+G)*G
pi1 = 0.016 per_day_per_item Reaction: => R; A, E, Rate Law: pi1*E*A

States:

Name Description
A [professional antigen presenting cell]
G [antigen]
E [effector T cell]
A im [defensive cell]
R [natural T-regulatory cell]

Observables: none

This is a transcriptional-based mathematical model centered on linear combinations of the clock controlled elements (CCE…

The molecular oscillator of the mammalian circadian clock consists in a dynamical network of genes and proteins whose main regulatory mechanisms occur at the transcriptional level. From a dynamical point of view, the mechanisms leading to an oscillatory solution with an orderly protein peak expression and a clear day/night phase distinction remain unclear. Our goal is to identify the essential interactions needed to generate phase opposition between the activating CLOCK:BMAL1 and the repressing PER:CRY complexes and to better distinguish these two main clock molecular phases relating to rest/activity and fast/feeding cycles. To do this, we develop a transcription-based mathematical model centered on linear combinations of the clock controlled elements (CCEs): E-box, R-box and D-box. Each CCE is responsive to activators and repressors. After model calibration with single-cell data, we explore entrainment and period tuning via interplay with metabolism. Variation of the PER degradation rate γp, relating to the tau mutation, results in asymmetric changes in the duration of the different clock molecular phases. Time spent at the state of high PER/PER:CRY decreases with γp, while time spent at the state of high BMAL1 and CRY1, both proteins with activity in promoting insulin sensitivity, remains constant. This result suggests a possible mechanism behind the altered metabolism of tau mutation animals. Furthermore, we expose the clock system to two regulatory inputs, one relating to the fast/feeding cycle and the other to the light-dependent synchronization signaling. We observe the phase difference between these signals to also affect the relative duration of molecular clock states. Simulated circadian misalignment, known to correlate with insulin resistance, leads to decreased duration of BMAL1 expression. Our results reveal a possible mechanism for clock-controlled metabolic homeostasis, whereby the circadian clock controls the relative duration of different molecular (and metabolic) states in response to signaling inputs. link: http://identifiers.org/pubmed/31539528

Parameters:

Name Description
gamma_cp = 0.141 Reaction: PERCRY => PER + CRY, Rate Law: compartment*gamma_cp*PERCRY
gamma_ror = 2.55 Reaction: ROR =>, Rate Law: compartment*gamma_ror*ROR
gamma_bp = 2.58 Reaction: BMAL1 => ; PERCRY, Rate Law: compartment*gamma_bp*BMAL1*PERCRY
gamma_E4 = 0.295 Reaction: E4BP4 =>, Rate Law: compartment*gamma_E4*E4BP4
R_box = 3.3887906702538 Reaction: => BMAL1, Rate Law: compartment*R_box
E_box = 0.140122086570477 Reaction: => ROR, Rate Law: compartment*E_box
gamma_c = 2.34 Reaction: CRY =>, Rate Law: compartment*gamma_c*CRY
gamma_p = 0.844 Reaction: PER =>, Rate Law: compartment*gamma_p*PER
gamma_pc = 0.191 Reaction: PER + CRY => PERCRY, Rate Law: compartment*gamma_pc*PER*CRY
gamma_db = 0.156 Reaction: DBP =>, Rate Law: compartment*gamma_db*DBP
gamma_rev = 0.241 Reaction: REV =>, Rate Law: compartment*gamma_rev*REV
D_box = 17.3257229391434 Reaction: => REV, Rate Law: compartment*D_box

States:

Name Description
PERCRY [PR:000012548; PR:000050151]
DBP [Q10586]
BMAL1 [Aryl hydrocarbon receptor nuclear translocator-like protein 1]
ROR [C29881]
CRY [PR:000050151]
PER [PR:000012548]
REV [P20393]
E4BP4 [PR:000011176]

Observables: none

This is a non-linear mathematical model of cancer immunosurveillance that takes into account intratumoral phenotypic het…

Human cancers display intra-tumor heterogeneity in many phenotypic features, such as expression of cell surface receptors, growth, and angiogenic, proliferative, and immunogenic factors, which represent obstacles to a successful immune response. In this paper, we propose a nonlinear mathematical model of cancer immunosurveillance that takes into account some of these features based on cell-mediated immune responses. The model describes phenomena that are seen in vivo, such as tumor dormancy, robustness, immunoselection over tumor heterogeneity (also called "cancer immunoediting") and strong sensitivity to initial conditions in the composition of tumor microenvironment. The results framework has as common element the tumor as an attractor for abnormal cells. Bifurcation analysis give us as tumor attractors fixed-points, limit cycles and chaotic attractors, the latter emerging from period-doubling cascade displaying Feigenbaum's universality. Finally, we simulated both elimination and escape tumor scenarios by means of a stochastic version of the model according to the Doob-Gillespie algorithm. link: http://identifiers.org/pubmed/30930063

Parameters:

Name Description
nu = 1.101E-9 Reaction: T_1 => ; T_2, Rate Law: compartment*nu*T_1*T_2
b = 2.0E-9; a = 0.514 Reaction: => T_1, Rate Law: compartment*a*T_1*(1-b*T_1)
d_2 = 3.42E-10 Reaction: E_2_Adaptive => ; T_1, Rate Law: compartment*d_2*T_1*E_2_Adaptive
nu = 1.101E-9; r = 1.5 Reaction: T_2 => ; T_1, Rate Law: compartment*r*nu*T_1*T_2
mu = 1.101E-7 Reaction: T_1 => ; E_1_Innate, Rate Law: compartment*mu*E_1_Innate*T_1
s = 1.0; c_4 = 0.1245; c_5 = 2.0193E7 Reaction: => E_1_Innate; T_1, T_2, Rate Law: compartment*c_4*(T_1+s*T_2)*E_1_Innate/(c_5+T_1+T_2)
c_2 = 0.0412 Reaction: E_1_Innate =>, Rate Law: compartment*c_2*E_1_Innate
c_1 = 13000.0 Reaction: => E_1_Innate, Rate Law: compartment*c_1
b = 2.0E-9; a = 0.514; p = 0.35 Reaction: => T_2, Rate Law: compartment*a*p*T_2*(1-b*T_2)
q = 1.0; mu = 1.101E-7 Reaction: T_2 => ; E_1_Innate, Rate Law: compartment*mu*q*E_1_Innate*T_2
d_1 = 1.1E-7 Reaction: => E_2_Adaptive; T_1, E_1_Innate, Rate Law: compartment*d_1*T_1*E_1_Innate
beta = 1.101E-10 Reaction: T_1 => ; E_2_Adaptive, Rate Law: compartment*beta*E_2_Adaptive*T_1
c_3 = 3.422E-10 Reaction: E_1_Innate => ; T_1, T_2, Rate Law: compartment*c_3*(T_1+T_2)*E_1_Innate
d_3 = 0.02 Reaction: E_2_Adaptive =>, Rate Law: compartment*d_3*E_2_Adaptive

States:

Name Description
T 2 [neoplastic cell]
E 2 Adaptive [T cell]
T 1 [neoplastic cell]
E 1 Innate [natural killer cell; innate lymphoid cell]

Observables: none

MODEL1112110000 @ v0.0.1

This a model from the article: The Feedback Control of Glucose: On the road to type II diabetes Alvehag, K.; Martin,…

This paper develops a mathematical model for the feedback control of glucose regulation in the healthy human being and is based on the work of Sorensen (1985). The proposed model serves as a starting point for modeling type II diabetes. Four agents - glucose and the three hormones insulin, glucagon, and incretins - are assumed to have an effect on glucose metabolism. By letting compartments represent anatomical organs, the model has a close resemblance to a real human body. Mass balance equations that account for blood flows, exchange between compartments, and metabolic sinks and sources are written, and these result in simultaneous differential equations that are solved numerically. The metabolic sinks and sources - removing or adding glucose, insulin, glucagon, and incretins - describe physiological processes in the body. These processes function as feedback control systems and have nonlinear behaviors. The results of simulations performed for three different clinical test types indicate that the model is successful in simulating intravenous glucose, oral glucose, and meals containing mainly carbohydrates link: http://identifiers.org/doi/10.1109/CDC.2006.377192

Parameters: none

States: none

Observables: none

MODEL1112110001 @ v0.0.1

This a model from the article: The Feedback Control of Glucose: On the road to type II diabetes Alvehag, K.; Martin,…

This paper develops a mathematical model for the feedback control of glucose regulation in the healthy human being and is based on the work of Sorensen (1985). The proposed model serves as a starting point for modeling type II diabetes. Four agents - glucose and the three hormones insulin, glucagon, and incretins - are assumed to have an effect on glucose metabolism. By letting compartments represent anatomical organs, the model has a close resemblance to a real human body. Mass balance equations that account for blood flows, exchange between compartments, and metabolic sinks and sources are written, and these result in simultaneous differential equations that are solved numerically. The metabolic sinks and sources - removing or adding glucose, insulin, glucagon, and incretins - describe physiological processes in the body. These processes function as feedback control systems and have nonlinear behaviors. The results of simulations performed for three different clinical test types indicate that the model is successful in simulating intravenous glucose, oral glucose, and meals containing mainly carbohydrates link: http://identifiers.org/doi/10.1109/CDC.2006.377192

Parameters: none

States: none

Observables: none

Mechanistic model of the Post-Replication Repair (PRR), the pathway involved in the bypass of DNA lesions induced by su…

The genome of living organisms is constantly exposed to several damaging agents that induce different types of DNA lesions, leading to cellular malfunctioning and onset of many diseases. To maintain genome stability, cells developed various repair and tolerance systems to counteract the effects of DNA damage. Here we focus on Post Replication Repair (PRR), the pathway involved in the bypass of DNA lesions induced by sunlight exposure and UV radiation. PRR acts through two different mechanisms, activated by mono- and poly-ubiquitylation of the DNA sliding clamp, called Proliferating Cell Nuclear Antigen (PCNA).We developed a novel protocol to measure the time-course ratios between mono-, di- and tri-ubiquitylated PCNA isoforms on a single western blot, which were used as the wet readout for PRR events in wild type and mutant S. cerevisiae cells exposed to acute UV radiation doses. Stochastic simulations of PCNA ubiquitylation dynamics, performed by exploiting a novel mechanistic model of PRR, well fitted the experimental data at low UV doses, but evidenced divergent behaviors at high UV doses, thus driving the design of further experiments to verify new hypothesis on the functioning of PRR. The model predicted the existence of a UV dose threshold for the proper functioning of the PRR model, and highlighted an overlapping effect of Nucleotide Excision Repair (the pathway effectively responsible to clean the genome from UV lesions) on the dynamics of PCNA ubiquitylation in different phases of the cell cycle. In addition, we showed that ubiquitin concentration can affect the rate of PCNA ubiquitylation in PRR, offering a possible explanation to the DNA damage sensitivity of yeast strains lacking deubiquitylating enzymes.We exploited an in vivo and in silico combinational approach to analyze for the first time in a Systems Biology context the events of PCNA ubiquitylation occurring in PRR in budding yeast cells. Our findings highlighted an intricate functional crosstalk between PRR and other events controlling genome stability, and evidenced that PRR is more complicated and still far less characterized than previously thought. link: http://identifiers.org/pubmed/23514624

Parameters:

Name Description
k1=1.0E-10 Reaction: species_16 => species_14 + species_15; species_16, Rate Law: compartment_1*k1*species_16
k1=1.0 Reaction: species_11 => species_4 + species_12; species_11, Rate Law: compartment_1*k1*species_11
k1=100000.0 Reaction: species_8 + species_18 => species_15; species_8, species_18, Rate Law: compartment_1*k1*species_8*species_18
k1=0.078 Reaction: species_14 + species_15 => species_16; species_14, species_15, Rate Law: compartment_1*k1*species_14*species_15
k1=0.05 Reaction: species_16 => species_18 + species_17; species_16, Rate Law: compartment_1*k1*species_16
k1=5.0E-6 Reaction: species_12 + species_13 => species_14; species_12, species_13, Rate Law: compartment_1*k1*species_12*species_13
k1=7.5E-6 Reaction: species_17 => species_13 + species_19; species_17, Rate Law: compartment_1*k1*species_17
k1=0.005 Reaction: species_22 => species_8 + species_23; species_22, Rate Law: compartment_1*k1*species_22
k1=0.0351 Reaction: species_7 + species_9 => species_10; species_7, species_9, Rate Law: compartment_1*k1*species_7*species_9
k1=8.0E-4 Reaction: species_19 => species_8 + species_23; species_19, Rate Law: compartment_1*k1*species_19
k1=3.0E-8 Reaction: species_12 => species_8 + species_23; species_12, Rate Law: compartment_1*k1*species_12
k1=0.01 Reaction: species_10 => species_6 + species_11; species_10, Rate Law: compartment_1*k1*species_10
k1=2.5E-7 Reaction: species_6 + species_8 => species_7; species_6, species_8, Rate Law: compartment_1*k1*species_6*species_8
k1=1000.0 Reaction: species_9 => species_3 + species_4; species_9, Rate Law: compartment_1*k1*species_9
k1=1.5E-8 Reaction: species_2 + species_1 => species_3; species_2, species_1, Rate Law: compartment_1*k1*species_2*species_1

States:

Name Description
species 9 [Postreplication repair E3 ubiquitin-protein ligase RAD18; Proliferating cell nuclear antigen]
species 1 [site of double-strand break]
species 18 [Ubiquitin-conjugating enzyme variant MMS2; Ubiquitin-conjugating enzyme E2 13]
species 20 [Proliferating cell nuclear antigen; Polyubiquitin; Ubiquitin-conjugating enzyme variant MMS2; Ubiquitin-conjugating enzyme E2 13; DNA repair protein RAD5; protein polyubiquitination]
species 16 [Ubiquitin-conjugating enzyme E2 13; Ubiquitin-conjugating enzyme variant MMS2; DNA repair protein RAD5; Proliferating cell nuclear antigen; MOD:01148]
species 4 [Postreplication repair E3 ubiquitin-protein ligase RAD18]
species 21 [DNA repair protein RAD5; Proliferating cell nuclear antigen; protein polyubiquitination]
species 8 [Polyubiquitin]
species 17 [DNA repair protein RAD5; Proliferating cell nuclear antigen; MOD:01148]
species 12 [Proliferating cell nuclear antigen; protein monoubiquitination]
species 5 [Postreplication repair E3 ubiquitin-protein ligase RAD18]
species 15 [Ubiquitin-conjugating enzyme E2 13; Ubiquitin-conjugating enzyme variant MMS2; protein monoubiquitination]
species 2 [Proliferating cell nuclear antigen]
species 6 [Ubiquitin-conjugating enzyme E2 2]
species 19 [Proliferating cell nuclear antigen; Polyubiquitin; MOD:01148]
species 10 [Postreplication repair E3 ubiquitin-protein ligase RAD18; Proliferating cell nuclear antigen; Ubiquitin-conjugating enzyme E2 2; protein monoubiquitination]
species 11 [Postreplication repair E3 ubiquitin-protein ligase RAD18; Proliferating cell nuclear antigen; protein monoubiquitination]
species 14 [DNA repair protein RAD5; Proliferating cell nuclear antigen; protein monoubiquitination]
species 22 [protein polyubiquitination; Proliferating cell nuclear antigen]
species 3 [Proliferating cell nuclear antigen]
species 23 [Proliferating cell nuclear antigen]
species 7 [Ubiquitin-conjugating enzyme E2 2; protein monoubiquitination]
species 13 [DNA repair protein RAD5]

Observables: none

Inhomogeneous blood coagulation model. Encoded model contains reactions of the intrinsic pathway with platelet activatio…

Multiple interacting mechanisms control the formation and dissolution of clots to maintain blood in a state of delicate balance. In addition to a myriad of biochemical reactions, rheological factors also play a crucial role in modulating the response of blood to external stimuli. To date, a comprehensive model for clot formation and dissolution, that takes into account the biochemical, medical and rheological factors, has not been put into place, the existing models emphasizing either one or the other of the factors. In this paper, after discussing the various biochemical, physiologic and rheological factors at some length, we develop a model for clot formation and dissolution that incorporates many of the relevant crucial factors that have a bearing on the problem. The model, though just a first step towards understanding a complex phenomenon, goes further than previous models in integrating the biochemical, physiologic and rheological factors that come into play. link: http://identifiers.org/doi/10.1080/10273660412331317415

Parameters: none

States: none

Observables: none

Andersen2009 - Genome-scale metabolic network of Aspergillus niger (iMA871)This model is described in the article: [Met…

The release of the genome sequences of two strains of Aspergillus niger has allowed systems-level investigations of this important microbial cell factory. To this end, tools for doing data integration of multi-ome data are necessary, and especially interesting in the context of metabolism. On the basis of an A. niger bibliome survey, we present the largest model reconstruction of a metabolic network reported for a fungal species. The reconstructed gapless metabolic network is based on the reportings of 371 articles and comprises 1190 biochemically unique reactions and 871 ORFs. Inclusion of isoenzymes increases the total number of reactions to 2240. A graphical map of the metabolic network is presented. All levels of the reconstruction process were based on manual curation. From the reconstructed metabolic network, a mathematical model was constructed and validated with data on yields, fluxes and transcription. The presented metabolic network and map are useful tools for examining systemwide data in a metabolic context. Results from the validated model show a great potential for expanding the use of A. niger as a high-yield production platform. link: http://identifiers.org/pubmed/18364712

Parameters: none

States: none

Observables: none

This is a mathematical model investigating the role of chronic inflammation in the development and progression of myelop…

The chronic Philadelphia-negative myeloproliferative neoplasms (MPNs) are acquired stem cell neoplasms which ultimately may transform to acute myelogenous leukemia. Most recently, chronic inflammation has been described as an important factor for the development and progression of MPNs in the biological continuum from early cancer stage to the advanced myelofibrosis stage, the MPNs being described as "A Human Inflammation Model for Cancer Development". This novel concept has been built upon clinical, experimental, genomic, immunological and not least epidemiological studies. Only a few studies have described the development of MPNs by mathematical models, and none have addressed the role of inflammation for clonal evolution and disease progression. Herein, we aim at using mathematical modelling to substantiate the concept of chronic inflammation as an important trigger and driver of MPNs.The basics of the model describe the proliferation from stem cells to mature cells including mutations of healthy stem cells to become malignant stem cells. We include a simple inflammatory coupling coping with cell death and affecting the basic model beneath. First, we describe the system without feedbacks or regulatory interactions. Next, we introduce inflammatory feedback into the system. Finally, we include other feedbacks and regulatory interactions forming the inflammatory-MPN model. Using mathematical modeling, we add further proof to the concept that chronic inflammation may be both a trigger of clonal evolution and an important driving force for MPN disease progression. Our findings support intervention at the earliest stage of cancer development to target the malignant clone and dampen concomitant inflammation. link: http://identifiers.org/pubmed/28859112

Parameters:

Name Description
dy0 = 0.002 Reaction: y0 => a, Rate Law: compartment*dy0*y0
rs = 3.0E-4 Reaction: => s; a, Rate Law: compartment*rs*a
rm = 2.0E-8 Reaction: x0 => y0; s, Rate Law: compartment*rm*s*x0
ax = 1.1E-5 Reaction: x0 =>, Rate Law: compartment*ax*x0
psi_y = 0.635402229467573; ry = 0.0013 Reaction: => y0; s, Rate Law: compartment*ry*psi_y*s*y0
Ay = 4.7E13; ay = 1.1E-5 Reaction: => y1; y0, Rate Law: compartment*ay*Ay*y0
dx0 = 0.002 Reaction: x0 => a, Rate Law: compartment*dx0*x0
dx1 = 129.0 Reaction: x1 => a, Rate Law: compartment*dx1*x1
ax = 1.1E-5; Ax = 4.7E13 Reaction: => x1; x0, Rate Law: compartment*ax*Ax*x0
ay = 1.1E-5 Reaction: y0 =>, Rate Law: compartment*ay*y0
psi_x = 0.635402229467573; rx = 8.7E-4 Reaction: => x0; s, Rate Law: compartment*x0*rx*psi_x*s
dy1 = 129.0 Reaction: y1 => a, Rate Law: compartment*dy1*y1
es = 2.0 Reaction: s =>, Rate Law: compartment*es*s
inflammation = 7.0 Reaction: => s, Rate Law: compartment*inflammation
ea = 2.0E9 Reaction: a => ; s, Rate Law: compartment*ea*a*s

States:

Name Description
x1 [hematopoietic stem cell; BTO:0002312]
y1 [Neoplastic Cell; BTO:0002312; C4345]
x0 [hematopoietic stem cell]
y0 [Neoplastic Cell; C4345]
a [cell; Dead]
s [inflammatory response]

Observables: none

This is a mathematical model describing tumor-CD4+-cytokine interactions, with specific emphasis on the role that CD4+ T…

Immunotherapies are important methods for controlling and curing malignant tumors. Based on recent observations that many tumors have been immuno-selected to evade recognition by the traditional cytotoxic T lymphocytes, we propose mathematical models of tumor-CD4+-cytokine interactions to investigate the role of CD4+ on tumor regression. Treatments of either CD4+ or cytokine are applied to study their effectiveness. It is found that doses of treatments are critical in determining the fate of the tumor, and tumor cells can be eliminated completely if doses of cytokine are large. Bistability is observed in models with either of the treatment strategies, which signifies that a careful planning of the treatment strategy is necessary for achieving a satisfactory outcome. link: http://identifiers.org/doi/10.1002/mma.3370

Parameters:

Name Description
b = 0.1; alpha = 0.1 Reaction: => z_Cytokine; x_Tumor_Cells, y_CD4_T_Cells, Rate Law: compartment*alpha*x_Tumor_Cells*y_CD4_T_Cells/(b+x_Tumor_Cells)
beta = 0.02; k = 10.0 Reaction: => y_CD4_T_Cells; x_Tumor_Cells, Rate Law: compartment*beta*x_Tumor_Cells*y_CD4_T_Cells/(k+x_Tumor_Cells)
I_2 = 0.0 Reaction: => z_Cytokine, Rate Law: compartment*I_2
K = 1000.0; r = 0.03 Reaction: => x_Tumor_Cells, Rate Law: compartment*r*x_Tumor_Cells*(1-x_Tumor_Cells/K)
m = 1.0; delta = 0.1 Reaction: x_Tumor_Cells => ; z_Cytokine, Rate Law: compartment*delta*x_Tumor_Cells*z_Cytokine/(m+x_Tumor_Cells)
mu = 47.0 Reaction: z_Cytokine =>, Rate Law: compartment*mu*z_Cytokine
I_1 = 10.0 Reaction: => y_CD4_T_Cells, Rate Law: compartment*I_1
a = 0.02 Reaction: y_CD4_T_Cells =>, Rate Law: compartment*a*y_CD4_T_Cells

States:

Name Description
x Tumor Cells [neoplastic cell]
y CD4 T Cells [CD4-positive helper T cell]
z Cytokine [Cytokine]

Observables: none

Multi-compartment metabolic model of the cicada Neotibicen canicularis and its endosymbionts Sulcia and Hodgkinia

Various intracellular bacterial symbionts that provide their host with essential nutrients have much-reduced genomes, attributed largely to genomic decay and relaxed selection. To obtain quantitative estimates of the metabolic function of these bacteria, we reconstructed genome- and transcriptome-informed metabolic models of three xylem-feeding insects that bear two bacterial symbionts with complementary metabolic functions: a primary symbiont, Sulcia, that has codiversified with the insects, and a coprimary symbiont of distinct taxonomic origin and with different degrees of genome reduction in each insect species (Hodgkinia in a cicada, Baumannia in a sharpshooter, and Sodalis in a spittlebug). Our simulations reveal extensive bidirectional flux of multiple metabolites between each symbiont and the host, but near-complete metabolic segregation (i.e., near absence of metabolic cross-feeding) between the two symbionts, a likely mode of host control over symbiont metabolism. Genome reduction of the symbionts is associated with an increased number of host metabolic inputs to the symbiont and also reduced metabolic cost to the host. In particular, Sulcia and Hodgkinia with genomes of ≤0.3 Mb are calculated to recycle ∼30 to 80% of host-derived nitrogen to essential amino acids returned to the host, while Baumannia and Sodalis with genomes of ≥0.6 Mb recycle 10 to 15% of host nitrogen. We hypothesize that genome reduction of symbionts may be driven by selection for increased host control and reduced host costs, as well as by the stochastic process of genomic decay and relaxed selection.IMPORTANCE Current understanding of many animal-microbial symbioses involving unculturable bacterial symbionts with much-reduced genomes derives almost entirely from nonquantitative inferences from genome data. To overcome this limitation, we reconstructed multipartner metabolic models that quantify both the metabolic fluxes within and between three xylem-feeding insects and their bacterial symbionts. This revealed near-complete metabolic segregation between cooccurring bacterial symbionts, despite extensive metabolite exchange between each symbiont and the host, suggestive of strict host controls over the metabolism of its symbionts. We extended the model analysis to investigate metabolic costs. The positive relationship between symbiont genome size and the metabolic cost incurred by the host points to fitness benefits to the host of bearing symbionts with small genomes. The multicompartment metabolic models developed here can be applied to other symbioses that are not readily tractable to experimental approaches. link: http://identifiers.org/pubmed/30254121

Parameters: none

States: none

Observables: none

Multi-compartment metabolic model of the sharpshooter Graphocephala coccinea and its endosymbionts Sulcia and Baumannia

Various intracellular bacterial symbionts that provide their host with essential nutrients have much-reduced genomes, attributed largely to genomic decay and relaxed selection. To obtain quantitative estimates of the metabolic function of these bacteria, we reconstructed genome- and transcriptome-informed metabolic models of three xylem-feeding insects that bear two bacterial symbionts with complementary metabolic functions: a primary symbiont, Sulcia, that has codiversified with the insects, and a coprimary symbiont of distinct taxonomic origin and with different degrees of genome reduction in each insect species (Hodgkinia in a cicada, Baumannia in a sharpshooter, and Sodalis in a spittlebug). Our simulations reveal extensive bidirectional flux of multiple metabolites between each symbiont and the host, but near-complete metabolic segregation (i.e., near absence of metabolic cross-feeding) between the two symbionts, a likely mode of host control over symbiont metabolism. Genome reduction of the symbionts is associated with an increased number of host metabolic inputs to the symbiont and also reduced metabolic cost to the host. In particular, Sulcia and Hodgkinia with genomes of ≤0.3 Mb are calculated to recycle ∼30 to 80% of host-derived nitrogen to essential amino acids returned to the host, while Baumannia and Sodalis with genomes of ≥0.6 Mb recycle 10 to 15% of host nitrogen. We hypothesize that genome reduction of symbionts may be driven by selection for increased host control and reduced host costs, as well as by the stochastic process of genomic decay and relaxed selection.IMPORTANCE Current understanding of many animal-microbial symbioses involving unculturable bacterial symbionts with much-reduced genomes derives almost entirely from nonquantitative inferences from genome data. To overcome this limitation, we reconstructed multipartner metabolic models that quantify both the metabolic fluxes within and between three xylem-feeding insects and their bacterial symbionts. This revealed near-complete metabolic segregation between cooccurring bacterial symbionts, despite extensive metabolite exchange between each symbiont and the host, suggestive of strict host controls over the metabolism of its symbionts. We extended the model analysis to investigate metabolic costs. The positive relationship between symbiont genome size and the metabolic cost incurred by the host points to fitness benefits to the host of bearing symbionts with small genomes. The multicompartment metabolic models developed here can be applied to other symbioses that are not readily tractable to experimental approaches. link: http://identifiers.org/pubmed/30254121

Parameters: none

States: none

Observables: none

Multi-compartment metabolic model of the spittlebug Philaenus spumarius and its endosymbionts Sulcia and Sodalis

Various intracellular bacterial symbionts that provide their host with essential nutrients have much-reduced genomes, attributed largely to genomic decay and relaxed selection. To obtain quantitative estimates of the metabolic function of these bacteria, we reconstructed genome- and transcriptome-informed metabolic models of three xylem-feeding insects that bear two bacterial symbionts with complementary metabolic functions: a primary symbiont, Sulcia, that has codiversified with the insects, and a coprimary symbiont of distinct taxonomic origin and with different degrees of genome reduction in each insect species (Hodgkinia in a cicada, Baumannia in a sharpshooter, and Sodalis in a spittlebug). Our simulations reveal extensive bidirectional flux of multiple metabolites between each symbiont and the host, but near-complete metabolic segregation (i.e., near absence of metabolic cross-feeding) between the two symbionts, a likely mode of host control over symbiont metabolism. Genome reduction of the symbionts is associated with an increased number of host metabolic inputs to the symbiont and also reduced metabolic cost to the host. In particular, Sulcia and Hodgkinia with genomes of ≤0.3 Mb are calculated to recycle ∼30 to 80% of host-derived nitrogen to essential amino acids returned to the host, while Baumannia and Sodalis with genomes of ≥0.6 Mb recycle 10 to 15% of host nitrogen. We hypothesize that genome reduction of symbionts may be driven by selection for increased host control and reduced host costs, as well as by the stochastic process of genomic decay and relaxed selection.IMPORTANCE Current understanding of many animal-microbial symbioses involving unculturable bacterial symbionts with much-reduced genomes derives almost entirely from nonquantitative inferences from genome data. To overcome this limitation, we reconstructed multipartner metabolic models that quantify both the metabolic fluxes within and between three xylem-feeding insects and their bacterial symbionts. This revealed near-complete metabolic segregation between cooccurring bacterial symbionts, despite extensive metabolite exchange between each symbiont and the host, suggestive of strict host controls over the metabolism of its symbionts. We extended the model analysis to investigate metabolic costs. The positive relationship between symbiont genome size and the metabolic cost incurred by the host points to fitness benefits to the host of bearing symbionts with small genomes. The multicompartment metabolic models developed here can be applied to other symbioses that are not readily tractable to experimental approaches. link: http://identifiers.org/pubmed/30254121

Parameters: none

States: none

Observables: none

Sulcia-Zinderia-Clastoptera multi-compartment metabolic model

Insects feeding on the nutrient-poor diet of xylem plant sap generally bear two microbial symbionts that are localized to different organs (bacteriomes) and provide complementary sets of essential amino acids (EAAs). Here, we investigate the metabolic basis for the apparent paradox that xylem-feeding insects are under intense selection for metabolic efficiency but incur the cost of maintaining two symbionts for functions mediated by one symbiont in other associations. Using stable isotope analysis of central carbon metabolism and metabolic modeling, we provide evidence that the bacteriomes of the spittlebug Clastoptera proteus display high rates of aerobic glycolysis, with syntrophic splitting of glucose oxidation. Specifically, our data suggest that one bacteriome (containing the bacterium Sulcia, which synthesizes seven EAAs) predominantly processes glucose glycolytically, producing pyruvate and lactate, and the exported pyruvate and lactate is assimilated by the second bacteriome (containing the bacterium Zinderia, which synthesizes three energetically costly EAAs) and channeled through the TCA cycle for energy generation by oxidative phosphorylation. We, furthermore, calculate that this metabolic arrangement supports the high ATP demand in Zinderia bacteriomes for Zinderia-mediated synthesis of energy-intensive EAAs. We predict that metabolite cross-feeding among host cells may be widespread in animal–microbe symbioses utilizing low-nutrient diets. link: http://identifiers.org/doi/10.1038/s41396-020-0661-z

Parameters: none

States: none

Observables: none

Multi-compartment Sulcia-Clastoptera (spittlebug) metabolic model

Insects feeding on the nutrient-poor diet of xylem plant sap generally bear two microbial symbionts that are localized to different organs (bacteriomes) and provide complementary sets of essential amino acids (EAAs). Here, we investigate the metabolic basis for the apparent paradox that xylem-feeding insects are under intense selection for metabolic efficiency but incur the cost of maintaining two symbionts for functions mediated by one symbiont in other associations. Using stable isotope analysis of central carbon metabolism and metabolic modeling, we provide evidence that the bacteriomes of the spittlebug Clastoptera proteus display high rates of aerobic glycolysis, with syntrophic splitting of glucose oxidation. Specifically, our data suggest that one bacteriome (containing the bacterium Sulcia, which synthesizes seven EAAs) predominantly processes glucose glycolytically, producing pyruvate and lactate, and the exported pyruvate and lactate is assimilated by the second bacteriome (containing the bacterium Zinderia, which synthesizes three energetically costly EAAs) and channeled through the TCA cycle for energy generation by oxidative phosphorylation. We, furthermore, calculate that this metabolic arrangement supports the high ATP demand in Zinderia bacteriomes for Zinderia-mediated synthesis of energy-intensive EAAs. We predict that metabolite cross-feeding among host cells may be widespread in animal–microbe symbioses utilizing low-nutrient diets. link: http://identifiers.org/doi/10.1038/s41396-020-0661-z

Parameters: none

States: none

Observables: none

Multi-compartment Zinderia-Clastoptera (spittlebug) metabolic model

Insects feeding on the nutrient-poor diet of xylem plant sap generally bear two microbial symbionts that are localized to different organs (bacteriomes) and provide complementary sets of essential amino acids (EAAs). Here, we investigate the metabolic basis for the apparent paradox that xylem-feeding insects are under intense selection for metabolic efficiency but incur the cost of maintaining two symbionts for functions mediated by one symbiont in other associations. Using stable isotope analysis of central carbon metabolism and metabolic modeling, we provide evidence that the bacteriomes of the spittlebug Clastoptera proteus display high rates of aerobic glycolysis, with syntrophic splitting of glucose oxidation. Specifically, our data suggest that one bacteriome (containing the bacterium Sulcia, which synthesizes seven EAAs) predominantly processes glucose glycolytically, producing pyruvate and lactate, and the exported pyruvate and lactate is assimilated by the second bacteriome (containing the bacterium Zinderia, which synthesizes three energetically costly EAAs) and channeled through the TCA cycle for energy generation by oxidative phosphorylation. We, furthermore, calculate that this metabolic arrangement supports the high ATP demand in Zinderia bacteriomes for Zinderia-mediated synthesis of energy-intensive EAAs. We predict that metabolite cross-feeding among host cells may be widespread in animal–microbe symbioses utilizing low-nutrient diets. link: http://identifiers.org/doi/10.1038/s41396-020-0661-z

Parameters: none

States: none

Observables: none

Genome scale metabolic model of Drosophila gut microbe Acetobacter fabarum Abstract - An important goal for many nutri…

An important goal for many nutrition-based microbiome studies is to identify the metabolic function of microbes in complex microbial communities and their impact on host physiology. This research can be confounded by poorly understood effects of community composition and host diet on the metabolic traits of individual taxa. Here, we investigated these multiway interactions by constructing and analyzing metabolic models comprising every combination of five bacterial members of the <i>Drosophila</i> gut microbiome (from single taxa to the five-member community of <i>Acetobacter</i> and <i>Lactobacillus</i> species) under three nutrient regimes. We show that the metabolic function of <i>Drosophila</i> gut bacteria is dynamic, influenced by community composition, and responsive to dietary modulation. Furthermore, we show that ecological interactions such as competition and mutualism identified from the growth patterns of gut bacteria are underlain by a diversity of metabolic interactions, and show that the bacteria tend to compete for amino acids and B vitamins more frequently than for carbon sources. Our results reveal that, in addition to fermentation products such as acetate, intermediates of the tricarboxylic acid (TCA) cycle, including 2-oxoglutarate and succinate, are produced at high flux and cross-fed between bacterial taxa, suggesting important roles for TCA cycle intermediates in modulating <i>Drosophila</i> gut microbe interactions and the potential to influence host traits. These metabolic models provide specific predictions of the patterns of ecological and metabolic interactions among gut bacteria under different nutrient regimes, with potentially important consequences for overall community metabolic function and nutritional interactions with the host.<b>IMPORTANCE</b> <i>Drosophila</i> is an important model for microbiome research partly because of the low complexity of its mostly culturable gut microbiota. Our current understanding of how <i>Drosophila</i> interacts with its gut microbes and how these interactions influence host traits derives almost entirely from empirical studies that focus on individual microbial taxa or classes of metabolites. These studies have failed to capture fully the complexity of metabolic interactions that occur between host and microbe. To overcome this limitation, we reconstructed and analyzed 31 metabolic models for every combination of the five principal bacterial taxa in the gut microbiome of <i>Drosophila</i> This revealed that metabolic interactions between <i>Drosophila</i> gut bacterial taxa are highly dynamic and influenced by cooccurring bacteria and nutrient availability. Our results generate testable hypotheses about among-microbe ecological interactions in the <i>Drosophila</i> gut and the diversity of metabolites available to influence host traits. link: http://identifiers.org/pubmed/33947801

Parameters: none

States: none

Observables: none

An important goal for many nutrition-based microbiome studies is to identify the metabolic function of microbes in compl…

An important goal for many nutrition-based microbiome studies is to identify the metabolic function of microbes in complex microbial communities and their impact on host physiology. This research can be confounded by poorly understood effects of community composition and host diet on the metabolic traits of individual taxa. Here, we investigated these multiway interactions by constructing and analyzing metabolic models comprising every combination of five bacterial members of the <i>Drosophila</i> gut microbiome (from single taxa to the five-member community of <i>Acetobacter</i> and <i>Lactobacillus</i> species) under three nutrient regimes. We show that the metabolic function of <i>Drosophila</i> gut bacteria is dynamic, influenced by community composition, and responsive to dietary modulation. Furthermore, we show that ecological interactions such as competition and mutualism identified from the growth patterns of gut bacteria are underlain by a diversity of metabolic interactions, and show that the bacteria tend to compete for amino acids and B vitamins more frequently than for carbon sources. Our results reveal that, in addition to fermentation products such as acetate, intermediates of the tricarboxylic acid (TCA) cycle, including 2-oxoglutarate and succinate, are produced at high flux and cross-fed between bacterial taxa, suggesting important roles for TCA cycle intermediates in modulating <i>Drosophila</i> gut microbe interactions and the potential to influence host traits. These metabolic models provide specific predictions of the patterns of ecological and metabolic interactions among gut bacteria under different nutrient regimes, with potentially important consequences for overall community metabolic function and nutritional interactions with the host.<b>IMPORTANCE</b> <i>Drosophila</i> is an important model for microbiome research partly because of the low complexity of its mostly culturable gut microbiota. Our current understanding of how <i>Drosophila</i> interacts with its gut microbes and how these interactions influence host traits derives almost entirely from empirical studies that focus on individual microbial taxa or classes of metabolites. These studies have failed to capture fully the complexity of metabolic interactions that occur between host and microbe. To overcome this limitation, we reconstructed and analyzed 31 metabolic models for every combination of the five principal bacterial taxa in the gut microbiome of <i>Drosophila</i> This revealed that metabolic interactions between <i>Drosophila</i> gut bacterial taxa are highly dynamic and influenced by cooccurring bacteria and nutrient availability. Our results generate testable hypotheses about among-microbe ecological interactions in the <i>Drosophila</i> gut and the diversity of metabolites available to influence host traits. link: http://identifiers.org/pubmed/33947801

Parameters: none

States: none

Observables: none

An important goal for many nutrition-based microbiome studies is to identify the metabolic function of microbes in compl…

An important goal for many nutrition-based microbiome studies is to identify the metabolic function of microbes in complex microbial communities and their impact on host physiology. This research can be confounded by poorly understood effects of community composition and host diet on the metabolic traits of individual taxa. Here, we investigated these multiway interactions by constructing and analyzing metabolic models comprising every combination of five bacterial members of the <i>Drosophila</i> gut microbiome (from single taxa to the five-member community of <i>Acetobacter</i> and <i>Lactobacillus</i> species) under three nutrient regimes. We show that the metabolic function of <i>Drosophila</i> gut bacteria is dynamic, influenced by community composition, and responsive to dietary modulation. Furthermore, we show that ecological interactions such as competition and mutualism identified from the growth patterns of gut bacteria are underlain by a diversity of metabolic interactions, and show that the bacteria tend to compete for amino acids and B vitamins more frequently than for carbon sources. Our results reveal that, in addition to fermentation products such as acetate, intermediates of the tricarboxylic acid (TCA) cycle, including 2-oxoglutarate and succinate, are produced at high flux and cross-fed between bacterial taxa, suggesting important roles for TCA cycle intermediates in modulating <i>Drosophila</i> gut microbe interactions and the potential to influence host traits. These metabolic models provide specific predictions of the patterns of ecological and metabolic interactions among gut bacteria under different nutrient regimes, with potentially important consequences for overall community metabolic function and nutritional interactions with the host.<b>IMPORTANCE</b> <i>Drosophila</i> is an important model for microbiome research partly because of the low complexity of its mostly culturable gut microbiota. Our current understanding of how <i>Drosophila</i> interacts with its gut microbes and how these interactions influence host traits derives almost entirely from empirical studies that focus on individual microbial taxa or classes of metabolites. These studies have failed to capture fully the complexity of metabolic interactions that occur between host and microbe. To overcome this limitation, we reconstructed and analyzed 31 metabolic models for every combination of the five principal bacterial taxa in the gut microbiome of <i>Drosophila</i> This revealed that metabolic interactions between <i>Drosophila</i> gut bacterial taxa are highly dynamic and influenced by cooccurring bacteria and nutrient availability. Our results generate testable hypotheses about among-microbe ecological interactions in the <i>Drosophila</i> gut and the diversity of metabolites available to influence host traits. link: http://identifiers.org/pubmed/33947801

Parameters: none

States: none

Observables: none

An important goal for many nutrition-based microbiome studies is to identify the metabolic function of microbes in compl…

An important goal for many nutrition-based microbiome studies is to identify the metabolic function of microbes in complex microbial communities and their impact on host physiology. This research can be confounded by poorly understood effects of community composition and host diet on the metabolic traits of individual taxa. Here, we investigated these multiway interactions by constructing and analyzing metabolic models comprising every combination of five bacterial members of the <i>Drosophila</i> gut microbiome (from single taxa to the five-member community of <i>Acetobacter</i> and <i>Lactobacillus</i> species) under three nutrient regimes. We show that the metabolic function of <i>Drosophila</i> gut bacteria is dynamic, influenced by community composition, and responsive to dietary modulation. Furthermore, we show that ecological interactions such as competition and mutualism identified from the growth patterns of gut bacteria are underlain by a diversity of metabolic interactions, and show that the bacteria tend to compete for amino acids and B vitamins more frequently than for carbon sources. Our results reveal that, in addition to fermentation products such as acetate, intermediates of the tricarboxylic acid (TCA) cycle, including 2-oxoglutarate and succinate, are produced at high flux and cross-fed between bacterial taxa, suggesting important roles for TCA cycle intermediates in modulating <i>Drosophila</i> gut microbe interactions and the potential to influence host traits. These metabolic models provide specific predictions of the patterns of ecological and metabolic interactions among gut bacteria under different nutrient regimes, with potentially important consequences for overall community metabolic function and nutritional interactions with the host.<b>IMPORTANCE</b> <i>Drosophila</i> is an important model for microbiome research partly because of the low complexity of its mostly culturable gut microbiota. Our current understanding of how <i>Drosophila</i> interacts with its gut microbes and how these interactions influence host traits derives almost entirely from empirical studies that focus on individual microbial taxa or classes of metabolites. These studies have failed to capture fully the complexity of metabolic interactions that occur between host and microbe. To overcome this limitation, we reconstructed and analyzed 31 metabolic models for every combination of the five principal bacterial taxa in the gut microbiome of <i>Drosophila</i> This revealed that metabolic interactions between <i>Drosophila</i> gut bacterial taxa are highly dynamic and influenced by cooccurring bacteria and nutrient availability. Our results generate testable hypotheses about among-microbe ecological interactions in the <i>Drosophila</i> gut and the diversity of metabolites available to influence host traits. link: http://identifiers.org/pubmed/33947801

Parameters: none

States: none

Observables: none

An important goal for many nutrition-based microbiome studies is to identify the metabolic function of microbes in compl…

An important goal for many nutrition-based microbiome studies is to identify the metabolic function of microbes in complex microbial communities and their impact on host physiology. This research can be confounded by poorly understood effects of community composition and host diet on the metabolic traits of individual taxa. Here, we investigated these multiway interactions by constructing and analyzing metabolic models comprising every combination of five bacterial members of the <i>Drosophila</i> gut microbiome (from single taxa to the five-member community of <i>Acetobacter</i> and <i>Lactobacillus</i> species) under three nutrient regimes. We show that the metabolic function of <i>Drosophila</i> gut bacteria is dynamic, influenced by community composition, and responsive to dietary modulation. Furthermore, we show that ecological interactions such as competition and mutualism identified from the growth patterns of gut bacteria are underlain by a diversity of metabolic interactions, and show that the bacteria tend to compete for amino acids and B vitamins more frequently than for carbon sources. Our results reveal that, in addition to fermentation products such as acetate, intermediates of the tricarboxylic acid (TCA) cycle, including 2-oxoglutarate and succinate, are produced at high flux and cross-fed between bacterial taxa, suggesting important roles for TCA cycle intermediates in modulating <i>Drosophila</i> gut microbe interactions and the potential to influence host traits. These metabolic models provide specific predictions of the patterns of ecological and metabolic interactions among gut bacteria under different nutrient regimes, with potentially important consequences for overall community metabolic function and nutritional interactions with the host.<b>IMPORTANCE</b> <i>Drosophila</i> is an important model for microbiome research partly because of the low complexity of its mostly culturable gut microbiota. Our current understanding of how <i>Drosophila</i> interacts with its gut microbes and how these interactions influence host traits derives almost entirely from empirical studies that focus on individual microbial taxa or classes of metabolites. These studies have failed to capture fully the complexity of metabolic interactions that occur between host and microbe. To overcome this limitation, we reconstructed and analyzed 31 metabolic models for every combination of the five principal bacterial taxa in the gut microbiome of <i>Drosophila</i> This revealed that metabolic interactions between <i>Drosophila</i> gut bacterial taxa are highly dynamic and influenced by cooccurring bacteria and nutrient availability. Our results generate testable hypotheses about among-microbe ecological interactions in the <i>Drosophila</i> gut and the diversity of metabolites available to influence host traits. link: http://identifiers.org/pubmed/33947801

Parameters: none

States: none

Observables: none

Archer2011 - Genome-scale metabolic model of Escherichia coli (iCA1273)This model is described in the article: [The gen…

BACKGROUND: Escherichia coli is a model prokaryote, an important pathogen, and a key organism for industrial biotechnology. E. coli W (ATCC 9637), one of four strains designated as safe for laboratory purposes, has not been sequenced. E. coli W is a fast-growing strain and is the only safe strain that can utilize sucrose as a carbon source. Lifecycle analysis has demonstrated that sucrose from sugarcane is a preferred carbon source for industrial bioprocesses. RESULTS: We have sequenced and annotated the genome of E. coli W. The chromosome is 4,900,968 bp and encodes 4,764 ORFs. Two plasmids, pRK1 (102,536 bp) and pRK2 (5,360 bp), are also present. W has unique features relative to other sequenced laboratory strains (K-12, B and Crooks): it has a larger genome and belongs to phylogroup B1 rather than A. W also grows on a much broader range of carbon sources than does K-12. A genome-scale reconstruction was developed and validated in order to interrogate metabolic properties. CONCLUSIONS: The genome of W is more similar to commensal and pathogenic B1 strains than phylogroup A strains, and therefore has greater utility for comparative analyses with these strains. W should therefore be the strain of choice, or 'type strain' for group B1 comparative analyses. The genome annotation and tools created here are expected to allow further utilization and development of E. coli W as an industrial organism for sucrose-based bioprocesses. Refinements in our E. coli metabolic reconstruction allow it to more accurately define E. coli metabolism relative to previous models. link: http://identifiers.org/pubmed/21208457

Parameters: none

States: none

Observables: none

This is a mathematical model consisting of a system of nonlinear ordinary differential equations describing tumor cells…

In this paper a mathematical model is presented that describes growth, immune escape, and siRNA treatment of tumors. The model consists of a system of nonlinear, ordinary differential equations describing tumor cells and immune effectors, as well as the immuno-stimulatory and suppressive cytokines IL-2 and TGF-β. TGF-β suppresses the immune system by inhibiting the activation of effector cells and reducing tumor antigen expression. It also stimulates tumor growth by promoting angiogenesis, explaining the inclusion of an angiogenic switch mechanism for TGF-β activity. The model predicts that increasing the rate of TGF-β production for reasonable values of tumor antigenicity enhances tumor growth and its ability to escape host detection. The model is then extended to include siRNA treatment which suppresses TGF-β production by targeting the mRNA that codes for TGF-β, thereby reducing the presence and effect of TGF-β in tumor cells. Comparison of tumor response to multiple injections of siRNA with behavior of untreated tumors demonstrates the effectiveness of this proposed treatment strategy. A second administration method, continuous infusion, is included to contrast the ideal outcome of siRNA treatment. The model's results predict conditions under which siRNA treatment can be successful in returning an aggressive, TGF-β producing tumor to its passive, non-immune evading state. link: http://identifiers.org/doi/10.3934/dcdsb.2004.4.39

Parameters: none

States: none

Observables: none

This model is from the article: A quantitative comparison of Calvin–Benson cycle models Anne Arnold, Zoran Nikoloski…

The Calvin-Benson cycle (CBC) provides the precursors for biomass synthesis necessary for plant growth. The dynamic behavior and yield of the CBC depend on the environmental conditions and regulation of the cellular state. Accurate quantitative models hold the promise of identifying the key determinants of the tightly regulated CBC function and their effects on the responses in future climates. We provide an integrative analysis of the largest compendium of existing models for photosynthetic processes. Based on the proposed ranking, our framework facilitates the discovery of best-performing models with regard to metabolomics data and of candidates for metabolic engineering. link: http://identifiers.org/pubmed/22001849

Parameters:

Name Description
J = 3.64863790509821; Nt = 0.5 Reaction: NADP => NADPH, Rate Law: chloroplast*J/2*NADP/Nt
Vcmax = 1.91141270310584; Rp = 3.2; Nt = 0.5 Reaction: PGA => RuBP; NADPH, Rate Law: chloroplast*PGA/Rp*NADPH/Nt*Vcmax
Vp = 0.942054655190967; Vj = 0.675554869049198; Vc = 0.822489884906092 Reaction: RuBP + CO2 + NADPH => PGA; O2, Rate Law: chloroplast*((((Vc+Vj)-abs(Vc-Vj))/2+Vp)-abs(((Vc+Vj)-abs(Vc-Vj))/2-Vp))/2
Vp = 0.942054655190967; phi = 0.025590660664217; Vj = 0.675554869049198; Vc = 0.822489884906092 Reaction: RuBP + O2 + NADPH => PGA; CO2, Rate Law: chloroplast*phi*((((Vc+Vj)-abs(Vc-Vj))/2+Vp)-abs(((Vc+Vj)-abs(Vc-Vj))/2-Vp))/2
Nt = 0.5 Reaction: NADP = Nt-NADPH, Rate Law: missing

States:

Name Description
NADPH [NADPH]
RuBP [D-ribulose 1,5-bisphosphate]
PGA [3-phosphoglyceric acid]
NADP [NADP]
CO2 [carbon dioxide]
O2 [dioxygen]

Observables: none

This model is from the article: A quantitative comparison of Calvin–Benson cycle models Anne Arnold, Zoran Nikoloski…

The Calvin-Benson cycle (CBC) provides the precursors for biomass synthesis necessary for plant growth. The dynamic behavior and yield of the CBC depend on the environmental conditions and regulation of the cellular state. Accurate quantitative models hold the promise of identifying the key determinants of the tightly regulated CBC function and their effects on the responses in future climates. We provide an integrative analysis of the largest compendium of existing models for photosynthetic processes. Based on the proposed ranking, our framework facilitates the discovery of best-performing models with regard to metabolomics data and of candidates for metabolic engineering. link: http://identifiers.org/pubmed/22001849

Parameters:

Name Description
j = 5.92307692307692; Nt = 0.5 Reaction: NADP => NADPH, Rate Law: chloroplast*j/2*NADP/Nt
Ko = 330.0; kc = 2.5; Kc = 460.0; phi = 0.267272727272727; E = 1.33846153846154 Reaction: RuBP + CO2 + NADPH => PGA; O2, Rate Law: chloroplast*phi*((kc*CO2/(CO2+Kc*(1+O2/Ko))*E+kc*CO2/(CO2+Kc*(1+O2/Ko))*RuBP)-abs(kc*CO2/(CO2+Kc*(1+O2/Ko))*E-kc*CO2/(CO2+Kc*(1+O2/Ko))*RuBP))/2
Nt = 0.5 Reaction: NADP = Nt-NADPH, Rate Law: missing
Rp = 3.2; kc = 2.5; E = 1.33846153846154; Nt = 0.5 Reaction: PGA => RuBP; NADPH, Rate Law: chloroplast*PGA/Rp*NADPH/Nt*kc*E
Ko = 330.0; kc = 2.5; Kc = 460.0; E = 1.33846153846154 Reaction: RuBP + CO2 + NADPH => PGA; O2, Rate Law: chloroplast*((kc*CO2/(CO2+Kc*(1+O2/Ko))*E+kc*CO2/(CO2+Kc*(1+O2/Ko))*RuBP)-abs(kc*CO2/(CO2+Kc*(1+O2/Ko))*E-kc*CO2/(CO2+Kc*(1+O2/Ko))*RuBP))/2

States:

Name Description
NADPH [NADPH]
RuBP [D-ribulose 1,5-bisphosphate]
PGA [3-phosphoglyceric acid]
NADP [NADP]
CO2 [carbon dioxide]

Observables: none

BIOMD0000000390 @ v0.0.1

This model is from the article: A quantitative comparison of Calvin–Benson cycle models Anne Arnold, Zoran Nikolosk…

The Calvin-Benson cycle (CBC) provides the precursors for biomass synthesis necessary for plant growth. The dynamic behavior and yield of the CBC depend on the environmental conditions and regulation of the cellular state. Accurate quantitative models hold the promise of identifying the key determinants of the tightly regulated CBC function and their effects on the responses in future climates. We provide an integrative analysis of the largest compendium of existing models for photosynthetic processes. Based on the proposed ranking, our framework facilitates the discovery of best-performing models with regard to metabolomics data and of candidates for metabolic engineering. link: http://identifiers.org/pubmed/22001849

Parameters:

Name Description
K2=1.0; K1=1.0; Vm=3.49; k=14.0 Reaction: PGA + ATP => ADP + TP + Pi, Rate Law: chloroplast*Vm*(PGA*ATP-ADP*TP*Pi/k)/(K1+PGA*ATP*K1/K2+ADP*TP*Pi/k)
k=0.504; K=0.04 Reaction: totRuBP + RuBP => PGA; E_RuBisCO, Rate Law: chloroplast*k/2*((E_RuBisCO+totRuBP+K)-((E_RuBisCO+totRuBP+K)^2-4*E_RuBisCO*totRuBP)^(0.5))
K3=0.05; Vm=4.81; K1=0.05; K2=0.5 Reaction: Ru5P + ATP => RuBP + ADP; Pi, Rate Law: chloroplast*Vm*Ru5P*ATP/(K1*K2+K2*ATP+Ru5P*ATP+K3*Pi)
Vm=1.06; K=0.4 Reaction: TP => Ru5P + Pi, Rate Law: chloroplast*Vm*TP/(TP+K)
K2=0.25; K1=0.08; Vm=3.3 Reaction: TP + Pic => TPc + Pi, Rate Law: Vm*(TP*Pic-TPc*Pi)/((TP+TPc)*K2+(Pic+Pi)*K1+K1*K2*(TP/K1+Pi/K2)*(Pic/K2+TPc/K1))
P_0 = 16.0 Reaction: totRuBP = 1/2*(P_0-(PGA+TP+Ru5P+Pi+ATP)), Rate Law: missing
V6 = 5.8801285588795; K2=0.5; K1=0.08 Reaction: ADP + Pi => ATP, Rate Law: chloroplast*V6*ADP*Pi/((ADP+K1)*(Pi+K2))

States:

Name Description
Ru5P [D-ribulose 5-phosphate]
ATP [ATP]
RuBP [D-ribulose 1,5-bisphosphate]
PGA [3-phosphoglyceric acid]
Pi [hydrogenphosphate]
totRuBP totRuBP
TP [24794350]
ADP [ADP]
Pic [hydrogenphosphate]
TPc [24794350]

Observables: none

This model is from the article: A quantitative comparison of Calvin–Benson cycle models Anne Arnold, Zoran Nikoloski…

The Calvin-Benson cycle (CBC) provides the precursors for biomass synthesis necessary for plant growth. The dynamic behavior and yield of the CBC depend on the environmental conditions and regulation of the cellular state. Accurate quantitative models hold the promise of identifying the key determinants of the tightly regulated CBC function and their effects on the responses in future climates. We provide an integrative analysis of the largest compendium of existing models for photosynthetic processes. Based on the proposed ranking, our framework facilitates the discovery of best-performing models with regard to metabolomics data and of candidates for metabolic engineering. link: http://identifiers.org/pubmed/22001849

Parameters:

Name Description
phi = 1.0E-4 Reaction: Suc => E, Rate Law: phi*Suc-phi*E
k1=0.0207 Reaction: PGA + ATP => TP + ADP + Pi, Rate Law: chloroplast*k1*PGA*ATP
k1=0.031 Reaction: HeP => TPGA + E4P, Rate Law: chloroplast*k1*HeP
k1=0.00755 Reaction: UDP + Pic => UTP, Rate Law: k1*UDP*Pic
k1=4.0; k2=0.0 Reaction: TP => HeP + Pi, Rate Law: chloroplast*(k1*TP^2-k2*HeP*Pi)
r = 3.0E-5 Reaction: Suc => Resp, Rate Law: r*Suc
k1=0.279 Reaction: ADP + Pi => ATP, Rate Law: chloroplast*k1*ADP*Pi
v_15 = 0.00998718 Reaction: HePc + UTP => Suc + UDP + Pic, Rate Law: v_15
k1=1.55 Reaction: TPc => HePc + Pic, Rate Law: cytosol*k1*TPc^2
k1=0.006 Reaction: RuBP + CO2 => PGA, Rate Law: chloroplast*k1*RuBP*CO2
k1=3.1 Reaction: E4P + TP => S7P + Pi, Rate Law: chloroplast*k1*E4P*TP
D = 1.0E-4 Reaction: Suc => SucV, Rate Law: D*Suc-D*SucV
k1=0.5 Reaction: TP + Pic => TPc + Pi, Rate Law: k1*TP*Pic
k1=4.0E-5 Reaction: GG + Pi => HeP, Rate Law: chloroplast*k1*GG*Pi
k1=0.002 Reaction: ATP + HeP => GG + ADP + Pi, Rate Law: chloroplast*k1*ATP*HeP
k1=6.2 Reaction: TPGA + TP => Ru5P, Rate Law: chloroplast*k1*TPGA*TP
k1=0.31 Reaction: S7P => TPGA + Ru5P, Rate Law: chloroplast*k1*S7P

States:

Name Description
ATP ATP
HePc [D-hexose phosphate]
TPc [24794350]
SucV [sucrose]
RuBP [D-ribulose 1,5-bisphosphate]
PGA [3-phosphoglyceric acid]
GG GG
UTP [UTP]
HeP [D-hexose phosphate]
Suc [sucrose]
TP [IPR000652]
Resp Resp
Pic [hydrogenphosphate]
TPGA [15938963; 756]
CO2 [carbon dioxide]
S7P [165007]
E4P [122357]
E E
UDP [UDP]
Ru5P [D-ribulose 5-phosphate]
Pi [hydrogenphosphate]
ADP ADP

Observables: none

This model is from the article: A quantitative comparison of Calvin–Benson cycle models Anne Arnold, Zoran Nikolosk…

The Calvin-Benson cycle (CBC) provides the precursors for biomass synthesis necessary for plant growth. The dynamic behavior and yield of the CBC depend on the environmental conditions and regulation of the cellular state. Accurate quantitative models hold the promise of identifying the key determinants of the tightly regulated CBC function and their effects on the responses in future climates. We provide an integrative analysis of the largest compendium of existing models for photosynthetic processes. Based on the proposed ranking, our framework facilitates the discovery of best-performing models with regard to metabolomics data and of candidates for metabolic engineering. link: http://identifiers.org/pubmed/22001849

Parameters:

Name Description
k1=3030.3 Reaction: ER + O2 + ATP => EPG + PGA + ADP, Rate Law: chloroplast*k1*ER*O2
k1=300000.0 Reaction: ER + CO2 => EPP, Rate Law: chloroplast*k1*ER*CO2
Ks1=3.2842E-5; Kp1=6.3429E-5; q=0.77294; Vm=0.011364; Kp2=0.0017914 Reaction: FBP => HeP + Pi; F6P, Rate Law: chloroplast*Vm*(FBP-F6P*Pi/q)/(Ks1*(1+FBP/Ks1+F6P/Kp1+F6P*Pi/(Kp1*Kp2)))
Ks=8.9213E-4; Kr4=9.4837E-5; Vm=0.0568182; Kr3=9.6372E-5; Kr1=9.3583E-5; Kr2=9.8597E-5; Kp=5.4107E-4 Reaction: PGA => PGAc; TP, Pi, TPc, Pic, Rate Law: Vm/(PGA/Ks+TP/Kr1+Pi/Kr2+PGAc/Kp+TPc/Kr3+Pic/Kr4+(PGA/Ks+TP/Kr1+Pi/Kr2)*(PGAc/Kp+TPc/Kr3+Pic/Kr4))*(PGA*(PGAc/Kp+TPc/Kr3+Pic/Kr4)/Ks-PGAc*(PGA/Ks+TP/Kr1+Pi/Kr2)/Kp)
Kp2=9.11825E-5; Ks1=3.63934E-5; Kp1=9.95868E-5; Vm=0.568182; Ks2=5.5117E-4; q=1.05289 Reaction: PeP + ATP => RuBP + ADP; Ru5P, Rate Law: chloroplast*Vm*(Ru5P*ATP-RuBP*ADP/q)/(Ks1*Ks2*(((1+Ru5P/Ks1)*(1+ATP/Ks2)+(1+RuBP/Kp1)*(1+ADP/Kp2))-1))
Kp1=2.10226E-5; Ks1=2.78407E-4; Vm=0.00568182; Ks2=3.74778E-4; q=1.00224 Reaction: TPc => FBPc; GAPc, DHAPc, Rate Law: cytosol*Vm*(GAPc*DHAPc-FBPc/q)/(Ks1*Ks2*((1+GAPc/Ks1)*(1+DHAPc/Ks2)+FBPc/Kp1))
k2=70000.0; k1=6.0 Reaction: EP => PGA + E, Rate Law: chloroplast*(k1*EP-k2*PGA*E)
ADTc = 0.001 Reaction: ADPc = ADTc-ATPc, Rate Law: missing
PiTc = 0.0170454545454545 Reaction: Pic = (PiTc-2*(FBPc+UTPc+ATPc+PiPic))-(PGAc+TPc+HePc+SucPc+UDPGc+UDPc+ADPc), Rate Law: missing
Ks2=3.6393E-4; Kp1=2.0129E-5; q=1.18815; Vm=0.011364; Ks1=1.7677E-4 Reaction: E4P + TP => SBP; DHAP, Rate Law: chloroplast*Vm*(E4P*DHAP-SBP/q)/(Ks1*Ks2*((1+E4P/Ks1)*(1+DHAP/Ks2)+SBP/Kp1))
Ks=9.3583E-5; Kr3=5.4107E-4; Kr4=9.4837E-5; Vm=0.0568182; Kp=9.6372E-5; Kr1=8.9213E-4; Kr2=9.8597E-5 Reaction: TP => TPc; PGA, Pi, PGAc, Pic, Rate Law: Vm/(TP/Ks+PGA/Kr1+Pi/Kr2+TPc/Kp+PGAc/Kr3+Pic/Kr4+(TP/Ks+PGA/Kr1+Pi/Kr2)*(TPc/Kp+PGAc/Kr3+Pic/Kr4))*(TP*(TPc/Kp+PGAc/Kr3+Pic/Kr4)/Ks-TPc*(TP/Ks+PGA/Kr1+Pi/Kr2)/Kp)
PiT = 0.0284090909090909 Reaction: Pi = (PiT-2*(EPP+EPG+RuBP+FBP+SBP+ATP+PiPi))-(EP+PGA+TP+HeP+E4P+S7P+PeP+ADP+ADPG), Rate Law: missing
k1=50000.0; k2=0.9 Reaction: RuBP + E => ER, Rate Law: chloroplast*(k1*RuBP*E-k2*ER)
Ks1=2.7035E-4; Ks2=3.6393E-4; Kp1=2.0129E-5; q=1.18815; Vm=0.022727 Reaction: TP => FBP; GAP, DHAP, Rate Law: chloroplast*Vm*(GAP*DHAP-FBP/q)/(Ks1*Ks2*((1+GAP/Ks1)*(1+DHAP/Ks2)+FBP/Kp1))
W4 = -0.00532314322950372 Reaction: EOP =>, Rate Law: chloroplast*W4
Kr1=0.001; Ks1=0.001; Vm=1.02614E-7; Kr2=0.0015 Reaction: HePc => F26BPc + ADPc; F6Pc, Pic, TPc, PGAc, Rate Law: cytosol*Vm*F6Pc/Ks1*(1+Pic/Kr1)/(1+(TPc+PGAc)/Kr2)
Kp1=5.3013E-4; Ks2=1.1023E-4; Vm=0.00113636; q=0.11059; Kp2=0.01951; Ks1=0.0010398 Reaction: HeP + ATP => ADPG + PiPi; G1P, PGA, Pi, Rate Law: chloroplast*Vm*(PGA/Pi)^2*(G1P*ATP-ADPG*PiPi/q)/(Ks1*Ks2*(((1+G1P/Ks1)*(1+ATP/Ks2)+(1+ADPG/Kp1)*(1+PiPi/Kp2))-1))
Ks1=0.0011122; Vm=0.0170455; Kp1=2.7035E-4; q1 = 0.129053067280279; Kp2=5.3013E-4; Kp3=0.0027397; Ks2=3.307E-4 Reaction: PGA + ATP => TP + ADP + Pi; GAP, Rate Law: chloroplast*Vm*(PGA*ATP-GAP*ADP*Pi/q1)/(Ks1*Ks2*((1+PGA/Ks1)*(1+ATP/Ks2)+GAP/Kp1+ADP/Kp2+Pi/Kp3+GAP*ADP*Pi/(Kp1*Kp2*Kp3)))
K2r1=5.407E-4; K1=6.1349E-4; q=0.99996; K2s1=1.7677E-4; K2s2=9.0464E-5; Vm=0.0821023; K1s2=2.7035E-4; K2=1.1438E-4 Reaction: S7P + TP => PeP; GAP, R5P, X5P, F6P, E4P, Rate Law: chloroplast*Vm*(q*S7P*GAP-R5P*X5P)/(K1*K2*(1+(1+GAP/K1s2)*(S7P/K2s1+F6P/K2r1)+GAP/K2s2+1/K2*(X5P*(1+R5P*E4P/K1)+R5P+E4P)))
Et = 0.0028030303030303 Reaction: E = Et-(ER+EPP+EPG+EP+EOP), Rate Law: missing
Ks1=3.2124E-5; q=1.6219; Kp1=1.4393E-4; Kp2=0.0013192; Vm=0.00410568; Ks2=2.364E-4 Reaction: HePc + UTPc => UDPGc + PiPic; G1Pc, Rate Law: cytosol*Vm*(G1Pc*UTPc-UDPGc*PiPic/q)/(Ks1*Ks2*(((1+G1Pc/Ks1)*(1+UTPc/Ks2)+(1+UDPGc/Kp1)*(1+PiPic/Kp2))-1))
Kp2=0.006744; q=0.77294; Vm=0.00568182; Ks1=1.2713E-5; Kp1=1.5597E-5 Reaction: SBP => S7P + Pi, Rate Law: chloroplast*Vm*(SBP-S7P*Pi/q)/(Ks1*(SBP/Ks1+(1+S7P/Kp1)*(1+Pi/Kp2)))
Kp1=6.36157E-4; Vm=0.00284091; Ks1=2.12052E-4; q=1.00326 Reaction: ADPG => ADP, Rate Law: chloroplast*Vm*(ADPG-ADP/q)/(Ks1*(1+ADPG/Ks1+ADP/Kp1))
k1=3.0 Reaction: EPG => EP, Rate Law: chloroplast*k1*EPG
Kp2=3.74778E-4; Ks1=2.78407E-4; Kr11=0.00920241; Ks2=1.10717E-4; Vm=7.38636E-5; Kr12=0.00164329; Kp1=6.42157E-4; q=1.00012 Reaction: HePc + UDPGc => UDPc + SucPc + Hc; F6Pc, Pic, Rate Law: cytosol*Vm*F6Pc*(F6Pc*UDPGc-UDPc*SucPc*Hc/q)/((Ks1*(1+Pic/Kr11))^2*Ks2*((((1+(F6Pc/(Ks1*(1+Pic/Kr11)))^2)*(1+UDPGc/Ks2)+(1+UDPc/Kp1)*(1+SucPc/Kp2))-1)+Pic/Kr12))
ADT = 0.0015 Reaction: ADP = ADT-ATP, Rate Law: missing
Kr1=1.1065E-6; Kp2=0.0018624; Vm=0.00113636; q=0.792367; Kp1=6.5319E-5; Ks1=2.2129E-5 Reaction: FBPc => HePc + Pic; F6Pc, F26BPc, Rate Law: cytosol*Vm*FBPc*(FBPc-F6Pc*Pic/q)/((Ks1*(1+F26BPc/Kr1))^2*((FBPc/(Ks1*(1+F26BPc/Kr1)))^2+(1+F6Pc/Kp1)*(1+Pic/Kp2)))
Ks1=3.1808E-4; Vm=0.0284091; q12 = 2.22786254125735E12; Kp12 = 224014.808032967; Ks2=3.1612E-4 Reaction: ADP + Pi => ATP, Rate Law: chloroplast*Vm*(ADP*Pi-ATP/q12)/(Ks1*Ks2*((1+ADP/Ks1)*(1+Pi/Ks2)+ATP/Kp12))
K1=6.1349E-4; K2r1=1.7677E-4; Vm=0.170455; K2s1=5.407E-4; q=0.99943; K2s2=9.0464E-5; K1s2=2.7035E-4; K2=1.1438E-4 Reaction: HeP + TP => E4P + PeP; F6P, GAP, X5P, S7P, R5P, Rate Law: chloroplast*Vm*(q*F6P*GAP-E4P*X5P)/(K1*K2*(1+(1+GAP/K1s2)*(F6P/K2s1+S7P/K2r1)+GAP/K2s2+1/K2*(X5P*(1+E4P*R5P/K1)+E4P+R5P)))
k1=6.0; k2=0.0 Reaction: EPP => PGA + EP, Rate Law: chloroplast*(k1*EPP-k2*PGA*EP)
Kr3=0.001; Kr1=0.002; Ks1=1.0E-9; Vm=2.05284E-10; Kr4=1.0E-4 Reaction: F26BPc => HePc + Pic; F6Pc, TPc, PGAc, Pic, HePc, Rate Law: cytosol*Vm*F26BPc/Ks1*(1+(TPc+PGAc)/Kr1)/(1+Pic/Kr3+HePc/Kr4)
q=1.35286; Kp1=0.01; Ks1=5.354E-5; Vm=0.0010267; Kp2=0.002191 Reaction: SucPc => Succ + Pic, Rate Law: cytosol*Vm*(SucPc-Succ*Pic/q)/(Ks1*(1+SucPc/Ks1+Succ/Kp1+Succ*Pic/(Kp1*Kp2)))

States:

Name Description
HePc [D-hexose phosphate]
G1P [65533]
PiPic PiPic
HeP [D-hexose phosphate]
TP [24794350]
EP EP
ADPc [ADP]
Pic [hydrogenphosphate]
X5P [23615403]
PeP [1005]
CO2 [carbon dioxide]
S7P [165007]
GAP [glyceraldehyde 3-phosphate]
SBP [164735]
Succ [sucrose]
UDPGc [8629]
GAPc [glyceraldehyde 3-phosphate]
Ru5P [D-ribulose 5-phosphate]
G6P [439958]
ADP [ADP]
G6Pc [5958]
F26BPc [105021]
G1Pc [65533]
SucPc [161554]
ATP [ATP]
EPP EPP
F6Pc [keto-D-fructose 6-phosphate]
FBP [keto-D-fructose 1,6-bisphosphate]
TPc [24794350]
DHAP [dihydroxyacetone phosphate]
PGA [3-phosphoglyceric acid]
ER ER
Hc [hydron]
RuBP [D-ribulose 1,5-bisphosphate]
PiPi PiPi
EPG EPG
EOP EOP
R5P [439167]
DHAPc [dihydroxyacetone phosphate]
UDPc [UDP]
F6P [keto-D-fructose 6-phosphate]
E E
E4P [122357]
FBPc [keto-D-fructose 1,6-bisphosphate]
ADPG [16500]
Pi [hydrogenphosphate]
UTPc [UTP]

Observables: none

This model is from the article: A quantitative comparison of Calvin–Benson cycle models Anne Arnold, Zoran Nikoloski…

The Calvin-Benson cycle (CBC) provides the precursors for biomass synthesis necessary for plant growth. The dynamic behavior and yield of the CBC depend on the environmental conditions and regulation of the cellular state. Accurate quantitative models hold the promise of identifying the key determinants of the tightly regulated CBC function and their effects on the responses in future climates. We provide an integrative analysis of the largest compendium of existing models for photosynthetic processes. Based on the proposed ranking, our framework facilitates the discovery of best-performing models with regard to metabolomics data and of candidates for metabolic engineering. link: http://identifiers.org/pubmed/22001849

Parameters:

Name Description
Rp = 3.2; Vcmax = 2.53232284114507; Nt = 0.5 Reaction: PGA => RuBP; NADPH, Rate Law: chloroplast*PGA/Rp*NADPH/Nt*Vcmax
J = 4.8582995951417; Nt = 0.5 Reaction: NADP => NADPH, Rate Law: chloroplast*J/2*NADP/Nt
Vc = 0.646926785453086; Vj = 0.899589030506691 Reaction: RuBP + CO2 + NADPH => PGA, Rate Law: chloroplast*((Vc+Vj)-abs(Vc-Vj))/2
Vc = 0.646926785453086; Vj = 0.899589030506691; phi = 0.263964911408408 Reaction: RuBP + O2 + NADPH => PGA, Rate Law: chloroplast*phi*((Vc+Vj)-abs(Vc-Vj))/2
Nt = 0.5 Reaction: NADP = Nt-NADPH, Rate Law: missing

States:

Name Description
NADPH [NADPH]
NADP [NADP]
RuBP [D-ribulose 1,5-bisphosphate]
PGA [3-phosphoglyceric acid]
CO2 [carbon dioxide]
O2 [dioxygen]

Observables: none

This model is from the article: A quantitative comparison of Calvin–Benson cycle models Anne Arnold, Zoran Nikolosk…

The Calvin-Benson cycle (CBC) provides the precursors for biomass synthesis necessary for plant growth. The dynamic behavior and yield of the CBC depend on the environmental conditions and regulation of the cellular state. Accurate quantitative models hold the promise of identifying the key determinants of the tightly regulated CBC function and their effects on the responses in future climates. We provide an integrative analysis of the largest compendium of existing models for photosynthetic processes. Based on the proposed ranking, our framework facilitates the discovery of best-performing models with regard to metabolomics data and of candidates for metabolic engineering. link: http://identifiers.org/pubmed/22001849

Parameters:

Name Description
KA=0.74; Vm=250.0; KR1=0.63; KR2=0.25; K=0.075; KR3=0.077 Reaction: GAP => ; Pext, Pi, PGA, DHAP, Rate Law: chloroplast*Vm*GAP/(GAP*(1+KA/Pext)+K*(1+(1+KA/Pext)*(Pi/KR1+PGA/KR2+DHAP/KR3)))
Vm=200.0; KR1=0.7; KR2=12.0; K=0.03 Reaction: FBP => F6P + Pi; F6P, Pi, Rate Law: chloroplast*Vm*FBP/(FBP+K*(1+F6P/KR1+Pi/KR2))
k2=3.84615E7; k1=5.0E8 Reaction: DHAP + E4P => SBP, Rate Law: chloroplast*(k1*DHAP*E4P-k2*SBP)
KR1=12.0; Vm=40.0; K=0.02 Reaction: SBP => S7P + Pi; Pi, Rate Law: chloroplast*Vm*SBP/(SBP+K*(1+Pi/KR1))
k1=5.0E8; k2=7.0423E7 Reaction: DHAP + GAP => FBP, Rate Law: chloroplast*(k1*DHAP*GAP-k2*FBP)
k1=5.0E8; k2=1.25E9 Reaction: R5P => Ru5P, Rate Law: chloroplast*(k1*R5P-k2*Ru5P)
k1=5.0E8; k2=31.25 Reaction: DPGA + NADPH + H => GAP + NADP + Pi, Rate Law: chloroplast*(k1*DPGA*NADPH*H-k2*GAP*NADP*Pi)
k1=5.0E8; k2=2.2727E7 Reaction: GAP => DHAP, Rate Law: chloroplast*(k1*GAP-k2*DHAP)
k2=7.46269E8; k1=5.0E8 Reaction: X5P => Ru5P, Rate Law: chloroplast*(k1*X5P-k2*Ru5P)
k2=1.6129E12; k1=5.0E8 Reaction: PGA + ATP => DPGA + ADP, Rate Law: chloroplast*(k1*PGA*ATP-k2*DPGA*ADP)
Vm=340.0; KR3=0.0075; KR4=0.9; KR1=0.84; KR5=0.07; K=0.02; KR2=0.04 Reaction: RuBP => PGA; PGA, FBP, SBP, Pi, NADPH, Rate Law: chloroplast*Vm*RuBP/(RuBP+K*(1+PGA/KR1+FBP/KR2+SBP/KR3+Pi/KR4+NADPH/KR5))
KA=0.74; Vm=250.0; KR1=0.63; KR2=0.075; KR3=0.077; K=0.25 Reaction: PGA => ; Pext, Pi, GAP, DHAP, Rate Law: chloroplast*Vm*PGA/(PGA*(1+KA/Pext)+K*(1+(1+KA/Pext)*(Pi/KR1+GAP/KR2+DHAP/KR3)))
KR1=0.05; Vm=40.0; K=0.1 Reaction: Pi => G1P; G1P, Rate Law: chloroplast*Vm*Pi/(Pi+K*(1+G1P/KR1))
k2=5.8824E8; k1=5.0E8 Reaction: GAP + S7P => X5P + R5P, Rate Law: chloroplast*(k1*GAP*S7P-k2*X5P*R5P)
KR41=2.5; Vm=1000.0; K1=0.05; KR2=0.7; KR1=2.0; KR3=4.0; K2=0.05; KR42=0.4 Reaction: Ru5P + ATP => RuBP + ADP; PGA, RuBP, Pi, ADP, Rate Law: chloroplast*Vm*Ru5P*ATP/((Ru5P+K1*(1+PGA/KR1+RuBP/KR2+Pi/KR3))*(ATP*(1+ADP/KR41)+K2*(1+ADP/KR42)))
k1=5.0E8; k2=8.621E9 Reaction: G6P => G1P, Rate Law: chloroplast*(k1*G6P-k2*G1P)
KA2=0.02; KR1=10.0; KA3=0.02; Vm=40.0; K2=0.08; KA1=0.1; K1=0.08 Reaction: G1P + ATP => ; ADP, Pi, PGA, F6P, FBP, Rate Law: chloroplast*Vm*G1P*ATP/((G1P+K1)*(1+ADP/KR1)*(ATP+K2*(1+K2*Pi/(KA1*PGA+KA2*F6P+KA3*FBP))))
k2=5.9524E9; k1=5.0E8 Reaction: GAP + F6P => X5P + E4P, Rate Law: chloroplast*(k1*GAP*F6P-k2*X5P*E4P)
KA=0.74; KR3=0.075; Vm=250.0; K=0.077; KR1=0.63; KR2=0.25 Reaction: DHAP => ; Pext, Pi, PGA, GAP, Rate Law: chloroplast*Vm*DHAP/(DHAP*(1+KA/Pext)+K*(1+(1+KA/Pext)*(Pi/KR1+PGA/KR2+GAP/KR3)))
k1=5.0E8; k2=2.174E8 Reaction: F6P => G6P, Rate Law: chloroplast*(k1*F6P-k2*G6P)
Vm=350.0; K1=0.014; K2=0.3 Reaction: ADP + Pi => ATP, Rate Law: chloroplast*Vm*ADP*Pi/((ADP+K1)*(Pi+K2))

States:

Name Description
ATP [ATP]
NADP [NADP]
R5P [aldehydo-D-ribose 5-phosphate]
DPGA [bisphosphoglyceric acid]
G1P [65533]
FBP [keto-D-fructose 1,6-bisphosphate]
X5P [D-xylulose 5-phosphate]
F6P [keto-D-fructose 6-phosphate]
DHAP [dihydroxyacetone phosphate; 668]
S7P [sedoheptulose 7-phosphate]
GAP [glyceraldehyde 3-phosphate]
E4P [122357]
NADPH [NADPH]
SBP [sedoheptulose 1,7-bisphosphate]
RuBP [D-ribulose 1,5-bisphosphate]
PGA [3-phosphoglyceric acid]
Ru5P [D-ribulose 5-phosphate]
G6P [439958]
Pi [hydrogenphosphate]
ADP [ADP]
H [hydron]

Observables: none

This model is from the article: A quantitative comparison of Calvin–Benson cycle models Anne Arnold, Zoran Nikoloski…

The Calvin-Benson cycle (CBC) provides the precursors for biomass synthesis necessary for plant growth. The dynamic behavior and yield of the CBC depend on the environmental conditions and regulation of the cellular state. Accurate quantitative models hold the promise of identifying the key determinants of the tightly regulated CBC function and their effects on the responses in future climates. We provide an integrative analysis of the largest compendium of existing models for photosynthetic processes. Based on the proposed ranking, our framework facilitates the discovery of best-performing models with regard to metabolomics data and of candidates for metabolic engineering. link: http://identifiers.org/pubmed/22001849

Parameters:

Name Description
Rp = 3.2; Nt = 0.5; Vcmax = 1.4749455852483 Reaction: PGA => RuBP; NADPH, Rate Law: chloroplast*PGA/Rp*NADPH/Nt*Vcmax
Nt = 0.5 Reaction: NADP = Nt-NADPH, Rate Law: missing
Vc = 0.666248728058741; Vj = 0.611525371598211; Vp = 0.768408279573721 Reaction: RuBP + CO2 + NADPH => PGA; O2, Rate Law: chloroplast*((((Vc+Vj)-abs(Vc-Vj))/2+Vp)-abs(((Vc+Vj)-abs(Vc-Vj))/2-Vp))/2
Vc = 0.666248728058741; Vj = 0.611525371598211; Vp = 0.768408279573721; phi = 0.116856926991465 Reaction: RuBP + O2 + NADPH => PGA; CO2, Rate Law: chloroplast*phi*((((Vc+Vj)-abs(Vc-Vj))/2+Vp)-abs(((Vc+Vj)-abs(Vc-Vj))/2-Vp))/2
J = 2.98814971559545; Nt = 0.5 Reaction: NADP => NADPH, Rate Law: chloroplast*J/2*NADP/Nt

States:

Name Description
NADPH [NADPH]
NADP [NADP]
RuBP [D-ribulose 1,5-bisphosphate]
PGA [3-phosphoglyceric acid]
CO2 [carbon dioxide]
O2 [dioxygen]

Observables: none

This model is from the article: A quantitative comparison of Calvin–Benson cycle models Anne Arnold, Zoran Nikoloski…

The Calvin-Benson cycle (CBC) provides the precursors for biomass synthesis necessary for plant growth. The dynamic behavior and yield of the CBC depend on the environmental conditions and regulation of the cellular state. Accurate quantitative models hold the promise of identifying the key determinants of the tightly regulated CBC function and their effects on the responses in future climates. We provide an integrative analysis of the largest compendium of existing models for photosynthetic processes. Based on the proposed ranking, our framework facilitates the discovery of best-performing models with regard to metabolomics data and of candidates for metabolic engineering. link: http://identifiers.org/pubmed/22001849

Parameters:

Name Description
Vc = 0.00892944491541968; phi = 0.286292104000314; Vp = 0.110677228404984; Vj = 0.00593820961819415 Reaction: RuBP + O2 + NADPH => PGA; CO2, Rate Law: chloroplast*phi*((((Vc+Vj)-abs(Vc-Vj))/2+Vp)-abs(((Vc+Vj)-abs(Vc-Vj))/2-Vp))/2
Rp = 3.2; Vcmax = 0.0307602623029146; Nt = 0.5 Reaction: PGA => RuBP; NADPH, Rate Law: chloroplast*PGA/Rp*NADPH/Nt*Vcmax
J = 0.0307678189755062; Nt = 0.5 Reaction: NADP => NADPH, Rate Law: chloroplast*J/2*NADP/Nt
Vc = 0.00892944491541968; Vp = 0.110677228404984; Vj = 0.00593820961819415 Reaction: RuBP + CO2 + NADPH => PGA; O2, Rate Law: chloroplast*((((Vc+Vj)-abs(Vc-Vj))/2+Vp)-abs(((Vc+Vj)-abs(Vc-Vj))/2-Vp))/2
Nt = 0.5 Reaction: NADP = Nt-NADPH, Rate Law: missing

States:

Name Description
NADPH [NADPH]
NADP [NADP]
RuBP [D-ribulose 1,5-bisphosphate]
PGA [3-phosphoglyceric acid]
CO2 [carbon dioxide]
O2 [dioxygen]

Observables: none

This model is from the article: A quantitative comparison of Calvin–Benson cycle models Anne Arnold, Zoran Nikolosk…

The Calvin-Benson cycle (CBC) provides the precursors for biomass synthesis necessary for plant growth. The dynamic behavior and yield of the CBC depend on the environmental conditions and regulation of the cellular state. Accurate quantitative models hold the promise of identifying the key determinants of the tightly regulated CBC function and their effects on the responses in future climates. We provide an integrative analysis of the largest compendium of existing models for photosynthetic processes. Based on the proposed ranking, our framework facilitates the discovery of best-performing models with regard to metabolomics data and of candidates for metabolic engineering. link: http://identifiers.org/pubmed/22001849

Parameters:

Name Description
KA=0.74; KR1=0.63; KR2=0.075; KR3=0.077; Vm=1.24333; K=0.25 Reaction: PGA => PGAc; Pic, Pi, GAP, DHAP, Rate Law: Vm*PGA/(PGA*(1+KA/Pic)+K*(1+(1+KA/Pic)*(Pi/KR1+GAP/KR2+DHAP/KR3)))
Ki=0.28; V=5.0; Km=0.39 Reaction: GCEAc => GCEA; GCAc, Rate Law: V*GCEAc/(Km+GCEAc+Km*GCAc/Ki)
q=250000.0; Vm=10.0098; Kr1=12.0; Ks1=0.09 Reaction: HPRc + NADH => GCEAc + NAD; HPRc, Rate Law: Vm*(HPRc*NADH-GCEAc*NAD/q)/(HPRc+Ks1*(1+HPRc/Kr1))
V=1.45611; Km=0.1 Reaction: GCAc => GOAc, Rate Law: cytosol*V*GCAc/(Km+GCAc)
KR4=0.9; KR3=0.075; Wo_min = 0.280229143229506; KR1=0.84; KR5=0.07; K=0.02; KR2=0.04 Reaction: RuBP => PGA + PGCA; PGA, FBP, SBP, Pi, NADPH, Rate Law: chloroplast*Wo_min*RuBP/(RuBP+K*(1+PGA/KR1+FBP/KR2+SBP/KR3+Pi/KR4+NADPH/KR5))
KA=0.74; KR1=0.63; KR2=0.25; K=0.075; KR3=0.077; Vm=1.24333 Reaction: TP => TPc; GAP, GAPc, Pic, Pi, PGA, DHAP, Rate Law: Vm*GAP/(GAP*(1+KA/Pic)+K*(1+(1+KA/Pic)*(Pi/KR1+PGA/KR2+DHAP/KR3)))
Kp1=0.02; Vm=0.107377; q=12.0; Ks1=0.3; Ks2=0.4 Reaction: TPc => FBPc; GAPc, DHAPc, Rate Law: cytosol*Vm*(GAPc*DHAPc-FBPc/q)/(Ks1*Ks2*((1+GAPc/Ks1)*(1+DHAPc/Ks2)+FBPc/Kp1))
q=6663.0; Kp2=12.0; Kp1=0.7; Vm=0.063979; K52a = 0.00277857142857143 Reaction: FBPc => HePc + Pic; F6Pc, Rate Law: cytosol*Vm*(FBPc-F6Pc*Pic/q)/(K52a*(FBPc/K52a+(1+F6Pc/Kp1)*(1+Pic/Kp2)))
Ks1=0.14; Kp1=0.12; Vm=0.115403; Kp2=0.11; q=0.31; Ks2=0.1 Reaction: HePc + UTPc => UDPGc + PiPic; G1Pc, Rate Law: cytosol*Vm*(G1Pc*UTPc-UDPGc*PiPic/q)/(Ks1*Ks2*(((1+G1Pc/Ks1)*(1+UTPc/Ks2)+(1+UDPGc/Kp1)*(1+PiPic/Kp2))-1))
Kr3=4.0; Ks1=0.05; q=6846.0; Kr41=2.5; Kr42=0.4; Vm=10.8348; Kr1=2.0; Kr2=0.7; Ks2=0.059 Reaction: PeP + ATP => RuBP + ADP; Ru5P, PGA, RuBP, Pi, ADP, Rate Law: chloroplast*Vm*(Ru5P*ATP-RuBP*ADP/q)/((Ru5P+Ks1*(1+PGA/Kr1+RuBP/Kr2+Pi/Kr3))*(ATP*(1+ADP/Kr41)+Ks2*(1+ADP/Kr42)))
cA = 1.5 Reaction: ADP = cA-ATP, Rate Law: missing
Ks1=2.7; Vm=3.30619; q=0.24; Kr1=33.0; Ks2=0.15 Reaction: SERc + GOAc => HPRc + GLYc; GLYc, Rate Law: Vm*(SERc*GOAc-HPRc*GLYc/q)/((SERc+Ks1*(1+GLYc/Kr1))*(GOAc+Ks2))
q=607.0; Ks1=1.7; Kr1=2.0; Vm=2.74582; Ks2=0.15 Reaction: GLUc + GOAc => KGc + GLYc; GLYc, Rate Law: cytosol*Vm*(GLUc*GOAc-KGc*GLYc/q)/((GLUc+Ks1*(1+GLYc/Kr1))*(GOAc+Ks2))
Vm=0.0555034; Kr5=11.0; Kr3=0.4; Kr4=50.0; Ks2=2.4; Ks1=0.8; q=10.0; Kr1=0.8; Kr2=0.7 Reaction: HePc + UDPGc => SucPc + UDPc; F6Pc, FBPc, UDPc, SucPc, Succ, Pic, Rate Law: cytosol*Vm*(F6Pc*UDPGc-SucPc*UDPc/q)/((F6Pc+Ks1*(1+FBPc/Kr1))*(UDPGc+Ks2*(1+UDPc/Kr2)*(1+SucPc/Kr3)*(1+Succ/Kr4)*(1+Pic/Kr5)))
q=1.17647; Vm=3.12207; Ks2=0.46; Kr1=0.1; Kr2=1.5; Ks1=0.072 Reaction: TP + S7P => PeP; GAP, X5P, R5P, Rate Law: chloroplast*Vm*(GAP*S7P-X5P*R5P/q)/((GAP+Ks1*(1+X5P/Kr1+R5P/Kr2))*(S7P+Ks2))
Kr1=94.0; Ks1=0.026; Vm=52.4199; Kr2=2.55 Reaction: PGCA => GCA; GCA, Pi, Rate Law: chloroplast*Vm*PGCA/(PGCA+Ks1*(1+GCA/Kr1)*(1+Pi/Kr2))
KA=0.74; KR3=0.075; K=0.077; KR1=0.63; KR2=0.25; Vm=1.24333 Reaction: TP => TPc; DHAP, DHAPc, Pic, Pi, PGA, GAP, Rate Law: Vm*DHAP/(DHAP*(1+KA/Pic)+K*(1+(1+KA/Pic)*(Pi/KR1+PGA/KR2+GAP/KR3)))
KA2=0.02; KR1=10.0; KA3=0.02; Vm=0.266843; K2=0.08; KA1=0.1; K1=0.08 Reaction: HeP + ATP => ; G1P, ADP, Pi, PGA, F6P, FBP, Rate Law: chloroplast*Vm*G1P*ATP/((G1P+K1)*(1+ADP/KR1)*(ATP+K2*(1+K2*Pi/(KA1*PGA+KA2*F6P+KA3*FBP))))
Vm=0.0168192; Kr2=0.1; Ks1=0.032; Kr1=0.5 Reaction: F26BPc => HePc + Pic; Pic, F6Pc, Rate Law: cytosol*Vm*F26BPc/((F26BPc+Ks1)*(1+Pic/Kr1)*(1+F6Pc/Kr2))
cPc = 15.0 Reaction: PiTc = (cPc-2*(FBPc+F26BPc))-(PGAc+TPc+HePc+SucPc+ATPc+UTPc), Rate Law: missing
V=0.5; Km=1.0 Reaction: PGAc =>, Rate Law: cytosol*V*PGAc/(Km+PGAc)
q=1.017; Vm=1.21889; Ks2=0.2; Ks1=0.4 Reaction: TP + E4P => SBP; DHAP, Rate Law: chloroplast*Vm*(DHAP*E4P-SBP/q)/((DHAP+Ks1)*(E4P+Ks2))
Ks1=0.1; Kr2=0.1; Vm=3.12207; Kr1=0.1; q=10.0; Ks2=0.1 Reaction: HeP + TP => E4P + PeP; F6P, GAP, X5P, E4P, Rate Law: chloroplast*Vm*(F6P*GAP-X5P*E4P/q)/((F6P+Ks1*(1+X5P/Kr1+E4P/Kr2))*(GAP+Ks2))
q=666000.0; Vm=0.72626; Kr2=12.0; Ks1=0.033; Kr1=0.7 Reaction: FBP => HeP + Pi; F6P, Pi, Rate Law: chloroplast*Vm*(FBP-F6P*Pi/q)/(FBP+Ks1*(1+F6P/Kr1+Pi/Kr2))
Ks1=0.24; Vm=30.1408; Kr1=0.23; Ks2=0.39 Reaction: PGA + ATP => DPGA + ADP; ADP, Rate Law: chloroplast*Vm*PGA*ATP/((PGA+Ks1)*(ATP+Ks2*(1+ADP/Kr1)))
V=2.0; Km=5.0 Reaction: Succ =>, Rate Law: cytosol*V*Succ/(Km+Succ)
KR4=0.9; KR3=0.075; KR1=0.84; Wc_min = 0.76667245633627; KR5=0.07; K=0.02; KR2=0.04 Reaction: RuBP => PGA; PGA, FBP, SBP, Pi, NADPH, Rate Law: chloroplast*Wc_min*RuBP/(RuBP+K*(1+PGA/KR1+FBP/KR2+SBP/KR3+Pi/KR4+NADPH/KR5))
cUc = 1.5 Reaction: UDPc = (cUc-UTPc)-UDPGc, Rate Law: missing
K1=0.004; K2=0.1; Vm=4.03948 Reaction: DPGA + NADPH => TP + NADP; GAP, Rate Law: chloroplast*Vm*DPGA*NADPH/((DPGA+K1)*(NADPH+K2))
Kp1=0.02; Vm=1.21889; q=7.1; Ks1=0.3; Ks2=0.4 Reaction: TP => FBP; GAP, DHAP, Rate Law: chloroplast*Vm*(GAP*DHAP-FBP/q)/(Ks1*Ks2*((1+GAP/Ks1)*(1+DHAP/Ks2)+FBP/Kp1))
Kr1=4.0; Ks1=6.0; Vm=2.49475 Reaction: GLYc => SERc; SERc, Rate Law: cytosol*Vm*GLYc/(GLYc+Ks1*(1+SERc/Kr1))
V=6.0; Km=0.2; Ki=0.22 Reaction: GCA => GCAc; GCEA, Rate Law: V*GCA/(Km+GCA+Km*GCEA/Ki)
Ks1=0.35; Vm=0.555034; q=780.0; Kr1=80.0 Reaction: SucPc => Succ + Pic; Succ, Rate Law: cytosol*Vm*(SucPc-Succ*Pic/q)/(SucPc+Ks1*(1+Succ/Kr1))
cAc = 1.0 Reaction: ADPc = cAc-ATPc, Rate Law: missing
Kr3=0.16; Vm=0.100915; Ks2=0.5; Kr1=0.021; Ks1=0.5; q=590.0; Kr2=0.7 Reaction: HePc + ATPc => F26BPc + ADPc; F6Pc, F26BPc, DHAPc, ADPc, Rate Law: cytosol*Vm*(F6Pc*ATPc-F26BPc*ADPc/q)/((F6Pc+Ks1*(1+F26BPc/Kr1)*(1+DHAPc/Kr2))*(ATPc+Ks2*(1+ADPc/Kr3)))
Kp1=0.3; Vm=15.0; Ks1=0.014; q=5.734; Ks2=0.3 Reaction: ADP + Pi => ATP, Rate Law: chloroplast*Vm*(ADP*Pi-ATP/q)/(Ks1*Ks2*((1+ADP/Ks1)*(1+Pi/Ks2)+ATP/Kp1))
Vm=5.71579; q=300.0; Ks1=0.21; Kr1=0.36; Ks2=0.25 Reaction: ATP + GCEA => PGA + ADP; PGA, Rate Law: chloroplast*Vm*(ATP*GCEA-PGA*ADP/q)/((ATP+Ks1*(1+PGA/Kr1))*(GCEA+Ks2))
q=666000.0; Ks1=0.05; Vm=0.324191; Kr1=12.0 Reaction: SBP => S7P + Pi; Pi, Rate Law: chloroplast*Vm*(SBP-S7P*Pi/q)/(SBP+Ks1*(1+Pi/Kr1))

States:

Name Description
GLYc [glycine]
HePc [D-hexose phosphate]
G1P [65533]
PGCA [126523595]
PiPic [hydrogenphosphate]
HeP [D-hexose phosphate]
TP [24794350]
PiTc [hydrogenphosphate]
NADP [NADP]
ADPc [ADP]
Pic [hydrogenphosphate]
X5P [42609827]
PeP [1005]
S7P [sedoheptulose 7-phosphate]
GAP [643984]
SBP [sedoheptulose 1,7-bisphosphate]
Succ [5988]
UDPGc [53477679]
GAPc [152025]
GCAc [841751]
G6P [439958]
GOAc [2775]
GCEA [3557]
ADP [ADP]
G6Pc [126700772]
NAD [NAD]
PGAc [668242]
ATPc [ATP]
G1Pc [49847001]
SucPc [sucrose 6(F)-phosphate]
ATP [ATP]
F6Pc [691766]
FBP [172313]
TPc [841076]
GLUc [glucose]
DHAP [668]
PGA [724]
RuBP [4337391]
HPRc [3468]
NADH [NADH]
R5P [439167]
DHAPc [53788488]
DPGA [44472828]
UDPc [20056717]
F6P [69507]
E4P [122357]
FBPc [56435918]
Pi [hydrogenphosphate]
GCEAc [3557]
UTPc [6133]
SERc [serine]
GCA [126523016]
KGc KGc

Observables: none

BIOMD0000000388 @ v0.0.1

This model is from the article: A quantitative comparison of Calvin–Benson cycle models Anne Arnold, Zoran Nikoloski…

The Calvin-Benson cycle (CBC) provides the precursors for biomass synthesis necessary for plant growth. The dynamic behavior and yield of the CBC depend on the environmental conditions and regulation of the cellular state. Accurate quantitative models hold the promise of identifying the key determinants of the tightly regulated CBC function and their effects on the responses in future climates. We provide an integrative analysis of the largest compendium of existing models for photosynthetic processes. Based on the proposed ranking, our framework facilitates the discovery of best-performing models with regard to metabolomics data and of candidates for metabolic engineering. link: http://identifiers.org/pubmed/22001849

Parameters:

Name Description
Km=0.84; V=3.05 Reaction: GAP => Ru5P, Rate Law: chloroplast*V*GAP/(Km+GAP)
V=3.78; Km=1.0 Reaction: RuBP => PGA, Rate Law: chloroplast*V*RuBP/(Km+RuBP)
Km=0.75; V=3.0 Reaction: PGA =>, Rate Law: chloroplast*V*PGA/(Km+PGA)
K2=0.059; Vm=8.0; K1=0.15 Reaction: Ru5P + ATP => RuBP + ADP, Rate Law: chloroplast*Vm*Ru5P*ATP/((Ru5P+K1)*(ATP+K2))
V=0.1; Km=5.0 Reaction: GAP =>, Rate Law: chloroplast*V*GAP/(Km+GAP)
V=5.04; Km=0.5 Reaction: DPGA => GAP + Pi, Rate Law: chloroplast*V*DPGA/(Km+DPGA)
K1=0.24; Vm=11.75; K2=0.39 Reaction: PGA + ATP => ADP + DPGA, Rate Law: chloroplast*Vm*PGA*ATP/((PGA+K1)*(ATP+K2))

States:

Name Description
Ru5P [D-ribulose 5-phosphate]
ATP [ATP]
Pi [hydrogenphosphate]
RuBP [D-ribulose 1,5-bisphosphate]
PGA [3-phosphoglyceric acid]
DPGA [bisphosphoglyceric acid]
ADP [ADP]
GAP [glyceraldehyde 3-phosphate]

Observables: none

<meta name="qrichtext" content="1" /> </head> <body style="font-size:13pt;font-family:Lucida Grande"> <p dir="ltr">Mo…

The nuclear factor kappaB (NF-kappaB) transcription factor regulates cellular stress responses and the immune response to infection. NF-kappaB activation results in oscillations in nuclear NF-kappaB abundance. To define the function of these oscillations, we treated cells with repeated short pulses of tumor necrosis factor-alpha at various intervals to mimic pulsatile inflammatory signals. At all pulse intervals that were analyzed, we observed synchronous cycles of NF-kappaB nuclear translocation. Lower frequency stimulations gave repeated full-amplitude translocations, whereas higher frequency pulses gave reduced translocation, indicating a failure to reset. Deterministic and stochastic mathematical models predicted how negative feedback loops regulate both the resetting of the system and cellular heterogeneity. Altering the stimulation intervals gave different patterns of NF-kappaB-dependent gene expression, which supports the idea that oscillation frequency has a functional role. link: http://identifiers.org/pubmed/19359585

Parameters: none

States: none

Observables: none

MODEL1006230065 @ v0.0.1

This a model from the article: Optimal velocity and safety of discontinuous conduction through the heterogeneous Purki…

Slow and discontinuous wave conduction through nonuniform junctions in cardiac tissues is generally considered unsafe and proarrythmogenic. However, the relationships between tissue structure, wave conduction velocity, and safety at such junctions are unknown. We have developed a structurally and electrophysiologically detailed model of the canine Purkinje-ventricular junction (PVJ) and varied its heterogeneity parameters to determine such relationships. We show that neither very fast nor very slow conduction is safe, and there exists an optimal velocity that provides the maximum safety factor for conduction through the junction. The resultant conduction time delay across the PVJ is a natural consequence of the electrophysiological and morphological differences between the Purkinje fiber and ventricular tissue. The delay allows the PVJ to accumulate and pass sufficient charge to excite the adjacent ventricular tissue, but is not long enough for the source-to-load mismatch at the junction to be enhanced over time. The observed relationships between the conduction velocity and safety factor can provide new insights into optimal conditions for wave propagation through nonuniform junctions between various cardiac tissues. link: http://identifiers.org/pubmed/19580741

Parameters: none

States: none

Observables: none

This a model from the article: Mechanisms of transition from normal to reentrant electrical activity in a model of rab…

Experimental evidence suggests that regional differences in action potential (AP) morphology can provide a substrate for initiation and maintenance of reentrant arrhythmias in the right atrium (RA), but the relationships between the complex electrophysiological and anatomical organization of the RA and the genesis of reentry are unclear. In this study, a biophysically detailed three-dimensional computer model of the right atrial tissue was constructed to study the role of tissue heterogeneity and anisotropy in arrhythmogenesis. The model of Lindblad et al. for a rabbit atrial cell was modified to incorporate experimental data on regional differences in several ionic currents (primarily, I(Na), I(CaL), I(K1), I(to), and I(sus)) between the crista terminalis and pectinate muscle cells. The modified model was validated by its ability to reproduce the AP properties measured experimentally. The anatomical model of the rabbit RA (including tissue geometry and fiber orientation) was based on a recent histological reconstruction. Simulations with the resultant electrophysiologically and anatomically detailed three-dimensional model show that complex organization of the RA tissue causes breakdown of regular AP conduction patterns at high pacing rates (>11.75 Hz): as the AP in the crista terminalis cells is longer, and electrotonic coupling transverse to fibers of the crista terminalis is weak, high-frequency pacing at the border between the crista terminalis and pectinate muscles results in a unidirectional conduction block toward the crista terminalis and generation of reentry. Contributions of the tissue heterogeneity and anisotropy to reentry initiation mechanisms are quantified by measuring action potential duration (APD) gradients at the border between the crista terminalis and pectinate muscles: the APD gradients are high in areas where both heterogeneity and anisotropy are high, such that intrinsic APD differences are not diminished by electrotonic interactions. Thus, our detailed computer model reconstructs complex electrical activity in the RA, and provides new insights into the mechanisms of transition from focal atrial tachycardia into reentry. link: http://identifiers.org/pubmed/19186122

Parameters: none

States: none

Observables: none

MODEL9147975215 @ v0.0.1

This is a complex model to examine mechanisms that govern MAPK pathway dynamics in Chinese hamster ovary (CHO) cell line…

Exploiting signaling pathways for the purpose of controlling cell function entails identifying and manipulating the information content of intracellular signals. As in the case of the ubiquitously expressed, eukaryotic mitogen-activated protein kinase (MAPK) signaling pathway, this information content partly resides in the signals' dynamical properties. Here, we utilize a mathematical model to examine mechanisms that govern MAPK pathway dynamics, particularly the role of putative negative feedback mechanisms in generating complete signal adaptation, a term referring to the reset of a signal to prestimulation levels. In addition to yielding adaptation of its direct target, feedback mechanisms implemented in our model also indirectly assist in the adaptation of signaling components downstream of the target under certain conditions. In fact, model predictions identify conditions yielding ultra-desensitization of signals in which complete adaptation of target and downstream signals culminates even while stimulus recognition (i.e., receptor-ligand binding) continues to increase. Moreover, the rate at which signal decays can follow first-order kinetics with respect to signal intensity, so that signal adaptation is achieved in the same amount of time regardless of signal intensity or ligand dose. All of these features are consistent with experimental findings recently obtained for the Chinese hamster ovary (CHO) cell lines (Asthagiri et al., J. Biol. Chem. 1999, 274, 27119-27127). Our model further predicts that although downstream effects are independent of whether an enzyme or adaptor protein is targeted by negative feedback, adaptor-targeted feedback can "back-propagate" effects upstream of the target, specifically resulting in increased steady-state upstream signal. Consequently, where these upstream components serve as nodes within a signaling network, feedback can transfer signaling through these nodes into alternate pathways, thereby promoting the sort of signaling cross-talk that is becoming more widely appreciated. link: http://identifiers.org/pubmed/11312698

Parameters: none

States: none

Observables: none

BIOMD0000000054 @ v0.0.1

The model reproduces ion and adenylate pool concentration corresponding to line 2 of Fig 3 of the publication. This mode…

A simplified mathematical model of cell metabolism describing ion pump, glycolysis and adenylate metabolism was developed and investigated in order to clarify the functional role of the adenylate metabolism system in human erythrocytes. The adenylate metabolism system was shown to be able to function as a specific regulatory system stabilizing intracellular ion concentration and, hence, erythrocyte volume under changes in the permeability of cell membrane. This stabilization is provided via an increase in adenylate pool in association with ATPases rate elevation. Proper regulation of adenylate pool size might be achieved even in the case when AMP synthesis rate remains constant and only AMP degradation rate varies. The best stabilization of intracellular ion concentration in the model is attained when the rate of AMP destruction is directly proportional to ATP concentration and is inversely proportional to AMP concentration. An optimal rate of adenylate metabolism in erythrocytes ranges from several tenths of a percent to several percent of the glycolytic flux. An increase in this rate results in deterioration of cell metabolism stability. Decrease in the rate of adenylate metabolism makes the functioning of this metabolic system inefficient, because the time necessary to achieve stabilization of intracellular ion concentration becomes comparable with erythrocyte life span. link: http://identifiers.org/pubmed/8733433

Parameters:

Name Description
T = 1.0; W3 = 13.48; M = 0.01 Reaction: => E, Rate Law: cell*W3*T^0.52*M^0.41
T = 1.0; W2 = 0.2 Reaction: I + E =>, Rate Law: cell*W2*I*T
P = 0.121; J = 100.0 Reaction: => I, Rate Law: cell*P*J
U = 0.02; n = 1.2; W = 0.01; T = 1.0; M = 0.01; k = -1.0 Reaction: => A, Rate Law: cell*U*(1-W*T^n*M^k)
U = 0.02 Reaction: E =>, Rate Law: cell*2*U

States:

Name Description
I [sodium(1+); Sodium cation]
A [AMP; ADP; ATP; AMP; ADP; ATP]
E [ATP; ADP; ADP; ATP]

Observables: none

Aubert2002 - Coupling between Brain electrical activity, Metabolism and HemodynamicsFelix Winter encoded this model in S…

In order to improve the interpretation of functional neuroimaging data, we implemented a mathematical model of the coupling between membrane ionic currents, energy metabolism (i.e., ATP regeneration via phosphocreatine buffer effect, glycolysis, and mitochondrial respiration), blood-brain barrier exchanges, and hemodynamics. Various hypotheses were tested for the variation of the cerebral metabolic rate of oxygen (CMRO(2)): (H1) the CMRO(2) remains at its baseline level; (H2) the CMRO(2) is enhanced as soon as the cerebral blood flow (CBF) increases; (H3) the CMRO(2) increase depends on intracellular oxygen and pyruvate concentrations, and intracellular ATP/ADP ratio; (H4) in addition to hypothesis H3, the CMRO(2) progressively increases, due to the action of a second messenger. A good agreement with experimental data from magnetic resonance imaging and spectroscopy (MRI and MRS) was obtained when we simulated sustained and repetitive activation protocols using hypotheses (H3) or (H4), rather than hypotheses (H1) or (H2). Furthermore, by studying the effect of the variation of some physiologically important parameters on the time course of the modeled blood-oxygenation-level-dependent (BOLD) signal, we were able to formulate hypotheses about the physiological or biochemical significance of functional magnetic resonance data, especially the poststimulus undershoot and the baseline drift. link: http://identifiers.org/pubmed/12414257

Parameters:

Name Description
parameter_20 = 42.6 Reaction: species_5 + species_6 => species_7 + species_9; species_3, species_5, species_3, species_6, species_7, Rate Law: compartment_1*parameter_20*species_5/compartment_1*species_3/compartment_1*species_6/compartment_1/(species_7/compartment_1)
parameter_30 = 20.0; parameter_29 = 3666.0 Reaction: species_11 + species_3 => species_12 + species_2; species_11, species_3, species_12, species_2, Rate Law: compartment_1*(parameter_29*species_11/compartment_1*species_3/compartment_1-parameter_30*species_12/compartment_1*species_2/compartment_1)
v_stim_constant = 0.23 Reaction: => species_1, Rate Law: compartment_1*v_stim_constant
parameter_33 = 0.0361; parameter_34 = 8.6; parameter_35 = 2.73; parameter_32 = 1.6 Reaction: species_19 => species_13; species_19, species_13, Rate Law: parameter_32*(parameter_33*(parameter_34/(species_19/compartment_3)-1)^((-1)/parameter_35)-species_13/compartment_1)
parameter_7 = 2.2 Reaction: species_2 = parameter_7*compartment_1, Rate Law: missing
parameter_22 = 0.186 Reaction: species_6 = parameter_22*compartment_1, Rate Law: missing
parameter_23 = 86.7 Reaction: species_3 + species_9 => species_2 + species_8; species_3, species_9, Rate Law: parameter_23*species_3*species_9/compartment_1
parameter_26 = 0.00628; parameter_27 = 0.5 Reaction: species_10 => species_18; species_10, species_18, Rate Law: parameter_26*(species_10/compartment_1/(species_10/compartment_1+parameter_27)-species_18/compartment_3/(species_18/compartment_3+parameter_27))
parameter_25 = 44.8; parameter_24 = 2000.0 Reaction: species_8 + species_7 => species_10 + species_6; species_8, species_7, species_10, species_6, Rate Law: compartment_1*(parameter_24*species_8/compartment_1*species_7/compartment_1-parameter_25*species_10/compartment_1*species_6/compartment_1)
parameter_1 = 90000.0; parameter_9 = 0.5; parameter_8 = 2.9E-7 Reaction: species_1 + species_2 => species_3; species_2, species_1, Rate Law: compartment_1*parameter_1*parameter_8*species_2/compartment_1*species_1/compartment_1/(1+species_2/compartment_1/parameter_9)
parameter_10 = 0.0119296850858459 Reaction: species_3 = parameter_10*compartment_1, Rate Law: missing
parameter_4 = 26.73; parameter_3 = 96500.0; parameter_5 = -70.0; parameter_1 = 90000.0; parameter_6 = 150.0; parameter_2 = 0.0039 Reaction: => species_1; species_1, Rate Law: compartment_1*parameter_1*parameter_2/parameter_3*(parameter_4*ln(parameter_6/(species_1/compartment_1))-parameter_5)
F_out = 0.012 Reaction: dHb => ; dHb, Rate Law: compartment_3*F_out*dHb/compartment_3/compartment_4
parameter_37 = 0.012 Reaction: => dHb; species_14, species_19, species_14, species_19, Rate Law: compartment_3*parameter_37*2*(species_14/compartment_2-species_19/compartment_3)
parameter_14 = 0.0476; parameter_15 = 9.0 Reaction: species_17 => species_4; species_17, species_4, Rate Law: parameter_14*(species_17/compartment_3/(species_17/compartment_3+parameter_15)-species_4/compartment_1/(species_4/compartment_1+parameter_15))
v_Mito_H3 = 0.0191965079261093 Reaction: species_8 + species_7 + species_13 => species_2, Rate Law: compartment_1*v_Mito_H3
parameter_16 = 0.12; parameter_19 = 0.05; parameter_18 = 4.0; parameter_17 = 1.0 Reaction: species_4 + species_2 => species_5 + species_3; species_2, species_4, Rate Law: compartment_1*parameter_16*species_2/compartment_1*species_4/compartment_1/(species_4/compartment_1+parameter_19)/(1+(species_2/compartment_1/parameter_17)^parameter_18)
parameter_31 = 10.0 Reaction: species_12 = (parameter_31-species_11/compartment_1)*compartment_1, Rate Law: missing
parameter_38 = 0.0055; parameter_37 = 0.012 Reaction: species_14 => species_19; species_14, species_19, Rate Law: 2*parameter_37/parameter_38*(species_14/compartment_2-species_19/compartment_3)
v=0.149 Reaction: species_2 =>, Rate Law: compartment_1*v

States:

Name Description
species 9 [phosphoenolpyruvate]
species 2 [ATP]
species 6 [NAD(+)]
species 13 [dioxygen]
species 19 [dioxygen]
species 10 [(S)-lactic acid]
species 11 [N-phosphocreatine]
species 1 [sodium(1+)]
species 18 [(S)-lactic acid]
species 4 [D-glucopyranose]
species 16 [(S)-lactic acid]
species 14 [dioxygen]
species 3 [ADP]
species 8 [pyruvate]
species 17 [D-glucopyranose]
species 12 [creatine]
species 7 [NADH]
species 5 [D-glyceraldehyde 3-phosphate]
species 15 [D-glucopyranose]
dHb [deoxyhemoglobin]

Observables: none

Aubert2005 - Interaction between astrocytes and neurons on energy metabolismEnocded non-curated model. Issues: - Confus…

Understanding cerebral energy metabolism in neurons and astrocytes is necessary for the interpretation of functional brain imaging data. It has been suggested that astrocytes can provide lactate as an energy fuel to neurons, a process referred to as astrocyte-neuron lactate shuttle (ANLS). Some authors challenged this hypothesis, defending the classical view that glucose is the major energy substrate of neurons, at rest as well as in response to a stimulation. To test the ANLS hypothesis from a theoretical point of view, we developed a mathematical model of compartmentalized energy metabolism between neurons and astrocytes, adopting hypotheses highly unfavorable to ANLS. Simulation results can be divided between two groups, depending on the relative neuron versus astrocyte stimulation. If this ratio is low, ANLS is observed during all the stimulus and poststimulus periods (continuous ANLS), but a high ratio induces ANLS only at the beginning of the stimulus and during the poststimulus period (triphasic behavior). Finally, our results show that current experimental data on lactate kinetics are compatible with the ANLS hypothesis, and that it is essential to assess the neuronal and astrocytic NADH/NAD+ ratio changes to test the ANLS hypothesis. link: http://identifiers.org/pubmed/15931164

Parameters: none

States: none

Observables: none

Fluid-phase endoeytosis (pinocytosis) kinetics were studied in Dictyostelium discoideum amoebae from the axenic strain A…

Fluid-phase endocytosis (pinocytosis) kinetics were studied inDictyostelium discoideum amoebae from the axenic strain Ax-2 that exhibits high rates of fluid-phase endocytosis when cultured in liquid nutrient media. Fluorescein-labelled dextran (FITC-dextran) was used as a marker in continuous uptake- and in pulse-chase exocytosis experiments. In the latter case, efflux of the marker was monitored on cells loaded for short periods of time and resuspended in marker-free medium. A multicompartmental model was developed which describes satisfactorily fluid-phase endocytosis kinetics. In particular, it accounts correctly for the extended latency period before exocytosis in pulse-chase experiments and it suggests the existence of some sorts of maturation stages in the pathway. link: http://identifiers.org/doi/10.1007/BF00713556

Parameters: none

States: none

Observables: none

Fluid-phase endoeytosis (pinocytosis) kinetics were studied in Dictyostelium discoideum amoebae from the axenic strain A…

Fluid-phase endocytosis (pinocytosis) kinetics were studied inDictyostelium discoideum amoebae from the axenic strain Ax-2 that exhibits high rates of fluid-phase endocytosis when cultured in liquid nutrient media. Fluorescein-labelled dextran (FITC-dextran) was used as a marker in continuous uptake- and in pulse-chase exocytosis experiments. In the latter case, efflux of the marker was monitored on cells loaded for short periods of time and resuspended in marker-free medium. A multicompartmental model was developed which describes satisfactorily fluid-phase endocytosis kinetics. In particular, it accounts correctly for the extended latency period before exocytosis in pulse-chase experiments and it suggests the existence of some sorts of maturation stages in the pathway. link: http://identifiers.org/doi/10.1007/BF00713556

Parameters: none

States: none

Observables: none

Auer2010 - Correlation between lag time and aggregation rate in protein aggregationThis model is described in the articl…

Under favorable conditions, many proteins can assemble into macroscopically large aggregates such as the amyloid fibrils that are associated with Alzheimer's, Parkinson's, and other neurological and systemic diseases. The overall process of protein aggregation is characterized by initial lag time during which no detectable aggregation occurs in the solution and by maximal aggregation rate at which the dissolved protein converts into aggregates. In this study, the correlation between the lag time and the maximal rate of protein aggregation is analyzed. It is found that the product of these two quantities depends on a single numerical parameter, the kinetic index of the curve quantifying the time evolution of the fraction of protein aggregated. As this index depends relatively little on the conditions and/or system studied, our finding provides insight into why for many experiments the values of the product of the lag time and the maximal aggregation rate are often equal or quite close to each other. It is shown how the kinetic index is related to a basic kinetic parameter of a recently proposed theory of protein aggregation. link: http://identifiers.org/pubmed/20602358

Parameters:

Name Description
n = 7.2; omega = 35.3 Reaction: Amyloid = 1-exp(-(time/omega)^n), Rate Law: missing

States:

Name Description
Amyloid [amyloid fibril; aggregated]

Observables: none

This a model from the article: A mathematical model of bone remodeling dynamics for normal bone cell populations and…

Multiple myeloma is a hematologic malignancy associated with the development of a destructive osteolytic bone disease.Mathematical models are developed for normal bone remodeling and for the dysregulated bone remodeling that occurs in myeloma bone disease. The models examine the critical signaling between osteoclasts (bone resorption) and osteoblasts (bone formation). The interactions of osteoclasts and osteoblasts are modeled as a system of differential equations for these cell populations, which exhibit stable oscillations in the normal case and unstable oscillations in the myeloma case. In the case of untreated myeloma, osteoclasts increase and osteoblasts decrease, with net bone loss as the tumor grows. The therapeutic effects of targeting both myeloma cells and cells of the bone marrow microenvironment on these dynamics are examined.The current model accurately reflects myeloma bone disease and illustrates how treatment approaches may be investigated using such computational approaches.This article was reviewed by Ariosto Silva and Mark P. Little. link: http://identifiers.org/pubmed/20406449

Parameters:

Name Description
k1 = 0.24; k2 = 0.0017; y1 = NaN; y2 = NaN Reaction: z = k2*y2-k1*y1, Rate Law: k2*y2-k1*y1
beta2 = 0.02; g22 = 0.0; g12 = 1.0; alpha2 = 4.0 Reaction: B = alpha2*C^g12*B^g22-beta2*B, Rate Law: alpha2*C^g12*B^g22-beta2*B
g11 = 0.5; beta1 = 0.2; alpha1 = 3.0; g21 = -0.5 Reaction: C = alpha1*C^g11*B^g21-beta1*C, Rate Law: alpha1*C^g11*B^g21-beta1*C

States:

Name Description
B [osteoblast]
C [osteoclast]
z [mass]

Observables: none

This a model from the article: A mathematical model of bone remodeling dynamics for normal bone cell populations and…

Multiple myeloma is a hematologic malignancy associated with the development of a destructive osteolytic bone disease.Mathematical models are developed for normal bone remodeling and for the dysregulated bone remodeling that occurs in myeloma bone disease. The models examine the critical signaling between osteoclasts (bone resorption) and osteoblasts (bone formation). The interactions of osteoclasts and osteoblasts are modeled as a system of differential equations for these cell populations, which exhibit stable oscillations in the normal case and unstable oscillations in the myeloma case. In the case of untreated myeloma, osteoclasts increase and osteoblasts decrease, with net bone loss as the tumor grows. The therapeutic effects of targeting both myeloma cells and cells of the bone marrow microenvironment on these dynamics are examined.The current model accurately reflects myeloma bone disease and illustrates how treatment approaches may be investigated using such computational approaches.This article was reviewed by Ariosto Silva and Mark P. Little. link: http://identifiers.org/pubmed/20406449

Parameters:

Name Description
y1 = NaN; k2 = 6.395E-4; y2 = NaN; k1 = 0.0748 Reaction: z = k2*y2-k1*y1, Rate Law: k2*y2-k1*y1
gammaT = 0.005; LT = 100.0 Reaction: Tumour = gammaT*Tumour*ln(LT/Tumour), Rate Law: gammaT*Tumour*ln(LT/Tumour)
r12 = 0.0; r22 = 0.2; beta2 = 0.02; g22 = 0.0; g12 = 1.0; alpha2 = 4.0; LT = 100.0 Reaction: B = alpha2*C^(g12/(1+r12*Tumour/LT))*B^(g22-r22*Tumour/LT)-beta2*B, Rate Law: alpha2*C^(g12/(1+r12*Tumour/LT))*B^(g22-r22*Tumour/LT)-beta2*B
r11 = 0.005; g11 = 1.1; beta1 = 0.2; alpha1 = 3.0; r21 = 0.0; g21 = -0.5; LT = 100.0 Reaction: C = alpha1*C^(g11*(1+r11*Tumour/LT))*B^(g21*(1+r21*Tumour/LT))-beta1*C, Rate Law: alpha1*C^(g11*(1+r11*Tumour/LT))*B^(g21*(1+r21*Tumour/LT))-beta1*C

States:

Name Description
B [osteoblast]
C [osteoclast]
Tumour [multiple myeloma cell]
z [mass]

Observables: none

This a model from the article: A mathematical model of bone remodeling dynamics for normal bone cell populations and…

Multiple myeloma is a hematologic malignancy associated with the development of a destructive osteolytic bone disease.Mathematical models are developed for normal bone remodeling and for the dysregulated bone remodeling that occurs in myeloma bone disease. The models examine the critical signaling between osteoclasts (bone resorption) and osteoblasts (bone formation). The interactions of osteoclasts and osteoblasts are modeled as a system of differential equations for these cell populations, which exhibit stable oscillations in the normal case and unstable oscillations in the myeloma case. In the case of untreated myeloma, osteoclasts increase and osteoblasts decrease, with net bone loss as the tumor grows. The therapeutic effects of targeting both myeloma cells and cells of the bone marrow microenvironment on these dynamics are examined.The current model accurately reflects myeloma bone disease and illustrates how treatment approaches may be investigated using such computational approaches.This article was reviewed by Ariosto Silva and Mark P. Little. link: http://identifiers.org/pubmed/20406449

Parameters:

Name Description
gammaT = 0.005; V2 = NaN; LT = 100.0 Reaction: Tumour = (gammaT-V2)*Tumour*ln(LT/Tumour), Rate Law: (gammaT-V2)*Tumour*ln(LT/Tumour)
y1 = NaN; k2 = 6.395E-4; y2 = NaN; k1 = 0.0748 Reaction: z = k2*y2-k1*y1, Rate Law: k2*y2-k1*y1
r12 = 0.0; r22 = 0.2; beta2 = 0.02; g22 = 0.0; g12 = 1.0; alpha2 = 4.0; LT = 100.0; V1 = NaN Reaction: B = alpha2*C^(g12/(1+r12*Tumour/LT))*B^(g22-r22*Tumour/LT)-(beta2-V1)*B, Rate Law: alpha2*C^(g12/(1+r12*Tumour/LT))*B^(g22-r22*Tumour/LT)-(beta2-V1)*B
r11 = 0.005; g11 = 1.1; beta1 = 0.2; alpha1 = 3.0; r21 = 0.0; g21 = -0.5; LT = 100.0 Reaction: C = alpha1*C^(g11*(1+r11*Tumour/LT))*B^(g21*(1+r21*Tumour/LT))-beta1*C, Rate Law: alpha1*C^(g11*(1+r11*Tumour/LT))*B^(g21*(1+r21*Tumour/LT))-beta1*C

States:

Name Description
B [osteoblast]
C [osteoclast]
Tumour [multiple myeloma cell]
z [mass]

Observables: none

B


Baart2007 - Genome-scale metabolic network of Neisseria meningitidis (iGB555)This model is described in the article: [M…

BACKGROUND: Neisseria meningitidis is a human pathogen that can infect diverse sites within the human host. The major diseases caused by N. meningitidis are responsible for death and disability, especially in young infants. In general, most of the recent work on N. meningitidis focuses on potential antigens and their functions, immunogenicity, and pathogenicity mechanisms. Very little work has been carried out on Neisseria primary metabolism over the past 25 years. RESULTS: Using the genomic database of N. meningitidis serogroup B together with biochemical and physiological information in the literature we constructed a genome-scale flux model for the primary metabolism of N. meningitidis. The validity of a simplified metabolic network derived from the genome-scale metabolic network was checked using flux-balance analysis in chemostat cultures. Several useful predictions were obtained from in silico experiments, including substrate preference. A minimal medium for growth of N. meningitidis was designed and tested successfully in batch and chemostat cultures. CONCLUSION: The verified metabolic model describes the primary metabolism of N. meningitidis in a chemostat in steady state. The genome-scale model is valuable because it offers a framework to study N. meningitidis metabolism as a whole, or certain aspects of it, and it can also be used for the purpose of vaccine process development (for example, the design of growth media). The flux distribution of the main metabolic pathways (that is, the pentose phosphate pathway and the Entner-Douderoff pathway) indicates that the major part of pyruvate (69%) is synthesized through the ED-cleavage, a finding that is in good agreement with literature. link: http://identifiers.org/pubmed/17617894

Parameters: none

States: none

Observables: none

BIOMD0000000758 @ v0.0.1

The paper describes a simple model of tumor immunotherapy. Created by COPASI 4.25 (Build 207) This model is descri…

The objective of this study was to create a clinically applicable mathematical model of immunotherapy for cancer and use it to explore differences between successful and unsuccessful treatment scenarios. The simplified predator-prey model includes four lumped parameters: tumor growth rate, g; immune cell killing efficiency, k; immune cell signaling factor, λ; and immune cell half-life decay, μ. The predator-prey equations as functions of time, t, for normalized tumor cell numbers, y, (the prey) and immunocyte numbers, ×, (the predators) are: dy/dt = gy - kx and dx/dt = λxy - μx. A parameter estimation procedure that capitalizes on available clinical data and the timing of clinically observable phenomena gives mid-range benchmarks for parameters representing the unstable equilibrium case in which the tumor neither grows nor shrinks. Departure from this equilibrium results in oscillations in tumor cell numbers and in many cases complete elimination of the tumor. Several paradoxical phenomena are predicted, including increasing tumor cell numbers prior to a population crash, apparent cure with late recurrence, one or more cycles of tumor growth prior to eventual tumor elimination, and improved tumor killing with initially weaker immune parameters or smaller initial populations of immune cells. The model and the parameter estimation techniques are easily adapted to various human cancers that evoke an immune response. They may help clinicians understand and predict certain strange and unexpected effects in the world of tumor immunity and lead to the design of clinical trials to test improved treatment protocols for patients. link: http://identifiers.org/pubmed/22432059

Parameters:

Name Description
l = 0.09 1/d Reaction: => I; T, Rate Law: tumor_microenvironment*l*T*I
k = 4.0 1/d Reaction: T => ; I, Rate Law: tumor_microenvironment*k*I
u = 0.1 1/d Reaction: I =>, Rate Law: tumor_microenvironment*u*I
g = 0.004 1/d Reaction: => T, Rate Law: tumor_microenvironment*g*T

States:

Name Description
I [effector T cell]
T [malignant cell; Tumor Size]

Observables: none

This is a dynamic pathway model examining the roles of of the two transcriptional negative feedback regulators of the su…

Cellular signal transduction is governed by multiple feedback mechanisms to elicit robust cellular decisions. The specific contributions of individual feedback regulators, however, remain unclear. Based on extensive time-resolved data sets in primary erythroid progenitor cells, we established a dynamic pathway model to dissect the roles of the two transcriptional negative feedback regulators of the suppressor of cytokine signaling (SOCS) family, CIS and SOCS3, in JAK2/STAT5 signaling. Facilitated by the model, we calculated the STAT5 response for experimentally unobservable Epo concentrations and provide a quantitative link between cell survival and the integrated response of STAT5 in the nucleus. Model predictions show that the two feedbacks CIS and SOCS3 are most effective at different ligand concentration ranges due to their distinct inhibitory mechanisms. This divided function of dual feedback regulation enables control of STAT5 responses for Epo concentrations that can vary 1000-fold in vivo. Our modeling approach reveals dose-dependent feedback control as key property to regulate STAT5-mediated survival decisions over a broad range of ligand concentrations. link: http://identifiers.org/pubmed/21772264

Parameters:

Name Description
CISRNATurn = 1000.0 Reaction: CISRNA =>, Rate Law: cyt*CISRNATurn*CISRNA
SOCS3Eqc = 173.653; SOCS3Inh = 10.408; EpoRActJAK2 = 0.267308; EpoRCISInh = 1000000.0 Reaction: EpoRpJAK2 => p2EpoRpJAK2; EpoRJAK2_CIS, SOCS3, Rate Law: cyt*3*EpoRActJAK2*EpoRpJAK2/((SOCS3Inh*SOCS3/SOCS3Eqc+1)*(EpoRCISInh*EpoRJAK2_CIS+1))
SOCS3Turn = 10000.0 Reaction: SOCS3 =>, Rate Law: cyt*SOCS3Turn*SOCS3
SHP1ActEpoR = 0.001; init_EpoRJAK2 = 3.97622 Reaction: SHP1 => SHP1Act; EpoRpJAK2, p12EpoRpJAK2, p1EpoRpJAK2, p2EpoRpJAK2, Rate Law: cyt*SHP1ActEpoR*SHP1*(EpoRpJAK2+p12EpoRpJAK2+p1EpoRpJAK2+p2EpoRpJAK2)/init_EpoRJAK2
SOCS3RNADelay = 1.06465 Reaction: SOCS3nRNA1 => SOCS3nRNA2, Rate Law: nuc*SOCS3RNADelay*SOCS3nRNA1
init_EpoRJAK2 = 3.97622; STAT5ActJAK2 = 0.0780965; SOCS3Eqc = 173.653; SOCS3Inh = 10.408 Reaction: STAT5 => pSTAT5; EpoRpJAK2, SOCS3, p12EpoRpJAK2, p1EpoRpJAK2, p2EpoRpJAK2, Rate Law: cyt*STAT5ActJAK2*STAT5*(EpoRpJAK2+p12EpoRpJAK2+p1EpoRpJAK2+p2EpoRpJAK2)/(init_EpoRJAK2*(SOCS3Inh*SOCS3/SOCS3Eqc+1))
CISTurn = 0.00839842; CISEqc = 432.871; CISRNAEqc = 1.0 Reaction: => CIS; CISRNA, Rate Law: cyt*CISEqc*CISTurn*CISRNA/CISRNAEqc
init_EpoRJAK2 = 3.97622; EpoRCISRemove = 5.42932 Reaction: EpoRJAK2_CIS => ; p12EpoRpJAK2, p1EpoRpJAK2, Rate Law: cyt*EpoRCISRemove*EpoRJAK2_CIS*(p12EpoRpJAK2+p1EpoRpJAK2)/init_EpoRJAK2
CISTurn = 0.00839842; CISEqc = 432.871; CISEqcOE = 0.530261; CISoe = 0.0 Reaction: => CIS, Rate Law: cyt*CISoe*CISEqc*CISTurn*CISEqcOE/cyt
SOCS3Eqc = 173.653; SOCS3Inh = 10.408; EpoRActJAK2 = 0.267308 Reaction: EpoRpJAK2 => p1EpoRpJAK2; SOCS3, Rate Law: cyt*EpoRActJAK2*EpoRpJAK2/(SOCS3Inh*SOCS3/SOCS3Eqc+1)
JAK2EpoRDeaSHP1 = 142.722; init_SHP1 = 26.7251 Reaction: p1EpoRpJAK2 => EpoRJAK2; SHP1Act, Rate Law: cyt*JAK2EpoRDeaSHP1*SHP1Act*p1EpoRpJAK2/init_SHP1
SHP1Dea = 0.00816391 Reaction: SHP1Act => SHP1, Rate Law: cyt*SHP1Dea*SHP1Act
ActD = 0.0; SOCS3RNATurn = 0.00830844; init_STAT5 = 79.7535; SOCS3RNAEqc = 1.0 Reaction: => SOCS3nRNA1; npSTAT5, Rate Law: nuc*(-SOCS3RNAEqc*SOCS3RNATurn*npSTAT5*(ActD-1)/init_STAT5*nuc)/nuc
STAT5Exp = 0.0745155 Reaction: npSTAT5 => STAT5, Rate Law: STAT5Exp*npSTAT5*nuc
CISRNADelay = 0.144775 Reaction: CISnRNA1 => CISnRNA2, Rate Law: nuc*CISRNADelay*CISnRNA1
STAT5Imp = 0.0268889 Reaction: pSTAT5 => npSTAT5, Rate Law: STAT5Imp*pSTAT5*cyt
SOCS3Eqc = 173.653; SOCS3Inh = 10.408; JAK2ActEpo = 633253.0 Reaction: EpoRJAK2 => EpoRpJAK2; Epo, SOCS3, Rate Law: cyt*JAK2ActEpo*Epo*EpoRJAK2/(SOCS3Inh*SOCS3/SOCS3Eqc+1)
SOCS3RNATurn = 0.00830844 Reaction: SOCS3RNA =>, Rate Law: cyt*SOCS3RNATurn*SOCS3RNA
ActD = 0.0; CISRNAEqc = 1.0; init_STAT5 = 79.7535; CISRNATurn = 1000.0 Reaction: => CISnRNA1; npSTAT5, Rate Law: nuc*(-CISRNAEqc*CISRNATurn*npSTAT5*(ActD-1)/init_STAT5*nuc)/nuc
CISTurn = 0.00839842 Reaction: CIS =>, Rate Law: cyt*CISTurn*CIS
SOCS3Eqc = 173.653; SOCS3Turn = 10000.0; SOCS3RNAEqc = 1.0 Reaction: => SOCS3; SOCS3RNA, Rate Law: cyt*SOCS3Eqc*SOCS3Turn*SOCS3RNA/SOCS3RNAEqc
SOCS3EqcOE = 0.679157; SOCS3Eqc = 173.653; SOCS3oe = 0.0; SOCS3Turn = 10000.0 Reaction: => SOCS3, Rate Law: cyt*SOCS3oe*SOCS3Eqc*SOCS3Turn*SOCS3EqcOE/cyt
init_EpoRJAK2 = 3.97622; SOCS3Eqc = 173.653; CISEqc = 432.871; SOCS3Inh = 10.408; STAT5ActEpoR = 38.9757; CISInh = 7.84653E8 Reaction: STAT5 => pSTAT5; CIS, SOCS3, p12EpoRpJAK2, p1EpoRpJAK2, Rate Law: cyt*STAT5ActEpoR*STAT5*(p12EpoRpJAK2+p1EpoRpJAK2)^2/(init_EpoRJAK2^2*(CISInh*CIS/CISEqc+1)*(SOCS3Inh*SOCS3/SOCS3Eqc+1))

States:

Name Description
p1EpoRpJAK2 [PR:000007142; PR:000009197; phosphorylated]
pSTAT5 [C28668; phosphorylated]
SOCS3nRNA4 [C97975; ribonucleic acid]
SOCS3RNA [; ribonucleic acid]
SHP1 [PR:000013461]
STAT5 [C28668]
CISnRNA1 [Q9NSE2; ribonucleic acid]
SOCS3nRNA2 [C97975; ribonucleic acid]
SOCS3nRNA1 [C97975; ribonucleic acid]
EpoRJAK2 [PR:000007142; PR:000009197]
CISnRNA3 [Q9NSE2; ribonucleic acid]
CISnRNA4 [Q9NSE2; ribonucleic acid]
SOCS3 [C97975]
EpoRJAK2 CIS [PR:000007142; PR:000009197]
SOCS3nRNA5 [C97975; ribonucleic acid]
SOCS3nRNA3 [C97975; ribonucleic acid]
CISnRNA5 [Q9NSE2; ribonucleic acid]
SHP1Act [PR:000013461; Activation]
npSTAT5 [C28668; nucleus; phosphorylated]
p12EpoRpJAK2 [PR:000007142; phosphorylated; PR:000009197; PR:000007142; phosphorylated]
p2EpoRpJAK2 [PR:000009197; PR:000007142; phosphorylated]
CIS [Q9NSE2]
EpoRpJAK2 [PR:000009197; PR:000007142; phosphorylated]
CISnRNA2 [Q9NSE2; ribonucleic acid]
CISRNA [Q9NSE2; ribonucleic acid]

Observables: none

BIOMD0000000347 @ v0.0.1

This model is from the article: Division of labor by dual feedback regulators controls JAK2/STAT5 signaling over broad…

Cellular signal transduction is governed by multiple feedback mechanisms to elicit robust cellular decisions. The specific contributions of individual feedback regulators, however, remain unclear. Based on extensive time-resolved data sets in primary erythroid progenitor cells, we established a dynamic pathway model to dissect the roles of the two transcriptional negative feedback regulators of the suppressor of cytokine signaling (SOCS) family, CIS and SOCS3, in JAK2/STAT5 signaling. Facilitated by the model, we calculated the STAT5 response for experimentally unobservable Epo concentrations and provide a quantitative link between cell survival and the integrated response of STAT5 in the nucleus. Model predictions show that the two feedbacks CIS and SOCS3 are most effective at different ligand concentration ranges due to their distinct inhibitory mechanisms. This divided function of dual feedback regulation enables control of STAT5 responses for Epo concentrations that can vary 1000-fold in vivo. Our modeling approach reveals dose-dependent feedback control as key property to regulate STAT5-mediated survival decisions over a broad range of ligand concentrations. link: http://identifiers.org/pubmed/21772264

Parameters:

Name Description
CISRNATurn = 1000.0 Reaction: CISRNA =>, Rate Law: CISRNATurn*CISRNA*cyt
SOCS3Eqc = 173.653; SOCS3Inh = 10.408; EpoRActJAK2 = 0.267308; EpoRCISInh = 1000000.0 Reaction: EpoRpJAK2 => p2EpoRpJAK2; EpoRJAK2_CIS, SOCS3, Rate Law: 3*EpoRActJAK2*EpoRpJAK2/((SOCS3Inh*SOCS3/SOCS3Eqc+1)*(EpoRCISInh*EpoRJAK2_CIS+1))*cyt
SOCS3Turn = 10000.0 Reaction: SOCS3 =>, Rate Law: SOCS3Turn*SOCS3*cyt
SHP1ActEpoR = 0.001; init_EpoRJAK2 = 3.97622 Reaction: SHP1 => SHP1Act; EpoRpJAK2, p12EpoRpJAK2, p1EpoRpJAK2, p2EpoRpJAK2, Rate Law: SHP1ActEpoR*SHP1*(EpoRpJAK2+p12EpoRpJAK2+p1EpoRpJAK2+p2EpoRpJAK2)/init_EpoRJAK2*cyt
CISTurn = 0.00839842; CISEqc = 432.871; CISRNAEqc = 1.0 Reaction: => CIS; CISRNA, Rate Law: CISEqc*CISTurn*CISRNA/CISRNAEqc*cyt
SOCS3RNADelay = 1.06465 Reaction: SOCS3nRNA1 => SOCS3nRNA2, Rate Law: SOCS3RNADelay*SOCS3nRNA1*nuc
init_EpoRJAK2 = 3.97622; STAT5ActJAK2 = 0.0780965; SOCS3Eqc = 173.653; SOCS3Inh = 10.408 Reaction: STAT5 => pSTAT5; EpoRpJAK2, SOCS3, p12EpoRpJAK2, p1EpoRpJAK2, p2EpoRpJAK2, Rate Law: STAT5ActJAK2*STAT5*(EpoRpJAK2+p12EpoRpJAK2+p1EpoRpJAK2+p2EpoRpJAK2)/(init_EpoRJAK2*(SOCS3Inh*SOCS3/SOCS3Eqc+1))*cyt
CISTurn = 0.00839842; CISEqc = 432.871; CISEqcOE = 0.530261; CISoe = 0.0 Reaction: => CIS, Rate Law: CISoe*CISEqc*CISTurn*CISEqcOE
init_EpoRJAK2 = 3.97622; EpoRCISRemove = 5.42932 Reaction: EpoRJAK2_CIS => ; p12EpoRpJAK2, p1EpoRpJAK2, Rate Law: EpoRCISRemove*EpoRJAK2_CIS*(p12EpoRpJAK2+p1EpoRpJAK2)/init_EpoRJAK2*cyt
SOCS3Eqc = 173.653; SOCS3Inh = 10.408; EpoRActJAK2 = 0.267308 Reaction: EpoRpJAK2 => p1EpoRpJAK2; SOCS3, Rate Law: EpoRActJAK2*EpoRpJAK2/(SOCS3Inh*SOCS3/SOCS3Eqc+1)*cyt
JAK2EpoRDeaSHP1 = 142.722; init_SHP1 = 26.7251 Reaction: EpoRpJAK2 => EpoRJAK2; SHP1Act, Rate Law: JAK2EpoRDeaSHP1*SHP1Act*EpoRpJAK2/init_SHP1*cyt
ActD = 0.0; SOCS3RNATurn = 0.00830844; init_STAT5 = 79.7535; SOCS3RNAEqc = 1.0 Reaction: => SOCS3nRNA1; npSTAT5, Rate Law: -SOCS3RNAEqc*SOCS3RNATurn*npSTAT5*(ActD-1)/init_STAT5*nuc
SHP1Dea = 0.00816391 Reaction: SHP1Act => SHP1, Rate Law: SHP1Dea*SHP1Act*cyt
STAT5Exp = 0.0745155 Reaction: npSTAT5 => STAT5, Rate Law: STAT5Exp*npSTAT5*nuc
CISRNADelay = 0.144775 Reaction: CISnRNA5 => CISRNA, Rate Law: CISRNADelay*CISnRNA5*nuc
STAT5Imp = 0.0268889 Reaction: pSTAT5 => npSTAT5, Rate Law: STAT5Imp*pSTAT5*cyt
SOCS3Eqc = 173.653; SOCS3Inh = 10.408; JAK2ActEpo = 633253.0 Reaction: EpoRJAK2 => EpoRpJAK2; Epo, SOCS3, Rate Law: JAK2ActEpo*Epo*EpoRJAK2/(SOCS3Inh*SOCS3/SOCS3Eqc+1)*cyt
SOCS3RNATurn = 0.00830844 Reaction: SOCS3RNA =>, Rate Law: SOCS3RNATurn*SOCS3RNA*cyt
ActD = 0.0; CISRNAEqc = 1.0; init_STAT5 = 79.7535; CISRNATurn = 1000.0 Reaction: => CISnRNA1; npSTAT5, Rate Law: -CISRNAEqc*CISRNATurn*npSTAT5*(ActD-1)/init_STAT5*nuc
CISTurn = 0.00839842 Reaction: CIS =>, Rate Law: CISTurn*CIS*cyt
SOCS3Eqc = 173.653; SOCS3Turn = 10000.0; SOCS3RNAEqc = 1.0 Reaction: => SOCS3; SOCS3RNA, Rate Law: SOCS3Eqc*SOCS3Turn*SOCS3RNA/SOCS3RNAEqc*cyt
SOCS3EqcOE = 0.679157; SOCS3Eqc = 173.653; SOCS3oe = 0.0; SOCS3Turn = 10000.0 Reaction: => SOCS3, Rate Law: SOCS3oe*SOCS3Eqc*SOCS3Turn*SOCS3EqcOE
init_EpoRJAK2 = 3.97622; SOCS3Eqc = 173.653; CISEqc = 432.871; SOCS3Inh = 10.408; STAT5ActEpoR = 38.9757; CISInh = 7.84653E8 Reaction: STAT5 => pSTAT5; CIS, SOCS3, p12EpoRpJAK2, p1EpoRpJAK2, Rate Law: STAT5ActEpoR*STAT5*(p12EpoRpJAK2+p1EpoRpJAK2)^2/(init_EpoRJAK2^2*(CISInh*CIS/CISEqc+1)*(SOCS3Inh*SOCS3/SOCS3Eqc+1))*cyt

States:

Name Description
p1EpoRpJAK2 [Erythropoietin receptor; Tyrosine-protein kinase JAK2; Phosphoprotein]
pSTAT5 [Signal transducer and activator of transcription 5A; Phosphoprotein]
SOCS3nRNA4 [messenger RNA]
SOCS3RNA [messenger RNA]
SHP1 [Tyrosine-protein phosphatase non-receptor type 6]
SOCS3nRNA2 [messenger RNA]
STAT5 [Signal transducer and activator of transcription 5A]
SOCS3nRNA1 [messenger RNA]
CISnRNA1 [messenger RNA]
EpoRJAK2 [Erythropoietin receptor; Tyrosine-protein kinase JAK2]
CISnRNA3 [messenger RNA]
CISnRNA4 [messenger RNA]
SOCS3 [Suppressor of cytokine signaling 3]
CISnRNA5 [messenger RNA]
SOCS3nRNA5 [messenger RNA]
SOCS3nRNA3 [messenger RNA]
EpoRJAK2 CIS [Erythropoietin receptor; Tyrosine-protein kinase JAK2; Cytokine-inducible SH2-containing protein]
SHP1Act [Tyrosine-protein phosphatase non-receptor type 6]
npSTAT5 [Signal transducer and activator of transcription 5A; Phosphoprotein]
p12EpoRpJAK2 [Erythropoietin receptor; Tyrosine-protein kinase JAK2; Phosphoprotein]
p2EpoRpJAK2 [Erythropoietin receptor; Tyrosine-protein kinase JAK2; Phosphoprotein]
CIS [Cytokine-inducible SH2-containing protein]
EpoRpJAK2 [Erythropoietin receptor; Tyrosine-protein kinase JAK2; Phosphoprotein]
CISnRNA2 [messenger RNA]
CISRNA [messenger RNA]

Observables: none

&lt;notes xmlns=&quot;http://www.sbml.org/sbml/level2/version4&quot;&gt; &lt;body xmlns=&quot;http://www.w3.org/1…

BACKGROUND:Oral administration of drugs is convenient and shows good compliance but it can be affected by many factors in the gastrointestinal (GI) system. Consumption of food is one of the major factors affecting the GI system and consequently the absorption of drugs. The aim of this study was to develop a mechanistic GI absorption model for explaining the effect of food on fenofibrate pharmacokinetics (PK), focusing on the food type and calorie content. METHODS:Clinical data from a fenofibrate PK study involving three different conditions (fasting, standard meals and high-fat meals) were used. The model was developed by nonlinear mixed effect modeling method. Both linear and nonlinear effects were evaluated to explain the impact of food intake on drug absorption. Similarly, to explain changes in gastric emptying time for the drug due to food effects was evaluated. RESULTS:The gastric emptying rate increased by 61.7% during the first 6.94 h after food consumption. Increased calories in the duodenum increased the absorption rate constant of the drug in fed conditions (standard meal = 16.5%, high-fat meal = 21.8%) compared with fasted condition. The final model displayed good prediction power and precision. CONCLUSIONS:A mechanistic GI absorption model for quantitatively evaluating the effects of food on fenofibrate absorption was successfully developed, and acceptable parameters were obtained. The mechanism-based PK model of fenofibrate can quantify the effects of food on drug absorption by food type and calorie content. link: http://identifiers.org/pubmed/29370865

Parameters: none

States: none

Observables: none

The circadian rhythms influence the metabolic activity from molecular level to tissue, organ, and host level. Disruption…

The circadian rhythms influence the metabolic activity from molecular level to tissue, organ, and host level. Disruption of the circadian rhythms manifests to the host's health as metabolic syndromes, including obesity, diabetes, and elevated plasma glucose, eventually leading to cardiovascular diseases. Therefore, it is imperative to understand the mechanism behind the relationship between circadian rhythms and metabolism. To start answering this question, we propose a semimechanistic mathematical model to study the effect of circadian disruption on hepatic gluconeogenesis in humans. Our model takes the light-dark cycle and feeding-fasting cycle as two environmental inputs that entrain the metabolic activity in the liver. The model was validated by comparison with data from mice and rat experimental studies. Formal sensitivity and uncertainty analyses were conducted to elaborate on the driving forces for hepatic gluconeogenesis. Furthermore, simulating the impact of Clock gene knockout suggests that modification to the local pathways tied most closely to the feeding-fasting rhythms may be the most efficient way to restore the disrupted glucose metabolism in liver. link: http://identifiers.org/pubmed/29351477

Parameters: none

States: none

Observables: none

MODEL1006230056 @ v0.0.1

This a model from the article: Bistability in apoptosis: roles of bax, bcl-2, and mitochondrial permeability transitio…

We propose a mathematical model for mitochondria-dependent apoptosis, in which kinetic cooperativity in formation of the apoptosome is a key element ensuring bistability. We examine the role of Bax and Bcl-2 synthesis and degradation rates, as well as the number of mitochondrial permeability transition pores (MPTPs), on the cell response to apoptotic stimuli. Our analysis suggests that cooperative apoptosome formation is a mechanism for inducing bistability, much more robust than that induced by other mechanisms, such as inhibition of caspase-3 by the inhibitor of apoptosis (IAP). Simulations predict a pathological state in which cells will exhibit a monostable cell survival if Bax degradation rate is above a threshold value, or if Bax expression rate is below a threshold value. Otherwise, cell death or survival occur depending on initial caspase-3 levels. We show that high expression rates of Bcl-2 can counteract the effects of Bax. Our simulations also demonstrate a monostable (pathological) apoptotic response if the number of MPTPs exceeds a threshold value. This study supports our contention, based on mathematical modeling, that cooperativity in apoptosome formation is critically important for determining the healthy responses to apoptotic stimuli, and helps define the roles of Bax, Bcl-2, and MPTP vis-à-vis apoptosome formation. link: http://identifiers.org/pubmed/16339882

Parameters: none

States: none

Observables: none

MODEL1006230064 @ v0.0.1

This a model from the article: Computational insights on the competing effects of nitric oxide in regulating apoptosis…

Despite the establishment of the important role of nitric oxide (NO) on apoptosis, a molecular-level understanding of the origin of its dichotomous pro- and anti-apoptotic effects has been elusive. We propose a new mathematical model for simulating the effects of nitric oxide (NO) on apoptosis. The new model integrates mitochondria-dependent apoptotic pathways with NO-related reactions, to gain insights into the regulatory effect of the reactive NO species N(2)O(3), non-heme iron nitrosyl species (FeL(n)NO), and peroxynitrite (ONOO(-)). The biochemical pathways of apoptosis coupled with NO-related reactions are described by ordinary differential equations using mass-action kinetics. In the absence of NO, the model predicts either cell survival or apoptosis (a bistable behavior) with shifts in the onset time of apoptotic response depending on the strength of extracellular stimuli. Computations demonstrate that the relative concentrations of anti- and pro-apoptotic reactive NO species, and their interplay with glutathione, determine the net anti- or pro-apoptotic effects at long time points. Interestingly, transient effects on apoptosis are also observed in these simulations, the duration of which may reach up to hours, despite the eventual convergence to an anti-apoptotic state. Our computations point to the importance of precise timing of NO production and external stimulation in determining the eventual pro- or anti-apoptotic role of NO. link: http://identifiers.org/pubmed/18509469

Parameters: none

States: none

Observables: none

MODEL1006230026 @ v0.0.1

This a model from the article: Computational insights on the competing effects of nitric oxide in regulating apoptosis…

Despite the establishment of the important role of nitric oxide (NO) on apoptosis, a molecular-level understanding of the origin of its dichotomous pro- and anti-apoptotic effects has been elusive. We propose a new mathematical model for simulating the effects of nitric oxide (NO) on apoptosis. The new model integrates mitochondria-dependent apoptotic pathways with NO-related reactions, to gain insights into the regulatory effect of the reactive NO species N(2)O(3), non-heme iron nitrosyl species (FeL(n)NO), and peroxynitrite (ONOO(-)). The biochemical pathways of apoptosis coupled with NO-related reactions are described by ordinary differential equations using mass-action kinetics. In the absence of NO, the model predicts either cell survival or apoptosis (a bistable behavior) with shifts in the onset time of apoptotic response depending on the strength of extracellular stimuli. Computations demonstrate that the relative concentrations of anti- and pro-apoptotic reactive NO species, and their interplay with glutathione, determine the net anti- or pro-apoptotic effects at long time points. Interestingly, transient effects on apoptosis are also observed in these simulations, the duration of which may reach up to hours, despite the eventual convergence to an anti-apoptotic state. Our computations point to the importance of precise timing of NO production and external stimulation in determining the eventual pro- or anti-apoptotic role of NO. link: http://identifiers.org/pubmed/18509469

Parameters: none

States: none

Observables: none

BIOMD0000000242 @ v0.0.1

This a model from the article: Theoretical and experimental evidence for hysteresis in cell proliferation. Bai S, Go…

We propose a mathematical model for the regulation of the G1-phase of the mammalian cell cycle taking into account interactions of cyclin D/cdk4, cyclin E/cdk2, Rb and E2F. Mathematical analysis of this model predicts that a change in the proliferative status in response to a change in concentrations of serum growth factors will exhibit the property of hysteresis: the concentration of growth factors required to induce proliferation is higher than the concentration required to maintain proliferation. We experimentally confirmed this prediction in mouse embryonic fibroblasts in vitro. In agreement with the mathematical model, this indicates that changes in proliferative mode caused by small changes in concentrations of growth factors are not easily reversible. Based on this study, we discuss the importance of proliferation hysteresis for cell cycle regulation. link: http://identifiers.org/pubmed/12695688

Parameters:

Name Description
qD_1 = 0.6; pD_1 = 0.48 Reaction: RS_1 => theta_1; RS_1, D_1, Rate Law: cell*pD_1*RS_1*D_1/(qD_1+RS_1+D_1)
f_1 = 0.35; g_1 = 0.528; aX_1 = 0.08 Reaction: => X_1; E_1, theta_1, X_1, Rate Law: cell*(aX_1*E_1+f_1*theta_1+g_1*X_1^2*E_1)
dX_1 = 1.04 Reaction: X_1 => ; X_1, Rate Law: cell*dX_1*X_1
GF_1 = 6.3; k2_1 = 1000.0; aE_1 = 0.16; aF_1 = 0.9 Reaction: => E_1; theta_1, Rate Law: cell*aE_1*(GF_1/(k2_1^(-1)+GF_1)+aF_1*theta_1)
qE_1 = 0.6; pE_1 = 0.096 Reaction: RS_1 => theta_1; RS_1, E_1, Rate Law: cell*pE_1*RS_1*E_1/(qE_1+RS_1+E_1)
aD_1 = 0.4; GF_1 = 6.3; k1_1 = 0.05 Reaction: => D_1, Rate Law: cell*aD_1*GF_1/(k1_1^(-1)+GF_1)
dtheta_1 = 0.12; qtheta_1 = 0.3 Reaction: theta_1 => ; theta_1, X_1, Rate Law: cell*dtheta_1*X_1/(qtheta_1+X_1)*theta_1
dE_1 = 0.2 Reaction: E_1 => ; X_1, E_1, Rate Law: cell*dE_1*X_1*E_1
qX_1 = 0.8; RT_1 = 2.5; pX_1 = 0.48 Reaction: => R_1; RS_1, R_1, X_1, Rate Law: cell*pX_1*((RT_1-RS_1)-R_1)*X_1/((((qX_1+RT_1)-RS_1)-R_1)+X_1)
GF_1 = 6.3; k3_1 = 1.5; atheta_1 = 0.05; fC_1_1 = 0.003 Reaction: => theta_1; theta_1, Rate Law: cell*atheta_1*(GF_1/(k3_1^(-1)+GF_1)+fC_1_1*theta_1)
dD_1 = 0.4 Reaction: D_1 => ; D_1, E_1, Rate Law: cell*dD_1*E_1*D_1
pS_1 = 0.6 Reaction: R_1 + theta_1 => RS_1, Rate Law: cell*pS_1*R_1*theta_1

States:

Name Description
theta 1 [Transcription factor E2F1]
R 1 [Retinoblastoma-associated protein]
X 1 X
D 1 [Cyclin dependent kinase 4; G1/S-specific cyclin-D1]
E 1 [Cyclin-dependent kinase 2; G1/S-specific cyclin-E1]
RS 1 [Transcription factor E2F1]

Observables: none

This model describes the interactions between tumor cells and virus particles, with particular reference to virus-induce…

The Edmonston vaccine strain of measles virus has potent and selective activity against a wide range of tumors. Tumor cells infected by this virus or genetically modified strains express viral proteins that allow them to fuse with neighboring cells to form syncytia that ultimately die. Moreover, infected cells may produce new virus particles that proceed to infect additional tumor cells. We present a model of tumor and virus interactions based on established biology and with proper accounting of the free virus population. The range of model parameters is estimated by fitting to available experimental data. The stability of equilibrium states corresponding to complete tumor eradication, therapy failure and partial tumor reduction is discussed. We use numerical simulations to explore conditions for which the model predicts successful therapy and tumor eradication. The model exhibits damped, as well as stable oscillations in a range of parameter values. These oscillatory states are organized by a Hopf bifurcation. link: http://identifiers.org/pubmed/18316099

Parameters:

Name Description
r = 0.2062134; epsilon = 1.648773; K = 2139.258 Reaction: => y_Tumor_Cell; y_Tumor_Cell, x_Infected_Cell, Rate Law: compartment*r*y_Tumor_Cell*(1-(y_Tumor_Cell+x_Infected_Cell)^epsilon/K^epsilon)
rho = 0.608 Reaction: y_Tumor_Cell => ; x_Infected_Cell, Rate Law: compartment*rho*x_Infected_Cell*y_Tumor_Cell
kappa = 4.48E-4 Reaction: y_Tumor_Cell + v_Virus => x_Infected_Cell, Rate Law: compartment*kappa*y_Tumor_Cell*v_Virus
omega = 0.3 Reaction: v_Virus =>, Rate Law: compartment*omega*v_Virus
delta = 0.309 Reaction: x_Infected_Cell =>, Rate Law: compartment*delta*x_Infected_Cell
alpha = 0.001 Reaction: => v_Virus; x_Infected_Cell, Rate Law: compartment*alpha*x_Infected_Cell

States:

Name Description
v Virus [Oncolytic Virus]
y Tumor Cell [neoplastic cell]
x Infected Cell [infected cell]

Observables: none

Baker2013 - Cytokine Mediated Inflammation in Rheumatoid ArthritisThis model by Baker M. 2013, describes the interaction…

Rheumatoid arthritis (RA) is a chronic inflammatory disease preferentially affecting the joints and leading, if untreated, to progressive joint damage and disability. Cytokines, a group of small inducible proteins, which act as intercellular messengers, are key regulators of the inflammation that characterizes RA. They can be classified into pro-inflammatory and anti-inflammatory groups. Numerous cytokines have been implicated in the regulation of RA with complex up and down regulatory interactions. This paper considers a two-variable model for the interactions between pro-inflammatory and anti-inflammatory cytokines, and demonstrates that mathematical modelling may be used to investigate the involvement of cytokines in the disease process. The model displays a range of possible behaviours, such as bistability and oscillations, which are strongly reminiscent of the behaviour of RA e.g. genetic susceptibility and remitting-relapsing disease. We also show that the dose regimen as well as the dose level are important factors in RA treatments. link: http://identifiers.org/pubmed/23002057

Parameters:

Name Description
parameter_5 = 1.25; parameter_2 = 1.0; parameter_1 = 0.025 Reaction: species_2 = (-parameter_5)*species_2+1/(1+species_1^2)*(parameter_1+parameter_2*species_2^2/(1+species_2^2)), Rate Law: (-parameter_5)*species_2+1/(1+species_1^2)*(parameter_1+parameter_2*species_2^2/(1+species_2^2))
parameter_4 = 3.5; parameter_3 = 0.5 Reaction: species_1 = (-species_1)+parameter_4*species_2^2/(parameter_3^2+species_2^2), Rate Law: (-species_1)+parameter_4*species_2^2/(parameter_3^2+species_2^2)

States:

Name Description
species 2 [Cytokine; inflammatory]
species 1 [anti-inflammatory agent]

Observables: none

Baker2013 - Cytokine Mediated Inflammation in Rheumatoid Arthritis - Age DependantThis model by Baker M. 2013, describes…

Rheumatoid arthritis (RA) is a chronic inflammatory disease preferentially affecting the joints and leading, if untreated, to progressive joint damage and disability. Cytokines, a group of small inducible proteins, which act as intercellular messengers, are key regulators of the inflammation that characterizes RA. They can be classified into pro-inflammatory and anti-inflammatory groups. Numerous cytokines have been implicated in the regulation of RA with complex up and down regulatory interactions. This paper considers a two-variable model for the interactions between pro-inflammatory and anti-inflammatory cytokines, and demonstrates that mathematical modelling may be used to investigate the involvement of cytokines in the disease process. The model displays a range of possible behaviours, such as bistability and oscillations, which are strongly reminiscent of the behaviour of RA e.g. genetic susceptibility and remitting-relapsing disease. We also show that the dose regimen as well as the dose level are important factors in RA treatments. link: http://identifiers.org/pubmed/23002057

Parameters:

Name Description
parameter_5 = 1.25; parameter_2 = 1.0; parameter_1 = 0.025 Reaction: species_2 = (-parameter_5)*species_2+1/(1+species_1^2)*(parameter_1+parameter_2*species_2^2/(1+species_2^2)), Rate Law: (-parameter_5)*species_2+1/(1+species_1^2)*(parameter_1+parameter_2*species_2^2/(1+species_2^2))
parameter_4 = 7.0; parameter_3 = 0.5 Reaction: species_1 = (-species_1)+parameter_4*species_2^2/(parameter_3^2+species_2^2), Rate Law: (-species_1)+parameter_4*species_2^2/(parameter_3^2+species_2^2)

States:

Name Description
species 2 [Cytokine; inflammatory]
species 1 [anti-inflammatory agent]

Observables: none

Baker2017 - The role of cytokines, MMPs and fibronectin fragments osteoarthritisThis model is described in the article:…

Osteoarthritis (OA) is a degenerative disease which causes pain and stiffness in joints. OA progresses through excessive degradation of joint cartilage, eventually leading to significant joint degeneration and loss of function. Cytokines, a group of cell signalling proteins, present in raised concentrations in OA joints, can be classified into pro-inflammatory and anti-inflammatory groups. They mediate cartilage degradation through several mechanisms, primarily the up-regulation of matrix metalloproteinases (MMPs), a group of collagen-degrading enzymes. In this paper we show that the interactions of cytokines within cartilage have a crucial role to play in OA progression and treatment. We develop a four-variable ordinary differential equation model for the interactions between pro- and anti-inflammatory cytokines, MMPs and fibronectin fragments (Fn-fs), a by-product of cartilage degradation and up-regulator of cytokines. We show that the model has four classes of dynamic behaviour: homoeostasis, bistable inflammation, tristable inflammation and persistent inflammation. We show that positive and negative feedbacks controlling cytokine production rates can determine either a pre-disposition to OA or initiation of OA. Further, we show that manipulation of cytokine, MMP and Fn-fs levels can be used to treat OA, but we suggest that multiple treatment targets may be essential to halt or slow disease progression. link: http://identifiers.org/pubmed/28213682

Parameters:

Name Description
Gamma_p = 1.0 Reaction: solution0 =>, Rate Law: compartmentOne*Gamma_p*solution0/compartmentOne
Gamma_m = 1.0 Reaction: solution2 =>, Rate Law: compartmentOne*Gamma_m*solution2/compartmentOne
Gamma_f = 1.0 Reaction: solution3 =>, Rate Law: compartmentOne*Gamma_f*solution3/compartmentOne
Pbp = 0.01 Reaction: solution1 => solution0 + solution1, Rate Law: compartmentOne*Pbp/(1+solution1^2)/compartmentOne
Ppp = 10.0 Reaction: solution0 + solution1 => solution0 + solution1, Rate Law: compartmentOne*1/(1+solution1^2)*Ppp/(1+solution0^2)*solution0^2/compartmentOne
Mbp = 0.01 Reaction: => solution2, Rate Law: compartmentOne*Mbp/compartmentOne
App = 10.0; Aph = 1.0 Reaction: solution0 => solution0 + solution1, Rate Law: compartmentOne*App*1/(Aph^2+solution0^2)*solution0^2/compartmentOne
Pfp = 10.0 Reaction: solution1 + solution3 => solution0 + solution1 + solution3, Rate Law: compartmentOne*1/(1+solution1^2)*Pfp/(1+solution3^2)*solution3^2/compartmentOne
Mph = 1.0; Mpp = 10.0 Reaction: solution0 => solution0 + solution2, Rate Law: compartmentOne*Mpp*1/(Mph^2+solution0^2)*solution0^2/compartmentOne
Afh = 1.0; Afp = 10.0 Reaction: solution3 => solution1 + solution3, Rate Law: compartmentOne*Afp*1/(Afh^2+solution3^2)*solution3^2/compartmentOne
Fdam = 0.0 Reaction: => solution3, Rate Law: compartmentOne*Fdam/compartmentOne

States:

Name Description
solution3 [D-threo-Aldono-1,5-lactone]
solution0 [Cytokine; Inflammation]
solution1 [Cytokine; Anti-inflammatory]
solution2 [Matrix Metalloproteinase]

Observables: none

Baker2017 - The role of cytokines, MMPs and fibronectin fragments osteoarthritisThis model is described in the article:…

Plants depend on the signalling of the phytohormone auxin for their development and for responding to environmental perturbations. The associated biomolecular signalling network involves a negative feedback on Aux/IAA proteins which mediate the influence of auxin (the signal) on the auxin response factor (ARF) transcription factors (the drivers of the response). To probe the role of this feedback, we consider alternative in silico signalling networks implementing different operating principles. By a comparative analysis, we find that the presence of a negative feedback allows the system to have a far larger sensitivity in its dynamical response to auxin and that this sensitivity does not prevent the system from being highly resilient. Given this insight, we build a new biomolecular signalling model for quantitatively describing such Aux/IAA and ARF responses. link: http://identifiers.org/pubmed/29410878

Parameters:

Name Description
lm = 0.9 Reaction: auxTIR1IAA => auxTIR1 + IAAstar, Rate Law: auxTIR1IAA*lm
muIAA = 0.003 Reaction: IAAp => null, Rate Law: IAAp*muIAA
delta = 4.0 Reaction: IAAm => IAAm + IAAp, Rate Law: delta*IAAm
muIAAstar = 0.1 Reaction: IAAstar => null, Rate Law: IAAstar*muIAAstar
la = 5.75 Reaction: auxTIR1 + IAAp => auxTIR1IAA, Rate Law: auxTIR1*IAAp*la
ka = 8.2E-4 Reaction: aux + TIR1 => auxTIR1, Rate Law: aux*ka*TIR1
muaux = 0.1 Reaction: aux => null, Rate Law: aux*muaux
qd = 0.44 Reaction: ARF2 => ARF, Rate Law: ARF2*qd
muIAAm = 0.003 Reaction: IAAm => null, Rate Law: IAAm*muIAAm
thetaARFIAA = 100.0; thetaARF = 100.0; lambda1 = 0.48; thetaARF2 = 100.0 Reaction: null => IAAm; ARF, ARF2, ARFIAA, Rate Law: ARF*lambda1*(thetaARF*(ARF*thetaARF^-1+ARF2*thetaARF2^-1+ARFIAA*thetaARFIAA^-1+1))^-1
pa = 1.0 Reaction: ARF + IAAp => ARFIAA, Rate Law: ARF*IAAp*pa
Sauxin = 0.02 Reaction: null => aux, Rate Law: Sauxin
kd = 0.33 Reaction: auxTIR1 => aux + TIR1, Rate Law: auxTIR1*kd
pd = 0.072 Reaction: ARFIAA => ARF + IAAp, Rate Law: ARFIAA*pd
ld = 0.045 Reaction: auxTIR1IAA => auxTIR1 + IAAp, Rate Law: auxTIR1IAA*ld
qa = 0.5 Reaction: ARF => ARF2, Rate Law: ARF^2*qa

States:

Name Description
ARF ARF
TIR1 TIR1
ARFIAA ARFIAA
aux aux
auxTIR1 auxTIR1
ARF2 ARF2
IAAm IAAm
IAAp IAAp
auxTIR1IAA auxTIR1IAA
IAAstar IAAstar

Observables: none

MODEL1101100000 @ v0.0.1

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/…

In trypanosomes the first part of glycolysis takes place in specialized microbodies, the glycosomes. Most glycolytic enzymes of Trypanosoma brucei have been purified and characterized kinetically. In this paper a mathematical model of glycolysis in the bloodstream form of this organism is developed on the basis of all available kinetic data. The fluxes and the cytosolic metabolite concentrations as predicted by the model were in accordance with available data as measured in non-growing trypanosomes, both under aerobic and under anaerobic conditions. The model also reproduced the inhibition of anaerobic glycolysis by glycerol, although the amount of glycerol needed to inhibit glycolysis completely was lower than experimentally determined. At low extracellular glucose concentrations the intracellular glucose concentration remained very low, and only at 5 mM of extracellular glucose, free glucose started to accumulate intracellularly, in close agreement with experimental observations. This biphasic relation could be related to the large difference between the affinities of the glucose transporter and hexokinase for intracellular glucose. The calculated intraglycosomal metabolite concentrations demonstrated that enzymes that have been shown to be near-equilibrium in the cytosol must work far from equilibrium in the glycosome in order to maintain the high glycolytic flux in the latter. link: http://identifiers.org/pubmed/9013556

Parameters: none

States: none

Observables: none

BIOMD0000000071 @ v0.0.1

. . . **[SBML](http://www.sbml.org/) level 2 code generated for the JWS Online project by Jacky Snoep using [PySCeS]…

Kinetoplastid protozoa compartmentalize the first seven enzymes of glycolysis and two enzymes of glycerol metabolism in a microbody, the glycosome. While in its mammalian host, Trypanosoma brucei depends entirely on glucose for ATP generation. Under aerobic conditions, most of the glucose is metabolized to pyruvate. Aerobic metabolism depends on the activities of glycosomal triosephosphate isomerase and a mitochondrial glycerophosphate oxidase, and on glycerophosphate<–>dihydroxyacetone phosphate exchange across the glycosomal membrane. Using a combination of genetics and computer modelling, we show that triosephosphate isomerase is probably essential for bloodstream trypanosome survival, but not for the insect-dwelling procyclics, which preferentially use amino acids as an energy source. When the enzyme level decreased to about 15% of that of the wild-type, the growth rate was halved. Below this level, a lethal rise in dihydroxyacetone phosphate was predicted. Expression of cytosolic triosephosphate isomerase inhibited cell growth. Attempts to knockout the trypanosome alternative oxidase genes (which are needed for glycerophosphate oxidase activity) were unsuccessful, but when we lowered the level of the corresponding mRNA by expressing a homologous double-stranded RNA, oxygen consumption was reduced fourfold and the rate of trypanosome growth was halved. link: http://identifiers.org/pubmed/11415442

Parameters:

Name Description
KeqAK = 0.442 dimensionless; sumAg = 6.0 mM Reaction: ATPg = ((Pg*(1-4*KeqAK)-sumAg)+((sumAg-(1-4*KeqAK)*Pg)^2+4*(1-4*KeqAK)*KeqAK*Pg^2)^0.5)/(2*(1-4*KeqAK)), Rate Law: missing
afac=0.75 dimensionless; K1Glc=2.0 mM; Vm1=106.2 nanomole_per_min_per_mg; Vt = 5.7 microlitre_per_mg Reaction: GlcE => GlcI, Rate Law: tot_cell/Vt*Vm1*(GlcE-GlcI)/(K1Glc+GlcE+GlcI+afac*GlcE*GlcI/K1Glc)
Vm14f=200.0 nanomole_per_min_per_mg; Vm14r=33400.0 nanomole_per_min_per_mg; K14Gly3Pg=5.1 mM; Vm14=1.0 dimensionless; K14ADPg=0.12 mM; K14Gly=0.12 mM; Vt = 5.7 microlitre_per_mg; K14ATPg=0.19 mM Reaction: Gly3P => Pg + Gly; ADPg, Gly3Pg, ATPg, Rate Law: tot_cell/Vt*Vm14*(Vm14f*ADPg*Gly3Pg/(K14ADPg*K14Gly3Pg)-Gly*Vm14r*ATPg/(K14ATPg*K14Gly))/((1+Gly/K14Gly+Gly3Pg/K14Gly3Pg)*(1+ATPg/K14ATPg+ADPg/K14ADPg))
Vm8=1.0 dimensionless; Vm8f=533.0 nanomole_per_min_per_mg; K8Gly3Pg=2.0 mM; Vm8r=149.24 nanomole_per_min_per_mg; K8DHAPg=0.1 mM; K8NADH=0.01 mM; K8NAD=0.4 mM; Vt = 5.7 microlitre_per_mg Reaction: DHAP + NADH => NAD + Gly3P; DHAPg, Gly3Pg, Rate Law: tot_cell/Vt*Vm8*Vm8f*(NADH*DHAPg/(K8DHAPg*K8NADH)-Vm8r*NAD*Gly3Pg/(K8Gly3Pg*K8NAD*Vm8f))/((1+NAD/K8NAD+NADH/K8NADH)*(1+DHAPg/K8DHAPg+Gly3Pg/K8Gly3Pg))
Vm6=842.0 nanomole_per_min_per_mg; K6GAP=0.25 mM; K6DHAPg=1.2 mM; TPIact = 1.0 dimensionless; Vt = 5.7 microlitre_per_mg Reaction: DHAP => GAP; DHAPg, Rate Law: tot_cell/Vt*TPIact*Vm6*(DHAPg/K6DHAPg-5.7*GAP/K6GAP)/(1+GAP/K6GAP+DHAPg/K6DHAPg)
Vm11r=18.56 nanomole_per_min_per_mg; K11BPGA13=0.05 dimensionless; Vm11f=640.0 nanomole_per_min_per_mg; K11ADPg=0.1 dimensionless; Vm11=1.0 dimensionless; Vt = 5.7 microlitre_per_mg; K11ATPg=0.29 mM; K11PGA3=1.62 mM Reaction: BPGA13 => Nb + Pg; ADPg, ATPg, PGAg, Rate Law: tot_cell/Vt*Vm11*Vm11f*((-Vm11r)*PGAg*ATPg/(K11ATPg*K11PGA3*Vm11f)+BPGA13*ADPg/(K11ADPg*K11BPGA13))/((1+BPGA13/K11BPGA13+PGAg/K11PGA3)*(1+ATPg/K11ATPg+ADPg/K11ADPg))
K4ATPg=0.026 mM; K4i1Fru16BP=15.8 mM; Vm4=780.0 nanomole_per_min_per_mg; K4i2Fru16BP=10.7 mM; Vt = 5.7 microlitre_per_mg; K4Fru6P=0.82 mM Reaction: Pg + Fru6P => Fru16BP; ATPg, Rate Law: tot_cell/Vt*K4i1Fru16BP*Vm4*Fru6P*ATPg/(K4ATPg*K4Fru6P*(K4i1Fru16BP+Fru16BP)*(1+Fru16BP/K4i2Fru16BP+Fru6P/K4Fru6P)*(1+ATPg/K4ATPg))
KeqAK = 0.442 dimensionless; sumAc = 3.9 mM Reaction: ATPc = ((Pc*(1-4*KeqAK)-sumAc)+((sumAc-(1-4*KeqAK)*Pc)^2+4*(1-4*KeqAK)*KeqAK*Pc^2)^0.5)/(2*(1-4*KeqAK)), Rate Law: missing
Vt = 5.7 microlitre_per_mg; K9Gly3Pc=1.7 mM; Vm9=368.0 nanomole_per_min_per_mg Reaction: Gly3P => DHAP; Gly3Pc, Rate Law: tot_cell/Vt*Vm9*Gly3Pc/(K9Gly3Pc*1+Gly3Pc)
sumc4 = 45.0 mM; Vg = NaN microlitre_per_mg; Vc = NaN microlitre_per_mg; sumc5 = 5.0 mM Reaction: DHAPc = sumc5*(1+Vc/Vg)*DHAP/((sumc4+sumc5*Vc/Vg)-(BPGA13+2*Fru16BP+Fru6P+GAP+Glc6P+Pg)), Rate Law: missing
K2GlcI=0.1 mM; Vm2=625.0 nanomole_per_min_per_mg; K2ADPg=0.126 mM; Vt = 5.7 microlitre_per_mg; K2Glc6P=12.0 mM; K2ATPg=0.116 mM Reaction: Pg + GlcI => Glc6P; ATPg, ADPg, Rate Law: tot_cell/Vt*Vm2*GlcI*ATPg/(K2ATPg*K2GlcI*(1+Glc6P/K2Glc6P+GlcI/K2GlcI)*(1+ATPg/K2ATPg+ADPg/K2ADPg))
Vm10=200.0 nanomole_per_min_per_mg; K10Pyr=1.96 mM; Vt = 5.7 microlitre_per_mg Reaction: Pyr => PyrE, Rate Law: tot_cell/Vt*Vm10*Pyr/K10Pyr/(1+Pyr/K10Pyr)
Keq_anti = 1.0 dimensionless Reaction: Gly3Pg = Gly3Pc*DHAPg/(Keq_anti*DHAPc), Rate Law: missing
K3Fru6P=0.12 mM; Vm3=848.0 nanomole_per_min_per_mg; K3Glc6P=0.4 mM; Vt = 5.7 microlitre_per_mg Reaction: Glc6P => Fru6P, Rate Law: tot_cell/Vt*Vm3*(Glc6P/K3Glc6P-Fru6P/K3Fru6P)/(1+Glc6P/K3Glc6P+Fru6P/K3Fru6P)
K12ADP=0.114 mM; n12=2.5 dimensionless; Vt = 5.7 microlitre_per_mg; Vm12=2600.0 nanomole_per_min_per_mg Reaction: Nb => Pc + Pyr; PEPc, ADPc, ATPc, Rate Law: tot_cell/Vt*Vm12*(PEPc/(0.34*(1+ADPc/0.57+ATPc/0.64)))^n12*ADPc/K12ADP/((1+(PEPc/(0.34*(1+ADPc/0.57+ATPc/0.64)))^n12)*(1+ADPc/K12ADP))
Vg = NaN microlitre_per_mg; Vc = NaN microlitre_per_mg; Vt = 5.7 microlitre_per_mg Reaction: DHAPg = (DHAP*Vt-DHAPc*Vc)/Vg, Rate Law: missing
Vm5r=219.555 nanomole_per_min_per_mg; Vm5f=184.5 nanomole_per_min_per_mg; K5GAPi=0.098 mM; K5GAP=0.067 mM; Vt = 5.7 microlitre_per_mg; sumAg = 6.0 mM; K5DHAP=0.015 mM Reaction: Fru16BP => GAP + DHAP; DHAPg, ATPg, ADPg, Rate Law: tot_cell/Vt*(Vm5f*Fru16BP/(0.009*(1+ATPg/0.68+ADPg/1.51+(sumAg-(ATPg+ADPg))/3.65))-Vm5r*GAP*DHAPg/(K5DHAP*K5GAP))/(1+GAP/K5GAP+DHAPg/K5DHAP+GAP*DHAPg/(K5DHAP*K5GAP)+Fru16BP/(0.009*(1+ATPg/0.68+ADPg/1.51+(sumAg-(ATPg+ADPg))/3.65))+Fru16BP*GAP/(K5GAPi*0.009*(1+ATPg/0.68+ADPg/1.51+(sumAg-(ATPg+ADPg))/3.65)))
sumc5 = 5.0 mM Reaction: Gly3Pc = sumc5-DHAPc, Rate Law: missing
Vt = 5.7 microlitre_per_mg; K13=50.0 nanomole_per_min_per_mg Reaction: Pc => ; ATPc, ADPc, Rate Law: tot_cell/Vt*K13*ATPc/ADPc
Keq_ENO = 6.7 dimensionless; Keq_PGM = 0.187 dimensionless Reaction: PEPc = Keq_ENO*Keq_PGM*PGAg, Rate Law: missing
Keq_ENO = 6.7 dimensionless; Vg = NaN microlitre_per_mg; Vc = NaN microlitre_per_mg; Keq_PGM = 0.187 dimensionless Reaction: PGAg = Nb*(1+Vc/Vg)/(1+(1+Keq_PGM+Keq_PGM*Keq_ENO)*Vc/Vg), Rate Law: missing
Vm7f=1470.0 nanomole_per_min_per_mg; K7BPGA13=0.1 mM; K7NADH=0.02 mM; Vm7=1.0 dimensionless; K7GAP=0.15 mM; Vt = 5.7 microlitre_per_mg; Vm7r=984.9 nanomole_per_min_per_mg; K7NAD=0.45 mM Reaction: GAP + NAD => NADH + BPGA13, Rate Law: tot_cell/Vt*Vm7*Vm7f*(GAP*NAD/K7GAP/K7NAD-Vm7r/Vm7f*BPGA13*NADH/K7BPGA13/K7NADH)/((1+GAP/K7GAP+BPGA13/K7BPGA13)*(1+NAD/K7NAD+NADH/K7NADH))

States:

Name Description
ATPc [ATP; ATP]
Nb [3-Phospho-D-glycerate; 2-Phospho-D-glycerate; Phosphoenolpyruvate; phosphoenolpyruvate; 3-phospho-D-glyceric acid; 2-phospho-D-glyceric acid]
Glc6P [alpha-D-glucose 6-phosphate; alpha-D-Glucose 6-phosphate]
Fru16BP [beta-D-fructofuranose 1,6-bisphosphate; beta-D-Fructose 1,6-bisphosphate]
DHAPg [dihydroxyacetone phosphate; Glycerone phosphate]
BPGA13 [3-phospho-D-glyceroyl dihydrogen phosphate; 3-Phospho-D-glyceroyl phosphate]
DHAP [dihydroxyacetone phosphate; Glycerone phosphate]
Gly3Pc [sn-glycerol 3-phosphate; sn-Glycerol 3-phosphate]
Pc [phosphate ion]
GlcE [glucose; C00293]
PyrE [pyruvic acid; Pyruvate]
GlcI [glucose; C00293]
NADH [NADH; NADH]
Gly [glycerol; Glycerol]
PGAg [phosphoenolpyruvate; Phosphoenolpyruvate]
DHAPc [dihydroxyacetone phosphate; Glycerone phosphate]
ADPc [ADP; ADP]
Pyr [pyruvate; Pyruvate]
ADPg [ADP; ADP]
GAP [D-glyceraldehyde 3-phosphate; D-Glyceraldehyde 3-phosphate]
Gly3Pg [sn-glycerol 3-phosphate; sn-Glycerol 3-phosphate]
PEPc [phosphoenolpyruvate; Phosphoenolpyruvate]
ATPg [ATP; ATP]
Gly3P [sn-glycerol 3-phosphate; sn-Glycerol 3-phosphate]
Pg [phosphate ion]
NAD [NAD(+); NAD+]
Fru6P [beta-D-fructofuranose 6-phosphate; beta-D-Fructose 6-phosphate]

Observables: none

This model is based on the publication: "Mathematical Modelling of Alternative Pathway of Complement System". Suruchi Ba…

The complement system (CS) is an integral part of innate immunity and can be activated via three different pathways. The alternative pathway (AP) has a central role in the function of the CS. The AP of complement system is implicated in several human disease pathologies. In the absence of triggers, the AP exists in a time-invariant resting state (physiological steady state). It is capable of rapid, potent and transient activation response upon challenge with a trigger. Previous models of AP have focused on the activation response. In order to understand the molecular machinery necessary for AP activation and regulation of a physiological steady state, we built parsimonious AP models using experimentally supported kinetic parameters. The models further allowed us to test quantitative roles played by negative and positive regulators of the pathway in order to test hypotheses regarding their mechanisms of action, thus providing more insight into the complex regulation of AP. link: http://identifiers.org/pubmed/32062771

Parameters: none

States: none

Observables: none

This model is based on the publication: "Mathematical Modelling of Alternative Pathway of Complement System". Suruchi Ba…

The complement system (CS) is an integral part of innate immunity and can be activated via three different pathways. The alternative pathway (AP) has a central role in the function of the CS. The AP of complement system is implicated in several human disease pathologies. In the absence of triggers, the AP exists in a time-invariant resting state (physiological steady state). It is capable of rapid, potent and transient activation response upon challenge with a trigger. Previous models of AP have focused on the activation response. In order to understand the molecular machinery necessary for AP activation and regulation of a physiological steady state, we built parsimonious AP models using experimentally supported kinetic parameters. The models further allowed us to test quantitative roles played by negative and positive regulators of the pathway in order to test hypotheses regarding their mechanisms of action, thus providing more insight into the complex regulation of AP. link: http://identifiers.org/pubmed/32062771

Parameters: none

States: none

Observables: none

This model is based on the publication: "Mathematical Modelling of Alternative Pathway of Complement System". Suruchi Ba…

The complement system (CS) is an integral part of innate immunity and can be activated via three different pathways. The alternative pathway (AP) has a central role in the function of the CS. The AP of complement system is implicated in several human disease pathologies. In the absence of triggers, the AP exists in a time-invariant resting state (physiological steady state). It is capable of rapid, potent and transient activation response upon challenge with a trigger. Previous models of AP have focused on the activation response. In order to understand the molecular machinery necessary for AP activation and regulation of a physiological steady state, we built parsimonious AP models using experimentally supported kinetic parameters. The models further allowed us to test quantitative roles played by negative and positive regulators of the pathway in order to test hypotheses regarding their mechanisms of action, thus providing more insight into the complex regulation of AP. link: http://identifiers.org/pubmed/32062771

Parameters: none

States: none

Observables: none

BIOMD0000000296 @ v0.0.1

This is the reduced model described in the article: **A synthetic Escherichia coli predator–prey ecosystem** Balagaddé…

We have constructed a synthetic ecosystem consisting of two Escherichia coli populations, which communicate bi-directionally through quorum sensing and regulate each other's gene expression and survival via engineered gene circuits. Our synthetic ecosystem resembles canonical predator-prey systems in terms of logic and dynamics. The predator cells kill the prey by inducing expression of a killer protein in the prey, while the prey rescue the predators by eliciting expression of an antidote protein in the predator. Extinction, coexistence and oscillatory dynamics of the predator and prey populations are possible depending on the operating conditions as experimentally validated by long-term culturing of the system in microchemostats. A simple mathematical model is developed to capture these system dynamics. Coherent interplay between experiments and mathematical analysis enables exploration of the dynamics of interacting populations in a predictable manner. link: http://identifiers.org/pubmed/18414488

Parameters:

Name Description
kA2 = NaN Reaction: source => A2; C2, Rate Law: environment*kA2*C2
D = 0.1125; dAA2 = 0.11 Reaction: A2 => sink, Rate Law: environment*(dAA2+D)*A2
K1 = 10.0; D = 0.1125; d1 = NaN Reaction: C1 => sink; A2, Rate Law: environment*(D+d1*K1/(K1+A2^2))*C1
kc1 = 0.8; Cm = 100.0 Reaction: source => C1; C2, Rate Law: environment*kc1*C1*(1-(C1+C2)/Cm)
D = 0.1125; dAA1 = 0.017 Reaction: A1 => sink, Rate Law: environment*(dAA1+D)*A1
d2 = 0.3; D = 0.1125; K2 = 10.0 Reaction: C2 => sink; A1, Rate Law: environment*(D+d2*A1^2/(K2+A1^2))*C2
kc2 = 0.4; Cm = 100.0 Reaction: source => C2; C1, Rate Law: environment*kc2*C2*(1-(C1+C2)/Cm)
kA1 = 0.1 Reaction: source => A1; C1, Rate Law: environment*kA1*C1

States:

Name Description
C1 [Escherichia coli]
A2 [N-acyl-L-homoserine lactone; N-Acyl-L-homoserine lactone]
sink sink
C2 [Escherichia coli]
source source
A1 [N-(3-oxododecanoyl)-D-homoserine lactone]

Observables: none

First mathematical model of thrombin production in flowing blood. Nondimensionalised ODE model has been curated.

This paper presents the first attempt to model the blood coagulation reactions in flowing blood. The model focuses on the common pathway and includes activation of factor X and prothrombin, including feedback activation of cofactors VIII and V by thrombin, and plasma inhibition of factor Xa and thrombin. In this paper, the first of two, the sparsely covered membrane (SCM) case is presented. This considers the limiting situation where platelet membrane binding sites are in excess, such that no membrane saturation or binding competition occurs. Under these conditions, the model predicts that the two positive feedback loops lead to multiple steady-state behavior in the range of intermediate mass transfer rates. It will be shown that this results in three parameter regions exhibiting very different thrombin production patterns. The model predicts the effect of flow on steady-state and dynamic thrombin production and attempts to explain the difference between venous and arterial thrombi. The reliance of thrombin production on precursor procoagulant protein concentrations is also assessed. link: http://identifiers.org/doi/10.1007/BF02368242

Parameters: none

States: none

Observables: none

MODEL4992089662 @ v0.0.1

This is a part of the model described in: **A physiological model of cerebral blood flow control** Murad Banaji, Ili…

The construction of a computational model of the human brain circulation is described. We combine an existing model of the biophysics of the circulatory system, a basic model of brain metabolic biochemistry, and a model of the functioning of vascular smooth muscle (VSM) into a single model. This represents a first attempt to understand how the numerous different feedback pathways by which cerebral blood flow is controlled interact with each other. The present work comprises the following: Descriptions of the physiology underlying the model; general comments on the processes by which this physiology is translated into mathematics; comments on parameter setting; and some simulation results. The simulations presented are preliminary, but show qualitative agreement between model behaviour and experimental results. link: http://identifiers.org/pubmed/15854674

Parameters: none

States: none

Observables: none

BIOMD0000000413 @ v0.0.1

This model is from the article: Root gravitropism is regulated by a transient lateral auxin gradient controlled by a…

Gravity profoundly influences plant growth and development. Plants respond to changes in orientation by using gravitropic responses to modify their growth. Cholodny and Went hypothesized over 80 years ago that plants bend in response to a gravity stimulus by generating a lateral gradient of a growth regulator at an organ's apex, later found to be auxin. Auxin regulates root growth by targeting Aux/IAA repressor proteins for degradation. We used an Aux/IAA-based reporter, domain II (DII)-VENUS, in conjunction with a mathematical model to quantify auxin redistribution following a gravity stimulus. Our multidisciplinary approach revealed that auxin is rapidly redistributed to the lower side of the root within minutes of a 90° gravity stimulus. Unexpectedly, auxin asymmetry was rapidly lost as bending root tips reached an angle of 40° to the horizontal. We hypothesize roots use a "tipping point" mechanism that operates to reverse the asymmetric auxin flow at the midpoint of root bending. These mechanistic insights illustrate the scientific value of developing quantitative reporters such as DII-VENUS in conjunction with parameterized mathematical models to provide high-resolution kinetics of hormone redistribution. link: http://identifiers.org/pubmed/22393022

Parameters:

Name Description
ld = 4.49 Reaction: auxinTIR1VENUS => auxinTIR1 + VENUS, Rate Law: ld*auxinTIR1VENUS
kd = 0.334 Reaction: auxinTIR1 => auxin + TIR1, Rate Law: kd*auxinTIR1
mu = 0.79 Reaction: auxin =>, Rate Law: mu*auxin
la = 1.15 Reaction: auxinTIR1 + VENUS => auxinTIR1VENUS, Rate Law: la*auxinTIR1*VENUS
lm = 0.175 Reaction: auxinTIR1VENUS => auxinTIR1, Rate Law: lm*auxinTIR1VENUS
alpha_tr = 30.5 Reaction: => auxin, Rate Law: alpha_tr
lambda = 0.00316 Reaction: VENUS =>, Rate Law: lambda*VENUS
delta = 0.486 Reaction: => VENUS, Rate Law: delta
ka = 8.22E-4 Reaction: auxin + TIR1 => auxinTIR1, Rate Law: ka*auxin*TIR1

States:

Name Description
TIR1 [Protein TRANSPORT INHIBITOR RESPONSE 1; GRR1-like protein 1; Protein AUXIN SIGNALING F-BOX 2; Protein AUXIN SIGNALING F-BOX 3]
auxinTIR1 [Auxin-responsive protein IAA1; Protein TRANSPORT INHIBITOR RESPONSE 1]
auxinTIR1VENUS [Auxin-responsive protein IAA1; Protein TRANSPORT INHIBITOR RESPONSE 1; Auxin-responsive protein IAA28; Protein FLUORESCENT IN BLUE LIGHT, chloroplastic]
auxin [Auxin-responsive protein IAA1]
VENUS [Protein FLUORESCENT IN BLUE LIGHT, chloroplastic; Auxin-responsive protein IAA28]

Observables: none

BIOMD0000000414 @ v0.0.1

This model is from the article: Root gravitropism is regulated by a transient lateral auxin gradient controlled by a…

Gravity profoundly influences plant growth and development. Plants respond to changes in orientation by using gravitropic responses to modify their growth. Cholodny and Went hypothesized over 80 years ago that plants bend in response to a gravity stimulus by generating a lateral gradient of a growth regulator at an organ's apex, later found to be auxin. Auxin regulates root growth by targeting Aux/IAA repressor proteins for degradation. We used an Aux/IAA-based reporter, domain II (DII)-VENUS, in conjunction with a mathematical model to quantify auxin redistribution following a gravity stimulus. Our multidisciplinary approach revealed that auxin is rapidly redistributed to the lower side of the root within minutes of a 90° gravity stimulus. Unexpectedly, auxin asymmetry was rapidly lost as bending root tips reached an angle of 40° to the horizontal. We hypothesize roots use a "tipping point" mechanism that operates to reverse the asymmetric auxin flow at the midpoint of root bending. These mechanistic insights illustrate the scientific value of developing quantitative reporters such as DII-VENUS in conjunction with parameterized mathematical models to provide high-resolution kinetics of hormone redistribution. link: http://identifiers.org/pubmed/22393022

Parameters:

Name Description
p1_star = 0.056; p2 = 0.0053; qj_star = 0.16 Reaction: VENUS =>, Rate Law: p2*VENUS/(p1_star*VENUS+qj_star)
lambda_star = 0.52; p2 = 0.0053 Reaction: VENUS =>, Rate Law: lambda_star*p2*VENUS
p2 = 0.0053 Reaction: => VENUS, Rate Law: p2

States:

Name Description
VENUS [Protein FLUORESCENT IN BLUE LIGHT, chloroplastic; Auxin-responsive protein IAA28]

Observables: none

IMMUNOTHERAPY WITH INTERLEUKIN-2: A STUDY BASED ON MATHEMATICAL MODELING S ANDIP B ANERJEE Indian Institute of Technolog…

The role of interleukin-2 (IL-2) in tumor dynamics is illustrated through mathematical modeling, using delay differentialequations with a discrete time delay (a modified version of the Kirshner-Panetta model). Theoretical analysis gives anexpression for the discrete time delay and the length of the time delay to preserve stability. Numerical analysis shows thatinterleukin-2 alone can cause the tumor cell population to regress.

Int. J. Appl. Math. Comput. Sci., 2008, Vol. 18, No. 3, 389–398 link: http://identifiers.org/doi/10.2478/v10006-008-0035-6

Parameters: none

States: none

Observables: none

T11 Target structure (T11TS), a membrane glycoprotein isolated from sheep erythrocytes, reverses the immune suppressed s…

T11 Target structure (T11TS), a membrane glycoprotein isolated from sheep erythrocytes, reverses the immune suppressed state of brain tumor induced animals by boosting the functional status of the immune cells. This study aims at aiding in the design of more efficacious brain tumor therapies with T11 target structure. We propose a mathematical model for brain tumor (glioma) and the immune system interactions, which aims in designing efficacious brain tumor therapy. The model encompasses considerations of the interactive dynamics of glioma cells, macrophages, cytotoxic T-lymphocytes (CD8(+) T-cells), TGF-β, IFN-γ and the T11TS. The system undergoes sensitivity analysis, that determines which state variables are sensitive to the given parameters and the parameters are estimated from the published data. Computer simulations were used for model verification and validation, which highlight the importance of T11 target structure in brain tumor therapy. link: http://identifiers.org/pubmed/25955428

Parameters: none

States: none

Observables: none

Influence of Intracellular Delay on the Dynamics of Hepatitis C Virus Sandip Banerjee 1· Ram Keval 2 Abstract In this…

In this paper, we present a delay induced model for hepatitis C virus incorporating the healthy and infected hepatocytes as well as infectious and noninfectious virions. The model is mathematically analyzed and characterized, both for the steady states and the dynamical behavior of the model. It is shown that time delay does not affect the local asymptotic stability of the uninfected steady state. However, it can destabilize the endemic equilibrium, leading to Hopf bifurcation to periodic solutions with realistic data sets. The model is also validated using 12 patient data obtained from the study, conducted at the University of Sao Paulo Hospital das clinicas. link: http://identifiers.org/doi/10.1080/17513750903261281

Parameters: none

States: none

Observables: none

We have developed a new model of the human airway epithelial cell by deriving the cell-specific metabolic reactions iden…

The coronavirus disease 2019 (COVID-19) pandemic caused by the new coronavirus (SARS-CoV-2) is currently responsible for over 500 thousand deaths in 216 countries across the world and is affecting over 10 million people. The absence of FDA approved drugs against the new SARS-CoV-2 virus has highlighted an urgent need to design new drugs. We developed an integrated model of the human cell and the SARS-CoV-2 virus to provide insight into the pathogenetic mechanism of the virus and to support current therapeutic strategies. We show the biochemical reactions required for the growth and general maintenance of the human cell, first of all, in its healthy state. We then demonstrate how the entry of the SARS-CoV-2 virus into the human cell causes biochemical and structural changes, leading to a change of cell functions or cell death. We have completed a comparative analysis of our model and other previously generated cell type models and highlight 48 pathways and over 800 reactions hijacked by the virus for its replication and survival. We designed a new tool which predicts 15 unique reactions as drug targets from our models (the integrated human macrophage, human airway epithelial cells and the SARS-CoV-2 virus) and provide a platform for future studies on viral entry inhibition and drug optimisation strategies. link: http://identifiers.org/doi/10.21203/rs.3.rs-46892/v2

Parameters: none

States: none

Observables: none

Barr2016 - All-or-nothing G1/S transitionThis model is described in the article: [A Dynamical Framework for the All-or-…

The transition from G1 into DNA replication (S phase) is an emergent behavior resulting from dynamic and complex interactions between cyclin-dependent kinases (Cdks), Cdk inhibitors (CKIs), and the anaphase-promoting complex/cyclosome (APC/C). Understanding the cellular decision to commit to S phase requires a quantitative description of these interactions. We apply quantitative imaging of single human cells to track the expression of G1/S regulators and use these data to parametrize a stochastic mathematical model of the G1/S transition. We show that a rapid, proteolytic, double-negative feedback loop between Cdk2:Cyclin and the Cdk inhibitor p27(Kip1) drives a switch-like entry into S phase. Furthermore, our model predicts that increasing Emi1 levels throughout S phase are critical in maintaining irreversibility of the G1/S transition, which we validate using Emi1 knockdown and live imaging of G1/S reporters. This work provides insight into the general design principles of the signaling networks governing the temporally abrupt transitions between cell-cycle phases. link: http://identifiers.org/pubmed/27136687

Parameters:

Name Description
kscyca = 0.0025 Reaction: => CycA, Rate Law: compartment*kscyca
ks27 = 0.008 Reaction: => p27, Rate Law: compartment*ks27
kicdh1a = 0.2; kicdh1e = 0.07; Inhibitor = 0.0 Reaction: Cdh1 => Cdh1p; CycE, CycA, Rate Law: compartment*(kicdh1e*CycE/(1+Inhibitor)+kicdh1a*CycA/(1+Inhibitor))*Cdh1
kscyce = 0.003 Reaction: => CycE, Rate Law: compartment*kscyce
kdcycee = 1.0E-4; kdcycea = 0.03; kdcyce = 0.001; Inhibitor = 0.0 Reaction: CycE => ; CycE, CycA, Rate Law: compartment*(kdcyce+kdcycee*CycE/(1+Inhibitor)+kdcycea*CycA/(1+Inhibitor))*CycE
kasec = 2.0; kdiec = 0.02 Reaction: Cdh1 + Emi1 => Emi1Cdh1, Rate Law: compartment*(kasec*Cdh1*Emi1-kdiec*Emi1Cdh1)
kdisa = 0.02; kassa = 1.0 Reaction: CycA + p27 => CycAp27, Rate Law: compartment*(kassa*CycA*p27-kdisa*CycAp27)
kdskp2c1 = 0.2; kdskp2 = 0.002 Reaction: Skp2 => ; Cdh1, Rate Law: compartment*(kdskp2+kdskp2c1*Cdh1)*Skp2
ksemi1 = 0.003 Reaction: => Emi1, Rate Law: compartment*ksemi1
kacdh1 = 0.02 Reaction: Cdh1p => Cdh1, Rate Law: compartment*kacdh1*Cdh1p
ksskp2 = 0.004 Reaction: => Skp2, Rate Law: compartment*ksskp2
kdise = 0.02; kasse = 1.0 Reaction: CycE + p27 => CycEp27, Rate Law: compartment*(kasse*CycE*p27-kdise*CycEp27)
kdcycac1 = 0.4; kdcyca = 0.002 Reaction: CycA => ; Cdh1, Rate Law: compartment*(kdcyca+kdcycac1*Cdh1)*CycA
kdemi1 = 0.001 Reaction: Emi1 =>, Rate Law: compartment*kdemi1*Emi1
kd27a = 2.0; kd27e = 2.0; kd27 = 0.004; Inhibitor = 0.0 Reaction: CycEp27 => CycE; CycE, CycA, Skp2, Rate Law: compartment*((kd27e*CycE/(1+Inhibitor)+kd27a*CycA/(1+Inhibitor))*Skp2+kd27)*CycEp27

States:

Name Description
CycE [cyclin-E]
CycET [cyclin-E]
Emi1Cdh1 [12550; 852125]
CycAp27 [urn:miriam:omit:OMIT%3A0024493; cyclin-A]
Emi1Cdh1p [12550; Phosphoprotein; 852125]
Cdh1T [12550]
Skp2 [S-phase kinase-associated protein 2]
Cdh1p [12550; Phosphoprotein]
Cdh1dp [12550]
Emi1 [852125]
EmiC [12550; 852125]
Emi1T [852125]
CycAT [cyclin-A]
p27 [urn:miriam:omit:OMIT%3A0024493]
Cdh1 [12550]
CycEp27 [cyclin-E]
p27T [urn:miriam:omit:OMIT%3A0024493]
CycA [cyclin-A]

Observables: none

Barr2017 - Dynamics of p21 in hTert-RPE1 cellsThis deteministic model reveals that a bistable switch created by Cdt2, pr…

Following DNA damage caused by exogenous sources, such as ionizing radiation, the tumour suppressor p53 mediates cell cycle arrest via expression of the CDK inhibitor, p21. However, the role of p21 in maintaining genomic stability in the absence of exogenous DNA-damaging agents is unclear. Here, using live single-cell measurements of p21 protein in proliferating cultures, we show that naturally occurring DNA damage incurred over S-phase causes p53-dependent accumulation of p21 during mother G2- and daughter G1-phases. High p21 levels mediate G1 arrest via CDK inhibition, yet lower levels have no impact on G1 progression, and the ubiquitin ligases CRL4Cdt2 and SCFSkp2 couple to degrade p21 prior to the G1/S transition. Mathematical modelling reveals that a bistable switch, created by CRL4Cdt2, promotes irreversible S-phase entry by keeping p21 levels low, preventing premature S-phase exit upon DNA damage. Thus, we characterize how p21 regulates the proliferation-quiescence decision to maintain genomic stability. link: http://identifiers.org/pubmed/28317845

Parameters:

Name Description
kSyCy = 0.005 Reaction: MrnaCy => MrnaCy + Cy, Rate Law: Cell*kSyCy*MrnaCy
kDeP21 = 0.0025; kDeP21aRc = 1.0; kDeP21Cy = 0.007 Reaction: CyP21 => Cy; Skp2, Cy, Cdt2, aRc, Rate Law: Cell*(kDeP21+kDeP21Cy*Skp2*Cy+kDeP21aRc*Cdt2*aRc)*CyP21
kDeP53 = 0.05; jP53 = 0.01 Reaction: P53 => ; Dam, Rate Law: Cell*kDeP53/(jP53+Dam)*P53
kSyMrnaP53 = 0.08 Reaction: P53 => MrnaP21 + P53, Rate Law: Cell*kSyMrnaP53*P53
kDsRcPc = 0.001; kAsRcPc = 0.01 Reaction: aPcna + pRc => aRc, Rate Law: Cell*(kAsRcPc*aPcna*pRc-kDsRcPc*aRc)
kGeDam = 0.001 Reaction: => Dam, Rate Law: Cell*kGeDam
kDsCyP21 = 0.05; kAsCyP21 = 1.0 Reaction: Cy + P21 => CyP21, Rate Law: Cell*(kAsCyP21*Cy*P21-kDsCyP21*CyP21)
kSyDna = 0.007 Reaction: aRc => aRc + Dna, Rate Law: Cell*kSyDna*aRc
kDsPcP21 = 0.01; kAsPcP21 = 100.0 Reaction: aPcna + P21 => iPcna, Rate Law: Cell*(kAsPcP21*aPcna*P21-kDsPcP21*iPcna)
kGeDamArc = 0.005 Reaction: aRc => aRc + Dam, Rate Law: Cell*kGeDamArc*aRc
kSyMrna = 0.02 Reaction: => MrnaCy, Rate Law: Cell*kSyMrna
kDeCyCy = 2.0E-4; kDeCy = 0.002 Reaction: CyP21 => P21; Skp2, Cy, Rate Law: Cell*(kDeCy+kDeCyCy*Skp2*Cy)*CyP21
kSyP21 = 0.0018 Reaction: MrnaP21 => MrnaP21 + P21, Rate Law: Cell*kSyP21*MrnaP21
kExPc = 0.006 Reaction: iPcna => P21, Rate Law: Cell*kExPc*iPcna
kDeMrna = 0.02 Reaction: MrnaP53 =>, Rate Law: Cell*kDeMrna*MrnaP53
kSyP53 = 0.05 Reaction: MrnaP53 => MrnaP53 + P53, Rate Law: Cell*kSyP53*MrnaP53
kImPc = 0.003 Reaction: => aPcna, Rate Law: Cell*kImPc
n = 6.0; kPhRc = 0.1; jCy = 1.8 Reaction: Rc => pRc; Cy, Rate Law: Cell*kPhRc*Cy^n/(jCy^n+Cy^n)*Rc
kReDamP53 = 0.005; kReDam = 0.001; jDam = 0.5 Reaction: Dam => ; P53, Rate Law: Cell*(kReDam+kReDamP53*P53/(jDam+Dam))*Dam
kDpRc = 0.01 Reaction: pRc => Rc, Rate Law: Cell*kDpRc*pRc

States:

Name Description
aRc [active; pre-replicative complex]
iRc [inactive; pre-replicative complex]
iPcna [Cyclin-dependent kinase inhibitor 1; Proliferating cell nuclear antigen]
CyP21 [G1/S-specific cyclin-E1; Cyclin-dependent kinase inhibitor 1; Cyclin-dependent kinase inhibitor 1; Cyclin-A1]
MrnaP53 [messenger RNA; Cellular tumor antigen p53]
tPcna [Proliferating cell nuclear antigen]
Rc [pre-replicative complex]
tP21 [Cyclin-dependent kinase inhibitor 1]
P53 [Cellular tumor antigen p53]
Dam [urn:miriam:ncit:NCIT_C16507]
tCy [Cyclin-A1; Cyclin-dependent kinase 2; G1/S-specific cyclin-E1; Cyclin-dependent kinase 2]
P21 [Cyclin-dependent kinase inhibitor 1]
MrnaCy [G1/S-specific cyclin-E1; messenger RNA; Cyclin-A1]
Cy [G1/S-specific cyclin-E1; Cyclin-A1]
aPcna [Proliferating cell nuclear antigen]
Dna [deoxyribonucleic acid]
MrnaP21 [Cyclin-dependent kinase inhibitor 1; messenger RNA]
pRc [pre-replicative complex]

Observables: none

Barrack2014 - Calcium/cell cycle coupling - Cyclin D dependent ATP releaseThis model is designed based on the hypothesis…

Most neocortical neurons formed during embryonic brain development arise from radial glial cells which communicate, in part, via ATP mediated calcium signals. Although the intercellular signalling mechanisms that regulate radial glia proliferation are not well understood, it has recently been demonstrated that ATP dependent intracellular calcium release leads to an increase of nearly 100% in overall cellular proliferation. It has been hypothesised that cytoplasmic calcium accelerates entry into S phase of the cell cycle and/or acts to recruit otherwise quiescent cells onto the cell cycle. In this paper we study this cell cycle acceleration and recruitment by forming a differential equation model for ATP mediated calcium-cell cycle coupling via Cyclin D in a single radial glial cell. Bifurcation analysis and numerical simulations suggest that the cell cycle period depends only weakly on cytoplasmic calcium. Therefore, the accelerative impact of calcium on the cell cycle can only account for a small fraction of the large increase in proliferation observed experimentally. Crucially however, our bifurcation analysis reveals that stable fixed point and stable limit cycle solutions can coexist, and that calcium dependent Cyclin D dynamics extend the oscillatory region to lower Cyclin D synthesis rates, thus rendering cells more susceptible to cycling. This supports the hypothesis that cycling glial cells recruit quiescent cells (in G0 phase) onto the cell cycle, via a calcium signalling mechanism, and that this may be the primary means by which calcium augments proliferation rates at the population scale. Numerical simulations of two coupled cells demonstrate that such a scenario is indeed feasible. link: http://identifiers.org/pubmed/24434742

Parameters:

Name Description
kr = 25.0 Reaction: ro = atp/(kr+atp), Rate Law: missing
kdeg = 0.0625; ip30 = 0.013; rhstar = 0.6 Reaction: delta = kg*kdeg*ip30/(rhstar-kdeg*ip30), Rate Law: missing
dcrit = 0.5 Reaction: dcon = (tanh((d-dcrit)/0.01)+1)/2, Rate Law: missing
p3 = 1.31319; p1 = 0.0159835; p2 = 0.514987; m = 24.1946; p4 = 0.332195; n = 9.79183; p5 = 0.787902 Reaction: ca = p1+p2*ip3^m/(p3^m+ip3^m)+p4*ip3^n/(p5^n+ip3^n), Rate Law: missing
ax = 0.08; f = 0.2; dxx = 1.04; yo = 1.5; g = 0.528 Reaction: x = (ax*e+f*(yo-rs)+g*x^2*e)-dxx*x, Rate Law: (ax*e+f*(yo-rs)+g*x^2*e)-dxx*x
ip3min = 0.012 Reaction: ip3con = (tanh((ip3-ip3min)/0.01)+1)/2, Rate Law: missing
scale = 3600.0; kdeg = 0.0625; rhstar = 0.6 Reaction: ip3 = scale*(rhstar*gstar-kdeg*ip3), Rate Law: scale*(rhstar*gstar-kdeg*ip3)
kkdeg = 50.0; krel = 10.0; vdeg = 2.0; ip3min = 0.012; scale = 3600.0; vatp_s = 50.0 Reaction: atp = scale*(vatp_s*(y-atp)*dcon*ip3con*(ip3-ip3min)/(krel+ip3)-vdeg*atp/(kkdeg+atp)), Rate Law: scale*(vatp_s*(y-atp)*dcon*ip3con*(ip3-ip3min)/(krel+ip3)-vdeg*atp/(kkdeg+atp))
kd = 0.15; ka = 0.017 Reaction: kg = kd/ka, Rate Law: missing
rt = 2.5; qx = 0.8; px = 0.48; yo = 1.5; ps = 0.6 Reaction: r = px*((rt-rs)-r)*x/(qx+((rt-rs)-r)+x)-ps*(yo-rs)*r, Rate Law: px*((rt-rs)-r)*x/(qx+((rt-rs)-r)+x)-ps*(yo-rs)*r
qd = 0.6; pe = 0.096; qe = 0.6; yo = 1.5; ps = 0.6; pd = 0.48 Reaction: rs = (ps*(yo-rs)*r-pd*rs*d/(qd+rs+d))-pe*rs*e/(qe+rs+e), Rate Law: (ps*(yo-rs)*r-pd*rs*d/(qd+rs+d))-pe*rs*e/(qe+rs+e)
ymax = 500.0; krel = 10.0; alpha = 0.083; ip3min = 0.012; scale = 3600.0; vatp_s = 50.0 Reaction: y = scale*(alpha*(ymax-y)-dcon*ip3con*vatp_s*(y-atp)*(ip3-ip3min)/(krel+ip3)), Rate Law: scale*(alpha*(ymax-y)-dcon*ip3con*vatp_s*(y-atp)*(ip3-ip3min)/(krel+ip3))
dee = 0.2; yo = 1.5; af = 0.9; ae = 0.16 Reaction: e = ae*(1+af*(yo-rs))-dee*x*e, Rate Law: ae*(1+af*(yo-rs))-dee*x*e
p1 = 0.0159835; gamma = 1.0; addash = 0.41 Reaction: ad = addash+gamma*(ca-p1), Rate Law: missing
ddd = 0.4; k = 0.05; gf = 6.3 Reaction: d = ad*k*gf/(1+k*gf)-ddd*e*d, Rate Law: ad*k*gf/(1+k*gf)-ddd*e*d

States:

Name Description
y [ATP]
kg kg
e [Cyclin-dependent kinase 2; G1/S-specific cyclin-E1]
x [protein polypeptide chain; indicator]
ip3con ip3con
r [Retinoblastoma-like protein 2]
delta delta
rs [Retinoblastoma-like protein 2; Transcription factor E2F1]
atp [ATP]
ad ad
dcon dcon
ip3 [1D-myo-inositol 1,4,5-trisphosphate]
ro ro
ca [calcium(2+)]
gstar gstar
d [Cyclin-dependent kinase 4; G1/S-specific cyclin-D1]

Observables: none

Barrack2014 - Calcium/cell cycle coupling - Rs dependent ATP releaseThis model is designed based on the hypothesis that…

Most neocortical neurons formed during embryonic brain development arise from radial glial cells which communicate, in part, via ATP mediated calcium signals. Although the intercellular signalling mechanisms that regulate radial glia proliferation are not well understood, it has recently been demonstrated that ATP dependent intracellular calcium release leads to an increase of nearly 100% in overall cellular proliferation. It has been hypothesised that cytoplasmic calcium accelerates entry into S phase of the cell cycle and/or acts to recruit otherwise quiescent cells onto the cell cycle. In this paper we study this cell cycle acceleration and recruitment by forming a differential equation model for ATP mediated calcium-cell cycle coupling via Cyclin D in a single radial glial cell. Bifurcation analysis and numerical simulations suggest that the cell cycle period depends only weakly on cytoplasmic calcium. Therefore, the accelerative impact of calcium on the cell cycle can only account for a small fraction of the large increase in proliferation observed experimentally. Crucially however, our bifurcation analysis reveals that stable fixed point and stable limit cycle solutions can coexist, and that calcium dependent Cyclin D dynamics extend the oscillatory region to lower Cyclin D synthesis rates, thus rendering cells more susceptible to cycling. This supports the hypothesis that cycling glial cells recruit quiescent cells (in G0 phase) onto the cell cycle, via a calcium signalling mechanism, and that this may be the primary means by which calcium augments proliferation rates at the population scale. Numerical simulations of two coupled cells demonstrate that such a scenario is indeed feasible. link: http://identifiers.org/pubmed/24434742

Parameters:

Name Description
kr = 25.0 Reaction: ro = atp/(kr+atp), Rate Law: missing
kdeg = 0.0625; ip30 = 0.013; rhstar = 0.6 Reaction: delta = kg*kdeg*ip30/(rhstar-kdeg*ip30), Rate Law: missing
rscrit = 1.0 Reaction: rscon = (tanh((rscrit-rs)/0.01)+1)/2, Rate Law: missing
p3 = 1.31319; p1 = 0.0159835; p2 = 0.514987; m = 24.1946; p4 = 0.332195; n = 9.79183; p5 = 0.787902 Reaction: ca = p1+p2*ip3^m/(p3^m+ip3^m)+p4*ip3^n/(p5^n+ip3^n), Rate Law: missing
ax = 0.08; f = 0.2; dxx = 1.04; yo = 1.5; g = 0.528 Reaction: x = (ax*e+f*(yo-rs)+g*x^2*e)-dxx*x, Rate Law: (ax*e+f*(yo-rs)+g*x^2*e)-dxx*x
ip3min = 0.012 Reaction: ip3con = (tanh((ip3-ip3min)/0.01)+1)/2, Rate Law: missing
kkdeg = 50.0; krel = 10.0; vdeg = 2.0; ip3min = 0.012; scale = 3600.0; vatp_s = 50.0 Reaction: atp = scale*(vatp_s*(y-atp)*rscon*ip3con*(ip3-ip3min)/(krel+ip3)-vdeg*atp/(kkdeg+atp)), Rate Law: scale*(vatp_s*(y-atp)*rscon*ip3con*(ip3-ip3min)/(krel+ip3)-vdeg*atp/(kkdeg+atp))
scale = 3600.0; kdeg = 0.0625; rhstar = 0.6 Reaction: ip3 = scale*(rhstar*gstar-kdeg*ip3), Rate Law: scale*(rhstar*gstar-kdeg*ip3)
kd = 0.15; ka = 0.017 Reaction: kg = kd/ka, Rate Law: missing
rt = 2.5; qx = 0.8; px = 0.48; yo = 1.5; ps = 0.6 Reaction: r = px*((rt-rs)-r)*x/(qx+((rt-rs)-r)+x)-ps*(yo-rs)*r, Rate Law: px*((rt-rs)-r)*x/(qx+((rt-rs)-r)+x)-ps*(yo-rs)*r
qd = 0.6; pe = 0.096; qe = 0.6; yo = 1.5; ps = 0.6; pd = 0.48 Reaction: rs = (ps*(yo-rs)*r-pd*rs*d/(qd+rs+d))-pe*rs*e/(qe+rs+e), Rate Law: (ps*(yo-rs)*r-pd*rs*d/(qd+rs+d))-pe*rs*e/(qe+rs+e)
ymax = 500.0; krel = 10.0; alpha = 0.083; ip3min = 0.012; scale = 3600.0; vatp_s = 50.0 Reaction: y = scale*(alpha*(ymax-y)-rscon*ip3con*vatp_s*(y-atp)*(ip3-ip3min)/(krel+ip3)), Rate Law: scale*(alpha*(ymax-y)-rscon*ip3con*vatp_s*(y-atp)*(ip3-ip3min)/(krel+ip3))
dee = 0.2; yo = 1.5; af = 0.9; ae = 0.16 Reaction: e = ae*(1+af*(yo-rs))-dee*x*e, Rate Law: ae*(1+af*(yo-rs))-dee*x*e
p1 = 0.0159835; gamma = 1.0; addash = 0.41 Reaction: ad = addash+gamma*(ca-p1), Rate Law: missing
ddd = 0.4; k = 0.05; gf = 6.3 Reaction: d = ad*k*gf/(1+k*gf)-ddd*e*d, Rate Law: ad*k*gf/(1+k*gf)-ddd*e*d

States:

Name Description
d [Cyclin-dependent kinase 4; G1/S-specific cyclin-D1]
kg kg
e [G1/S-specific cyclin-E1; Cyclin-dependent kinase 2]
rscon rscon
x [indicator; protein polypeptide chain]
ip3con ip3con
r [Retinoblastoma-like protein 2]
delta delta
rs [Retinoblastoma-like protein 2; Transcription factor E2F1]
atp [ATP]
ad ad
ip3 [1D-myo-inositol 1,4,5-trisphosphate]
ro ro
ca [calcium(2+)]
gstar gstar
y [ATP]

Observables: none

A mathematical model (CART-math) studying the impact of CAR-T cells therapy on haematological cancer cell line which in…

Immunotherapy has gained great momentum with chimeric antigen receptor T cell (CAR-T) therapy, in which patient's T lymphocytes are genetically manipulated to recognize tumor-specific antigens, increasing tumor elimination efficiency. In recent years, CAR-T cell immunotherapy for hematological malignancies achieved a great response rate in patients and is a very promising therapy for several other malignancies. Each new CAR design requires a preclinical proof-of-concept experiment using immunodeficient mouse models. The absence of a functional immune system in these mice makes them simple and suitable for use as mathematical models. In this work, we develop a three-population mathematical model to describe tumor response to CAR-T cell immunotherapy in immunodeficient mouse models, encompassing interactions between a non-solid tumor and CAR-T cells (effector and long-term memory). We account for several phenomena, such as tumor-induced immunosuppression, memory pool formation, and conversion of memory into effector CAR-T cells in the presence of new tumor cells. Individual donor and tumor specificities are considered uncertainties in the model parameters. Our model is able to reproduce several CAR-T cell immunotherapy scenarios, with different CAR receptors and tumor targets reported in the literature. We found that therapy effectiveness mostly depends on specific parameters such as the differentiation of effector to memory CAR-T cells, CAR-T cytotoxic capacity, tumor growth rate, and tumor-induced immunosuppression. In summary, our model can contribute to reducing and optimizing the number of in vivo experiments with in silico tests to select specific scenarios that could be tested in experimental research. Such an in silico laboratory is an easy-to-run open-source simulator, built on a Shiny R-based platform called CART<i>math</i>. It contains the results of this manuscript as examples and documentation. The developed model together with the CART<i>math</i> platform have potential use in assessing different CAR-T cell immunotherapy protocols and its associated efficacy, becoming an accessory for in silico trials. link: http://identifiers.org/pubmed/34208323

Parameters: none

States: none

Observables: none

A mathematical model (CART-math) studying the impact of CAR-T cells therapy on haematological cancer cell lines which in…

Immunotherapy has gained great momentum with chimeric antigen receptor T cell (CAR-T) therapy, in which patient's T lymphocytes are genetically manipulated to recognize tumor-specific antigens, increasing tumor elimination efficiency. In recent years, CAR-T cell immunotherapy for hematological malignancies achieved a great response rate in patients and is a very promising therapy for several other malignancies. Each new CAR design requires a preclinical proof-of-concept experiment using immunodeficient mouse models. The absence of a functional immune system in these mice makes them simple and suitable for use as mathematical models. In this work, we develop a three-population mathematical model to describe tumor response to CAR-T cell immunotherapy in immunodeficient mouse models, encompassing interactions between a non-solid tumor and CAR-T cells (effector and long-term memory). We account for several phenomena, such as tumor-induced immunosuppression, memory pool formation, and conversion of memory into effector CAR-T cells in the presence of new tumor cells. Individual donor and tumor specificities are considered uncertainties in the model parameters. Our model is able to reproduce several CAR-T cell immunotherapy scenarios, with different CAR receptors and tumor targets reported in the literature. We found that therapy effectiveness mostly depends on specific parameters such as the differentiation of effector to memory CAR-T cells, CAR-T cytotoxic capacity, tumor growth rate, and tumor-induced immunosuppression. In summary, our model can contribute to reducing and optimizing the number of in vivo experiments with in silico tests to select specific scenarios that could be tested in experimental research. Such an in silico laboratory is an easy-to-run open-source simulator, built on a Shiny R-based platform called CART<i>math</i>. It contains the results of this manuscript as examples and documentation. The developed model together with the CART<i>math</i> platform have potential use in assessing different CAR-T cell immunotherapy protocols and its associated efficacy, becoming an accessory for in silico trials. link: http://identifiers.org/pubmed/34208323

Parameters: none

States: none

Observables: none

BIOMD0000000197 @ v0.0.1

SBML model exported from PottersWheel on 2007-09-19 15:35:47. The values for parameters and the inital concentrations o…

Vectorial transport of endogenous small molecules, toxins, and drugs across polarized epithelial cells contributes to their half-life in the organism and to detoxification. To study vectorial transport in a quantitative manner, an in vitro model was used that includes polarized MDCKII cells stably expressing the recombinant human uptake transporter OATP1B3 in their basolateral membrane and the recombinant ATP-driven efflux pump ABCC2 in their apical membrane. These double-transfected cells enabled mathematical modeling of the vectorial transport of the anionic prototype substance bromosulfophthalein (BSP) that has frequently been used to examine hepatobiliary transport. Time-dependent analyses of (3)H-labeled BSP in the basolateral, intracellular, and apical compartments of cells cultured on filter membranes and efflux experiments in cells preloaded with BSP were performed. A mathematical model was fitted to the experimental data. Data-based modeling was optimized by including endogenous transport processes in addition to the recombinant transport proteins. The predominant contributions to the overall vectorial transport of BSP were mediated by OATP1B3 (44%) and ABCC2 (28%). Model comparison predicted a previously unrecognized endogenous basolateral efflux process as a negative contribution to total vectorial transport, amounting to 19%, which is in line with the detection of the basolateral efflux pump Abcc4 in MDCKII cells. Rate-determining steps in the vectorial transport were identified by calculating control coefficients. Data-based mathematical modeling of vectorial transport of BSP as a model substance resulted in a quantitative description of this process and its components. The same systems biology approach may be applied to other cellular systems and to different substances. link: http://identifiers.org/pubmed/17548463

Parameters:

Name Description
p7 = 0.0397 permin Reaction: x2 => x1, Rate Law: p7*x2
p5 = 0.0091 permin Reaction: x3 => x5, Rate Law: p5*x3
p12 = 3.0E-4 ml_per_min Reaction: x1 => x5, Rate Law: p12*(x1/basolat-x5/apical)
p9 = 0.0098 per_nmole_per_ml; p11 = 1000.0 nmole Reaction: x3 => x4, Rate Law: p9*x3*(p11-x4)
p4 = 0.0827 permin Reaction: x3 => x1, Rate Law: p4*x3
p3 = 0.0013 permin Reaction: x1 => x3, Rate Law: p3*x1
p10 = 1.6 permin Reaction: x4 => x3, Rate Law: p10*x4
p1 = 0.0025 permin Reaction: x1 => x3, Rate Law: p1*x1
p6 = 6.4E-5 per_nmole_per_ml; p8 = 1000.0 nmole Reaction: x1 => x2, Rate Law: p6*x1*(p8-x2)
p2 = 0.0784 permin Reaction: x3 => x5, Rate Law: p2*x3

States:

Name Description
x5 [Sulfobromophthalein; bromosulfophthalein]
x1 [Sulfobromophthalein; bromosulfophthalein]
BSP cell [Sulfobromophthalein; bromosulfophthalein]
BSP tot [Sulfobromophthalein; bromosulfophthalein]
x4 [Sulfobromophthalein; bromosulfophthalein]
x2 [Sulfobromophthalein; bromosulfophthalein]
x3 [Sulfobromophthalein; bromosulfophthalein]

Observables: none

MODEL8478881246 @ v0.0.1

This SBML file is a translation of a MatLab model utilized in the following paper. It describes the interplay of IkB and…

Inflammatory NF-kappaB/RelA activation is mediated by the three canonical inhibitors, IkappaBalpha, -beta, and -epsilon. We report here the characterization of a fourth inhibitor, nfkappab2/p100, that forms two distinct inhibitory complexes with RelA, one of which mediates developmental NF-kappaB activation. Our genetic evidence confirms that p100 is required and sufficient as a fourth IkappaB protein for noncanonical NF-kappaB signaling downstream of NIK and IKK1. We develop a mathematical model of the four-IkappaB-containing NF-kappaB signaling module to account for NF-kappaB/RelA:p50 activation in response to inflammatory and developmental stimuli and find signaling crosstalk between them that determines gene-expression programs. Further combined computational and experimental studies reveal that mutant cells with altered balances between canonical and noncanonical IkappaB proteins may exhibit inappropriate inflammatory gene expression in response to developmental signals. Our results have important implications for physiological and pathological scenarios in which inflammatory and developmental signals converge. link: http://identifiers.org/pubmed/17254973

Parameters: none

States: none

Observables: none

Basic mathematical model of the formation of coagulation factor Xa involving TF:VIIa and its inhibition by TFPI.

Tissue factor (TF) pathway inhibitor (TFPI) regulates factor X activation through the sequential inhibition of factor Xa and the VIIa.TF complex. Factor Xa formation was studied in a purified, reconstituted system, at plasma concentrations of factor X and TFPI, saturating concentrations of factor VIIa, and increasing concentrations of TF reconstituted into phosphatidylcholine:phosphatidylserine membranes (TF/PCPS) or PC membranes (TF/PC). The initial rate of factor Xa formation was equivalent in the presence or absence of 2.4 nM TFPI. However, reaction extent was small (<20%) relative to that observed in the absence of TFPI, implying the rapid inhibition of VIIa.TF during factor X activation. Initiation of factor Xa formation using increasing concentrations of TF/PCPS or TF/PC in the presence of TFPI yielded families of progress curves where both initial rate and reaction extent were linearly proportional to the concentration of VIIa.TF. These observations were consistent with a kinetic model in which the rate-limiting step represents the initial inhibition of newly formed factor Xa. Numerical analyses of progress curves yielded a rate constant for inhibition of VIIa.TF by Xa.TFPI (>10(8) M-1.s-1) that was substantially greater than the value (7.34 +/- 0.8 x 10(6) M-1.s-1) directly measured. Thus, VIIa.TF is inhibited at near diffusion-limited rates by Xa.TFPI formed during catalysis which cannot be explained by studies of the isolated reaction. We propose that the predominant inhibitory pathway during factor X activation may involve the initial inhibition of factor Xa either bound to or in the near vicinity of VIIa.TF on the membrane surface. As a result, VIIa.TF inhibition is unexpectedly rapid, and the concentration of active factor Xa that escapes regulation is linearly dependent on the availability of TF. link: http://identifiers.org/pubmed/9468488

Parameters: none

States: none

Observables: none

Mathematical description of the interactions of CycE/Cdk2, Cdc25A, and P27Kip1 in a core cancer subnetwork. Model is enc…

The Eukaryotic cell cycle is a repeated sequence of events that enables the division of a cell into two daughter cells. The cell cycle is classically divided into four phases: gap 1 (G1), synthesis (S), gap 2 (G2), and mitosis (M). In the G1 phase of the cell cycle, the cell physically grows and prepares for DNA replication. In the following, S, phase, the DNA is copied,while in the G2 phase, final preparations for cell division are made within the nucleus of the cell. In the last, M, phase, the cell divides into two daughter cells, which then begin a new cycle of division [1–4]. During the cell cycle process, there are different checkpoints that allow the cell to check for and repair DNA damage, as well as to control cell progression: the restriction (R) checkpoint between the G1 and S phases; the G2 checkpoint between the G2 and M phases; and the metaphase checkpoint between the metaphase and anaphases of the cell cycle [5]. At the R-checkpoint [6], either the cell commits to division and then progresses to the S phase or exits the cell cycle and enters the quiescent state (G0) [4]. In this study, we are particularly interested in the dynamics of the gene expression levels at the R-checkpoint. The cell-cycle process is orchestrated by the production and balance of chemical signals that activate and inhibit the cell-cycle progression genes that form a complex and highly integrated network [2]. In this network, activating and inhibitory signal molecules interact, forming positive-feedback and negative-feedback loops, which ultimately control the dynamics of the cell cycle. The two types of genes that are particularly important for regulating cell-cycle process are oncogenes (which are responsible for growth signals and promotion of cell-cycle progression) and tumor suppressor genes (TSGs) (which are responsible for inhibitory signals and retard or halt the cell cycle). If either (or both) of these genes malfunction, then cancer initiation (carcinogenesis) may occur. link: http://identifiers.org/doi/10.1002/mma.4213

Parameters: none

States: none

Observables: none

Bazzani2012 - Genome scale networks of P.falciparum and human hepatocyteThis model is described in the article: Networ…

BACKGROUND: The search for new drug targets for antibiotics against Plasmodium falciparum, a major cause of human deaths, is a pressing scientific issue, as multiple resistance strains spread rapidly. Metabolic network-based analyses may help to identify those parasite's essential enzymes whose homologous counterparts in the human host cells are either absent, non-essential or relatively less essential. RESULTS: Using the well-curated metabolic networks PlasmoNet of the parasite Plasmodium falciparum and HepatoNet1 of the human hepatocyte, the selectivity of 48 experimental antimalarial drug targets was analyzed. Applying in silico gene deletions, 24 of these drug targets were found to be perfectly selective, in that they were essential for the parasite but non-essential for the human cell. The selectivity of a subset of enzymes, that were essential in both models, was evaluated with the reduced fitness concept. It was, then, possible to quantify the reduction in functional fitness of the two networks under the progressive inhibition of the same enzymatic activity. Overall, this in silico analysis provided a selectivity ranking that was in line with numerous in vivo and in vitro observations. CONCLUSIONS: Genome-scale models can be useful to depict and quantify the effects of enzymatic inhibitions on the impaired production of biomass components. From the perspective of a host-pathogen metabolic interaction, an estimation of the drug targets-induced consequences can be beneficial for the development of a selective anti-parasitic drug. link: http://identifiers.org/pubmed/22937810

Parameters: none

States: none

Observables: none

MODEL4151491057 @ v0.0.1

This is the model described in the article: A biophysical model of the mitochondrial respiratory system and oxidative…

A computational model for the mitochondrial respiratory chain that appropriately balances mass, charge, and free energy transduction is introduced and analyzed based on a previously published set of data measured on isolated cardiac mitochondria. The basic components included in the model are the reactions at complexes I, III, and IV of the electron transport system, ATP synthesis at F1F0 ATPase, substrate transporters including adenine nucleotide translocase and the phosphate-hydrogen co-transporter, and cation fluxes across the inner membrane including fluxes through the K+/H+ antiporter and passive H+ and K+ permeation. Estimation of 16 adjustable parameter values is based on fitting model simulations to nine independent data curves. The identified model is further validated by comparison to additional datasets measured from mitochondria isolated from rat heart and liver and observed at low oxygen concentration. To obtain reasonable fits to the available data, it is necessary to incorporate inorganic-phosphate-dependent activation of the dehydrogenase activity and the electron transport system. Specifically, it is shown that a model incorporating phosphate-dependent activation of complex III is able to reasonably reproduce the observed data. The resulting validated and verified model provides a foundation for building larger and more complex systems models and investigating complex physiological and pathophysiological interactions in cardiac energetics. link: http://identifiers.org/pubmed/16163394

Parameters: none

States: none

Observables: none

Becker2005 - Genome-scale metabolic network of Staphylococcus aureus (iSB619)This model is described in the article: [G…

BACKGROUND: Several strains of bacteria have sequenced and annotated genomes, which have been used in conjunction with biochemical and physiological data to reconstruct genome-scale metabolic networks. Such reconstruction amounts to a two-dimensional annotation of the genome. These networks have been analyzed with a constraint-based formalism and a variety of biologically meaningful results have emerged. Staphylococcus aureus is a pathogenic bacterium that has evolved resistance to many antibiotics, representing a significant health care concern. We present the first manually curated elementally and charge balanced genome-scale reconstruction and model of S. aureus' metabolic networks and compute some of its properties. RESULTS: We reconstructed a genome-scale metabolic network of S. aureus strain N315. This reconstruction, termed iSB619, consists of 619 genes that catalyze 640 metabolic reactions. For 91% of the reactions, open reading frames are explicitly linked to proteins and to the reaction. All but three of the metabolic reactions are both charge and elementally balanced. The reaction list is the most complete to date for this pathogen. When the capabilities of the reconstructed network were analyzed in the context of maximal growth, we formed hypotheses regarding growth requirements, the efficiency of growth on different carbon sources, and potential drug targets. These hypotheses can be tested experimentally and the data gathered can be used to improve subsequent versions of the reconstruction. CONCLUSION: iSB619 represents comprehensive biochemically and genetically structured information about the metabolism of S. aureus to date. The reconstructed metabolic network can be used to predict cellular phenotypes and thus advance our understanding of a troublesome pathogen. link: http://identifiers.org/pubmed/15752426

Parameters: none

States: none

Observables: none

BIOMD0000000272 @ v0.0.1

This is the auxiliary model described in the article: Covering a Broad Dynamic Range: Information Processing at the Er…

Cell surface receptors convert extracellular cues into receptor activation, thereby triggering intracellular signaling networks and controlling cellular decisions. A major unresolved issue is the identification of receptor properties that critically determine processing of ligand-encoded information. We show by mathematical modeling of quantitative data and experimental validation that rapid ligand depletion and replenishment of the cell surface receptor are characteristic features of the erythropoietin (Epo) receptor (EpoR). The amount of Epo-EpoR complexes and EpoR activation integrated over time corresponds linearly to ligand input; this process is carried out over a broad range of ligand concentrations. This relation depends solely on EpoR turnover independent of ligand binding, which suggests an essential role of large intracellular receptor pools. These receptor properties enable the system to cope with basal and acute demand in the hematopoietic system. link: http://identifiers.org/pubmed/20488988

Parameters:

Name Description
koff_SAv = 0.00679946 (60*s)^(-1) Reaction: SAv_EpoR => SAv + EpoR, Rate Law: koff_SAv*SAv_EpoR*cell
Bmax_SAv = 76.0 1E-12*mol*l^(-1); kt = 0.0329366 (60*s)^(-1) Reaction: => EpoR, Rate Law: kt*Bmax_SAv*cell
kt = 0.0329366 (60*s)^(-1) Reaction: EpoR =>, Rate Law: kt*EpoR*cell
kex_SAv = 0.01101 (60*s)^(-1) Reaction: SAv_EpoRi => SAv, Rate Law: kex_SAv*SAv_EpoRi*cell
kde = 0.0164042 (60*s)^(-1) Reaction: SAv_EpoRi => dSAve, Rate Law: kde*SAv_EpoRi*cell
kdi = 0.00317871 (60*s)^(-1) Reaction: SAv_EpoRi => dSAvi, Rate Law: kdi*SAv_EpoRi*cell
kon_SAv = 2.29402E-6 (60*s)^(-1)*(1E-12*mol)^(-1)*l Reaction: SAv + EpoR => SAv_EpoR, Rate Law: kon_SAv*SAv*EpoR*cell

States:

Name Description
EpoR [Erythropoietin receptor; EPOR]
SAv EpoR [Erythropoietin receptor; Streptavidin]
SAv EpoRi [Erythropoietin receptor; Streptavidin]
dSAve dSAve
SAv [Streptavidin]
dSAvi dSAvi

Observables: none

BIOMD0000000271 @ v0.0.1

This is the core model described in the article: Covering a Broad Dynamic Range: Information Processing at the Erythro…

Cell surface receptors convert extracellular cues into receptor activation, thereby triggering intracellular signaling networks and controlling cellular decisions. A major unresolved issue is the identification of receptor properties that critically determine processing of ligand-encoded information. We show by mathematical modeling of quantitative data and experimental validation that rapid ligand depletion and replenishment of the cell surface receptor are characteristic features of the erythropoietin (Epo) receptor (EpoR). The amount of Epo-EpoR complexes and EpoR activation integrated over time corresponds linearly to ligand input; this process is carried out over a broad range of ligand concentrations. This relation depends solely on EpoR turnover independent of ligand binding, which suggests an essential role of large intracellular receptor pools. These receptor properties enable the system to cope with basal and acute demand in the hematopoietic system. link: http://identifiers.org/pubmed/20488988

Parameters:

Name Description
kon = 1.0496E-4 (60*s)^(-1)*(1E-12*mol)^(-1)*l Reaction: Epo + EpoR => Epo_EpoR, Rate Law: kon*Epo*EpoR*cell
koff = 0.0172135 (60*s)^(-1) Reaction: Epo_EpoR => Epo + EpoR, Rate Law: koff*Epo_EpoR*cell
kt = 0.0329366 (60*s)^(-1) Reaction: EpoR =>, Rate Law: kt*EpoR*cell
kde = 0.0164042 (60*s)^(-1) Reaction: Epo_EpoRi => dEpoe, Rate Law: kde*Epo_EpoRi*cell
kex = 0.00993805 (60*s)^(-1) Reaction: Epo_EpoRi => Epo + EpoR, Rate Law: kex*Epo_EpoRi*cell
kdi = 0.00317871 (60*s)^(-1) Reaction: Epo_EpoRi => dEpoi, Rate Law: kdi*Epo_EpoRi*cell
ke = 0.0748267 (60*s)^(-1) Reaction: Epo_EpoR => Epo_EpoRi, Rate Law: ke*Epo_EpoR*cell
Bmax = 516.0 1E-12*mol*l^(-1); kt = 0.0329366 (60*s)^(-1) Reaction: => EpoR, Rate Law: kt*Bmax*cell

States:

Name Description
EpoR [Epor; Erythropoietin receptor; EPOR]
Epo EpoR [Erythropoietin receptor; Erythropoietin; EPOR; EPO]
Epo [Epo; Erythropoietin; EPO]
dEpoe dEpoe
Epo EpoRi [Erythropoietin receptor; Erythropoietin; EPOR; EPO]
dEpoi dEpoi

Observables: none

This is the model described in the article: Reconstruction of the action potential of ventricular myocardial fibres.…

  1. A mathematical model of membrane action potentials of mammalian ventricular myocardial fibres is described. The reconstruction model is based as closely as possible on ionic currents which have been measured by the voltage-clamp method.2. Four individual components of ionic current were formulated mathematically in terms of Hodgkin-Huxley type equations. The model incorporates two voltage- and time-dependent inward currents, the excitatory inward sodium current, i(Na), and a secondary or slow inward current, i(s), primarily carried by calcium ions. A time-independent outward potassium current, i(K1), exhibiting inward-going rectification, and a voltage- and time-dependent outward current, i(x1), primarily carried by potassium ions, are further elements of the model.3. The i(Na) is primarily responsible for the rapid upstroke of the action potential, while the other current components determine the configuration of the plateau of the action potential and the re-polarization phase. The relative importance of inactivation of i(s) and of activation of i(x1) for termination of the plateau is evaluated by the model.4. Experimental phenomena like slow recovery of the sodium system from inactivation, frequency dependence of the action potential duration, all-or-nothing re-polarization, membrane oscillations are adequately described by the model.5. Possible inadequacies and shortcomings of the model are discussed.

link: http://identifiers.org/pubmed/874889

Parameters: none

States: none

Observables: none

Begitt2014 - STAT1 cooperative DNA binding - double GAS polymer modelThe importance of STAT1-cooperative DNA binding in…

STAT1 is an indispensable component of a heterotrimer (ISGF3) and a STAT1 homodimer (GAF) that function as transcription regulators in type 1 and type 2 interferon signaling, respectively. To investigate the importance of STAT1-cooperative DNA binding, we generated gene-targeted mice expressing cooperativity-deficient STAT1 with alanine substituted for Phe77. Neither ISGF3 nor GAF bound DNA cooperatively in the STAT1F77A mouse strain, but type 1 and type 2 interferon responses were affected differently. Type 2 interferon-mediated transcription and antibacterial immunity essentially disappeared owing to defective promoter recruitment of GAF. In contrast, STAT1 recruitment to ISGF3 binding sites and type 1 interferon-dependent responses, including antiviral protection, remained intact. We conclude that STAT1 cooperativity is essential for its biological activity and underlies the cellular responses to type 2, but not type 1 interferon. link: http://identifiers.org/pubmed/24413774

Parameters:

Name Description
Kon_P1 = 60000.0; Koff_P1 = 100.0 Reaction: DNA1100 => DNA1_100; DNA1100, DNA1_100, Rate Law: nucleus*(Kon_P1*DNA1100-Koff_P1*DNA1_100)/nucleus
Kon_G1 = 2.0E10; Koff_G1 = 100.0 Reaction: DNA0011 + S1 => DNA0111; DNA0011, DNA0111, S1, Rate Law: nucleus*(Kon_G1*DNA0011*S1-Koff_G1*DNA0111)/nucleus
Koff_NG1 = 5000.0; Kon_NG1 = 2.0E10 Reaction: DNA1000 + S1 => DNA1001; DNA1000, DNA1001, S1, Rate Law: nucleus*(Kon_NG1*DNA1000*S1-Koff_NG1*DNA1001)/nucleus

States:

Name Description
DNA1010 DNA1010
DNA111 1 DNA111_1
DNA0100 DNA0100
DNA1011 DNA1011
DNA11 1 1 DNA11_1_1
DNA1 1 1 1 DNA1_1_1_1
DNA0101 DNA0101
DNA1 111 DNA1_111
DNA1100 DNA1100
DNA1111 DNA1111
S1 [Signal transducer and activator of transcription 1]
DNA1 1 11 DNA1_1_11
DNA101 1 DNA101_1
DNA0011 DNA0011
DNA11 10 DNA11_10
DNA0111 DNA0111
DNA0110 DNA0110
DNA011 1 DNA011_1
DNA0010 DNA0010
DNA1001 DNA1001
DNA001 1 DNA001_1
DNA1 1 10 DNA1_1_10
DNA0000 DNA0000
DNA1 100 DNA1_100
DNA01 10 DNA01_10
DNA1000 DNA1000
DNA1 110 DNA1_110
DNA1 11 1 DNA1_11_1
DNA0001 DNA0001
DNA1101 DNA1101

Observables: none

Begitt2014 - STAT1 cooperative DNA binding - single GAS polymer modelThe importance of STAT1-cooperative DNA binding in…

STAT1 is an indispensable component of a heterotrimer (ISGF3) and a STAT1 homodimer (GAF) that function as transcription regulators in type 1 and type 2 interferon signaling, respectively. To investigate the importance of STAT1-cooperative DNA binding, we generated gene-targeted mice expressing cooperativity-deficient STAT1 with alanine substituted for Phe77. Neither ISGF3 nor GAF bound DNA cooperatively in the STAT1F77A mouse strain, but type 1 and type 2 interferon responses were affected differently. Type 2 interferon-mediated transcription and antibacterial immunity essentially disappeared owing to defective promoter recruitment of GAF. In contrast, STAT1 recruitment to ISGF3 binding sites and type 1 interferon-dependent responses, including antiviral protection, remained intact. We conclude that STAT1 cooperativity is essential for its biological activity and underlies the cellular responses to type 2, but not type 1 interferon. link: http://identifiers.org/pubmed/24413774

Parameters:

Name Description
Kon_P1 = 60000.0; Koff_P1 = 100.0 Reaction: DNA_110 => DNA_1B10; DNA_110, DNA_1B10, Rate Law: nucleus*(Kon_P1*DNA_110-Koff_P1*DNA_1B10)/nucleus
Kon_NG1 = 2.0E10; Koff_NG1 = 20000.0 Reaction: DNA_001 + S1 => DNA_101; DNA_001, DNA_101, S1, Rate Law: nucleus*(Kon_NG1*DNA_001*S1-Koff_NG1*DNA_101)/nucleus
Kon_G1 = 2.0E10; Koff_G1 = 100.0 Reaction: DNA_101 + S1 => DNA_111; DNA_101, DNA_111, S1, Rate Law: nucleus*(Kon_G1*DNA_101*S1-Koff_G1*DNA_111)/nucleus

States:

Name Description
DNA 1B1B1 DNA_1B1B1
DNA 01B1 DNA_01B1
DNA 011 DNA_011
DNA 1B11 DNA_1B11
DNA 010 DNA_010
DNA 111 DNA_111
DNA 000 DNA_000
DNA 001 DNA_001
DNA 11B1 DNA_11B1
DNA 100 DNA_100
S1 [Signal transducer and activator of transcription 1]
DNA 101 DNA_101
DNA 110 DNA_110
DNA 1B10 DNA_1B10

Observables: none

# Orthologous iso-enzyme metabolic network for Bos taurus Copy number alterations in the mammalian metabolic network co-…

Using two high-quality human metabolic networks, we employed comparative genomics techniques to infer metabolic network structures for seven other mammals. We then studied copy number alterations (CNAs) in these networks. Using a graph-theoretic approach, we show that the pattern of CNAs is distinctly different from the random distributions expected under genetic drift. Instead, we find that changes in copy number are most common among transporter genes and that the CNAs differ depending on the mammalian lineage in question. Thus, we find an excess of transporter genes in cattle involved in the milk production, secretion, and regulation. These results suggest a potential role for dosage selection in the evolution of mammalian metabolic networks. link: http://identifiers.org/pubmed/21051442

Parameters: none

States: none

Observables: none

# Orthologous iso-enzyme metabolic network for Pan troglodytes Copy number alterations in the mammalian metabolic networ…

Using two high-quality human metabolic networks, we employed comparative genomics techniques to infer metabolic network structures for seven other mammals. We then studied copy number alterations (CNAs) in these networks. Using a graph-theoretic approach, we show that the pattern of CNAs is distinctly different from the random distributions expected under genetic drift. Instead, we find that changes in copy number are most common among transporter genes and that the CNAs differ depending on the mammalian lineage in question. Thus, we find an excess of transporter genes in cattle involved in the milk production, secretion, and regulation. These results suggest a potential role for dosage selection in the evolution of mammalian metabolic networks. link: http://identifiers.org/pubmed/21051442

Parameters: none

States: none

Observables: none

# Orthologous iso-enzyme metabolic network for Canis familiaris Copy number alterations in the mammalian metabolic netwo…

Using two high-quality human metabolic networks, we employed comparative genomics techniques to infer metabolic network structures for seven other mammals. We then studied copy number alterations (CNAs) in these networks. Using a graph-theoretic approach, we show that the pattern of CNAs is distinctly different from the random distributions expected under genetic drift. Instead, we find that changes in copy number are most common among transporter genes and that the CNAs differ depending on the mammalian lineage in question. Thus, we find an excess of transporter genes in cattle involved in the milk production, secretion, and regulation. These results suggest a potential role for dosage selection in the evolution of mammalian metabolic networks. link: http://identifiers.org/pubmed/21051442

Parameters: none

States: none

Observables: none

# Orthologous iso-enzyme metabolic network for Equus caballus Copy number alterations in the mammalian metabolic network…

Using two high-quality human metabolic networks, we employed comparative genomics techniques to infer metabolic network structures for seven other mammals. We then studied copy number alterations (CNAs) in these networks. Using a graph-theoretic approach, we show that the pattern of CNAs is distinctly different from the random distributions expected under genetic drift. Instead, we find that changes in copy number are most common among transporter genes and that the CNAs differ depending on the mammalian lineage in question. Thus, we find an excess of transporter genes in cattle involved in the milk production, secretion, and regulation. These results suggest a potential role for dosage selection in the evolution of mammalian metabolic networks. link: http://identifiers.org/pubmed/21051442

Parameters: none

States: none

Observables: none

# Orthologous iso-enzyme metabolic network for Macaca mulatta Copy number alterations in the mammalian metabolic network…

Using two high-quality human metabolic networks, we employed comparative genomics techniques to infer metabolic network structures for seven other mammals. We then studied copy number alterations (CNAs) in these networks. Using a graph-theoretic approach, we show that the pattern of CNAs is distinctly different from the random distributions expected under genetic drift. Instead, we find that changes in copy number are most common among transporter genes and that the CNAs differ depending on the mammalian lineage in question. Thus, we find an excess of transporter genes in cattle involved in the milk production, secretion, and regulation. These results suggest a potential role for dosage selection in the evolution of mammalian metabolic networks. link: http://identifiers.org/pubmed/21051442

Parameters: none

States: none

Observables: none

# Orthologous iso-enzyme metabolic network for Mus musculus Copy number alterations in the mammalian metabolic network c…

Using two high-quality human metabolic networks, we employed comparative genomics techniques to infer metabolic network structures for seven other mammals. We then studied copy number alterations (CNAs) in these networks. Using a graph-theoretic approach, we show that the pattern of CNAs is distinctly different from the random distributions expected under genetic drift. Instead, we find that changes in copy number are most common among transporter genes and that the CNAs differ depending on the mammalian lineage in question. Thus, we find an excess of transporter genes in cattle involved in the milk production, secretion, and regulation. These results suggest a potential role for dosage selection in the evolution of mammalian metabolic networks. link: http://identifiers.org/pubmed/21051442

Parameters: none

States: none

Observables: none

# Orthologous iso-enzyme metabolic network for Rattus norvegicus Copy number alterations in the mammalian metabolic netw…

Using two high-quality human metabolic networks, we employed comparative genomics techniques to infer metabolic network structures for seven other mammals. We then studied copy number alterations (CNAs) in these networks. Using a graph-theoretic approach, we show that the pattern of CNAs is distinctly different from the random distributions expected under genetic drift. Instead, we find that changes in copy number are most common among transporter genes and that the CNAs differ depending on the mammalian lineage in question. Thus, we find an excess of transporter genes in cattle involved in the milk production, secretion, and regulation. These results suggest a potential role for dosage selection in the evolution of mammalian metabolic networks. link: http://identifiers.org/pubmed/21051442

Parameters: none

States: none

Observables: none

Bekaert2012 - Reconstruction of D.rerio Metabolic NetworkDanio rerio metabolic model accounting for subcellular compart…

Plant and microbial metabolic engineering is commonly used in the production of functional foods and quality trait improvement. Computational model-based approaches have been used in this important endeavour. However, to date, fish metabolic models have only been scarcely and partially developed, in marked contrast to their prominent success in metabolic engineering. In this study we present the reconstruction of fully compartmentalised models of the Danio rerio (zebrafish) on a global scale. This reconstruction involves extraction of known biochemical reactions in D. rerio for both primary and secondary metabolism and the implementation of methods for determining subcellular localisation and assignment of enzymes. The reconstructed model (ZebraGEM) is amenable for constraint-based modelling analysis, and accounts for 4,988 genes coding for 2,406 gene-associated reactions and only 418 non-gene-associated reactions. A set of computational validations (i.e., simulations of known metabolic functionalities and experimental data) strongly testifies to the predictive ability of the model. Overall, the reconstructed model is expected to lay down the foundations for computational-based rational design of fish metabolic engineering in aquaculture. link: http://identifiers.org/pubmed/23166792

Parameters: none

States: none

Observables: none

This network was obtained by combining (with an OR logical operator) the following list of IL1B_secretion activators: TX…

Knowledgebases play an increasingly important role in scientific research, where the expert curation of biological knowledge in forms that are amenable to computational analysis (using ontologies for example)–provides a significant added value and enables new types of computational analyses for high throughput datasets. In this work, we demonstrate how expert curation can also play a more direct role in research, by supporting the use of network-based dynamical models to study a specific biological process. This curation effort is focused on the regulatory interactions between biological entities, such as genes or proteins and compounds, which may interact with each other in a complex manner, including regulatory complexes and conditional dependencies between co-regulators. This critical information has to be captured and encoded in a computable manner, which is currently far beyond the current capabilities of automatically constructed network. As a case study, we report here the prior knowledge network constructed by the sysVASC consortium to model the biological events leading to the formation of atherosclerotic plaques, during the onset of cardiovascular disease and discuss some specific examples to illustrate the main pitfalls and added value provided by the expert curation during this endeavor. link: http://identifiers.org/pubmed/29688381

Parameters: none

States: none

Observables: none

This network was obtained by combining (with an OR logical operator) the following list of MAPK1_3 activators: PDGFRB -…

Knowledgebases play an increasingly important role in scientific research, where the expert curation of biological knowledge in forms that are amenable to computational analysis (using ontologies for example)–provides a significant added value and enables new types of computational analyses for high throughput datasets. In this work, we demonstrate how expert curation can also play a more direct role in research, by supporting the use of network-based dynamical models to study a specific biological process. This curation effort is focused on the regulatory interactions between biological entities, such as genes or proteins and compounds, which may interact with each other in a complex manner, including regulatory complexes and conditional dependencies between co-regulators. This critical information has to be captured and encoded in a computable manner, which is currently far beyond the current capabilities of automatically constructed network. As a case study, we report here the prior knowledge network constructed by the sysVASC consortium to model the biological events leading to the formation of atherosclerotic plaques, during the onset of cardiovascular disease and discuss some specific examples to illustrate the main pitfalls and added value provided by the expert curation during this endeavor. link: http://identifiers.org/pubmed/29688381

Parameters: none

States: none

Observables: none

This network was obtained by combining (with an OR logical operator) the following list of PPARA activators: SIRT1 AND P…

Knowledgebases play an increasingly important role in scientific research, where the expert curation of biological knowledge in forms that are amenable to computational analysis (using ontologies for example)–provides a significant added value and enables new types of computational analyses for high throughput datasets. In this work, we demonstrate how expert curation can also play a more direct role in research, by supporting the use of network-based dynamical models to study a specific biological process. This curation effort is focused on the regulatory interactions between biological entities, such as genes or proteins and compounds, which may interact with each other in a complex manner, including regulatory complexes and conditional dependencies between co-regulators. This critical information has to be captured and encoded in a computable manner, which is currently far beyond the current capabilities of automatically constructed network. As a case study, we report here the prior knowledge network constructed by the sysVASC consortium to model the biological events leading to the formation of atherosclerotic plaques, during the onset of cardiovascular disease and discuss some specific examples to illustrate the main pitfalls and added value provided by the expert curation during this endeavor. link: http://identifiers.org/pubmed/29688381

Parameters: none

States: none

Observables: none

BIOMD0000000368 @ v0.0.1

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/…

A hierarchy of enzyme-catalyzed positive feedback loops is examined by mathematical and numerical analysis. Four systems are described, from the simplest, in which an enzyme catalyzes its own formation from an inactive precursor, to the most complex, in which two sequential feedback loops act in a cascade. In the latter we also examine the function of a long-range feedback, in which the final enzyme produced in the second loop activates the initial step in the first loop. When the enzymes generated are subject to inhibition or inactivation, all four systems exhibit threshold properties akin to excitable systems like neuron firing. For those that are amenable to mathematical analysis, expressions are derived that relate the excitation threshold to the kinetics of enzyme generation and inhibition and the initial conditions. For the most complex system, it was expedient to employ numerical simulation to demonstrate threshold behavior, and in this case long-range feedback was seen to have two distinct effects. At sufficiently high catalytic rates, this feedback is capable of exciting an otherwise subthreshold system. At lower catalytic rates, where the long-range feedback does not significantly affect the threshold, it nonetheless has a major effect in potentiating the response above the threshold. In particular, oscillatory behavior observed in simulations of sequential feedback loops is abolished when a long-range feedback is present. link: http://identifiers.org/pubmed/7568009

Parameters:

Name Description
mu23 = 0.1; mu3 = 0.1; k3 = 5.0 Reaction: E3 = (mu23*E2+mu3*E4)*Z3-k3*E3, Rate Law: (mu23*E2+mu3*E4)*Z3-k3*E3
mu4 = 0.1; k4 = 5.0 Reaction: E4 = mu4*E3*Z4-k4*E4, Rate Law: mu4*E3*Z4-k4*E4
mu5 = 1.0; k1 = 1.0; mu1 = 1.0 Reaction: Z1 = (-(mu1*E2+mu5*E4))*Z1+k1*E1, Rate Law: (-(mu1*E2+mu5*E4))*Z1+k1*E1
mu2 = 0.1; k2 = 1.0 Reaction: E2 = mu2*E1*Z2-k2*E2, Rate Law: mu2*E1*Z2-k2*E2

States:

Name Description
Z4 Z4
E3 E3
E2 E2
Z2 Z2
Z3 Z3
E1 E1
Z1 Z1
E4 E4

Observables: none

BIOMD0000000369 @ v0.0.1

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/…

A hierarchy of enzyme-catalyzed positive feedback loops is examined by mathematical and numerical analysis. Four systems are described, from the simplest, in which an enzyme catalyzes its own formation from an inactive precursor, to the most complex, in which two sequential feedback loops act in a cascade. In the latter we also examine the function of a long-range feedback, in which the final enzyme produced in the second loop activates the initial step in the first loop. When the enzymes generated are subject to inhibition or inactivation, all four systems exhibit threshold properties akin to excitable systems like neuron firing. For those that are amenable to mathematical analysis, expressions are derived that relate the excitation threshold to the kinetics of enzyme generation and inhibition and the initial conditions. For the most complex system, it was expedient to employ numerical simulation to demonstrate threshold behavior, and in this case long-range feedback was seen to have two distinct effects. At sufficiently high catalytic rates, this feedback is capable of exciting an otherwise subthreshold system. At lower catalytic rates, where the long-range feedback does not significantly affect the threshold, it nonetheless has a major effect in potentiating the response above the threshold. In particular, oscillatory behavior observed in simulations of sequential feedback loops is abolished when a long-range feedback is present. link: http://identifiers.org/pubmed/7568009

Parameters:

Name Description
mu4 = 1.0; k4 = 1.0 Reaction: E4 = mu4*E3*Z4-k4*E4, Rate Law: mu4*E3*Z4-k4*E4
mu4 = 1.0 Reaction: Z4 = (-mu4)*E3*Z4, Rate Law: (-mu4)*E3*Z4
mu2 = 0.1; C = 0.001 Reaction: Z2 = (-mu2)*(1+C)*E1*Z2, Rate Law: (-mu2)*(1+C)*E1*Z2
mu2 = 0.1; mu3 = 1.0; k2 = 1.0 Reaction: E2 = (mu2*E1*Z2-mu3*E4*E2)-k2*E2, Rate Law: (mu2*E1*Z2-mu3*E4*E2)-k2*E2
mu5 = 0.0; mu1 = 1.0 Reaction: Z1 = (-(mu1*E2+mu5*E4))*Z1, Rate Law: (-(mu1*E2+mu5*E4))*Z1
mu2 = 0.1; k3 = 1.0; mu3 = 1.0; C = 0.001 Reaction: E3 = (mu2*C*E1*Z2+mu3*E4*E2)-k3*E3, Rate Law: (mu2*C*E1*Z2+mu3*E4*E2)-k3*E3
mu5 = 0.0; k1 = 1.0; mu1 = 1.0 Reaction: E1 = (mu1*E2+mu5*E4)*Z1-k1*E1, Rate Law: (mu1*E2+mu5*E4)*Z1-k1*E1

States:

Name Description
Z4 Z4
E3 E3
Z2 Z2
E2 E2
E1 E1
E4 E4
Z1 Z1

Observables: none

It's a mathematical model studying feedback control of B-catenin pathway by HOS and FWD1 using Lee2003 model as base.

The Wnt/β-catenin signalling pathway is involved in the regulation of a multitude of cellular processes by controlling the concentration of the transcriptional regulator β-catenin. Proteasomal degradation of β-catenin is mediated by two β-transducin repeat-containing protein paralogues, homologous to Slimb protein (HOS) and F-box/WD repeat-containing protein 1A (FWD1), which are functionally interchangeable and thereby considered to function redundantly in the pathway. HOS and FWD1 are both regulated by Wnt/β-catenin signalling, albeit in opposite directions, thus establishing interlocked negative and positive feedback loops. The functional relevance of the opposite regulation of HOS and FWD1 by Wnt/β-catenin signalling in conjunction with their redundant activities in proteasomal degradation of β-catenin remains unresolved. Using a detailed ordinary differential equation model, we investigated the specific influence of each individual feedback mechanism and their combination on Wnt/β-catenin signal transduction under wild-type and cancerous conditions. We found that, under wild-type conditions, the signalling dynamics are predominantly affected by the HOS feedback as a result of a higher concentration of HOS than FWD1. Transcriptional up-regulation of FWD1 by other signalling pathways reduced the impact of the HOS feedback. The opposite regulation of HOS and FWD1 expression by Wnt/β-catenin signalling allows the FWD1 feedback to be employed as a compensation mechanism against aberrant pathway activation as a result of a reduced HOS concentration. By contrast, the FWD1 feedback provides no protection against aberrant activation in adenomatous polyposis coli protein mutant cancer cells. link: http://identifiers.org/pubmed/25601154

Parameters: none

States: none

Observables: none

its a mathematical model studying impact of b_TrCP on NFKB nuclear dynamics. This model is derived from Lipniacki2004 (P…

The canonical nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) signaling pathway regulates central processes in mammalian cells and plays a fundamental role in the regulation of inflammation and immunity. Aberrant regulation of the activation of the transcription factor NF-κB is associated with severe diseases such as inflammatory bowel disease and arthritis. In the canonical pathway, the inhibitor IκB suppresses NF-κB's transcriptional activity. NF-κB becomes active upon the degradation of IκB, a process that is, in turn, regulated by the β-transducin repeat-containing protein (β-TrCP). β-TrCP has therefore been proposed as a promising pharmacological target in the development of novel therapeutic approaches to control NF-κB's activity in diseases. This study explores the extent to which β-TrCP affects the dynamics of nuclear NF-κB using a computational model of canonical NF-κB signaling. The analysis predicts that β-TrCP influences the steady-state concentration of nuclear NF-κB, as well as changes characteristic dynamic properties of nuclear NF-κB, such as fold-change and the duration of its response to pathway stimulation. The results suggest that the modulation of β-TrCP has a high potential to regulate the transcriptional activity of NF-κB. link: http://identifiers.org/pubmed/31137887

Parameters:

Name Description
k2 = 0.006 Reaction: IKK_active => IKK_inact; TNF, A20, Rate Law: Cytosol*function_for_R26(k2, TNF, A20, IKK_active)
a1 = 0.03 Reaction: NFKB + IkB => IkB_NFKB, Rate Law: Cytosol*a1*NFKB*IkB
c5a = 0.006 Reaction: IkB =>, Rate Law: Cytosol*c5a*IkB
a2 = 0.012 Reaction: IKK_active + IkB => IKKactive_IkB, Rate Law: Cytosol*a2*IKK_active*IkB
t1 = 6.0 Reaction: IKKactive_IkB => IKK_active; b_TrCP, Rate Law: Cytosol*function_for_R3(t1, b_TrCP, IKKactive_IkB)
c3a = 0.024 Reaction: IkB_mRNA =>, Rate Law: Nucleus*c3a*IkB_mRNA
c4a = 30.0 Reaction: => IkB; IkB_mRNA, Rate Law: function_for_substrateless_production(c4a, IkB_mRNA)
Kprod = 1.5 Reaction: => IKK_neutral, Rate Law: Cytosol*Constant_flux__irreversible(Kprod)
c3 = 0.024 Reaction: A20_mRNA =>, Rate Law: Nucleus*c3*A20_mRNA
c6a = 0.0012 Reaction: IkB_NFKB => NFKB, Rate Law: Cytosol*c6a*IkB_NFKB
c3c = 0.024 Reaction: cgen_mRNA =>, Rate Law: Nucleus*c3c*cgen_mRNA
i1a = 0.06 Reaction: IkB =>, Rate Law: Cytosol*i1a*IkB
e1a = 0.03 Reaction: => IkB; IkB_nuc, Rate Law: function_for_indirect_production(e1a, IkB_nuc)
i1 = 0.15 Reaction: NFKB =>, Rate Law: Cytosol*i1*NFKB
Kv = 5.0; i1 = 0.15 Reaction: => NFKB_nuc; NFKB, Rate Law: function_for_indirect_transport(i1, Kv, NFKB)
a3 = 0.06 Reaction: IKK_active + IkB_NFKB => IKKactive_IkB_NFKB, Rate Law: Cytosol*a3*IKK_active*IkB_NFKB
e2a = 0.6 Reaction: => IkB_NFKB; IkB_NFKB_nuc, Rate Law: function_for_indirect_production(e2a, IkB_NFKB_nuc)
c1 = 3.0E-5 Reaction: => A20_mRNA; NFKB_nuc, Rate Law: Nucleus*function_for_substrateless_production(c1, NFKB_nuc)
c1c = 3.0E-5 Reaction: => cgen_mRNA; NFKB_nuc, Rate Law: Nucleus*function_for_substrateless_production(c1c, NFKB_nuc)
Kv = 5.0; i1a = 0.06 Reaction: => IkB_nuc; IkB, Rate Law: function_for_indirect_transport(i1a, Kv, IkB)
TNF_R = 0.0 Reaction: TNF = TNF_R, Rate Law: missing
Kv = 5.0; e2a = 0.6 Reaction: IkB_NFKB_nuc =>, Rate Law: Nucleus*function_for_transport(e2a, Kv, IkB_NFKB_nuc)
c1a = 3.0E-5 Reaction: => IkB_mRNA; NFKB_nuc, Rate Law: Nucleus*function_for_substrateless_production(c1a, NFKB_nuc)
t2 = 6.0 Reaction: IKKactive_IkB_NFKB => IKK_active + NFKB; b_TrCP, Rate Law: Cytosol*function_for_R3(t2, b_TrCP, IKKactive_IkB_NFKB)
c4 = 30.0 Reaction: => A20; A20_mRNA, Rate Law: function_for_substrateless_production(c4, A20_mRNA)
k1 = 0.15 Reaction: IKK_neutral => IKK_active; TNF, Rate Law: Cytosol*function_for_R3(k1, TNF, IKK_neutral)
e1a = 0.03; Kv = 5.0 Reaction: IkB_nuc =>, Rate Law: Nucleus*function_for_transport(e1a, Kv, IkB_nuc)
c5 = 0.018 Reaction: A20 =>, Rate Law: Cytosol*c5*A20
k3 = 0.09 Reaction: IKK_active => IKK_inact, Rate Law: Cytosol*k3*IKK_active
Kdeg = 0.0075 Reaction: IKK_inact =>, Rate Law: Cytosol*Kdeg*IKK_inact

States:

Name Description
IKKactive IkB NFKB [NF-kappa-B inhibitor alpha; Inhibitor of nuclear factor kappa-B kinase subunit alpha; Nuclear factor NF-kappa-B p105 subunit]
IKK inact [Inhibitor of nuclear factor kappa-B kinase subunit alpha]
TNF [Tumor necrosis factor]
A20 [Tumor necrosis factor alpha-induced protein 3]
IkB mRNA [NF-kappa-B inhibitor alpha]
IkB NFKB nuc [NF-kappa-B inhibitor alpha; Nuclear factor NF-kappa-B p105 subunit]
IKK active [Inhibitor of nuclear factor kappa-B kinase subunit alpha]
IkB [NF-kappa-B inhibitor alpha]
IKKactive IkB [NF-kappa-B inhibitor alpha; Inhibitor of nuclear factor kappa-B kinase subunit alpha]
NFKB nuc [Nuclear factor NF-kappa-B p105 subunit]
IkB NFKB [Nuclear factor NF-kappa-B p105 subunit; NF-kappa-B inhibitor alpha]
cgen mRNA cgen_mRNA
IkB nuc [NF-kappa-B inhibitor alpha]
IKK neutral [Inhibitor of nuclear factor kappa-B kinase subunit alpha]
NFKB [Nuclear factor NF-kappa-B p105 subunit]
A20 mRNA [Tumor necrosis factor alpha-induced protein 3]

Observables: none

Benedict2011 - Genome-scale metoblic network of Methanosarcina acetivorans (iMB745)This model is described in the articl…

Methanosarcina acetivorans strain C2A is a marine methanogenic archaeon notable for its substrate utilization, genetic tractability, and novel energy conservation mechanisms. To help probe the phenotypic implications of this organism's unique metabolism, we have constructed and manually curated a genome-scale metabolic model of M. acetivorans, iMB745, which accounts for 745 of the 4,540 predicted protein-coding genes (16%) in the M. acetivorans genome. The reconstruction effort has identified key knowledge gaps and differences in peripheral and central metabolism between methanogenic species. Using flux balance analysis, the model quantitatively predicts wild-type phenotypes and is 96% accurate in knockout lethality predictions compared to currently available experimental data. The model was used to probe the mechanisms and energetics of by-product formation and growth on carbon monoxide, as well as the nature of the reaction catalyzed by the soluble heterodisulfide reductase HdrABC in M. acetivorans. The genome-scale model provides quantitative and qualitative hypotheses that can be used to help iteratively guide additional experiments to further the state of knowledge about methanogenesis. link: http://identifiers.org/pubmed/22139506

Parameters: none

States: none

Observables: none

MODEL1006230078 @ v0.0.1

This a model from the article: The canine virtual ventricular wall: a platform for dissecting pharmacological effects…

We have constructed computational models of canine ventricular cells and tissues, ultimately combining detailed tissue architecture and heterogeneous transmural electrophysiology. The heterogeneity is introduced by modifying the Hund-Rudy canine cell model in order to reproduce experimentally reported electrophysiological properties of endocardial, midmyocardial (M) and epicardial cells. These models are validated against experimental data for individual ionic current and action potential characteristics, and their rate dependencies. 1D and 3D heterogeneous virtual tissues are constructed, with detailed tissue architecture (anisotropy and orthotropy, due to fibre orientation and sheet structure) of the left ventricular wall wedge extracted from a diffusion tensor imaging data set. The models are used to study the effects of tissue heterogeneity and class III drugs on transmural propagation and tissue vulnerability to re-entry. We have determined relationships between the transmural dispersion of action potential duration (APD) and the vulnerable window in the 1D virtual ventricular wall, and demonstrated how changes in the transmural heterogeneity, and hence tissue vulnerability, can lead to generation of re-entry in the 3D ventricular wedge. Two class III drugs with opposite qualitative effects on transmural APD heterogeneity are considered: d-sotalol that increases transmural APD dispersion, and amiodarone that decreases it. Simulations with the 1D virtual ventricular wall show that under d-sotalol conditions the vulnerable window is substantially wider compared to amiodarone conditions, primarily in the epicardial region where unidirectional conduction block persists until the adjacent M cells are fully repolarised. Further simulations with the 3D ventricular wedge have shown that ectopic stimulation of the epicardial region results in generation of sustained re-entry under d-sotalol conditions, but not under amiodarone conditions or in control. Again, APD increase in M cells was identified as the major contributor to tissue vulnerability–re-entry was initiated primarily due to ectopic excitation propagating around the unidirectional conduction block in the M cell region. This suggests an electrophysiological mechanism for the anti- and proarrhythmic effects of the class III drugs: the relative safety of amiodarone in comparison to d-sotalol can be explained by relatively low transmural APD dispersion, and hence, a narrow vulnerable window and low probability of re-entry in the tissue. link: http://identifiers.org/pubmed/17915298

Parameters: none

States: none

Observables: none

MODEL1006230101 @ v0.0.1

This a model from the article: The canine virtual ventricular wall: a platform for dissecting pharmacological effects…

We have constructed computational models of canine ventricular cells and tissues, ultimately combining detailed tissue architecture and heterogeneous transmural electrophysiology. The heterogeneity is introduced by modifying the Hund-Rudy canine cell model in order to reproduce experimentally reported electrophysiological properties of endocardial, midmyocardial (M) and epicardial cells. These models are validated against experimental data for individual ionic current and action potential characteristics, and their rate dependencies. 1D and 3D heterogeneous virtual tissues are constructed, with detailed tissue architecture (anisotropy and orthotropy, due to fibre orientation and sheet structure) of the left ventricular wall wedge extracted from a diffusion tensor imaging data set. The models are used to study the effects of tissue heterogeneity and class III drugs on transmural propagation and tissue vulnerability to re-entry. We have determined relationships between the transmural dispersion of action potential duration (APD) and the vulnerable window in the 1D virtual ventricular wall, and demonstrated how changes in the transmural heterogeneity, and hence tissue vulnerability, can lead to generation of re-entry in the 3D ventricular wedge. Two class III drugs with opposite qualitative effects on transmural APD heterogeneity are considered: d-sotalol that increases transmural APD dispersion, and amiodarone that decreases it. Simulations with the 1D virtual ventricular wall show that under d-sotalol conditions the vulnerable window is substantially wider compared to amiodarone conditions, primarily in the epicardial region where unidirectional conduction block persists until the adjacent M cells are fully repolarised. Further simulations with the 3D ventricular wedge have shown that ectopic stimulation of the epicardial region results in generation of sustained re-entry under d-sotalol conditions, but not under amiodarone conditions or in control. Again, APD increase in M cells was identified as the major contributor to tissue vulnerability–re-entry was initiated primarily due to ectopic excitation propagating around the unidirectional conduction block in the M cell region. This suggests an electrophysiological mechanism for the anti- and proarrhythmic effects of the class III drugs: the relative safety of amiodarone in comparison to d-sotalol can be explained by relatively low transmural APD dispersion, and hence, a narrow vulnerable window and low probability of re-entry in the tissue. link: http://identifiers.org/pubmed/17915298

Parameters: none

States: none

Observables: none

MODEL1006230087 @ v0.0.1

This a model from the article: The canine virtual ventricular wall: a platform for dissecting pharmacological effects…

We have constructed computational models of canine ventricular cells and tissues, ultimately combining detailed tissue architecture and heterogeneous transmural electrophysiology. The heterogeneity is introduced by modifying the Hund-Rudy canine cell model in order to reproduce experimentally reported electrophysiological properties of endocardial, midmyocardial (M) and epicardial cells. These models are validated against experimental data for individual ionic current and action potential characteristics, and their rate dependencies. 1D and 3D heterogeneous virtual tissues are constructed, with detailed tissue architecture (anisotropy and orthotropy, due to fibre orientation and sheet structure) of the left ventricular wall wedge extracted from a diffusion tensor imaging data set. The models are used to study the effects of tissue heterogeneity and class III drugs on transmural propagation and tissue vulnerability to re-entry. We have determined relationships between the transmural dispersion of action potential duration (APD) and the vulnerable window in the 1D virtual ventricular wall, and demonstrated how changes in the transmural heterogeneity, and hence tissue vulnerability, can lead to generation of re-entry in the 3D ventricular wedge. Two class III drugs with opposite qualitative effects on transmural APD heterogeneity are considered: d-sotalol that increases transmural APD dispersion, and amiodarone that decreases it. Simulations with the 1D virtual ventricular wall show that under d-sotalol conditions the vulnerable window is substantially wider compared to amiodarone conditions, primarily in the epicardial region where unidirectional conduction block persists until the adjacent M cells are fully repolarised. Further simulations with the 3D ventricular wedge have shown that ectopic stimulation of the epicardial region results in generation of sustained re-entry under d-sotalol conditions, but not under amiodarone conditions or in control. Again, APD increase in M cells was identified as the major contributor to tissue vulnerability–re-entry was initiated primarily due to ectopic excitation propagating around the unidirectional conduction block in the M cell region. This suggests an electrophysiological mechanism for the anti- and proarrhythmic effects of the class III drugs: the relative safety of amiodarone in comparison to d-sotalol can be explained by relatively low transmural APD dispersion, and hence, a narrow vulnerable window and low probability of re-entry in the tissue. link: http://identifiers.org/pubmed/17915298

Parameters: none

States: none

Observables: none

Benson2013 - Identification of key drug targets in nerve growth factor pathwayThis model is described in the article: […

The nerve growth factor (NGF) pathway is of great interest as a potential source of drug targets, for example in the management of certain types of pain. However, selecting targets from this pathway either by intuition or by non-contextual measures is likely to be challenging. An alternative approach is to construct a mathematical model of the system and via sensitivity analysis rank order the targets in the known pathway, with respect to an endpoint such as the diphosphorylated extracellular signal-regulated kinase concentration in the nucleus. Using the published literature, a model was created and, via sensitivity analysis, it was concluded that, after NGF itself, tropomyosin receptor kinase A (TrkA) was one of the most sensitive druggable targets. This initial model was subsequently used to develop a further model incorporating physiological and pharmacological parameters. This allowed the exploration of the characteristics required for a successful hypothetical TrkA inhibitor. Using these systems models, we were able to identify candidates for the optimal drug targets in the known pathway. These conclusions were consistent with clinical and human genetic data. We also found that incorporating appropriate physiological context was essential to drawing accurate conclusions about important parameters such as the drug dose required to give pathway inhibition. Furthermore, the importance of the concentration of key reactants such as TrkA kinase means that appropriate contextual data are required before clear conclusions can be drawn. Such models could be of great utility in selecting optimal targets and in the clinical evaluation of novel drugs. link: http://identifiers.org/pubmed/24427523

Parameters:

Name Description
kf_20 = 600.0 1/(micromolarity*minute); kb_20 = 12.0 1/minute Reaction: pTrkA + Grb2_SOS_pShc => Grb2_SOS_pShc_pTrkA; Grb2_SOS_pShc, pTrkA, Grb2_SOS_pShc_pTrkA, Rate Law: kf_20*Grb2_SOS_pShc*pTrkA-kb_20*Grb2_SOS_pShc_pTrkA
kf_23 = 600.0 1/(micromolarity*minute); kb_23 = 12.0 1/minute Reaction: pTrkA_endo + Grb2_SOS_pShc => Grb2_SOS_pShc_pTrkA_endo; Grb2_SOS_pShc, pTrkA_endo, Grb2_SOS_pShc_pTrkA_endo, Rate Law: kf_23*Grb2_SOS_pShc*pTrkA_endo-kb_23*Grb2_SOS_pShc_pTrkA_endo
kf_67 = 60.0 1/(micromolarity*minute); kb_67 = 12.0 1/minute Reaction: pFRS2_pTrkA_endo + Crk_C3G => Crk_C3G_pFRS2_pTrkA_endo; Crk_C3G, pFRS2_pTrkA_endo, Crk_C3G_pFRS2_pTrkA_endo, Rate Law: kf_67*Crk_C3G*pFRS2_pTrkA_endo-kb_67*Crk_C3G_pFRS2_pTrkA_endo
kf_1 = 0.049998 1/minute; kb_1 = 0.0166668 1/minute Reaction: mwf82ad06a_b8aa_40fa_a532_a1da44e3425f => NGFR + mwf82ad06a_b8aa_40fa_a532_a1da44e3425f; mwf82ad06a_b8aa_40fa_a532_a1da44e3425f, NGFR, Rate Law: kf_1*mwf82ad06a_b8aa_40fa_a532_a1da44e3425f-kb_1*NGFR
kf_47 = 1.8 1/(micromolarity*minute); kb_47 = 1.008 1/minute Reaction: Grb2 + pSOS => Grb2_pSOS; Grb2, pSOS, Grb2_pSOS, Rate Law: kf_47*Grb2*pSOS-kb_47*Grb2_pSOS
Km_41 = 0.1 micromolarity; Vmax_41 = 1.2 1/minute Reaction: Dok + pShc_pTrkA => pDok + pShc_pTrkA; pShc_pTrkA, Dok, Rate Law: Vmax_41*pShc_pTrkA*Dok/(Km_41+Dok)
kb_74 = 30.0 1/minute; kf_74 = 3600.0 1/(micromolarity*minute) Reaction: Ras_GTP + B_Raf => B_Raf_Ras_GTP; B_Raf, Ras_GTP, B_Raf_Ras_GTP, Rate Law: kf_74*B_Raf*Ras_GTP-kb_74*B_Raf_Ras_GTP
Vmax_90 = 18.0 1/minute; Km_90 = 0.16 micromolarity Reaction: pMEKcyt + B_Raf_Rap1_GTP => ppMEKcyt + B_Raf_Rap1_GTP; B_Raf_Rap1_GTP, pMEKcyt, Rate Law: Vmax_90*B_Raf_Rap1_GTP*pMEKcyt/(Km_90+pMEKcyt)
Km_63 = 1.0 micromolarity; Vmax_63 = 600.0 1/minute Reaction: B_Raf_Ras_GTP + pDok_RasGAP => Ras_GDP + B_Raf + pDok_RasGAP; pDok_RasGAP, B_Raf_Ras_GTP, Rate Law: Vmax_63*pDok_RasGAP*B_Raf_Ras_GTP/(Km_63+B_Raf_Ras_GTP)
kf_68 = 0.3 1/minute Reaction: pFRS2 => FRS2; pFRS2, Rate Law: kf_68*pFRS2
kf_76 = 9.0 1/minute Reaction: ppMEKcyt_ERKcyt => ppMEKcyt + ppERKcyt; ppMEKcyt_ERKcyt, Rate Law: kf_76*ppMEKcyt_ERKcyt
kf_53 = 0.12 1/minute Reaction: pSOS => SOS; pSOS, Rate Law: kf_53*pSOS
Km_44 = 0.1 micromolarity; Vmax_44 = 1.2 1/minute Reaction: Dok + pFRS2_pTrkA => pDok + pFRS2_pTrkA; pFRS2_pTrkA, Dok, Rate Law: Vmax_44*pFRS2_pTrkA*Dok/(Km_44+Dok)
Km_71 = 1.0 micromolarity; Vmax_71 = 120.0 1/minute; Rap1GAP = 0.012 micromolarity Reaction: Rap1_GTP => Rap1_GDP; Rap1_GTP, Rate Law: Vmax_71*Rap1GAP*Rap1_GTP/(Km_71+Rap1_GTP)
kf_31 = 120.0 1/minute Reaction: FRS2_pTrkA_endo => pFRS2_pTrkA_endo; FRS2_pTrkA_endo, Rate Law: kf_31*FRS2_pTrkA_endo
kf_21 = 600.0 1/(micromolarity*minute); kb_21 = 12.0 1/minute Reaction: pTrkA_endo + Shc => Shc_pTrkA_endo; Shc, pTrkA_endo, Shc_pTrkA_endo, Rate Law: kf_21*Shc*pTrkA_endo-kb_21*Shc_pTrkA_endo
Vmax_64 = 600.0 1/minute; Km_64 = 1.0 micromolarity Reaction: c_Raf_Ras_GTP + pDok_RasGAP => Ras_GDP + c_Raf + pDok_RasGAP; pDok_RasGAP, c_Raf_Ras_GTP, Rate Law: Vmax_64*pDok_RasGAP*c_Raf_Ras_GTP/(Km_64+c_Raf_Ras_GTP)
kf_80 = 978.24 1/(micromolarity*minute); kb_80 = 36.0 1/minute Reaction: ppMEKcyt + ERKcyt => ppMEKcyt_ERKcyt; ppMEKcyt, ERKcyt, ppMEKcyt_ERKcyt, Rate Law: kf_80*ppMEKcyt*ERKcyt-kb_80*ppMEKcyt_ERKcyt
kb_79 = 36.0 1/minute; kf_79 = 978.24 1/(micromolarity*minute) Reaction: ERKcyt + pMEKcyt => pMEKcyt_ERKcyt; pMEKcyt, ERKcyt, pMEKcyt_ERKcyt, Rate Law: kf_79*pMEKcyt*ERKcyt-kb_79*pMEKcyt_ERKcyt
mwd74ca4a6_566f_4161_859e_2b05bf2851fc=1.0E7 1/(molarity*second); mw924e0439_7ac5_4812_b1c2_11e46b4737b8=0.001 1/second Reaction: mwd4cc05d6_6e19_4e2e_b540_45954f2df4f0 + NGFR => mw5afa8250_0cf0_40a2_a97a_f7cf20a9cfbd; mwd4cc05d6_6e19_4e2e_b540_45954f2df4f0, NGFR, mw5afa8250_0cf0_40a2_a97a_f7cf20a9cfbd, Rate Law: mwd74ca4a6_566f_4161_859e_2b05bf2851fc*mwd4cc05d6_6e19_4e2e_b540_45954f2df4f0*NGFR-mw924e0439_7ac5_4812_b1c2_11e46b4737b8*mw5afa8250_0cf0_40a2_a97a_f7cf20a9cfbd
Vmax_39 = 1.2 1/minute; Km_39 = 0.1 micromolarity Reaction: Dok + pTrkA => pDok + pTrkA; pTrkA, Dok, Rate Law: Vmax_39*pTrkA*Dok/(Km_39+Dok)
Vmax_40 = 1.2 1/minute; Km_40 = 0.1 micromolarity Reaction: Dok + Shc_pTrkA => pDok + Shc_pTrkA; Shc_pTrkA, Dok, Rate Law: Vmax_40*Shc_pTrkA*Dok/(Km_40+Dok)
Km_58 = 0.02 micromolarity; Vmax_58 = 120.0 1/minute Reaction: Ras_GDP + Grb2_SOS_pShc_pTrkA => Ras_GTP + Grb2_SOS_pShc_pTrkA; Grb2_SOS_pShc_pTrkA, Ras_GDP, Rate Law: Vmax_58*Grb2_SOS_pShc_pTrkA*Ras_GDP/(Km_58+Ras_GDP)
kf_55 = 3.0 1/(micromolarity*minute); kb_55 = 1.99999998 1/minute Reaction: pDok + RasGAP => pDok_RasGAP; pDok, RasGAP, pDok_RasGAP, Rate Law: kf_55*pDok*RasGAP-kb_55*pDok_RasGAP
Km_87 = 0.16 micromolarity; Vmax_87 = 12.0 1/minute Reaction: MEKcyt_ERKcyt + B_Raf_Ras_GTP => pMEKcyt_ERKcyt + B_Raf_Ras_GTP; B_Raf_Ras_GTP, MEKcyt_ERKcyt, Rate Law: Vmax_87*B_Raf_Ras_GTP*MEKcyt_ERKcyt/(Km_87+MEKcyt_ERKcyt)
Vmax_43 = 1.2 1/minute; Km_43 = 0.1 micromolarity Reaction: Dok + FRS2_pTrkA => pDok + FRS2_pTrkA; FRS2_pTrkA, Dok, Rate Law: Vmax_43*FRS2_pTrkA*Dok/(Km_43+Dok)
kf_117 = 2.1 1/minute Reaction: MEKnuc_ERKnuc => ; MEKnuc_ERKnuc, Rate Law: kf_117*MEKnuc_ERKnuc
kf_54 = 0.12 1/minute Reaction: Grb2_pSOS => Grb2_SOS; Grb2_pSOS, Rate Law: kf_54*Grb2_pSOS
Km_88 = 0.16 micromolarity; Vmax_88 = 12.0 1/minute Reaction: pMEKcyt_ERKcyt + B_Raf_Ras_GTP => ppMEKcyt_ERKcyt + B_Raf_Ras_GTP; B_Raf_Ras_GTP, pMEKcyt_ERKcyt, Rate Law: Vmax_88*B_Raf_Ras_GTP*pMEKcyt_ERKcyt/(Km_88+pMEKcyt_ERKcyt)
kf_65 = 60.0 1/(micromolarity*minute); kb_65 = 0.12 1/minute Reaction: Crk + C3G => Crk_C3G; C3G, Crk, Crk_C3G, Rate Law: kf_65*C3G*Crk-kb_65*Crk_C3G
Vmax_59 = 60.0 1/minute; Km_59 = 25.641 micromolarity Reaction: SOS + dppERKcyt => pSOS + dppERKcyt; dppERKcyt, SOS, Rate Law: Vmax_59*dppERKcyt*SOS/(Km_59+SOS)
kb_18 = 12.0 1/minute; mwdfa3719d_20cc_4f14_b45e_3f097c3aff65 = 600.0 1/(micromolarity*minute) Reaction: pTrkA + Shc => Shc_pTrkA; Shc, pTrkA, Shc_pTrkA, Rate Law: mwdfa3719d_20cc_4f14_b45e_3f097c3aff65*Shc*pTrkA-kb_18*Shc_pTrkA
kf_48 = 600.0 1/(micromolarity*minute); kb_48 = 12.0 1/minute Reaction: Grb2_SOS + pShc => Grb2_SOS_pShc; Grb2_SOS, pShc, Grb2_SOS_pShc, Rate Law: kf_48*Grb2_SOS*pShc-kb_48*Grb2_SOS_pShc
kf_33 = 0.132 1/minute Reaction: Shc_pTrkA => Shc; Shc_pTrkA, Rate Law: kf_33*Shc_pTrkA
PP2Acyt = 0.24 micromolarity; Km_95 = 15.657 micromolarity; Vmax_95 = 180.0 1/minute Reaction: pMEKcyt_ERKcyt => MEKcyt_ERKcyt; pMEKcyt_ERKcyt, Rate Law: Vmax_95*PP2Acyt*pMEKcyt_ERKcyt/(Km_95+pMEKcyt_ERKcyt)
mw3716109a_c83e_4fd4_911e_ccc67b036bb7=0.001 1/second; mwfc8fe87e_6841_4214_9c2f_5d821794f38d=1.0E7 1/(molarity*second) Reaction: L_NGFR + mwd4cc05d6_6e19_4e2e_b540_45954f2df4f0 => mwe009ad7f_90fd_4186_8855_77780724ddb8; L_NGFR, mwd4cc05d6_6e19_4e2e_b540_45954f2df4f0, mwe009ad7f_90fd_4186_8855_77780724ddb8, Rate Law: mwfc8fe87e_6841_4214_9c2f_5d821794f38d*L_NGFR*mwd4cc05d6_6e19_4e2e_b540_45954f2df4f0-mw3716109a_c83e_4fd4_911e_ccc67b036bb7*mwe009ad7f_90fd_4186_8855_77780724ddb8
Km_62 = 1.0 micromolarity; Vmax_62 = 600.0 1/minute Reaction: Ras_GTP + pDok_RasGAP => Ras_GDP + pDok_RasGAP; pDok_RasGAP, Ras_GTP, Rate Law: Vmax_62*pDok_RasGAP*Ras_GTP/(Km_62+Ras_GTP)
Vmax_70 = 2.88 1/minute; Km_70 = 0.01 micromolarity Reaction: Rap1_GDP + Crk_C3G_pFRS2_pTrkA_endo => Rap1_GTP + Crk_C3G_pFRS2_pTrkA_endo; Crk_C3G_pFRS2_pTrkA_endo, Rap1_GDP, Rate Law: Vmax_70*Crk_C3G_pFRS2_pTrkA_endo*Rap1_GDP/(Km_70+Rap1_GDP)
kf_46 = 1.8 1/(micromolarity*minute); kb_46 = 1.008 1/minute Reaction: Grb2 + SOS => Grb2_SOS; Grb2, SOS, Grb2_SOS, Rate Law: kf_46*Grb2*SOS-kb_46*Grb2_SOS
Vmax_45 = 1.2 1/minute; Km_45 = 0.1 micromolarity Reaction: Dok + Crk_C3G_pFRS2_pTrkA => pDok + Crk_C3G_pFRS2_pTrkA; Crk_C3G_pFRS2_pTrkA, Dok, Rate Law: Vmax_45*Crk_C3G_pFRS2_pTrkA*Dok/(Km_45+Dok)
kf_36 = 0.132 1/minute Reaction: FRS2_pTrkA => FRS2; FRS2_pTrkA, Rate Law: kf_36*FRS2_pTrkA
kf_12 = 0.0252 1/minute Reaction: Shc_pTrkA_endo => Shc; Shc_pTrkA_endo, Rate Law: kf_12*Shc_pTrkA_endo
kf_4 = 0.0378 1/minute Reaction: pTrkA => pTrkA_endo; pTrkA, Rate Law: kf_4*pTrkA
Km_42 = 0.1 micromolarity; Vmax_42 = 1.2 1/minute Reaction: Dok + Grb2_SOS_pShc_pTrkA => pDok + Grb2_SOS_pShc_pTrkA; Grb2_SOS_pShc_pTrkA, Dok, Rate Law: Vmax_42*Grb2_SOS_pShc_pTrkA*Dok/(Km_42+Dok)
kf_32 = 0.132 1/minute Reaction: pTrkA => ; pTrkA, Rate Law: kf_32*pTrkA
kf_116 = 0.42 1/minute Reaction: MEKnuc_ERKnuc => MEKcyt_ERKcyt + MEKnuc_ERKnuc; MEKnuc_ERKnuc, Rate Law: kf_116*MEKnuc_ERKnuc
kb_25 = 6.0 1/minute; kf_25 = 300.0 1/(micromolarity*minute) Reaction: pTrkA + pFRS2 => pFRS2_pTrkA; pFRS2, pTrkA, pFRS2_pTrkA, Rate Law: kf_25*pFRS2*pTrkA-kb_25*pFRS2_pTrkA
PP2Anuc = 0.08 micromolarity; Km_106 = 15.657 micromolarity; Vmax_106 = 180.0 1/minute Reaction: pMEKnuc_ERKnuc => MEKnuc_ERKnuc; pMEKnuc_ERKnuc, Rate Law: Vmax_106*PP2Anuc*pMEKnuc_ERKnuc/(Km_106+pMEKnuc_ERKnuc)
Km_89 = 0.16 micromolarity; Vmax_89 = 18.0 1/minute Reaction: MEKcyt + B_Raf_Rap1_GTP => pMEKcyt + B_Raf_Rap1_GTP; B_Raf_Rap1_GTP, MEKcyt, Rate Law: Vmax_89*B_Raf_Rap1_GTP*MEKcyt/(Km_89+MEKcyt)
kb_49 = 12.0 1/minute; kf_49 = 600.0 1/(micromolarity*minute) Reaction: pShc_pTrkA + Grb2_SOS => Grb2_SOS_pShc_pTrkA; Grb2_SOS, pShc_pTrkA, Grb2_SOS_pShc_pTrkA, Rate Law: kf_49*Grb2_SOS*pShc_pTrkA-kb_49*Grb2_SOS_pShc_pTrkA
Vmax_84 = 30.0 1/minute; Km_84 = 0.16 micromolarity Reaction: pMEKcyt_ERKcyt + c_Raf_Ras_GTP => ppMEKcyt_ERKcyt + c_Raf_Ras_GTP; c_Raf_Ras_GTP, pMEKcyt_ERKcyt, Rate Law: Vmax_84*c_Raf_Ras_GTP*pMEKcyt_ERKcyt/(Km_84+pMEKcyt_ERKcyt)
kf_3 = 60.0 1/minute Reaction: L_NGFR => pTrkA; L_NGFR, Rate Law: kf_3*L_NGFR
kf_52 = 0.3 1/minute Reaction: Grb2_SOS_pShc => Shc + Grb2_SOS; Grb2_SOS_pShc, Rate Law: kf_52*Grb2_SOS_pShc
kf_101 = 978.24 1/(micromolarity*minute); kb_101 = 36.0 1/minute Reaction: MEKnuc + ERKnuc => MEKnuc_ERKnuc; MEKnuc, ERKnuc, MEKnuc_ERKnuc, Rate Law: kf_101*MEKnuc*ERKnuc-kb_101*MEKnuc_ERKnuc
kf_56 = 0.12 1/minute; kb_56 = 6.0E-4 1/minute Reaction: pDok => Dok; pDok, Dok, Rate Law: kf_56*pDok-kb_56*Dok
Km_61 = 25.641 micromolarity; Vmax_61 = 60.0 1/minute Reaction: Grb2_SOS + dppERKcyt => Grb2_pSOS + dppERKcyt; dppERKcyt, Grb2_SOS, Rate Law: Vmax_61*dppERKcyt*Grb2_SOS/(Km_61+Grb2_SOS)
kf_10 = 0.0378 1/minute Reaction: Crk_C3G_pFRS2_pTrkA => Crk_C3G_pFRS2_pTrkA_endo; Crk_C3G_pFRS2_pTrkA, Rate Law: kf_10*Crk_C3G_pFRS2_pTrkA
kb_50 = 12.0 1/minute; kf_50 = 600.0 1/(micromolarity*minute) Reaction: pShc_pTrkA_endo + Grb2_SOS => Grb2_SOS_pShc_pTrkA_endo; Grb2_SOS, pShc_pTrkA_endo, Grb2_SOS_pShc_pTrkA_endo, Rate Law: kf_50*Grb2_SOS*pShc_pTrkA_endo-kb_50*Grb2_SOS_pShc_pTrkA_endo
kf_26 = 300.0 1/(micromolarity*minute); kb_26 = 6.0 1/minute Reaction: pTrkA_endo + FRS2 => FRS2_pTrkA_endo; FRS2, pTrkA_endo, FRS2_pTrkA_endo, Rate Law: kf_26*FRS2*pTrkA_endo-kb_26*FRS2_pTrkA_endo
kb_73 = 30.0 1/minute; kf_73 = 3600.0 1/(micromolarity*minute) Reaction: Ras_GTP + c_Raf => c_Raf_Ras_GTP; c_Raf, Ras_GTP, c_Raf_Ras_GTP, Rate Law: kf_73*c_Raf*Ras_GTP-kb_73*c_Raf_Ras_GTP
kf_38 = 0.132 1/minute Reaction: Crk_C3G_pFRS2_pTrkA => Crk_C3G + pFRS2; Crk_C3G_pFRS2_pTrkA, Rate Law: kf_38*Crk_C3G_pFRS2_pTrkA
kf_17 = 0.0252 1/minute Reaction: Crk_C3G_pFRS2_pTrkA_endo => Crk_C3G + pFRS2; Crk_C3G_pFRS2_pTrkA_endo, Rate Law: kf_17*Crk_C3G_pFRS2_pTrkA_endo
kf_114 = 3.12 1/minute Reaction: MEKcyt_ERKcyt => ; MEKcyt_ERKcyt, Rate Law: kf_114*MEKcyt_ERKcyt
kf_57 = 0.0070002 1/minute Reaction: Ras_GTP => Ras_GDP; Ras_GTP, Rate Law: kf_57*Ras_GTP
kf_115 = 15.6 1/minute Reaction: MEKcyt_ERKcyt => MEKnuc_ERKnuc + MEKcyt_ERKcyt; MEKcyt_ERKcyt, Rate Law: kf_115*MEKcyt_ERKcyt
kf_66 = 60.0 1/(micromolarity*minute); kb_66 = 12.0 1/minute Reaction: pFRS2_pTrkA + Crk_C3G => Crk_C3G_pFRS2_pTrkA; Crk_C3G, pFRS2_pTrkA, Crk_C3G_pFRS2_pTrkA, Rate Law: kf_66*Crk_C3G*pFRS2_pTrkA-kb_66*Crk_C3G_pFRS2_pTrkA
kf_75 = 3600.0 1/(micromolarity*minute); kb_75 = 30.0 1/minute Reaction: B_Raf + Rap1_GTP => B_Raf_Rap1_GTP; B_Raf, Rap1_GTP, B_Raf_Rap1_GTP, Rate Law: kf_75*B_Raf*Rap1_GTP-kb_75*B_Raf_Rap1_GTP
Km_72 = 1.0 micromolarity; Vmax_72 = 120.0 1/minute; Rap1GAP = 0.012 micromolarity Reaction: B_Raf_Rap1_GTP => B_Raf + Rap1_GDP; B_Raf_Rap1_GTP, Rate Law: Vmax_72*Rap1GAP*B_Raf_Rap1_GTP/(Km_72+B_Raf_Rap1_GTP)
Km_85 = 0.16 micromolarity; Vmax_85 = 12.0 1/minute Reaction: MEKcyt + B_Raf_Ras_GTP => pMEKcyt + B_Raf_Ras_GTP; B_Raf_Ras_GTP, MEKcyt, Rate Law: Vmax_85*B_Raf_Ras_GTP*MEKcyt/(Km_85+MEKcyt)
mwda0271e2_458c_4419_9c7d_8fb1bf692c13=2000.0 1/minute; mwc3897a3e_bec3_478d_9450_afc4751c2775=2000.0 1/minute Reaction: NGFR => mwe979ec8f_a55c_470c_a554_9fa8013eab74; NGFR, mwe979ec8f_a55c_470c_a554_9fa8013eab74, Rate Law: mwc3897a3e_bec3_478d_9450_afc4751c2775*NGFR*mw3bc142df_1951_4fa9_b0a7_011c95012bbf-mwda0271e2_458c_4419_9c7d_8fb1bf692c13*mwe979ec8f_a55c_470c_a554_9fa8013eab74*mwc2fe3668_8fb0_4cfb_b795_99057e61e290
mw5d69c45e_20e6_4a18_b22a_b79692b9c57d=2000.0 1/minute; mw88063cbd_d06b_40bd_bbed_3f8a4a9ee082=2000.0 1/minute Reaction: mw6782adfa_29ee_41a8_acbb_4c86c6c81596 => L_NGFR; mw6782adfa_29ee_41a8_acbb_4c86c6c81596, L_NGFR, Rate Law: mw5d69c45e_20e6_4a18_b22a_b79692b9c57d*mw6782adfa_29ee_41a8_acbb_4c86c6c81596*mwc2fe3668_8fb0_4cfb_b795_99057e61e290-mw88063cbd_d06b_40bd_bbed_3f8a4a9ee082*L_NGFR*mw3bc142df_1951_4fa9_b0a7_011c95012bbf

States:

Name Description
B Raf B_Raf
Shc Shc
pTrkA endo pTrkA_endo
Grb2 SOS Grb2_SOS
Rap1 GTP Rap1_GTP
pMEKcyt pMEKcyt
Ras GTP Ras_GTP
Crk C3G pFRS2 pTrkA Crk_C3G_pFRS2_pTrkA
MEKnuc ERKnuc MEKnuc_ERKnuc
B Raf Ras GTP B_Raf_Ras_GTP
L NGFR L_NGFR
Crk C3G pFRS2 pTrkA endo Crk_C3G_pFRS2_pTrkA_endo
Crk C3G Crk_C3G
SOS SOS
Grb2 SOS pShc Grb2_SOS_pShc
MEKcyt ERKcyt MEKcyt_ERKcyt
pFRS2 pTrkA endo pFRS2_pTrkA_endo
Ras GDP Ras_GDP
FRS2 FRS2
pTrkA pTrkA
Grb2 Grb2
Dok Dok
ppMEKcyt ERKcyt ppMEKcyt_ERKcyt
c Raf c_Raf
NGFR NGFR
pDok pDok

Observables: none

Benson2014 - FAAH inhibitors for the treatment of osteoarthritic painEvaluation of fatty acid amide hydrolase (FAAH) as…

The level of the endocannabinoid anandamide is controlled by fatty acid amide hydrolase (FAAH). In 2011, PF-04457845, an irreversible inhibitor of FAAH, was progressed to phase II clinical trials for osteoarthritic pain. This article discusses a prospective, integrated systems pharmacology model evaluation of FAAH as a target for pain in humans, using physiologically based pharmacokinetic and systems biology approaches. The model integrated physiological compartments; endocannabinoid production, degradation, and disposition data; PF-04457845 pharmacokinetics and pharmacodynamics, and cannabinoid receptor CB1-binding kinetics. The modeling identified clear gaps in our understanding and highlighted key risks going forward, in particular relating to whether methods are in place to demonstrate target engagement and pharmacological effect. The value of this modeling exercise will be discussed in detail and in the context of the clinical phase II data, together with recommendations to enable optimal future evaluation of FAAH inhibitors.CPT: Pharmacometrics Systems Pharmacology (2014) 3, e91; doi:10.1038/psp.2013.72; published online 15 January 2014. link: http://identifiers.org/pubmed/24429592

Parameters:

Name Description
kcat_FAAH = 18000.0; FAAH_D_m = 0.0; a_FAAH_S = 1.0; Km_FAAH_S = 10000.0 Reaction: S_m => ; FAAH_m, FAAH_m, S_m, Rate Law: MEC*FAAH_m*kcat_FAAH*a_FAAH_S*S_m/(Km_FAAH_S*FAAH_D_m)
ktr_r_p = 100.0; Ktr_p_r_A = 0.62; Km_p_m_A = 1.0 Reaction: A_r => A_p; A_r, A_p, Rate Law: PLASMA*ktr_r_p*(A_r-A_p*Ktr_p_r_A)/(A_r+A_p+Km_p_m_A)
b_NAT_Brain = 1.667; Vmax_NAT = 300.0; p_O = 0.098; a_NAT_O = 13.0 Reaction: => NOPE_b, Rate Law: BRAIN*Vmax_NAT*p_O*a_NAT_O*b_NAT_Brain
Vmax_NAT = 300.0; p_O = 0.098; a_NAT_O = 13.0; c_NAT_ROB = 0.0 Reaction: => NOPE_r, Rate Law: Vmax_NAT*p_O*a_NAT_O*c_NAT_ROB
kcat_FAAH = 18000.0; a_FAAH_P = 37.8; FAAH_D_m = 0.0; Km_FAAH_P = 543000.0 Reaction: P_m => ; FAAH_m, FAAH_m, P_m, Rate Law: MEC*FAAH_m*kcat_FAAH*a_FAAH_P*P_m/(Km_FAAH_P*FAAH_D_m)
Ktr_p_m_A = 1.89; ktr_m_p_A = 150.0; Km_p_m_A = 1.0 Reaction: A_m => A_p; A_m, A_p, Rate Law: MEC*ktr_m_p_A*(A_m-A_p*Ktr_p_m_A)/(A_m+A_p+Km_p_m_A)
kcat_FAAH = 18000.0; FAAH_D_r = 0.0; a_FAAH_S = 1.0; Km_FAAH_S = 10000.0 Reaction: S_r => ; FAAH_r, FAAH_r, S_r, Rate Law: ROB*FAAH_r*kcat_FAAH*a_FAAH_S*S_r/(Km_FAAH_S*FAAH_D_r)
den_b = 0.0; Km_NS_PE = 3400.0; k_NS_PE = 280.0; PLD_b = 1.0E7 Reaction: NSPE_b => S_b; NSPE_b, Rate Law: BRAIN*PLD_b*k_NS_PE*NSPE_b/Km_NS_PE/den_b
k_inh = 1.1; PF_r = 0.0 Reaction: FAAH_r => FAAHinh_r; FAAH_r, Rate Law: ROB*k_inh*FAAH_r*PF_r
kin_PFM = 0.117; kout_PFM = 0.18 Reaction: PFM_p => PFM_r; PFM_p, PFM_r, Rate Law: kout_PFM*PFM_p-kin_PFM*PFM_r
p_P = 0.615; a_NAT_P = 0.42; b_NAT_Brain = 1.667; Vmax_NAT = 300.0 Reaction: => NPPE_b, Rate Law: BRAIN*Vmax_NAT*p_P*a_NAT_P*b_NAT_Brain
kcat_FAAH = 18000.0; FAAH_D_r = 0.0; Km_FAAH_O = 52200.0; a_FAAH_O = 5.7 Reaction: O_r => ; FAAH_r, FAAH_r, O_r, Rate Law: ROB*FAAH_r*kcat_FAAH*a_FAAH_O*O_r/(Km_FAAH_O*FAAH_D_r)
ktr_r_p = 100.0; Ktr_p_r_S = 9.19 Reaction: S_r => S_p; S_r, S_p, Rate Law: PLASMA*ktr_r_p*(S_r-S_p*Ktr_p_r_S)
PLD_r = 1.0E7; k_NA_PE = 202.0; Km_NA_PE = 2800.0; den_r = 0.0 Reaction: NAPE_r => A_r; NAPE_r, Rate Law: ROB*PLD_r*k_NA_PE*NAPE_r/Km_NA_PE/den_r
a_NAT_L = 8.6; p_L = 0.016; b_NAT_Brain = 1.667; Vmax_NAT = 300.0 Reaction: => NLPE_b, Rate Law: BRAIN*Vmax_NAT*p_L*a_NAT_L*b_NAT_Brain
kcl_A = 1.74; b_FAAH_Brain = 0.197 Reaction: A_b => ; A_b, Rate Law: BRAIN*b_FAAH_Brain*kcl_A*A_b
Ktr_p_r_P = 0.85; ktr_r_p = 100.0 Reaction: P_r => P_p; P_r, P_p, Rate Law: PLASMA*ktr_r_p*(P_r-P_p*Ktr_p_r_P)
den_b = 0.0; Km_NO_PE = 2900.0; k_NO_PE = 230.0; PLD_b = 1.0E7 Reaction: NOPE_b => O_b; NOPE_b, Rate Law: BRAIN*PLD_b*k_NO_PE*NOPE_b/Km_NO_PE/den_b
b_FAAH_Brain = 0.197; kcl_S = 1.2 Reaction: S_b => ; S_b, Rate Law: BRAIN*b_FAAH_Brain*kcl_S*S_b
c_NAAA_ROB = 0.0; kcl_O = 2.5 Reaction: O_r => ; O_r, Rate Law: c_NAAA_ROB*kcl_O*O_r
ktr_m_p_L = 0.0 Reaction: L_b => L_m; L_b, L_m, Rate Law: MEC*ktr_m_p_L*(L_b-L_m)
kcl_P = 2.61; b_FAAH_Brain = 0.197 Reaction: P_b => ; P_b, Rate Law: BRAIN*b_FAAH_Brain*kcl_P*P_b
kcl_O = 2.5; b_FAAH_Brain = 0.197 Reaction: O_b => ; O_b, Rate Law: BRAIN*b_FAAH_Brain*kcl_O*O_b
kabs_PFM = 2.2; MD = 0.0 Reaction: PFM_gut => PFM_p, Rate Law: kabs_PFM*MD
kcat_FAAH = 18000.0; FAAH_D_m = 0.0; Km_FAAH_A = 8200.0; a_FAAH_A = 1.0 Reaction: A_m => ; FAAH_m, FAAH_m, A_m, Rate Law: MEC*FAAH_m*kcat_FAAH*a_FAAH_A*A_m/(Km_FAAH_A*FAAH_D_m)
c_NAAA_ROB = 0.0; kcl_S = 1.2 Reaction: S_r => ; S_r, Rate Law: c_NAAA_ROB*kcl_S*S_r
PF_m = 0.0; k_inh = 1.1 Reaction: FAAH_m => FAAHinh_m; FAAH_m, Rate Law: MEC*k_inh*FAAH_m*PF_m
kcat_FAAH = 18000.0; FAAH_D_m = 0.0; a_FAAH_L = 1.15; Km_FAAH_L = 10800.0 Reaction: L_m => ; FAAH_m, FAAH_m, L_m, Rate Law: MEC*FAAH_m*kcat_FAAH*a_FAAH_L*L_m/(Km_FAAH_L*FAAH_D_m)
b_FAAH_MEC = 0.137; k_deg_FAAH = 0.0051; FAAH_t = 78.0 Reaction: => FAAH_m, Rate Law: MEC*FAAH_t*b_FAAH_MEC*k_deg_FAAH
FAAH_D_b = 0.0; kcat_FAAH = 18000.0; a_FAAH_P = 37.8; Km_FAAH_P = 543000.0 Reaction: P_b => ; FAAH_b, FAAH_b, P_b, Rate Law: BRAIN*FAAH_b*kcat_FAAH*a_FAAH_P*P_b/(Km_FAAH_P*FAAH_D_b)
den_b = 0.0; k_NA_PE = 202.0; Km_NA_PE = 2800.0; PLD_b = 1.0E7 Reaction: NAPE_b => A_b; NAPE_b, Rate Law: BRAIN*PLD_b*k_NA_PE*NAPE_b/Km_NA_PE/den_b
a_NAT_A = 1.0; b_NAT_Brain = 1.667; Vmax_NAT = 300.0; p_A = 0.051 Reaction: => NAPE_b, Rate Law: BRAIN*Vmax_NAT*p_A*a_NAT_A*b_NAT_Brain
PLD_r = 1.0E7; Km_NL_PE = 1000.0; k_NL_PE = 100.0; den_r = 0.0 Reaction: NLPE_r => L_r; NLPE_r, Rate Law: ROB*PLD_r*k_NL_PE*NLPE_r/Km_NL_PE/den_r
klinear_PFM = 0.0803; Km_PFM = 26.1; Vm_PFM = 1511.0; Vss_PFM = 58.328 Reaction: PFM_p => ; PFM_p, Rate Law: klinear_PFM*PFM_p+Vm_PFM*PFM_p/(Km_PFM+PFM_p/Vss_PFM)/Vss_PFM
kcat_FAAH = 18000.0; a_FAAH_P = 37.8; Km_FAAH_P = 543000.0; FAAH_D_r = 0.0 Reaction: P_r => ; FAAH_r, FAAH_r, P_r, Rate Law: ROB*FAAH_r*kcat_FAAH*a_FAAH_P*P_r/(Km_FAAH_P*FAAH_D_r)
ktr_r_p = 100.0; Ktr_p_r_L = 0.89 Reaction: L_r => L_p; L_r, L_p, Rate Law: PLASMA*ktr_r_p*(L_r-L_p*Ktr_p_r_L)
PLD_r = 1.0E7; Km_NO_PE = 2900.0; k_NO_PE = 230.0; den_r = 0.0 Reaction: NOPE_r => O_r; NOPE_r, Rate Law: ROB*PLD_r*k_NO_PE*NOPE_r/Km_NO_PE/den_r
kcat_FAAH = 18000.0; FAAH_D_r = 0.0; Km_FAAH_A = 8200.0; a_FAAH_A = 1.0 Reaction: A_r => ; FAAH_r, FAAH_r, A_r, Rate Law: ROB*FAAH_r*kcat_FAAH*a_FAAH_A*A_r/(Km_FAAH_A*FAAH_D_r)
k_deg_FAAH = 0.0051 Reaction: FAAH_r => ; FAAH_r, Rate Law: ROB*k_deg_FAAH*FAAH_r
a_NAT_S = 1.0; Vmax_NAT = 300.0; p_S = 0.191; c_NAT_ROB = 0.0 Reaction: => NSPE_r, Rate Law: Vmax_NAT*p_S*a_NAT_S*c_NAT_ROB
Ktr_p_r_O = 2.8; ktr_r_p = 100.0 Reaction: O_r => O_p; O_r, O_p, Rate Law: PLASMA*ktr_r_p*(O_r-O_p*Ktr_p_r_O)
kcat_FAAH = 18000.0; a_FAAH_L = 1.15; FAAH_D_r = 0.0; Km_FAAH_L = 10800.0 Reaction: L_r => ; FAAH_r, FAAH_r, L_r, Rate Law: ROB*FAAH_r*kcat_FAAH*a_FAAH_L*L_r/(Km_FAAH_L*FAAH_D_r)
den_b = 0.0; Km_NL_PE = 1000.0; k_NL_PE = 100.0; PLD_b = 1.0E7 Reaction: NLPE_b => L_b; NLPE_b, Rate Law: BRAIN*PLD_b*k_NL_PE*NLPE_b/Km_NL_PE/den_b
ktr_m_p_A = 150.0; Km_p_m_A = 1.0 Reaction: A_b => A_m; A_b, A_m, Rate Law: MEC*ktr_m_p_A*(A_b-A_m)/(A_m+A_b+Km_p_m_A)
c_NAAA_ROB = 0.0; kcl_A = 1.74 Reaction: A_r => ; A_r, Rate Law: c_NAAA_ROB*kcl_A*A_r
Ktr_p_m_P = 2.65; ktr_m_p_P = 10.0 Reaction: P_m => P_p; P_m, P_p, Rate Law: MEC*ktr_m_p_P*(P_m-P_p*Ktr_p_m_P)
k_deg_FAAH = 0.0051; b_FAAH_Brain = 0.197; FAAH_t = 78.0 Reaction: => FAAH_b, Rate Law: BRAIN*FAAH_t*b_FAAH_Brain*k_deg_FAAH
c_NAAA_ROB = 0.0; kcl_P = 2.61 Reaction: P_r => ; P_r, Rate Law: c_NAAA_ROB*kcl_P*P_r
ktr_m_p_P = 10.0 Reaction: P_b => P_m; P_b, P_m, Rate Law: MEC*ktr_m_p_P*(P_b-P_m)
den_b = 0.0; k_NP_PE = 270.0; PLD_b = 1.0E7; Km_NP_PE = 3300.0 Reaction: NPPE_b => P_b; NPPE_b, Rate Law: BRAIN*PLD_b*k_NP_PE*NPPE_b/Km_NP_PE/den_b
FAAH_D_b = 0.0; kcat_FAAH = 18000.0; a_FAAH_S = 1.0; Km_FAAH_S = 10000.0 Reaction: S_b => ; FAAH_b, FAAH_b, S_b, Rate Law: BRAIN*FAAH_b*kcat_FAAH*a_FAAH_S*S_b/(Km_FAAH_S*FAAH_D_b)
ktr_m_p_S = 10.0; Ktr_p_m_S = 30.01 Reaction: S_m => S_p; S_m, S_p, Rate Law: MEC*ktr_m_p_S*(S_m-S_p*Ktr_p_m_S)
a_NAT_S = 1.0; b_NAT_Brain = 1.667; Vmax_NAT = 300.0; p_S = 0.191 Reaction: => NSPE_b, Rate Law: BRAIN*Vmax_NAT*p_S*a_NAT_S*b_NAT_Brain
kcl_L = 1.25; b_FAAH_Brain = 0.197 Reaction: L_b => ; L_b, Rate Law: BRAIN*b_FAAH_Brain*kcl_L*L_b
c_FAAH_ROB = 0.0; k_deg_FAAH = 0.0051; FAAH_t = 78.0 Reaction: => FAAH_r, Rate Law: FAAH_t*c_FAAH_ROB*k_deg_FAAH
FAAH_D_b = 0.0; kcat_FAAH = 18000.0; a_FAAH_L = 1.15; Km_FAAH_L = 10800.0 Reaction: L_b => ; FAAH_b, FAAH_b, L_b, Rate Law: BRAIN*FAAH_b*kcat_FAAH*a_FAAH_L*L_b/(Km_FAAH_L*FAAH_D_b)
ktr_m_p_O = 10.0 Reaction: O_b => O_m; O_b, O_m, Rate Law: MEC*ktr_m_p_O*(O_b-O_m)
c_NAAA_ROB = 0.0; kcl_L = 1.25 Reaction: L_r => ; L_r, Rate Law: c_NAAA_ROB*kcl_L*L_r
PLD_r = 1.0E7; Km_NS_PE = 3400.0; k_NS_PE = 280.0; den_r = 0.0 Reaction: NSPE_r => S_r; NSPE_r, Rate Law: ROB*PLD_r*k_NS_PE*NSPE_r/Km_NS_PE/den_r
k_inh = 1.1; PF_b = 0.0 Reaction: FAAH_b => FAAHinh_b; FAAH_b, Rate Law: BRAIN*k_inh*FAAH_b*PF_b
ktr_m_p_S = 10.0 Reaction: S_b => S_m; S_b, S_m, Rate Law: MEC*ktr_m_p_S*(S_b-S_m)
a_NAT_A = 1.0; Vmax_NAT = 300.0; p_A = 0.051; c_NAT_ROB = 0.0 Reaction: => NAPE_r, Rate Law: Vmax_NAT*p_A*a_NAT_A*c_NAT_ROB
kcat_FAAH = 18000.0; FAAH_D_m = 0.0; Km_FAAH_O = 52200.0; a_FAAH_O = 5.7 Reaction: O_m => ; FAAH_m, FAAH_m, O_m, Rate Law: MEC*FAAH_m*kcat_FAAH*a_FAAH_O*O_m/(Km_FAAH_O*FAAH_D_m)
Ktr_p_m_L = 2.77; ktr_m_p_L = 0.0 Reaction: L_m => L_p; L_m, L_p, Rate Law: MEC*ktr_m_p_L*(L_m-L_p*Ktr_p_m_L)
Ktr_p_m_O = 9.07; ktr_m_p_O = 10.0 Reaction: O_m => O_p; O_m, O_p, Rate Law: MEC*ktr_m_p_O*(O_m-O_p*Ktr_p_m_O)
PLD_r = 1.0E7; k_NP_PE = 270.0; Km_NP_PE = 3300.0; den_r = 0.0 Reaction: NPPE_r => P_r; NPPE_r, Rate Law: ROB*PLD_r*k_NP_PE*NPPE_r/Km_NP_PE/den_r
FAAH_D_b = 0.0; kcat_FAAH = 18000.0; Km_FAAH_O = 52200.0; a_FAAH_O = 5.7 Reaction: O_b => ; FAAH_b, FAAH_b, O_b, Rate Law: BRAIN*FAAH_b*kcat_FAAH*a_FAAH_O*O_b/(Km_FAAH_O*FAAH_D_b)
FAAH_D_b = 0.0; kcat_FAAH = 18000.0; Km_FAAH_A = 8200.0; a_FAAH_A = 1.0 Reaction: A_b => ; FAAH_b, FAAH_b, A_b, Rate Law: BRAIN*FAAH_b*kcat_FAAH*a_FAAH_A*A_b/(Km_FAAH_A*FAAH_D_b)

States:

Name Description
NAPE r [anandamide]
L b [linoleoyl ethanolamide]
S b [27902]
A r [anandamide]
P b [palmitoyl ethanolamide]
O b [oleoyl ethanolamide]
FAAHinh b [EC 3.5.1.99 (fatty acid amide hydrolase) inhibitor]
O r [oleoyl ethanolamide]
A b [anandamide]
L p [linoleoyl ethanolamide]
A p [anandamide]
L r [linoleoyl ethanolamide]
S r [27902]
PFM gut [CHEMBL1651534]
NLPE b [linoleoyl ethanolamide]
FAAH b [Fatty-acid amide hydrolase 1]
O m [oleoyl ethanolamide]
PFM r [CHEMBL1651534]
NLPE r [linoleoyl ethanolamide]
NSPE r [27902]
L m [linoleoyl ethanolamide]
NSPE b [27902]
FAAH r [Fatty-acid amide hydrolase 1]
NAPE b [anandamide]
FAAHinh r [EC 3.5.1.99 (fatty acid amide hydrolase) inhibitor]
FAAHinh m [EC 3.5.1.99 (fatty acid amide hydrolase) inhibitor]
P m [palmitoyl ethanolamide]
P r [palmitoyl ethanolamide]
NPPE b [palmitoyl ethanolamide]
A m [anandamide]
FAAH m [Fatty-acid amide hydrolase 1]
O p [oleoyl ethanolamide]
PFM p [CHEMBL1651534]
S m [27902]
NOPE r [oleoyl ethanolamide]
P p [palmitoyl ethanolamide]
S p [27902]
NOPE b [oleoyl ethanolamide]

Observables: none

BensonWattersonetal_SystemsPharmacology_MultidrugThis model is described in the article: [Is systems pharmacology ready…

An ever-growing wealth of information on current drugs and their pharmacological effects is available from online databases. As our understanding of systems biology increases, we have the opportunity to predict, model and quantify how drug combinations can be introduced that outperform conventional single-drug therapies. Here, we explore the feasibility of such systems pharmacology approaches with an analysis of the mevalonate branch of the cholesterol biosynthesis pathway.Using open online resources, we assembled a computational model of the mevalonate pathway and compiled a set of inhibitors directed against targets in this pathway. We used computational optimisation to identify combination and dose options that show not only maximal efficacy of inhibition on the cholesterol producing branch but also minimal impact on the geranylation branch, known to mediate the side effects of pharmaceutical treatment.We describe serious impediments to systems pharmacology studies arising from limitations in the data, incomplete coverage and inconsistent reporting. By curating a more complete dataset, we demonstrate the utility of computational optimization for identifying multi-drug treatments with high efficacy and minimal off-target effects.We suggest solutions that facilitate systems pharmacology studies, based on the introduction of standards for data capture that increase the power of experimental data. We propose a systems pharmacology work-flow for the refinement of data and the generation of future therapeutic hypotheses. link: http://identifiers.org/pubmed/28910500

Parameters: none

States: none

Observables: none

The coronavirus disease 2019 (COVID-19) pandemic has placed epidemic modeling at the forefront of worldwide public polic…

The coronavirus disease 2019 (COVID-19) pandemic has placed epidemic modeling at the forefront of worldwide public policy making. Nonetheless, modeling and forecasting the spread of COVID-19 remains a challenge. Here, we detail three regional-scale models for forecasting and assessing the course of the pandemic. This work demonstrates the utility of parsimonious models for early-time data and provides an accessible framework for generating policy-relevant insights into its course. We show how these models can be connected to each other and to time series data for a particular region. Capable of measuring and forecasting the impacts of social distancing, these models highlight the dangers of relaxing nonpharmaceutical public health interventions in the absence of a vaccine or antiviral therapies. link: http://identifiers.org/pubmed/32616574

Parameters: none

States: none

Observables: none

BIOMD0000000374 @ v0.0.1

This a model from the article: A role for calcium release-activated current (CRAC) in cholinergic modulation of elec…

S. Bordin and colleagues have proposed that the depolarizing effects of acetylcholine and other muscarinic agonists on pancreatic beta-cells are mediated by a calcium release-activated current (CRAC). We support this hypothesis with additional data, and present a theoretical model which accounts for most known data on muscarinic effects. Additional phenomena, such as the biphasic responses of beta-cells to changes in glucose concentration and the depolarizing effects of the sarco-endoplasmic reticulum calcium ATPase pump poison thapsigargin, are also accounted for by our model. The ability of this single hypothesis, that CRAC is present in beta-cells, to explain so many phenomena motivates a more complete characterization of this current. link: http://identifiers.org/pubmed/7647236

Parameters:

Name Description
lambda_er = 250.0; J_er_tot = 0.0463972850678733; sigma_er = 5.0 Reaction: Ca_er_Ca_equations = (-J_er_tot)/(lambda_er*sigma_er), Rate Law: (-J_er_tot)/(lambda_er*sigma_er)
i_K_Ca = 3.45489443378119; i_leak = 0.0; i_CRAC = -11.3122509416041; Cm = 6158.0; i_Ca = -1342.58335216182; i_K = 17.55; i_K_ATP = 1350.0 Reaction: V_membrane = (-(i_Ca+i_K+i_K_ATP+i_K_Ca+i_CRAC+i_leak))/Cm, Rate Law: (-(i_Ca+i_K+i_K_ATP+i_K_Ca+i_CRAC+i_leak))/Cm
tau_n = 9.085746273364; lambda_n = 1.85; n_infinity = 4.67956725632935E-4 Reaction: n = lambda_n*(n_infinity-n)/tau_n, Rate Law: lambda_n*(n_infinity-n)/tau_n
J_mem_tot = -2.8573018487523E-5; lambda_er = 250.0; J_er_tot = 0.0463972850678733 Reaction: Ca_i = J_er_tot/lambda_er+J_mem_tot, Rate Law: J_er_tot/lambda_er+J_mem_tot
jm_infinity = 0.0179862099620915; tau_j = 8145.05572085199 Reaction: jm = (jm_infinity-jm)/tau_j, Rate Law: (jm_infinity-jm)/tau_j

States:

Name Description
Ca i [calcium(2+)]
V membrane [membrane potential]
jm [variant]
Ca er Ca equations [calcium(2+)]
n [delayed rectifier potassium channel activity]

Observables: none

This a model from the article: The phantom burster model for pancreatic beta-cells. Bertram R, Previte J, Sherman A…

Pancreatic beta-cells exhibit bursting oscillations with a wide range of periods. Whereas periods in isolated cells are generally either a few seconds or a few minutes, in intact islets of Langerhans they are intermediate (10-60 s). We develop a mathematical model for beta-cell electrical activity capable of generating this wide range of bursting oscillations. Unlike previous models, bursting is driven by the interaction of two slow processes, one with a relatively small time constant (1-5 s) and the other with a much larger time constant (1-2 min). Bursting on the intermediate time scale is generated without need for a slow process having an intermediate time constant, hence phantom bursting. The model suggests that isolated cells exhibiting a fast pattern may nonetheless possess slower processes that can be brought out by injecting suitable exogenous currents. Guided by this, we devise an experimental protocol using the dynamic clamp technique that reliably elicits islet-like, medium period oscillations from isolated cells. Finally, we show that strong electrical coupling between a fast burster and a slow burster can produce synchronized medium bursting, suggesting that islets may be composed of cells that are intrinsically either fast or slow, with few or none that are intrinsically medium. link: http://identifiers.org/pubmed/11106596

Parameters:

Name Description
Is2 = 513.856; Cm = 4524.0; Il = -75.0; ICa = -2295.26000299071; IK = 1443.0; Is1 = 74.0 Reaction: V = (-(ICa+IK+Il+Is1+Is2))/Cm, Rate Law: (-(ICa+IK+Il+Is1+Is2))/Cm
s2inf = 0.0758581800212435; taus2 = 120000.0 Reaction: s2 = (s2inf-s2)/taus2, Rate Law: (s2inf-s2)/taus2
taun = 8.03194764300286; ninf = 0.0322954646984505 Reaction: n = (ninf-n)/taun, Rate Law: (ninf-n)/taun
s1inf = 0.00247262315663477; taus1 = 1000.0 Reaction: s1 = (s1inf-s1)/taus1, Rate Law: (s1inf-s1)/taus1

States:

Name Description
s1 [variant]
V [membrane potential]
s2 [variant]
n [delayed rectifier potassium channel activity]

Observables: none

MODEL1006230024 @ v0.0.1

This a model from the article: Role for G protein Gbetagamma isoform specificity in synaptic signal processing: a comp…

Computational modeling is used to investigate the functional impact of G protein-mediated presynaptic autoinhibition on synaptic filtering properties. It is demonstrated that this form of autoinhibition, which is relieved by depolarization, acts as a high-pass filter. This contrasts with vesicle depletion, which acts as a low-pass filter. Model parameters are adjusted to reproduce kinetic slowing data from different Gbetagamma dimeric isoforms, which produce different degrees of slowing. With these sets of parameter values, we demonstrate that the range of frequencies filtered out by the autoinhibition varies greatly depending on the Gbetagamma isoform activated by the autoreceptors. It is shown that G protein autoinhibition can enhance the spatial contrast between a spatially distributed high-frequency signal and surrounding low-frequency noise, providing an alternate mechanism to lateral inhibition. It is also shown that autoinhibition can increase the fidelity of coincidence detection by increasing the signal-to-noise ratio in the postsynaptic cell. The filter cut, the input frequency below which signals are filtered, depends on several biophysical parameters in addition to those related to Gbetagamma binding and unbinding. By varying one such parameter, the rate at which transmitter unbinds from autoreceptors, we show that the filter cut can be adjusted up or down for several of the Gbetagamma isoforms. This allows for great synapse-to-synapse variability in the distinction between signal and noise. link: http://identifiers.org/pubmed/11976397

Parameters: none

States: none

Observables: none

MODEL1006230071 @ v0.0.1

This a model from the article: Calcium and glycolysis mediate multiple bursting modes in pancreatic islets. Bertram…

Pancreatic islets of Langerhans produce bursts of electrical activity when exposed to stimulatory glucose levels. These bursts often have a regular repeating pattern, with a period of 10-60 s. In some cases, however, the bursts are episodic, clustered into bursts of bursts, which we call compound bursting. Consistent with this are recordings of free Ca2+ concentration, oxygen consumption, mitochondrial membrane potential, and intraislet glucose levels that exhibit very slow oscillations, with faster oscillations superimposed. We describe a new mathematical model of the pancreatic beta-cell that can account for these multimodal patterns. The model includes the feedback of cytosolic Ca2+ onto ion channels that can account for bursting, and a metabolic subsystem that is capable of producing slow oscillations driven by oscillations in glycolysis. This slow rhythm is responsible for the slow mode of compound bursting in the model. We also show that it is possible for glycolytic oscillations alone to drive a very slow form of bursting, which we call "glycolytic bursting." Finally, the model predicts that there is bistability between stationary and oscillatory glycolysis for a range of parameter values. We provide experimental support for this model prediction. Overall, the model can account for a diversity of islet behaviors described in the literature over the past 20 years. link: http://identifiers.org/pubmed/15347584

Parameters: none

States: none

Observables: none

BIOMD0000000373 @ v0.0.1

This a model from the article: Calcium and glycolysis mediate multiple bursting modes in pancreatic islets. Bertram…

Pancreatic islets of Langerhans produce bursts of electrical activity when exposed to stimulatory glucose levels. These bursts often have a regular repeating pattern, with a period of 10-60 s. In some cases, however, the bursts are episodic, clustered into bursts of bursts, which we call compound bursting. Consistent with this are recordings of free Ca2+ concentration, oxygen consumption, mitochondrial membrane potential, and intraislet glucose levels that exhibit very slow oscillations, with faster oscillations superimposed. We describe a new mathematical model of the pancreatic beta-cell that can account for these multimodal patterns. The model includes the feedback of cytosolic Ca2+ onto ion channels that can account for bursting, and a metabolic subsystem that is capable of producing slow oscillations driven by oscillations in glycolysis. This slow rhythm is responsible for the slow mode of compound bursting in the model. We also show that it is possible for glycolytic oscillations alone to drive a very slow form of bursting, which we call "glycolytic bursting." Finally, the model predicts that there is bistability between stationary and oscillatory glycolysis for a range of parameter values. We provide experimental support for this model prediction. Overall, the model can account for a diversity of islet behaviors described in the literature over the past 20 years. link: http://identifiers.org/pubmed/15347584

Parameters:

Name Description
Jmem = -0.0368247126576742; fcyt = 0.01; Jer = -0.06305 Reaction: c = fcyt*(Jmem+Jer), Rate Law: fcyt*(Jmem+Jer)
IKCa = 1800.0; IK = 1012.5; Cm = 5300.0; IKATP = 2669.03575460448; ICa = -2927.84163162795 Reaction: V = (-(IK+ICa+IKCa+IKATP))/Cm, Rate Law: (-(IK+ICa+IKCa+IKATP))/Cm
lambda = 0.005; Rgk = 0.2; pfk = 0.550829288131395 Reaction: g6p = lambda*(Rgk-pfk), Rate Law: lambda*(Rgk-pfk)
fback = 1.24703296147847; atp = 1899.74679486529; taua = 300000.0; r1 = 0.35 Reaction: adp = (atp-adp*exp(fback*(1-c/r1)))/(taua*1), Rate Law: (atp-adp*exp(fback*(1-c/r1)))/(taua*1)
ninf = 1.50710358059757E-4; taun = 20.0 Reaction: n = (ninf-n)/taun, Rate Law: (ninf-n)/taun
Jer = -0.06305; sigmaV = 31.0; fer = 0.01 Reaction: cer = (-fer)*sigmaV*Jer, Rate Law: (-fer)*sigmaV*Jer
lambda = 0.005; pfk = 0.550829288131395; rgpdh = 1.26491106406735 Reaction: fbp = lambda*(pfk/1-0.5*rgpdh), Rate Law: lambda*(pfk/1-0.5*rgpdh)

States:

Name Description
g6p [D-glucose 6-phosphate]
c [calcium(2+)]
cer [calcium(2+)]
adp [ADP]
V [membrane potential]
fbp [keto-D-fructose 1,6-bisphosphate]
n [delayed rectifier potassium channel activity]

Observables: none

MODEL1006230114 @ v0.0.1

This a model from the article: A simplified model for mitochondrial ATP production. Bertram R, Gram Pedersen M, Luci…

Most of the adenosine triphosphate (ATP) synthesized during glucose metabolism is produced in the mitochondria through oxidative phosphorylation. This is a complex reaction powered by the proton gradient across the mitochondrial inner membrane, which is generated by mitochondrial respiration. A detailed model of this reaction, which includes dynamic equations for the key mitochondrial variables, was developed earlier by Magnus and Keizer. However, this model is extraordinarily complicated. We develop a simpler model that captures the behavior of the original model but is easier to use and to understand. We then use it to investigate the mitochondrial responses to glycolytic and calcium input. We use the model to explain experimental observations of the opposite effects of raising cytosolic Ca(2+)in low and high glucose, and to predict the effects of a mutation in the mitochondrial enzyme nicotinamide nucleotide transhydrogenase (Nnt) in pancreatic beta-cells. link: http://identifiers.org/pubmed/16945388

Parameters: none

States: none

Observables: none

BIOMD0000000128 @ v0.0.1

The model is according to the paper *Endothelin Action on Pituitary Lactotrophs: One Receptor, Many GTP-Binding Proteins…

The endothelins are a family of hormones that have a biphasic action on pituitary lactotrophs. The initial effect is stimulatory, followed later by inhibition that persists long after the agonist has been removed. Recent research has uncovered several G protein pathways that mediate these effects. link: http://identifiers.org/pubmed/16434725

Parameters:

Name Description
cAMPlow = 0.2; ETswitch = 0.0; taudir = 20000.0 Reaction: => cAMP, Rate Law: cell*ETswitch*(cAMPlow-cAMP)/taudir
jertot = NaN; jmemtot = NaN; f = 0.01 Reaction: => c, Rate Law: cell*f*(jertot+jmemtot)
sigmav = 10.0; jertot = NaN; fer = 0.01 Reaction: => cer, Rate Law: (-fer)*sigmav*jertot*cell

States:

Name Description
cer [calcium(2+); Calcium cation]
c [calcium(2+); Calcium cation]
cAMP [3',5'-cyclic AMP; 3',5'-Cyclic AMP]

Observables: none

BIOMD0000000376 @ v0.0.1

This is the model described in the article: Interaction of glycolysis and mitochondrial respiration in metabolic os…

Insulin secretion from pancreatic beta-cells is oscillatory, with a typical period of 2-7 min, reflecting oscillations in membrane potential and the cytosolic Ca(2+) concentration. Our central hypothesis is that the slow 2-7 min oscillations are due to glycolytic oscillations, whereas faster oscillations that are superimposed are due to Ca(2+) feedback onto metabolism or ion channels. We extend a previous mathematical model based on this hypothesis to include a more detailed description of mitochondrial metabolism. We demonstrate that this model can account for typical oscillatory patterns of membrane potential and Ca(2+) concentration in islets. It also accounts for temporal data on oxygen consumption in islets. A recent challenge to the notion that glycolytic oscillations drive slow Ca(2+) oscillations in islets are data showing that oscillations in Ca(2+), mitochondrial oxygen consumption, and NAD(P)H levels are all terminated by membrane hyperpolarization. We demonstrate that these data are in fact compatible with a model in which glycolytic oscillations are the key player in rhythmic islet activity. Finally, we use the model to address the recent finding that the activity of islets from some mice is uniformly fast, whereas that from islets of other mice is slow. We propose a mechanism for this dichotomy. link: http://identifiers.org/pubmed/17172305

Parameters:

Name Description
cm = 5300.0; Ik = 0.0; Ikatp = 2433.43025793791; Ica = -2927.84163162795; Ikca = 466.296163499462 Reaction: Vm = (-(Ik+Ica+Ikca+Ikatp))/cm, Rate Law: (-(Ik+Ica+Ikca+Ikatp))/cm
JO = 0.446813558235194; JPDH = 0.451601160351069; gamma = 0.001 Reaction: NADHm = gamma*(JPDH-JO), Rate Law: gamma*(JPDH-JO)
delta = 0.0733082706766917; JANT = 1.1239508472473; Jhyd = 0.0797355 Reaction: adp = (-delta)*JANT+Jhyd, Rate Law: (-delta)*JANT+Jhyd
gamma = 0.001; JANT = 1.1239508472473; JF1F0 = 1.12901593707623 Reaction: ADPm = gamma*(JANT-JF1F0), Rate Law: gamma*(JANT-JF1F0)
JHatp = 3.38704781122868; Cmito = 1.8; JHleak = 0.298; JNaCa = 0.162244429551387; JANT = 1.1239508472473; JHres = 5.21282484607726; Juni = 0.157794 Reaction: delta_psi = (JHres-(JHatp+JANT+JHleak+JNaCa+2*Juni))/Cmito, Rate Law: (JHres-(JHatp+JANT+JHleak+JNaCa+2*Juni))/Cmito
JPFK_ms = 3.74364085279847E-4; JGK_ms = 4.0E-4 Reaction: G6P = JGK_ms-JPFK_ms, Rate Law: JGK_ms-JPFK_ms
Vc_Ver = 31.0; Jer = 9.65999999999995E-4; fer = 0.01 Reaction: Caer = (-fer)*Vc_Ver*Jer, Rate Law: (-fer)*Vc_Ver*Jer
n_infinity = 1.50710358059757E-4; tau_n = 20.0 Reaction: n = (n_infinity-n)/tau_n, Rate Law: (n_infinity-n)/tau_n
JPFK_ms = 3.74364085279847E-4; JGPDH = 7.34846922834953E-4 Reaction: FBP = JPFK_ms-0.5*JGPDH, Rate Law: JPFK_ms-0.5*JGPDH
delta = 0.0733082706766917; Jmito = 0.00445042955138744; fcyt = 0.01; Jmem = 0.00117528734232577; Jer = 9.65999999999995E-4 Reaction: c = fcyt*(Jmem+Jer+delta*Jmito), Rate Law: fcyt*(Jmem+Jer+delta*Jmito)
fmito = 0.01; Jmito = 0.00445042955138744 Reaction: Cam = (-fmito)*Jmito, Rate Law: (-fmito)*Jmito

States:

Name Description
Cam [calcium(2+)]
delta psi delta_psi
c [calcium(2+)]
ADPm [ADP]
Caer [calcium(2+)]
FBP [keto-D-fructose 1,6-bisphosphate]
G6P [D-glucose 6-phosphate]
adp [ADP]
NADHm [NADH]
Vm [membrane potential]
n [delayed rectifier potassium channel activity]

Observables: none

Besozzi2012 - Oscillatory regimes in the Ras/cAMP/PKA pathway in S.cerevisiaeMechanistic model of the Ras/cAMP/PKA in ye…

: In the yeast Saccharomyces cerevisiae, the Ras/cAMP/PKA pathway is involved in the regulation of cell growth and proliferation in response to nutritional sensing and stress conditions. The pathway is tightly regulated by multiple feedback loops, exerted by the protein kinase A (PKA) on a few pivotal components of the pathway. In this article, we investigate the dynamics of the second messenger cAMP by performing stochastic simulations and parameter sweep analysis of a mechanistic model of the Ras/cAMP/PKA pathway, to determine the effects that the modulation of these feedback mechanisms has on the establishment of stable oscillatory regimes. In particular, we start by studying the role of phosphodiesterases, the enzymes that catalyze the degradation of cAMP, which represent the major negative feedback in this pathway. Then, we show the results on cAMP oscillations when perturbing the amount of protein Cdc25 coupled with the alteration of the intracellular ratio of the guanine nucleotides (GTP/GDP), which are known to regulate the switch of the GTPase Ras protein. This multi-level regulation of the amplitude and frequency of oscillations in the Ras/cAMP/PKA pathway might act as a fine tuning mechanism for the downstream targets of PKA, as also recently evidenced by some experimental investigations on the nucleocytoplasmic shuttling of the transcription factor Msn2 in yeast cells. link: http://identifiers.org/pubmed/22818197

Parameters:

Name Description
K27 = 0.1 s^(-1) Reaction: cAMP_Pde1f => cAMP + Pde1f; cAMP_Pde1f, Rate Law: K27*cAMP_Pde1f
K18 = 0.1 s^(-1) Reaction: IIIcAMP_PKA => cAMP + IIcAMP_PKA; IIIcAMP_PKA, Rate Law: K18*IIIcAMP_PKA
K8 = 0.01 s^(-1) Reaction: Ras2_GTP + Ira2 => Ras2_GTP_Ira2; Ras2_GTP, Ira2, Rate Law: K8*Ras2_GTP*Ira2
K0 = 1.0 s^(-1) Reaction: Ras2_GDP + Cdc25 => Ras2_GDP_Cdc25; Ras2_GDP, Cdc25, Rate Law: K0*Ras2_GDP*Cdc25
K34 = 0.01 s^(-1) Reaction: PPA2 + Cdc25f => Cdc25 + PPA2; PPA2, Cdc25f, Rate Law: K34*PPA2*Cdc25f
K35 = 0.001 s^(-1) Reaction: Ira2 + C => C + Ira2P; Ira2, C, Rate Law: K35*Ira2*C
K17 = 0.1 s^(-1) Reaction: IVcAMP_PKA => cAMP + IIIcAMP_PKA; IVcAMP_PKA, Rate Law: K17*IVcAMP_PKA
K14 = 1.0E-5 s^(-1) Reaction: cAMP + cAMP_PKA => IIcAMP_PKA; cAMP, cAMP_PKA, Rate Law: K14*cAMP*cAMP_PKA
K28 = 7.5 s^(-1) Reaction: cAMP_Pde1f => Pde1f + AMP; cAMP_Pde1f, Rate Law: K28*cAMP_Pde1f
K13 = 1.0E-5 s^(-1) Reaction: cAMP + PKA => cAMP_PKA; cAMP, PKA, Rate Law: K13*cAMP*PKA
K12 = 0.001 s^(-1) Reaction: Ira2 + Ras2_GTP_CYR1 => Ras2_GDP + Ira2 + CYR1; Ira2, Ras2_GTP_CYR1, Rate Law: K12*Ira2*Ras2_GTP_CYR1
K1 = 1.0 s^(-1) Reaction: Ras2_GDP_Cdc25 => Ras2_GDP + Cdc25; Ras2_GDP_Cdc25, Rate Law: K1*Ras2_GDP_Cdc25
K29 = 1.0E-4 s^(-1) Reaction: Pde1f + PPA2 => Pde1 + PPA2; Pde1f, PPA2, Rate Law: K29*Pde1f*PPA2
K7 = 1.0 s^(-1) Reaction: Cdc25 + Ras2_GTP => Ras2_GTP_Cdc25; Cdc25, Ras2_GTP, Rate Law: K7*Cdc25*Ras2_GTP
K3 = 1.0 s^(-1) Reaction: Ras2_Cdc25 + GDP => Ras2_GDP_Cdc25; Ras2_Cdc25, GDP, Rate Law: K3*Ras2_Cdc25*GDP
K4 = 1.0 s^(-1) Reaction: Ras2_Cdc25 + GTP => Ras2_GTP_Cdc25; Ras2_Cdc25, GTP, Rate Law: K4*Ras2_Cdc25*GTP
K26 = 0.1 s^(-1) Reaction: cAMP + Pde1f => cAMP_Pde1f; cAMP, Pde1f, Rate Law: K26*cAMP*Pde1f
K9 = 0.25 s^(-1) Reaction: Ras2_GTP_Ira2 => Ras2_GDP + Ira2; Ras2_GTP_Ira2, Rate Law: K9*Ras2_GTP_Ira2
K16 = 1.0E-5 s^(-1) Reaction: cAMP + IIIcAMP_PKA => IVcAMP_PKA; cAMP, IIIcAMP_PKA, Rate Law: K16*cAMP*IIIcAMP_PKA
K30 = 1.0E-4 s^(-1) Reaction: cAMP + Pde2 => cAMP_Pde2; cAMP, Pde2, Rate Law: K30*cAMP*Pde2
K31 = 1.0 s^(-1) Reaction: cAMP_Pde2 => cAMP + Pde2; cAMP_Pde2, Rate Law: K31*cAMP_Pde2
K32 = 1.7 s^(-1) Reaction: cAMP_Pde2 => AMP + Pde2; cAMP_Pde2, Rate Law: K32*cAMP_Pde2
K33 = 1.0 s^(-1) Reaction: Cdc25 + C => C + Cdc25f; Cdc25, C, Rate Law: K33*Cdc25*C
K24 = 1.0 s^(-1) Reaction: R_C => PKA; R_C, Rate Law: K24*R_C*(R_C-1)/2
K10 = 0.001 s^(-1) Reaction: Ras2_GTP + CYR1 => Ras2_GTP_CYR1; Ras2_GTP, CYR1, Rate Law: K10*Ras2_GTP*CYR1
K36 = 1.25 s^(-1) Reaction: Ras2_GTP + Ira2P => Ras2_GTP_Ira2P; Ras2_GTP, Ira2P, Rate Law: K36*Ras2_GTP*Ira2P
K38 = 10.0 s^(-1) Reaction: Ira2P => Ira2; Ira2P, Rate Law: K38*Ira2P
K37 = 2.5 s^(-1) Reaction: Ras2_GTP_Ira2P => Ras2_GDP + Ira2P; Ras2_GTP_Ira2P, Rate Law: K37*Ras2_GTP_Ira2P
K22 = 1.0 s^(-1) Reaction: R_2cAMP => cAMP + R; R_2cAMP, Rate Law: K22*R_2cAMP
K20 = 0.1 s^(-1) Reaction: cAMP_PKA => cAMP + PKA; cAMP_PKA, Rate Law: K20*cAMP_PKA
K5 = 1.0 s^(-1) Reaction: Ras2_GTP_Cdc25 => Ras2_Cdc25 + GTP; Ras2_GTP_Cdc25, Rate Law: K5*Ras2_GTP_Cdc25
K15 = 1.0E-5 s^(-1) Reaction: cAMP + IIcAMP_PKA => IIIcAMP_PKA; cAMP, IIcAMP_PKA, Rate Law: K15*cAMP*IIcAMP_PKA
K23 = 0.75 s^(-1) Reaction: C + R => R_C; C, R, Rate Law: K23*C*R
K6 = 1.0 s^(-1) Reaction: Ras2_GTP_Cdc25 => Cdc25 + Ras2_GTP; Ras2_GTP_Cdc25, Rate Law: K6*Ras2_GTP_Cdc25
K19 = 0.1 s^(-1) Reaction: IIcAMP_PKA => cAMP + cAMP_PKA; IIcAMP_PKA, Rate Law: K19*IIcAMP_PKA
K11 = 2.1E-6 s^(-1) Reaction: Ras2_GTP_CYR1 + ATP => Ras2_GTP_CYR1 + cAMP; Ras2_GTP_CYR1, ATP, Rate Law: K11*Ras2_GTP_CYR1*ATP
K2 = 1.5 s^(-1) Reaction: Ras2_GDP_Cdc25 => Ras2_Cdc25 + GDP; Ras2_GDP_Cdc25, Rate Law: K2*Ras2_GDP_Cdc25

States:

Name Description
cAMP Pde1f [3',5'-cyclic AMP; 3',5'-cyclic-nucleotide phosphodiesterase 1; phosphorylated]
ATP [ATP]
Pde1f [3',5'-cyclic-nucleotide phosphodiesterase 1; phosphorylated]
Ras2 GTP Ira2P [GTP; Ras-like protein 2; Inhibitory regulator protein IRA2]
Ras2 GTP CYR1 [GTP; Adenylate cyclase; Ras-like protein 2]
AMP [AMP]
GTP [GTP]
IIcAMP PKA [3',5'-cyclic AMP; cAMP-dependent protein kinase, catalytic subunit-like]
Ras2 GDP [GDP; Ras-like protein 2]
cAMP [3',5'-cyclic AMP]
Cdc25f [Cell division control protein 25]
Ras2 GTP [GTP; Ras-like protein 2]
PPA2 [Inorganic pyrophosphatase, mitochondrial]
cAMP PKA [3',5'-cyclic AMP; cAMP-dependent protein kinase, catalytic subunit-like]
cAMP Pde2 [3',5'-cyclic AMP; 3',5'-cyclic-nucleotide phosphodiesterase 2]
Ras2 GTP Ira2 [GTP; Inhibitory regulator protein IRA2; Ras-like protein 2]
GDP [GDP]
Pde2 [3',5'-cyclic-nucleotide phosphodiesterase 2]
Ras2 GTP Cdc25 [GTP; Cell division control protein 25; Ras-like protein 2]
PKA [cAMP-dependent protein kinase, catalytic subunit-like]
Ras2 GDP Cdc25 [GDP; Cell division control protein 25; Ras-like protein 2]
Ras2 Cdc25 [Cell division control protein 25; Ras-like protein 2]
Ira2 [Inhibitory regulator protein IRA2]
Cdc25 [Cell division control protein 25]
R C [cAMP-dependent protein kinase regulatory subunit; cAMP-dependent protein kinase type 3; cAMP-dependent protein kinase type 2; cAMP-dependent protein kinase type 1]
CYR1 [Adenylate cyclase]
Ira2P [Inhibitory regulator protein IRA2; phosphorylated]
R [cAMP-dependent protein kinase regulatory subunit]
IIIcAMP PKA [3',5'-cyclic AMP; cAMP-dependent protein kinase, catalytic subunit-like]

Observables: none

Best2009 - Homeostatic mechanisms in dopamine synthesis and releaseEncoded non-curated model. Issues: - Initial concent…

Dopamine is a catecholamine that is used as a neurotransmitter both in the periphery and in the central nervous system. Dysfunction in various dopaminergic systems is known to be associated with various disorders, including schizophrenia, Parkinson's disease, and Tourette's syndrome. Furthermore, microdialysis studies have shown that addictive drugs increase extracellular dopamine and brain imaging has shown a correlation between euphoria and psycho-stimulant-induced increases in extracellular dopamine 1. These consequences of dopamine dysfunction indicate the importance of maintaining dopamine functionality through homeostatic mechanisms that have been attributed to the delicate balance between synthesis, storage, release, metabolism, and reuptake.We construct a mathematical model of dopamine synthesis, release, and reuptake and use it to study homeostasis in single dopaminergic neuron terminals. We investigate the substrate inhibition of tyrosine hydroxylase by tyrosine, the consequences of the rapid uptake of extracellular dopamine by the dopamine transporters, and the effects of the autoreceoptors on dopaminergic function. The main focus is to understand the regulation and control of synthesis and release and to explicate and interpret experimental findings.We show that the substrate inhibition of tyrosine hydroxylase by tyrosine stabilizes cytosolic and vesicular dopamine against changes in tyrosine availability due to meals. We find that the autoreceptors dampen the fluctuations in extracellular dopamine caused by changes in tyrosine hydroxylase expression and changes in the rate of firing. We show that short bursts of action potentials create significant dopamine signals against the background of tonic firing. We explain the observed time courses of extracellular dopamine responses to stimulation in wild type mice and mice that have genetically altered dopamine transporter densities and the observed half-lives of extracellular dopamine under various treatment protocols.Dopaminergic systems must respond robustly to important biological signals such as bursts, while at the same time maintaining homeostasis in the face of normal biological fluctuations in inputs, expression levels, and firing rates. This is accomplished through the cooperative effect of many different homeostatic mechanisms including special properties of tyrosine hydroxylase, the dopamine transporters, and the dopamine autoreceptors. link: http://identifiers.org/pubmed/19740446

Parameters: none

States: none

Observables: none

Beste2007 - Genome-scale metabolic network of Mycobacterium tuberculosis (GSMN_TB)This model is described in the article…

An impediment to the rational development of novel drugs against tuberculosis (TB) is a general paucity of knowledge concerning the metabolism of Mycobacterium tuberculosis, particularly during infection. Constraint-based modeling provides a novel approach to investigating microbial metabolism but has not yet been applied to genome-scale modeling of M. tuberculosis.GSMN-TB, a genome-scale metabolic model of M. tuberculosis, was constructed, consisting of 849 unique reactions and 739 metabolites, and involving 726 genes. The model was calibrated by growing Mycobacterium bovis bacille Calmette Guérin in continuous culture and steady-state growth parameters were measured. Flux balance analysis was used to calculate substrate consumption rates, which were shown to correspond closely to experimentally determined values. Predictions of gene essentiality were also made by flux balance analysis simulation and were compared with global mutagenesis data for M. tuberculosis grown in vitro. A prediction accuracy of 78% was achieved. Known drug targets were predicted to be essential by the model. The model demonstrated a potential role for the enzyme isocitrate lyase during the slow growth of mycobacteria, and this hypothesis was experimentally verified. An interactive web-based version of the model is available.The GSMN-TB model successfully simulated many of the growth properties of M. tuberculosis. The model provides a means to examine the metabolic flexibility of bacteria and predict the phenotype of mutants, and it highlights previously unexplored features of M. tuberculosis metabolism. link: http://identifiers.org/pubmed/17521419

Parameters: none

States: none

Observables: none

MODEL9071122126 @ v0.0.1

This model is based closely on the one from <a href = "http://www.ncbi.nlm.nih.gov:80/entrez/query.fcgi?cmd=Retrieve&db=…

Many distinct signaling pathways allow the cell to receive, process, and respond to information. Often, components of different pathways interact, resulting in signaling networks. Biochemical signaling networks were constructed with experimentally obtained constants and analyzed by computational methods to understand their role in complex biological processes. These networks exhibit emergent properties such as integration of signals across multiple time scales, generation of distinct outputs depending on input strength and duration, and self-sustaining feedback loops. Feedback can result in bistable behavior with discrete steady-state activities, well-defined input thresholds for transition between states and prolonged signal output, and signal modulation in response to transient stimuli. These properties of signaling networks raise the possibility that information for "learned behavior" of biological systems may be stored within intracellular biochemical reactions that comprise signaling pathways. link: http://identifiers.org/pubmed/9888852

Parameters: none

States: none

Observables: none

MODEL9071773985 @ v0.0.1

This model relates to figure 5 in <a href = "http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=A…

Biological signaling networks comprised of cellular components including signaling proteins and small molecule messengers control the many cell function in responses to various extracellular and intracellular signals including hormone and neurotransmitter inputs, and genetic events. Many signaling pathways have motifs familiar to electronics and control theory design. Feedback loops are among the most common of these. Using experimentally derived parameters, we modeled a positive feedback loop in signaling pathways used by growth factors to trigger cell proliferation. This feedback loop is bistable under physiological conditions, although the system can move to a monostable state as well. We find that bistability persists under a wide range of regulatory conditions, even when core enzymes in the feedback loop deviate from physiological values. We did not observe any other phenomena in the core feedback loop, but the addition of a delayed inhibitory feedback was able to generate oscillations under rather extreme parameter conditions. Such oscillations may not be of physiological relevance. We propose that the kinetic properties of this feedback loop have evolved to support bistability and flexibility in going between bistable and monostable modes, while simultaneously being very refractory to oscillatory states. (c) 2001 American Institute of Physics. link: http://identifiers.org/pubmed/12779455

Parameters: none

States: none

Observables: none

MODEL9077438479 @ v0.0.1

This is a model of the canonical cAMP signaling pathway:<br">Ligand->Receptor->G-protein->Cyclase->cAMP->PKA.<br>It also…

link: http://identifiers.org/pubmed/11665614

Parameters: none

States: none

Observables: none

MODEL9079179924 @ v0.0.1

Model for figure 1c in <a href = "http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&lis…

Intracellular signaling networks receive and process information to control cellular machines. The mitogen-activated protein kinase (MAPK) 1,2/protein kinase C (PKC) system is one such network that regulates many cellular machines, including the cell cycle machinery and autocrine/paracrine factor synthesizing machinery. We used a combination of computational analysis and experiments in mouse NIH-3T3 fibroblasts to understand the design principles of this controller network. We find that the growth factor-stimulated signaling network containing MAPK 1, 2/PKC can operate with one (monostable) or two (bistable) stable states. At low concentrations of MAPK phosphatase, the system exhibits bistable behavior, such that brief stimulus results in sustained MAPK activation. The MAPK-induced increase in the amounts of MAPK phosphatase eliminates the prolonged response capability and moves the network to a monostable state, in which it behaves as a proportional response system responding acutely to stimulus. Thus, the MAPK 1, 2/PKC controller network is flexibly designed, and MAPK phosphatase may be critical for this flexible response. link: http://identifiers.org/pubmed/12169734

Parameters: none

States: none

Observables: none

MODEL9070467164 @ v0.0.1

This is a network involving the MAPK-PKC feedback loop with input from the PDGFR in the synapse. The distinctive feature…

Intracellular signaling networks receive and process information to control cellular machines. The mitogen-activated protein kinase (MAPK) 1,2/protein kinase C (PKC) system is one such network that regulates many cellular machines, including the cell cycle machinery and autocrine/paracrine factor synthesizing machinery. We used a combination of computational analysis and experiments in mouse NIH-3T3 fibroblasts to understand the design principles of this controller network. We find that the growth factor-stimulated signaling network containing MAPK 1, 2/PKC can operate with one (monostable) or two (bistable) stable states. At low concentrations of MAPK phosphatase, the system exhibits bistable behavior, such that brief stimulus results in sustained MAPK activation. The MAPK-induced increase in the amounts of MAPK phosphatase eliminates the prolonged response capability and moves the network to a monostable state, in which it behaves as a proportional response system responding acutely to stimulus. Thus, the MAPK 1, 2/PKC controller network is flexibly designed, and MAPK phosphatase may be critical for this flexible response. link: http://identifiers.org/pubmed/12169734

Parameters: none

States: none

Observables: none

MODEL9080747936 @ v0.0.1

Model of regulation of CaMKII by Calcium, including parallel excitatory input from CaM and inhibitory input from PP1 as…

Many cellular signaling events occur in small subcellular volumes and involve low-abundance molecular species. This context introduces two major differences from mass-action analyses of nondiffusive signaling. First, reactions involving small numbers of molecules occur in a probabilistic manner which introduces scatter in chemical activities. Second, the timescale of diffusion of molecules between subcellular compartments and the rest of the cell is comparable to the timescale of many chemical reactions, altering the dynamics and outcomes of signaling reactions. This study examines both these effects on information flow through four protein kinase regulatory pathways. The analysis uses Monte Carlo simulations in a subcellular volume diffusively coupled to a bulk cellular volume. Diffusion constants and the volume of the subcellular compartment are systematically varied to account for a range of cellular conditions. Each pathway is characterized in terms of the probabilistic scatter in active kinase levels as a measure of "noise" on the pathway output. Under the conditions reported here, most signaling outcomes in a volume below one femtoliter are severely degraded. Diffusion and subcellular compartmentalization influence the signaling chemistry to give a diversity of signaling outcomes. These outcomes may include washout of the signal, reinforcement of signals, and conversion of steady responses to transients. link: http://identifiers.org/pubmed/15298882

Parameters: none

States: none

Observables: none

MODEL9085850385 @ v0.0.1

Model of MAPK activation by EGFR in the synapse. Demonstration programs using this model are available <a href = "http:/…

The synaptic signaling network is capable of sophisticated cellular computations. These include the ability to respond selectively to different patterns of input, and to sustain changes in response over long periods. The small volume of the synapse complicates the analysis of signaling because the chemical environment is strongly affected by diffusion and stochasticity. This study is based on an updated version of a previously proposed synaptic signaling circuit (Bhalla and Iyengar, 1999) and analyzes three network computation properties in small volumes: bistability, thresholding, and pattern selectivity. Simulations show that although there are diffusive regimes in which bistability may persist, chemical noise at small volumes overwhelms bistability. In the deterministic situation, the network exhibits a sharp threshold for transition between lower and upper stable states. This transition is broadened and individual runs partition between lower and upper states, when stochasticity is considered. The third network property, pattern selectivity, is severely degraded at synaptic volumes. However, there are regimes in which a process similar to stochastic resonance operates and amplifies pattern selectivity. These results imply that simple scaling of signaling conditions to femtoliter volumes is unlikely, and microenvironments, such as reaction complex formation, may be essential for reliable small-volume signaling. link: http://identifiers.org/pubmed/15298883

Parameters: none

States: none

Observables: none

MODEL9081220742 @ v0.0.1

This is a network model of many pathways present at the neuronal synapse. The network has properties of temporal tuning…

The synaptic signaling network is capable of sophisticated cellular computations. These include the ability to respond selectively to different patterns of input, and to sustain changes in response over long periods. The small volume of the synapse complicates the analysis of signaling because the chemical environment is strongly affected by diffusion and stochasticity. This study is based on an updated version of a previously proposed synaptic signaling circuit (Bhalla and Iyengar, 1999) and analyzes three network computation properties in small volumes: bistability, thresholding, and pattern selectivity. Simulations show that although there are diffusive regimes in which bistability may persist, chemical noise at small volumes overwhelms bistability. In the deterministic situation, the network exhibits a sharp threshold for transition between lower and upper stable states. This transition is broadened and individual runs partition between lower and upper states, when stochasticity is considered. The third network property, pattern selectivity, is severely degraded at synaptic volumes. However, there are regimes in which a process similar to stochastic resonance operates and amplifies pattern selectivity. These results imply that simple scaling of signaling conditions to femtoliter volumes is unlikely, and microenvironments, such as reaction complex formation, may be essential for reliable small-volume signaling. link: http://identifiers.org/pubmed/15298883

Parameters: none

States: none

Observables: none

MODEL9079740062 @ v0.0.1

This model consists of receptor-ligand interaction, G-protein activation, Adenylyl cyclase mediated formation of cAMP an…

Many cellular signaling events occur in small subcellular volumes and involve low-abundance molecular species. This context introduces two major differences from mass-action analyses of nondiffusive signaling. First, reactions involving small numbers of molecules occur in a probabilistic manner which introduces scatter in chemical activities. Second, the timescale of diffusion of molecules between subcellular compartments and the rest of the cell is comparable to the timescale of many chemical reactions, altering the dynamics and outcomes of signaling reactions. This study examines both these effects on information flow through four protein kinase regulatory pathways. The analysis uses Monte Carlo simulations in a subcellular volume diffusively coupled to a bulk cellular volume. Diffusion constants and the volume of the subcellular compartment are systematically varied to account for a range of cellular conditions. Each pathway is characterized in terms of the probabilistic scatter in active kinase levels as a measure of "noise" on the pathway output. Under the conditions reported here, most signaling outcomes in a volume below one femtoliter are severely degraded. Diffusion and subcellular compartmentalization influence the signaling chemistry to give a diversity of signaling outcomes. These outcomes may include washout of the signal, reinforcement of signals, and conversion of steady responses to transients. link: http://identifiers.org/pubmed/15298882

Parameters: none

States: none

Observables: none

MODEL9080388197 @ v0.0.1

This model consists of receptor-ligand interaction, G-protein activation, Adenylyl cyclase mediated formation of cAMP an…

Many cellular signaling events occur in small subcellular volumes and involve low-abundance molecular species. This context introduces two major differences from mass-action analyses of nondiffusive signaling. First, reactions involving small numbers of molecules occur in a probabilistic manner which introduces scatter in chemical activities. Second, the timescale of diffusion of molecules between subcellular compartments and the rest of the cell is comparable to the timescale of many chemical reactions, altering the dynamics and outcomes of signaling reactions. This study examines both these effects on information flow through four protein kinase regulatory pathways. The analysis uses Monte Carlo simulations in a subcellular volume diffusively coupled to a bulk cellular volume. Diffusion constants and the volume of the subcellular compartment are systematically varied to account for a range of cellular conditions. Each pathway is characterized in terms of the probabilistic scatter in active kinase levels as a measure of "noise" on the pathway output. Under the conditions reported here, most signaling outcomes in a volume below one femtoliter are severely degraded. Diffusion and subcellular compartmentalization influence the signaling chemistry to give a diversity of signaling outcomes. These outcomes may include washout of the signal, reinforcement of signals, and conversion of steady responses to transients. link: http://identifiers.org/pubmed/15298882

Parameters: none

States: none

Observables: none

BIOMD0000000062 @ v0.0.1

[SBML](http://www.sbml.org/) level 2 code originaly generated for the JWS Online project by Jacky Snoep using [PySCeS](…

A mathematical model has been developed to study the effect of external tryptophan on the trp operon. The model accounts for the effect of feedback repression by tryptophan through the Hill equation. We demonstrate that the trp operon maintains an intracellular steady-state concentration in a fivefold range irrespective of extracellular conditions. Dynamic behavior of the trp operon corresponding to varying levels of extracellular tryptophan illustrates the adaptive nature of regulation. Depending on the external tryptophan level in the medium, the transient response ranges from a rapid and underdamped to a sluggish and highly overdamped response. To test model fidelity, simulation results are compared with experimental data available in the literature. We further demonstrate the significance of the biological structure of the operon on the overall performance. Our analysis suggests that the tryptophan operon has evolved to a truly optimal design. link: http://identifiers.org/pubmed/12787031

Parameters:

Name Description
ki1=3.53 microM; Ot=0.0033 microM; nH=1.92 dimensionless; k1=65.0 per_min Reaction: => Enz; Tt, Rate Law: compartment*k1*ki1^nH*Ot/(ki1^nH+Tt^nH)
k2=25.0 per_min; Ki2=810.0 microM Reaction: => Ts; Enz, Tt, Rate Law: compartment*k2*Enz*Ki2/(Ki2+Tt)
f_val = 380.0 microM; Tomax = 100.0 microM; Tex = 0.14 microM; e_val = 0.9 microM Reaction: To = Tomax*Tex/(Tex*(1+Ts/f_val)+e_val), Rate Law: missing
mu=0.01 per_min Reaction: Enz =>, Rate Law: compartment*mu*Enz
g=25.0 microM_per_min; Kg=0.2 microM Reaction: Ts =>, Rate Law: compartment*g*Ts/(Kg+Ts)

States:

Name Description
Tt [tryptophan; Tryptophan]
Ts [tryptophan; Tryptophan]
Enz [Anthranilate synthase component 1]
To exog. Trp

Observables: none

MODEL0318212660 @ v0.0.1

This model is from the article: Time Scale Simulation of Vmax of Urea Cycle Enzymes. Pradip Bhattacharya, Alok Sriva…

The objective of this study was to initialize the time-scale simulation of urea cycle enzymes based on the very limited amount of experimental data. As a model example, Vmax of each four enzymes was simulated with varying time for some organisms. These results indicated that the values of Vmax of time-scale simulation of all four enzymatic reactions of urea cycle were very close to 0.09-0.4, and were comparable (deviation â?¤0.01-0.1) for steady state kinetic measurements. Enzymes of several organisms were included in this study protocol. link: https://www.novapublishers.com/catalog/productinfo.php?productsid=26885

Parameters: none

States: none

Observables: none

This is a deterministic nonlinear ordinary differential equation mathematical model of the sterol regulatory element bin…

Cholesterol is one of the key constituents for maintaining the cellular membrane and thus the integrity of the cell itself. In contrast high levels of cholesterol in the blood are known to be a major risk factor in the development of cardiovascular disease. We formulate a deterministic nonlinear ordinary differential equation model of the sterol regulatory element binding protein 2 (SREBP-2) cholesterol genetic regulatory pathway in a hepatocyte. The mathematical model includes a description of genetic transcription by SREBP-2 which is subsequently translated to mRNA leading to the formation of 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR), a main regulator of cholesterol synthesis. Cholesterol synthesis subsequently leads to the regulation of SREBP-2 via a negative feedback formulation. Parameterised with data from the literature, the model is used to understand how SREBP-2 transcription and regulation affects cellular cholesterol concentration. Model stability analysis shows that the only positive steady-state of the system exhibits purely oscillatory, damped oscillatory or monotic behaviour under certain parameter conditions. In light of our findings we postulate how cholesterol homeostasis is maintained within the cell and the advantages of our model formulation are discussed with respect to other models of genetic regulation within the literature. link: http://identifiers.org/pubmed/24444765

Parameters:

Name Description
kappa_m = 1.0E-4; y = 4.0; mu_m = 1.9E-10; x = 3.0; kappa_c = 0.001 Reaction: => m; c, Rate Law: compartment*mu_m/(1+(kappa_m*(1+(c/kappa_c)^y))^x)
delta_h = 0.00193 Reaction: h =>, Rate Law: compartment*delta_h*h
delta_c = 0.0036 Reaction: c =>, Rate Law: compartment*delta_c*c
delta_m = 0.00135 Reaction: m =>, Rate Law: compartment*delta_m*m
mu_c = 0.462 Reaction: => c; h, Rate Law: compartment*mu_c*h

States:

Name Description
c [cholesterol]
m [C54701]
h [C54701]

Observables: none

MODEL1107050000 @ v0.0.1

This model is from the article: Systems biology analysis of programmed cell death Shani Bialik, Einat Zalckvar, Yaar…

Systems biology, a combined computational and experimental approach to analyzing complex biological systems, has recently been applied to understanding the pathways that regulate programmed cell death. This approach has become especially crucial because recent advances have resulted in an expanded view of the network, to include not just a single death module (apoptosis) but multiple death programs, including programmed necrosis and autophagic cell death. Current research directions in the systems biology field range from quantitative analysis of subprocesses of individual death pathways to the study of interconnectivity among the various death modules of the larger network. These initial studies have provided great advances in our understanding of programmed cell death and have important clinical implications for drug target research. link: http://identifiers.org/pubmed/20537543

Parameters: none

States: none

Observables: none

This model describes the humoral and cellular response of the immune system to a tumor associate antigen and the recogni…

The definition of artificial immunity, realized through vaccinations, is nowadays a practice widely developed in order to eliminate cancer disease. The present paper deals with an improved version of a mathematical model recently analyzed and related to the competition between immune system cells and mammary carcinoma cells under the action of a vaccine (Triplex). The model describes in detail both the humoral and cellular response of the immune system to the tumor associate antigen and the recognition process between B cells, T cells and antigen presenting cells. The control of the tumor cells growth occurs through the definition of different vaccine protocols. The performed numerical simulations of the model are in agreement with in vivo experiments on transgenic mice. link: http://identifiers.org/pubmed/23281916

Parameters: none

States: none

Observables: none

Persistence analysis in a Kolmogorov-type model for cancer-immune system competition AIP Conference Proceedings 1558, 17…

This paper is concerned with analytical investigations on the competition between cancer cells and immune system cells. Specifically the role of the B-cells and T-cells in the evolution of cancer cells is taken into account. The mathematical model is a Kolmogorov-type system of three evolution equations where the growth rate of the cells is described by logistic law and the response of B-cells and T-cells is modeled according to Holling type-II function. The stability analysis of equilibrium points is performed and the persistence of the model is proved. link: http://identifiers.org/doi/10.1063/1.4825874

Parameters:

Name Description
delta_2 = 0.173286795139986 Reaction: T =>, Rate Law: compartment*delta_2*T
gamma_2 = 0.65; gamma_1 = 0.5 Reaction: C => ; B, T, Rate Law: compartment*(gamma_1/(1+gamma_1*C)*C*B+gamma_2/(1+gamma_2*C)*C*T)
beta_1 = 10.0; alpha_1 = 0.05 Reaction: => C, Rate Law: compartment*alpha_1*C*(1-C/beta_1)
alpha_2 = 0.31; beta_2 = 3.0; gamma_1 = 0.5 Reaction: => B; C, Rate Law: compartment*alpha_2*C*B*(1-B/beta_2)*gamma_1/(1+gamma_1*C)
beta_3 = 3.0; gamma_2 = 0.65; alpha_3 = 0.5 Reaction: => T; C, Rate Law: compartment*alpha_3*C*T*(1-T/beta_3)*gamma_2/(1+gamma_2*C)
delta_1 = 0.0990210257942779 Reaction: B =>, Rate Law: compartment*delta_1*B

States:

Name Description
B [BTO:0000776]
T [T-lymphocyte]
C [cancer]

Observables: none

Bianconi2012 - EGFR and IGF1R pathway in lung cancerEGFR and IGF1R pathways play a key role in various human cancers and…

In this paper we propose a Systems Biology approach to understand the molecular biology of the Epidermal Growth Factor Receptor (EGFR, also known as ErbB1/HER1) and type 1 Insulin-like Growth Factor (IGF1R) pathways in non-small cell lung cancer (NSCLC). This approach, combined with Translational Oncology methodologies, is used to address the experimental evidence of a close relationship among EGFR and IGF1R protein expression, by immunohistochemistry (IHC) and gene amplification, by in situ hybridization (FISH) and the corresponding ability to develop a more aggressive behavior. We develop a detailed in silico model, based on ordinary differential equations, of the pathways and study the dynamic implications of receptor alterations on the time behavior of the MAPK cascade down to ERK, which in turn governs proliferation and cell migration. In addition, an extensive sensitivity analysis of the proposed model is carried out and a simplified model is proposed which allows us to infer a similar relationship among EGFR and IGF1R activities and disease outcome. link: http://identifiers.org/pubmed/21620944

Parameters:

Name Description
gamma_EGFR = 0.02 Reaction: EGFR_active => ; EGFR_active, Rate Law: gamma_EGFR*EGFR_active
n_RasActiveRasGap=1.0; KM_RasActiveRasGap=1432410.0; k_RasActiveRasGap=1509.36 Reaction: RasGapActive + Ras_active => Ras + RasGapActive; RasGapActive, Ras_active, Rate Law: RasGapActive*k_RasActiveRasGap*Ras_active^n_RasActiveRasGap/(KM_RasActiveRasGap^n_RasActiveRasGap+Ras_active^n_RasActiveRasGap)
KM_MekActivePP2A=518753.0; k_MekActivePP2A=2.83243; n_MekActivePP2A=1.0 Reaction: PP2A + Mek_active => Mek + PP2A; PP2A, Mek_active, Rate Law: PP2A*k_MekActivePP2A*Mek_active^n_MekActivePP2A/(KM_MekActivePP2A^n_MekActivePP2A+Mek_active^n_MekActivePP2A)
k_ERKactive_PP2A=8.8912; n_ERKactive_PP2A=1.0; KM_ERKactive_PP2A=3496490.0 Reaction: ERK_active + PP2A => ERK + PP2A; PP2A, ERK_active, Rate Law: PP2A*k_ERKactive_PP2A*ERK_active^n_ERKactive_PP2A/(KM_ERKactive_PP2A^n_ERKactive_PP2A+ERK_active^n_ERKactive_PP2A)
n_Mek_PP2A=1.0; KM_MekPP2A=4768350.0; k_Mek_PP2A=185.759 Reaction: Raf_active + Mek => Mek_active + Raf_active; Raf_active, Mek, Rate Law: Raf_active*k_Mek_PP2A*Mek^n_Mek_PP2A/(KM_MekPP2A^n_Mek_PP2A+Mek^n_Mek_PP2A)
k_ERK_MekActive=9.85367; KM_ERK_MekActive=1007340.0 Reaction: ERK + Mek_active => ERK_active + Mek_active; Mek_active, ERK, Rate Law: Mek_active*k_ERK_MekActive*ERK/(KM_ERK_MekActive+ERK)
n_Raf_AKT=1.0; k_Raf_AKT=15.1212; KM_Raf_AKT=119355.0 Reaction: AKT_active + Raf_active => Raf + AKT_active; AKT_active, Raf_active, Rate Law: AKT_active*k_Raf_AKT*Raf_active^n_Raf_AKT/(KM_Raf_AKT^n_Raf_AKT+Raf_active^n_Raf_AKT)
kd_AKT=0.005 Reaction: AKT_active => AKT; AKT_active, Rate Law: kd_AKT*AKT_active
k_Ras_SOS=32.344; n_Ras_SOS=1.0; KM_Ras_SOS=35954.3 Reaction: A_SOS + Ras => Ras_active + A_SOS; A_SOS, Ras, Rate Law: A_SOS*k_Ras_SOS*Ras^n_Ras_SOS/(KM_Ras_SOS^n_Ras_SOS+Ras^n_Ras_SOS)
KM_PI3K_IGF1R=184912.0; k_PI3K_IGF1R=10.6737; n_PI3K_I=1.0 Reaction: PI3KCA + IGFR_active => PI3KCA_active + IGFR_active; IGFR_active, PI3KCA, Rate Law: IGFR_active*k_PI3K_IGF1R*PI3KCA^n_PI3K_I/(KM_PI3K_IGF1R^n_PI3K_I+PI3KCA^n_PI3K_I)
kd_PI3K_a = 0.005 Reaction: PI3KCA_active => PI3KCA; PI3KCA_active, Rate Law: kd_PI3K_a*PI3KCA_active
n_SOS=1.0; KM_SOS_E=6086070.0; k_SOS_E=694.731 Reaction: D_SOS + EGFR_active => A_SOS + EGFR_active; EGFR_active, D_SOS, Rate Law: k_SOS_E*EGFR_active*D_SOS^n_SOS/(KM_SOS_E^n_SOS+D_SOS^n_SOS)
kd_P90Rsk=0.005 Reaction: P90Rsk_Active => P90RskInactive; P90Rsk_Active, Rate Law: kd_P90Rsk*P90Rsk_Active
n_D_SOS=1.0; KM_D_SOS_P90Rsk=896896.0; k_D_SOS_P90Rsk=161197.0 Reaction: P90Rsk_Active + A_SOS => D_SOS + P90Rsk_Active; P90Rsk_Active, A_SOS, Rate Law: P90Rsk_Active*k_D_SOS_P90Rsk*A_SOS^n_D_SOS/(KM_D_SOS_P90Rsk^n_D_SOS+A_SOS^n_D_SOS)
n_PI3K_E=1.0; k_PI3K_EGF1R=10.6737; KM_PI3K_EGF1R=184912.0 Reaction: PI3KCA + EGFR_active => PI3KCA_active + EGFR_active; EGFR_active, PI3KCA, Rate Law: EGFR_active*k_PI3K_EGF1R*EGFR_active*PI3KCA^n_PI3K_E/(KM_PI3K_EGF1R^n_PI3K_E+PI3KCA^n_PI3K_E)
KM_RasActive_RafPP=1061.71; n_RasActive_RafPP=1.0; k_RasActive_RafPP=0.126329 Reaction: RafPP + Raf_active => Raf + RafPP; RafPP, Raf_active, Rate Law: RafPP*k_RasActive_RafPP*Raf_active^n_RasActive_RafPP/(KM_RasActive_RafPP^n_RasActive_RafPP+Raf_active^n_RasActive_RafPP)
KM_P90Rsk_ERKActive = 763523.0; k_P90Rsk_ERKActive = 0.0213697 Reaction: P90RskInactive + ERK_active => P90Rsk_Active + ERK_active; ERK_active, P90RskInactive, Rate Law: ERK_active*k_P90Rsk_ERKActive*P90RskInactive/(KM_P90Rsk_ERKActive+P90RskInactive)
k_AKT_PI3K=0.0566279; n_AKT_PI3K=1.0; KM_AKT_PI3K=653951.0 Reaction: AKT + PI3KCA_active => AKT_active + PI3KCA_active; PI3KCA_active, AKT, Rate Law: PI3KCA_active*k_AKT_PI3K*AKT^n_AKT_PI3K/(KM_AKT_PI3K^n_AKT_PI3K+AKT^n_AKT_PI3K)
gamma_IGFR = 0.02 Reaction: IGFR_active => ; IGFR_active, Rate Law: gamma_IGFR*IGFR_active
k_PI3K_Ras=0.0771067; KM_PI3K_Ras=272056.0; n_PI3K_Ras=1.0 Reaction: PI3KCA + Ras_active => PI3KCA_active + Ras_active; Ras_active, PI3KCA, Rate Law: Ras_active*k_PI3K_Ras*PI3KCA^n_PI3K_Ras/(KM_PI3K_Ras^n_PI3K_Ras+PI3KCA^n_PI3K_Ras)
n_A_SOS_I=1.0; KM_A_SOS_I=100000.0; k_A_SOS_I=500.0 Reaction: IGFR_active + D_SOS => A_SOS + IGFR_active; IGFR_active, D_SOS, Rate Law: IGFR_active*k_A_SOS_I*D_SOS^n_A_SOS_I/(KM_A_SOS_I^n_A_SOS_I+D_SOS^n_A_SOS_I)
n_Raf_RasActive=1.0; k_Raf_RasActive=0.884096; KM_Raf_RasActive=62464.6 Reaction: Ras_active + Raf => Raf_active + Ras_active; Ras_active, Raf, Rate Law: Ras_active*k_Raf_RasActive*Raf^n_Raf_RasActive/(KM_Raf_RasActive+Raf^n_Raf_RasActive)

States:

Name Description
IGFR active [IGF-like family receptor 1]
P90RskInactive [Ribosomal protein S6 kinase alpha-6]
AKT [RAC-beta serine/threonine-protein kinase]
A SOS [Son of sevenless homolog 1]
Raf active [RAF proto-oncogene serine/threonine-protein kinase]
RafPP [RAF proto-oncogene serine/threonine-protein kinase]
RasGapActive [Ras-related protein R-Ras2]
D SOS [Son of sevenless homolog 1]
PI3KCA active [Phosphatidylinositol 4-phosphate 3-kinase C2 domain-containing subunit alpha]
ERK active [Mitogen-activated protein kinase 3]
PP2A [protein phosphatase type 2A complex]
Raf [RAF proto-oncogene serine/threonine-protein kinase]
Ras active [Ras-related protein R-Ras2]
Mek active [Dual specificity mitogen-activated protein kinase kinase 1]
Ras [Ras-related protein R-Ras2]
PI3KCA [Phosphatidylinositol 4-phosphate 3-kinase C2 domain-containing subunit alpha]
ERK [Mitogen-activated protein kinase 3]
AKT active [RAC-beta serine/threonine-protein kinase]
Mek [Dual specificity mitogen-activated protein kinase kinase 1]
EGFR active [Receptor protein-tyrosine kinase]
P90Rsk Active [Ribosomal protein S6 kinase alpha-6]

Observables: none

BIOMD0000000453 @ v0.0.1

Bidkhori2012 - EGFR signalling in NSCLCThe paper describes and compares two models on EGFR signalling between normal and…

EGFR signaling plays a very important role in NSCLC. It activates Ras/ERK, PI3K/Akt and STAT activation pathways. These are the main pathways for cell proliferation and survival. We have developed two mathematical models to relate to the different EGFR signaling in NSCLC and normal cells in the presence or absence of EGFR and PTEN mutations. The dynamics of downstream signaling pathways vary in the disease state and activation of some factors can be indicative of drug resistance. Our simulation denotes the effect of EGFR mutations and increased expression of certain factors in NSCLC EGFR signaling on each of the three pathways where levels of pERK, pSTAT and pAkt are increased. Over activation of ERK, Akt and STAT3 which are the main cell proliferation and survival factors act as promoting factors for tumor progression in NSCLC. In case of loss of PTEN, Akt activity level is considerably increased. Our simulation results show that in the presence of erlotinib, downstream factors i.e. pAkt, pSTAT3 and pERK are inhibited. However, in case of loss of PTEN expression in the presence of erlotinib, pAkt level would not decrease which demonstrates that these cells are resistant to erlotinib. link: http://identifiers.org/pubmed/23133538

Parameters:

Name Description
mwa4c71b8d_fb74_465b_b76e_cec4e4c95484=16.0 Reaction: mwcedf8ecd_67bd_4b91_aa04_d58782dec2a4 => mwf816df4c_4593_4d23_990f_0d7c15ddde5d + mwcc894c94_0ddf_42cc_913e_cdcc4d471d94; mwcedf8ecd_67bd_4b91_aa04_d58782dec2a4, Rate Law: mwa4c71b8d_fb74_465b_b76e_cec4e4c95484*mwcedf8ecd_67bd_4b91_aa04_d58782dec2a4
mw56f1bdc0_66fd_47c0_806a_beeaf123e2f2=0.8; mwc489f472_68ce_44e7_aad1_f8d2f6dda4ff=14.3 Reaction: mwf816df4c_4593_4d23_990f_0d7c15ddde5d + mwf9e2a044_7774_400b_a74e_a111b4a21f30 => mwcb572fe2_c3ac_40e7_8141_da7d55fce18a; mwf816df4c_4593_4d23_990f_0d7c15ddde5d, mwf9e2a044_7774_400b_a74e_a111b4a21f30, mwcb572fe2_c3ac_40e7_8141_da7d55fce18a, Rate Law: mwc489f472_68ce_44e7_aad1_f8d2f6dda4ff*mwf816df4c_4593_4d23_990f_0d7c15ddde5d*mwf9e2a044_7774_400b_a74e_a111b4a21f30-mw56f1bdc0_66fd_47c0_806a_beeaf123e2f2*mwcb572fe2_c3ac_40e7_8141_da7d55fce18a
mw1decb177_5075_41f3_a348_ca13b8f4497e=5.0E-4 Reaction: mwa455ec7e_1a12_4659_95a2_a5695d09ca60 => mw19122f7d_f92e_4dc0_922f_6b681db65b0b + mwb2366216_0b3c_4f28_8303_fec92c68dd57; mwa455ec7e_1a12_4659_95a2_a5695d09ca60, Rate Law: mw1decb177_5075_41f3_a348_ca13b8f4497e*mwa455ec7e_1a12_4659_95a2_a5695d09ca60
mw9cc637fe_d9ca_47d2_a4dc_66009d458094=0.18; mw5639395a_a5cd_46dd_81b8_30fe72400a2e=202.9 Reaction: mwf8cc7834_bf4f_4ccd_8235_d0890badf0f6 + mw8f5a7b5c_ca4c_4a4c_85b1_e5d640c426bf => mw28464aad_8013_4a23_ae09_a406954859a6; mwf8cc7834_bf4f_4ccd_8235_d0890badf0f6, mw8f5a7b5c_ca4c_4a4c_85b1_e5d640c426bf, mw28464aad_8013_4a23_ae09_a406954859a6, Rate Law: mw5639395a_a5cd_46dd_81b8_30fe72400a2e*mwf8cc7834_bf4f_4ccd_8235_d0890badf0f6*mw8f5a7b5c_ca4c_4a4c_85b1_e5d640c426bf-mw9cc637fe_d9ca_47d2_a4dc_66009d458094*mw28464aad_8013_4a23_ae09_a406954859a6
mw289fed85_e6ee_43e6_a69f_77b5f487a452=10.0; mw8768b5c7_b227_4825_aa55_a525b0d915c2=1.0 Reaction: mw504578d8_96c3_471f_8a7e_8c14e7535d3d + mwe57c3282_5935_405c_8c0b_7fadb7a5de17 => mw45ab688a_6467_4a3e_a779_2118fa84d69e; mw504578d8_96c3_471f_8a7e_8c14e7535d3d, mwe57c3282_5935_405c_8c0b_7fadb7a5de17, mw45ab688a_6467_4a3e_a779_2118fa84d69e, Rate Law: mw289fed85_e6ee_43e6_a69f_77b5f487a452*mw504578d8_96c3_471f_8a7e_8c14e7535d3d*mwe57c3282_5935_405c_8c0b_7fadb7a5de17-mw8768b5c7_b227_4825_aa55_a525b0d915c2*mw45ab688a_6467_4a3e_a779_2118fa84d69e
mw11e520e6_b1f1_4802_af71_92a2bd9cb644=0.001; mw65e1222f_39ad_4a29_ae76_04b7d591af38=1.0 Reaction: mw3d81860d_d786_4fcc_b8bb_64f1a2d7739d => mw16796ffe_4764_4a9f_942e_149f42c1cd28 + mwd7f41594_8377_4e2e_9528_45d5a82ffdb4; mw3d81860d_d786_4fcc_b8bb_64f1a2d7739d, mw16796ffe_4764_4a9f_942e_149f42c1cd28, mwd7f41594_8377_4e2e_9528_45d5a82ffdb4, Rate Law: mw65e1222f_39ad_4a29_ae76_04b7d591af38*mw3d81860d_d786_4fcc_b8bb_64f1a2d7739d-mw11e520e6_b1f1_4802_af71_92a2bd9cb644*mw16796ffe_4764_4a9f_942e_149f42c1cd28*mwd7f41594_8377_4e2e_9528_45d5a82ffdb4
mw134431c3_e8e5_4375_89a0_2c51a03d65dd=25.0 Reaction: mw014cc419_b720_4b90_9192_2ec6e706c87d => mw78e207c4_4faf_4b48_8e22_1ee666e9cc4c + mwd7f41594_8377_4e2e_9528_45d5a82ffdb4; mw014cc419_b720_4b90_9192_2ec6e706c87d, Rate Law: mw134431c3_e8e5_4375_89a0_2c51a03d65dd*mw014cc419_b720_4b90_9192_2ec6e706c87d
mw11bb74b8_d908_46f0_ac4d_06e8dd1aa5ae=3.0; mwb44117f5_20b2_495e_adf3_3467cd119fd6=0.033 Reaction: mwf816df4c_4593_4d23_990f_0d7c15ddde5d + mw7e23b961_186b_47a0_a8b5_5e9957766792 => mwcedf8ecd_67bd_4b91_aa04_d58782dec2a4; mwf816df4c_4593_4d23_990f_0d7c15ddde5d, mw7e23b961_186b_47a0_a8b5_5e9957766792, mwcedf8ecd_67bd_4b91_aa04_d58782dec2a4, Rate Law: mw11bb74b8_d908_46f0_ac4d_06e8dd1aa5ae*mwf816df4c_4593_4d23_990f_0d7c15ddde5d*mw7e23b961_186b_47a0_a8b5_5e9957766792-mwb44117f5_20b2_495e_adf3_3467cd119fd6*mwcedf8ecd_67bd_4b91_aa04_d58782dec2a4
mw91a84697_3231_4fa6_b6ff_d69ee86056dc=3.372E-4; mwf3d00ca5_89dc_4693_92ec_a47db8150144=33.72 Reaction: mw0dc4e5eb_4366_4799_bebc_cfcffe5c06f5 => mw1e591998_65c0_484e_8a3b_537a38d94de1; mw0dc4e5eb_4366_4799_bebc_cfcffe5c06f5, mw1e591998_65c0_484e_8a3b_537a38d94de1, Rate Law: mwf3d00ca5_89dc_4693_92ec_a47db8150144*mw0dc4e5eb_4366_4799_bebc_cfcffe5c06f5-mw91a84697_3231_4fa6_b6ff_d69ee86056dc*mw1e591998_65c0_484e_8a3b_537a38d94de1
mw0aa92e25_f9aa_461e_92b8_23b1b5b3ab92=0.2661 Reaction: mwf9999977_6f0e_4e35_9b73_75587f3448e9 => mw3c2e1b43_29ca_491a_93e9_c723a993d6fb + mwe57c3282_5935_405c_8c0b_7fadb7a5de17; mwf9999977_6f0e_4e35_9b73_75587f3448e9, Rate Law: mw0aa92e25_f9aa_461e_92b8_23b1b5b3ab92*mwf9999977_6f0e_4e35_9b73_75587f3448e9
mwe1743f7b_ca2c_47d4_91d7_aed2748d98c5=2.661 Reaction: mwbf5cb039_b830_4282_aa22_a3dda6272ec1 => mwa8f2e7b2_0927_4ab4_a817_dddc43bb4fa3 + mw7cff9a0e_094d_498e_bf7f_7b162c61d63a + mwe57c3282_5935_405c_8c0b_7fadb7a5de17; mwbf5cb039_b830_4282_aa22_a3dda6272ec1, Rate Law: mwe1743f7b_ca2c_47d4_91d7_aed2748d98c5*mwbf5cb039_b830_4282_aa22_a3dda6272ec1
mw21d22acd_ddd4_4794_9700_52201984f75b=0.2; mw8cbe6595_6f16_4704_afe2_0dd043a175fa=1.0 Reaction: mw4f575c55_7dff_45d7_94ad_cda9621d5b63 + mwcea1f1c1_2f85_4af1_98ea_ef14cf580c09 => mw472d5cb9_120e_4f60_bbae_1ae2552837dd; mw4f575c55_7dff_45d7_94ad_cda9621d5b63, mwcea1f1c1_2f85_4af1_98ea_ef14cf580c09, mw472d5cb9_120e_4f60_bbae_1ae2552837dd, Rate Law: mw8cbe6595_6f16_4704_afe2_0dd043a175fa*mw4f575c55_7dff_45d7_94ad_cda9621d5b63*mwcea1f1c1_2f85_4af1_98ea_ef14cf580c09-mw21d22acd_ddd4_4794_9700_52201984f75b*mw472d5cb9_120e_4f60_bbae_1ae2552837dd
mwba545ecf_c7d4_4a6c_8c47_9e91f052d5a9=1.0; mw01c5ceef_57a1_4baa_b2cd_fd39e9588a10=0.2 Reaction: mwe3fd7f65_b0d1_44d9_b6f3_d2f7d332f664 + mw0e1be972_fded_4bff_a93d_091ec942485f => mw8c85ff7f_6368_4b11_a2ed_ce83481b55e6; mwe3fd7f65_b0d1_44d9_b6f3_d2f7d332f664, mw0e1be972_fded_4bff_a93d_091ec942485f, mw8c85ff7f_6368_4b11_a2ed_ce83481b55e6, Rate Law: mwba545ecf_c7d4_4a6c_8c47_9e91f052d5a9*mwe3fd7f65_b0d1_44d9_b6f3_d2f7d332f664*mw0e1be972_fded_4bff_a93d_091ec942485f-mw01c5ceef_57a1_4baa_b2cd_fd39e9588a10*mw8c85ff7f_6368_4b11_a2ed_ce83481b55e6
mwafd23622_952d_44b3_a437_4aa12422add7=0.25; mw9d9a7d08_b19a_44f1_a806_151597049345=0.5 Reaction: mw9b25f809_18a1_4c14_8f4b_cf18e6d93c28 + mwf9e2a044_7774_400b_a74e_a111b4a21f30 => mwa0acc0ac_5fac_4a42_a3be_e36db44994b0; mw9b25f809_18a1_4c14_8f4b_cf18e6d93c28, mwf9e2a044_7774_400b_a74e_a111b4a21f30, mwa0acc0ac_5fac_4a42_a3be_e36db44994b0, Rate Law: mwafd23622_952d_44b3_a437_4aa12422add7*mw9b25f809_18a1_4c14_8f4b_cf18e6d93c28*mwf9e2a044_7774_400b_a74e_a111b4a21f30-mw9d9a7d08_b19a_44f1_a806_151597049345*mwa0acc0ac_5fac_4a42_a3be_e36db44994b0
mwab1ef4d4_2acc_4fa2_b07c_fac51fb7bfaf=0.3; mw9e24066c_51a5_4c7a_af7c_4656155a4eb0=4.481 Reaction: mwa98802cb_c977_4fe0_9e67_5000904c2c36 => mwbfcf6773_1915_432c_b1d2_1f246094cc74 + mwa0349407_8187_48fc_9e94_5698ccc4e06d; mwa98802cb_c977_4fe0_9e67_5000904c2c36, mwbfcf6773_1915_432c_b1d2_1f246094cc74, mwa0349407_8187_48fc_9e94_5698ccc4e06d, Rate Law: mw9e24066c_51a5_4c7a_af7c_4656155a4eb0*mwa98802cb_c977_4fe0_9e67_5000904c2c36-mwab1ef4d4_2acc_4fa2_b07c_fac51fb7bfaf*mwbfcf6773_1915_432c_b1d2_1f246094cc74*mwa0349407_8187_48fc_9e94_5698ccc4e06d
mwbc2119ce_ade3_4e2a_a3bc_a29cd77adf72=8.898; mw54b0e5e9_710f_438e_a8d3_749c594667bc=1.0 Reaction: mwd784228d_0cb5_468a_ac70_02d8f04b3d9c + mwfbda4e09_0cbb_49bc_ae69_f88b7a79ed21 => mw5babe3d5_a9af_4dfd_ac01_35474ef64af2; mwd784228d_0cb5_468a_ac70_02d8f04b3d9c, mwfbda4e09_0cbb_49bc_ae69_f88b7a79ed21, mw5babe3d5_a9af_4dfd_ac01_35474ef64af2, Rate Law: mwbc2119ce_ade3_4e2a_a3bc_a29cd77adf72*mwd784228d_0cb5_468a_ac70_02d8f04b3d9c*mwfbda4e09_0cbb_49bc_ae69_f88b7a79ed21-mw54b0e5e9_710f_438e_a8d3_749c594667bc*mw5babe3d5_a9af_4dfd_ac01_35474ef64af2
mw58c37b3e_91e7_445e_846e_77cd0b2320af=0.01833; mw11cdaca9_941c_4a59_ba2a_3bfeafb65aeb=4.0 Reaction: mwaff92910_ed3d_40b9_a29c_e4866167e828 + mw9b25f809_18a1_4c14_8f4b_cf18e6d93c28 => mw12ba4000_d452_420c_be63_96d2848aca32; mwaff92910_ed3d_40b9_a29c_e4866167e828, mw9b25f809_18a1_4c14_8f4b_cf18e6d93c28, mw12ba4000_d452_420c_be63_96d2848aca32, Rate Law: mw11cdaca9_941c_4a59_ba2a_3bfeafb65aeb*mwaff92910_ed3d_40b9_a29c_e4866167e828*mw9b25f809_18a1_4c14_8f4b_cf18e6d93c28-mw58c37b3e_91e7_445e_846e_77cd0b2320af*mw12ba4000_d452_420c_be63_96d2848aca32
mwba77a9ba_078d_4ec6_a8b8_d7042a2cefe7=0.2; mwb4c6ed27_c7ec_438f_bafd_4a09a9f356f1=3.114 Reaction: mwd39388fd_4f85_4d1c_b2a3_37857c595a2d + mwe57c3282_5935_405c_8c0b_7fadb7a5de17 => mwbf5cb039_b830_4282_aa22_a3dda6272ec1; mwd39388fd_4f85_4d1c_b2a3_37857c595a2d, mwe57c3282_5935_405c_8c0b_7fadb7a5de17, mwbf5cb039_b830_4282_aa22_a3dda6272ec1, Rate Law: mwb4c6ed27_c7ec_438f_bafd_4a09a9f356f1*mwd39388fd_4f85_4d1c_b2a3_37857c595a2d*mwe57c3282_5935_405c_8c0b_7fadb7a5de17-mwba77a9ba_078d_4ec6_a8b8_d7042a2cefe7*mwbf5cb039_b830_4282_aa22_a3dda6272ec1
mwd3e2533f_8d57_407c_834d_e0dde30b7f4a=4.7E-6; mwbd416b7b_f9b6_4464_b9e8_be4ac001d13d=2.297E-6 Reaction: mw7033dfd6_53c5_433b_a132_f8cb34dea20f => mwfc4a9c3d_3ebb_4033_8b7d_f4d7613d2078 + mw2ba1db9a_4483_44fa_a3a2_b4a5ea66898c; mw7033dfd6_53c5_433b_a132_f8cb34dea20f, mwfc4a9c3d_3ebb_4033_8b7d_f4d7613d2078, mw2ba1db9a_4483_44fa_a3a2_b4a5ea66898c, Rate Law: mwd3e2533f_8d57_407c_834d_e0dde30b7f4a*mw7033dfd6_53c5_433b_a132_f8cb34dea20f-mwbd416b7b_f9b6_4464_b9e8_be4ac001d13d*mwfc4a9c3d_3ebb_4033_8b7d_f4d7613d2078*mw2ba1db9a_4483_44fa_a3a2_b4a5ea66898c
mw1df2caba_8e41_4fe5_a1b5_7777eb98ed1c=0.005 Reaction: mw4f575c55_7dff_45d7_94ad_cda9621d5b63 => mw4110f531_7513_4786_8896_7c9d969ff558; mw4f575c55_7dff_45d7_94ad_cda9621d5b63, Rate Law: mw1df2caba_8e41_4fe5_a1b5_7777eb98ed1c*mw4f575c55_7dff_45d7_94ad_cda9621d5b63
mwa17c895f_29d8_4977_a99f_cf9bf6216785=0.058 Reaction: mwcb572fe2_c3ac_40e7_8141_da7d55fce18a => mw9b25f809_18a1_4c14_8f4b_cf18e6d93c28 + mwf9e2a044_7774_400b_a74e_a111b4a21f30; mwcb572fe2_c3ac_40e7_8141_da7d55fce18a, Rate Law: mwa17c895f_29d8_4977_a99f_cf9bf6216785*mwcb572fe2_c3ac_40e7_8141_da7d55fce18a
mwff6f49f7_268a_4f08_8d36_3ad8449d7472=0.2; mw7e889122_d26c_4d09_bae4_d313b992dc8e=3.114 Reaction: mwbfcf6773_1915_432c_b1d2_1f246094cc74 + mwe57c3282_5935_405c_8c0b_7fadb7a5de17 => mw954e8fcb_ac0a_459d_8878_f19080208a17; mwbfcf6773_1915_432c_b1d2_1f246094cc74, mwe57c3282_5935_405c_8c0b_7fadb7a5de17, mw954e8fcb_ac0a_459d_8878_f19080208a17, Rate Law: mw7e889122_d26c_4d09_bae4_d313b992dc8e*mwbfcf6773_1915_432c_b1d2_1f246094cc74*mwe57c3282_5935_405c_8c0b_7fadb7a5de17-mwff6f49f7_268a_4f08_8d36_3ad8449d7472*mw954e8fcb_ac0a_459d_8878_f19080208a17
mwb1b46773_a218_4f99_a000_a98fbc1275d7=1.0; mwd2d0b340_bbdb_40bd_9eac_992a2a402b94=10.0 Reaction: mw16796ffe_4764_4a9f_942e_149f42c1cd28 + mw11a8b702_b8ac_4513_b4aa_063e51089812 => mwa6e82fc9_a0ce_461c_93c8_17f3c807c1a1; mw16796ffe_4764_4a9f_942e_149f42c1cd28, mw11a8b702_b8ac_4513_b4aa_063e51089812, mwa6e82fc9_a0ce_461c_93c8_17f3c807c1a1, Rate Law: mwd2d0b340_bbdb_40bd_9eac_992a2a402b94*mw16796ffe_4764_4a9f_942e_149f42c1cd28*mw11a8b702_b8ac_4513_b4aa_063e51089812-mwb1b46773_a218_4f99_a000_a98fbc1275d7*mwa6e82fc9_a0ce_461c_93c8_17f3c807c1a1
mw7e974605_8d9c_4250_8f69_072aab1f24f7=3.5 Reaction: mw4628f984_eb87_4922_9760_4975095ce6eb => mwaff92910_ed3d_40b9_a29c_e4866167e828 + mw9b25f809_18a1_4c14_8f4b_cf18e6d93c28; mw4628f984_eb87_4922_9760_4975095ce6eb, Rate Law: mw7e974605_8d9c_4250_8f69_072aab1f24f7*mw4628f984_eb87_4922_9760_4975095ce6eb
mwa8f70790_9f44_4548_988e_49d13016d2f1=71.7; mwaad540b6_783e_4576_8862_ad522fd897db=0.2 Reaction: mwaff92910_ed3d_40b9_a29c_e4866167e828 + mwbaaeb210_4806_4076_9d60_219f4ed945b6 => mw19a33ad5_5ba4_46c7_84eb_c1287f02bcd5; mwaff92910_ed3d_40b9_a29c_e4866167e828, mwbaaeb210_4806_4076_9d60_219f4ed945b6, mw19a33ad5_5ba4_46c7_84eb_c1287f02bcd5, Rate Law: mwa8f70790_9f44_4548_988e_49d13016d2f1*mwaff92910_ed3d_40b9_a29c_e4866167e828*mwbaaeb210_4806_4076_9d60_219f4ed945b6-mwaad540b6_783e_4576_8862_ad522fd897db*mw19a33ad5_5ba4_46c7_84eb_c1287f02bcd5
mw4f6f44d9_408e_49b2_bedf_d34b2448725e=0.595 Reaction: mwbd6bb050_89bd_41df_8cea_d2e1fb77bafe => mw7033dfd6_53c5_433b_a132_f8cb34dea20f; mwbd6bb050_89bd_41df_8cea_d2e1fb77bafe, Rate Law: mw4f6f44d9_408e_49b2_bedf_d34b2448725e*mwbd6bb050_89bd_41df_8cea_d2e1fb77bafe
mw81384973_14a0_4498_ab21_f70666d46d7f=0.003 Reaction: mw472d5cb9_120e_4f60_bbae_1ae2552837dd => mwd2c465fb_eea7_499a_8ea4_f318a64cb9ee + mwcea1f1c1_2f85_4af1_98ea_ef14cf580c09; mw472d5cb9_120e_4f60_bbae_1ae2552837dd, Rate Law: mw81384973_14a0_4498_ab21_f70666d46d7f*mw472d5cb9_120e_4f60_bbae_1ae2552837dd
mwe5304629_3bf5_4912_b431_190349f23010=0.2; mw53f2f6aa_0608_4b23_bfe6_f27b10b55fe5=0.005 Reaction: mw0dc4e5eb_4366_4799_bebc_cfcffe5c06f5 + mw19122f7d_f92e_4dc0_922f_6b681db65b0b => mwbedcc124_dbf3_41ab_989e_6b0900d7590a; mw0dc4e5eb_4366_4799_bebc_cfcffe5c06f5, mw19122f7d_f92e_4dc0_922f_6b681db65b0b, mwbedcc124_dbf3_41ab_989e_6b0900d7590a, Rate Law: mwe5304629_3bf5_4912_b431_190349f23010*mw0dc4e5eb_4366_4799_bebc_cfcffe5c06f5*mw19122f7d_f92e_4dc0_922f_6b681db65b0b-mw53f2f6aa_0608_4b23_bfe6_f27b10b55fe5*mwbedcc124_dbf3_41ab_989e_6b0900d7590a
mwd12a67b3_6d98_40e9_a54b_282a577498eb=2.661 Reaction: mw45ab688a_6467_4a3e_a779_2118fa84d69e => mwa8f2e7b2_0927_4ab4_a817_dddc43bb4fa3 + mwa0349407_8187_48fc_9e94_5698ccc4e06d + mwf430a579_ecbf_48ba_80c2_06e455808f2a + mwe57c3282_5935_405c_8c0b_7fadb7a5de17; mw45ab688a_6467_4a3e_a779_2118fa84d69e, Rate Law: mwd12a67b3_6d98_40e9_a54b_282a577498eb*mw45ab688a_6467_4a3e_a779_2118fa84d69e
mwb0744746_88a2_488e_a483_266747a044c6=0.2661 Reaction: mw954e8fcb_ac0a_459d_8878_f19080208a17 => mwa8f2e7b2_0927_4ab4_a817_dddc43bb4fa3 + mwe57c3282_5935_405c_8c0b_7fadb7a5de17; mw954e8fcb_ac0a_459d_8878_f19080208a17, Rate Law: mwb0744746_88a2_488e_a483_266747a044c6*mw954e8fcb_ac0a_459d_8878_f19080208a17
mwc6b3c76f_af7b_488c_8751_28f1d9ab90a1=5.0E-4 Reaction: mw06b8aada_c92a_48eb_8ee7_af3778cfe62f => mw19122f7d_f92e_4dc0_922f_6b681db65b0b + mw1093b3af_1864_4ba3_a541_6009a9921282 + mwb2366216_0b3c_4f28_8303_fec92c68dd57 + mwa0349407_8187_48fc_9e94_5698ccc4e06d; mw06b8aada_c92a_48eb_8ee7_af3778cfe62f, Rate Law: mwc6b3c76f_af7b_488c_8751_28f1d9ab90a1*mw06b8aada_c92a_48eb_8ee7_af3778cfe62f
mwa5567196_f821_479b_973b_f0967a3eb761=17.0 Reaction: mwd7f41594_8377_4e2e_9528_45d5a82ffdb4 => mwb561d9f3_a9ed_4bdb_8d40_87be5cc3237a; mwd7f41594_8377_4e2e_9528_45d5a82ffdb4, Rate Law: mwa5567196_f821_479b_973b_f0967a3eb761*mwd7f41594_8377_4e2e_9528_45d5a82ffdb4
mw92d81b3b_fa59_4637_8540_8cb8482490d9=0.0025; mw90873203_7a5d_4fca_a789_5e989ff0c999=0.2 Reaction: mwfbda4e09_0cbb_49bc_ae69_f88b7a79ed21 + mw19122f7d_f92e_4dc0_922f_6b681db65b0b => mwb1bc2058_e6d8_4680_9e6c_d27bb366cde0; mwfbda4e09_0cbb_49bc_ae69_f88b7a79ed21, mw19122f7d_f92e_4dc0_922f_6b681db65b0b, mwb1bc2058_e6d8_4680_9e6c_d27bb366cde0, Rate Law: mw90873203_7a5d_4fca_a789_5e989ff0c999*mwfbda4e09_0cbb_49bc_ae69_f88b7a79ed21*mw19122f7d_f92e_4dc0_922f_6b681db65b0b-mw92d81b3b_fa59_4637_8540_8cb8482490d9*mwb1bc2058_e6d8_4680_9e6c_d27bb366cde0
mwa617804d_95cc_4197_a39b_264a2c66b5a3=0.3 Reaction: mw35f5adaa_d1c0_433c_817d_76e317f4cb15 => mw7e23b961_186b_47a0_a8b5_5e9957766792 + mwd087f76b_65dc_47f1_ba21_c43774457686; mw35f5adaa_d1c0_433c_817d_76e317f4cb15, Rate Law: mwa617804d_95cc_4197_a39b_264a2c66b5a3*mw35f5adaa_d1c0_433c_817d_76e317f4cb15
mwbb727dc5_30e8_45f4_9d15_3b34be5c1e93=0.1; mw7ae1ee96_563e_4684_bc9a_8f4ef373620e=0.0015 Reaction: mwf430a579_ecbf_48ba_80c2_06e455808f2a + mw9dcaa655_a755_426e_a3fa_1ad7c3c45575 => mw1093b3af_1864_4ba3_a541_6009a9921282; mwf430a579_ecbf_48ba_80c2_06e455808f2a, mw9dcaa655_a755_426e_a3fa_1ad7c3c45575, mw1093b3af_1864_4ba3_a541_6009a9921282, Rate Law: mwbb727dc5_30e8_45f4_9d15_3b34be5c1e93*mwf430a579_ecbf_48ba_80c2_06e455808f2a*mw9dcaa655_a755_426e_a3fa_1ad7c3c45575-mw7ae1ee96_563e_4684_bc9a_8f4ef373620e*mw1093b3af_1864_4ba3_a541_6009a9921282
mw880a5942_7549_4466_bd19_0e1768a3a533=0.6; mwf1697f55_a3f4_4fb6_ae1d_f96f09ad1daa=90.0 Reaction: mwbfcf6773_1915_432c_b1d2_1f246094cc74 + mw3c2e1b43_29ca_491a_93e9_c723a993d6fb => mw5198d3c2_879c_4f0d_b4f8_cd40efe0b1cf; mwbfcf6773_1915_432c_b1d2_1f246094cc74, mw3c2e1b43_29ca_491a_93e9_c723a993d6fb, mw5198d3c2_879c_4f0d_b4f8_cd40efe0b1cf, Rate Law: mwf1697f55_a3f4_4fb6_ae1d_f96f09ad1daa*mwbfcf6773_1915_432c_b1d2_1f246094cc74*mw3c2e1b43_29ca_491a_93e9_c723a993d6fb-mw880a5942_7549_4466_bd19_0e1768a3a533*mw5198d3c2_879c_4f0d_b4f8_cd40efe0b1cf
mwe2aded94_f2b5_4513_8670_71a86abf7968=10.0; mw8d6eacb6_7184_4564_8cde_53e93add2146=1.0 Reaction: mw62bf5275_ce02_4e86_b3b6_3f87a335e1de + mw6e01967b_3e2a_433d_bec6_9f9cf3ba243c => mw6353aa36_d4a4_4254_8a1f_1f7f571d4233; mw62bf5275_ce02_4e86_b3b6_3f87a335e1de, mw6e01967b_3e2a_433d_bec6_9f9cf3ba243c, mw6353aa36_d4a4_4254_8a1f_1f7f571d4233, Rate Law: mwe2aded94_f2b5_4513_8670_71a86abf7968*mw62bf5275_ce02_4e86_b3b6_3f87a335e1de*mw6e01967b_3e2a_433d_bec6_9f9cf3ba243c-mw8d6eacb6_7184_4564_8cde_53e93add2146*mw6353aa36_d4a4_4254_8a1f_1f7f571d4233
mw3d07dc22_f821_49a5_9712_820ba9592353=5.7 Reaction: mw6cb74b27_ffef_49bb_8ffb_622d552caa9e => mwf816df4c_4593_4d23_990f_0d7c15ddde5d + mwd784228d_0cb5_468a_ac70_02d8f04b3d9c; mw6cb74b27_ffef_49bb_8ffb_622d552caa9e, Rate Law: mw3d07dc22_f821_49a5_9712_820ba9592353*mw6cb74b27_ffef_49bb_8ffb_622d552caa9e
mw93f832d7_eefb_43dd_853c_a0d7a76023cf=0.0214; mw6ac313e2_e8a9_42a9_b13a_27e55c1012a2=10.0 Reaction: mw504578d8_96c3_471f_8a7e_8c14e7535d3d + mw9dcaa655_a755_426e_a3fa_1ad7c3c45575 => mwfbda4e09_0cbb_49bc_ae69_f88b7a79ed21; mw504578d8_96c3_471f_8a7e_8c14e7535d3d, mw9dcaa655_a755_426e_a3fa_1ad7c3c45575, mwfbda4e09_0cbb_49bc_ae69_f88b7a79ed21, Rate Law: mw6ac313e2_e8a9_42a9_b13a_27e55c1012a2*mw504578d8_96c3_471f_8a7e_8c14e7535d3d*mw9dcaa655_a755_426e_a3fa_1ad7c3c45575-mw93f832d7_eefb_43dd_853c_a0d7a76023cf*mwfbda4e09_0cbb_49bc_ae69_f88b7a79ed21
mw64664eb9_353a_4f1d_a8dc_e22bcb06e2c2=25.0; mw0573df9d_f365_40b7_83d4_3846a05aefdc=3.5 Reaction: mw78e207c4_4faf_4b48_8e22_1ee666e9cc4c + mwb561d9f3_a9ed_4bdb_8d40_87be5cc3237a => mw014cc419_b720_4b90_9192_2ec6e706c87d; mw78e207c4_4faf_4b48_8e22_1ee666e9cc4c, mwb561d9f3_a9ed_4bdb_8d40_87be5cc3237a, mw014cc419_b720_4b90_9192_2ec6e706c87d, Rate Law: mw64664eb9_353a_4f1d_a8dc_e22bcb06e2c2*mw78e207c4_4faf_4b48_8e22_1ee666e9cc4c*mwb561d9f3_a9ed_4bdb_8d40_87be5cc3237a-mw0573df9d_f365_40b7_83d4_3846a05aefdc*mw014cc419_b720_4b90_9192_2ec6e706c87d
mw74529c03_0e18_4c1b_8704_a9816a9ea3d0=5.0E-4 Reaction: mw741407c8_029b_44ed_9799_02eb9d90ec9a => mw13abe2a6_9905_40e5_8c23_3fc8834b572a + mw19122f7d_f92e_4dc0_922f_6b681db65b0b + mwb2366216_0b3c_4f28_8303_fec92c68dd57; mw741407c8_029b_44ed_9799_02eb9d90ec9a, Rate Law: mw74529c03_0e18_4c1b_8704_a9816a9ea3d0*mw741407c8_029b_44ed_9799_02eb9d90ec9a
mwfa680314_051c_4b10_afc9_7e7fbee49e3f=0.2; mw97b9ab43_02ae_4e42_a524_6b781633a255=5.0E-4 Reaction: mwbfcf6773_1915_432c_b1d2_1f246094cc74 + mw19122f7d_f92e_4dc0_922f_6b681db65b0b => mwec1b368b_8f73_47eb_9636_9956389836eb; mwbfcf6773_1915_432c_b1d2_1f246094cc74, mw19122f7d_f92e_4dc0_922f_6b681db65b0b, mwec1b368b_8f73_47eb_9636_9956389836eb, Rate Law: mwfa680314_051c_4b10_afc9_7e7fbee49e3f*mwbfcf6773_1915_432c_b1d2_1f246094cc74*mw19122f7d_f92e_4dc0_922f_6b681db65b0b-mw97b9ab43_02ae_4e42_a524_6b781633a255*mwec1b368b_8f73_47eb_9636_9956389836eb
mw77c60377_28ae_4aad_b911_5768fc8b824f=4.0; mw2eed2db0_ba78_435b_b2c8_ee91efdba1b4=0.01833 Reaction: mwaff92910_ed3d_40b9_a29c_e4866167e828 + mw0834731b_0477_4217_a53b_30cef851191b => mw4628f984_eb87_4922_9760_4975095ce6eb; mwaff92910_ed3d_40b9_a29c_e4866167e828, mw0834731b_0477_4217_a53b_30cef851191b, mw4628f984_eb87_4922_9760_4975095ce6eb, Rate Law: mw77c60377_28ae_4aad_b911_5768fc8b824f*mwaff92910_ed3d_40b9_a29c_e4866167e828*mw0834731b_0477_4217_a53b_30cef851191b-mw2eed2db0_ba78_435b_b2c8_ee91efdba1b4*mw4628f984_eb87_4922_9760_4975095ce6eb
mw19173345_925d_427b_8658_add0978e5931=2.854; mw9f6790d7_19ce_41d9_b4de_a1658c047501=0.96 Reaction: mwa54a9c38_c98b_45e5_8432_4119fb777e44 + mw7cff9a0e_094d_498e_bf7f_7b162c61d63a => mwdf82303e_323f_4c51_a858_56a59233cd98; mwa54a9c38_c98b_45e5_8432_4119fb777e44, mw7cff9a0e_094d_498e_bf7f_7b162c61d63a, mwdf82303e_323f_4c51_a858_56a59233cd98, Rate Law: mw19173345_925d_427b_8658_add0978e5931*mwa54a9c38_c98b_45e5_8432_4119fb777e44*mw7cff9a0e_094d_498e_bf7f_7b162c61d63a-mw9f6790d7_19ce_41d9_b4de_a1658c047501*mwdf82303e_323f_4c51_a858_56a59233cd98
mwa0806e7a_a90d_4187_9c37_6d9ea569a447=2.0E-4; mw95cb9071_56e2_447d_b7c7_59ac96baa623=0.2 Reaction: mw960bddeb_e567_46dd_b2f3_ed5e6a5c7972 + mwe3fd7f65_b0d1_44d9_b6f3_d2f7d332f664 => mw9686f53e_d343_45fd_b441_9c992219546a; mw960bddeb_e567_46dd_b2f3_ed5e6a5c7972, mwe3fd7f65_b0d1_44d9_b6f3_d2f7d332f664, mw9686f53e_d343_45fd_b441_9c992219546a, Rate Law: mwa0806e7a_a90d_4187_9c37_6d9ea569a447*mw960bddeb_e567_46dd_b2f3_ed5e6a5c7972*mwe3fd7f65_b0d1_44d9_b6f3_d2f7d332f664-mw95cb9071_56e2_447d_b7c7_59ac96baa623*mw9686f53e_d343_45fd_b441_9c992219546a
mw693f22fe_7af9_4af8_a026_faace261163b=0.2; mw4e34dd0b_2ef1_4805_ba4a_2c859bdcb5e2=0.005 Reaction: mw2fd710a6_7fe2_4484_bca6_59c187bade8b + mw19122f7d_f92e_4dc0_922f_6b681db65b0b => mw9da19d39_6d91_41d0_b101_f7748391705a; mw2fd710a6_7fe2_4484_bca6_59c187bade8b, mw19122f7d_f92e_4dc0_922f_6b681db65b0b, mw9da19d39_6d91_41d0_b101_f7748391705a, Rate Law: mw693f22fe_7af9_4af8_a026_faace261163b*mw2fd710a6_7fe2_4484_bca6_59c187bade8b*mw19122f7d_f92e_4dc0_922f_6b681db65b0b-mw4e34dd0b_2ef1_4805_ba4a_2c859bdcb5e2*mw9da19d39_6d91_41d0_b101_f7748391705a
mw084cd67b_f328_48a7_8e16_1d6256c8c137=10.0; mw43f177dc_f522_4dd1_b8e5_21b2b8fdfdba=0.06 Reaction: mwd9462e5b_a272_4b66_ab66_fde9266b1a43 + mw9dcaa655_a755_426e_a3fa_1ad7c3c45575 => mwf8cc7834_bf4f_4ccd_8235_d0890badf0f6; mwd9462e5b_a272_4b66_ab66_fde9266b1a43, mw9dcaa655_a755_426e_a3fa_1ad7c3c45575, mwf8cc7834_bf4f_4ccd_8235_d0890badf0f6, Rate Law: mw084cd67b_f328_48a7_8e16_1d6256c8c137*mwd9462e5b_a272_4b66_ab66_fde9266b1a43*mw9dcaa655_a755_426e_a3fa_1ad7c3c45575-mw43f177dc_f522_4dd1_b8e5_21b2b8fdfdba*mwf8cc7834_bf4f_4ccd_8235_d0890badf0f6
mw2dfc8a19_1792_4e12_af38_8bfbda31a577=0.18; mw7e09242b_bd80_4af0_90c8_e0cddace89fe=202.9 Reaction: mwfbda4e09_0cbb_49bc_ae69_f88b7a79ed21 + mw8f5a7b5c_ca4c_4a4c_85b1_e5d640c426bf => mwf40d6176_abfc_4a30_886f_83a19fcffc48; mwfbda4e09_0cbb_49bc_ae69_f88b7a79ed21, mw8f5a7b5c_ca4c_4a4c_85b1_e5d640c426bf, mwf40d6176_abfc_4a30_886f_83a19fcffc48, Rate Law: mw7e09242b_bd80_4af0_90c8_e0cddace89fe*mwfbda4e09_0cbb_49bc_ae69_f88b7a79ed21*mw8f5a7b5c_ca4c_4a4c_85b1_e5d640c426bf-mw2dfc8a19_1792_4e12_af38_8bfbda31a577*mwf40d6176_abfc_4a30_886f_83a19fcffc48
mw94cadd24_0432_4f89_a6fc_96cb0475c44e=0.1764; mw901b5284_bdae_4040_b77d_10f1ec267f06=0.09 Reaction: mw0dc4e5eb_4366_4799_bebc_cfcffe5c06f5 => mwbfcf6773_1915_432c_b1d2_1f246094cc74 + mw78e207c4_4faf_4b48_8e22_1ee666e9cc4c; mw0dc4e5eb_4366_4799_bebc_cfcffe5c06f5, mwbfcf6773_1915_432c_b1d2_1f246094cc74, mw78e207c4_4faf_4b48_8e22_1ee666e9cc4c, Rate Law: mw901b5284_bdae_4040_b77d_10f1ec267f06*mw0dc4e5eb_4366_4799_bebc_cfcffe5c06f5-mw94cadd24_0432_4f89_a6fc_96cb0475c44e*mwbfcf6773_1915_432c_b1d2_1f246094cc74*mw78e207c4_4faf_4b48_8e22_1ee666e9cc4c
mw98405e53_330b_4a64_a700_a62bb3f21426=0.1; mw11f8de84_6639_486d_bf17_8f7021f54b66=0.005 Reaction: mwc1935afc_56b1_4a87_923c_ae6d82455d80 => mw3d81860d_d786_4fcc_b8bb_64f1a2d7739d + mw6e01967b_3e2a_433d_bec6_9f9cf3ba243c; mwc1935afc_56b1_4a87_923c_ae6d82455d80, mw3d81860d_d786_4fcc_b8bb_64f1a2d7739d, mw6e01967b_3e2a_433d_bec6_9f9cf3ba243c, Rate Law: mw98405e53_330b_4a64_a700_a62bb3f21426*mwc1935afc_56b1_4a87_923c_ae6d82455d80-mw11f8de84_6639_486d_bf17_8f7021f54b66*mw3d81860d_d786_4fcc_b8bb_64f1a2d7739d*mw6e01967b_3e2a_433d_bec6_9f9cf3ba243c
mwb881f20a_cf8a_493a_aa84_59ee90f26dd9=7.76 Reaction: mwd7bf31ba_b05c_4c45_bb2f_6a2468a2a507 => mwd39388fd_4f85_4d1c_b2a3_37857c595a2d + mw8f5a7b5c_ca4c_4a4c_85b1_e5d640c426bf; mwd7bf31ba_b05c_4c45_bb2f_6a2468a2a507, Rate Law: mwb881f20a_cf8a_493a_aa84_59ee90f26dd9*mwd7bf31ba_b05c_4c45_bb2f_6a2468a2a507
mw3676a900_b098_4a74_a511_e15984ca0cd2=10.0; mwf68a0726_94b5_4be1_933f_1ac48053601d=1.0 Reaction: mwc1935afc_56b1_4a87_923c_ae6d82455d80 + mw11a8b702_b8ac_4513_b4aa_063e51089812 => mw57a44eb0_ace7_4294_905a_219e87d3c281; mwc1935afc_56b1_4a87_923c_ae6d82455d80, mw11a8b702_b8ac_4513_b4aa_063e51089812, mw57a44eb0_ace7_4294_905a_219e87d3c281, Rate Law: mw3676a900_b098_4a74_a511_e15984ca0cd2*mwc1935afc_56b1_4a87_923c_ae6d82455d80*mw11a8b702_b8ac_4513_b4aa_063e51089812-mwf68a0726_94b5_4be1_933f_1ac48053601d*mw57a44eb0_ace7_4294_905a_219e87d3c281
mwe483687f_b591_4c42_9abc_7ea9f47470bf=2.845; mwcf964aba_9db6_46c5_b687_beafc5d89169=0.96 Reaction: mwd39388fd_4f85_4d1c_b2a3_37857c595a2d + mwa54a9c38_c98b_45e5_8432_4119fb777e44 => mwd7bf31ba_b05c_4c45_bb2f_6a2468a2a507; mwd39388fd_4f85_4d1c_b2a3_37857c595a2d, mwa54a9c38_c98b_45e5_8432_4119fb777e44, mwd7bf31ba_b05c_4c45_bb2f_6a2468a2a507, Rate Law: mwe483687f_b591_4c42_9abc_7ea9f47470bf*mwd39388fd_4f85_4d1c_b2a3_37857c595a2d*mwa54a9c38_c98b_45e5_8432_4119fb777e44-mwcf964aba_9db6_46c5_b687_beafc5d89169*mwd7bf31ba_b05c_4c45_bb2f_6a2468a2a507
mw6d852e8c_c64a_4926_80c4_781a9c04b20e=0.001; mw4d614bfc_3e20_450e_8890_6326afd0a0d7=0.001 Reaction: mw9b937ca3_0d82_46d5_8f5a_0f9701002797 => mw62bf5275_ce02_4e86_b3b6_3f87a335e1de + mw11a8b702_b8ac_4513_b4aa_063e51089812; mw9b937ca3_0d82_46d5_8f5a_0f9701002797, mw62bf5275_ce02_4e86_b3b6_3f87a335e1de, mw11a8b702_b8ac_4513_b4aa_063e51089812, Rate Law: mw6d852e8c_c64a_4926_80c4_781a9c04b20e*mw9b937ca3_0d82_46d5_8f5a_0f9701002797-mw4d614bfc_3e20_450e_8890_6326afd0a0d7*mw62bf5275_ce02_4e86_b3b6_3f87a335e1de*mw11a8b702_b8ac_4513_b4aa_063e51089812
mwc4824ff0_2b51_4d66_ad48_1145f670a6e1=3.114; mw0f1d282f_1c6b_455c_8254_3760632c6ecc=0.2 Reaction: mwa0349407_8187_48fc_9e94_5698ccc4e06d + mwe57c3282_5935_405c_8c0b_7fadb7a5de17 => mwf9999977_6f0e_4e35_9b73_75587f3448e9; mwa0349407_8187_48fc_9e94_5698ccc4e06d, mwe57c3282_5935_405c_8c0b_7fadb7a5de17, mwf9999977_6f0e_4e35_9b73_75587f3448e9, Rate Law: mwc4824ff0_2b51_4d66_ad48_1145f670a6e1*mwa0349407_8187_48fc_9e94_5698ccc4e06d*mwe57c3282_5935_405c_8c0b_7fadb7a5de17-mw0f1d282f_1c6b_455c_8254_3760632c6ecc*mwf9999977_6f0e_4e35_9b73_75587f3448e9
mw432640ec_11b9_484d_ba26_415538ab9a10=2.9 Reaction: mw12ba4000_d452_420c_be63_96d2848aca32 => mwaff92910_ed3d_40b9_a29c_e4866167e828 + mwf816df4c_4593_4d23_990f_0d7c15ddde5d; mw12ba4000_d452_420c_be63_96d2848aca32, Rate Law: mw432640ec_11b9_484d_ba26_415538ab9a10*mw12ba4000_d452_420c_be63_96d2848aca32
mw6a4e035b_11a7_4155_9a78_cfba13631cb1=0.05 Reaction: mwa6e82fc9_a0ce_461c_93c8_17f3c807c1a1 => mw236a3250_4c96_4f6e_b94c_ab3d12852801; mwa6e82fc9_a0ce_461c_93c8_17f3c807c1a1, Rate Law: mw6a4e035b_11a7_4155_9a78_cfba13631cb1*mwa6e82fc9_a0ce_461c_93c8_17f3c807c1a1
mw10c97b8e_72aa_4f56_b3b9_c94baad7e213=0.1; mw0b6eb5f7_b133_4b3d_bf15_9fd6c2e9332d=0.01 Reaction: mwbfcf6773_1915_432c_b1d2_1f246094cc74 + mw7cff9a0e_094d_498e_bf7f_7b162c61d63a => mwd39388fd_4f85_4d1c_b2a3_37857c595a2d; mwbfcf6773_1915_432c_b1d2_1f246094cc74, mw7cff9a0e_094d_498e_bf7f_7b162c61d63a, mwd39388fd_4f85_4d1c_b2a3_37857c595a2d, Rate Law: mw10c97b8e_72aa_4f56_b3b9_c94baad7e213*mwbfcf6773_1915_432c_b1d2_1f246094cc74*mw7cff9a0e_094d_498e_bf7f_7b162c61d63a-mw0b6eb5f7_b133_4b3d_bf15_9fd6c2e9332d*mwd39388fd_4f85_4d1c_b2a3_37857c595a2d
mw193f2553_1ab3_4b07_9b4b_201ee9e08c96=10.0; mwb7292ff5_dd13_41aa_b9b8_2c0c75d35fb1=1.0 Reaction: mw3d81860d_d786_4fcc_b8bb_64f1a2d7739d + mw11a8b702_b8ac_4513_b4aa_063e51089812 => mw1a0cb97a_b657_430b_963c_92217f643081; mw3d81860d_d786_4fcc_b8bb_64f1a2d7739d, mw11a8b702_b8ac_4513_b4aa_063e51089812, mw1a0cb97a_b657_430b_963c_92217f643081, Rate Law: mw193f2553_1ab3_4b07_9b4b_201ee9e08c96*mw3d81860d_d786_4fcc_b8bb_64f1a2d7739d*mw11a8b702_b8ac_4513_b4aa_063e51089812-mwb7292ff5_dd13_41aa_b9b8_2c0c75d35fb1*mw1a0cb97a_b657_430b_963c_92217f643081
mw95e2190d_8e39_419b_ad26_7cc141f7b87b=0.4 Reaction: mw2fd710a6_7fe2_4484_bca6_59c187bade8b => mwbfcf6773_1915_432c_b1d2_1f246094cc74 + mwb6a9aa2c_62e7_410f_9c33_dbe36dfcc4af; mw2fd710a6_7fe2_4484_bca6_59c187bade8b, Rate Law: mw95e2190d_8e39_419b_ad26_7cc141f7b87b*mw2fd710a6_7fe2_4484_bca6_59c187bade8b
mwa18578d7_236f_4939_baca_52259e38fe15=0.1; mwe879a9ac_4b8d_4c9a_a157_a3751761cf63=3.0 Reaction: mwa98802cb_c977_4fe0_9e67_5000904c2c36 + mwf430a579_ecbf_48ba_80c2_06e455808f2a => mw504578d8_96c3_471f_8a7e_8c14e7535d3d; mwa98802cb_c977_4fe0_9e67_5000904c2c36, mwf430a579_ecbf_48ba_80c2_06e455808f2a, mw504578d8_96c3_471f_8a7e_8c14e7535d3d, Rate Law: mwe879a9ac_4b8d_4c9a_a157_a3751761cf63*mwa98802cb_c977_4fe0_9e67_5000904c2c36*mwf430a579_ecbf_48ba_80c2_06e455808f2a-mwa18578d7_236f_4939_baca_52259e38fe15*mw504578d8_96c3_471f_8a7e_8c14e7535d3d
mwf59d397b_cfee_4a84_9279_134cc951db8c=1.0; mw22510791_ef7e_4373_907c_9eecbc8adda7=10.0 Reaction: mwcef73e0e_d195_4077_ae71_723664ee1602 + mwd7f41594_8377_4e2e_9528_45d5a82ffdb4 => mw62bf5275_ce02_4e86_b3b6_3f87a335e1de; mwcef73e0e_d195_4077_ae71_723664ee1602, mwd7f41594_8377_4e2e_9528_45d5a82ffdb4, mw62bf5275_ce02_4e86_b3b6_3f87a335e1de, Rate Law: mw22510791_ef7e_4373_907c_9eecbc8adda7*mwcef73e0e_d195_4077_ae71_723664ee1602*mwd7f41594_8377_4e2e_9528_45d5a82ffdb4-mwf59d397b_cfee_4a84_9279_134cc951db8c*mw62bf5275_ce02_4e86_b3b6_3f87a335e1de
mwbc5340b6_06b7_4081_bd0c_e7a397f06a92=10.0; mw0df80c0e_c32b_4f90_99bd_e8f90e4c8109=0.045 Reaction: mwa98802cb_c977_4fe0_9e67_5000904c2c36 + mw1093b3af_1864_4ba3_a541_6009a9921282 => mwfbda4e09_0cbb_49bc_ae69_f88b7a79ed21; mwa98802cb_c977_4fe0_9e67_5000904c2c36, mw1093b3af_1864_4ba3_a541_6009a9921282, mwfbda4e09_0cbb_49bc_ae69_f88b7a79ed21, Rate Law: mwbc5340b6_06b7_4081_bd0c_e7a397f06a92*mwa98802cb_c977_4fe0_9e67_5000904c2c36*mw1093b3af_1864_4ba3_a541_6009a9921282-mw0df80c0e_c32b_4f90_99bd_e8f90e4c8109*mwfbda4e09_0cbb_49bc_ae69_f88b7a79ed21
mw6eebbe41_cf28_46e8_930c_26f50e08d602=0.001; mw751c2663_d807_482f_991b_c8032cb6d996=0.001 Reaction: mw236a3250_4c96_4f6e_b94c_ab3d12852801 => mwcef73e0e_d195_4077_ae71_723664ee1602 + mw11a8b702_b8ac_4513_b4aa_063e51089812; mw236a3250_4c96_4f6e_b94c_ab3d12852801, mwcef73e0e_d195_4077_ae71_723664ee1602, mw11a8b702_b8ac_4513_b4aa_063e51089812, Rate Law: mw6eebbe41_cf28_46e8_930c_26f50e08d602*mw236a3250_4c96_4f6e_b94c_ab3d12852801-mw751c2663_d807_482f_991b_c8032cb6d996*mwcef73e0e_d195_4077_ae71_723664ee1602*mw11a8b702_b8ac_4513_b4aa_063e51089812
mw23e16d40_acbb_4658_a336_be5d0b0dd86a=7.76 Reaction: mwdf82303e_323f_4c51_a858_56a59233cd98 => mw8f5a7b5c_ca4c_4a4c_85b1_e5d640c426bf + mw7cff9a0e_094d_498e_bf7f_7b162c61d63a; mwdf82303e_323f_4c51_a858_56a59233cd98, Rate Law: mw23e16d40_acbb_4658_a336_be5d0b0dd86a*mwdf82303e_323f_4c51_a858_56a59233cd98
mw85c8ff7d_8d7c_4403_8a58_4996a3e6ac28=0.038; mw688106ee_719d_4995_b1a0_faeefdb0af5a=1.0 Reaction: mwfc4a9c3d_3ebb_4033_8b7d_f4d7613d2078 + mw78e207c4_4faf_4b48_8e22_1ee666e9cc4c => mwbd6bb050_89bd_41df_8cea_d2e1fb77bafe; mwfc4a9c3d_3ebb_4033_8b7d_f4d7613d2078, mw78e207c4_4faf_4b48_8e22_1ee666e9cc4c, mwbd6bb050_89bd_41df_8cea_d2e1fb77bafe, Rate Law: mw688106ee_719d_4995_b1a0_faeefdb0af5a*mwfc4a9c3d_3ebb_4033_8b7d_f4d7613d2078*mw78e207c4_4faf_4b48_8e22_1ee666e9cc4c-mw85c8ff7d_8d7c_4403_8a58_4996a3e6ac28*mwbd6bb050_89bd_41df_8cea_d2e1fb77bafe
mw5a798f7a_b4eb_4a27_b413_4ff3956b90e9=20.0; mw54178365_18c1_47e0_94ee_6b96582c52ef=0.1 Reaction: mwe3fd7f65_b0d1_44d9_b6f3_d2f7d332f664 + mwe3fd7f65_b0d1_44d9_b6f3_d2f7d332f664 => mw4110f531_7513_4786_8896_7c9d969ff558; mwe3fd7f65_b0d1_44d9_b6f3_d2f7d332f664, mw4110f531_7513_4786_8896_7c9d969ff558, Rate Law: mw5a798f7a_b4eb_4a27_b413_4ff3956b90e9*mwe3fd7f65_b0d1_44d9_b6f3_d2f7d332f664*mwe3fd7f65_b0d1_44d9_b6f3_d2f7d332f664-mw54178365_18c1_47e0_94ee_6b96582c52ef*mw4110f531_7513_4786_8896_7c9d969ff558
mw8b269d52_eda9_4dd1_8616_ebcf29c971fa=0.2; mw1ff4e75e_fce5_4a7a_907b_05df4981f80b=1.0 Reaction: mw4110f531_7513_4786_8896_7c9d969ff558 + mw0e1be972_fded_4bff_a93d_091ec942485f => mw0facb8f2_95cf_4ddf_a959_b24ba64f320b; mw4110f531_7513_4786_8896_7c9d969ff558, mw0e1be972_fded_4bff_a93d_091ec942485f, mw0facb8f2_95cf_4ddf_a959_b24ba64f320b, Rate Law: mw1ff4e75e_fce5_4a7a_907b_05df4981f80b*mw4110f531_7513_4786_8896_7c9d969ff558*mw0e1be972_fded_4bff_a93d_091ec942485f-mw8b269d52_eda9_4dd1_8616_ebcf29c971fa*mw0facb8f2_95cf_4ddf_a959_b24ba64f320b
mw1ddaf9f4_dcab_4dc2_a6fa_5ce85b9d7a3a=0.0426 Reaction: mw5babe3d5_a9af_4dfd_ac01_35474ef64af2 => mwd784228d_0cb5_468a_ac70_02d8f04b3d9c + mwbfcf6773_1915_432c_b1d2_1f246094cc74 + mwa0349407_8187_48fc_9e94_5698ccc4e06d + mwf430a579_ecbf_48ba_80c2_06e455808f2a + mw31ac308f_da36_4f73_830f_67f3e5b945d9; mw5babe3d5_a9af_4dfd_ac01_35474ef64af2, Rate Law: mw1ddaf9f4_dcab_4dc2_a6fa_5ce85b9d7a3a*mw5babe3d5_a9af_4dfd_ac01_35474ef64af2

States:

Name Description
mw6e01967b 3e2a 433d bec6 9f9cf3ba243c [3-phosphoinositide-dependent protein kinase 1]
mw0dc4e5eb 4366 4799 bebc cfcffe5c06f5 [Pro-epidermal growth factor; Epidermal growth factor receptor; Phosphatidylinositol 3-kinase catalytic subunit type 3; Phosphatidylinositol 3-kinase regulatory subunit beta; phosphorylated]
mwd39388fd 4f85 4d1c b2a3 37857c595a2d [Pro-epidermal growth factor; Epidermal growth factor receptor; GTPase HRas; Ras GTPase-activating protein 1; phosphorylated]
mwcedf8ecd 67bd 4b91 aa04 d58782dec2a4 [Dual specificity mitogen-activated protein kinase kinase 1; Mitogen-activated protein kinase 1; phosphorylated]
mw2ba1db9a 4483 44fa a3a2 b4a5ea66898c [Phosphatidylinositol 3-kinase catalytic subunit type 3; Phosphatidylinositol 3-kinase regulatory subunit beta]
mw4110f531 7513 4786 8896 7c9d969ff558 [Signal transducer and activator of transcription 3; phosphorylated]
mwf816df4c 4593 4d23 990f 0d7c15ddde5d [Dual specificity mitogen-activated protein kinase kinase 1; phosphorylated]
mwf9999977 6f0e 4e35 9b73 75587f3448e9 [SHC-transforming protein 2; Nuclear receptor subfamily 0 group B member 2; phosphorylated]
mw472d5cb9 120e 4f60 bbae 1ae2552837dd [Signal transducer and activator of transcription 3; Phosphatidylinositol 3,4,5-trisphosphate 3-phosphatase and dual-specificity protein phosphatase PTEN; phosphorylated]
mw1e591998 65c0 484e 8a3b 537a38d94de1 [Pro-epidermal growth factor; Epidermal growth factor receptor; Phosphatidylinositol 3-kinase catalytic subunit type 3; Phosphatidylinositol 3-kinase regulatory subunit beta; phosphorylated]
mw62bf5275 ce02 4e86 b3b6 3f87a335e1de [RAC-beta serine/threonine-protein kinase]
mw78e207c4 4faf 4b48 8e22 1ee666e9cc4c [Phosphatidylinositol 3-kinase catalytic subunit type 3; Phosphatidylinositol 3-kinase regulatory subunit beta; phosphorylated]
mwf430a579 ecbf 48ba 80c2 06e455808f2a [Growth factor receptor-bound protein 2]
mw3d81860d d786 4fcc b8bb 64f1a2d7739d [RAC-beta serine/threonine-protein kinase; phosphorylated]
mwcc894c94 0ddf 42cc 913e cdcc4d471d94 [Mitogen-activated protein kinase 1; phosphorylated]
mwbd6bb050 89bd 41df 8cea d2e1fb77bafe [Phosphatidylinositol 3-kinase catalytic subunit type 3; Phosphatidylinositol 3-kinase regulatory subunit beta; Phosphatidylinositol 3,4,5-trisphosphate 5-phosphatase 2]
mwcef73e0e d195 4077 ae71 723664ee1602 [RAC-beta serine/threonine-protein kinase]
mw7e23b961 186b 47a0 a8b5 5e9957766792 [Mitogen-activated protein kinase 1]
mw504578d8 96c3 471f 8a7e 8c14e7535d3d [Pro-epidermal growth factor; Epidermal growth factor receptor; SHC-transforming protein 2; Growth factor receptor-bound protein 2; phosphorylated]
mw236a3250 4c96 4f6e b94c ab3d12852801 [protein polypeptide chain; RAC-beta serine/threonine-protein kinase]
mwfc4a9c3d 3ebb 4033 8b7d f4d7613d2078 [Phosphatidylinositol 3,4,5-trisphosphate 5-phosphatase 2]
mwb561d9f3 a9ed 4bdb 8d40 87be5cc3237a [phosphatidylinositol bisphosphate]
mwa98802cb c977 4fe0 9e67 5000904c2c36 [Pro-epidermal growth factor; Epidermal growth factor receptor; SHC-transforming protein 2; phosphorylated]
mwaff92910 ed3d 40b9 a29c e4866167e828 [RAF proto-oncogene serine/threonine-protein kinase]
mwbfcf6773 1915 432c b1d2 1f246094cc74 [Pro-epidermal growth factor; Epidermal growth factor receptor; phosphorylated]
mwfbda4e09 0cbb 49bc ae69 f88b7a79ed21 [Pro-epidermal growth factor; Epidermal growth factor receptor; SHC-transforming protein 2; Growth factor receptor-bound protein 2; Son of sevenless homolog 1; phosphorylated]
mw11a8b702 b8ac 4513 b4aa 063e51089812 [protein polypeptide chain]
mw12ba4000 d452 420c be63 96d2848aca32 [RAF proto-oncogene serine/threonine-protein kinase; Dual specificity mitogen-activated protein kinase kinase 1; phosphorylated]
mw7cff9a0e 094d 498e bf7f 7b162c61d63a [GTPase HRas; Ras GTPase-activating protein 1]
mwd7f41594 8377 4e2e 9528 45d5a82ffdb4 [24755492]
mw28464aad 8013 4a23 ae09 a406954859a6 [GDP; Pro-epidermal growth factor; Epidermal growth factor receptor; Growth factor receptor-bound protein 2; Son of sevenless homolog 1; GTPase HRas; phosphorylated]
mwa0349407 8187 48fc 9e94 5698ccc4e06d [SHC-transforming protein 2; phosphorylated]
mw0e1be972 fded 4bff a93d 091ec942485f [Putative uncharacterized protein PTEN2]
mw5198d3c2 879c 4f0d b4f8 cd40efe0b1cf [Pro-epidermal growth factor; Epidermal growth factor receptor; SHC-transforming protein 2; phosphorylated]
mw4628f984 eb87 4922 9760 4975095ce6eb [RAF proto-oncogene serine/threonine-protein kinase; Dual specificity mitogen-activated protein kinase kinase 1]
mw9dcaa655 a755 426e a3fa 1ad7c3c45575 [Son of sevenless homolog 1]
mw3c2e1b43 29ca 491a 93e9 c723a993d6fb [SHC-transforming protein 2]
mw9b25f809 18a1 4c14 8f4b cf18e6d93c28 [Dual specificity mitogen-activated protein kinase kinase 1; phosphorylated]
mwdf82303e 323f 4c51 a858 56a59233cd98 [GTP; GTPase HRas; Ras GTPase-activating protein 1]
mw4f575c55 7dff 45d7 94ad cda9621d5b63 [Signal transducer and activator of transcription 3; phosphorylated]
mw6353aa36 d4a4 4254 8a1f 1f7f571d4233 [RAC-beta serine/threonine-protein kinase; 3-phosphoinositide-dependent protein kinase 1]
mw7033dfd6 53c5 433b a132 f8cb34dea20f [Phosphatidylinositol 3-kinase catalytic subunit type 3; Phosphatidylinositol 3-kinase regulatory subunit beta; Phosphatidylinositol 3,4,5-trisphosphate 5-phosphatase 2]
mw19122f7d f92e 4dc0 922f 6b681db65b0b [E3 ubiquitin-protein ligase CBL]
mwe57c3282 5935 405c 8c0b 7fadb7a5de17 [Nuclear receptor subfamily 0 group B member 2]
mwcea1f1c1 2f85 4af1 98ea ef14cf580c09 [Phosphatidylinositol 3,4,5-trisphosphate 3-phosphatase and dual-specificity protein phosphatase PTEN]
mwe3fd7f65 b0d1 44d9 b6f3 d2f7d332f664 [Signal transducer and activator of transcription 3; phosphorylated]
mw16796ffe 4764 4a9f 942e 149f42c1cd28 [RAC-beta serine/threonine-protein kinase; phosphorylated]
mwa6e82fc9 a0ce 461c 93c8 17f3c807c1a1 [protein polypeptide chain; RAC-beta serine/threonine-protein kinase; phosphorylated]

Observables: none

BIOMD0000000452 @ v0.0.1

Bidkhori2012 - normal EGFR signallingThe paper describes and compares two models on EGFR signalling between normal and N…

EGFR signaling plays a very important role in NSCLC. It activates Ras/ERK, PI3K/Akt and STAT activation pathways. These are the main pathways for cell proliferation and survival. We have developed two mathematical models to relate to the different EGFR signaling in NSCLC and normal cells in the presence or absence of EGFR and PTEN mutations. The dynamics of downstream signaling pathways vary in the disease state and activation of some factors can be indicative of drug resistance. Our simulation denotes the effect of EGFR mutations and increased expression of certain factors in NSCLC EGFR signaling on each of the three pathways where levels of pERK, pSTAT and pAkt are increased. Over activation of ERK, Akt and STAT3 which are the main cell proliferation and survival factors act as promoting factors for tumor progression in NSCLC. In case of loss of PTEN, Akt activity level is considerably increased. Our simulation results show that in the presence of erlotinib, downstream factors i.e. pAkt, pSTAT3 and pERK are inhibited. However, in case of loss of PTEN expression in the presence of erlotinib, pAkt level would not decrease which demonstrates that these cells are resistant to erlotinib. link: http://identifiers.org/pubmed/23133538

Parameters:

Name Description
mw9f1a7f64_0b37_42df_9dd5_e1a44efdcbba=2.0E-4; mw366e6f17_4081_4cdc_9fa5_0aeb354d692c=0.2 Reaction: mw13abe2a6_9905_40e5_8c23_3fc8834b572a + mwb6a9aa2c_62e7_410f_9c33_dbe36dfcc4af => mwd2c465fb_eea7_499a_8ea4_f318a64cb9ee; mw13abe2a6_9905_40e5_8c23_3fc8834b572a, mwb6a9aa2c_62e7_410f_9c33_dbe36dfcc4af, mwd2c465fb_eea7_499a_8ea4_f318a64cb9ee, Rate Law: mw9f1a7f64_0b37_42df_9dd5_e1a44efdcbba*mw13abe2a6_9905_40e5_8c23_3fc8834b572a*mwb6a9aa2c_62e7_410f_9c33_dbe36dfcc4af-mw366e6f17_4081_4cdc_9fa5_0aeb354d692c*mwd2c465fb_eea7_499a_8ea4_f318a64cb9ee
mw9cc637fe_d9ca_47d2_a4dc_66009d458094=0.18; mw5639395a_a5cd_46dd_81b8_30fe72400a2e=202.9 Reaction: mwf8cc7834_bf4f_4ccd_8235_d0890badf0f6 + mw8f5a7b5c_ca4c_4a4c_85b1_e5d640c426bf => mw28464aad_8013_4a23_ae09_a406954859a6; mwf8cc7834_bf4f_4ccd_8235_d0890badf0f6, mw8f5a7b5c_ca4c_4a4c_85b1_e5d640c426bf, mw28464aad_8013_4a23_ae09_a406954859a6, Rate Law: mw5639395a_a5cd_46dd_81b8_30fe72400a2e*mwf8cc7834_bf4f_4ccd_8235_d0890badf0f6*mw8f5a7b5c_ca4c_4a4c_85b1_e5d640c426bf-mw9cc637fe_d9ca_47d2_a4dc_66009d458094*mw28464aad_8013_4a23_ae09_a406954859a6
mw289fed85_e6ee_43e6_a69f_77b5f487a452=10.0; mw8768b5c7_b227_4825_aa55_a525b0d915c2=1.0 Reaction: mw504578d8_96c3_471f_8a7e_8c14e7535d3d + mwe57c3282_5935_405c_8c0b_7fadb7a5de17 => mw45ab688a_6467_4a3e_a779_2118fa84d69e; mw504578d8_96c3_471f_8a7e_8c14e7535d3d, mwe57c3282_5935_405c_8c0b_7fadb7a5de17, mw45ab688a_6467_4a3e_a779_2118fa84d69e, Rate Law: mw289fed85_e6ee_43e6_a69f_77b5f487a452*mw504578d8_96c3_471f_8a7e_8c14e7535d3d*mwe57c3282_5935_405c_8c0b_7fadb7a5de17-mw8768b5c7_b227_4825_aa55_a525b0d915c2*mw45ab688a_6467_4a3e_a779_2118fa84d69e
mw11e520e6_b1f1_4802_af71_92a2bd9cb644=0.001; mw65e1222f_39ad_4a29_ae76_04b7d591af38=1.0 Reaction: mw3d81860d_d786_4fcc_b8bb_64f1a2d7739d => mw16796ffe_4764_4a9f_942e_149f42c1cd28 + mwd7f41594_8377_4e2e_9528_45d5a82ffdb4; mw3d81860d_d786_4fcc_b8bb_64f1a2d7739d, mw16796ffe_4764_4a9f_942e_149f42c1cd28, mwd7f41594_8377_4e2e_9528_45d5a82ffdb4, Rate Law: mw65e1222f_39ad_4a29_ae76_04b7d591af38*mw3d81860d_d786_4fcc_b8bb_64f1a2d7739d-mw11e520e6_b1f1_4802_af71_92a2bd9cb644*mw16796ffe_4764_4a9f_942e_149f42c1cd28*mwd7f41594_8377_4e2e_9528_45d5a82ffdb4
mw134431c3_e8e5_4375_89a0_2c51a03d65dd=25.0 Reaction: mw014cc419_b720_4b90_9192_2ec6e706c87d => mw78e207c4_4faf_4b48_8e22_1ee666e9cc4c + mwd7f41594_8377_4e2e_9528_45d5a82ffdb4; mw014cc419_b720_4b90_9192_2ec6e706c87d, Rate Law: mw134431c3_e8e5_4375_89a0_2c51a03d65dd*mw014cc419_b720_4b90_9192_2ec6e706c87d
mw56f1be7e_e303_4a72_be17_5bd08e3eb1f2=0.1; mwcc0d3fcd_9b9e_4390_b588_e57b57d89d22=5.0 Reaction: mwec1b368b_8f73_47eb_9636_9956389836eb + mwb2366216_0b3c_4f28_8303_fec92c68dd57 => mwa455ec7e_1a12_4659_95a2_a5695d09ca60; mwec1b368b_8f73_47eb_9636_9956389836eb, mwb2366216_0b3c_4f28_8303_fec92c68dd57, mwa455ec7e_1a12_4659_95a2_a5695d09ca60, Rate Law: mwcc0d3fcd_9b9e_4390_b588_e57b57d89d22*mwec1b368b_8f73_47eb_9636_9956389836eb*mwb2366216_0b3c_4f28_8303_fec92c68dd57-mw56f1be7e_e303_4a72_be17_5bd08e3eb1f2*mwa455ec7e_1a12_4659_95a2_a5695d09ca60
mwfbc395b5_05b8_4e27_9696_c3ba52edaf74=1.0 Reaction: mw19a33ad5_5ba4_46c7_84eb_c1287f02bcd5 => mw66ac98c4_7e7b_4071_954d_43eb17584220 + mwbaaeb210_4806_4076_9d60_219f4ed945b6; mw19a33ad5_5ba4_46c7_84eb_c1287f02bcd5, Rate Law: mwfbc395b5_05b8_4e27_9696_c3ba52edaf74*mw19a33ad5_5ba4_46c7_84eb_c1287f02bcd5
mw9cafad09_6002_46e1_8336_bb91c3716d70=17.0 Reaction: mwd7f41594_8377_4e2e_9528_45d5a82ffdb4 => mwb561d9f3_a9ed_4bdb_8d40_87be5cc3237a; mwd7f41594_8377_4e2e_9528_45d5a82ffdb4, Rate Law: mw9cafad09_6002_46e1_8336_bb91c3716d70*mwd7f41594_8377_4e2e_9528_45d5a82ffdb4
mwfc146e94_8070_4727_8416_fb55829068cb=0.1434 Reaction: mwf40d6176_abfc_4a30_886f_83a19fcffc48 => mwfbda4e09_0cbb_49bc_ae69_f88b7a79ed21 + mwa54a9c38_c98b_45e5_8432_4119fb777e44; mwf40d6176_abfc_4a30_886f_83a19fcffc48, Rate Law: mwfc146e94_8070_4727_8416_fb55829068cb*mwf40d6176_abfc_4a30_886f_83a19fcffc48
mwe1743f7b_ca2c_47d4_91d7_aed2748d98c5=2.661 Reaction: mwbf5cb039_b830_4282_aa22_a3dda6272ec1 => mwa8f2e7b2_0927_4ab4_a817_dddc43bb4fa3 + mw7cff9a0e_094d_498e_bf7f_7b162c61d63a + mwe57c3282_5935_405c_8c0b_7fadb7a5de17; mwbf5cb039_b830_4282_aa22_a3dda6272ec1, Rate Law: mwe1743f7b_ca2c_47d4_91d7_aed2748d98c5*mwbf5cb039_b830_4282_aa22_a3dda6272ec1
mw21d22acd_ddd4_4794_9700_52201984f75b=0.2; mw8cbe6595_6f16_4704_afe2_0dd043a175fa=1.0 Reaction: mw4f575c55_7dff_45d7_94ad_cda9621d5b63 + mwcea1f1c1_2f85_4af1_98ea_ef14cf580c09 => mw472d5cb9_120e_4f60_bbae_1ae2552837dd; mw4f575c55_7dff_45d7_94ad_cda9621d5b63, mwcea1f1c1_2f85_4af1_98ea_ef14cf580c09, mw472d5cb9_120e_4f60_bbae_1ae2552837dd, Rate Law: mw8cbe6595_6f16_4704_afe2_0dd043a175fa*mw4f575c55_7dff_45d7_94ad_cda9621d5b63*mwcea1f1c1_2f85_4af1_98ea_ef14cf580c09-mw21d22acd_ddd4_4794_9700_52201984f75b*mw472d5cb9_120e_4f60_bbae_1ae2552837dd
mw90b25c4b_ad1a_4ee5_ae20_c60451484516=0.005 Reaction: mw0facb8f2_95cf_4ddf_a959_b24ba64f320b => mw9686f53e_d343_45fd_b441_9c992219546a + mw0e1be972_fded_4bff_a93d_091ec942485f; mw0facb8f2_95cf_4ddf_a959_b24ba64f320b, Rate Law: mw90b25c4b_ad1a_4ee5_ae20_c60451484516*mw0facb8f2_95cf_4ddf_a959_b24ba64f320b
mwba545ecf_c7d4_4a6c_8c47_9e91f052d5a9=1.0; mw01c5ceef_57a1_4baa_b2cd_fd39e9588a10=0.2 Reaction: mwe3fd7f65_b0d1_44d9_b6f3_d2f7d332f664 + mw0e1be972_fded_4bff_a93d_091ec942485f => mw8c85ff7f_6368_4b11_a2ed_ce83481b55e6; mwe3fd7f65_b0d1_44d9_b6f3_d2f7d332f664, mw0e1be972_fded_4bff_a93d_091ec942485f, mw8c85ff7f_6368_4b11_a2ed_ce83481b55e6, Rate Law: mwba545ecf_c7d4_4a6c_8c47_9e91f052d5a9*mwe3fd7f65_b0d1_44d9_b6f3_d2f7d332f664*mw0e1be972_fded_4bff_a93d_091ec942485f-mw01c5ceef_57a1_4baa_b2cd_fd39e9588a10*mw8c85ff7f_6368_4b11_a2ed_ce83481b55e6
mw60892818_7ef4_4f65_8003_9700a708c66c=8.898; mw6843d346_6e9f_43d5_97f6_1059f164aa16=1.0 Reaction: mwd784228d_0cb5_468a_ac70_02d8f04b3d9c + mwf8cc7834_bf4f_4ccd_8235_d0890badf0f6 => mw31261227_9cd6_4059_a0bb_04dbf4888080; mwd784228d_0cb5_468a_ac70_02d8f04b3d9c, mwf8cc7834_bf4f_4ccd_8235_d0890badf0f6, mw31261227_9cd6_4059_a0bb_04dbf4888080, Rate Law: mw60892818_7ef4_4f65_8003_9700a708c66c*mwd784228d_0cb5_468a_ac70_02d8f04b3d9c*mwf8cc7834_bf4f_4ccd_8235_d0890badf0f6-mw6843d346_6e9f_43d5_97f6_1059f164aa16*mw31261227_9cd6_4059_a0bb_04dbf4888080
mwafd23622_952d_44b3_a437_4aa12422add7=0.25; mw9d9a7d08_b19a_44f1_a806_151597049345=0.5 Reaction: mw9b25f809_18a1_4c14_8f4b_cf18e6d93c28 + mwf9e2a044_7774_400b_a74e_a111b4a21f30 => mwa0acc0ac_5fac_4a42_a3be_e36db44994b0; mw9b25f809_18a1_4c14_8f4b_cf18e6d93c28, mwf9e2a044_7774_400b_a74e_a111b4a21f30, mwa0acc0ac_5fac_4a42_a3be_e36db44994b0, Rate Law: mwafd23622_952d_44b3_a437_4aa12422add7*mw9b25f809_18a1_4c14_8f4b_cf18e6d93c28*mwf9e2a044_7774_400b_a74e_a111b4a21f30-mw9d9a7d08_b19a_44f1_a806_151597049345*mwa0acc0ac_5fac_4a42_a3be_e36db44994b0
mwb9547c37_09b7_4258_95ab_8039d4088298=0.025; mwfa6a58ab_0ca5_4c05_92b0_870593ac135d=2.734 Reaction: mwbfcf6773_1915_432c_b1d2_1f246094cc74 + mw1093b3af_1864_4ba3_a541_6009a9921282 => mwf8cc7834_bf4f_4ccd_8235_d0890badf0f6; mwbfcf6773_1915_432c_b1d2_1f246094cc74, mw1093b3af_1864_4ba3_a541_6009a9921282, mwf8cc7834_bf4f_4ccd_8235_d0890badf0f6, Rate Law: mwfa6a58ab_0ca5_4c05_92b0_870593ac135d*mwbfcf6773_1915_432c_b1d2_1f246094cc74*mw1093b3af_1864_4ba3_a541_6009a9921282-mwb9547c37_09b7_4258_95ab_8039d4088298*mwf8cc7834_bf4f_4ccd_8235_d0890badf0f6
mwbc2119ce_ade3_4e2a_a3bc_a29cd77adf72=8.898; mw54b0e5e9_710f_438e_a8d3_749c594667bc=1.0 Reaction: mwd784228d_0cb5_468a_ac70_02d8f04b3d9c + mwfbda4e09_0cbb_49bc_ae69_f88b7a79ed21 => mw5babe3d5_a9af_4dfd_ac01_35474ef64af2; mwd784228d_0cb5_468a_ac70_02d8f04b3d9c, mwfbda4e09_0cbb_49bc_ae69_f88b7a79ed21, mw5babe3d5_a9af_4dfd_ac01_35474ef64af2, Rate Law: mwbc2119ce_ade3_4e2a_a3bc_a29cd77adf72*mwd784228d_0cb5_468a_ac70_02d8f04b3d9c*mwfbda4e09_0cbb_49bc_ae69_f88b7a79ed21-mw54b0e5e9_710f_438e_a8d3_749c594667bc*mw5babe3d5_a9af_4dfd_ac01_35474ef64af2
mwba77a9ba_078d_4ec6_a8b8_d7042a2cefe7=0.2; mwb4c6ed27_c7ec_438f_bafd_4a09a9f356f1=3.114 Reaction: mwd39388fd_4f85_4d1c_b2a3_37857c595a2d + mwe57c3282_5935_405c_8c0b_7fadb7a5de17 => mwbf5cb039_b830_4282_aa22_a3dda6272ec1; mwd39388fd_4f85_4d1c_b2a3_37857c595a2d, mwe57c3282_5935_405c_8c0b_7fadb7a5de17, mwbf5cb039_b830_4282_aa22_a3dda6272ec1, Rate Law: mwb4c6ed27_c7ec_438f_bafd_4a09a9f356f1*mwd39388fd_4f85_4d1c_b2a3_37857c595a2d*mwe57c3282_5935_405c_8c0b_7fadb7a5de17-mwba77a9ba_078d_4ec6_a8b8_d7042a2cefe7*mwbf5cb039_b830_4282_aa22_a3dda6272ec1
mwd3e2533f_8d57_407c_834d_e0dde30b7f4a=4.7E-6; mwbd416b7b_f9b6_4464_b9e8_be4ac001d13d=2.297E-6 Reaction: mw7033dfd6_53c5_433b_a132_f8cb34dea20f => mwfc4a9c3d_3ebb_4033_8b7d_f4d7613d2078 + mw2ba1db9a_4483_44fa_a3a2_b4a5ea66898c; mw7033dfd6_53c5_433b_a132_f8cb34dea20f, mwfc4a9c3d_3ebb_4033_8b7d_f4d7613d2078, mw2ba1db9a_4483_44fa_a3a2_b4a5ea66898c, Rate Law: mwd3e2533f_8d57_407c_834d_e0dde30b7f4a*mw7033dfd6_53c5_433b_a132_f8cb34dea20f-mwbd416b7b_f9b6_4464_b9e8_be4ac001d13d*mwfc4a9c3d_3ebb_4033_8b7d_f4d7613d2078*mw2ba1db9a_4483_44fa_a3a2_b4a5ea66898c
mw26688d02_8ab9_4123_89c4_022b981cb72c=0.1434 Reaction: mw28464aad_8013_4a23_ae09_a406954859a6 => mwf8cc7834_bf4f_4ccd_8235_d0890badf0f6 + mwa54a9c38_c98b_45e5_8432_4119fb777e44; mw28464aad_8013_4a23_ae09_a406954859a6, Rate Law: mw26688d02_8ab9_4123_89c4_022b981cb72c*mw28464aad_8013_4a23_ae09_a406954859a6
mwa17c895f_29d8_4977_a99f_cf9bf6216785=0.058 Reaction: mwcb572fe2_c3ac_40e7_8141_da7d55fce18a => mw9b25f809_18a1_4c14_8f4b_cf18e6d93c28 + mwf9e2a044_7774_400b_a74e_a111b4a21f30; mwcb572fe2_c3ac_40e7_8141_da7d55fce18a, Rate Law: mwa17c895f_29d8_4977_a99f_cf9bf6216785*mwcb572fe2_c3ac_40e7_8141_da7d55fce18a
mwff6f49f7_268a_4f08_8d36_3ad8449d7472=0.2; mw7e889122_d26c_4d09_bae4_d313b992dc8e=3.114 Reaction: mwbfcf6773_1915_432c_b1d2_1f246094cc74 + mwe57c3282_5935_405c_8c0b_7fadb7a5de17 => mw954e8fcb_ac0a_459d_8878_f19080208a17; mwbfcf6773_1915_432c_b1d2_1f246094cc74, mwe57c3282_5935_405c_8c0b_7fadb7a5de17, mw954e8fcb_ac0a_459d_8878_f19080208a17, Rate Law: mw7e889122_d26c_4d09_bae4_d313b992dc8e*mwbfcf6773_1915_432c_b1d2_1f246094cc74*mwe57c3282_5935_405c_8c0b_7fadb7a5de17-mwff6f49f7_268a_4f08_8d36_3ad8449d7472*mw954e8fcb_ac0a_459d_8878_f19080208a17
mw4f6f44d9_408e_49b2_bedf_d34b2448725e=0.595 Reaction: mwbd6bb050_89bd_41df_8cea_d2e1fb77bafe => mw7033dfd6_53c5_433b_a132_f8cb34dea20f; mwbd6bb050_89bd_41df_8cea_d2e1fb77bafe, Rate Law: mw4f6f44d9_408e_49b2_bedf_d34b2448725e*mwbd6bb050_89bd_41df_8cea_d2e1fb77bafe
mwa8f70790_9f44_4548_988e_49d13016d2f1=71.7; mwaad540b6_783e_4576_8862_ad522fd897db=0.2 Reaction: mwaff92910_ed3d_40b9_a29c_e4866167e828 + mwbaaeb210_4806_4076_9d60_219f4ed945b6 => mw19a33ad5_5ba4_46c7_84eb_c1287f02bcd5; mwaff92910_ed3d_40b9_a29c_e4866167e828, mwbaaeb210_4806_4076_9d60_219f4ed945b6, mw19a33ad5_5ba4_46c7_84eb_c1287f02bcd5, Rate Law: mwa8f70790_9f44_4548_988e_49d13016d2f1*mwaff92910_ed3d_40b9_a29c_e4866167e828*mwbaaeb210_4806_4076_9d60_219f4ed945b6-mwaad540b6_783e_4576_8862_ad522fd897db*mw19a33ad5_5ba4_46c7_84eb_c1287f02bcd5
mwd12a67b3_6d98_40e9_a54b_282a577498eb=2.661 Reaction: mw45ab688a_6467_4a3e_a779_2118fa84d69e => mwa8f2e7b2_0927_4ab4_a817_dddc43bb4fa3 + mwa0349407_8187_48fc_9e94_5698ccc4e06d + mwf430a579_ecbf_48ba_80c2_06e455808f2a + mwe57c3282_5935_405c_8c0b_7fadb7a5de17; mw45ab688a_6467_4a3e_a779_2118fa84d69e, Rate Law: mwd12a67b3_6d98_40e9_a54b_282a577498eb*mw45ab688a_6467_4a3e_a779_2118fa84d69e
mwf59d397b_cfee_4a84_9279_134cc951db8c=3.0; mw22510791_ef7e_4373_907c_9eecbc8adda7=10.0 Reaction: mwcef73e0e_d195_4077_ae71_723664ee1602 + mwd7f41594_8377_4e2e_9528_45d5a82ffdb4 => mw62bf5275_ce02_4e86_b3b6_3f87a335e1de; mwcef73e0e_d195_4077_ae71_723664ee1602, mwd7f41594_8377_4e2e_9528_45d5a82ffdb4, mw62bf5275_ce02_4e86_b3b6_3f87a335e1de, Rate Law: mw22510791_ef7e_4373_907c_9eecbc8adda7*mwcef73e0e_d195_4077_ae71_723664ee1602*mwd7f41594_8377_4e2e_9528_45d5a82ffdb4-mwf59d397b_cfee_4a84_9279_134cc951db8c*mw62bf5275_ce02_4e86_b3b6_3f87a335e1de
mwdaa378da_64fe_4ea4_b79d_c25733837b9f=0.0426 Reaction: mw31261227_9cd6_4059_a0bb_04dbf4888080 => mwd784228d_0cb5_468a_ac70_02d8f04b3d9c + mwbfcf6773_1915_432c_b1d2_1f246094cc74 + mwf430a579_ecbf_48ba_80c2_06e455808f2a + mw31ac308f_da36_4f73_830f_67f3e5b945d9; mw31261227_9cd6_4059_a0bb_04dbf4888080, Rate Law: mwdaa378da_64fe_4ea4_b79d_c25733837b9f*mw31261227_9cd6_4059_a0bb_04dbf4888080
mw1351daea_68be_404a_b7b0_105920ff3371=0.1; mwcc2a950d_261b_4fd7_9c08_9f3c194ba09d=5.0 Reaction: mwb1bc2058_e6d8_4680_9e6c_d27bb366cde0 + mwb2366216_0b3c_4f28_8303_fec92c68dd57 => mw06b8aada_c92a_48eb_8ee7_af3778cfe62f; mwb1bc2058_e6d8_4680_9e6c_d27bb366cde0, mwb2366216_0b3c_4f28_8303_fec92c68dd57, mw06b8aada_c92a_48eb_8ee7_af3778cfe62f, Rate Law: mwcc2a950d_261b_4fd7_9c08_9f3c194ba09d*mwb1bc2058_e6d8_4680_9e6c_d27bb366cde0*mwb2366216_0b3c_4f28_8303_fec92c68dd57-mw1351daea_68be_404a_b7b0_105920ff3371*mw06b8aada_c92a_48eb_8ee7_af3778cfe62f
mw81384973_14a0_4498_ab21_f70666d46d7f=0.003 Reaction: mw472d5cb9_120e_4f60_bbae_1ae2552837dd => mwd2c465fb_eea7_499a_8ea4_f318a64cb9ee + mwcea1f1c1_2f85_4af1_98ea_ef14cf580c09; mw472d5cb9_120e_4f60_bbae_1ae2552837dd, Rate Law: mw81384973_14a0_4498_ab21_f70666d46d7f*mw472d5cb9_120e_4f60_bbae_1ae2552837dd
mwb0744746_88a2_488e_a483_266747a044c6=0.2661 Reaction: mw954e8fcb_ac0a_459d_8878_f19080208a17 => mwa8f2e7b2_0927_4ab4_a817_dddc43bb4fa3 + mwe57c3282_5935_405c_8c0b_7fadb7a5de17; mw954e8fcb_ac0a_459d_8878_f19080208a17, Rate Law: mwb0744746_88a2_488e_a483_266747a044c6*mw954e8fcb_ac0a_459d_8878_f19080208a17
mw92d81b3b_fa59_4637_8540_8cb8482490d9=0.005; mw90873203_7a5d_4fca_a789_5e989ff0c999=0.5 Reaction: mwfbda4e09_0cbb_49bc_ae69_f88b7a79ed21 + mw19122f7d_f92e_4dc0_922f_6b681db65b0b => mwb1bc2058_e6d8_4680_9e6c_d27bb366cde0; mwfbda4e09_0cbb_49bc_ae69_f88b7a79ed21, mw19122f7d_f92e_4dc0_922f_6b681db65b0b, mwb1bc2058_e6d8_4680_9e6c_d27bb366cde0, Rate Law: mw90873203_7a5d_4fca_a789_5e989ff0c999*mwfbda4e09_0cbb_49bc_ae69_f88b7a79ed21*mw19122f7d_f92e_4dc0_922f_6b681db65b0b-mw92d81b3b_fa59_4637_8540_8cb8482490d9*mwb1bc2058_e6d8_4680_9e6c_d27bb366cde0
mwbb727dc5_30e8_45f4_9d15_3b34be5c1e93=0.1; mw7ae1ee96_563e_4684_bc9a_8f4ef373620e=0.0015 Reaction: mwf430a579_ecbf_48ba_80c2_06e455808f2a + mw9dcaa655_a755_426e_a3fa_1ad7c3c45575 => mw1093b3af_1864_4ba3_a541_6009a9921282; mwf430a579_ecbf_48ba_80c2_06e455808f2a, mw9dcaa655_a755_426e_a3fa_1ad7c3c45575, mw1093b3af_1864_4ba3_a541_6009a9921282, Rate Law: mwbb727dc5_30e8_45f4_9d15_3b34be5c1e93*mwf430a579_ecbf_48ba_80c2_06e455808f2a*mw9dcaa655_a755_426e_a3fa_1ad7c3c45575-mw7ae1ee96_563e_4684_bc9a_8f4ef373620e*mw1093b3af_1864_4ba3_a541_6009a9921282
mw3d07dc22_f821_49a5_9712_820ba9592353=5.7 Reaction: mw6cb74b27_ffef_49bb_8ffb_622d552caa9e => mwf816df4c_4593_4d23_990f_0d7c15ddde5d + mwd784228d_0cb5_468a_ac70_02d8f04b3d9c; mw6cb74b27_ffef_49bb_8ffb_622d552caa9e, Rate Law: mw3d07dc22_f821_49a5_9712_820ba9592353*mw6cb74b27_ffef_49bb_8ffb_622d552caa9e
mw3f5e2165_9bb6_4ac3_992e_50943dd2ea05=0.002 Reaction: mw31ac308f_da36_4f73_830f_67f3e5b945d9 => mw9dcaa655_a755_426e_a3fa_1ad7c3c45575; mw31ac308f_da36_4f73_830f_67f3e5b945d9, Rate Law: mw3f5e2165_9bb6_4ac3_992e_50943dd2ea05*mw31ac308f_da36_4f73_830f_67f3e5b945d9
mw93f832d7_eefb_43dd_853c_a0d7a76023cf=0.0214; mw6ac313e2_e8a9_42a9_b13a_27e55c1012a2=10.0 Reaction: mw504578d8_96c3_471f_8a7e_8c14e7535d3d + mw9dcaa655_a755_426e_a3fa_1ad7c3c45575 => mwfbda4e09_0cbb_49bc_ae69_f88b7a79ed21; mw504578d8_96c3_471f_8a7e_8c14e7535d3d, mw9dcaa655_a755_426e_a3fa_1ad7c3c45575, mwfbda4e09_0cbb_49bc_ae69_f88b7a79ed21, Rate Law: mw6ac313e2_e8a9_42a9_b13a_27e55c1012a2*mw504578d8_96c3_471f_8a7e_8c14e7535d3d*mw9dcaa655_a755_426e_a3fa_1ad7c3c45575-mw93f832d7_eefb_43dd_853c_a0d7a76023cf*mwfbda4e09_0cbb_49bc_ae69_f88b7a79ed21
mw64664eb9_353a_4f1d_a8dc_e22bcb06e2c2=25.0; mw0573df9d_f365_40b7_83d4_3846a05aefdc=3.5 Reaction: mw78e207c4_4faf_4b48_8e22_1ee666e9cc4c + mwb561d9f3_a9ed_4bdb_8d40_87be5cc3237a => mw014cc419_b720_4b90_9192_2ec6e706c87d; mw78e207c4_4faf_4b48_8e22_1ee666e9cc4c, mwb561d9f3_a9ed_4bdb_8d40_87be5cc3237a, mw014cc419_b720_4b90_9192_2ec6e706c87d, Rate Law: mw64664eb9_353a_4f1d_a8dc_e22bcb06e2c2*mw78e207c4_4faf_4b48_8e22_1ee666e9cc4c*mwb561d9f3_a9ed_4bdb_8d40_87be5cc3237a-mw0573df9d_f365_40b7_83d4_3846a05aefdc*mw014cc419_b720_4b90_9192_2ec6e706c87d
mwb6701ead_d3f2_4eb3_8b08_341cea49a4b2=1.0; mwa5016035_3f9f_44fc_9f69_1d7a0155eb36=0.2 Reaction: mwb6a9aa2c_62e7_410f_9c33_dbe36dfcc4af + mwcea1f1c1_2f85_4af1_98ea_ef14cf580c09 => mwdc34472c_a6f9_4002_951d_e0e8da64eb42; mwb6a9aa2c_62e7_410f_9c33_dbe36dfcc4af, mwcea1f1c1_2f85_4af1_98ea_ef14cf580c09, mwdc34472c_a6f9_4002_951d_e0e8da64eb42, Rate Law: mwb6701ead_d3f2_4eb3_8b08_341cea49a4b2*mwb6a9aa2c_62e7_410f_9c33_dbe36dfcc4af*mwcea1f1c1_2f85_4af1_98ea_ef14cf580c09-mwa5016035_3f9f_44fc_9f69_1d7a0155eb36*mwdc34472c_a6f9_4002_951d_e0e8da64eb42
mwf44d37d0_fe7f_4e47_bf10_1e734fbc3391=0.05; mwc585e0e4_b7e7_4290_8a6d_10fcd9759a2d=3.0 Reaction: mwbfcf6773_1915_432c_b1d2_1f246094cc74 + mwf430a579_ecbf_48ba_80c2_06e455808f2a => mwd9462e5b_a272_4b66_ab66_fde9266b1a43; mwbfcf6773_1915_432c_b1d2_1f246094cc74, mwf430a579_ecbf_48ba_80c2_06e455808f2a, mwd9462e5b_a272_4b66_ab66_fde9266b1a43, Rate Law: mwc585e0e4_b7e7_4290_8a6d_10fcd9759a2d*mwbfcf6773_1915_432c_b1d2_1f246094cc74*mwf430a579_ecbf_48ba_80c2_06e455808f2a-mwf44d37d0_fe7f_4e47_bf10_1e734fbc3391*mwd9462e5b_a272_4b66_ab66_fde9266b1a43
mw31eb851a_c381_419d_b694_f158b7f5cfb6=0.05 Reaction: mw960bddeb_e567_46dd_b2f3_ed5e6a5c7972 => mw13abe2a6_9905_40e5_8c23_3fc8834b572a; mw960bddeb_e567_46dd_b2f3_ed5e6a5c7972, Rate Law: mw31eb851a_c381_419d_b694_f158b7f5cfb6*mw960bddeb_e567_46dd_b2f3_ed5e6a5c7972
mw77a6c207_ff8c_463c_9b4e_8a7d96652b79=0.005; mwe09b67b9_0d2a_4b82_91ef_5284216beb94=0.5 Reaction: mw2fd710a6_7fe2_4484_bca6_59c187bade8b + mw19122f7d_f92e_4dc0_922f_6b681db65b0b => mw548c81c2_c626_4df8_9177_a1a6fc3d4ce8; mw2fd710a6_7fe2_4484_bca6_59c187bade8b, mw19122f7d_f92e_4dc0_922f_6b681db65b0b, mw548c81c2_c626_4df8_9177_a1a6fc3d4ce8, Rate Law: mwe09b67b9_0d2a_4b82_91ef_5284216beb94*mw2fd710a6_7fe2_4484_bca6_59c187bade8b*mw19122f7d_f92e_4dc0_922f_6b681db65b0b-mw77a6c207_ff8c_463c_9b4e_8a7d96652b79*mw548c81c2_c626_4df8_9177_a1a6fc3d4ce8
mwac85fd83_4e73_43f1_9c42_01773349d50f=0.058 Reaction: mwa0acc0ac_5fac_4a42_a3be_e36db44994b0 => mw0834731b_0477_4217_a53b_30cef851191b + mwf9e2a044_7774_400b_a74e_a111b4a21f30; mwa0acc0ac_5fac_4a42_a3be_e36db44994b0, Rate Law: mwac85fd83_4e73_43f1_9c42_01773349d50f*mwa0acc0ac_5fac_4a42_a3be_e36db44994b0
mw19173345_925d_427b_8658_add0978e5931=2.854; mw9f6790d7_19ce_41d9_b4de_a1658c047501=0.96 Reaction: mwa54a9c38_c98b_45e5_8432_4119fb777e44 + mw7cff9a0e_094d_498e_bf7f_7b162c61d63a => mwdf82303e_323f_4c51_a858_56a59233cd98; mwa54a9c38_c98b_45e5_8432_4119fb777e44, mw7cff9a0e_094d_498e_bf7f_7b162c61d63a, mwdf82303e_323f_4c51_a858_56a59233cd98, Rate Law: mw19173345_925d_427b_8658_add0978e5931*mwa54a9c38_c98b_45e5_8432_4119fb777e44*mw7cff9a0e_094d_498e_bf7f_7b162c61d63a-mw9f6790d7_19ce_41d9_b4de_a1658c047501*mwdf82303e_323f_4c51_a858_56a59233cd98
mw254868f8_c9fb_493c_bc1d_807cc83c18e6=5.0; mw78a41659_4abc_4614_9e83_38cbfe1c5262=0.5 Reaction: mwcc894c94_0ddf_42cc_913e_cdcc4d471d94 + mwd087f76b_65dc_47f1_ba21_c43774457686 => mw35f5adaa_d1c0_433c_817d_76e317f4cb15; mwcc894c94_0ddf_42cc_913e_cdcc4d471d94, mwd087f76b_65dc_47f1_ba21_c43774457686, mw35f5adaa_d1c0_433c_817d_76e317f4cb15, Rate Law: mw254868f8_c9fb_493c_bc1d_807cc83c18e6*mwcc894c94_0ddf_42cc_913e_cdcc4d471d94*mwd087f76b_65dc_47f1_ba21_c43774457686-mw78a41659_4abc_4614_9e83_38cbfe1c5262*mw35f5adaa_d1c0_433c_817d_76e317f4cb15
mw2a4ed8a2_fce4_44a4_adb9_edc24a06b4e1=0.005 Reaction: mwa0349407_8187_48fc_9e94_5698ccc4e06d => mw3c2e1b43_29ca_491a_93e9_c723a993d6fb; mwa0349407_8187_48fc_9e94_5698ccc4e06d, Rate Law: mw2a4ed8a2_fce4_44a4_adb9_edc24a06b4e1*mwa0349407_8187_48fc_9e94_5698ccc4e06d
mwa0806e7a_a90d_4187_9c37_6d9ea569a447=2.0E-4; mw95cb9071_56e2_447d_b7c7_59ac96baa623=0.2 Reaction: mw960bddeb_e567_46dd_b2f3_ed5e6a5c7972 + mwe3fd7f65_b0d1_44d9_b6f3_d2f7d332f664 => mw9686f53e_d343_45fd_b441_9c992219546a; mw960bddeb_e567_46dd_b2f3_ed5e6a5c7972, mwe3fd7f65_b0d1_44d9_b6f3_d2f7d332f664, mw9686f53e_d343_45fd_b441_9c992219546a, Rate Law: mwa0806e7a_a90d_4187_9c37_6d9ea569a447*mw960bddeb_e567_46dd_b2f3_ed5e6a5c7972*mwe3fd7f65_b0d1_44d9_b6f3_d2f7d332f664-mw95cb9071_56e2_447d_b7c7_59ac96baa623*mw9686f53e_d343_45fd_b441_9c992219546a
mw9fe16c2b_7271_4e4f_b6de_c149721a3198=20.0; mw74ea5b55_ead0_4b6f_8da0_fd1dcf7e231d=0.1 Reaction: mwb6a9aa2c_62e7_410f_9c33_dbe36dfcc4af + mwb6a9aa2c_62e7_410f_9c33_dbe36dfcc4af => mw4f575c55_7dff_45d7_94ad_cda9621d5b63; mwb6a9aa2c_62e7_410f_9c33_dbe36dfcc4af, mw4f575c55_7dff_45d7_94ad_cda9621d5b63, Rate Law: mw9fe16c2b_7271_4e4f_b6de_c149721a3198*mwb6a9aa2c_62e7_410f_9c33_dbe36dfcc4af*mwb6a9aa2c_62e7_410f_9c33_dbe36dfcc4af-mw74ea5b55_ead0_4b6f_8da0_fd1dcf7e231d*mw4f575c55_7dff_45d7_94ad_cda9621d5b63
mw084cd67b_f328_48a7_8e16_1d6256c8c137=10.0; mw43f177dc_f522_4dd1_b8e5_21b2b8fdfdba=0.06 Reaction: mwd9462e5b_a272_4b66_ab66_fde9266b1a43 + mw9dcaa655_a755_426e_a3fa_1ad7c3c45575 => mwf8cc7834_bf4f_4ccd_8235_d0890badf0f6; mwd9462e5b_a272_4b66_ab66_fde9266b1a43, mw9dcaa655_a755_426e_a3fa_1ad7c3c45575, mwf8cc7834_bf4f_4ccd_8235_d0890badf0f6, Rate Law: mw084cd67b_f328_48a7_8e16_1d6256c8c137*mwd9462e5b_a272_4b66_ab66_fde9266b1a43*mw9dcaa655_a755_426e_a3fa_1ad7c3c45575-mw43f177dc_f522_4dd1_b8e5_21b2b8fdfdba*mwf8cc7834_bf4f_4ccd_8235_d0890badf0f6
mw2dfc8a19_1792_4e12_af38_8bfbda31a577=0.18; mw7e09242b_bd80_4af0_90c8_e0cddace89fe=202.9 Reaction: mwfbda4e09_0cbb_49bc_ae69_f88b7a79ed21 + mw8f5a7b5c_ca4c_4a4c_85b1_e5d640c426bf => mwf40d6176_abfc_4a30_886f_83a19fcffc48; mwfbda4e09_0cbb_49bc_ae69_f88b7a79ed21, mw8f5a7b5c_ca4c_4a4c_85b1_e5d640c426bf, mwf40d6176_abfc_4a30_886f_83a19fcffc48, Rate Law: mw7e09242b_bd80_4af0_90c8_e0cddace89fe*mwfbda4e09_0cbb_49bc_ae69_f88b7a79ed21*mw8f5a7b5c_ca4c_4a4c_85b1_e5d640c426bf-mw2dfc8a19_1792_4e12_af38_8bfbda31a577*mwf40d6176_abfc_4a30_886f_83a19fcffc48
mwf4c4d7a7_1498_4f6c_9d72_cd5cb012146c=0.6; mwd23d026b_c5b7_4742_aab9_b9beb18ec9bc=7.0 Reaction: mwd784228d_0cb5_468a_ac70_02d8f04b3d9c + mwd087f76b_65dc_47f1_ba21_c43774457686 => mwa7e3103a_6394_472c_b0f4_8ed527f68604; mwd784228d_0cb5_468a_ac70_02d8f04b3d9c, mwd087f76b_65dc_47f1_ba21_c43774457686, mwa7e3103a_6394_472c_b0f4_8ed527f68604, Rate Law: mwd23d026b_c5b7_4742_aab9_b9beb18ec9bc*mwd784228d_0cb5_468a_ac70_02d8f04b3d9c*mwd087f76b_65dc_47f1_ba21_c43774457686-mwf4c4d7a7_1498_4f6c_9d72_cd5cb012146c*mwa7e3103a_6394_472c_b0f4_8ed527f68604
mw736e4a7b_4a25_4d32_b96b_b088e3bd41e7=2.661 Reaction: mw925b938a_fe73_4664_ba6f_e72e57780891 => mwa8f2e7b2_0927_4ab4_a817_dddc43bb4fa3 + mwf430a579_ecbf_48ba_80c2_06e455808f2a + mwe57c3282_5935_405c_8c0b_7fadb7a5de17; mw925b938a_fe73_4664_ba6f_e72e57780891, Rate Law: mw736e4a7b_4a25_4d32_b96b_b088e3bd41e7*mw925b938a_fe73_4664_ba6f_e72e57780891
mw1decb177_5075_41f3_a348_ca13b8f4497e=0.001 Reaction: mwa455ec7e_1a12_4659_95a2_a5695d09ca60 => mw19122f7d_f92e_4dc0_922f_6b681db65b0b + mwb2366216_0b3c_4f28_8303_fec92c68dd57; mwa455ec7e_1a12_4659_95a2_a5695d09ca60, Rate Law: mw1decb177_5075_41f3_a348_ca13b8f4497e*mwa455ec7e_1a12_4659_95a2_a5695d09ca60
mwe3e5abe4_9f92_43eb_92e4_cea771f5bf14=0.27 Reaction: mwa7e3103a_6394_472c_b0f4_8ed527f68604 => mwcc894c94_0ddf_42cc_913e_cdcc4d471d94 + mwd087f76b_65dc_47f1_ba21_c43774457686; mwa7e3103a_6394_472c_b0f4_8ed527f68604, Rate Law: mwe3e5abe4_9f92_43eb_92e4_cea771f5bf14*mwa7e3103a_6394_472c_b0f4_8ed527f68604
mw6d852e8c_c64a_4926_80c4_781a9c04b20e=0.001; mw4d614bfc_3e20_450e_8890_6326afd0a0d7=0.001 Reaction: mw9b937ca3_0d82_46d5_8f5a_0f9701002797 => mw62bf5275_ce02_4e86_b3b6_3f87a335e1de + mw11a8b702_b8ac_4513_b4aa_063e51089812; mw9b937ca3_0d82_46d5_8f5a_0f9701002797, mw62bf5275_ce02_4e86_b3b6_3f87a335e1de, mw11a8b702_b8ac_4513_b4aa_063e51089812, Rate Law: mw6d852e8c_c64a_4926_80c4_781a9c04b20e*mw9b937ca3_0d82_46d5_8f5a_0f9701002797-mw4d614bfc_3e20_450e_8890_6326afd0a0d7*mw62bf5275_ce02_4e86_b3b6_3f87a335e1de*mw11a8b702_b8ac_4513_b4aa_063e51089812
mwe645e76e_bb00_4c22_b25e_a2e77a6aada2=0.5838 Reaction: mw5198d3c2_879c_4f0d_b4f8_cd40efe0b1cf => mwa98802cb_c977_4fe0_9e67_5000904c2c36; mw5198d3c2_879c_4f0d_b4f8_cd40efe0b1cf, Rate Law: mwe645e76e_bb00_4c22_b25e_a2e77a6aada2*mw5198d3c2_879c_4f0d_b4f8_cd40efe0b1cf
mwa18578d7_236f_4939_baca_52259e38fe15=0.1; mwe879a9ac_4b8d_4c9a_a157_a3751761cf63=3.0 Reaction: mwa98802cb_c977_4fe0_9e67_5000904c2c36 + mwf430a579_ecbf_48ba_80c2_06e455808f2a => mw504578d8_96c3_471f_8a7e_8c14e7535d3d; mwa98802cb_c977_4fe0_9e67_5000904c2c36, mwf430a579_ecbf_48ba_80c2_06e455808f2a, mw504578d8_96c3_471f_8a7e_8c14e7535d3d, Rate Law: mwe879a9ac_4b8d_4c9a_a157_a3751761cf63*mwa98802cb_c977_4fe0_9e67_5000904c2c36*mwf430a579_ecbf_48ba_80c2_06e455808f2a-mwa18578d7_236f_4939_baca_52259e38fe15*mw504578d8_96c3_471f_8a7e_8c14e7535d3d
mw26164d03_adda_4a21_b5ac_59e1d5a8d8ab=0.003 Reaction: mwdc34472c_a6f9_4002_951d_e0e8da64eb42 => mw13abe2a6_9905_40e5_8c23_3fc8834b572a + mwcea1f1c1_2f85_4af1_98ea_ef14cf580c09; mwdc34472c_a6f9_4002_951d_e0e8da64eb42, Rate Law: mw26164d03_adda_4a21_b5ac_59e1d5a8d8ab*mwdc34472c_a6f9_4002_951d_e0e8da64eb42
mwf9c81339_e73a_45b5_a714_0854b718d44f=0.5; mw587125c7_6092_4627_9cdd_2415b77a8307=0.005 Reaction: mwf8cc7834_bf4f_4ccd_8235_d0890badf0f6 + mw19122f7d_f92e_4dc0_922f_6b681db65b0b => mw481cd12b_61ba_44e5_93bf_8b88c6c4a4e7; mwf8cc7834_bf4f_4ccd_8235_d0890badf0f6, mw19122f7d_f92e_4dc0_922f_6b681db65b0b, mw481cd12b_61ba_44e5_93bf_8b88c6c4a4e7, Rate Law: mwf9c81339_e73a_45b5_a714_0854b718d44f*mwf8cc7834_bf4f_4ccd_8235_d0890badf0f6*mw19122f7d_f92e_4dc0_922f_6b681db65b0b-mw587125c7_6092_4627_9cdd_2415b77a8307*mw481cd12b_61ba_44e5_93bf_8b88c6c4a4e7
mwc4824ff0_2b51_4d66_ad48_1145f670a6e1=3.114; mw0f1d282f_1c6b_455c_8254_3760632c6ecc=0.2 Reaction: mwa0349407_8187_48fc_9e94_5698ccc4e06d + mwe57c3282_5935_405c_8c0b_7fadb7a5de17 => mwf9999977_6f0e_4e35_9b73_75587f3448e9; mwa0349407_8187_48fc_9e94_5698ccc4e06d, mwe57c3282_5935_405c_8c0b_7fadb7a5de17, mwf9999977_6f0e_4e35_9b73_75587f3448e9, Rate Law: mwc4824ff0_2b51_4d66_ad48_1145f670a6e1*mwa0349407_8187_48fc_9e94_5698ccc4e06d*mwe57c3282_5935_405c_8c0b_7fadb7a5de17-mw0f1d282f_1c6b_455c_8254_3760632c6ecc*mwf9999977_6f0e_4e35_9b73_75587f3448e9
mw7aba6db3_c7ec_4192_bb5e_0ac4b466c1a5=0.005 Reaction: mw8c85ff7f_6368_4b11_a2ed_ce83481b55e6 => mw960bddeb_e567_46dd_b2f3_ed5e6a5c7972 + mw0e1be972_fded_4bff_a93d_091ec942485f; mw8c85ff7f_6368_4b11_a2ed_ce83481b55e6, Rate Law: mw7aba6db3_c7ec_4192_bb5e_0ac4b466c1a5*mw8c85ff7f_6368_4b11_a2ed_ce83481b55e6
mw76d68ace_272d_4178_bba2_74dfdf260c70=5.0; mwe37b936f_7781_4a01_b59b_96bd7db0c49e=0.5 Reaction: mwbfcf6773_1915_432c_b1d2_1f246094cc74 + mwb6a9aa2c_62e7_410f_9c33_dbe36dfcc4af => mw341082a0_8017_4cc7_9d00_b1211a196072; mwbfcf6773_1915_432c_b1d2_1f246094cc74, mwb6a9aa2c_62e7_410f_9c33_dbe36dfcc4af, mw341082a0_8017_4cc7_9d00_b1211a196072, Rate Law: mw76d68ace_272d_4178_bba2_74dfdf260c70*mwbfcf6773_1915_432c_b1d2_1f246094cc74*mwb6a9aa2c_62e7_410f_9c33_dbe36dfcc4af-mwe37b936f_7781_4a01_b59b_96bd7db0c49e*mw341082a0_8017_4cc7_9d00_b1211a196072
mw3d564c3c_aa54_4c16_90be_662cfcbf8bc8=10.0; mw371642bb_3836_4ded_93a5_68fa9b464896=1.0 Reaction: mwd9462e5b_a272_4b66_ab66_fde9266b1a43 + mwe57c3282_5935_405c_8c0b_7fadb7a5de17 => mw925b938a_fe73_4664_ba6f_e72e57780891; mwd9462e5b_a272_4b66_ab66_fde9266b1a43, mwe57c3282_5935_405c_8c0b_7fadb7a5de17, mw925b938a_fe73_4664_ba6f_e72e57780891, Rate Law: mw3d564c3c_aa54_4c16_90be_662cfcbf8bc8*mwd9462e5b_a272_4b66_ab66_fde9266b1a43*mwe57c3282_5935_405c_8c0b_7fadb7a5de17-mw371642bb_3836_4ded_93a5_68fa9b464896*mw925b938a_fe73_4664_ba6f_e72e57780891
mwc6b3c76f_af7b_488c_8751_28f1d9ab90a1=0.001 Reaction: mw06b8aada_c92a_48eb_8ee7_af3778cfe62f => mw19122f7d_f92e_4dc0_922f_6b681db65b0b + mw1093b3af_1864_4ba3_a541_6009a9921282 + mwb2366216_0b3c_4f28_8303_fec92c68dd57 + mwa0349407_8187_48fc_9e94_5698ccc4e06d; mw06b8aada_c92a_48eb_8ee7_af3778cfe62f, Rate Law: mwc6b3c76f_af7b_488c_8751_28f1d9ab90a1*mw06b8aada_c92a_48eb_8ee7_af3778cfe62f
mwbc5340b6_06b7_4081_bd0c_e7a397f06a92=10.0; mw0df80c0e_c32b_4f90_99bd_e8f90e4c8109=0.045 Reaction: mwa98802cb_c977_4fe0_9e67_5000904c2c36 + mw1093b3af_1864_4ba3_a541_6009a9921282 => mwfbda4e09_0cbb_49bc_ae69_f88b7a79ed21; mwa98802cb_c977_4fe0_9e67_5000904c2c36, mw1093b3af_1864_4ba3_a541_6009a9921282, mwfbda4e09_0cbb_49bc_ae69_f88b7a79ed21, Rate Law: mwbc5340b6_06b7_4081_bd0c_e7a397f06a92*mwa98802cb_c977_4fe0_9e67_5000904c2c36*mw1093b3af_1864_4ba3_a541_6009a9921282-mw0df80c0e_c32b_4f90_99bd_e8f90e4c8109*mwfbda4e09_0cbb_49bc_ae69_f88b7a79ed21
mw23e16d40_acbb_4658_a336_be5d0b0dd86a=7.76 Reaction: mwdf82303e_323f_4c51_a858_56a59233cd98 => mw8f5a7b5c_ca4c_4a4c_85b1_e5d640c426bf + mw7cff9a0e_094d_498e_bf7f_7b162c61d63a; mwdf82303e_323f_4c51_a858_56a59233cd98, Rate Law: mw23e16d40_acbb_4658_a336_be5d0b0dd86a*mwdf82303e_323f_4c51_a858_56a59233cd98
mw8bff2fe0_b582_4020_8f05_83f14451b1c0=0.033; mwc40b3165_cc16_4f78_86b5_e34f2731dcbb=3.0 Reaction: mwf816df4c_4593_4d23_990f_0d7c15ddde5d + mwcc894c94_0ddf_42cc_913e_cdcc4d471d94 => mw6cb74b27_ffef_49bb_8ffb_622d552caa9e; mwf816df4c_4593_4d23_990f_0d7c15ddde5d, mwcc894c94_0ddf_42cc_913e_cdcc4d471d94, mw6cb74b27_ffef_49bb_8ffb_622d552caa9e, Rate Law: mwc40b3165_cc16_4f78_86b5_e34f2731dcbb*mwf816df4c_4593_4d23_990f_0d7c15ddde5d*mwcc894c94_0ddf_42cc_913e_cdcc4d471d94-mw8bff2fe0_b582_4020_8f05_83f14451b1c0*mw6cb74b27_ffef_49bb_8ffb_622d552caa9e
mwa137184a_0eb0_4bcb_971c_8e19231b2c07=0.001 Reaction: mw1d5948e7_5504_4224_9d71_227911b4f1ee => mw19122f7d_f92e_4dc0_922f_6b681db65b0b + mw1093b3af_1864_4ba3_a541_6009a9921282 + mwb2366216_0b3c_4f28_8303_fec92c68dd57; mw1d5948e7_5504_4224_9d71_227911b4f1ee, Rate Law: mwa137184a_0eb0_4bcb_971c_8e19231b2c07*mw1d5948e7_5504_4224_9d71_227911b4f1ee
mw1ddaf9f4_dcab_4dc2_a6fa_5ce85b9d7a3a=0.0426 Reaction: mw5babe3d5_a9af_4dfd_ac01_35474ef64af2 => mwd784228d_0cb5_468a_ac70_02d8f04b3d9c + mwbfcf6773_1915_432c_b1d2_1f246094cc74 + mwa0349407_8187_48fc_9e94_5698ccc4e06d + mwf430a579_ecbf_48ba_80c2_06e455808f2a + mw31ac308f_da36_4f73_830f_67f3e5b945d9; mw5babe3d5_a9af_4dfd_ac01_35474ef64af2, Rate Law: mw1ddaf9f4_dcab_4dc2_a6fa_5ce85b9d7a3a*mw5babe3d5_a9af_4dfd_ac01_35474ef64af2

States:

Name Description
mw2ba1db9a 4483 44fa a3a2 b4a5ea66898c [Phosphatidylinositol 3-kinase regulatory subunit beta; Phosphatidylinositol 3-kinase catalytic subunit type 3]
mw925b938a fe73 4664 ba6f e72e57780891 [Epidermal growth factor receptor; Pro-epidermal growth factor; Nuclear receptor subfamily 0 group B member 2; Growth factor receptor-bound protein 2; phosphorylated]
mwa0acc0ac 5fac 4a42 a3be e36db44994b0 [Dual specificity mitogen-activated protein kinase kinase 1; Dual specificity protein phosphatase 3]
mw960bddeb e567 46dd b2f3 ed5e6a5c7972 [Signal transducer and activator of transcription 3]
mw19a33ad5 5ba4 46c7 84eb c1287f02bcd5 [RAF proto-oncogene serine/threonine-protein kinase; Dual specificity protein phosphatase 1]
mw0facb8f2 95cf 4ddf a959 b24ba64f320b [Signal transducer and activator of transcription 3; Putative uncharacterized protein PTEN2; phosphorylated]
mwb1bc2058 e6d8 4680 9e6c d27bb366cde0 [Epidermal growth factor receptor; Pro-epidermal growth factor; SHC-transforming protein 2; Growth factor receptor-bound protein 2; Son of sevenless homolog 1; E3 ubiquitin-protein ligase CBL; phosphorylated]
mwf9999977 6f0e 4e35 9b73 75587f3448e9 [SHC-transforming protein 2; Nuclear receptor subfamily 0 group B member 2; phosphorylated]
mwd784228d 0cb5 468a ac70 02d8f04b3d9c [Mitogen-activated protein kinase 1; phosphorylated]
mw62bf5275 ce02 4e86 b3b6 3f87a335e1de [RAC-beta serine/threonine-protein kinase; messenger RNA]
mw78e207c4 4faf 4b48 8e22 1ee666e9cc4c [Phosphatidylinositol 3-kinase catalytic subunit type 3; Phosphatidylinositol 3-kinase regulatory subunit beta; phosphorylated]
mwf430a579 ecbf 48ba 80c2 06e455808f2a [Growth factor receptor-bound protein 2]
mwf8cc7834 bf4f 4ccd 8235 d0890badf0f6 [Epidermal growth factor receptor; Pro-epidermal growth factor; Growth factor receptor-bound protein 2; Son of sevenless homolog 1; phosphorylated]
mwbd6bb050 89bd 41df 8cea d2e1fb77bafe [Phosphatidylinositol 3-kinase catalytic subunit type 3; Phosphatidylinositol 3-kinase regulatory subunit beta; Phosphatidylinositol 3,4,5-trisphosphate 5-phosphatase 2; phosphorylated]
mwcc894c94 0ddf 42cc 913e cdcc4d471d94 [Mitogen-activated protein kinase 1; phosphorylated]
mw341082a0 8017 4cc7 9d00 b1211a196072 [phosphorylated; Epidermal growth factor receptor; Signal transducer and activator of transcription 3; Pro-epidermal growth factor]
mwcef73e0e d195 4077 ae71 723664ee1602 [RAC-beta serine/threonine-protein kinase]
mw954e8fcb ac0a 459d 8878 f19080208a17 [Epidermal growth factor receptor; Pro-epidermal growth factor; Nuclear receptor subfamily 0 group B member 2; phosphorylated]
mw504578d8 96c3 471f 8a7e 8c14e7535d3d [Epidermal growth factor receptor; Pro-epidermal growth factor; SHC-transforming protein 2; Growth factor receptor-bound protein 2]
mw481cd12b 61ba 44e5 93bf 8b88c6c4a4e7 [Epidermal growth factor receptor; Pro-epidermal growth factor; Growth factor receptor-bound protein 2; Son of sevenless homolog 1; E3 ubiquitin-protein ligase CBL]
mw06b8aada c92a 48eb 8ee7 af3778cfe62f [Epidermal growth factor receptor; Pro-epidermal growth factor; SHC-transforming protein 2; Growth factor receptor-bound protein 2; Son of sevenless homolog 1; Epsin-1]
mw1093b3af 1864 4ba3 a541 6009a9921282 [Growth factor receptor-bound protein 2; Son of sevenless homolog 1]
mwa455ec7e 1a12 4659 95a2 a5695d09ca60 [Epidermal growth factor receptor; Pro-epidermal growth factor; E3 ubiquitin-protein ligase CBL; Epsin-1]
mwfc4a9c3d 3ebb 4033 8b7d f4d7613d2078 [Phosphatidylinositol 3,4,5-trisphosphate 5-phosphatase 2]
mwb2366216 0b3c 4f28 8303 fec92c68dd57 [Epsin-1]
mwbaaeb210 4806 4076 9d60 219f4ed945b6 [Dual specificity protein phosphatase 1]
mwb561d9f3 a9ed 4bdb 8d40 87be5cc3237a [5497157]
mw8c85ff7f 6368 4b11 a2ed ce83481b55e6 [Signal transducer and activator of transcription 3; Putative uncharacterized protein PTEN2; phosphorylated]
mwd087f76b 65dc 47f1 ba21 c43774457686 [Dual specificity protein phosphatase 6]
mwfbda4e09 0cbb 49bc ae69 f88b7a79ed21 [Epidermal growth factor receptor; Pro-epidermal growth factor; SHC-transforming protein 2; Growth factor receptor-bound protein 2; Son of sevenless homolog 1; phosphorylated]
mw7cff9a0e 094d 498e bf7f 7b162c61d63a [GTPase HRas; Ras GTPase-activating protein 1]
mwd7f41594 8377 4e2e 9528 45d5a82ffdb4 [24755492]
mwa0349407 8187 48fc 9e94 5698ccc4e06d [SHC-transforming protein 2; phosphorylated]
mw0e1be972 fded 4bff a93d 091ec942485f [Putative uncharacterized protein PTEN2]
mw5198d3c2 879c 4f0d b4f8 cd40efe0b1cf [Epidermal growth factor receptor; Pro-epidermal growth factor; SHC-transforming protein 2; phosphorylated]
mw9dcaa655 a755 426e a3fa 1ad7c3c45575 [Son of sevenless homolog 1]
mwe57c3282 5935 405c 8c0b 7fadb7a5de17 [Nuclear receptor subfamily 0 group B member 2]
mw19122f7d f92e 4dc0 922f 6b681db65b0b [E3 ubiquitin-protein ligase CBL]
mw7033dfd6 53c5 433b a132 f8cb34dea20f [Phosphatidylinositol 3-kinase catalytic subunit type 3; Phosphatidylinositol 3-kinase regulatory subunit beta; Phosphatidylinositol 3,4,5-trisphosphate 5-phosphatase 2]
mw8f5a7b5c ca4c 4a4c 85b1 e5d640c426bf [GDP; GTPase HRas]
mwcea1f1c1 2f85 4af1 98ea ef14cf580c09 [Phosphatidylinositol 3,4,5-trisphosphate 3-phosphatase and dual-specificity protein phosphatase PTEN]
mwdc34472c a6f9 4002 951d e0e8da64eb42 [Signal transducer and activator of transcription 3; Phosphatidylinositol 3,4,5-trisphosphate 3-phosphatase and dual-specificity protein phosphatase PTEN; phosphorylated]
mw9686f53e d343 45fd b441 9c992219546a [Signal transducer and activator of transcription 3; phosphorylated]
mwb6a9aa2c 62e7 410f 9c33 dbe36dfcc4af [Signal transducer and activator of transcription 3; phosphorylated]
mwd9462e5b a272 4b66 ab66 fde9266b1a43 [Epidermal growth factor receptor; Pro-epidermal growth factor; Growth factor receptor-bound protein 2; phosphorylated]
mw6cb74b27 ffef 49bb 8ffb 622d552caa9e [Dual specificity mitogen-activated protein kinase kinase 1; Mitogen-activated protein kinase 1; phosphorylated]
mwf9e2a044 7774 400b a74e a111b4a21f30 [Dual specificity protein phosphatase 4]

Observables: none

Mathematical modeling representing Tcell actiavtion regualtion by TCR, IL2 and other players.

T-cell activation is a crucial step in mounting of the immune response. The dynamics of T-cell receptor (TCR) specific recognition of peptide presented by major histocompatibility complex (MHC) molecule decides the fate of the T cell. Several biochemical interactions interfere resulting in a highly complex mechanism that would be difficult to understand without computer help. The aim of the present study was to define a mathematical model in order to approach the kinetics of monoclonal T-cell-specific activation. The reaction scheme was first described and the model was tested using experimental parameters from the published data. Simulations were concordant with experimental data showing proportional decrease of membrane TCR and production of interleukin-2 (IL-2). Agonist and antagonist peptides induce different levels of intracellular signal that could make the yes or no decision for entry to cell cycle. Different conditions (peptide concentrations, initial TCR density and exogenous IL-2 levels) can be tested. Several parameters are missing for parameters estimation and adjustment before it could be adapted for a polyclonal T-cell reaction model. However, the model should be of interest in setting experiments, simulation of clinical responses and optimization of preventive or therapeutic immunotherapy. link: http://identifiers.org/pubmed/18271720

Parameters: none

States: none

Observables: none

BIOMD0000000254 @ v0.0.1

This a model from the article: How yeast cells synchronize their glycolytic oscillations: a perturbation analytic tre…

Of all the lifeforms that obtain their energy from glycolysis, yeast cells are among the most basic. Under certain conditions the concentrations of the glycolytic intermediates in yeast cells can oscillate. Individual yeast cells in a suspension can synchronize their oscillations to get in phase with each other. Although the glycolytic oscillations originate in the upper part of the glycolytic chain, the signaling agent in this synchronization appears to be acetaldehyde, a membrane-permeating metabolite at the bottom of the anaerobic part of the glycolytic chain. Here we address the issue of how a metabolite remote from the pacemaking origin of the oscillation may nevertheless control the synchronization. We present a quantitative model for glycolytic oscillations and their synchronization in terms of chemical kinetics. We show that, in essence, the common acetaldehyde concentration can be modeled as a small perturbation on the "pacemaker" whose effect on the period of the oscillations of cells in the same suspension is indeed such that a synchronization develops. link: http://identifiers.org/pubmed/10692299

Parameters:

Name Description
k1 = 0.02; km = 13.0; kp = 6.0; epsilon = 0.01 Reaction: T2 = (2*k1*G2*T2-kp*T2/(km+T2))-epsilon*(T2-T1), Rate Law: (2*k1*G2*T2-kp*T2/(km+T2))-epsilon*(T2-T1)
k1 = 0.02; V_in = 0.36 Reaction: G1 = V_in-k1*G1*T1, Rate Law: V_in-k1*G1*T1

States:

Name Description
G1 [glucose; C00293]
T1 [ATP; ATP]
T2 [ATP; ATP]
G2 [glucose; C00293]

Observables: none

BIOMD0000000058 @ v0.0.1

The model reproduces the same amplitude antiphase calcium oscillations of coupled cells depicted in Figure 5B of the pub…

In many cell types, asynchronous or synchronous oscillations in the concentration of intracellular free calcium occur in adjacent cells that are coupled by gap junctions. Such oscillations are believed to underlie oscillatory intercellular calcium waves in some cell types, and thus it is important to understand how they occur and are modified by intercellular coupling. Using a previous model of intracellular calcium oscillations in pancreatic acinar cells, this article explores the effects of coupling two cells with a simple linear diffusion term. Depending on the concentration of a signal molecule, inositol (1,4,5)-trisphosphate, coupling two identical cells by diffusion can give rise to synchronized in-phase oscillations, as well as different-amplitude in-phase oscillations and same-amplitude antiphase oscillations. Coupling two nonidentical cells leads to more complex behaviors such as cascades of period doubling and multiply periodic solutions. This study is a first step towards understanding the role and significance of the diffusion of calcium through gap junctions in the coordination of oscillatory calcium waves in a variety of cell types. (c) 2001 American Institute of Physics. link: http://identifiers.org/pubmed/12779457

Parameters:

Name Description
D=0.01 Reaction: c2 => c1, Rate Law: compartment*D*(c2-c1)
Phi_minus1_c1 = 0.0; Phi1_c1 = 0.0; Phi2_c1 = 0.0; p=0.2778 Reaction: h1 =>, Rate Law: compartment*Phi1_c1*Phi2_c1*h1*p/(Phi1_c1*p+Phi_minus1_c1)
Phi3_c1 = 0.0 Reaction: => h1, Rate Law: compartment*Phi3_c1*(1-h1)
kf=28.0; p=0.2778; Phi1_c2 = 0.0; Phi_minus1_c2 = 0.0 Reaction: => c2; h2, Rate Law: compartment*kf*(p*h2*Phi1_c2/(Phi1_c2*p+Phi_minus1_c2))^4
Phi2_c2 = 0.0; p=0.2778; Phi1_c2 = 0.0; Phi_minus1_c2 = 0.0 Reaction: h2 =>, Rate Law: compartment*Phi1_c2*Phi2_c2*h2*p/(Phi1_c2*p+Phi_minus1_c2)
Phi_minus1_c1 = 0.0; kf=28.0; Phi1_c1 = 0.0; p=0.2778 Reaction: => c1; h1, Rate Law: compartment*kf*(p*h1*Phi1_c1/(Phi1_c1*p+Phi_minus1_c1))^4
Vp=1.2; Kp=0.18 Reaction: c1 =>, Rate Law: compartment*Vp*c1^2/(Kp^2+c1^2)
Phi3_c2 = 0.0 Reaction: => h2, Rate Law: compartment*Phi3_c2*(1-h2)
Jleak=0.2 Reaction: => c1, Rate Law: compartment*Jleak

States:

Name Description
c2 [calcium(2+); Calcium cation]
c1 [calcium(2+); Calcium cation]
h1 [IPR000493]
h2 [IPR000493]

Observables: none

MODEL1172200168 @ v0.0.1

This a model from the article: A mathematical model of the regulation system of the secretion of glucocorticoids Liu…

We propose a mathematical model for the regulation system of the secretion of glucocorticoids and determined the coefficients in the system of ordinary differential equations. Some results are calculated which agree with the experimental results. link: http://identifiers.org/doi/10.1007/BF00386598

Parameters: none

States: none

Observables: none

BIOMD0000000175 @ v0.0.1

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 T…

Deregulation of ErbB signaling plays a key role in the progression of multiple human cancers. To help understand ErbB signaling quantitatively, in this work we combine traditional experiments with computational modeling, building a model that describes how stimulation of all four ErbB receptors with epidermal growth factor (EGF) and heregulin (HRG) leads to activation of two critical downstream proteins, extracellular-signal-regulated kinase (ERK) and Akt. Model analysis and experimental validation show that (i) ErbB2 overexpression, which occurs in approximately 25% of all breast cancers, transforms transient EGF-induced signaling into sustained signaling, (ii) HRG-induced ERK activity is much more robust to the ERK cascade inhibitor U0126 than EGF-induced ERK activity, and (iii) phosphoinositol-3 kinase is a major regulator of post-peak but not pre-peak EGF-induced ERK activity. Sensitivity analysis leads to the hypothesis that ERK activation is robust to parameter perturbation at high ligand doses, while Akt activation is not. link: http://identifiers.org/pubmed/18004277

Parameters:

Name Description
kf15 = 1.3565; kPTP15 = 60.2628; KmPY = 486.1398; VmaxPY = 223.8776 Reaction: E44 => E44P; SigT, Rate Law: membrane*((kf15*E44-VmaxPY*E44P/(KmPY+E44P))-kPTP15*SigT*E44P)
kPTP12 = 11.4211; kf12 = 1.8012; KmPY = 486.1398; VmaxPY = 223.8776 Reaction: E23 => E23P; SigT, Rate Law: membrane*((kf12*E23-VmaxPY*E23P/(KmPY+E23P))-kPTP12*SigT*E23P)
kon20 = 0.0478; koff20 = 0.6761; eps = 1.0E-16 Reaction: E12P + S => E12S + SigS; SigSP, SigSP_G, Rate Law: membrane*(6*kon20*E12P*S-koff20*SigS/(SigS+SigSP+SigSP_G+eps)*E12S)
kon25 = 0.0995; eps = 1.0E-16; koff25 = 2.225 Reaction: E23P + R => E23R + SigR; SigRP, Rate Law: membrane*(2*kon25*E23P*R-koff25*SigR/(SigR+SigRP+eps)*E23R)
Kmr54 = 336.183; Kmf54 = 457.9645; kf54 = 0.0538; Vmaxr54 = 588.2671 Reaction: O => OP; ERKstar, Rate Law: membrane*(kf54*O*ERKstar/(Kmf54+O)-Vmaxr54*OP/(Kmr54+OP))
kon36 = 0.0043; koff36 = 1.2567 Reaction: E44P + I => E44I + SigI, Rate Law: membrane*(2*kon36*E44P*I-koff36*E44I)
koff57 = 0.4526; kon57 = 0.0039 Reaction: G + P3_A => SigA_G, Rate Law: membrane*(kon57*P3_A*G-koff57*SigA_G)
koff24 = 4.4226; kon24 = 0.005 Reaction: E23P + I => E23I + SigI, Rate Law: membrane*(3*kon24*E23P*I-koff24*E23I)
kcat94 = 0.9966 Reaction: ERKstar_ERKpase => pERK + ERKpase, Rate Law: membrane*kcat94*ERKstar_ERKpase
koff60 = 4.9981; kon60 = 1.1994E-4 Reaction: SigG_A + O => A_SigG_O + SigO, Rate Law: membrane*(kon60*SigG_A*O-koff60*A_SigG_O)
kf85 = 6.7591; Kmr85 = 290.7667; Kmf85 = 179.6486; Vmaxr85 = 369.2261 Reaction: H_E4 => H_E4_PT; ERKstar, Rate Law: membrane*(kf85*H_E4*ERKstar/(Kmf85+H_E4)-Vmaxr85*H_E4_PT/(Kmr85+H_E4_PT))
kon6 = 0.2283; koff6 = 2.6619 Reaction: H_E3 + E2 => E23, Rate Law: membrane*(kon6*H_E3*E2-koff6*E23)
koff66 = 2.2988; kon66 = 1.9264E-4; eps = 1.0E-16 Reaction: E13P + S => E13S + SigS; SigSP, SigSP_G, Rate Law: membrane*(5*kon66*E13P*S-koff66*SigS/(SigS+SigSP+SigSP_G+eps)*E13S)
kcat96 = 19.9851 Reaction: pERK_ERKpase => ERK + ERKpase, Rate Law: membrane*kcat96*pERK_ERKpase
kon71 = 0.0078; koff71 = 2.2988 Reaction: E14P + I => E14I + SigI, Rate Law: membrane*(1*kon71*E14P*I-koff71*E14I)
eps = 1.0E-16; kon21 = 0.0114; koff21 = 4.7291 Reaction: E12P + R => E12R + SigR; SigRP, Rate Law: membrane*(2*kon21*E12P*R-koff21*SigR/(SigR+SigRP+eps)*E12R)
kon65 = 0.0123; koff65 = 0.1185; eps = 1.0E-16 Reaction: E13P + G => E13G + SigG; SigG_A, SigG_O, A_SigG_O, Rate Law: membrane*(4*kon65*E13P*G-koff65*SigG/(SigG+SigG_A+SigG_O+A_SigG_O+eps)*E13G)
koff40 = 3.1051; kon40 = 0.0191 Reaction: SigG + O => SigG_O + SigO, Rate Law: membrane*(kon40*SigG*O-koff40*SigG_O)
kon26 = 0.0355; koff26 = 0.0103; eps = 1.0E-16 Reaction: E34P + G => E34G + SigG; SigG_A, SigG_O, A_SigG_O, Rate Law: membrane*(4*kon26*E34P*G-koff26*SigG/(SigG+SigG_A+SigG_O+A_SigG_O+eps)*E34G)
kf14 = 6.1726; KmPY = 486.1398; kPTP14 = 57.7506; VmaxPY = 223.8776 Reaction: E24 => E24P; SigT, Rate Law: membrane*((kf14*E24-VmaxPY*E24P/(KmPY+E24P))-kPTP14*SigT*E24P)
kon74 = 0.0133; koff74 = 1.2496 Reaction: E12P + T => E12T + SigT, Rate Law: membrane*(3*kon74*E12P*T-koff74*E12T)
kon31 = 0.0032; koff31 = 1.2204; eps = 1.0E-16 Reaction: E24P + S => E24S + SigS; SigSP, SigSP_G, Rate Law: membrane*(4*kon31*E24P*S-koff31*SigS/(SigS+SigSP+SigSP_G+eps)*E24S)
kon69 = 0.0084; eps = 1.0E-16; koff69 = 3.97 Reaction: E14P + G => E14G + SigG; SigG_A, SigG_O, A_SigG_O, Rate Law: membrane*(4*kon69*E14P*G-koff69*SigG/(SigG+SigG_A+SigG_O+A_SigG_O+eps)*E14G)
koff77 = 1.2237; kon77 = 0.0101 Reaction: E24P + T => E24T + SigT, Rate Law: membrane*(2*kon77*E24P*T-koff77*E24T)
kf13 = 0.8875; KmPY = 486.1398; VmaxPY = 223.8776; kPTP13 = 55.2104 Reaction: E34 => E34P; SigT, Rate Law: membrane*((kf13*E34-VmaxPY*E34P/(KmPY+E34P))-kPTP13*SigT*E34P)
koff80 = 2.9373; kon80 = 2.0E-4 Reaction: E14P + T => E14T + SigT, Rate Law: membrane*(3*kon80*E14P*T-koff80*E14T)
koff79 = 1.1852; kon79 = 0.0078 Reaction: E13P + T => E13T + SigT, Rate Law: membrane*(3*kon79*E13P*T-koff79*E13T)
kon9 = 2.2868; koff9 = 5.5425 Reaction: H_E4 + H_E4 => E44, Rate Law: membrane*(kon9*H_E4*H_E4-koff9*E44)
koff19 = 2.3361; eps = 1.0E-16; kon19 = 0.0896 Reaction: E12P + G => E12G + SigG; SigG_A, SigG_O, A_SigG_O, Rate Law: membrane*(3*kon19*E12P*G-koff19*SigG/(SigG+SigG_A+SigG_O+A_SigG_O+eps)*E12G)
kon42 = 0.0023; eps = 1.0E-16; koff42 = 3.5195 Reaction: SigSP + G => SigSP_G + SigG; SigG_A, SigG_O, A_SigG_O, Rate Law: membrane*(kon42*SigSP*G-koff42*SigSP_G*SigG/(SigG+SigG_A+SigG_O+A_SigG_O+eps))
kon7 = 1.0606; koff7 = 8.0557 Reaction: H_E3 + H_E4 => E34, Rate Law: membrane*(kon7*H_E3*H_E4-koff7*E34)
koff35 = 1.8696; eps = 1.0E-16; kon35 = 0.0602 Reaction: E44P + S => E44S + SigS; SigSP, SigSP_G, Rate Law: membrane*(4*kon35*E44P*S-koff35*SigS/(SigS+SigSP+SigSP_G+eps)*E44S)
kon58 = 0.0215; koff58 = 6.3059 Reaction: SigA_G + O => SigA_G_O + SigO, Rate Law: membrane*(kon58*SigA_G*O-koff58*SigA_G_O)
kdeg = 0.0259 Reaction: E11G + SigG => G, Rate Law: membrane*kdeg*E11G
kon33 = 0.0335; eps = 1.0E-16; koff33 = 1.2817 Reaction: E24P + R => E24R + SigR; SigRP, Rate Law: membrane*(2*kon33*E24P*R-koff33*SigR/(SigR+SigRP+eps)*E24R)
koff5 = 4.3985; kon5 = 2.5427 Reaction: E_E1 + E2 => E12, Rate Law: membrane*(kon5*E_E1*E2-koff5*E12)
koff78 = 0.2007; kon78 = 0.0076 Reaction: E44P + T => E44T + SigT, Rate Law: membrane*(2*kon78*E44P*T-koff78*E44T)
eps = 1.0E-16; koff22 = 3.6962; kon22 = 7.0E-4 Reaction: E23P + G => E23G + SigG; SigG_A, SigG_O, A_SigG_O, Rate Law: membrane*(3*kon22*E23P*G-koff22*SigG/(SigG+SigG_A+SigG_O+A_SigG_O+eps)*E23G)
kon32 = 9.0E-4; koff32 = 3.8752 Reaction: E24P + I => E24I + SigI, Rate Law: membrane*(1*kon32*E24P*I-koff32*E24I)
kon72 = 0.0355; koff72 = 0.907; eps = 1.0E-16 Reaction: E14P + R => E14R + SigR; SigRP, Rate Law: membrane*(2*kon72*E14P*R-koff72*SigR/(SigR+SigRP+eps)*E14R)
kon18 = 0.0117; eps = 1.0E-16; koff18 = 2.2768 Reaction: E11P + R => E11R + SigR; SigRP, Rate Law: membrane*(2*kon18*E11P*R-koff18*SigR/(SigR+SigRP+eps)*E11R)
kon4 = 0.5005; koff4 = 0.1717 Reaction: E_E1 + E_E1 => E11, Rate Law: membrane*(kon4*E_E1*E_E1-koff4*E11)
kon23 = 0.0138; eps = 1.0E-16; koff23 = 2.3619 Reaction: E23P + S => E23S + SigS; SigSP, SigSP_G, Rate Law: membrane*(3*kon23*E23P*S-koff23*SigS/(SigS+SigSP+SigSP_G+eps)*E23S)
KmPY = 486.1398; kf10 = 0.6496; kPTP10 = 29.8531; VmaxPY = 223.8776 Reaction: E11 => E11P; SigT, Rate Law: membrane*((kf10*E11-VmaxPY*E11P/(KmPY+E11P))-kPTP10*SigT*E11P)
kPTP11 = 78.204; KmPY = 486.1398; kf11 = 0.3721; VmaxPY = 223.8776 Reaction: E12 => E12P; SigT, Rate Law: membrane*((kf11*E12-VmaxPY*E12P/(KmPY+E12P))-kPTP11*SigT*E12P)
eps = 1.0E-16; kon41 = 0.0051; koff41 = 7.0487 Reaction: SigG + A => SigG_A + SigA; SigAP, SigAP_S, SigAP_R, SigAP_I, SigAP_T, Rate Law: membrane*(kon41*SigG*A-koff41*SigG_A*SigA/(eps+SigA+SigAP+SigAP_S+SigAP_R+SigAP_I+SigAP_T))
koff45 = 3.9967; eps = 1.0E-16; kon45 = 0.0028 Reaction: SigAP + R => SigAP_R + SigR; SigRP, Rate Law: membrane*(2*kon45*SigAP*R-koff45*SigAP_R*SigR/(SigR+SigRP+eps))
kf38 = 279.9929; kPTP38 = 83.4465; KmPY = 486.1398; VmaxPY = 223.8776 Reaction: SigS => SigSP; E11P, E12P, E23P, E24P, E34P, E44P, E13P, E14P, SigT, Rate Law: membrane*((kf38*SigS*(E11P+E12P+E23P+E24P+E34P+E44P+E13P+E14P)-VmaxPY*SigSP/(KmPY+SigSP))-kPTP38*SigT*SigSP)
koff37 = 0.4059; kon37 = 0.0791; eps = 1.0E-16 Reaction: E44P + R => E44R + SigR; SigRP, Rate Law: membrane*(2*kon37*E44P*R-koff37*SigR/(SigR+SigRP+eps)*E44R)
koff27 = 1.8922; eps = 1.0E-16; kon27 = 0.0201 Reaction: E34P + S => E34S + SigS; SigSP, SigSP_G, Rate Law: membrane*(3*kon27*E34P*S-koff27*SigS/(SigS+SigSP+SigSP_G+eps)*E34S)
koff30 = 4.9936; kon30 = 0.002; eps = 1.0E-16 Reaction: E24P + G => E24G + SigG; SigG_A, SigG_O, A_SigG_O, Rate Law: membrane*(3*kon30*E24P*G-koff30*SigG/(SigG+SigG_A+SigG_O+A_SigG_O+eps)*E24G)
kon70 = 0.0116; eps = 1.0E-16; koff70 = 2.6069 Reaction: E14P + S => E14S + SigS; SigSP, SigSP_G, Rate Law: membrane*(6*kon70*E14P*S-koff70*SigS/(SigS+SigSP+SigSP_G+eps)*E14S)
koff17 = 4.6874; eps = 1.0E-16; kon17 = 0.0049 Reaction: E11P + S => E11S + SigS; SigSP, SigSP_G, Rate Law: membrane*(8*kon17*E11P*S-koff17*SigS/(SigS+SigSP+SigSP_G+eps)*E11S)
eps = 1.0E-16; kon68 = 0.0045; koff68 = 2.8871 Reaction: E13P + R => E13R + SigR; SigRP, Rate Law: membrane*(2*kon68*E13P*R-koff68*SigR/(SigR+SigRP+eps)*E13R)
koff28 = 4.6432; kon28 = 0.0074 Reaction: E34P + I => E34I + SigI, Rate Law: membrane*(4*kon28*E34P*I-koff28*E34I)
kPTP63 = 7.4766; kf63 = 0.9297; KmPY = 486.1398; VmaxPY = 223.8776 Reaction: E13 => E13P; SigT, Rate Law: membrane*((kf63*E13-VmaxPY*E13P/(KmPY+E13P))-kPTP63*SigT*E13P)
kPTP64 = 48.6335; KmPY = 486.1398; kf64 = 1.2083; VmaxPY = 223.8776 Reaction: E14 => E14P; SigT, Rate Law: membrane*((kf64*E14-VmaxPY*E14P/(KmPY+E14P))-kPTP64*SigT*E14P)
kon46 = 0.0148; koff46 = 0.5194; eps = 1.0E-16 Reaction: P3 + A => P3_A + SigA; SigAP, SigAP_S, SigAP_R, SigAP_I, SigAP_T, Rate Law: membrane*(kon46*P3*A-koff46*P3_A*SigA/(eps+SigA+SigAP+SigAP_S+SigAP_R+SigAP_I+SigAP_T))
kon16 = 0.0097; eps = 1.0E-16; koff16 = 0.5737 Reaction: E11P + G => E11G + SigG; SigG_A, SigG_O, A_SigG_O, Rate Law: membrane*(4*kon16*E11P*G-koff16*SigG/(SigG+SigG_A+SigG_O+A_SigG_O+eps)*E11G)
koff43 = 0.5441; eps = 1.0E-16; kon43 = 0.0127 Reaction: SigAP + S => SigAP_S + SigS; SigSP, SigSP_G, Rate Law: membrane*(3*kon43*SigAP*S-koff43*SigAP_S*SigS/(SigS+SigSP+SigSP_G+eps))
kon67 = 6.6667E-5; koff67 = 1.6142 Reaction: E13P + I => E13I + SigI, Rate Law: membrane*(3*kon67*E13P*I-koff67*E13I)
kon73 = 0.0116; koff73 = 3.0048 Reaction: E11P + T => E11T + SigT, Rate Law: membrane*(4*kon73*E11P*T-koff73*E11T)

States:

Name Description
E34G E34-Grb2
T [176885; Tyrosine-protein phosphatase non-receptor type 1]
E34P E34_p
E11 [Epidermal growth factor receptor; Pro-epidermal growth factor]
E24P E24_p
E24 [Receptor tyrosine-protein kinase erbB-2; Pro-neuregulin-1, membrane-bound isoform; Receptor tyrosine-protein kinase erbB-4]
pERK ERKpase p_ERK-ERKpase
SigS Sum Shc
ERKstar ERKpase ERK*-ERKpase
E44P E44_p
E14 ErbB1-ErbB4
E14S E14-Shc
E14R E14-RasGAP
SigR Sum RasGAP
O [Son of sevenless homolog 1; 182530]
E11P [Phosphoprotein; Epidermal growth factor receptor]
H E4 [Pro-neuregulin-1, membrane-bound isoform; Receptor tyrosine-protein kinase erbB-4]
E13P ErbB1-ErbB3_p
E44 [Pro-neuregulin-1, membrane-bound isoform; Receptor tyrosine-protein kinase erbB-4]
ERKpase [602747; 600714; 602748; Dual specificity protein phosphatase 1; Dual specificity protein phosphatase 4; Dual specificity protein phosphatase 6]
E23R E23-RasGAP
norm Erk star normalized Erk*
A [GRB2-associated-binding protein 1; 604439]
E23 [Receptor tyrosine-protein kinase erbB-2; Pro-neuregulin-1, membrane-bound isoform; Receptor tyrosine-protein kinase erbB-3]
E34 [Pro-neuregulin-1, membrane-bound isoform; Receptor tyrosine-protein kinase erbB-4; Receptor tyrosine-protein kinase erbB-3]
E11G E11-Grb2
E23G E23-Grb2
E14P ErbB1-ErbB3_p
G [Growth factor receptor-bound protein 2; 108355]
E13R E13-RasGAP
SigI Sum PI-3K
S [SHC-transforming protein 2; 605217]
I [Phosphoinositide 3-kinase regulatory subunit 5; Phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit alpha isoform]
E13S E13-Shc
E11R E11-RasGAP
E11S E11-Shc
norm Akt star normalized Akt*
R [139150; Ras GTPase-activating protein 1; IPR001936]
E14G E14-Grb2
E12 [Epidermal growth factor receptor; Pro-epidermal growth factor; Receptor tyrosine-protein kinase erbB-2]
E13G E13-Grb2

Observables: none

&lt;notes xmlns=&quot;http://www.sbml.org/sbml/level2/version4&quot;&gt; &lt;body xmlns=&quot;http://www.w3.org/1…

The striatum, the input structure of the basal ganglia, is a major site of learning and memory for goal-directed actions and habit formation. Spiny projection neurons of the striatum integrate cortical, thalamic, and nigral inputs to learn associations, with cortico-striatal synaptic plasticity as a learning mechanism. Signaling molecules implicated in synaptic plasticity are altered in alcohol withdrawal, which may contribute to overly strong learning and increased alcohol seeking and consumption. To understand how interactions among signaling molecules produce synaptic plasticity, we implemented a mechanistic model of signaling pathways activated by dopamine D1 receptors, acetylcholine receptors, and glutamate. We use our novel, computationally efficient simulator, NeuroRD, to simulate stochastic interactions both within and between dendritic spines. Dopamine release during theta burst and 20-Hz stimulation was extrapolated from fast-scan cyclic voltammetry data collected in mouse striatal slices. Our results show that the combined activity of several key plasticity molecules correctly predicts the occurrence of either LTP, LTD, or no plasticity for numerous experimental protocols. To investigate spatial interactions, we stimulate two spines, either adjacent or separated on a 20-μm dendritic segment. Our results show that molecules underlying LTP exhibit spatial specificity, whereas 2-arachidonoylglycerol exhibits a spatially diffuse elevation. We also implement changes in NMDA receptors, adenylyl cyclase, and G protein signaling that have been measured following chronic alcohol treatment. Simulations under these conditions suggest that the molecular changes can predict changes in synaptic plasticity, thereby accounting for some aspects of alcohol use disorder. link: http://identifiers.org/pubmed/29602186

Parameters: none

States: none

Observables: none

Aphid Buchnera Hamiltonella multi-compartment metabolic model, FBA

Beneficial microorganisms associated with animals derive their nutritional requirements entirely from the animal host, but the impact of these microorganisms on host metabolism is largely unknown. The focus of this study was the experimentally tractable tripartite symbiosis between the pea aphid Acyrthosiphon pisum, its obligate intracellular bacterial symbiont Buchnera, and the facultative bacterium Hamiltonella which is localized primarily to the aphid hemolymph (blood). Metabolome experiments on, first, multiple aphid genotypes that naturally bear or lack Hamiltonella and, second, one aphid genotype from which Hamiltonella was experimentally eliminated revealed no significant effects of Hamiltonella on aphid metabolite profiles, indicating that Hamiltonella does not cause major reconfiguration of host metabolism. However, the titer of just one metabolite, 5-aminoimidazole-4-carboxamide ribonucleotide (AICAR), displayed near-significant enrichment in Hamiltonella-positive aphids in both metabolome experiments. AICAR is a byproduct of biosynthesis of the essential amino acid histidine in Buchnera and, hence, an index of histidine biosynthetic rates, suggesting that Buchnera-mediated histidine production is elevated in Hamiltonella-bearing aphids. Consistent with this prediction, aphids fed on [13C]histidine yielded a significantly elevated 12C/13C ratio of histidine in Hamiltonella-bearing aphids, indicative of increased (~25%) histidine synthesized de novo by Buchnera. However, in silico analysis predicted an increase of only 0.8% in Buchnera histidine synthesis in Hamiltonella-bearing aphids. We hypothesize that Hamiltonella imposes increased host demand for histidine, possibly for heightened immune-related functions. These results demonstrate that facultative bacteria can alter the dynamics of host metabolic interactions with co-occurring microorganisms, even when the overall metabolic homeostasis of the host is not substantially perturbed. link: http://identifiers.org/doi/10.1128/mBio.00402-20

Parameters: none

States: none

Observables: none

BIOMD0000000077 @ v0.0.1

# A mathematical model quantifying GnRH-induced LH secretion from gonadotropes by Blum et al (2000) This paper includes…

A mathematical model is developed to investigate the rate of release of luteinizing hormone (LH) from pituitary gonadotropes in response to short pulses of gonadotropin-releasing hormone (GnRH). The model includes binding of the hormone to its receptor, dimerization, interaction with a G protein, production of inositol 1,4, 5-trisphosphate, release of Ca(2+) from the endoplasmic reticulum, entrance of Ca(2+) into the cytosol via voltage-gated membrane channels, pumping of Ca(2+) out of the cytosol via membrane and endoplasmic reticulum pumps, and release of LH. Cytosolic Ca(2+) dynamics are simplified (i.e., oscillations are not included in the model), and it is assumed that there is only one pool of releasable LH. Despite these and other simplifications, the model explains the qualitative features of LH release in response to GnRH pulses of various durations and different concentrations in the presence and absence of external Ca(2+). link: http://identifiers.org/pubmed/10662710

Parameters:

Name Description
k1=4000.0; k2=200.0 Reaction: HRRH + GQ => E, Rate Law: cell*(k1*HRRH*GQ-k2*E)
alpha = 2.0 nmol; beta = 4.0 unit for beta Reaction: CHO = 0.001*alpha*IP3*(0.3+0.3*beta*time*exp(1-beta*time))/(1+0.001*alpha*IP3), Rate Law: missing
k2=5.0; k1=2500.0 Reaction: HR => HRRH, Rate Law: cell*(k1*HR^2-k2*HRRH)
k=2.0E7 Reaction: => IP3; E, Rate Law: cell*k*E
k1=10.0 Reaction: IP3 =>, Rate Law: cell*k1*IP3
k2=5.0; k1=2.5 Reaction: H + R => HR, Rate Law: cell*(k1*H*R-k2*HR)

States:

Name Description
IP3 [1D-myo-inositol 1,4,5-trisphosphate; D-myo-Inositol 1,4,5-trisphosphate]
HR [Progonadoliberin-1; Gonadotropin-releasing hormone receptor]
HRRH [protein complex]
CHO [IPR000699]
GQ [heterotrimeric G-protein complex]
R [Gonadotropin-releasing hormone receptor]
E [PIRSF005483]
H [Progonadoliberin IIA; Progonadoliberin-1]

Observables: none

Boada2016 - Incoherent type 1 feed-forward loop (I1-FFL)A synthetic-biology mathematical modelling framework that was co…

Model based design plays a fundamental role in synthetic biology. Exploiting modularity, i.e. using biological parts and interconnecting them to build new and more complex biological circuits is one of the key issues. In this context, mathematical models have been used to generate predictions of the behavior of the designed device. Designers not only want the ability to predict the circuit behavior once all its components have been determined, but also to help on the design and selection of its biological parts, i.e. to provide guidelines for the experimental implementation. This is tantamount to obtaining proper values of the model parameters, for the circuit behavior results from the interplay between model structure and parameters tuning. However, determining crisp values for parameters of the involved parts is not a realistic approach. Uncertainty is ubiquitous to biology, and the characterization of biological parts is not exempt from it. Moreover, the desired dynamical behavior for the designed circuit usually results from a trade-off among several goals to be optimized.We propose the use of a multi-objective optimization tuning framework to get a model-based set of guidelines for the selection of the kinetic parameters required to build a biological device with desired behavior. The design criteria are encoded in the formulation of the objectives and optimization problem itself. As a result, on the one hand the designer obtains qualitative regions/intervals of values of the circuit parameters giving rise to the predefined circuit behavior; on the other hand, he obtains useful information for its guidance in the implementation process. These parameters are chosen so that they can effectively be tuned at the wet-lab, i.e. they are effective biological tuning knobs. To show the proposed approach, the methodology is applied to the design of a well known biological circuit: a genetic incoherent feed-forward circuit showing adaptive behavior.The proposed multi-objective optimization design framework is able to provide effective guidelines to tune biological parameters so as to achieve a desired circuit behavior. Moreover, it is easy to analyze the impact of the context on the synthetic device to be designed. That is, one can analyze how the presence of a downstream load influences the performance of the designed circuit, and take it into account. link: http://identifiers.org/pubmed/26968941

Parameters:

Name Description
d_AI2 = 0.035 Reaction: x4 =>, Rate Law: Cell*d_AI2*x4
d_mC = 0.3624 Reaction: x7 =>, Rate Law: Cell*d_mC*x7
M = 0.0; k_2r = 20.0 Reaction: => x2 + x3, Rate Law: Cell*k_2r*M
k_mA_C_gA = 104.0 Reaction: => x1, Rate Law: Cell*k_mA_C_gA
d_C = 0.2784 Reaction: x8 =>, Rate Law: Cell*d_C*x8
d_mA = 0.3624 Reaction: x1 =>, Rate Law: Cell*d_mA*x1
k_3r = 1.0 Reaction: x4 =>, Rate Law: Cell*k_3r*x4
d_B = 0.016 Reaction: x6 =>, Rate Law: Cell*d_B*x6
d_A = 0.035 Reaction: x2 =>, Rate Law: Cell*d_A*x2
k_pA = 80.0 Reaction: x1 => x1 + x2, Rate Law: Cell*k_pA*x1
d_Ie = 0.0164 Reaction: x9 =>, Rate Law: Extracellular*d_Ie*x9
gamma_1 = 107.4; k_mB_C_gB = 1.0 Reaction: => x5; x4, Rate Law: Cell*k_mB_C_gB*x4/(gamma_1+x4)
k_pC = 11.42 Reaction: x7 => x7 + x8, Rate Law: Cell*k_pC*x7
K_cells = 1.33333333333333E-9; k_d = 0.06 Reaction: x9 =>, Rate Law: Extracellular*K_cells*k_d*x9
k_2f = 0.1 Reaction: x2 + x3 =>, Rate Law: Cell*k_2f*x2*x3
k_3f = 0.1; M = 0.0 Reaction: => x4, Rate Law: Cell*k_3f*M*M
gamma_5 = 8.56; gamma_3 = 0.01; Beta_2 = 0.05; k_mC_C_gC = 1.0; gamma_4 = 1.15; gamma_2 = 0.2; Beta_1 = 0.05 Reaction: => x7; x4, x6, Rate Law: Cell*k_mC_C_gC*(x4+Beta_1*gamma_4*x6+Beta_2*gamma_5*x4*x6)/(gamma_2+gamma_3*x4+gamma_4*x6+gamma_5*x4*x6)
d_I = 0.0164 Reaction: x3 =>, Rate Law: Cell*d_I*x3
d_mB = 0.3624 Reaction: x5 =>, Rate Law: Cell*d_mB*x5
k_d = 0.06 Reaction: x3 =>, Rate Law: Cell*k_d*x3
k_pB = 1.0 Reaction: x5 => x5 + x6, Rate Law: Cell*k_pB*x5

States:

Name Description
x5 [mRNA]
x9 [Inducer]
x1 [mRNA]
x7 [mRNA]
x8 [protein]
x4 [urn:miriam:sbo:SBO%3A0000607]
x2 [protein]
x6 [protein]
x3 [Inducer]

Observables: none

Boehm2014 - isoform-specific dimerization of pSTAT5A and pSTAT5BTo study STAT5 activation, the authors build a dynamic m…

STAT5A and STAT5B are important transcription factors that dimerize and transduce activation signals of cytokine receptors directly to the nucleus. A typical cytokine that mediates STAT5 activation is erythropoietin (Epo). Differential functions of STAT5A and STAT5B have been reported. However, the extent to which phosphorylated STAT5A and STAT5B (pSTAT5A, pSTAT5B) form homo- or heterodimers is not understood, nor is how this might influence the signal transmission to the nucleus. To study this, we designed a concept to investigate the isoform-specific dimerization behavior of pSTAT5A and pSTAT5B that comprises isoform-specific immunoprecipitation (IP), measurement of the degree of phosphorylation, and isoform ratio determination between STAT5A and STAT5B. For the main analytical method, we employed quantitative label-free and -based mass spectrometry. For the cellular model system, we used Epo receptor (EpoR)-expressing BaF3 cells (BaF3-EpoR) stimulated with Epo. Three hypotheses of dimer formation between pSTAT5A and pSTAT5B were used to explain the analytical results by a static mathematical model: formation of (i) homodimers only, (ii) heterodimers only, and (iii) random formation of homo- and heterodimers. The best agreement between experimental data and model simulations was found for the last case. Dynamics of cytoplasmic STAT5 dimerization could be explained by distinct nuclear import rates and individual nuclear retention for homo- and heterodimers of phosphorylated STAT5. link: http://identifiers.org/pubmed/25333863

Parameters:

Name Description
k_exp_homo = 0.0061723799618614 Reaction: nucpBpB => STAT5B; nucpBpB, Rate Law: k_exp_homo*nucpBpB
k_imp_homo = 96807.6817909446 Reaction: pApA => nucpApA; pApA, Rate Law: k_imp_homo*pApA
k_exp_hetero = 1.00097114635938E-5 Reaction: nucpApB => STAT5A + STAT5B; nucpApB, Rate Law: k_exp_hetero*nucpApB
k_imp_hetero = 0.0163701561812467 Reaction: pApB => nucpApB; pApB, Rate Law: k_imp_hetero*pApB
Epo_degradation_BaF3 = 0.0269765368088175; k_phos = 15767.6469913504 Reaction: STAT5A + STAT5B => pApB; STAT5A, STAT5B, Rate Law: 1.25E-7*STAT5A*STAT5B*k_phos*exp((-Epo_degradation_BaF3)*time)

States:

Name Description
pApA [Signal transducer and activator of transcription 5A]
nucpApB [Signal transducer and activator of transcription 5A; Signal transducer and activator of transcription 5B]
STAT5A [Signal transducer and activator of transcription 5A]
nucpBpB [Signal transducer and activator of transcription 5B]
STAT5B [Signal transducer and activator of transcription 5B]
pApB [Signal transducer and activator of transcription 5B; Signal transducer and activator of transcription 5A]
nucpApA [Signal transducer and activator of transcription 5A]
pBpB [Signal transducer and activator of transcription 5B]

Observables: none

In this paper we present a model of the macrophage T lymphocyte interactions that generate an anti-tumor immune response…

In this paper we present a model of the macrophage T lymphocyte interactions that generate an anti-tumor immune response. The model specifies i) induction of cytotoxic T lymphocytes, ii) antigen presentation by macrophages, which leads to iii) activation of helper T cells, and iv) production of lymphoid factors, which induce a) cytotoxic macrophages, b) T lymphocyte proliferation, and c) an inflammation reaction. Tumor escape mechanisms (suppression, antigenic heterogeneity) have been deliberately omitted from the model. This research combines hitherto unrelated or even contradictory data within the range of behavior of one model. In the model behavior, helper T cells play a crucial role: Tumors that differ minimally in antigenicity (i.e., helper reactivity) can differ markedly in rejectability. Immunization yields protection against tumor doses that would otherwise be lethal, because it increases the number of helper T cells. The magnitude of the cytotoxic effector cell response depends on the time at which helper T cells become activated: early helper activity steeply increases the magnitude of the immune response. The type of cytotoxic effector cells that eradicates the tumor depends on tumor antigenicity: lowly antigenic tumors are attacked mainly by macrophages, whereas large highly antigenic tumors can be eradicated by cytotoxic T lymphocytes only. link: http://identifiers.org/pubmed/3156189

Parameters: none

States: none

Observables: none

Interactions between Macrophages and T-lymphocytes: Tumor Sneaking Through Intrinsic to Helper T cell Dynamics ROB J. DE…

In a mathematical model of the cellular immune response we investigate immune reactions to tumors that are introduced in various doses. The model represents macrophage T-lymphocyte interactions that generate cytotoxic macrophages and cytotoxic T-lymphocytes. In this model antigens (tumors) can induce infinitely large T-lymphocyte effector populations because effector T-lymphocytes are capable of repeated proliferation and we have omitted immunosuppression. In this (proliferative) model small doses of weakly antigenic tumors grow infinitely large (i.e. sneak through) eliciting an immune response of limited magnitude. Intermediate doses of the same tumor induce larger immune responses and are hence rejected. Large doses of the tumor break through, but their progressive growth is accompanied by a strong immune response involving extensive lymphocyte proliferation. Similarly a more antigenic tumor is rejected in intermediate doses and breaks through in large doses. Initially small doses however lead to tumor dormancy. Thus although the model is devoid of explicit regulatory mechanisms that limit the magnitude of its response (immunosuppression is such a mechanism), the immune response to large increasing tumors may either be a stable reaction of limited magnitude (experimentally known as tolerance or unresponsiveness) or a strong and ever increasing reaction. Unresponsiveness can evolve because in this model net T-lymphocyte proliferation requires the presence of a minimum number of helper T cells (i.e. a proliferation threshold). Unresponsiveness is caused by depletion of helper T cell precursors. link: http://identifiers.org/pubmed/2946899

Parameters: none

States: none

Observables: none

MODEL1150151512 @ v0.0.1

This the model from the article: Computer model of action potential of mouse ventricular myocytes. Bondarenko VE, Sz…

We have developed a mathematical model of the mouse ventricular myocyte action potential (AP) from voltage-clamp data of the underlying currents and Ca2+ transients. Wherever possible, we used Markov models to represent the molecular structure and function of ion channels. The model includes detailed intracellular Ca2+ dynamics, with simulations of localized events such as sarcoplasmic Ca2+ release into a small intracellular volume bounded by the sarcolemma and sarcoplasmic reticulum. Transporter-mediated Ca2+ fluxes from the bulk cytosol are closely matched to the experimentally reported values and predict stimulation rate-dependent changes in Ca2+ transients. Our model reproduces the properties of cardiac myocytes from two different regions of the heart: the apex and the septum. The septum has a relatively prolonged AP, which reflects a relatively small contribution from the rapid transient outward K+ current in the septum. The attribution of putative molecular bases for several of the component currents enables our mouse model to be used to simulate the behavior of genetically modified transgenic mice. link: http://identifiers.org/pubmed/15142845

Parameters: none

States: none

Observables: none

MODEL1011090002 @ v0.0.1

This is the joined genome scale reconstruction of both Mycobacterium tuberculosis and the human alveloar macrophage meta…

Metabolic coupling of Mycobacterium tuberculosis to its host is foundational to its pathogenesis. Computational genome-scale metabolic models have shown utility in integrating -omic as well as physiologic data for systemic, mechanistic analysis of metabolism. To date, integrative analysis of host-pathogen interactions using in silico mass-balanced, genome-scale models has not been performed. We, therefore, constructed a cell-specific alveolar macrophage model, iAB-AMØ-1410, from the global human metabolic reconstruction, Recon 1. The model successfully predicted experimentally verified ATP and nitric oxide production rates in macrophages. This model was then integrated with an M. tuberculosis H37Rv model, iNJ661, to build an integrated host-pathogen genome-scale reconstruction, iAB-AMØ-1410-Mt-661. The integrated host-pathogen network enables simulation of the metabolic changes during infection. The resulting reaction activity and gene essentiality targets of the integrated model represent an altered infectious state. High-throughput data from infected macrophages were mapped onto the host-pathogen network and were able to describe three distinct pathological states. Integrated host-pathogen reconstructions thus form a foundation upon which understanding the biology and pathophysiology of infections can be developed. link: http://identifiers.org/pubmed/2095