SBMLBioModels: P - S

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P


Padala2017- ERK, PI3K/Akt and Wnt signalling network (PTEN mutation)Crosstalk model of the ERK, Wnt and Akt Signalling p…

Perturbations in molecular signaling pathways are a result of genetic or epigenetic alterations, which may lead to malignant transformation of cells. Despite cellular robustness, specific genetic or epigenetic changes of any gene can trigger a cascade of failures, which result in the malfunctioning of cell signaling pathways and lead to cancer phenotypes. The extent of cellular robustness has a link with the architecture of the network such as feedback and feedforward loops. Perturbation in components within feedback loops causes a transition from a regulated to a persistently activated state and results in uncontrolled cell growth. This work represents the mathematical and quantitative modeling of ERK, PI3K/Akt, and Wnt/β-catenin signaling crosstalk to show the dynamics of signaling responses during genetic and epigenetic changes in cancer. ERK, PI3K/Akt, and Wnt/β-catenin signaling crosstalk networks include both intra and inter-pathway feedback loops which function in a controlled fashion in a healthy cell. Our results show that cancerous perturbations of components such as EGFR, Ras, B-Raf, PTEN, and components of the destruction complex cause extreme fragility in the network and constitutively activate inter-pathway positive feedback loops. We observed that the aberrant signaling response due to the failure of specific network components is transmitted throughout the network via crosstalk, generating an additive effect on cancer growth and proliferation. link: http://identifiers.org/pubmed/28367561

Parameters:

Name Description
k33a2 = 0.8333; k33a1 = 0.01667 Reaction: APC + Axin => APCAxin, Rate Law: Cell*(k33a1*Axin*APC-k33a2*APCAxin)/Cell
Kcat24 = 32.344; Km24 = 35954.3 Reaction: pC3G + Rap1 => pC3G + pRap1, Rate Law: Cell*Kcat24*pC3G*Rap1/(Rap1+Km24)/Cell
Kcat17b = 15.1212; Km17b = 119355.0 Reaction: pAkt + pBRaf => pAkt + BRaf, Rate Law: Cell*Kcat17b*pBRaf*pAkt/(Km17b+pBRaf)/Cell
Km16b = 62464.6; Kcat16b = 0.8841 Reaction: pRap1 + BRaf => pRap1 + pBRaf, Rate Law: Cell*Kcat16b*pRap1*BRaf/(BRaf+Km16b)/Cell
k6a = 2.5 Reaction: pSOS => SOS, Rate Law: Cell*k6a*pSOS/Cell
k3 = 0.00125 Reaction: fEGFR => null, Rate Law: Cell*k3*fEGFR/Cell
Km22b = 100.0; Kcat22b = 48.667 Reaction: pAkt => Akt, Rate Law: Cell*Kcat22b*pAkt/(Km22b+pAkt)/Cell
Kcat23a = 694.73; Km23a = 6086100.0 Reaction: bEGFR + C3G => bEGFR + pC3G, Rate Law: Cell*Kcat23a*bEGFR*C3G/(C3G+Km23a)/Cell
Kcat8b = 1509.36; Km8b = 1432410.0 Reaction: RasGap + pRas => RasGap + Ras, Rate Law: Cell*Kcat8b*RasGap*pRas/(pRas+Km8b)/Cell
k18a = 0.005 Reaction: pP90Rsk => P90Rsk, Rate Law: Cell*k18a*pP90Rsk/Cell
Km20 = 4.0; Kcat20 = 4.0 Reaction: pPI3K + PIP2 => pPI3K + PIP3, Rate Law: Cell*Kcat20*pPI3K*PIP2/(PIP2+Km20)/Cell
Km22a = 100.0; Kcat22a = 0.33 Reaction: PIP3 + Akt => PIP3 + pAkt, Rate Law: Cell*Kcat22a*PIP3*Akt/(Akt+Km22a)/Cell
Km7 = 35954.3; Kcat7 = 32.644 Reaction: pSOS + Ras => pSOS + pRas, Rate Law: Cell*Kcat7*pSOS*Ras/(Ras+Km7)/Cell
Kcat13 = 9.8537; Km13 = 1007300.0 Reaction: pMEK + ERK => pMEK + pERK, Rate Law: Cell*Kcat13*pMEK*ERK/(ERK+Km13)/Cell
k35 = 3.433 Reaction: pAPCpAxinGSK3BBCatenin => pAPCpAxinGSK3BpBCatenin, Rate Law: Cell*k35*pAPCpAxinGSK3BBCatenin/Cell
k19c = 0.005 Reaction: pPI3K => PI3K, Rate Law: Cell*k19c*pPI3K/Cell
k26b = 3.85E-4 Reaction: PKCD => null, Rate Law: Cell*k26b*PKCD/Cell
k36 = 3.433 Reaction: pAPCpAxinGSK3BpBCatenin => pBCatenin + pAPCpAxinGSK3B, Rate Law: Cell*k36*pAPCpAxinGSK3BpBCatenin/Cell
Km9a = 62464.6; Kcat9a = 0.884096 Reaction: pRas + Raf1 => pRas + pRaf1, Rate Law: Cell*Kcat9a*pRas*Raf1/(Raf1+Km9a)/Cell
Kcat12 = 2.8324; Km12 = 518750.0 Reaction: pMEK + PP2A => MEK + PP2A, Rate Law: Cell*Kcat12*PP2A*pMEK/(pMEK+Km12)/Cell
k53 = 2.8833E-4; k52 = 3.85E-5; k54 = 1.5; k51 = 0.003465 Reaction: PKCD + pERK + bEGFR + SOS => PKCD + pERK + bEGFR + pSOS, Rate Law: Cell*(k51*bEGFR+k52+k53*PKCD)/(1+pERK/k54)/Cell
k37a2 = 20.0; k37a1 = 0.01667 Reaction: BCatenin + APC => APCBCatenin, Rate Law: Cell*(k37a1*APC*BCatenin-k37a2*APCBCatenin)/Cell
Kcat14 = 8.8912; Km14 = 3496500.0 Reaction: pERK + PP2A => ERK + PP2A, Rate Law: Cell*Kcat14*PP2A*pERK/(pERK+Km14)/Cell
k312 = 0.01515; k311 = 0.001515 Reaction: APCAxin + GSK3B => APCAxinGSK3B, Rate Law: Cell*(k311*GSK3B*APCAxin-k312*APCAxinGSK3B)/Cell
Kcat19b = 0.07711; Km19b = 272056.0 Reaction: PI3K => pPI3K; pRas, Rate Law: Cell*Kcat19b*pRas*PI3K/(PI3K+Km19b)/Cell
k23b = 2.5 Reaction: pC3G => C3G, Rate Law: Cell*k23b*pC3G/Cell
k37c = 4.283E-6 Reaction: BCatenin => null, Rate Law: Cell*k37c*BCatenin/Cell
V37b = 0.00705 Reaction: null => BCatenin, Rate Law: Cell*V37b/Cell
Km9b = 15.0; W = 0.0; k9b = 0.025 Reaction: X + Raf1 => X + pRaf1, Rate Law: Cell*k9b*W*X*Raf1/(Km9b+Raf1)/Cell
Kcat6b = 1611.97; Km6b = 896896.0 Reaction: pP90Rsk + pSOS => pP90Rsk + SOS, Rate Law: Cell*Kcat6b*pP90Rsk*pSOS/(pSOS+Km6b)/Cell
Kcat18b = 0.02137; Km18b = 763523.0 Reaction: pERK + P90Rsk => pERK + pP90Rsk, Rate Law: Cell*Kcat18b*pERK*P90Rsk/(P90Rsk+Km18b)/Cell
k32b = 0.002217 Reaction: pAPCpAxinGSK3B => APCAxinGSK3B, Rate Law: Cell*k32b*pAPCpAxinGSK3B/Cell
k22 = 0.121008; k21 = 2.18503E-5 Reaction: fEGFR + EGF => bEGFR, Rate Law: Cell*(k21*EGF*fEGFR-k22*bEGFR)/Cell
V15b = 4.0 Reaction: pRKIP => RKIP, Rate Law: Cell*V15b*pRKIP/Cell
k32a = 0.00445 Reaction: APCAxinGSK3B => pAPCpAxinGSK3B, Rate Law: Cell*k32a*APCAxinGSK3B/Cell
Kcat10b = 15.1212; Km10b = 119355.0 Reaction: pAkt + pRaf1 => pAkt + Raf1, Rate Law: Cell*Kcat10b*pAkt*pRaf1/(pRaf1+Km10b)/Cell
k11b2 = 120.0; k11b1 = 1.1167E-5 Reaction: pRKIP + pRaf1 + MEK => pRKIP + pRaf1 + pMEK; RKIP, Rate Law: Cell*k11b1*pRaf1*MEK/(1+((RKIP-pRKIP)/k11b2)^2)/Cell
k342 = 2.0; k341 = 0.01667 Reaction: BCatenin + pAPCpAxinGSK3B => pAPCpAxinGSK3BBCatenin, Rate Law: Cell*(k341*pAPCpAxinGSK3B*BCatenin-k342*pAPCpAxinGSK3BBCatenin)/Cell
Km10a = 1061.7; Kcat10a = 0.12633 Reaction: RafPPtase + pRaf1 => RafPPtase + Raf1, Rate Law: Cell*Kcat10a*RafPPtase*pRaf1/(pRaf1+Km10a)/Cell
Km19a = 184912.0; Kcat19a = 10.6737 Reaction: bEGFR + PI3K => bEGFR + pPI3K, Rate Law: Cell*Kcat19a*bEGFR*PI3K/(PI3K+Km19a)/Cell
Kcat11a = 185.76; Km11a = 4768400.0 Reaction: pBRaf + MEK => pBRaf + pMEK, Rate Law: Cell*Kcat11a*pBRaf*MEK/(MEK+Km11a)/Cell
k4 = 0.2 Reaction: bEGFR => null, Rate Law: Cell*k4*bEGFR/Cell
k381 = 0.01667; k382 = 0.5 Reaction: TCF + BCatenin => TCFBCatenin, Rate Law: Cell*(k381*BCatenin*TCF-k382*TCFBCatenin)/Cell
Kcat16a = 0.8841; Km16a = 62645.0 Reaction: pRas + BRaf => pRas + pBRaf, Rate Law: Cell*Kcat16a*pRas*BRaf/(BRaf+Km16a)/Cell
V1 = 100.0 Reaction: pEGFR => fEGFR, Rate Law: Cell*V1/Cell
Km25 = 1432400.0; Kcat25 = 1509.4 Reaction: Rap1Gap + pRap1 => Rap1Gap + Rap1, Rate Law: Cell*Kcat25*Rap1Gap*pRap1/(pRap1+Km25)/Cell
Kcat17a = 0.12633; Km17a = 1061.71 Reaction: RafPPtase + pBRaf => RafPPtase + BRaf, Rate Law: Cell*Kcat17a*RafPPtase*pBRaf/(Km17a+RafPPtase)/Cell
V26a = 0.00154; k26a = 20.0 Reaction: GSK3B => GSK3B + PKCD, Rate Law: Cell*V26a/(1+(GSK3B/k26a)^2.5)/Cell
Kcat27d = 0.01541 Reaction: pGSK3B => GSK3B, Rate Law: Cell*Kcat27d*pGSK3B/Cell
k41 = 0.00695 Reaction: pBCatenin => null, Rate Law: Cell*k41*pBCatenin/Cell
Kcat27b = 0.04596 Reaction: pAkt + GSK3B => pAkt + pGSK3B, Rate Law: Cell*Kcat27b*GSK3B*pAkt/Cell
k33b = 0.002783 Reaction: Axin => null, Rate Law: Cell*k33b*Axin/Cell
k30 = 8.33E-4 Reaction: Dsha + APCAxinGSK3B => GSK3B + APCAxin + Dsha, Rate Law: Cell*k30*Dsha*APCAxinGSK3B/Cell
k15a = 1.3 Reaction: pERK + RKIP => pERK + pRKIP, Rate Law: Cell*k15a*pERK*(RKIP-pRKIP)/Cell
k33c1 = 1.37E-6; k33c2 = 1.667E-8 Reaction: BCatenin + TCFBCatenin => BCatenin + TCFBCatenin + Axin, Rate Law: Cell*(k33c1+k33c2*(TCFBCatenin+BCatenin))/Cell

States:

Name Description
pC3G [Complement C3; phosphorylated]
pBCatenin [Catenin beta-1; phosphorylated]
bEGFR [Epidermal growth factor receptor; Pro-epidermal growth factor]
pPI3K [0027264; phosphorylated]
APCAxin [Axin-1; Adenomatous polyposis coli protein]
Akt [RAC-alpha serine/threonine-protein kinase]
pSOS [Son of sevenless homolog 1; phosphorylated]
pAPCpAxinGSK3BBCatenin [Axin-1; Glycogen synthase kinase-3 beta; Catenin beta-1; Adenomatous polyposis coli protein; phosphorylated]
EGF [Pro-epidermal growth factor]
pP90Rsk [Ribosomal protein S6 kinase alpha-1; phosphorylated]
pMEK [Dual specificity mitogen-activated protein kinase kinase 1; phosphorylated]
BCatenin [Catenin beta-1]
pAkt [RAC-alpha serine/threonine-protein kinase; phosphorylated]
pRKIP [Phosphatidylethanolamine-binding protein 1; phosphorylated]
BRaf [Serine/threonine-protein kinase B-raf]
RKIP [Phosphatidylethanolamine-binding protein 1]
PIP3 [0016618]
pEGFR [Epidermal growth factor receptor; phosphorylated]
PKCD [Protein kinase C delta type]
pRaf1 [RAF proto-oncogene serine/threonine-protein kinase; phosphorylated]
MEK [Dual specificity mitogen-activated protein kinase kinase 1]
C3G [Rap guanine nucleotide exchange factor 1]
Dsha [Segment polarity protein dishevelled homolog DVL-1; phosphorylated]
pAPCpAxinGSK3B [Adenomatous polyposis coli protein; Axin-1; Glycogen synthase kinase-3 beta; phosphorylated]
P90Rsk [Ribosomal protein S6 kinase alpha-1]
pGSK3B [Glycogen synthase kinase-3 beta; phosphorylated]
Raf1 [RAF proto-oncogene serine/threonine-protein kinase]
PP2A [Serine/threonine-protein phosphatase 2A catalytic subunit alpha isoform]
pRas [Ras-related protein R-Ras2; phosphorylated]
APCBCatenin [Catenin beta-1; Adenomatous polyposis coli protein]
pERK [Mitogen-activated protein kinase 1; phosphorylated]
fEGFR [Epidermal growth factor receptor]
APC [Adenomatous polyposis coli protein]
pBRaf [Serine/threonine-protein kinase B-raf; phosphorylated]
GSK3B [Glycogen synthase kinase-3 beta]
ERK [Mitogen-activated protein kinase 3]
pRap1 [Ras-related protein Rap-1A; phosphorylated]
APCAxinGSK3B [Axin-1; Adenomatous polyposis coli protein; Glycogen synthase kinase-3 beta]
Axin [Axin-1]
pAPCpAxinGSK3BpBCatenin [Catenin beta-1; Glycogen synthase kinase-3 beta; Axin-1; Adenomatous polyposis coli protein; phosphorylated]

Observables: none

Padala2017- ERK, PI3K/Akt and Wnt signalling network (Ras mutated)Crosstalk model of the ERK, Wnt and Akt signalling pat…

Perturbations in molecular signaling pathways are a result of genetic or epigenetic alterations, which may lead to malignant transformation of cells. Despite cellular robustness, specific genetic or epigenetic changes of any gene can trigger a cascade of failures, which result in the malfunctioning of cell signaling pathways and lead to cancer phenotypes. The extent of cellular robustness has a link with the architecture of the network such as feedback and feedforward loops. Perturbation in components within feedback loops causes a transition from a regulated to a persistently activated state and results in uncontrolled cell growth. This work represents the mathematical and quantitative modeling of ERK, PI3K/Akt, and Wnt/β-catenin signaling crosstalk to show the dynamics of signaling responses during genetic and epigenetic changes in cancer. ERK, PI3K/Akt, and Wnt/β-catenin signaling crosstalk networks include both intra and inter-pathway feedback loops which function in a controlled fashion in a healthy cell. Our results show that cancerous perturbations of components such as EGFR, Ras, B-Raf, PTEN, and components of the destruction complex cause extreme fragility in the network and constitutively activate inter-pathway positive feedback loops. We observed that the aberrant signaling response due to the failure of specific network components is transmitted throughout the network via crosstalk, generating an additive effect on cancer growth and proliferation. link: http://identifiers.org/pubmed/28367561

Parameters:

Name Description
k27c = 1.5E-4 Reaction: RKIP => RKIP + GSK3B, Rate Law: Cell*k27c*RKIP/Cell
k33a2 = 0.8333; k33a1 = 0.01667 Reaction: APC + Axin => APCAxin, Rate Law: Cell*(k33a1*Axin*APC-k33a2*APCAxin)/Cell
W = 0.0; k28 = 0.003 Reaction: Dshi => Dsha, Rate Law: Cell*k28*Dshi*W/Cell
Kcat24 = 32.344; Km24 = 35954.3 Reaction: pC3G + Rap1 => pC3G + pRap1, Rate Law: Cell*Kcat24*pC3G*Rap1/(Rap1+Km24)/Cell
Kcat17b = 15.1212; Km17b = 119355.0 Reaction: pAkt + pBRaf => pAkt + BRaf, Rate Law: Cell*Kcat17b*pBRaf*pAkt/(Km17b+pBRaf)/Cell
Km16b = 62464.6; Kcat16b = 0.8841 Reaction: pRap1 + BRaf => pRap1 + pBRaf, Rate Law: Cell*Kcat16b*pRap1*BRaf/(BRaf+Km16b)/Cell
k6a = 2.5 Reaction: pSOS => SOS, Rate Law: Cell*k6a*pSOS/Cell
Kcat23a = 694.73; Km23a = 6086100.0 Reaction: bEGFR + C3G => bEGFR + pC3G, Rate Law: Cell*Kcat23a*bEGFR*C3G/(C3G+Km23a)/Cell
Km22b = 100.0; Kcat22b = 48.667 Reaction: pAkt => Akt, Rate Law: Cell*Kcat22b*pAkt/(Km22b+pAkt)/Cell
k18a = 0.005 Reaction: pP90Rsk => P90Rsk, Rate Law: Cell*k18a*pP90Rsk/Cell
Km20 = 4.0; Kcat20 = 4.0 Reaction: pPI3K + PIP2 => pPI3K + PIP3, Rate Law: Cell*Kcat20*pPI3K*PIP2/(PIP2+Km20)/Cell
Km22a = 100.0; Kcat22a = 0.33 Reaction: PIP3 + Akt => PIP3 + pAkt, Rate Law: Cell*Kcat22a*PIP3*Akt/(Akt+Km22a)/Cell
Km7 = 35954.3; Kcat7 = 32.644 Reaction: pSOS + Ras => pSOS + pRas, Rate Law: Cell*Kcat7*pSOS*Ras/(Ras+Km7)/Cell
k35 = 3.433 Reaction: pAPCpAxinGSK3BBCatenin => pAPCpAxinGSK3BpBCatenin, Rate Law: Cell*k35*pAPCpAxinGSK3BBCatenin/Cell
k19c = 0.005 Reaction: pPI3K => PI3K, Rate Law: Cell*k19c*pPI3K/Cell
k26b = 3.85E-4 Reaction: PKCD => null, Rate Law: Cell*k26b*PKCD/Cell
k36 = 3.433 Reaction: pAPCpAxinGSK3BpBCatenin => pBCatenin + pAPCpAxinGSK3B, Rate Law: Cell*k36*pAPCpAxinGSK3BpBCatenin/Cell
V8a = 0.0717 Reaction: null => Ras, Rate Law: Cell*V8a/Cell
Km9a = 62464.6; Kcat9a = 0.884096 Reaction: pRas + Raf1 => pRas + pRaf1, Rate Law: Cell*Kcat9a*pRas*Raf1/(Raf1+Km9a)/Cell
Kcat12 = 2.8324; Km12 = 518750.0 Reaction: pMEK + PP2A => MEK + PP2A, Rate Law: Cell*Kcat12*PP2A*pMEK/(pMEK+Km12)/Cell
k53 = 2.8833E-4; k52 = 3.85E-5; k54 = 1.5; k51 = 0.003465 Reaction: PKCD + pERK + bEGFR + SOS => PKCD + pERK + bEGFR + pSOS, Rate Law: Cell*(k51*bEGFR+k52+k53*PKCD)/(1+pERK/k54)/Cell
k37a2 = 20.0; k37a1 = 0.01667 Reaction: BCatenin + APC => APCBCatenin, Rate Law: Cell*(k37a1*APC*BCatenin-k37a2*APCBCatenin)/Cell
Kcat14 = 8.8912; Km14 = 3496500.0 Reaction: pERK + PP2A => ERK + PP2A, Rate Law: Cell*Kcat14*PP2A*pERK/(pERK+Km14)/Cell
k312 = 0.01515; k311 = 0.001515 Reaction: APCAxin + GSK3B => APCAxinGSK3B, Rate Law: Cell*(k311*GSK3B*APCAxin-k312*APCAxinGSK3B)/Cell
Kcat19b = 0.07711; Km19b = 272056.0 Reaction: PI3K => pPI3K; pRas, Rate Law: Cell*Kcat19b*pRas*PI3K/(PI3K+Km19b)/Cell
k23b = 2.5 Reaction: pC3G => C3G, Rate Law: Cell*k23b*pC3G/Cell
Km9b = 15.0; W = 0.0; k9b = 0.025 Reaction: X + Raf1 => X + pRaf1, Rate Law: Cell*k9b*W*X*Raf1/(Km9b+Raf1)/Cell
Kcat6b = 1611.97; Km6b = 896896.0 Reaction: pP90Rsk + pSOS => pP90Rsk + SOS, Rate Law: Cell*Kcat6b*pP90Rsk*pSOS/(pSOS+Km6b)/Cell
Kcat18b = 0.02137; Km18b = 763523.0 Reaction: pERK + P90Rsk => pERK + pP90Rsk, Rate Law: Cell*Kcat18b*pERK*P90Rsk/(P90Rsk+Km18b)/Cell
k22 = 0.121008; k21 = 2.18503E-5 Reaction: fEGFR + EGF => bEGFR, Rate Law: Cell*(k21*EGF*fEGFR-k22*bEGFR)/Cell
V15b = 4.0 Reaction: pRKIP => RKIP, Rate Law: Cell*V15b*pRKIP/Cell
k32a = 0.00445 Reaction: APCAxinGSK3B => pAPCpAxinGSK3B, Rate Law: Cell*k32a*APCAxinGSK3B/Cell
Kcat21 = 5.5; Km21 = 0.08 Reaction: PTEN + PIP3 => PTEN + PIP2, Rate Law: Cell*Kcat21*PTEN*PIP3/(PIP3+Km21)/Cell
Kcat10b = 15.1212; Km10b = 119355.0 Reaction: pAkt + pRaf1 => pAkt + Raf1, Rate Law: Cell*Kcat10b*pAkt*pRaf1/(pRaf1+Km10b)/Cell
k11b2 = 120.0; k11b1 = 1.1167E-5 Reaction: pRKIP + pRaf1 + MEK => pRKIP + pRaf1 + pMEK; RKIP, Rate Law: Cell*k11b1*pRaf1*MEK/(1+((RKIP-pRKIP)/k11b2)^2)/Cell
Km10a = 1061.7; Kcat10a = 0.12633 Reaction: RafPPtase + pRaf1 => RafPPtase + Raf1, Rate Law: Cell*Kcat10a*RafPPtase*pRaf1/(pRaf1+Km10a)/Cell
Kcat11a = 185.76; Km11a = 4768400.0 Reaction: pBRaf + MEK => pBRaf + pMEK, Rate Law: Cell*Kcat11a*pBRaf*MEK/(MEK+Km11a)/Cell
Km19a = 184912.0; Kcat19a = 10.6737 Reaction: bEGFR + PI3K => bEGFR + pPI3K, Rate Law: Cell*Kcat19a*bEGFR*PI3K/(PI3K+Km19a)/Cell
k29 = 0.003 Reaction: Dsha => Dshi, Rate Law: Cell*k29*Dsha/Cell
k381 = 0.01667; k382 = 0.5 Reaction: TCF + BCatenin => TCFBCatenin, Rate Law: Cell*(k381*BCatenin*TCF-k382*TCFBCatenin)/Cell
Kcat16a = 0.8841; Km16a = 62645.0 Reaction: pRas + BRaf => pRas + pBRaf, Rate Law: Cell*Kcat16a*pRas*BRaf/(BRaf+Km16a)/Cell
Kcat17a = 0.12633; Km17a = 1061.71 Reaction: RafPPtase + pBRaf => RafPPtase + BRaf, Rate Law: Cell*Kcat17a*RafPPtase*pBRaf/(Km17a+RafPPtase)/Cell
Km25 = 1432400.0; Kcat25 = 1509.4 Reaction: Rap1Gap + pRap1 => Rap1Gap + Rap1, Rate Law: Cell*Kcat25*Rap1Gap*pRap1/(pRap1+Km25)/Cell
V26a = 0.00154; k26a = 20.0 Reaction: GSK3B => GSK3B + PKCD, Rate Law: Cell*V26a/(1+(GSK3B/k26a)^2.5)/Cell
Kcat27d = 0.01541 Reaction: pGSK3B => GSK3B, Rate Law: Cell*Kcat27d*pGSK3B/Cell
k41 = 0.00695 Reaction: pBCatenin => null, Rate Law: Cell*k41*pBCatenin/Cell
Kcat27b = 0.04596 Reaction: pAkt + GSK3B => pAkt + pGSK3B, Rate Law: Cell*Kcat27b*GSK3B*pAkt/Cell
Km39 = 15.0; k39 = 0.01 Reaction: TCFBCatenin => X + TCFBCatenin, Rate Law: Cell*k39*TCFBCatenin^2/(Km39^2+TCFBCatenin^2)/Cell
k30 = 8.33E-4 Reaction: Dsha + APCAxinGSK3B => GSK3B + APCAxin + Dsha, Rate Law: Cell*k30*Dsha*APCAxinGSK3B/Cell
k15a = 1.3 Reaction: pERK + RKIP => pERK + pRKIP, Rate Law: Cell*k15a*pERK*(RKIP-pRKIP)/Cell
k33c1 = 1.37E-6; k33c2 = 1.667E-8 Reaction: BCatenin + TCFBCatenin => BCatenin + TCFBCatenin + Axin, Rate Law: Cell*(k33c1+k33c2*(TCFBCatenin+BCatenin))/Cell

States:

Name Description
pC3G [Complement C3; phosphorylated]
pBCatenin [Catenin beta-1; phosphorylated]
PIP2 [0018348]
PTEN [Phosphatidylinositol 3,4,5-trisphosphate 3-phosphatase and dual-specificity protein phosphatase PTEN]
pPI3K [0027264; phosphorylated]
bEGFR [Epidermal growth factor receptor; Pro-epidermal growth factor]
APCAxin [Axin-1; Adenomatous polyposis coli protein]
pSOS [Son of sevenless homolog 1; phosphorylated]
EGF [Pro-epidermal growth factor]
RafPPtase [Serine/threonine-protein phosphatase 2A catalytic subunit alpha isoform]
pP90Rsk [Ribosomal protein S6 kinase alpha-1; phosphorylated]
BRaf [Serine/threonine-protein kinase B-raf]
pAkt [RAC-alpha serine/threonine-protein kinase; phosphorylated]
pRKIP [Phosphatidylethanolamine-binding protein 1; phosphorylated]
PIP3 [0016618]
PKCD [Protein kinase C delta type]
RKIP [Phosphatidylethanolamine-binding protein 1]
Ras [GTPase HRas; 0010192]
SOS [Son of sevenless homolog 1]
pRaf1 [RAF proto-oncogene serine/threonine-protein kinase; phosphorylated]
MEK [Dual specificity mitogen-activated protein kinase kinase 1]
PI3K [0027264]
C3G [Rap guanine nucleotide exchange factor 1]
Rap1 [Ras-related protein Rap-1A]
TCFBCatenin [Lymphoid enhancer-binding factor 1; Catenin beta-1]
Dsha [Segment polarity protein dishevelled homolog DVL-1; phosphorylated]
X X
TCF [Lymphoid enhancer-binding factor 1]
P90Rsk [Ribosomal protein S6 kinase alpha-1]
Raf1 [RAF proto-oncogene serine/threonine-protein kinase]
pAPCpAxinGSK3B [Adenomatous polyposis coli protein; Axin-1; Glycogen synthase kinase-3 beta; phosphorylated]
PP2A [Serine/threonine-protein phosphatase 2A catalytic subunit alpha isoform]
Dshi [Segment polarity protein dishevelled homolog DVL-1]
pRas [Ras-related protein R-Ras2; phosphorylated; 0010192]
APCBCatenin [Catenin beta-1; Adenomatous polyposis coli protein]
APC [Adenomatous polyposis coli protein]
pBRaf [Serine/threonine-protein kinase B-raf; phosphorylated]
GSK3B [Glycogen synthase kinase-3 beta]
pRap1 [Ras-related protein Rap-1A; phosphorylated]
pAPCpAxinGSK3BpBCatenin [Catenin beta-1; Glycogen synthase kinase-3 beta; Axin-1; Adenomatous polyposis coli protein; phosphorylated]
APCAxinGSK3B [Axin-1; Adenomatous polyposis coli protein; Glycogen synthase kinase-3 beta]

Observables: none

This paper proposes a dynamic model to describe and forecast the dynamics of the coronavirus disease COVID-19 transmissi…

This paper proposes a dynamic model to describe and forecast the dynamics of the coronavirus disease COVID-19 transmission. The model is based on an approach previously used to describe the Middle East Respiratory Syndrome (MERS) epidemic. This methodology is used to describe the COVID-19 dynamics in six countries where the pandemic is widely spread, namely China, Italy, Spain, France, Germany, and the USA. For this purpose, data from the European Centre for Disease Prevention and Control (ECDC) are adopted. It is shown how the model can be used to forecast new infection cases and new deceased and how the uncertainties associated to this prediction can be quantified. This approach has the advantage of being relatively simple, grouping in few mathematical parameters the many conditions which affect the spreading of the disease. On the other hand, it requires previous data from the disease transmission in the country, being better suited for regions where the epidemic is not at a very early stage. With the estimated parameters at hand, one can use the model to predict the evolution of the disease, which in turn enables authorities to plan their actions. Moreover, one key advantage is the straightforward interpretation of these parameters and their influence over the evolution of the disease, which enables altering some of them, so that one can evaluate the effect of public policy, such as social distancing. The results presented for the selected countries confirm the accuracy to perform predictions. link: http://identifiers.org/pubmed/32735581

Parameters: none

States: none

Observables: none

BIOMD0000000325 @ v0.0.1

This is the model of the minmal 2 feedback switch described in the article: **Synthetic conversion of a graded recepto…

The ability to engineer an all-or-none cellular response to a given signaling ligand is important in applications ranging from biosensing to tissue engineering. However, synthetic gene network 'switches' have been limited in their applicability and tunability due to their reliance on specific components to function. Here, we present a strategy for reversible switch design that instead relies only on a robust, easily constructed network topology with two positive feedback loops and we apply the method to create highly ultrasensitive (n(H)>20), bistable cellular responses to a synthetic ligand/receptor complex. Independent modulation of the two feedback strengths enables rational tuning and some decoupling of steady-state (ultrasensitivity, signal amplitude, switching threshold, and bistability) and kinetic (rates of system activation and deactivation) response properties. Our integrated computational and synthetic biology approach elucidates design rules for building cellular switches with desired properties, which may be of utility in engineering signal-transduction pathways. link: http://identifiers.org/pubmed/21451590

Parameters:

Name Description
kdegR = 0.005 Reaction: R =>, Rate Law: cell*kdegR*R
kdegC = 0.01 Reaction: C =>, Rate Law: cell*kdegC*C
kdegX = 0.005 Reaction: X =>, Rate Law: cell*kdegX*X
Rs = 3.0; BR = 0.005; KD = 200.0 Reaction: => R; A, Rate Law: cell*(BR+Rs*A/(KD+A))
k3 = 45.0 Reaction: X => C + A, Rate Law: cell*k3*X
kdegA = 0.005 Reaction: A =>, Rate Law: cell*kdegA*A
koff = 0.05; kon = 0.001 Reaction: R + L => C, Rate Law: cell*(kon*L*R-koff*C)
kdegI = 0.005 Reaction: I =>, Rate Law: cell*kdegI*I
k2 = 5.0; k1 = 1.0 Reaction: C + I => X, Rate Law: cell*(k1*C*I-k2*X)
BI = 0.005; TFs = 3.0; KD = 200.0 Reaction: => I; A, Rate Law: cell*(BI+TFs*A/(KD+A))

States:

Name Description
I [Transcription factor SKN7]
A [obsolete transcription activator activity; phosphorylated L-histidine; Transcription factor SKN7]
X [transmembrane histidine kinase cytokinin receptor activity; Transcription factor SKN7; Histidine kinase 4; N(6)-isopentenyladenosine]
C [transmembrane histidine kinase cytokinin receptor activity; Histidine kinase 4; N(6)-isopentenyladenosine]
L [cytokinin; N(6)-isopentenyladenosine; CHEMBL1163500]
R [Histidine kinase 4]

Observables: none

Interleukin-7 (IL-7) is an essential cytokine for the development and homeostatic maintenance of T and B lymphocytes. Bi…

Interleukin-7 (IL-7) is an essential cytokine for the development and homeostatic maintenance of T and B lymphocytes. Binding of IL-7 to its cognate receptor, the IL-7 receptor (IL-7R), activates multiple pathways that regulate lymphocyte survival, glucose uptake, proliferation and differentiation. There has been much interest in understanding how IL-7 receptor signaling is modulated at multiple interconnected network levels. This review examines how the strength of the signal through the IL-7 receptor is modulated in T and B cells, including the use of shared receptor components, signaling crosstalk, shared interaction domains, feedback loops, integrated gene regulation, multimerization and ligand competition. We discuss how these network control mechanisms could integrate to govern the properties of IL-7R signaling in lymphocytes in health and disease. Analysis of IL-7 receptor signaling at a network level in a systematic manner will allow for a comprehensive approach to understanding the impact of multiple signaling pathways on lymphocyte biology. link: http://identifiers.org/pubmed/18445337

Parameters: none

States: none

Observables: none

Palmer2014 - Effect of IL-1β-Blocking therapies in T2DM - Disease Condition This is the model with disease state initia…

Recent clinical studies suggest sustained treatment effects of interleukin-1β (IL-1β)-blocking therapies in type 2 diabetes mellitus. The underlying mechanisms of these effects, however, remain underexplored. Using a quantitative systems pharmacology modeling approach, we combined ex vivo data of IL-1β effects on β-cell function and turnover with a disease progression model of the long-term interactions between insulin, glucose, and β-cell mass in type 2 diabetes mellitus. We then simulated treatment effects of the IL-1 receptor antagonist anakinra. The result was a substantial and partly sustained symptomatic improvement in β-cell function, and hence also in HbA1C, fasting plasma glucose, and proinsulin-insulin ratio, and a small increase in β-cell mass. We propose that improved β-cell function, rather than mass, is likely to explain the main IL-1β-blocking effects seen in current clinical data, but that improved β-cell mass might result in disease-modifying effects not clearly distinguishable until >1 year after treatment. link: http://identifiers.org/pubmed/24918743

Parameters:

Name Description
taus = 0.5 Reaction: TigB =>, Rate Law: taus*TigB
Kxg = 1.6E-5 Reaction: Glucose =>, Rate Law: Kxg*Glucose
placebo_on = 0.0; kplacebo = 0.00137 Reaction: => IL1b, Rate Law: placebo_on*kplacebo
vfg = 4.0; tauf = 0.5; kmf = 0.021; IL1R = 0.02341920375; kf = 0.00957754; kmfg = 9.0; xfg = 4.0; vf = 0.4 Reaction: => f; Glucose, Rate Law: tauf*kf*(1+vfg*Glucose^xfg/(kmfg^xfg+Glucose^xfg))*(1+vf*IL1R/(kmf+IL1R))
replication = 5.12314779E-4 Reaction: => B, Rate Law: replication*B
Kxgi = 2.24E-5 Reaction: Glucose => ; Insulin, Rate Law: Kxgi*Insulin*Glucose
Tgl = 0.025405 Reaction: => Glucose, Rate Law: Tgl
Kglucose = 2.92E-4; lambda = 0.743; Kin = 1.05; Ktr = 0.12 Reaction: rbc1 = (Kin-Ktr*rbc1)-Kglucose*Glucose^lambda*rbc1, Rate Law: (Kin-Ktr*rbc1)-Kglucose*Glucose^lambda*rbc1
Gh = 9.0; vh = 4.0 Reaction: => Proinsulin; TigB, B, f, Glucose, Rate Law: f*(Glucose/Gh)^vh/(1+(Glucose/Gh)^vh)*TigB*B
apoptosis = 7.543653797E-4 Reaction: B =>, Rate Law: apoptosis*B
kab = 3.94; Vp = 48.0 Reaction: => Anakinra; Anakinrasc, Rate Law: kab*Anakinrasc/Vp
k1 = 0.2; placebo_on = 0.0; k2 = 0.0025 Reaction: IL1b =>, Rate Law: piecewise((1-placebo_on)*k1*IL1b, time < 91, (1-placebo_on)*k2*IL1b)
k1 = 0.2; il1bH = 0.05; placebo_on = 0.0; il1b0 = 5.0; kplacebo = 0.00137; k2 = 0.0025 Reaction: => IL1b, Rate Law: piecewise((1-placebo_on)*k1*il1bH, time < 91, (1-placebo_on)*k2*(il1b0+kplacebo*time))
CL = 432.0; Vp = 48.0 Reaction: Anakinra =>, Rate Law: CL/Vp*Anakinra
IL1R = 0.02341920375; taus = 0.5; vs = 0.7; ks = 0.291008; kms = 0.021 Reaction: => TigB, Rate Law: taus*ks*(1-vs*IL1R/(kms+IL1R))
Kglucose = 2.92E-4; lambda = 0.743; Ktr = 0.12 Reaction: rbc5 = (Ktr*rbc4-Ktr*rbc5)-Kglucose*Glucose^lambda*rbc5, Rate Law: (Ktr*rbc4-Ktr*rbc5)-Kglucose*Glucose^lambda*rbc5
Kxi = 0.05 Reaction: Proinsulin =>, Rate Law: 0.1*Kxi*Proinsulin
kab = 3.94 Reaction: Anakinrasc =>, Rate Law: kab*Anakinrasc
tauf = 0.5 Reaction: f =>, Rate Law: tauf*f

States:

Name Description
Glucose [glucose]
f [insulin secretion]
a1c5 [urn:miriam:efo:EFO%3A0004541]
a1c1 [urn:miriam:efo:EFO%3A0004541]
a1c8 [urn:miriam:efo:EFO%3A0004541]
a1c3 [urn:miriam:efo:EFO%3A0004541]
rbc12 [erythrocyte]
IL1b [Interleukin-1 beta]
rbc6 [erythrocyte]
B [pancreatic beta cell]
a1c12 [urn:miriam:efo:EFO%3A0004541]
rbc3 [erythrocyte]
rbc1 [erythrocyte]
Proinsulin [Insulin]
Anakinra [Interleukin-1 receptor antagonist protein; pharmaceutical]
a1c7 [urn:miriam:efo:EFO%3A0004541]
a1c11 [urn:miriam:efo:EFO%3A0004541]
rbc2 [erythrocyte]
rbc11 [erythrocyte]
a1c6 [urn:miriam:efo:EFO%3A0004541]
rbc9 [erythrocyte]
TigB [insulin secretion]
rbc5 [erythrocyte]
Anakinrasc [Interleukin-1 receptor antagonist protein]
rbc7 [erythrocyte]
a1c9 [urn:miriam:efo:EFO%3A0004541]
Insulin [Insulin]
a1c10 [urn:miriam:efo:EFO%3A0004541]
a1c4 [urn:miriam:efo:EFO%3A0004541]
rbc4 [erythrocyte]
rbc8 [erythrocyte]
rbc10 [erythrocyte]
a1c2 [urn:miriam:efo:EFO%3A0004541]
hba1c [urn:miriam:efo:EFO%3A0004541]

Observables: none

Palmer2014 - Effect of IL-1β-Blocking therapies in T2DM - Healthy Condition This is the model with healthy state initia…

Recent clinical studies suggest sustained treatment effects of interleukin-1β (IL-1β)-blocking therapies in type 2 diabetes mellitus. The underlying mechanisms of these effects, however, remain underexplored. Using a quantitative systems pharmacology modeling approach, we combined ex vivo data of IL-1β effects on β-cell function and turnover with a disease progression model of the long-term interactions between insulin, glucose, and β-cell mass in type 2 diabetes mellitus. We then simulated treatment effects of the IL-1 receptor antagonist anakinra. The result was a substantial and partly sustained symptomatic improvement in β-cell function, and hence also in HbA1C, fasting plasma glucose, and proinsulin-insulin ratio, and a small increase in β-cell mass. We propose that improved β-cell function, rather than mass, is likely to explain the main IL-1β-blocking effects seen in current clinical data, but that improved β-cell mass might result in disease-modifying effects not clearly distinguishable until >1 year after treatment. link: http://identifiers.org/pubmed/24918743

Parameters:

Name Description
taus = 0.5 Reaction: TigB =>, Rate Law: taus*TigB
Kxg = 1.6E-5 Reaction: Glucose =>, Rate Law: Kxg*Glucose
placebo_on = 0.0; kplacebo = 0.00137 Reaction: => IL1b, Rate Law: placebo_on*kplacebo
il1b0 = 0.05; k1 = 0.2; il1bH = 0.05; placebo_on = 0.0; kplacebo = 0.00137; k2 = 0.0025 Reaction: => IL1b, Rate Law: piecewise((1-placebo_on)*k1*il1bH, time < 91, (1-placebo_on)*k2*(il1b0+kplacebo*time))
Tgl = 0.025405 Reaction: => Glucose, Rate Law: Tgl
Kglucose = 2.92E-4; lambda = 0.743; Kin = 1.05; Ktr = 0.12 Reaction: rbc1 = (Kin-Ktr*rbc1)-Kglucose*Glucose^lambda*rbc1, Rate Law: (Kin-Ktr*rbc1)-Kglucose*Glucose^lambda*rbc1
IL1R = 3.743916136E-4; taus = 0.5; vs = 0.7; ks = 0.291008; kms = 0.021 Reaction: => TigB, Rate Law: taus*ks*(1-vs*IL1R/(kms+IL1R))
Gh = 9.0; vh = 4.0 Reaction: => Proinsulin; TigB, B, f, Glucose, Rate Law: f*(Glucose/Gh)^vh/(1+(Glucose/Gh)^vh)*TigB*B
kab = 3.94; Vp = 48.0 Reaction: => Anakinra; Anakinrasc, Rate Law: kab*Anakinrasc/Vp
k1 = 0.2; placebo_on = 0.0; k2 = 0.0025 Reaction: IL1b =>, Rate Law: piecewise((1-placebo_on)*k1*IL1b, time < 91, (1-placebo_on)*k2*IL1b)
CL = 432.0; Vp = 48.0 Reaction: Anakinra =>, Rate Law: CL/Vp*Anakinra
Kxgi = 1.0E-4 Reaction: Glucose => ; Insulin, Rate Law: Kxgi*Insulin*Glucose
Kglucose = 2.92E-4; lambda = 0.743; Ktr = 0.12 Reaction: a1c5 = (Kglucose*Glucose^lambda*rbc5+Ktr*a1c4)-Ktr*a1c5, Rate Law: (Kglucose*Glucose^lambda*rbc5+Ktr*a1c4)-Ktr*a1c5
replication = 2.740001106E-4 Reaction: => B, Rate Law: replication*B
Kxi = 0.05 Reaction: Proinsulin =>, Rate Law: 0.1*Kxi*Proinsulin
vfg = 4.0; tauf = 0.5; kmf = 0.021; IL1R = 3.743916136E-4; kf = 0.00957754; kmfg = 9.0; xfg = 4.0; vf = 0.4 Reaction: => f; Glucose, Rate Law: tauf*kf*(1+vfg*Glucose^xfg/(kmfg^xfg+Glucose^xfg))*(1+vf*IL1R/(kmf+IL1R))
kab = 3.94 Reaction: Anakinrasc =>, Rate Law: kab*Anakinrasc
apoptosis = 2.740002397E-4 Reaction: B =>, Rate Law: apoptosis*B
tauf = 0.5 Reaction: f =>, Rate Law: tauf*f

States:

Name Description
Glucose [glucose]
f [insulin secretion]
a1c5 [urn:miriam:efo:EFO%3A0004541]
a1c1 [urn:miriam:efo:EFO%3A0004541]
a1c8 [urn:miriam:efo:EFO%3A0004541]
rbc12 [erythrocyte]
a1c3 [urn:miriam:efo:EFO%3A0004541]
IL1b [Interleukin-1 beta]
rbc6 [erythrocyte]
B [pancreatic beta cell]
a1c12 [urn:miriam:efo:EFO%3A0004541]
rbc3 [erythrocyte]
rbc1 [erythrocyte]
Proinsulin [Insulin]
Anakinra [Interleukin-1 receptor antagonist protein; pharmaceutical]
a1c7 [urn:miriam:efo:EFO%3A0004541]
a1c11 [urn:miriam:efo:EFO%3A0004541]
rbc2 [erythrocyte]
rbc11 [erythrocyte]
a1c6 [urn:miriam:efo:EFO%3A0004541]
rbc9 [erythrocyte]
rbc5 [erythrocyte]
TigB [insulin secretion]
Anakinrasc [Interleukin-1 receptor antagonist protein]
a1c9 [urn:miriam:efo:EFO%3A0004541]
Insulin [Insulin]
a1c2 [urn:miriam:efo:EFO%3A0004541]
a1c10 [urn:miriam:efo:EFO%3A0004541]
rbc10 [erythrocyte]
a1c4 [urn:miriam:efo:EFO%3A0004541]
rbc4 [erythrocyte]
rbc7 [erythrocyte]
rbc8 [erythrocyte]
hba1c [urn:miriam:efo:EFO%3A0004541]

Observables: none

Palsson2013 - Fully-integration immune response model (FIRM)FIRM (The Fully-integrated Immune Response Modeling) is a hy…

BACKGROUND: The complexity and multiscale nature of the mammalian immune response provides an excellent test bed for the potential of mathematical modeling and simulation to facilitate mechanistic understanding. Historically, mathematical models of the immune response focused on subsets of the immune system and/or specific aspects of the response. Mathematical models have been developed for the humoral side of the immune response, or for the cellular side, or for cytokine kinetics, but rarely have they been proposed to encompass the overall system complexity. We propose here a framework for integration of subset models, based on a system biology approach. RESULTS: A dynamic simulator, the Fully-integrated Immune Response Model (FIRM), was built in a stepwise fashion by integrating published subset models and adding novel features. The approach used to build the model includes the formulation of the network of interacting species and the subsequent introduction of rate laws to describe each biological process. The resulting model represents a multi-organ structure, comprised of the target organ where the immune response takes place, circulating blood, lymphoid T, and lymphoid B tissue. The cell types accounted for include macrophages, a few T-cell lineages (cytotoxic, regulatory, helper 1, and helper 2), and B-cell activation to plasma cells. Four different cytokines were accounted for: IFN-γ, IL-4, IL-10 and IL-12. In addition, generic inflammatory signals are used to represent the kinetics of IL-1, IL-2, and TGF-β. Cell recruitment, differentiation, replication, apoptosis and migration are described as appropriate for the different cell types. The model is a hybrid structure containing information from several mammalian species. The structure of the network was built to be physiologically and biochemically consistent. Rate laws for all the cellular fate processes, growth factor production rates and half-lives, together with antibody production rates and half-lives, are provided. The results demonstrate how this framework can be used to integrate mathematical models of the immune response from several published sources and describe qualitative predictions of global immune system response arising from the integrated, hybrid model. In addition, we show how the model can be expanded to include novel biological findings. Case studies were carried out to simulate TB infection, tumor rejection, response to a blood borne pathogen and the consequences of accounting for regulatory T-cells. CONCLUSIONS: The final result of this work is a postulated and increasingly comprehensive representation of the mammalian immune system, based on physiological knowledge and susceptible to further experimental testing and validation. We believe that the integrated nature of FIRM has the potential to simulate a range of responses under a variety of conditions, from modeling of immune responses after tuberculosis (TB) infection to tumor formation in tissues. FIRM also has the flexibility to be expanded to include both complex and novel immunological response features as our knowledge of the immune system advances. link: http://identifiers.org/pubmed/24074340

Parameters:

Name Description
q72e = 1.0E-4 Reaction: x_3 => x_3 + x_35, Rate Law: q72e*x_3
volLymphT = 10.0; Rho21 = 100.0 Reaction: x_11 => x_12, Rate Law: Rho21*volLymphT
w9 = 0.14; Mu9 = 0.04 Reaction: x_3 => x_1 + x_3, Rate Law: Mu9*w9*x_3
q72b = 5.0E-5 Reaction: x_8 => x_35 + x_8, Rate Law: q72b*x_8
Eta47 = 0.0024 Reaction: x_19 => x_16 + x_43, Rate Law: Eta47*x_19
volLymphT = 10.0; Mu22 = 0.9; c22 = 3000.0 Reaction: x_12 => x_12, Rate Law: x_12*Mu22/(c22+(x_12/volLymphT)^2)
Mu8 = 0.1; volLung = 1000.0; v14 = 0.0; k3 = 0.11; K3s = 50.0 Reaction: x_3 + x_5 => x_3, Rate Law: Mu8*x_3*x_5*(x_5/x_3)^2/((x_5/x_3)^2+K3s^2)/x_3+x_3*x_5*volLung*v14/x_3/x_3+x_3*x_5*x_3/x_3*(x_5/x_3)^2*k3/((x_5/x_3)^2+K3s^2)/x_3
c12 = 500000.0; volLung = 1000.0; Delta12 = 0.4; cf12 = 150.0; fi12 = 2.333 Reaction: x_1 + x_33 + x_39 + x_4 + x_5 => x_1 + x_2 + x_33 + x_39 + x_4 + x_5, Rate Law: Delta12*x_1*x_39/(x_39+fi12*x_33+cf12*volLung)*x_4/(c12*volLung+x_4+x_5)
Mu86 = 1.0; volLung = 1000.0; FACTOR = NaN; cf86 = 50.0; cF = 1000.0 Reaction: x_29 + x_8 => x_29 + x_8, Rate Law: x_8*x_8/volLung*x_29*volLung*Mu86/(cF+x_29)/(cf86+FACTOR)
volBlood = 4500.0; Rho34 = 10.0 Reaction: => x_16 + x_43, Rate Law: Rho34*volBlood
Delta11 = 0.36; volLung = 1000.0; cf11 = 100.0 Reaction: x_2 + x_35 => x_1 + x_35, Rate Law: Delta11*x_2*x_35/(x_35+cf11*volLung)
Delta54 = 0.001; volLung = 1000.0 Reaction: x_26 + x_29 => x_27 + x_29, Rate Law: x_26*x_29*Delta54/volLung
Delta43 = 2.4 Reaction: x_20 + x_47 + x_48 => x_21 + x_47 + x_48, Rate Law: Delta43*x_20*x_47/(x_47+x_48+1E-5)
Alpha61 = 10.0 Reaction: x_2 + x_29 => x_2 + x_30, Rate Law: Alpha61*x_2*x_29/(100000+x_29)
volLung = 1000.0; Mu15 = 0.02; c15 = 150000.0 Reaction: x_4 => x_4 + x_6, Rate Law: volLung*x_4*Mu15/(c15*volLung+x_4)
Eta13 = 1.0 Reaction: x_2 =>, Rate Law: Eta13*x_2
Delta36 = 2.4 Reaction: x_16 + x_43 + x_44 => x_17 + x_44 + x_45, Rate Law: Delta36*x_16*x_44/(x_43+x_44+1E-5)
c25 = 100000.0; volLung = 1000.0; Mu25 = 0.4 Reaction: x_2 + x_7 => x_2 + x_7, Rate Law: Mu25*x_7*x_2/(c25*volLung+x_2)
Gamma31 = 0.3333 Reaction: x_9 => x_14, Rate Law: Gamma31*x_9
k3 = 0.11; K3s = 50.0 Reaction: x_3 + x_5 => x_5, Rate Law: k3*x_3/((x_5/x_3)^2+K3s^2)*(x_5/x_3)^2
ci12 = 1000.0; Deltai12 = 0.009 Reaction: x_1 + x_29 => x_2 + x_29, Rate Law: Deltai12*x_1*x_29/(ci12+x_29)
volLung = 1000.0; MuI = 9.0; cF = 1000.0; cI = 50.0; Gamma17 = 0.2 Reaction: x_29 + x_6 => x_29, Rate Law: x_6/volLung*x_29*volLung*MuI*Gamma17/(cF+x_29)/(cI+x_29/(cF+x_29))
Gamma32 = 0.9 Reaction: x_14 => x_15, Rate Law: Gamma32*x_14
Eta16 = 0.01 Reaction: x_6 =>, Rate Law: Eta16*x_6
Mu8 = 0.1 Reaction: x_3 + x_5 => x_3 + x_5, Rate Law: Mu8*x_3*x_5/x_3
volLung = 1000.0; c4 = 0.15; Alpha4 = 0.5 Reaction: x_3 + x_5 + x_8 => x_3 + x_8, Rate Law: x_3*x_5*x_3/x_3*x_8*Alpha4/x_3/(x_8/x_3+c4*volLung)/x_3
fi27 = 4.1; Delta27 = 0.1; fii27 = 4.8; volLung = 1000.0; cf27 = 30.0 Reaction: x_33 + x_35 + x_36 + x_38 + x_7 => x_33 + x_35 + x_36 + x_38 + x_8, Rate Law: x_36*x_38*Delta27/volLung*x_7/(x_36+fi27*x_33+fii27*x_35+cf27*volLung)
Alpha5 = 1.25E-7; volLung = 1000.0 Reaction: x_2 + x_4 => x_2, Rate Law: x_2*x_4*Alpha5/volLung
Rho19 = 1000.0; volLymphT = 10.0 Reaction: => x_11, Rate Law: Rho19*volLymphT
Delta38 = 2.4 Reaction: x_18 + x_47 + x_48 => x_19 + x_48, Rate Law: Delta38*x_18*x_47/(x_47+x_48+1E-5)
q68a = 0.0029 Reaction: x_7 => x_33 + x_7, Rate Law: q68a*x_7
Eta53 = 0.02 Reaction: x_26 =>, Rate Law: Eta53*x_26
volLung = 1000.0; Rho21 = 100.0; MuI = 9.0; cF = 1000.0; cI = 50.0; Rho50 = 100.0 Reaction: x_29 => x_29 + x_6, Rate Law: x_29*volLung*Rho21*MuI/(cF+x_29)/(cI+x_29/(cF+x_29))+x_29*volLung*Rho50*MuI/(cF+x_29)/(cI+x_29/(cF+x_29))
volLung = 1000.0; k7 = 1.0E-7 Reaction: x_4 + x_6 => x_6, Rate Law: x_4*x_6*k7/volLung
Eta10 = 0.05 Reaction: x_1 =>, Rate Law: Eta10*x_1
c24 = 15000.0; volLung = 1000.0; Gamma24 = 0.9 Reaction: x_13 + x_2 => x_2 + x_7, Rate Law: Gamma24*x_13*x_2/(c24*volLung+x_2)
volLung = 1000.0; v14 = 0.0; k3 = 0.11; K3s = 50.0 Reaction: x_3 + x_5 => x_3 + x_4 + x_5, Rate Law: volLung*x_5*v14/x_3+k3*x_3*1/((x_5/x_3)^2+K3s^2)*x_5/x_3*(x_5/x_3)^2
volLymphT = 10.0; Delta21 = 1.0E-4 Reaction: x_10 + x_11 => x_10 + x_12, Rate Law: x_10*x_11*Delta21/volLymphT
Mui9 = 125000.0; volLung = 1000.0; MuI = 9.0; cF = 1000.0; cI = 50.0 Reaction: x_29 => x_1 + x_29, Rate Law: x_29*volLung*Mui9*MuI/(cF+x_29)/(cI+x_29/(cF+x_29))
K93 = 100.0; Beta90 = 1000.0 Reaction: x_52 => x_4 + x_51, Rate Law: Beta90*x_52/K93
q68b = 0.0218 Reaction: x_9 => x_33 + x_9, Rate Law: q68b*x_9
Gamma40 = 0.9 Reaction: x_18 + x_47 => x_20, Rate Law: Gamma40*x_18
q72d = 1.0E-4 Reaction: x_7 => x_35 + x_7, Rate Law: q72d*x_7
volLung = 1000.0; Rho80 = 700.0; c80 = 5000.0; ci80 = 50.0 Reaction: x_38 + x_4 + x_5 => x_38 + x_39 + x_4 + x_5, Rate Law: Rho80*volLung*x_38*x_4/(ci80*volLung+x_38)/(c80*volLung+x_4+x_5)+Rho80*volLung*x_38*x_5/(ci80*volLung+x_38)/(c80*volLung+x_4+x_5)
volLung = 1000.0; Rho9 = 5000.0 Reaction: => x_1, Rate Law: Rho9*volLung
k2 = 0.4; c2 = 1000000.0; volLung = 1000.0 Reaction: x_1 + x_4 => x_3, Rate Law: k2*x_1*x_4/(x_4+c2*volLung)
volLung = 1000.0; Beta90 = 1000.0 Reaction: x_4 + x_51 => x_52, Rate Law: Beta90*x_4*x_51/volLung
Deltai27 = 0.001 Reaction: x_2 + x_30 + x_7 => x_2 + x_30 + x_7 + x_8, Rate Law: Deltai27*x_2*x_30*x_7/(10000000+x_30)
v67 = 0.0; volLung = 1000.0 Reaction: x_32 =>, Rate Law: v67*volLung
Eta28 = 0.3333 Reaction: x_8 =>, Rate Law: Eta28*x_8
Mu9 = 0.04 Reaction: x_2 => x_1 + x_2, Rate Law: Mu9*x_2
volLung = 1000.0; cf29 = 2.0; Delta29 = 0.05; fi29 = 0.12 Reaction: x_33 + x_39 + x_7 => x_33 + x_39 + x_9, Rate Law: Delta29*x_7*x_33/(x_33+fi29*x_39+cf29*volLung)
volLung = 1000.0; v30 = 0.0 Reaction: x_9 =>, Rate Law: v30*volLung
volLung = 1000.0; c17 = 10000.0; Gamma17 = 0.2 Reaction: x_4 + x_6 => x_10 + x_4, Rate Law: Gamma17*x_6*x_4/(c17*volLung+x_4)
volLung = 1000.0; c52 = 15000.0; Gamma52 = 0.9 Reaction: x_2 + x_25 => x_2 + x_26, Rate Law: Gamma52*x_25*x_2/(c52*volLung+x_2)
Eta33 = 0.3333 Reaction: x_15 =>, Rate Law: Eta33*x_15
Gamma23 = 0.9 Reaction: x_12 => x_13, Rate Law: Gamma23*x_12
volLung = 1000.0; Mu55 = 1.0; FACTOR = NaN; cF = 1000.0 Reaction: x_27 + x_29 + x_8 => x_27 + x_29 + x_8, Rate Law: x_8*x_27/volLung*x_29*volLung*Mu55/(cF+x_29)/(cF+FACTOR)
ci72 = 0.05; volLung = 1000.0; c72 = 51.0; q72a = 0.006 Reaction: x_2 + x_35 + x_39 => x_2 + x_35 + x_39, Rate Law: c72*q72a*x_2*1/(x_35+ci72*x_39+c72*volLung)
Eta26 = 0.3333 Reaction: x_7 =>, Rate Law: Eta26*x_7
v41 = 0.0; volLymphB = 150.0 Reaction: x_20 => x_18 + x_47, Rate Law: v41*volLymphB
volLung = 1000.0; cii80 = 100000.0; q80 = 0.02 Reaction: x_2 + x_8 => x_2 + x_39 + x_8, Rate Law: volLung*x_2*x_8*q80/(cii80*volLung+x_2)

States:

Name Description
x 6 x_6
x 3 x_3
x 33 x_33
x 29 x_29
x 22 x_22
x 11 x_11
x 17 x_17
x 5 x_5
x 15 x_15
x 18 x_18
x 32 x_32
x 14 x_14
x 16 x_16
x 35 x_35
x 1 x_1
x 8 x_8
x 21 x_21
x 7 x_7
x 4 x_4
x 19 x_19
x 9 x_9
x 10 x_10
x 12 x_12
x 27 x_27
x 20 x_20
x 2 x_2
x 26 x_26
x 13 x_13

Observables: none

Cells switch between quiescence and proliferation states for maintaining tissue homeostasis and regeneration. At the res…

Cells switch between quiescence and proliferation states for maintaining tissue homeostasis and regeneration. At the restriction point (R-point), cells become irreversibly committed to the completion of the cell cycle independent of mitogen. The mechanism involving hyper-phosphorylation of retinoblastoma (Rb) and activation of transcription factor E2F is linked to the R-point passage. However, stress stimuli trigger exit from the cell cycle back to the mitogen-sensitive quiescent state after Rb hyper-phosphorylation but only until APC/CCdh1 inactivation. In this study, we developed a mathematical model to investigate the reversible transition between quiescence and proliferation in mammalian cells with respect to mitogen and stress signals. The model integrates the current mechanistic knowledge and accounts for the recent experimental observations with cells exiting quiescence and proliferating cells. We show that Cyclin E:Cdk2 couples Rb-E2F and APC/CCdh1 bistable switches and temporally segregates the R-point and the G1/S transition. A redox-dependent mutual antagonism between APC/CCdh1 and its inhibitor Emi1 makes the inactivation of APC/CCdh1 bistable. We show that the levels of Cdk inhibitor (CKI) and mitogen control the reversible transition between quiescence and proliferation. Further, we propose that shifting of the mitogen-induced transcriptional program to G2-phase in proliferating cells might result in an intermediate Cdk2 activity at the mitotic exit and in the immediate inactivation of APC/CCdh1. Our study builds a coherent framework and generates hypotheses that can be further explored by experiments. link: http://identifiers.org/pubmed/29856829

Parameters: none

States: none

Observables: none

MODEL8685104549 @ v0.0.1

This a model from the article: A mathematical model of the electrophysiological alterations in rat ventricular myocyte…

Our mathematical model of the rat ventricular myocyte (Pandit et al., 2001) was utilized to explore the ionic mechanism(s) that underlie the altered electrophysiological characteristics associated with the short-term model of streptozotocin-induced, type-I diabetes. The simulations show that the observed reductions in the Ca(2+)-independent transient outward K(+) current (I(t)) and the steady-state outward K(+) current (I(ss)), along with slowed inactivation of the L-type Ca(2+) current (I(CaL)), can result in the prolongation of the action potential duration, a well-known experimental finding. In addition, the model demonstrates that the slowed reactivation kinetics of I(t) in diabetic myocytes can account for the more pronounced rate-dependent action potential duration prolongation in diabetes, and that a decrease in the electrogenic Na(+)-K(+) pump current (I(NaK)) results in a small depolarization in the resting membrane potential (V(rest)). This depolarization reduces the availability of the Na(+) channels (I(Na)), thereby resulting in a slower upstroke (dV/dt(max)) of the diabetic action potential. Additional simulations suggest that a reduction in the magnitude of I(CaL), in combination with impaired sarcoplasmic reticulum uptake can lead to a decreased sarcoplasmic reticulum Ca(2+) load. These factors contribute to characteristic abnormal Ca(2+) homeostasis (reduced peak systolic value and rate of decay) in myocytes from diabetic animals. In combination, these simulation results provide novel information and integrative insights concerning plausible ionic mechanisms for the observed changes in cardiac repolarization and excitation-contraction coupling in rat ventricular myocytes in the setting of streptozotocin-induced, type-I diabetes. link: http://identifiers.org/pubmed/12547767

Parameters: none

States: none

Observables: none

MODEL1708210000 @ v0.0.1

Hass2017-PanRTK model for single cell lineThe model structure comprises heterodimerization and receptor trafficking as d…

Targeted therapies have shown significant patient benefit in about 5-10% of solid tumors that are addicted to a single oncogene. Here, we explore the idea of ligand addiction as a driver of tumor growth. High ligand levels in tumors have been shown to be associated with impaired patient survival, but targeted therapies have not yet shown great benefit in unselected patient populations. Using an approach of applying Bagged Decision Trees (BDT) to high-dimensional signaling features derived from a computational model, we can predict ligand dependent proliferation across a set of 58 cell lines. This mechanistic, multi-pathway model that features receptor heterodimerization, was trained on seven cancer cell lines and can predict signaling across two independent cell lines by adjusting only the receptor expression levels for each cell line. Interestingly, for patient samples the predicted tumor growth response correlates with high growth factor expression in the tumor microenvironment, which argues for a co-evolution of both factors in vivo. link: http://identifiers.org/pubmed/28944080

Parameters: none

States: none

Observables: none

BIOMD0000000359 @ v0.0.1

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

We have analyzed several mathematical models that describe inhibition of the factor VIIa-tissue factor complex (VIIa-TF) by tissue factor pathway inhibitor (TFPI). At the core of these models is a common mechanism of TFPI action suggesting that only the Xa-TFPI complex is the inhibitor of the extrinsic tenase activity. However, the model based on this hypothesis could not explain well all the available experimental data. Here, we show that a good quantitative description of all experimental data could be achieved in a model that contains two more assumptions. The first assumption is based on the hypothesis originally proposed by Baugh et al. [Baugh, R.J., Broze, G.J. Jr & Krishnaswamy, S. (1998) J. Biol. Chem. 273, 4378-4386], which suggests that TFPI could inhibit the enzyme-product complex Xa-VIIa-TF. The second assumption proposes an interaction between the X-VIIa-TF complex and the factor Xa-TFPI complex. Experiments to test these hypotheses are suggested. link: http://identifiers.org/pubmed/11985578

Parameters:

Name Description
k1=6.0; k2=0.02 Reaction: VIIa_TF_Xa + TFPI => VIIa_TF_Xa_TFPI, Rate Law: compartment*(k1*VIIa_TF_Xa*TFPI-k2*VIIa_TF_Xa_TFPI)
k2=0.02; k1=0.054 Reaction: Xa + TFPI => Xa_TFPI, Rate Law: compartment*(k1*Xa*TFPI-k2*Xa_TFPI)
k1=420.0 Reaction: VIIa_TF_X => VIIa_TF_Xa, Rate Law: compartment*k1*VIIa_TF_X
k1=0.44; k2=0.0 Reaction: VIIa_TF + Xa_TFPI => Xa_TFPI_VIIa_TF, Rate Law: compartment*(k1*VIIa_TF*Xa_TFPI-k2*Xa_TFPI_VIIa_TF)
k2=770.0; k1=5.0 Reaction: X + VIIa_TF => VIIa_TF_X, Rate Law: compartment*(k1*X*VIIa_TF-k2*VIIa_TF_X)
k2=5.0; k1=770.0 Reaction: VIIa_TF_Xa => Xa + VIIa_TF, Rate Law: compartment*(k1*VIIa_TF_Xa-k2*Xa*VIIa_TF)
k1=0.0; k2=0.0 Reaction: VIIa_TF_Xa_TFPI => Xa_TFPI_VIIa_TF, Rate Law: compartment*(k1*VIIa_TF_Xa_TFPI-k2*Xa_TFPI_VIIa_TF)
k1=20.0; k2=0.0 Reaction: VIIa_TF_X + Xa_TFPI => X + VIIa_TF_Xa_TFPI, Rate Law: compartment*(k1*VIIa_TF_X*Xa_TFPI-k2*X*VIIa_TF_Xa_TFPI)

States:

Name Description
Xa TFPI [Tissue factor pathway inhibitor; Coagulation factor X]
Xa TFPI VIIa TF [Tissue factor; Coagulation factor VII; Tissue factor pathway inhibitor; Coagulation factor X]
VIIa TF X [Coagulation factor X; Tissue factor; Coagulation factor VII]
X [Coagulation factor X]
TFPI [Tissue factor pathway inhibitor]
VIIa TF Xa TFPI [Tissue factor; Coagulation factor VII; Coagulation factor X; Tissue factor pathway inhibitor]
VIIa TF [Tissue factor; Coagulation factor VII]
Xa [Coagulation factor X]
VIIa TF Xa [Coagulation factor X; Tissue factor; Coagulation factor VII]

Observables: none

Full and reduced mathematical model of blood coagulation focusing on fibrin formation and the response to varied TF and…

Analysis of complex time-dependent biological networks is an important challenge in the current postgenomic era. We propose a middle-out approach for decomposition and analysis of complex time-dependent biological networks based on: 1), creation of a detailed mechanism-driven mathematical model of the network; 2), network response decomposition into several physiologically relevant subtasks; and 3), subsequent decomposition of the model, with the help of task-oriented necessity and sensitivity analysis into several modules that each control a single specific subtask, which is followed by further simplification employing temporal hierarchy reduction. The technique is tested and illustrated by studying blood coagulation. Five subtasks (threshold, triggering, control by blood flow velocity, spatial propagation, and localization), together with responsible modules, can be identified for the coagulation network. We show that the task of coagulation triggering is completely regulated by a two-step pathway containing a single positive feedback of factor V activation by thrombin. These theoretical predictions are experimentally confirmed by studies of fibrin generation in normal, factor V-, and factor VIII-deficient plasmas. The function of the factor V-dependent feedback is to minimize temporal and parametrical intervals of fibrin clot instability. We speculate that this pathway serves to lessen possibility of fibrin clot disruption by flow and subsequent thromboembolism. link: http://identifiers.org/pubmed/20441738

Parameters:

Name Description
k_01 = 1.1; k03 = 0.4; k02 = 0.0014; k01 = 4.2 Reaction: VII_TF = ((k01*VII*TF-k_01*VII_TF)-k02*VII_TF*IIa_F)-k03*VII_TF*Xa_F, Rate Law: ((k01*VII*TF-k_01*VII_TF)-k02*VII_TF*IIa_F)-k03*VII_TF*Xa_F
k15 = 54.0; K15 = 147.0; h14 = 0.35 Reaction: VIIIa = k15*VIII*IIa_F/(K15+IIa_F)-h14*VIIIa, Rate Law: k15*VIII*IIa_F/(K15+IIa_F)-h14*VIIIa
h17 = 2.6E-5; h20 = 1.4E-4; k17 = 0.03; h18 = 6.0E-6; h16 = 1.9E-5; h19 = 0.0054 Reaction: XIa = k17*Phospholipid*XI*IIa_F-(h16*AT_III+h17*alpha2_antiplasmin+h18*alpha1_antitrypsin+h19*ProteinC_Inhibitor+h20*C1_inhibitor)*XIa, Rate Law: k17*Phospholipid*XI*IIa_F-(h16*AT_III+h17*alpha2_antiplasmin+h18*alpha1_antitrypsin+h19*ProteinC_Inhibitor+h20*C1_inhibitor)*XIa
K26 = 470.0; n25 = 16000.0; K25 = 320.0 Reaction: X_B = X*Phospholipid*n25/(K25*(1+X/K25+II/K26)), Rate Law: missing
K05 = 200.0; k04 = 15.0; K04 = 210.0; k05 = 5.8 Reaction: IX = (-k04/K04)*IX*VIIa_TF_F-k05*IX*XIa/(K05+IX), Rate Law: (-k04/K04)*IX*VIIa_TF_F-k05*IX*XIa/(K05+IX)
k07 = 0.06; k08 = 6350.0; k06 = 435.0; K06 = 238.0; K07 = 230.0; K09 = 278.0 Reaction: X = ((-k06/K06)*X*VIIa_TF_F-k07*IXa_B_F*X_B/(Phospholipid*K07))-k08*IXa_B_F*VIIIa_B_F*X_B/(Phospholipid^2*K09*k08), Rate Law: ((-k06/K06)*X*VIIa_TF_F-k07*IXa_B_F*X_B/(Phospholipid*K07))-k08*IXa_B_F*VIIIa_B_F*X_B/(Phospholipid^2*K09*k08)
h21 = 6.0E-6; h22 = 6.0E-6; h23 = 7.0E-7; k18 = 2.0E-5; h24 = 3.9E-4 Reaction: APC = k18*PC*IIa_F-(h21*alpha2_macroglobulin+h22*alpha2_antiplasmin+h23*alpha1_antitrypsin+h24*ProteinC_Inhibitor)*APC, Rate Law: k18*PC*IIa_F-(h21*alpha2_macroglobulin+h22*alpha2_antiplasmin+h23*alpha1_antitrypsin+h24*ProteinC_Inhibitor)*APC
K20 = 2.57; n20 = 260.0 Reaction: IXa_B_F = IXa*Phospholipid*n20/(K20+IXa), Rate Law: missing
k_01 = 1.1; k02 = 0.0014; k01 = 4.2 Reaction: VII = (-(k01*VII*TF-k_01*VII_TF))-k02*VII*IIa_F, Rate Law: (-(k01*VII*TF-k_01*VII_TF))-k02*VII*IIa_F
k11 = 0.052; k_11 = 0.02; h02 = 6.0 Reaction: TFPI = (-(k11*Xa_F*TFPI-k_11*Xa_TFPI))-h02*Xa_VIIa_TF*TFPI, Rate Law: (-(k11*Xa_F*TFPI-k_11*Xa_TFPI))-h02*Xa_VIIa_TF*TFPI
K21 = 1.5; K10 = 1655.0; K22 = 150.0; n21 = 750.0 Reaction: VIIIa_B_F = VIIIa*Phospholipid*n21/((K21+VIIIa)*(1+X_B/(Phospholipid*K10)*(1+ProteinS_inhibitor/K22))), Rate Law: missing
k_01 = 1.1; h01 = 0.44; k03 = 0.4; k02 = 0.0014; k01 = 4.2; h02 = 6.0 Reaction: VIIa_TF = (((k01*VIIa*TF-k_01*VIIa_TF_F)+k02*VII_TF*IIa_F+k03*VII_TF*Xa_F)-h01*VIIa_TF_F*Xa_TFPI)-h02*Xa_VIIa_TF*TFPI, Rate Law: (((k01*VIIa*TF-k_01*VIIa_TF_F)+k02*VII_TF*IIa_F+k03*VII_TF*Xa_F)-h01*VIIa_TF_F*Xa_TFPI)-h02*Xa_VIIa_TF*TFPI
k15 = 54.0; K15 = 147.0 Reaction: VIII = (-k15)*VIII*IIa_F/(K15+IIa_F), Rate Law: (-k15)*VIII*IIa_F/(K15+IIa_F)
n27 = 2700.0; K27 = 2.9 Reaction: Va_B = Va*Phospholipid*n27/(K27+Va), Rate Law: missing
h03 = 8.2E-6; h08 = 2.2E-5; h04 = 1.5E-4; h09 = 4.1E-4; h16 = 1.9E-5 Reaction: AT_III = (-(h03*IXa+h04*Xa_F+h08*Xa_Va_b+h09*IIa_F+h16*XIa))*AT_III, Rate Law: (-(h03*IXa+h04*Xa_F+h08*Xa_Va_b+h09*IIa_F+h16*XIa))*AT_III
k_01 = 1.1; k01 = 4.2 Reaction: TF = (-(k01*VIIa*TF-k_01*VIIa_TF_F))-(k01*VII*TF-k_01*VII_TF), Rate Law: (-(k01*VIIa*TF-k_01*VIIa_TF_F))-(k01*VII*TF-k_01*VII_TF)
K23 = 0.118; K24 = 200.0 Reaction: Xa_Va_b = Xa*Va_B/(K23*(1+ProteinS_inhibitor/K24+Xa/K23)+Va_B), Rate Law: missing
k17 = 0.03 Reaction: XI = (-k17)*Phospholipid*XI*IIa_F, Rate Law: (-k17)*Phospholipid*XI*IIa_F
K14 = 7200.0 Reaction: IIa_F = IIa/(1+(fibrin+fibrinogen)/K14), Rate Law: missing
k11 = 0.052; h08 = 2.2E-5; h07 = 0.0012; k_11 = 0.02; K06 = 238.0; K07 = 230.0; h05 = 4.0E-5; k07 = 0.06; k08 = 6350.0; h04 = 1.5E-4; k06 = 435.0; h06 = 1.36E-5; K09 = 278.0 Reaction: Xa = (((k06/K06*X*VIIa_TF_F+k07*IXa_B_F*X_B/(Phospholipid*K07)+k08*IXa_B_F*VIIIa_B_F*X_B/(Phospholipid^2*K09*k08))-(k11*Xa_F*TFPI-k_11*Xa_TFPI))-(h04*AT_III+h05*alpha2_macroglobulin+h06*alpha1_antitrypsin+h07*ProteinC_Inhibitor)*Xa_F)-h08*AT_III*Xa_Va_b, Rate Law: (((k06/K06*X*VIIa_TF_F+k07*IXa_B_F*X_B/(Phospholipid*K07)+k08*IXa_B_F*VIIIa_B_F*X_B/(Phospholipid^2*K09*k08))-(k11*Xa_F*TFPI-k_11*Xa_TFPI))-(h04*AT_III+h05*alpha2_macroglobulin+h06*alpha1_antitrypsin+h07*ProteinC_Inhibitor)*Xa_F)-h08*AT_III*Xa_Va_b
k11 = 0.052; h01 = 0.44; k_11 = 0.02 Reaction: Xa_TFPI = (k11*Xa_F*TFPI-k_11*Xa_TFPI)-h01*VIIa_TF_F*Xa_TFPI, Rate Law: (k11*Xa_F*TFPI-k_11*Xa_TFPI)-h01*VIIa_TF_F*Xa_TFPI
k16 = 14.0; K16 = 71.7 Reaction: V = (-k16)*V*IIa_F/(K16+IIa_F), Rate Law: (-k16)*V*IIa_F/(K16+IIa_F)
K06 = 238.0; K04 = 210.0 Reaction: VIIa_TF_F = VIIa_TF/(1+IX/K04+X/K06), Rate Law: missing
k13 = 1.44; k12 = 45.0 Reaction: II = (-k12)*Phospholipid*Xa_F*II-k13*Xa_Va_b*II_B/Phospholipid, Rate Law: (-k12)*Phospholipid*Xa_F*II-k13*Xa_Va_b*II_B/Phospholipid
k18 = 2.0E-5 Reaction: PC = (-k18)*PC*IIa_F, Rate Law: (-k18)*PC*IIa_F
k14 = 5040.0; K14 = 7200.0 Reaction: fibrin = k14/K14*fibrinogen*IIa_F, Rate Law: k14/K14*fibrinogen*IIa_F
k06 = 435.0; k_19 = 770.0; K06 = 238.0 Reaction: Xa_VIIa_TF = k06/(K06*k_19)*X*VIIa_TF_F, Rate Law: missing
h03 = 8.2E-6; K05 = 200.0; k04 = 15.0; K04 = 210.0; k05 = 5.8 Reaction: IXa = (k04/K04*IX*VIIa_TF_F+k05*IX*XIa/(K05+IX))-h03*Xa_TFPI*IXa, Rate Law: (k04/K04*IX*VIIa_TF_F+k05*IX*XIa/(K05+IX))-h03*Xa_TFPI*IXa
k16 = 14.0; K16 = 71.7; h15 = 7.7 Reaction: Va = k16*V*IIa_F/(K16+IIa_F)-h15*APC*Va_B_F, Rate Law: k16*V*IIa_F/(K16+IIa_F)-h15*APC*Va_B_F
k13 = 1.44; h10 = 1.0E-4; h12 = 3.7E-4; h11 = 3.0E-6; h09 = 4.1E-4; k12 = 45.0; h13 = 6.3E-5 Reaction: IIa = (k12*Phospholipid*Xa_F*II+k13*Xa_Va_b*II_B/Phospholipid)-(h09*AT_III+h10*alpha2_macroglobulin+h11*alpha1_antitrypsin+h12*ProteinC_Inhibitor+h13*heparin_cofactor2)*IIa_F, Rate Law: (k12*Phospholipid*Xa_F*II+k13*Xa_Va_b*II_B/Phospholipid)-(h09*AT_III+h10*alpha2_macroglobulin+h11*alpha1_antitrypsin+h12*ProteinC_Inhibitor+h13*heparin_cofactor2)*IIa_F

States:

Name Description
fibrin [Fibrin]
VIII [Coagulation Factor VIII]
TFPI [TFPI Gene]
X B [Coagulation Factor X]
II B [Thrombin]
V [Coagulation Factor V]
Xa VIIa TF [Coagulation Factor VII; Coagulation Factor X; Tissue Factor; Coagulation Factor VII; Coagulation Factor X]
Xa [Coagulation Factor X]
Va B [Coagulation Factor V]
VIIIa B F [Coagulation Factor VIII]
PC [Protein C]
VII TF [Coagulation Factor VII; Tissue Factor]
TF [Tissue Factor]
XIa [121660]
X [Coagulation Factor X]
IIa F [Thrombin]
VIIIa [Coagulation Factor VIII]
AT III [Therapeutic Human Antithrombin-III]
Va [Coagulation Factor V]
IIa [Thrombin]
Xa TFPI [Coagulation Factor X; TFPI Gene; Coagulation Factor X]
VIIa [Coagulation Factor VII]
Xa Va b [Coagulation Factor V; Coagulation Factor X]
fibrinogen [Fibrinogen]
XI [121660]
APC [Protein C]
VIIa TF F [Tissue Factor; Coagulation Factor VII]
VIIa TF [Coagulation Factor VII; Tissue Factor]
IXa [Coagulation Factor IX]
Xa F [Coagulation Factor X]
Va B F [Coagulation Factor V]
VII [Coagulation Factor VII]
II [Thrombin]
IX [Coagulation Factor IX]
IXa B F [Coagulation Factor IX]

Observables: none

Pappalardo2016 - PI3K/AKT and MAPK Signaling Pathways in Melanoma CancerThis model is described in the article: [Comput…

Malignant melanoma is an aggressive tumor of the skin and seems to be resistant to current therapeutic approaches. Melanocytic transformation is thought to occur by sequential accumulation of genetic and molecular alterations able to activate the Ras/Raf/MEK/ERK (MAPK) and/or the PI3K/AKT (AKT) signalling pathways. Specifically, mutations of B-RAF activate MAPK pathway resulting in cell cycle progression and apoptosis prevention. According to these findings, MAPK and AKT pathways may represent promising therapeutic targets for an otherwise devastating disease.Here we show a computational model able to simulate the main biochemical and metabolic interactions in the PI3K/AKT and MAPK pathways potentially involved in melanoma development. Overall, this computational approach may accelerate the drug discovery process and encourages the identification of novel pathway activators with consequent development of novel antioncogenic compounds to overcome tumor cell resistance to conventional therapeutic agents. The source code of the various versions of the model are available as S1 Archive. link: http://identifiers.org/pubmed/27015094

Parameters:

Name Description
Kcat=8.8912; km=3496490.0 Reaction: species_10 => species_11; species_26, Rate Law: compartment_0*Kcat*species_26*species_10/(km+species_10)
km=62464.6; Kcat=0.884096 Reaction: species_7 => species_6; species_4, Rate Law: compartment_0*Kcat*species_4*species_7/(km+species_7)
Kcat=10.6737; km=184912.0 Reaction: species_15 => species_14; species_0, Rate Law: compartment_0*Kcat*species_0*species_15/(km+species_15)
Kcat=3.19E13; km=3200.0 Reaction: bRafMutated => bRafMutatedInactive; Dabrafenib, Rate Law: compartment_0*Kcat*Dabrafenib*bRafMutated/(km+bRafMutated)
Kcat=0.0213697; km=763523.0 Reaction: species_2 => species_3; species_10, Rate Law: compartment_0*Kcat*species_10*species_2/(km+species_2)
Kcat=0.126329; km=1061.71 Reaction: species_6 => species_7; species_27, Rate Law: compartment_0*Kcat*species_27*species_6/(km+species_6)
Kcat=2.83243; km=518753.0 Reaction: species_8 => species_9; species_26, Rate Law: compartment_0*Kcat*species_26*species_8/(km+species_8)
v=100.0 Reaction: probRafMutated => bRafMutated, Rate Law: compartment_0*v
km=896896.0; Kcat=1611.97 Reaction: species_2 => species_3; species_12, Rate Law: compartment_0*Kcat*species_12*species_2/(km+species_2)
Kcat=185.759; km=4768350.0 Reaction: species_9 => species_8; species_6, Rate Law: compartment_0*Kcat*species_6*species_9/(km+species_9)
k1=2.18503E-5; k2=0.121008 Reaction: species_25 + species_1 => species_0, Rate Law: compartment_0*(k1*species_25*species_1-k2*species_0)
km=1432410.0; Kcat=1509.36 Reaction: species_4 => species_5; species_28, Rate Law: compartment_0*Kcat*species_28*species_4/(km+species_4)
k1=1.92527E-5 Reaction: Dabrafenib =>, Rate Law: compartment_0*k1*Dabrafenib
Kcat=32.344; km=35954.3 Reaction: species_5 => species_4; species_2, Rate Law: compartment_0*Kcat*species_2*species_5/(km+species_5)
Kcat=9.85367; km=1007340.0 Reaction: species_11 => species_10; species_8, Rate Law: compartment_0*Kcat*species_8*species_11/(km+species_11)
k1=2.5 Reaction: species_19 => species_20, Rate Law: compartment_0*k1*species_19
k1=0.2 Reaction: species_0 =>, Rate Law: compartment_0*k1*species_0
k1=0.005 Reaction: species_12 => species_13, Rate Law: compartment_0*k1*species_12
km=6086070.0; Kcat=694.731 Reaction: species_20 => species_19; species_0, Rate Law: compartment_0*Kcat*species_0*species_20/(km+species_20)
Kcat=15.1212; km=119355.0 Reaction: species_6 => species_7; species_16, Rate Law: compartment_0*Kcat*species_16*species_6/(km+species_6)
Kcat=0.0566279; km=653951.0 Reaction: species_17 => species_16; species_14, Rate Law: compartment_0*Kcat*species_14*species_17/(km+species_17)
k1=0.00125 Reaction: species_1 =>, Rate Law: compartment_0*k1*species_1
Kcat=0.0771067; km=272056.0 Reaction: species_15 => species_14; species_4, Rate Law: compartment_0*Kcat*species_4*species_15/(km+species_15)

States:

Name Description
species 9 [Dual specificity mitogen-activated protein kinase kinase 1; K04368; Protein kinase byr1]
species 1 [Receptor Tyrosine Kinase; Protein sevenless]
species 20 [Rap guanine nucleotide exchange factor 1; K06277]
species 4 [K07829; Ras-related protein R-Ras]
species 16 [AKT kinase; Putative serine/threonine-protein kinase-like protein CCR3]
PIP3Active [Phosphatidylinositol-3,4,5-trisphosphate]
PDK1Inactive [[Pyruvate dehydrogenase (acetyl-transferring)] kinase isozyme 1, mitochondrial; Probable [pyruvate dehydrogenase (acetyl-transferring)] kinase, mitochondrial; K12077]
IRS1Active [K16172; Insulin receptor substrate 1]
species 0 [Receptor Tyrosine Kinase; Protein sevenless]
species 21 [Ras-related protein Rap-1A; K04353]
species 8 [Dual specificity mitogen-activated protein kinase kinase 1; Protein kinase byr1; K04368]
species 17 [Putative serine/threonine-protein kinase-like protein CCR3; AKT kinase]
species 12 [ribosomal protein S6 kinase alpha; Putative serine/threonine-protein kinase-like protein CCR3]
species 25 [Growth Factor]
species 5 [K07829; Ras-related protein R-Ras]
species 15 [K00914; Phosphatidylinositol 3-kinase age-1]
S6K1Active [Ribosomal protein S6 kinase beta-1; Putative serine/threonine-protein kinase-like protein CCR3; K04688]
Dabrafenib [D10064; dabrafenib]
species 2 [K03099; Son of sevenless homolog 1]
species 6 [Putative serine/threonine-protein kinase-like protein CCR3; RAF proto-oncogene serine/threonine-protein kinase; K04366]
mTORC1Inactive [Serine/threonine-protein kinase mTOR; TORC1 complex]
species 19 [Rap guanine nucleotide exchange factor 1; K06277]
species 10 [Mitogen-activated protein kinase 1; K05111]
S6K1Inactive [Ribosomal protein S6 kinase beta-1; Putative serine/threonine-protein kinase-like protein CCR3; K04688]
species 11 [Mitogen-activated protein kinase 1; K05111]
bRafMutatedInactive [Putative serine/threonine-protein kinase-like protein CCR3; K04365; Serine/threonine-protein kinase B-raf; BRAF Gene Mutation]
species 30 proRTK
IRS1Inactive [Insulin receptor substrate 1; K16172]
mTORC1Active [Serine/threonine-protein kinase mTOR; TORC1 complex]
species 14 [Phosphatidylinositol 3-kinase age-1; K00914]
species 22 [K04353; Ras-related protein Rap-1A]
PDK1Active [K12077; [Pyruvate dehydrogenase (acetyl-transferring)] kinase isozyme 1, mitochondrial; Probable [pyruvate dehydrogenase (acetyl-transferring)] kinase, mitochondrial]
bRafMutated [K04365; Serine/threonine-protein kinase B-raf; Putative serine/threonine-protein kinase-like protein CCR3; BRAF Gene Mutation]
species 3 [Son of sevenless homolog 1; K03099]
PIP3Inactive [Phosphatidylinositol-3,4,5-trisphosphate]
species 7 [RAF proto-oncogene serine/threonine-protein kinase; K04366; Putative serine/threonine-protein kinase-like protein CCR3]
probRafMutated probRafMutated
species 13 [Putative serine/threonine-protein kinase-like protein CCR3; ribosomal protein S6 kinase alpha]

Observables: none

It's an experimental + mathematical paper explaining probable targets for Cetixumab resistance in colorectal cancer.

Cetuximab (CTX), a monoclonal antibody against epidermal growth factor receptor, is being widely used for colorectal cancer (CRC) with wild-type (WT) KRAS. However, its responsiveness is still very limited and WT KRAS is not enough to indicate such responsiveness. Here, by analyzing the gene expression data of CRC patients treated with CTX monotherapy, we have identified DUSP4, ETV5, GNB5, NT5E, and PHLDA1 as potential targets to overcome CTX resistance. We found that knockdown of any of these five genes can increase CTX sensitivity in KRAS WT cells. Interestingly, we further found that GNB5 knockdown can increase CTX sensitivity even for KRAS mutant cells. We unraveled that GNB5 overexpression contributes to CTX resistance by modulating the Akt signaling pathway from experiments and mathematical simulation. Overall, these results indicate that GNB5 might be a promising target for combination therapy with CTX irrespective of KRAS mutation. link: http://identifiers.org/pubmed/30719834

Parameters: none

States: none

Observables: none

This model is an attempt to provide a mathematical description of IL-7 dependent T cell homeostasis at the molecular and…

Interleukin-7 (IL7) plays a nonredundant role in T cell survival and homeostasis, which is illustrated in the severe T cell lymphopenia of IL7-deficient mice, or demonstrated in animals or humans that lack expression of either the IL7Rα or γ c chain, the two subunits that constitute the functional IL7 receptor. Remarkably, IL7 is not expressed by T cells themselves, but produced in limited amounts by radio-resistant stromal cells. Thus, T cells need to constantly compete for IL7 to survive. How T cells maintain homeostasis and further maximize the size of the peripheral T cell pool in face of such competition are important questions that have fascinated both immunologists and mathematicians for a long time. Exceptionally, IL7 downregulates expression of its own receptor, so that IL7-signaled T cells do not consume extracellular IL7, and thus, the remaining extracellular IL7 can be shared among unsignaled T cells. Such an altruistic behavior of the IL7Rα chain is quite unique among members of the γ c cytokine receptor family. However, the consequences of this altruistic signaling behavior at the molecular, single cell and population levels are less well understood and require further investigation. In this regard, mathematical modeling of how a limited resource can be shared, while maintaining the clonal diversity of the T cell pool, can help decipher the molecular or cellular mechanisms that regulate T cell homeostasis. Thus, the current review aims to provide a mathematical modeling perspective of IL7-dependent T cell homeostasis at the molecular, cellular and population levels, in the context of recent advances in our understanding of the IL7 biology. This article is categorized under: Models of Systems Properties and Processes > Organ, Tissue, and Physiological Models Biological Mechanisms > Cell Signaling Models of Systems Properties and Processes > Mechanistic Models Analytical and Computational Methods > Computational Methods. link: http://identifiers.org/pubmed/31137085

Parameters:

Name Description
k_f_4 = 1.66054E-5 Reaction: IL15 + IL15Ru => IL15Rb, Rate Law: compartment*k_f_4*IL15*IL15Ru
k_f_3 = 1.66054E-4 Reaction: IL7 + IL7Ru => IL7Rb, Rate Law: compartment*k_f_3*IL7*IL7Ru
k_f_2 = 1.66054E-4 Reaction: IL15Rbeta + gamma_c => IL15Ru, Rate Law: compartment*k_f_2*IL15Rbeta*gamma_c
k_r_4 = 0.1 Reaction: IL15Rb => IL15 + IL15Ru, Rate Law: compartment*k_r_4*IL15Rb
k_r_3 = 0.1 Reaction: IL7Rb => IL7 + IL7Ru, Rate Law: compartment*k_r_3*IL7Rb
k_r_2 = 0.1 Reaction: IL15Ru => IL15Rbeta + gamma_c, Rate Law: compartment*k_r_2*IL15Ru
k_r_1 = 0.1 Reaction: IL7Ru => IL7Ra + gamma_c, Rate Law: compartment*k_r_1*IL7Ru
k_f_1 = 1.66054E-4 Reaction: IL7Ra + gamma_c => IL7Ru, Rate Law: compartment*k_f_1*IL7Ra*gamma_c

States:

Name Description
IL7Ra [Interleukin-7 Receptor Subunit Alpha]
IL15Ru [Interleukin-15 Receptor]
IL15Rbeta [Interleukin-2 Receptor Subunit Beta]
gamma c [PR:P31785]
IL15Rb [interleukin-15 receptor complex]
IL7Ru [159734]
IL15 [Interleukin-15]
IL7 [Interleukin-7]
IL7Rb [interleukin-7 receptor complex]

Observables: none

Mathematical model approach to describe tumour response in mice after vaccine administration and its applicability to im…

Immunotherapy is a growing therapeutic strategy in oncology based on the stimulation of innate and adaptive immune systems to induce the death of tumour cells. In this paper, we have developed a population semi-mechanistic model able to characterize the mechanisms implied in tumour growth dynamic after the administration of CyaA-E7, a vaccine able to target antigen to dendritic cells, thus triggering a potent immune response. The mathematical model developed presented the following main components: (1) tumour progression in the animals without treatment was described with a linear model, (2) vaccine effects were modelled assuming that vaccine triggers a non-instantaneous immune response inducing cell death. Delayed response was described with a series of two transit compartments, (3) a resistance effect decreasing vaccine efficiency was also incorporated through a regulator compartment dependent upon tumour size, and (4) a mixture model at the level of the elimination of the induced signal vaccine (k 2) to model tumour relapse after treatment, observed in a small percentage of animals (15.6%). The proposed model structure was successfully applied to describe antitumor effect of IL-12, suggesting its applicability to different immune-stimulatory therapies. In addition, a simulation exercise to evaluate in silico the impact on tumour size of possible combination therapies has been shown. This type of mathematical approaches may be helpful to maximize the information obtained from experiments in mice, reducing the number of animals and the cost of developing new antitumor immunotherapies. link: http://identifiers.org/pubmed/23605806

Parameters:

Name Description
k1 = 0.0907 Reaction: VAC =>, Rate Law: compartment*k1*VAC
gamma = 5.24; REG_50 = 3.18; k3 = 1.08 Reaction: Ts => ; REG, SVAC, Rate Law: compartment*k3*REG_50^gamma/(REG_50^gamma+REG^gamma)*Ts*SVAC
k2_pop2 = 0.0907 Reaction: SVAC =>, Rate Law: compartment*k2_pop2*SVAC
gamma = 5.24 Reaction: => Ts, Rate Law: compartment*gamma
k4 = 0.039 Reaction: => REG; Ts, Rate Law: compartment*k4*Ts

States:

Name Description
VAC [Vaccine]
Ts [Tumor Mass]
SVAC [Signal; Vaccine; Signal]
TRAN TRAN
REG [Regulator]

Observables: none

SBML and SBGN-ML models of atherosclerosis and atheroma formation. This model is described in the publication: New mod…

Motivation Atherosclerosis is amongst the leading causes of death globally. However, it is challenging to study in vivo or in vitro and no detailed, openly-available computational models exist. Clinical studies hint that pharmaceutical therapy may be possible. Here we develop the first detailed, computational model of atherosclerosis and use it to develop multi-drug therapeutic hypotheses.

Results We assembled a network describing atheroma development from the literature. Maps and mathematical models were produced using the Systems Biology Graphical Notation (SBGN) and Systems Biology Markup Language (SBML), respectively. The model was constrained against clinical and laboratory data. We identified five drugs that together potentially reverse advanced atheroma formation.

Availability and Implementation The map is available in the supplementary information in SBGN-ML format. The model is available in the supplementary material and from BioModels, a repository of SBML models, containing CellDesigner markup.

Supplementary Information Available from Bioinformatics online. link: http://identifiers.org/doi/10.1093/bioinformatics/bty980

Parameters: none

States: none

Observables: none

MODEL0406553884 @ v0.0.1

This a model from the article: The functional role of cardiac T-tubules explored in a model of rat ventricular myocyte…

The morphology of the cardiac transverse-axial tubular system (TATS) has been known for decades, but its function has received little attention. To explore the possible role of this system in the physiological modulation of electrical and contractile activity, we have developed a mathematical model of rat ventricular cardiomyocytes in which the TATS is described as a single compartment. The geometrical characteristics of the TATS, the biophysical characteristics of ion transporters and their distribution between surface and tubular membranes were based on available experimental data. Biophysically realistic values of mean access resistance to the tubular lumen and time constants for ion exchange with the bulk extracellular solution were included. The fraction of membrane in the TATS was set to 56%. The action potentials initiated in current-clamp mode are accompanied by transient K+ accumulation and transient Ca2+ depletion in the TATS lumen. The amplitude of these changes relative to external ion concentrations was studied at steady-state stimulation frequencies of 1-5 Hz. Ca2+ depletion increased from 7 to 13.1% with stimulation frequency, while K+ accumulation decreased from 4.1 to 2.7%. These ionic changes (particularly Ca2+ depletion) implicated significant decrease of intracellular Ca2+ load at frequencies natural for rat heart. link: http://identifiers.org/pubmed/16608703

Parameters: none

States: none

Observables: none

MODEL0406793751 @ v0.0.1

This a model from the article: A model of the guinea-pig ventricular cardiac myocyte incorporating a transverse-axial…

A model of the guinea-pig cardiac ventricular myocyte has been developed that includes a representation of the transverse-axial tubular system (TATS), including heterogeneous distribution of ion flux pathways between the surface and tubular membranes. The model reproduces frequency-dependent changes of action potential shape and intracellular ion concentrations and can replicate experimental data showing ion diffusion between the tubular lumen and external solution in guinea-pig myocytes. The model is stable at rest and during activity and returns to rested state after perturbation. Theoretical analysis and model simulations show that, due to tight electrical coupling, tubular and surface membranes behave as a homogeneous whole during voltage and current clamp (maximum difference 0.9 mV at peak tubular INa of -38 nA). However, during action potentials, restricted diffusion and ionic currents in TATS cause depletion of tubular Ca2+ and accumulation of tubular K+ (up to -19.8% and +3.4%, respectively, of bulk extracellular values, at 6 Hz). These changes, in turn, decrease ion fluxes across the TATS membrane and decrease sarcoplasmic reticulum (SR) Ca2+ load. Thus, the TATS plays a potentially important role in modulating the function of guinea-pig ventricular myocyte in physiological conditions. link: http://identifiers.org/pubmed/17888503

Parameters: none

States: none

Observables: none

BIOMD0000000287 @ v0.0.1

This is the model described in: **Feedback between p21 and reactive oxygen production is necessary for cell senescence.*…

Cellular senescence–the permanent arrest of cycling in normally proliferating cells such as fibroblasts–contributes both to age-related loss of mammalian tissue homeostasis and acts as a tumour suppressor mechanism. The pathways leading to establishment of senescence are proving to be more complex than was previously envisaged. Combining in-silico interactome analysis and functional target gene inhibition, stochastic modelling and live cell microscopy, we show here that there exists a dynamic feedback loop that is triggered by a DNA damage response (DDR) and, which after a delay of several days, locks the cell into an actively maintained state of 'deep' cellular senescence. The essential feature of the loop is that long-term activation of the checkpoint gene CDKN1A (p21) induces mitochondrial dysfunction and production of reactive oxygen species (ROS) through serial signalling through GADD45-MAPK14(p38MAPK)-GRB2-TGFBR2-TGFbeta. These ROS in turn replenish short-lived DNA damage foci and maintain an ongoing DDR. We show that this loop is both necessary and sufficient for the stability of growth arrest during the establishment of the senescent phenotype. link: http://identifiers.org/pubmed/20160708

Parameters:

Name Description
kdegp53 = 8.25E-4 Reaction: Mdm2_p53 => Mdm2, Rate Law: kdegp53*Mdm2_p53
kdephosp38 = 0.1 Reaction: p38_P => p38, Rate Law: kdephosp38*p38_P
krepair = 6.0E-5 Reaction: damDNA => Sink, Rate Law: krepair*damDNA
kphosp38 = 0.008 Reaction: p38 + GADD45 => p38_P + GADD45, Rate Law: kphosp38*p38*GADD45
krelMdm2p53 = 1.155E-6 Reaction: Mdm2_p53 => p53 + Mdm2, Rate Law: krelMdm2p53*Mdm2_p53
kactATM = 2.0E-5 Reaction: damDNA + ATMI => damDNA + ATMA, Rate Law: kactATM*damDNA*ATMI
kdegMdm2 = 4.33E-4 Reaction: Mdm2 => Sink, Rate Law: kdegMdm2*Mdm2
kdam = 0.007 Reaction: IR => IR + damDNA, Rate Law: kdam*IR
kdephosMdm2 = 0.5 Reaction: Mdm2_P => Mdm2, Rate Law: kdephosMdm2*Mdm2_P
kdamROS = 1.0E-5 Reaction: ROS => ROS + damDNA, Rate Law: kdamROS*ROS
kdephosp53 = 0.5 Reaction: p53_P => p53, Rate Law: kdephosp53*p53_P
ksynp21mRNAp53P = 6.0E-6 Reaction: p53_P => p53_P + p21_mRNA, Rate Law: ksynp21mRNAp53P*p53_P
ksynp21step3 = 4.0E-5 Reaction: p21step2 => p21, Rate Law: ksynp21step3*p21step2
kdegp53mdm2ind = 8.25E-7 Reaction: p53 => Sink, Rate Law: kdegp53mdm2ind*p53
ksynp21mRNAp53 = 6.0E-8 Reaction: p53 => p53 + p21_mRNA, Rate Law: ksynp21mRNAp53*p53
kbinMdm2p53 = 0.001155 Reaction: p53 + Mdm2 => Mdm2_p53, Rate Law: kbinMdm2p53*p53*Mdm2
kremROS = 3.83E-4 Reaction: ROS => Sink, Rate Law: kremROS*ROS
kinactATM = 5.0E-4 Reaction: ATMA => ATMI, Rate Law: kinactATM*ATMA
kGADD45 = 4.0E-6 Reaction: p21 => p21 + GADD45, Rate Law: kGADD45*p21
kdamBasalROS = 1.0E-9 Reaction: basalROS => basalROS + damDNA, Rate Law: kdamBasalROS*basalROS
kgenROSp38 = 4.5E-4; kp38ROS = 1.0 Reaction: p38_P => p38_P + ROS, Rate Law: kgenROSp38*p38_P*kp38ROS
ksynp21step1 = 4.0E-4 Reaction: p21_mRNA => p21_mRNA + p21step1, Rate Law: ksynp21step1*p21_mRNA
ksynp53 = 0.006 Reaction: p53_mRNA => p53 + p53_mRNA, Rate Law: ksynp53*p53_mRNA
kphosMdm2 = 2.0 Reaction: Mdm2 + ATMA => Mdm2_P + ATMA, Rate Law: kphosMdm2*Mdm2*ATMA
kdegGADD45 = 1.0E-5 Reaction: GADD45 => Sink, Rate Law: kdegGADD45*GADD45
kdegMdm2mRNA = 1.0E-4 Reaction: Mdm2_mRNA => Sink, Rate Law: kdegMdm2mRNA*Mdm2_mRNA
kdegATMMdm2 = 4.0E-4 Reaction: Mdm2_P => Sink, Rate Law: kdegATMMdm2*Mdm2_P
ksynp53mRNA = 0.001 Reaction: Source => p53_mRNA, Rate Law: ksynp53mRNA*Source
ksynMdm2 = 4.95E-4 Reaction: Mdm2_mRNA => Mdm2_mRNA + Mdm2, Rate Law: ksynMdm2*Mdm2_mRNA
kphosp53 = 0.006 Reaction: p53 + ATMA => p53_P + ATMA, Rate Law: kphosp53*p53*ATMA
ksynp21step2 = 4.0E-5 Reaction: p21step1 => p21step2, Rate Law: ksynp21step2*p21step1
kdegp53mRNA = 1.0E-4 Reaction: p53_mRNA => Sink, Rate Law: kdegp53mRNA*p53_mRNA
kdegp21 = 1.9E-4 Reaction: p21 => Sink, Rate Law: kdegp21*p21
ksynMdm2mRNA = 1.0E-4 Reaction: p53 => p53 + Mdm2_mRNA, Rate Law: ksynMdm2mRNA*p53
kdegp21mRNA = 2.4E-5 Reaction: p21_mRNA => Sink, Rate Law: kdegp21mRNA*p21_mRNA

States:

Name Description
Mdm2 P [E3 ubiquitin-protein ligase Mdm2]
p21 mRNA [Cyclin-dependent kinase inhibitor 1]
basalROS [reactive oxygen species]
GADD45 [Growth arrest and DNA damage-inducible protein GADD45 alpha; Growth arrest and DNA damage-inducible protein GADD45 beta; Growth arrest and DNA damage-inducible protein GADD45 gamma]
p38 P [Mitogen-activated protein kinase 14]
p53 [Cellular tumor antigen p53]
Source Source
p53 P [Cellular tumor antigen p53]
IR IR
Mdm2 [E3 ubiquitin-protein ligase Mdm2]
ROS [reactive oxygen species]
damDNA [deoxyribonucleic acid]
p53 mRNA [Cellular tumor antigen p53]
p38 [Mitogen-activated protein kinase 14]
ATMA [Serine-protein kinase ATM]
p21step2 [Cyclin-dependent kinase inhibitor 1]
Mdm2 p53 [E3 ubiquitin-protein ligase Mdm2; Cellular tumor antigen p53]
p21 [Cyclin-dependent kinase inhibitor 1]
ATMI [Serine-protein kinase ATM]
Sink Sink
Mdm2 mRNA [E3 ubiquitin-protein ligase Mdm2]
p21step1 [Cyclin-dependent kinase inhibitor 1]

Observables: none

Pastick2009 - Genome-scale metabolic network of Streptococcus thermophilus (iMP429)This model is described in the articl…

In this report, we describe the amino acid metabolism and amino acid dependency of the dairy bacterium Streptococcus thermophilus LMG18311 and compare them with those of two other characterized lactic acid bacteria, Lactococcus lactis and Lactobacillus plantarum. Through the construction of a genome-scale metabolic model of S. thermophilus, the metabolic differences between the three bacteria were visualized by direct projection on a metabolic map. The comparative analysis revealed the minimal amino acid auxotrophy (only histidine and methionine or cysteine) of S. thermophilus LMG18311 and the broad variety of volatiles produced from amino acids compared to the other two bacteria. It also revealed the limited number of pyruvate branches, forcing this strain to use the homofermentative metabolism for growth optimization. In addition, some industrially relevant features could be identified in S. thermophilus, such as the unique pathway for acetaldehyde (yogurt flavor) production and the absence of a complete pentose phosphate pathway. link: http://identifiers.org/pubmed/19346354

Parameters: none

States: none

Observables: none

Pathak2013 - MAPK activation in response to various abiotic stressesMAPK activation mechanism in response to various abi…

Mitogen-Activated Protein Kinases (MAPKs) cascade plays an important role in regulating plant growth and development, generating cellular responses to the extracellular stimuli. MAPKs cascade mainly consist of three sub-families i.e. mitogen-activated protein kinase kinase kinase (MAPKKK), mitogen-activated protein kinase kinase (MAPKK) and mitogen activated protein kinase (MAPK), several cascades of which are activated by various abiotic and biotic stresses. In this work we have modeled the holistic molecular mechanisms essential to MAPKs activation in response to several abiotic and biotic stresses through a system biology approach and performed its simulation studies. As extent of abiotic and biotic stresses goes on increasing, the process of cell division, cell growth and cell differentiation slow down in time dependent manner. The models developed depict the combinatorial and multicomponent signaling triggered in response to several abiotic and biotic factors. These models can be used to predict behavior of cells in event of various stresses depending on their time and exposure through activation of complex signaling cascades. link: http://identifiers.org/pubmed/23847397

Parameters:

Name Description
kass_re81 = 1.0 s^(-1); kdiss_re81 = 1.0 s^(-1) Reaction: s42 => s57, Rate Law: kass_re81*s42-kdiss_re81*s57
kass_re52 = 1.0 s^(-1); kdiss_re52 = 1.0 s^(-1) Reaction: s47 => s48, Rate Law: kass_re52*s47-kdiss_re52*s48
kdiss_re38 = 1.0 s^(-1); kass_re38 = 1.0 s^(-1) Reaction: s28 => s30, Rate Law: kass_re38*s28-kdiss_re38*s30
kdiss_re78 = 1.0 s^(-1); kass_re78 = 1.0 s^(-1) Reaction: s48 => s57, Rate Law: kass_re78*s48-kdiss_re78*s57
kass_re31 = 1.0 s^(-1); kdiss_re31 = 1.0 s^(-1) Reaction: s18 => s26, Rate Law: kass_re31*s18-kdiss_re31*s26
kdiss_re55 = 1.0 s^(-1); kass_re55 = 1.0 s^(-1) Reaction: s29 => s37, Rate Law: kass_re55*s29-kdiss_re55*s37
kdiss_re64 = 1.0 s^(-1); kass_re64 = 1.0 s^(-1) Reaction: s32 => s45, Rate Law: kass_re64*s32-kdiss_re64*s45
kass_re30 = 1.0 s^(-1); kdiss_re30 = 1.0 s^(-1) Reaction: s18 => s25, Rate Law: kass_re30*s18-kdiss_re30*s25
kass_re35 = 1.0 s^(-1); kdiss_re35 = 1.0 s^(-1) Reaction: s15 => s20, Rate Law: kass_re35*s15-kdiss_re35*s20
kass_re68 = 1.0 s^(-1); kdiss_re68 = 1.0 s^(-1) Reaction: s28 => s51, Rate Law: kass_re68*s28-kdiss_re68*s51
kass_re48 = 1.0 s^(-1); kdiss_re48 = 1.0 s^(-1) Reaction: s39 => s40, Rate Law: kass_re48*s39-kdiss_re48*s40
kass_re23 = 1.0 s^(-1); kdiss_re23 = 1.0 s^(-1) Reaction: s14 => s17, Rate Law: kass_re23*s14-kdiss_re23*s17
kass_re25 = 1.0 s^(-1); kdiss_re25 = 1.0 s^(-1) Reaction: s18 => s20, Rate Law: kass_re25*s18-kdiss_re25*s20
kdiss_re47 = 1.0 s^(-1); kass_re47 = 1.0 s^(-1) Reaction: s37 => s38, Rate Law: kass_re47*s37-kdiss_re47*s38
kass_re58 = 1.0 s^(-1); kdiss_re58 = 1.0 s^(-1) Reaction: s30 => s41, Rate Law: kass_re58*s30-kdiss_re58*s41
kass_re49 = 1.0 s^(-1); kdiss_re49 = 1.0 s^(-1) Reaction: s41 => s42, Rate Law: kass_re49*s41-kdiss_re49*s42
kass_re40 = 1.0 s^(-1); kdiss_re40 = 1.0 s^(-1) Reaction: s28 => s32, Rate Law: kass_re40*s28-kdiss_re40*s32
kdiss_re57 = 1.0 s^(-1); kass_re57 = 1.0 s^(-1) Reaction: s30 => s35, Rate Law: kass_re57*s30-kdiss_re57*s35
kdiss_re67 = 1.0 s^(-1); kass_re67 = 1.0 s^(-1) Reaction: s28 => s49, Rate Law: kass_re67*s28-kdiss_re67*s49
kdiss_re69 = 1.0 s^(-1); kass_re69 = 1.0 s^(-1) Reaction: s28 => s53, Rate Law: kass_re69*s28-kdiss_re69*s53
kass_re84 = 1.0 s^(-1); kdiss_re84 = 1.0 s^(-1) Reaction: s36 => s57, Rate Law: kass_re84*s36-kdiss_re84*s57
kass_re62 = 1.0 s^(-1); kdiss_re62 = 1.0 s^(-1) Reaction: s31 => s39, Rate Law: kass_re62*s31-kdiss_re62*s39
kass_re44 = 1.0 s^(-1); kdiss_re44 = 1.0 s^(-1) Reaction: s26 => s30, Rate Law: kass_re44*s26-kdiss_re44*s30
kdiss_re76 = 1.0 s^(-1); kass_re76 = 1.0 s^(-1) Reaction: s50 => s57, Rate Law: kass_re76*s50-kdiss_re76*s57
kass_re15 = 1.0 s^(-1); kdiss_re15 = 1.0 s^(-1) Reaction: s9 => s13, Rate Law: kass_re15*s9-kdiss_re15*s13
kdiss_re29 = 1.0 s^(-1); kass_re29 = 1.0 s^(-1) Reaction: s18 => s24, Rate Law: kass_re29*s18-kdiss_re29*s24
kdiss_re79 = 1.0 s^(-1); kass_re79 = 1.0 s^(-1) Reaction: s30 => s43, Rate Law: kass_re79*s30-kdiss_re79*s43
kdiss_re60 = 1.0 s^(-1); kass_re60 = 1.0 s^(-1) Reaction: s31 => s33, Rate Law: kass_re60*s31-kdiss_re60*s33
kass_re46 = 1.0 s^(-1); kdiss_re46 = 1.0 s^(-1) Reaction: s35 => s36, Rate Law: kass_re46*s35-kdiss_re46*s36
kdiss_re83 = 1.0 s^(-1); kass_re83 = 1.0 s^(-1) Reaction: s38 => s57, Rate Law: kass_re83*s38-kdiss_re83*s57
kass_re36 = 1.0 s^(-1); kdiss_re36 = 1.0 s^(-1) Reaction: s16 => s26, Rate Law: kass_re36*s16-kdiss_re36*s26
kdiss_re24 = 1.0 s^(-1); kass_re24 = 1.0 s^(-1) Reaction: s18 => s19, Rate Law: kass_re24*s18-kdiss_re24*s19
kdiss_re32 = 1.0 s^(-1); kass_re32 = 1.0 s^(-1) Reaction: s27 => s28, Rate Law: kass_re32*s27-kdiss_re32*s28
kdiss_re28 = 1.0 s^(-1); kass_re28 = 1.0 s^(-1) Reaction: s18 => s23, Rate Law: kass_re28*s18-kdiss_re28*s23
kass_re61 = 1.0 s^(-1); kdiss_re61 = 1.0 s^(-1) Reaction: s31 => s45, Rate Law: kass_re61*s31-kdiss_re61*s45
kdiss_re65 = 1.0 s^(-1); kass_re65 = 1.0 s^(-1) Reaction: s32 => s35, Rate Law: kass_re65*s32-kdiss_re65*s35
kdiss_re19 = 1.0 s^(-1); kass_re19 = 1.0 s^(-1) Reaction: s14 => s16, Rate Law: kass_re19*s14-kdiss_re19*s16
kass_re71 = 1.0 s^(-1); kdiss_re71 = 1.0 s^(-1) Reaction: s28 => s55, Rate Law: kass_re71*s28-kdiss_re71*s55
kass_re85 = 1.0 s^(-1); kdiss_re85 = 1.0 s^(-1) Reaction: s34 => s57, Rate Law: kass_re85*s34-kdiss_re85*s57
kass_re66 = 1.0 s^(-1); kdiss_re66 = 1.0 s^(-1) Reaction: s28 => s56, Rate Law: kass_re66*s28-kdiss_re66*s56
kass_re17 = 1.0 s^(-1); kdiss_re17 = 1.0 s^(-1) Reaction: s14 => s15, Rate Law: kass_re17*s14-kdiss_re17*s15
kdiss_re22 = 1.0 s^(-1); kass_re22 = 1.0 s^(-1) Reaction: s17 => s18, Rate Law: kass_re22*s17-kdiss_re22*s18
kass_re26 = 1.0 s^(-1); kdiss_re26 = 1.0 s^(-1) Reaction: s18 => s21, Rate Law: kass_re26*s18-kdiss_re26*s21
kass_re50 = 1.0 s^(-1); kdiss_re50 = 1.0 s^(-1) Reaction: s43 => s44, Rate Law: kass_re50*s43-kdiss_re50*s44
kass_re39 = 1.0 s^(-1); kdiss_re39 = 1.0 s^(-1) Reaction: s28 => s31, Rate Law: kass_re39*s28-kdiss_re39*s31
kass_re54 = 1.0 s^(-1); kdiss_re54 = 1.0 s^(-1) Reaction: s51 => s52, Rate Law: kass_re54*s51-kdiss_re54*s52
kass_re11 = 1.0 s^(-1); kdiss_re11 = 1.0 s^(-1) Reaction: s5 => s7, Rate Law: kass_re11*s5-kdiss_re11*s7
kass_re86 = 1.0 s^(-1); kdiss_re86 = 1.0 s^(-1) Reaction: s46 => s57, Rate Law: kass_re86*s46-kdiss_re86*s57
kdiss_re45 = 1.0 s^(-1); kass_re45 = 1.0 s^(-1) Reaction: s33 => s34, Rate Law: kass_re45*s33-kdiss_re45*s34
kdiss_re33 = 1.0 s^(-1); kass_re33 = 1.0 s^(-1) Reaction: s18 => s27, Rate Law: kass_re33*s18-kdiss_re33*s27
kass_re70 = 1.0 s^(-1); kdiss_re70 = 1.0 s^(-1) Reaction: s28 => s54, Rate Law: kass_re70*s28-kdiss_re70*s54
kass_re59 = 1.0 s^(-1); kdiss_re59 = 1.0 s^(-1) Reaction: s30 => s47, Rate Law: kass_re59*s30-kdiss_re59*s47
kdiss_re21 = 1.0 s^(-1); kass_re21 = 1.0 s^(-1) Reaction: s12 => s16, Rate Law: kass_re21*s12-kdiss_re21*s16
kdiss_re34 = 1.0 s^(-1); kass_re34 = 1.0 s^(-1) Reaction: s15 => s19, Rate Law: kass_re34*s15-kdiss_re34*s19
kdiss_re2 = 1.0 s^(-1); kass_re2 = 1.0 s^(-1) Reaction: s2 => s7, Rate Law: kass_re2*s2-kdiss_re2*s7
kass_re43 = 1.0 s^(-1); kdiss_re43 = 1.0 s^(-1) Reaction: s20 => s32, Rate Law: kass_re43*s20-kdiss_re43*s32
kass_re27 = 1.0 s^(-1); kdiss_re27 = 1.0 s^(-1) Reaction: s18 => s22, Rate Law: kass_re27*s18-kdiss_re27*s22
kdiss_re1 = 1.0 s^(-1); kass_re1 = 1.0 s^(-1) Reaction: s1 => s7, Rate Law: kass_re1*s1-kdiss_re1*s7
kdiss_re42 = 1.0 s^(-1); kass_re42 = 1.0 s^(-1) Reaction: s20 => s31, Rate Law: kass_re42*s20-kdiss_re42*s31
kass_re53 = 1.0 s^(-1); kdiss_re53 = 1.0 s^(-1) Reaction: s49 => s50, Rate Law: kass_re53*s49-kdiss_re53*s50
kass_re20 = 1.0 s^(-1); kdiss_re20 = 1.0 s^(-1) Reaction: s11 => s16, Rate Law: kass_re20*s11-kdiss_re20*s16
kass_re37 = 1.0 s^(-1); kdiss_re37 = 1.0 s^(-1) Reaction: s28 => s29, Rate Law: kass_re37*s28-kdiss_re37*s29
kdiss_re10 = 1.0 s^(-1); kass_re10 = 1.0 s^(-1) Reaction: s4 => s7, Rate Law: kass_re10*s4-kdiss_re10*s7
kdiss_re18 = 1.0 s^(-1); kass_re18 = 1.0 s^(-1) Reaction: s7 => s15, Rate Law: kass_re18*s7-kdiss_re18*s15
kdiss_re63 = 1.0 s^(-1); kass_re63 = 1.0 s^(-1) Reaction: s32 => s47, Rate Law: kass_re63*s32-kdiss_re63*s47
kdiss_re51 = 1.0 s^(-1); kass_re51 = 1.0 s^(-1) Reaction: s45 => s46, Rate Law: kass_re51*s45-kdiss_re51*s46
kdiss_re82 = 1.0 s^(-1); kass_re82 = 1.0 s^(-1) Reaction: s44 => s57, Rate Law: kass_re82*s44-kdiss_re82*s57
kass_re56 = 1.0 s^(-1); kdiss_re56 = 1.0 s^(-1) Reaction: s29 => s33, Rate Law: kass_re56*s29-kdiss_re56*s33
kass_re72 = 1.0 s^(-1); kdiss_re72 = 1.0 s^(-1) Reaction: s40 => s57, Rate Law: kass_re72*s40-kdiss_re72*s57

States:

Name Description
s5 [cellular response to metal ion]
s14 [Mitogen-activated protein kinase kinase kinase 5]
s18 [Mitogen-activated protein kinase kinase 1]
s37 [Probable WRKY transcription factor 8]
s40 [Probable WRKY transcription factor 25]
s20 [Mitogen-activated protein kinase kinase 2]
s35 [Probable WRKY transcription factor 12]
s44 [Probable WRKY transcription factor 29]
s57 [cellular response to stress]
s43 [Probable WRKY transcription factor 29]
s19 [Mitogen-activated protein kinase kinase 1]
s31 [Mitogen-activated protein kinase 4]
s36 [Probable WRKY transcription factor 12]
s34 [WRKY transcription factor 1]
s50 [ATMYB2At2g47190MYB transcription factorMYB transcription factor (Atmyb2)MYB transcription factor Atmyb2Myb domain protein 2]
s38 [Probable WRKY transcription factor 8]
s47 [Probable WRKY transcription factor 28]
s32 [Mitogen-activated protein kinase 6]
s46 [Probable WRKY transcription factor 33]
s15 [Mitogen-activated protein kinase kinase kinase 1]
s51 [Transcription repressor MYB4]
s45 [Probable WRKY transcription factor 33]
s1 [decreased temperature]
s48 [Probable WRKY transcription factor 28]
s17 [Mitogen-activated protein kinase kinase 1]
s41 [WRKY transcription factor 22]
s25 [Dual specificity mitogen-activated protein kinase kinase 7]
s13 [Mitogen-activated protein kinase kinase kinase 5]
s2 [sodium chloride]
s49 [ATMYB2At2g47190MYB transcription factorMYB transcription factor (Atmyb2)MYB transcription factor Atmyb2Myb domain protein 2]
s33 [WRKY transcription factor 1]
s16 [Serine/threonine-protein kinase CTR1]
s4 [hydrogen peroxide]
s30 [Mitogen-activated protein kinase 3]
s26 [Dual specificity mitogen-activated protein kinase kinase 1]
s42 [WRKY transcription factor 22]
s28 [Mitogen-activated protein kinase 3]
s39 [Probable WRKY transcription factor 25]
s29 [Mitogen-activated protein kinase]
s27 [Mitogen-activated protein kinase 3]

Observables: none

Pathak2013 - MAPK activation in response to various biotic stressesMAPK activation mechanism in response to various biot…

Mitogen-Activated Protein Kinases (MAPKs) cascade plays an important role in regulating plant growth and development, generating cellular responses to the extracellular stimuli. MAPKs cascade mainly consist of three sub-families i.e. mitogen-activated protein kinase kinase kinase (MAPKKK), mitogen-activated protein kinase kinase (MAPKK) and mitogen activated protein kinase (MAPK), several cascades of which are activated by various abiotic and biotic stresses. In this work we have modeled the holistic molecular mechanisms essential to MAPKs activation in response to several abiotic and biotic stresses through a system biology approach and performed its simulation studies. As extent of abiotic and biotic stresses goes on increasing, the process of cell division, cell growth and cell differentiation slow down in time dependent manner. The models developed depict the combinatorial and multicomponent signaling triggered in response to several abiotic and biotic factors. These models can be used to predict behavior of cells in event of various stresses depending on their time and exposure through activation of complex signaling cascades. link: http://identifiers.org/pubmed/23847397

Parameters:

Name Description
kass_re81 = 1.0 s^(-1); kdiss_re81 = 1.0 s^(-1) Reaction: s37 => s52, Rate Law: kass_re81*s37-kdiss_re81*s52
kdiss_re38 = 1.0 s^(-1); kass_re38 = 1.0 s^(-1) Reaction: s16 => s22, Rate Law: kass_re38*s16-kdiss_re38*s22
kass_re31 = 1.0 s^(-1); kdiss_re31 = 1.0 s^(-1) Reaction: s15 => s20, Rate Law: kass_re31*s15-kdiss_re31*s20
kdiss_re55 = 1.0 s^(-1); kass_re55 = 1.0 s^(-1) Reaction: s22 => s28, Rate Law: kass_re55*s22-kdiss_re55*s28
kass_re5 = 1.0 s^(-1); kdiss_re5 = 1.0 s^(-1) Reaction: s2 => s5, Rate Law: kass_re5*s2-kdiss_re5*s5
kass_re30 = 1.0 s^(-1); kdiss_re30 = 1.0 s^(-1) Reaction: s20 => s21, Rate Law: kass_re30*s20-kdiss_re30*s21
kass_re35 = 1.0 s^(-1); kdiss_re35 = 1.0 s^(-1) Reaction: s21 => s25, Rate Law: kass_re35*s21-kdiss_re35*s25
kass_re68 = 1.0 s^(-1); kdiss_re68 = 1.0 s^(-1) Reaction: s25 => s36, Rate Law: kass_re68*s25-kdiss_re68*s36
kass_re14 = 1.0 s^(-1); kdiss_re14 = 1.0 s^(-1) Reaction: s8 => s11, Rate Law: kass_re14*s8-kdiss_re14*s11
kass_re23 = 1.0 s^(-1); kdiss_re23 = 1.0 s^(-1) Reaction: s15 => s17, Rate Law: kass_re23*s15-kdiss_re23*s17
kass_re48 = 1.0 s^(-1); kdiss_re48 = 1.0 s^(-1) Reaction: s34 => s35, Rate Law: kass_re48*s34-kdiss_re48*s35
kdiss_re13 = 1.0 s^(-1); kass_re13 = 1.0 s^(-1) Reaction: s8 => s10, Rate Law: kass_re13*s8-kdiss_re13*s10
kass_re25 = 1.0 s^(-1); kdiss_re25 = 1.0 s^(-1) Reaction: s15 => s19, Rate Law: kass_re25*s15-kdiss_re25*s19
kdiss_re47 = 1.0 s^(-1); kass_re47 = 1.0 s^(-1) Reaction: s32 => s33, Rate Law: kass_re47*s32-kdiss_re47*s33
kass_re49 = 1.0 s^(-1); kdiss_re49 = 1.0 s^(-1) Reaction: s36 => s37, Rate Law: kass_re49*s36-kdiss_re49*s37
kass_re40 = 1.0 s^(-1); kdiss_re40 = 1.0 s^(-1) Reaction: s17 => s23, Rate Law: kass_re40*s17-kdiss_re40*s23
kdiss_re69 = 1.0 s^(-1); kass_re69 = 1.0 s^(-1) Reaction: s21 => s30, Rate Law: kass_re69*s21-kdiss_re69*s30
kdiss_re41 = 1.0 s^(-1); kass_re41 = 1.0 s^(-1) Reaction: s18 => s23, Rate Law: kass_re41*s18-kdiss_re41*s23
kass_re62 = 1.0 s^(-1); kdiss_re62 = 1.0 s^(-1) Reaction: s25 => s46, Rate Law: kass_re62*s25-kdiss_re62*s46
kass_re12 = 1.0 s^(-1); kdiss_re12 = 1.0 s^(-1) Reaction: s8 => s9, Rate Law: kass_re12*s8-kdiss_re12*s9
kass_re44 = 1.0 s^(-1); kdiss_re44 = 1.0 s^(-1) Reaction: s18 => s25, Rate Law: kass_re44*s18-kdiss_re44*s25
kdiss_re76 = 1.0 s^(-1); kass_re76 = 1.0 s^(-1) Reaction: s31 => s52, Rate Law: kass_re76*s31-kdiss_re76*s52
kass_re15 = 1.0 s^(-1); kdiss_re15 = 1.0 s^(-1) Reaction: s8 => s12, Rate Law: kass_re15*s8-kdiss_re15*s12
kdiss_re29 = 1.0 s^(-1); kass_re29 = 1.0 s^(-1) Reaction: s11 => s19, Rate Law: kass_re29*s11-kdiss_re29*s19
kass_re6 = 1.0 s^(-1); kdiss_re6 = 1.0 s^(-1) Reaction: s2 => s6, Rate Law: kass_re6*s2-kdiss_re6*s6
kass_re74 = 1.0 s^(-1); kdiss_re74 = 1.0 s^(-1) Reaction: s24 => s34, Rate Law: kass_re74*s24-kdiss_re74*s34
kass_re16 = 1.0 s^(-1); kdiss_re16 = 1.0 s^(-1) Reaction: s6 => s9, Rate Law: kass_re16*s6-kdiss_re16*s9
kdiss_re88 = 1.0 s^(-1); kass_re88 = 1.0 s^(-1) Reaction: s33 => s52, Rate Law: kass_re88*s33-kdiss_re88*s52
kass_re36 = 1.0 s^(-1); kdiss_re36 = 1.0 s^(-1) Reaction: s21 => s26, Rate Law: kass_re36*s21-kdiss_re36*s26
kdiss_re24 = 1.0 s^(-1); kass_re24 = 1.0 s^(-1) Reaction: s15 => s18, Rate Law: kass_re24*s15-kdiss_re24*s18
kdiss_re32 = 1.0 s^(-1); kass_re32 = 1.0 s^(-1) Reaction: s21 => s22, Rate Law: kass_re32*s21-kdiss_re32*s22
kdiss_re28 = 1.0 s^(-1); kass_re28 = 1.0 s^(-1) Reaction: s9 => s18, Rate Law: kass_re28*s9-kdiss_re28*s18
kdiss_re19 = 1.0 s^(-1); kass_re19 = 1.0 s^(-1) Reaction: s5 => s11, Rate Law: kass_re19*s5-kdiss_re19*s11
kass_re66 = 1.0 s^(-1); kdiss_re66 = 1.0 s^(-1) Reaction: s25 => s44, Rate Law: kass_re66*s25-kdiss_re66*s44
kass_re71 = 1.0 s^(-1); kdiss_re71 = 1.0 s^(-1) Reaction: s21 => s49, Rate Law: kass_re71*s21-kdiss_re71*s49
kass_re17 = 1.0 s^(-1); kdiss_re17 = 1.0 s^(-1) Reaction: s8 => s13, Rate Law: kass_re17*s8-kdiss_re17*s13
kass_re73 = 1.0 s^(-1); kdiss_re73 = 1.0 s^(-1) Reaction: s21 => s50, Rate Law: kass_re73*s21-kdiss_re73*s50
kass_re3 = 1.0 s^(-1); kdiss_re3 = 1.0 s^(-1) Reaction: s1 => s5, Rate Law: kass_re3*s1-kdiss_re3*s5
kdiss_re22 = 1.0 s^(-1); kass_re22 = 1.0 s^(-1) Reaction: s15 => s16, Rate Law: kass_re22*s15-kdiss_re22*s16
kass_re26 = 1.0 s^(-1); kdiss_re26 = 1.0 s^(-1) Reaction: s9 => s16, Rate Law: kass_re26*s9-kdiss_re26*s16
kass_re50 = 1.0 s^(-1); kdiss_re50 = 1.0 s^(-1) Reaction: s38 => s39, Rate Law: kass_re50*s38-kdiss_re50*s39
kass_re39 = 1.0 s^(-1); kdiss_re39 = 1.0 s^(-1) Reaction: s17 => s22, Rate Law: kass_re39*s17-kdiss_re39*s22
kass_re11 = 1.0 s^(-1); kdiss_re11 = 1.0 s^(-1) Reaction: s6 => s7, Rate Law: kass_re11*s6-kdiss_re11*s7
kdiss_re33 = 1.0 s^(-1); kass_re33 = 1.0 s^(-1) Reaction: s21 => s23, Rate Law: kass_re33*s21-kdiss_re33*s23
kass_re8 = 1.0 s^(-1); kdiss_re8 = 1.0 s^(-1) Reaction: s3 => s7, Rate Law: kass_re8*s3-kdiss_re8*s7
kass_re70 = 1.0 s^(-1); kdiss_re70 = 1.0 s^(-1) Reaction: s21 => s48, Rate Law: kass_re70*s21-kdiss_re70*s48
kass_re59 = 1.0 s^(-1); kdiss_re59 = 1.0 s^(-1) Reaction: s24 => s38, Rate Law: kass_re59*s24-kdiss_re59*s38
kdiss_re7 = 1.0 s^(-1); kass_re7 = 1.0 s^(-1) Reaction: s7 => s8, Rate Law: kass_re7*s7-kdiss_re7*s8
kdiss_re21 = 1.0 s^(-1); kass_re21 = 1.0 s^(-1) Reaction: s8 => s14, Rate Law: kass_re21*s8-kdiss_re21*s14
kdiss_re34 = 1.0 s^(-1); kass_re34 = 1.0 s^(-1) Reaction: s21 => s24, Rate Law: kass_re34*s21-kdiss_re34*s24
kdiss_re2 = 1.0 s^(-1); kass_re2 = 1.0 s^(-1) Reaction: s1 => s4, Rate Law: kass_re2*s1-kdiss_re2*s4
kass_re43 = 1.0 s^(-1); kdiss_re43 = 1.0 s^(-1) Reaction: s16 => s24, Rate Law: kass_re43*s16-kdiss_re43*s24
kass_re27 = 1.0 s^(-1); kdiss_re27 = 1.0 s^(-1) Reaction: s9 => s17, Rate Law: kass_re27*s9-kdiss_re27*s17
kass_re9 = 1.0 s^(-1); kdiss_re9 = 1.0 s^(-1) Reaction: s4 => s7, Rate Law: kass_re9*s4-kdiss_re9*s7
kdiss_re1 = 1.0 s^(-1); kass_re1 = 1.0 s^(-1) Reaction: s1 => s3, Rate Law: kass_re1*s1-kdiss_re1*s3
kdiss_re42 = 1.0 s^(-1); kass_re42 = 1.0 s^(-1) Reaction: s17 => s25, Rate Law: kass_re42*s17-kdiss_re42*s25
kass_re20 = 1.0 s^(-1); kdiss_re20 = 1.0 s^(-1) Reaction: s14 => s15, Rate Law: kass_re20*s14-kdiss_re20*s15
kass_re37 = 1.0 s^(-1); kdiss_re37 = 1.0 s^(-1) Reaction: s21 => s27, Rate Law: kass_re37*s21-kdiss_re37*s27
kdiss_re10 = 1.0 s^(-1); kass_re10 = 1.0 s^(-1) Reaction: s5 => s7, Rate Law: kass_re10*s5-kdiss_re10*s7
kdiss_re18 = 1.0 s^(-1); kass_re18 = 1.0 s^(-1) Reaction: s5 => s13, Rate Law: kass_re18*s5-kdiss_re18*s13
kdiss_re63 = 1.0 s^(-1); kass_re63 = 1.0 s^(-1) Reaction: s25 => s32, Rate Law: kass_re63*s25-kdiss_re63*s32
kass_re56 = 1.0 s^(-1); kdiss_re56 = 1.0 s^(-1) Reaction: s24 => s28, Rate Law: kass_re56*s24-kdiss_re56*s28
kdiss_re4 = 1.0 s^(-1); kass_re4 = 1.0 s^(-1) Reaction: s2 => s4, Rate Law: kass_re4*s2-kdiss_re4*s4
kass_re72 = 1.0 s^(-1); kdiss_re72 = 1.0 s^(-1) Reaction: s21 => s51, Rate Law: kass_re72*s21-kdiss_re72*s51

States:

Name Description
s8 [Mitogen-activated protein kinase kinase kinase 5]
s5 [LRR receptor-like serine/threonine-protein kinase FLS2]
s7 [Mitogen-activated protein kinase kinase kinase 5]
s14 [Mannosyl-oligosaccharide 1,2-alpha-mannosidase MNS2]
s18 [Dual specificity mitogen-activated protein kinase kinase 5]
s20 [Mitogen-activated protein kinase 3]
s23 [Mitogen-activated protein kinase 3]
s24 [Mitogen-activated protein kinase 4]
s37 [Transcription repressor MYB4]
s9 [Mitogen-activated protein kinase kinase kinase 1]
s19 [Dual specificity mitogen-activated protein kinase kinase 1]
s31 [ATMYB2At2g47190MYB transcription factorMYB transcription factor (Atmyb2)MYB transcription factor Atmyb2Myb domain protein 2]
s10 [Mitogen-activated protein kinase kinase kinase 18]
s34 [WRKY transcription factor 6]
s36 [Transcription repressor MYB4]
s38 [Probable WRKY transcription factor 25]
s6 [Probable leucine-rich repeat receptor-like serine/threonine-protein kinase At3g14840]
s32 [Probable WRKY transcription factor 33]
s22 [Mitogen-activated protein kinase]
s11 [Mitogen-activated protein kinase kinase kinase 19Protein kinase-like protein]
s15 [Mannosyl-oligosaccharide 1,2-alpha-mannosidase MNS2]
s3 [LysM domain-containing GPI-anchored protein 1]
s1 [173629; pathogen]
s17 [Dual specificity mitogen-activated protein kinase kinase 4]
s13 [Serine/threonine-protein kinase EDR1]
s25 [Mitogen-activated protein kinase 6]
s2 [Bacteria Latreille et al. 1825; pathogen]
s4 [Pinoresinol reductase 1]
s33 [Probable WRKY transcription factor 33]
s16 [Mitogen-activated protein kinase kinase 2]
s21 [Mitogen-activated protein kinase 3]
s28 [WRKY transcription factor 1]
s39 [Probable WRKY transcription factor 25]

Observables: none

a possible mechanism of MP in determining HP versus LP outcomes, and how different interventions might affect infection…

The World Health Organization identifies influenza as a major public health problem. While the strains commonly circulating in humans usually do not cause severe pathogenicity in healthy adults, some strains that have infected humans, such as H5N1, can cause high morbidity and mortality. Based on the severity of the disease, influenza viruses are sometimes categorized as either being highly pathogenic (HP) or having low pathogenicity (LP). The reasons why some strains are LP and others HP are not fully understood. While there are likely multiple mechanisms of interaction between the virus and the immune response that determine LP versus HP outcomes, we focus here on one component, namely macrophages (MP). There is some evidence that MP may both help fight the infection and become productively infected with HP influenza viruses. We developed mathematical models for influenza infections which explicitly included the dynamics and action of MP. We fit these models to viral load and macrophage count data from experimental infections of mice with LP and HP strains. Our results suggest that MP may not only help fight an influenza infection but may contribute to virus production in infections with HP viruses. We also explored the impact of combination therapies with antivirals and anti-inflammatory drugs on HP infections. Our study suggests a possible mechanism of MP in determining HP versus LP outcomes, and how different interventions might affect infection dynamics. link: http://identifiers.org/pubmed/26918620

Parameters: none

States: none

Observables: none

Physiologically based pharmacokinetic (PBPK) models were developed using MATLAB Simulink(®) to predict diurnal variation…

Physiologically based pharmacokinetic (PBPK) models were developed using MATLAB Simulink(®) to predict diurnal variations of endogenous melatonin with light as well as pharmacokinetics of exogenous melatonin via different routes of administration. The model was structured using whole body, including pineal and saliva compartments, and parameterized based on the literature values for endogenous melatonin. It was then optimized by including various intensities of light and various dosage and formulation of melatonin. The model predictions generally have a good fit with available experimental data as evaluated by mean squared errors and ratios between model-predicted and observed values considering large variations in melatonin secretion and pharmacokinetics as reported in the literature. It also demonstrates the capability and usefulness in simulating plasma and salivary concentrations of melatonin under different light conditions and the interaction of endogenous melatonin with the pharmacokinetics of exogenous melatonin. Given the mechanistic approach and programming flexibility of MATLAB Simulink(®), the PBPK model could provide predictions of endogenous melatonin rhythms and pharmacokinetic changes in response to environmental (light) and experimental (dosage and route of administration) conditions. Furthermore, the model may be used to optimize the combined treatment using light exposure and exogenous melatonin for maximal phase advances or delays. link: http://identifiers.org/pubmed/24120727

Parameters: none

States: none

Observables: none

MODEL1001150000 @ v0.0.1

This the full model from the article: A dynamic model of interactions of Ca2+, calmodulin, and catalytic subunits of C…

During the acquisition of memories, influx of Ca2+ into the postsynaptic spine through the pores of activated N-methyl-D-aspartate-type glutamate receptors triggers processes that change the strength of excitatory synapses. The pattern of Ca2+influx during the first few seconds of activity is interpreted within the Ca2+-dependent signaling network such that synaptic strength is eventually either potentiated or depressed. Many of the critical signaling enzymes that control synaptic plasticity,including Ca2+/calmodulin-dependent protein kinase II (CaMKII), are regulated by calmodulin, a small protein that can bindup to 4 Ca2+ ions. As a first step toward clarifying how the Ca2+-signaling network decides between potentiation or depression, we have created a kinetic model of the interactions of Ca2+, calmodulin, and CaMKII that represents our best understanding of the dynamics of these interactions under conditions that resemble those in a postsynaptic spine. We constrained parameters of the model from data in the literature, or from our own measurements, and then predicted time courses of activation and autophosphorylation of CaMKII under a variety of conditions. Simulations showed that species of calmodulin with fewer than four bound Ca2+ play a significant role in activation of CaMKII in the physiological regime,supporting the notion that processing of Ca2+ signals in a spine involves competition among target enzymes for binding to unsaturated species of CaM in an environment in which the concentration of Ca2+ is fluctuating rapidly. Indeed, we showed that dependence of activation on the frequency of Ca2+ transients arises from the kinetics of interaction of fluctuating Ca2+with calmodulin/CaMKII complexes. We used parameter sensitivity analysis to identify which parameters will be most beneficial to measure more carefully to improve the accuracy of predictions. This model provides a quantitative base from which to build more complex dynamic models of postsynaptic signal transduction during learning. link: http://identifiers.org/pubmed/20168991

Parameters: none

States: none

Observables: none

The model, iBP722, was reconstructed based on the functional reannotation of the complete genome sequence of A. succinog…

Actinobacillus succinogenes is a promising bacterial catalyst for the bioproduction of succinic acid from low-cost raw materials. In this work, a genome-scale metabolic model was reconstructed and used to assess the metabolic capabilities of this microorganism under producing conditions.The model, iBP722, was reconstructed based on the functional reannotation of the complete genome sequence of A. succinogenes 130Z and manual inspection of metabolic pathways, covering 1072 enzymatic reactions associated with 722 metabolic genes that involve 713 metabolites. The highly curated model was effective in capturing the growth of A. succinogenes on various carbon sources, as well as the SA production under various growth conditions with fair agreement between experimental and predicted data. Calculated flux distributions under different conditions show that a number of metabolic pathways are affected by the activity of some metabolic enzymes at key nodes in metabolism, including the transport mechanism of carbon sources and the ability to fix carbon dioxide.The established genome-scale metabolic model can be used for model-driven strain design and medium alteration to improve succinic acid yields. link: http://identifiers.org/pubmed/29843739

Parameters: none

States: none

Observables: none

This a model from the article: Dynamics of HIV infection of CD4+ T cells. Perelson AS, Kirschner DE, De Boer R. Math…

We examine a model for the interaction of HIV with CD4+ T cells that considers four populations: uninfected T cells, latently infected T cells, actively infected T cells, and free virus. Using this model we show that many of the puzzling quantitative features of HIV infection can be explained simply. We also consider effects of AZT on viral growth and T-cell population dynamics. The model exhibits two steady states, an uninfected state in which no virus is present and an endemically infected state, in which virus and infected T cells are present. We show that if N, the number of infectious virions produced per actively infected T cell, is less a critical value, Ncrit, then the uninfected state is the only steady state in the nonnegative orthant, and this state is stable. For N > Ncrit, the uninfected state is unstable, and the endemically infected state can be either stable, or unstable and surrounded by a stable limit cycle. Using numerical bifurcation techniques we map out the parameter regimes of these various behaviors. oscillatory behavior seems to lie outside the region of biologically realistic parameter values. When the endemically infected state is stable, it is characterized by a reduced number of T cells compared with the uninfected state. Thus T-cell depletion occurs through the establishment of a new steady state. The dynamics of the establishment of this new steady state are examined both numerically and via the quasi-steady-state approximation. We develop approximations for the dynamics at early times in which the free virus rapidly binds to T cells, during an intermediate time scale in which the virus grows exponentially, and a third time scale on which viral growth slows and the endemically infected steady state is approached. Using the quasi-steady-state approximation the model can be simplified to two ordinary differential equations the summarize much of the dynamical behavior. We compute the level of T cells in the endemically infected state and show how that level varies with the parameters in the model. The model predicts that different viral strains, characterized by generating differing numbers of infective virions within infected T cells, can cause different amounts of T-cell depletion and generate depletion at different rates. Two versions of the model are studied. In one the source of T cells from precursors is constant, whereas in the other the source of T cells decreases with viral load, mimicking the infection and killing of T-cell precursors.(ABSTRACT TRUNCATED AT 400 WORDS) link: http://identifiers.org/pubmed/8096155

Parameters:

Name Description
N = 1000.0; mu_b = 0.24 Reaction: => V; T_2, Rate Law: COMpartment*N*mu_b*T_2
k_1 = 2.4E-5; mu_V = 2.4 Reaction: V => ; T, Rate Law: COMpartment*(k_1*V*T+mu_V*V)
s = 10.0; r = 0.03 Reaction: => T, Rate Law: COMpartment*(s+r*T)
k_1 = 2.4E-5 Reaction: => T_1; V, T, Rate Law: COMpartment*k_1*V*T
mu_T = 0.02; k_2 = 0.003 Reaction: T_1 =>, Rate Law: COMpartment*(mu_T*T_1+k_2*T_1)
k_2 = 0.003 Reaction: => T_2; T_1, Rate Law: COMpartment*k_2*T_1
k_1 = 2.4E-5; mu_T = 0.02; T_max = 1500.0; r = 0.03 Reaction: T => ; V, T_1, T_2, Rate Law: COMpartment*(mu_T*T+k_1*V*T+r*T*(T+T_1+T_2)/T_max)
mu_b = 0.24 Reaction: T_2 =>, Rate Law: COMpartment*mu_b*T_2

States:

Name Description
T [P01730]
T 2 [P01730]
T 1 [P01730]
V V

Observables: none

This a model from the article: Dynamics of HIV infection of CD4+ T cells. Perelson AS, Kirschner DE, De Boer R. Math…

We examine a model for the interaction of HIV with CD4+ T cells that considers four populations: uninfected T cells, latently infected T cells, actively infected T cells, and free virus. Using this model we show that many of the puzzling quantitative features of HIV infection can be explained simply. We also consider effects of AZT on viral growth and T-cell population dynamics. The model exhibits two steady states, an uninfected state in which no virus is present and an endemically infected state, in which virus and infected T cells are present. We show that if N, the number of infectious virions produced per actively infected T cell, is less a critical value, Ncrit, then the uninfected state is the only steady state in the nonnegative orthant, and this state is stable. For N > Ncrit, the uninfected state is unstable, and the endemically infected state can be either stable, or unstable and surrounded by a stable limit cycle. Using numerical bifurcation techniques we map out the parameter regimes of these various behaviors. oscillatory behavior seems to lie outside the region of biologically realistic parameter values. When the endemically infected state is stable, it is characterized by a reduced number of T cells compared with the uninfected state. Thus T-cell depletion occurs through the establishment of a new steady state. The dynamics of the establishment of this new steady state are examined both numerically and via the quasi-steady-state approximation. We develop approximations for the dynamics at early times in which the free virus rapidly binds to T cells, during an intermediate time scale in which the virus grows exponentially, and a third time scale on which viral growth slows and the endemically infected steady state is approached. Using the quasi-steady-state approximation the model can be simplified to two ordinary differential equations the summarize much of the dynamical behavior. We compute the level of T cells in the endemically infected state and show how that level varies with the parameters in the model. The model predicts that different viral strains, characterized by generating differing numbers of infective virions within infected T cells, can cause different amounts of T-cell depletion and generate depletion at different rates. Two versions of the model are studied. In one the source of T cells from precursors is constant, whereas in the other the source of T cells decreases with viral load, mimicking the infection and killing of T-cell precursors.(ABSTRACT TRUNCATED AT 400 WORDS) link: http://identifiers.org/pubmed/8096155

Parameters: none

States: none

Observables: none

This a model from the article: Dynamics of HIV infection of CD4+ T cells. Perelson AS, Kirschner DE, De Boer R. Math…

We examine a model for the interaction of HIV with CD4+ T cells that considers four populations: uninfected T cells, latently infected T cells, actively infected T cells, and free virus. Using this model we show that many of the puzzling quantitative features of HIV infection can be explained simply. We also consider effects of AZT on viral growth and T-cell population dynamics. The model exhibits two steady states, an uninfected state in which no virus is present and an endemically infected state, in which virus and infected T cells are present. We show that if N, the number of infectious virions produced per actively infected T cell, is less a critical value, Ncrit, then the uninfected state is the only steady state in the nonnegative orthant, and this state is stable. For N > Ncrit, the uninfected state is unstable, and the endemically infected state can be either stable, or unstable and surrounded by a stable limit cycle. Using numerical bifurcation techniques we map out the parameter regimes of these various behaviors. oscillatory behavior seems to lie outside the region of biologically realistic parameter values. When the endemically infected state is stable, it is characterized by a reduced number of T cells compared with the uninfected state. Thus T-cell depletion occurs through the establishment of a new steady state. The dynamics of the establishment of this new steady state are examined both numerically and via the quasi-steady-state approximation. We develop approximations for the dynamics at early times in which the free virus rapidly binds to T cells, during an intermediate time scale in which the virus grows exponentially, and a third time scale on which viral growth slows and the endemically infected steady state is approached. Using the quasi-steady-state approximation the model can be simplified to two ordinary differential equations the summarize much of the dynamical behavior. We compute the level of T cells in the endemically infected state and show how that level varies with the parameters in the model. The model predicts that different viral strains, characterized by generating differing numbers of infective virions within infected T cells, can cause different amounts of T-cell depletion and generate depletion at different rates. Two versions of the model are studied. In one the source of T cells from precursors is constant, whereas in the other the source of T cells decreases with viral load, mimicking the infection and killing of T-cell precursors.(ABSTRACT TRUNCATED AT 400 WORDS) link: http://identifiers.org/pubmed/8096155

Parameters: none

States: none

Observables: none

This a model from the article: Dynamics of HIV infection of CD4+ T cells. Perelson AS, Kirschner DE, De Boer R. Math…

We examine a model for the interaction of HIV with CD4+ T cells that considers four populations: uninfected T cells, latently infected T cells, actively infected T cells, and free virus. Using this model we show that many of the puzzling quantitative features of HIV infection can be explained simply. We also consider effects of AZT on viral growth and T-cell population dynamics. The model exhibits two steady states, an uninfected state in which no virus is present and an endemically infected state, in which virus and infected T cells are present. We show that if N, the number of infectious virions produced per actively infected T cell, is less a critical value, Ncrit, then the uninfected state is the only steady state in the nonnegative orthant, and this state is stable. For N > Ncrit, the uninfected state is unstable, and the endemically infected state can be either stable, or unstable and surrounded by a stable limit cycle. Using numerical bifurcation techniques we map out the parameter regimes of these various behaviors. oscillatory behavior seems to lie outside the region of biologically realistic parameter values. When the endemically infected state is stable, it is characterized by a reduced number of T cells compared with the uninfected state. Thus T-cell depletion occurs through the establishment of a new steady state. The dynamics of the establishment of this new steady state are examined both numerically and via the quasi-steady-state approximation. We develop approximations for the dynamics at early times in which the free virus rapidly binds to T cells, during an intermediate time scale in which the virus grows exponentially, and a third time scale on which viral growth slows and the endemically infected steady state is approached. Using the quasi-steady-state approximation the model can be simplified to two ordinary differential equations the summarize much of the dynamical behavior. We compute the level of T cells in the endemically infected state and show how that level varies with the parameters in the model. The model predicts that different viral strains, characterized by generating differing numbers of infective virions within infected T cells, can cause different amounts of T-cell depletion and generate depletion at different rates. Two versions of the model are studied. In one the source of T cells from precursors is constant, whereas in the other the source of T cells decreases with viral load, mimicking the infection and killing of T-cell precursors.(ABSTRACT TRUNCATED AT 400 WORDS) link: http://identifiers.org/pubmed/8096155

Parameters: none

States: none

Observables: none

This is a model built by COPASI4.24(Build 197) This a model from the article: Computational design of improved standa…

Here we put forward a mathematical model describing the response of low-grade (WHO grade II) oligodendrogliomas (LGO) to temozolomide (TMZ). The model describes the longitudinal volumetric dynamics of tumor response to TMZ of a cohort of 11 LGO patients treated with TMZ. After finding patient-specific parameters, different therapeutic strategies were tried computationally on the 'in-silico twins' of those patients. Chemotherapy schedules with larger-than-standard rest periods between consecutive cycles had either the same or better long-term efficacy than the standard 28-day cycles. The results were confirmed in a large trial of 2000 virtual patients. These long-cycle schemes would also have reduced toxicity and defer the appearance of resistances. On the basis of those results, a combination scheme consisting of five induction TMZ cycles given monthly plus 12 maintenance cycles given every three months was found to provide substantial survival benefits for the in-silico twins of the 11 LGO patients (median 5.69 years, range: 0.67 to 68.45 years) and in a large virtual trial including 2000 patients. We used 220 sets of experiments in-silico to show that a clinical trial incorporating 100 patients per arm (standard intensive treatment versus 5 + 12 scheme) could demonstrate the superiority of the novel scheme after a follow-up period of 10 years. Thus, the proposed treatment plan could be the basis for a standardized TMZ treatment for LGO patients with survival benefits. link: http://identifiers.org/pubmed/31306418

Parameters:

Name Description
K = 261.799 m^3; kappa = 1.0; rho = 0.002931927433 1/d Reaction: Damaged_Tumor_Cells_D => ; Tumor_Cell_Population_P, Rate Law: compartment*rho*Damaged_Tumor_Cells_D*(1-(Tumor_Cell_Population_P+Damaged_Tumor_Cells_D)/K)/kappa
lambda = 8.3184 1/d Reaction: Drug_Concentration_C =>, Rate Law: compartment*lambda*Drug_Concentration_C
alpha_2 = 0.1396877593 Reaction: Tumor_Cell_Population_P => ; Drug_Concentration_C, Rate Law: compartment*alpha_2*Tumor_Cell_Population_P*Drug_Concentration_C
K = 261.799 m^3; rho = 0.002931927433 1/d Reaction: => Tumor_Cell_Population_P; Damaged_Tumor_Cells_D, Rate Law: compartment*rho*Tumor_Cell_Population_P*(1-(Tumor_Cell_Population_P+Damaged_Tumor_Cells_D)/K)
alpha_1 = 0.1027971308 Reaction: Tumor_Cell_Population_P => Damaged_Tumor_Cells_D; Drug_Concentration_C, Rate Law: compartment*alpha_1*Tumor_Cell_Population_P*Drug_Concentration_C

States:

Name Description
Tumor Cell Population P [cancer]
Drug Concentration C [Chemotherapy; Concentration]
Damaged Tumor Cells D [cancer; Abnormal]

Observables: none

BIOMD0000000610 @ v0.0.1

Petelenz-kurdzeil2013 - Osmo adaptation gpd1DThis model is described in the article: [Quantitative analysis of glycerol…

We provide an integrated dynamic view on a eukaryotic osmolyte system, linking signaling with regulation of gene expression, metabolic control and growth. Adaptation to osmotic changes enables cells to adjust cellular activity and turgor pressure to an altered environment. The yeast Saccharomyces cerevisiae adapts to hyperosmotic stress by activating the HOG signaling cascade, which controls glycerol accumulation. The Hog1 kinase stimulates transcription of genes encoding enzymes required for glycerol production (Gpd1, Gpp2) and glycerol import (Stl1) and activates a regulatory enzyme in glycolysis (Pfk26/27). In addition, glycerol outflow is prevented by closure of the Fps1 glycerol facilitator. In order to better understand the contributions to glycerol accumulation of these different mechanisms and how redox and energy metabolism as well as biomass production are maintained under such conditions we collected an extensive dataset. Over a period of 180 min after hyperosmotic shock we monitored in wild type and different mutant cells the concentrations of key metabolites and proteins relevant for osmoadaptation. The dataset was used to parameterize an ODE model that reproduces the generated data very well. A detailed computational analysis using time-dependent response coefficients showed that Pfk26/27 contributes to rerouting glycolytic flux towards lower glycolysis. The transient growth arrest following hyperosmotic shock further adds to redirecting almost all glycolytic flux from biomass towards glycerol production. Osmoadaptation is robust to loss of individual adaptation pathways because of the existence and upregulation of alternative routes of glycerol accumulation. For instance, the Stl1 glycerol importer contributes to glycerol accumulation in a mutant with diminished glycerol production capacity. In addition, our observations suggest a role for trehalose accumulation in osmoadaptation and that Hog1 probably directly contributes to the regulation of the Fps1 glycerol facilitator. Taken together, we elucidated how different metabolic adaptation mechanisms cooperate and provide hypotheses for further experimental studies. link: http://identifiers.org/pubmed/23762021

Parameters:

Name Description
volchangespeed = 6.30627442832138E-7 Reaction: cin => ; cellvol, Rate Law: intra*cin*volchangespeed/cellvol
CellSurface = 0.0296468313433281; Turgor = -0.580000000000118; vV_2 = 0.00116532; OsmoE = 0.355586; vV_T = 298.5; vV_R = 8.314; vV_1 = 3.56294E-5 Reaction: => cellvol; glycerol_e, glycerol_i, cin, Rate Law: intra*vV_1*CellSurface*(Turgor-vV_2*vV_R*vV_T*((glycerol_e+OsmoE)-(glycerol_i+cin)))
kv7_2 = 0.317879; kv7_1 = 0.00983997 Reaction: trioseP => pyruvate, Rate Law: intra*kv7_1*trioseP/(kv7_2+trioseP)
kv23r_1 = 2.09875E-4 Reaction: AOG => AOGi, Rate Law: intra*kv23r_1*AOG
v10speed = 1.30212691282784E-11; initcellnum = 6954722.464; cellnum = 1.11543272115466E7 Reaction: => trehalose_e, Rate Law: extra*(v10speed*cellnum/initcellnum-v10speed)
v13aspeed = 5.0275596254809E-8; initcellnum = 6954722.464; cellnum = 1.11543272115466E7 Reaction: => glycerol_e, Rate Law: extra*(v13aspeed*cellnum/initcellnum-v13aspeed)
v1speed = 5.33353293880484E-7; initcellnum = 6954722.464; cellnum = 1.11543272115466E7 Reaction: glucose_e =>, Rate Law: extra*(v1speed*cellnum/initcellnum-v1speed)
kv18r_1 = 1.32549E-4 Reaction: Gpd1 =>, Rate Law: intra*kv18r_1*Gpd1
kv16r_1VARIABLE = 0.444296 Reaction: Hog1PP => Hog1, Rate Law: intra*kv16r_1VARIABLE*Hog1PP
kv13a_1 = 6.28899E-6; CellSurface = 0.0296468313433281; kDiff=1.0 Reaction: glycerol_i => glycerol_e; Fps1r, Rate Law: kv13a_1*CellSurface*Fps1r*(glycerol_i-kDiff*glycerol_e)
kv20r_1 = 7.05933E-4 Reaction: stl1mRNA =>, Rate Law: intra*kv20r_1*stl1mRNA
kv13b_2 = 3.69196E-7; kv13b_1 = 1.27001E-7 Reaction: glycerol_e => glycerol_i; Stl1, Rate Law: glycerol_e*kv13b_1*Stl1/(kv13b_2+glycerol_e)
kv4_3 = 0.00171631; kv4_1 = 0.0628885; kv4_4 = 2.67143; kv4_2 = 0.00230714; kv4_5 = 0.583865 Reaction: G6P => F16DP; F26DP, Rate Law: intra*(kv4_2*(1-F26DP^kv4_5/(F26DP+kv4_3)^kv4_5)+kv4_1*F26DP^kv4_5/(F26DP+kv4_3)^kv4_5)*(G6P/kv4_4)^2/(1+(G6P/kv4_4)^2)
kv15r_2 = 3.3187E-5; kv15r_1 = 1.84829E-7 Reaction: F26DP => G6P, Rate Law: intra*kv15r_1*F26DP/(kv15r_2+F26DP)
kv2_1 = 0.00303855; kv2_2 = 0.40864 Reaction: glucose_i => G6P, Rate Law: intra*kv2_1*glucose_i/(kv2_2+glucose_i)
kv19r_1 = 0.0605655 Reaction: Pfk2627a => Pfk2627i, Rate Law: intra*kv19r_1*Pfk2627a
kv22_2 = 0.0215179; Turgor = -0.580000000000118; kv22_1 = 8.0075; kv22_3 = 0.0554729 Reaction: => Fps1r; Hog1PP, Rate Law: intra*(kv22_1*(-Turgor)/(kv22_3+(-Turgor))*1.5*(1-Hog1PP/(Hog1PP+kv22_2))-kv22_1*Fps1r)
kv8_2 = 1.50827; kv8_1 = 0.0135676 Reaction: pyruvate => acetate_i, Rate Law: intra*kv8_1*pyruvate/(kv8_2+pyruvate)
Vm = 4.80000000000001E-4; kv16f_3 = 14.9448; OsmoE = 0.355586; kv16f_1 = 0.156118; kv16f_2 = 4.52424E-4 Reaction: Hog1 => Hog1PP, Rate Law: intra*Hog1*kv16f_1*OsmoE*(kv16f_2/Vm)^kv16f_3
kv19f_1 = 0.299127 Reaction: Pfk2627i => Pfk2627a; Hog1PP, Rate Law: intra*kv19f_1*Hog1PP*Pfk2627i
v11speed = 9.21581643247704E-8; initcellnum = 6954722.464; cellnum = 1.11543272115466E7 Reaction: => acetate_e, Rate Law: extra*(v11speed*cellnum/initcellnum-v11speed)
kv18f_1 = 0.00646553 Reaction: => Gpd1; gpd1mRNA, Rate Law: intra*kv18f_1*gpd1mRNA
kv23f_1 = 8.80535E-6; Vm = 4.80000000000001E-4; kv23f_3 = 6.95727; kv23f_2 = 5.1235E-4 Reaction: AOGi => AOG, Rate Law: intra*AOGi*kv23f_1*(kv23f_2/Vm)^kv23f_3
kv1_2 = 0.899814; kv1_1 = 5.05249E-6 Reaction: glucose_e => glucose_i, Rate Law: kv1_1*glucose_e/(kv1_2+glucose_e)
kv5_1 = 0.00383315; kv5_2 = 1.74463; kv5_3 = 0.00656128; kv5_4 = 1.13994 Reaction: F16DP => trioseP, Rate Law: intra*(kv5_1*F16DP/kv5_2/(1+F16DP/kv5_2)-kv5_3*trioseP/kv5_4/(1+trioseP/kv5_4))
kv17r_1 = 0.00151498 Reaction: gpd1mRNA =>, Rate Law: intra*kv17r_1*gpd1mRNA
kv9_1 = 0.214937; kv9_2 = 0.923665 Reaction: pyruvate => ethanol_i, Rate Law: intra*kv9_1*pyruvate/(kv9_2+pyruvate)
CellSurface = 0.0296468313433281; kv12_2 = 0.148586; kv12_1 = 1.00927E-5 Reaction: ethanol_i => ethanol_e, Rate Law: kv12_1*CellSurface*(ethanol_i-kv12_2*ethanol_e)
kv14_5 = 1.23049; kv14_2 = 6.05922E-6; OsmoE = 0.355586; kv14_4 = 0.420621; kv14_1 = 0.808051; kv14_3 = 2.05157 Reaction: G6P => biomass; cellvol, Rate Law: intra*kv14_1*cellvol^kv14_3/(cellvol^kv14_3+kv14_2)*(1-OsmoE/(OsmoE+kv14_4))*G6P/kv14_5/(1+G6P/kv14_5)
kv20f_3 = 4.05843E-6; kv20f_2 = 0.0167845; kv20f_x = 1.55858; kv20f_1 = 9.81887E-5 Reaction: => stl1mRNA; Hog1PP, Rate Law: intra*(kv20f_1*Hog1PP^kv20f_x/(Hog1PP^kv20f_x+kv20f_2)+kv20f_3)
kv6b_4 = 4.61918E-5; kv6b_x = 28.5; kv6b_5 = 0.292627 Reaction: trioseP => glycerol_i, Rate Law: intra*kv6b_x*kv6b_4*trioseP^2/kv6b_5/(1+trioseP^2/kv6b_5)
initcellnum = 6954722.464; cellnum = 1.11543272115466E7; v13bspeed = 1.59327705289657E-11 Reaction: glycerol_e =>, Rate Law: extra*(v13bspeed*cellnum/initcellnum-v13bspeed)
kv21r_1 = 2.14247E-4 Reaction: Stl1 =>, Rate Law: intra*kv21r_1*Stl1
kv10_1 = 1.83291E-7; CellSurface = 0.0296468313433281; kv10_2 = 4.26512 Reaction: trehalose => trehalose_e, Rate Law: kv10_1*CellSurface*(trehalose-kv10_2*trehalose_e)
kv15f_2 = 6.95877; kv15f_1 = 4.99507E-5 Reaction: G6P => F26DP; Pfk2627a, Rate Law: intra*G6P*kv15f_1*Pfk2627a/(kv15f_2+G6P)
kv21f_1 = 0.00121673 Reaction: => Stl1; stl1mRNA, Rate Law: intra*kv21f_1*stl1mRNA
kv3_4 = 0.166996; kv3_1 = 6.17387E-6; kv3_3 = 7.37808E-4; kv3_2 = 0.81114 Reaction: G6P => trehalose, Rate Law: intra*(kv3_1*G6P/kv3_2-kv3_3*trehalose/kv3_4)/(1+G6P/kv3_2+trehalose/kv3_4)
CellSurface = 0.0296468313433281; kv11_2 = 1.17279; kv11_1 = 3.2863E-6 Reaction: acetate_i => acetate_e, Rate Law: kv11_1*CellSurface*(acetate_i-kv11_2*acetate_e)
initcellnum = 6954722.464; cellnum = 1.11543272115466E7; v12speed = 2.88652220351019E-6 Reaction: => ethanol_e, Rate Law: extra*(v12speed*cellnum/initcellnum-v12speed)

States:

Name Description
glucose i [glucose; intracellular]
acetate e [acetate]
trehalose e [trehalose]
gpd1mRNA [S000002180]
Stl1 [Sugar transporter STL1]
Pfk2627i [S000005496; S000001369]
Hog1PP [Mitogen-activated protein kinase HOG1]
stl1mRNA [S000002944]
glucose e [glucose]
F26DP [105021]
glycerol i [glycerol]
pyruvate [pyruvate]
ethanol e [ethanol]
Pfk2627a [S000005496; S000001369]
AOG [positive regulation of transcription, DNA-templated]
acetate i [acetate]
Hog1 [Mitogen-activated protein kinase HOG1]
F16DP [keto-D-fructose 1,6-bisphosphate]
cellvol cellvol
biomass biomass
ethanol i [ethanol]
trioseP [4643300; 729]
G6P [alpha-D-glucose 6-phosphate]
AOGi [positive regulation of transcription, DNA-templated]
Fps1r [Glycerol uptake/efflux facilitator protein]
cin [osmolyte]
glycerol e [glycerol]
trehalose [trehalose]
Gpd1 [Glycerol-3-phosphate dehydrogenase [NAD(+)] 1]

Observables: none

&lt;notes xmlns=&quot;http://www.sbml.org/sbml/level3/version1/core&quot;&gt; &lt;body xmlns=&quot;http://www.w3.…

Bone biology is physiologically complex and intimately linked to calcium homeostasis. The literature provides a wealth of qualitative and/or quantitative descriptions of cellular mechanisms, bone dynamics, associated organ dynamics, related disease sequela, and results of therapeutic interventions. We present a physiologically based mathematical model of integrated calcium homeostasis and bone biology constructed from literature data. The model includes relevant cellular aspects with major controlling mechanisms for bone remodeling and calcium homeostasis and appropriately describes a broad range of clinical and therapeutic conditions. These include changes in plasma parathyroid hormone (PTH), calcitriol, calcium and phosphate (PO4), and bone-remodeling markers as manifested by hypoparathyroidism and hyperparathyroidism, renal insufficiency, daily PTH 1-34 administration, and receptor activator of NF-kappaB ligand (RANKL) inhibition. This model highlights the utility of systems approaches to physiologic modeling in the bone field. The presented bone and calcium homeostasis model provides an integrated mathematical construct to conduct hypothesis testing of influential system aspects, to visualize elements of this complex endocrine system, and to continue to build upon iteratively with the results of ongoing scientific research. link: http://identifiers.org/pubmed/19732857

Parameters:

Name Description
J14 = NaN Reaction: Q => P, Rate Law: J14
koutRNK = 0.00323667 Reaction: RNK => ; RNK, Rate Law: koutRNK*RNK
k2 = 0.112013 Reaction: N => ; N, Rate Law: k2*N
kinOC2 = NaN Reaction: => OC, Rate Law: kinOC2
kbslow = NaN Reaction: OBslow => ; OBslow, Rate Law: kbslow*OBslow
k3 = 6.24E-6 Reaction: L + RNK => M; RNK, L, Rate Law: k3*RNK*L
pO = NaN Reaction: => O, Rate Law: pO
TERIPK = NaN Reaction: TERISC => PTH, Rate Law: TERIPK
koutL = 0.00293273 Reaction: L => ; L, Rate Law: koutL*L
kO = 15.8885 Reaction: O => ; O, Rate Law: kO*O
crebKout = 0.00279513 Reaction: CREB => ; CREB, Rate Law: crebKout*CREB
T76 = NaN Reaction: => S; S, Rate Law: (1-S)*T76
J48 = NaN Reaction: ECCPhos =>, Rate Law: J48
RX2Kout = NaN Reaction: RX2 => ; RX2, Rate Law: RX2Kout*RX2
RX2Kin = NaN Reaction: => RX2, Rate Law: RX2Kin
kout = NaN Reaction: PTH => ; PTH, Rate Law: kout*PTH
J40 = NaN Reaction: T => P, Rate Law: J40
J53 = NaN Reaction: PhosGut => ECCPhos, Rate Law: J53
OralCa = NaN; F11 = NaN Reaction: => T, Rate Law: OralCa*F11
KPT = NaN Reaction: ROB1 => ; ROB1, Rate Law: KPT*ROB1
crebKin = NaN Reaction: => CREB, Rate Law: crebKin
F12 = 0.7; OralPhos = NaN Reaction: => PhosGut, Rate Law: OralPhos*F12
IPTHint = 0.0 Reaction: => SC, Rate Law: IPTHint
koutTGFact = NaN Reaction: TGFBact => ; TGFBact, Rate Law: koutTGFact*TGFBact
SPTH = NaN Reaction: => PTH, Rate Law: SPTH
J14a = NaN Reaction: Qbone => Q, Rate Law: J14a
J15 = NaN Reaction: P => Q, Rate Law: J15
D = NaN; FracOBfast = 0.797629; Frackb = 0.313186; PicOB = NaN; bigDb = NaN Reaction: => OBslow, Rate Law: bigDb/PicOB*D*(1-FracOBfast)*Frackb
J42 = NaN Reaction: ECCPhos =>, Rate Law: J42
koutTGFeqn = NaN Reaction: TGFB => TGFBact, Rate Law: koutTGFeqn
kinL = NaN Reaction: => L, Rate Law: kinL
J41 = NaN Reaction: => ECCPhos, Rate Law: J41
kbfast = NaN Reaction: OBfast => ; OBfast, Rate Law: kbfast*OBfast
J27 = NaN Reaction: P =>, Rate Law: J27
SE = NaN Reaction: => A, Rate Law: SE
J15a = NaN Reaction: Q => Qbone, Rate Law: J15a
PTin = NaN Reaction: => PTmax, Rate Law: PTin
PTout = 1.604E-4 Reaction: PTmax => ; PTmax, Rate Law: PTout*PTmax
T64 = 0.05 Reaction: A => ; A, Rate Law: T64*A
kinRNKgam = 0.151825; kinRNK = NaN Reaction: => RNK; TGFBact, TGFBact, Rate Law: kinRNK*TGFBact^kinRNKgam
kLShap = NaN Reaction: HAp => ; HAp, Rate Law: kLShap*HAp
T36 = NaN Reaction: => R; R, Rate Law: T36*(1-R)
T37 = NaN Reaction: R => ; R, Rate Law: T37*R
ROBin = NaN Reaction: => ROB1, Rate Law: ROBin
J56 = NaN Reaction: IntraPO => ECCPhos, Rate Law: J56
k4 = 0.112013 Reaction: M => L + RNK; M, Rate Law: k4*M
D = NaN; FracOBfast = 0.797629; PicOB = NaN; bigDb = NaN; Frackb2 = NaN Reaction: => OBfast, Rate Law: bigDb/PicOB*D*FracOBfast*Frackb2
J54 = NaN Reaction: ECCPhos => IntraPO, Rate Law: J54
bcl2Kout = 0.693 Reaction: BCL2 => ; BCL2, Rate Law: bcl2Kout*BCL2
k1 = 6.24E-6 Reaction: => N; O, L, O, L, Rate Law: k1*O*L
T69 = 0.1 Reaction: B => ; B, Rate Law: T69*B
Osteoblast = NaN; kinTGF = NaN; OB0 = NaN; OBtgfGAM = 0.0111319 Reaction: => TGFB, Rate Law: kinTGF*(Osteoblast/OB0)^OBtgfGAM
Osteoblast = NaN; kHApIn = NaN Reaction: => HAp, Rate Law: kHApIn*Osteoblast
T75 = NaN Reaction: S => ; S, Rate Law: S*T75
bcl2Kin = NaN Reaction: => BCL2, Rate Law: bcl2Kin
KLSoc = NaN Reaction: OC => ; OC, Rate Law: KLSoc*OC

States:

Name Description
Q [calcium(2+); intracellular]
TGFB [Transforming growth factor beta-1]
IntraPO [phosphate ion]
T [calcium(2+)]
RNK [Tumor necrosis factor receptor superfamily member 11A]
P [calcium(2+)]
L [Tumor necrosis factor ligand superfamily member 11]
PTH [Parathyroid hormone]
OC [osteoclast]
O [Tumor necrosis factor receptor superfamily member 11B]
TGFBact [Transforming growth factor beta-1; active]
B [calcitriol]
M [protein complex; Tumor necrosis factor ligand superfamily member 11; Tumor necrosis factor receptor superfamily member 11A]
N [protein complex; Tumor necrosis factor ligand superfamily member 11; Tumor necrosis factor receptor superfamily member 11B]
ECCPhos [phosphate ion]
A [25-hydroxyvitamin D-1 alpha hydroxylase, mitochondrial]
CREB [Cyclic AMP-responsive element-binding protein 1]
SC [subcutaneous adipose tissue; Parathyroid hormone; pharmaceutical]
RX2 [Runt-related transcription factor 2]
BCL2 [Apoptosis regulator Bcl-2]
TERISC [16132393]
OBslow [osteoblast]
PTmax [Parathyroid hormone]
S [Parathyroid hormone]
OBfast [osteoblast]
Qbone [calcium(2+); extracellular region]
HAp [apatite]
ROB1 [osteoclast; urn:miriam:pato:PATO%3A0000487+]
R [intestine; calcium(2+)]
PhosGut [phosphate ion]

Observables: none

C-547, a candidate drug, is a potent slow-binding inhibitor of acetyl-cholinesterase, and the focus of this PK/PD model,…

C-547, a potent slow-binding inhibitor of acetylcholinesterase (AChE) was intravenously administered to rat (0.05 mg/kg). Pharmacokinetic profiles were determined in blood and different organs: extensor digitorum longus muscle, heart, liver, lungs and kidneys as a function of time. Pharmacokinetics (PK) was studied using non-compartmental and compartmental analyses. A 3-compartment model describes PK in blood. Most of injected C-547 binds to albumin in the bloodstream. The steady-state volume of distribution (3800 ml/kg) is 15 times larger than the distribution volume, indicating a good tissue distribution. C-547 is slowly eliminated (kel = 0.17 h-1; T1/2 = 4 h) from the bloodstream. Effect of C-547 on animal model of myasthenia gravis persists for more than 72 h, even though the drug is not analytically detectable in the blood. A PK/PD model was built to account for such a pharmacodynamical (PD) effect. Long-lasting effect results from micro-PD mechanisms: the slow-binding nature of inhibition, high affinity for AChE and long residence time on target at neuromuscular junction (NMJ). In addition, NMJ spatial constraints i.e. high concentration of AChE in a small volume, and slow diffusion rate of free C-547 out of NMJ, make possible effective rebinding of ligand. Thus, compared to other cholinesterase inhibitors used for palliative treatment of myasthenia gravis, C-547 is the most selective drug, displays a slow pharmacokinetics, and has the longest duration of action. This makes C-547 a promising drug leader for treatment of myasthenia gravis, and a template for development of other drugs against neurological diseases and for neuroprotection. link: http://identifiers.org/pubmed/29277489

Parameters: none

States: none

Observables: none

Peyraud2016 - Metabolic reconstruction (iRP1476) of Ralstonia solanacearum GMI1000This model is described in the article…

Bacterial pathogenicity relies on a proficient metabolism and there is increasing evidence that metabolic adaptation to exploit host resources is a key property of infectious organisms. In many cases, colonization by the pathogen also implies an intensive multiplication and the necessity to produce a large array of virulence factors, which may represent a significant cost for the pathogen. We describe here the existence of a resource allocation trade-off mechanism in the plant pathogen R. solanacearum. We generated a genome-scale reconstruction of the metabolic network of R. solanacearum, together with a macromolecule network module accounting for the production and secretion of hundreds of virulence determinants. By using a combination of constraint-based modeling and metabolic flux analyses, we quantified the metabolic cost for production of exopolysaccharides, which are critical for disease symptom production, and other virulence factors. We demonstrated that this trade-off between virulence factor production and bacterial proliferation is controlled by the quorum-sensing-dependent regulatory protein PhcA. A phcA mutant is avirulent but has a better growth rate than the wild-type strain. Moreover, a phcA mutant has an expanded metabolic versatility, being able to metabolize 17 substrates more than the wild-type. Model predictions indicate that metabolic pathways are optimally oriented towards proliferation in a phcA mutant and we show that this enhanced metabolic versatility in phcA mutants is to a large extent a consequence of not paying the cost for virulence. This analysis allowed identifying candidate metabolic substrates having a substantial impact on bacterial growth during infection. Interestingly, the substrates supporting well both production of virulence factors and growth are those found in higher amount within the plant host. These findings also provide an explanatory basis to the well-known emergence of avirulent variants in R. solanacearum populations in planta or in stressful environments. link: http://identifiers.org/pubmed/27732672

Parameters: none

States: none

Observables: none

This model is from the article: Cooperation and Competition in the Evolution of ATP-Producing Pathways Thomas Pfeiff…

Heterotrophic organisms generally face a trade-off between rate and yield of adenosine triphosphate (ATP) production. This trade-off may result in an evolutionary dilemma, because cells with a higher rate but lower yield of ATP production may gain a selective advantage when competing for shared energy resources. Using an analysis of model simulations and biochemical observations, we show that ATP production with a low rate and high yield can be viewed as a form of cooperative resource use and may evolve in spatially structured environments. Furthermore, we argue that the high ATP yield of respiration may have facilitated the evolutionary transition from unicellular to undifferentiated multicellular organisms. link: http://identifiers.org/pubmed/11283355

Parameters:

Name Description
v = 10.0 dimensionless Reaction: => S, Rate Law: v
d = 1.0 dimensionless Reaction: N1 =>, Rate Law: d*N1

States:

Name Description
S [energy]
N1 [cell]
N2 [cell]

Observables: none

The paper describes a model on the key components for tumor–immune dynamics in multiple myeloma. Created by COPASI 4.2…

The complexity of the immune responses is a major challenge in current virotherapy. This study incorporates the innate immune response into our basic model for virotherapy and investigates how the innate immunity affects the outcome of virotherapy. The viral therapeutic dynamics is largely determined by the viral burst size, relative innate immune killing rate, and relative innate immunity decay rate. The innate immunity may complicate virotherapy in the way of creating more equilibria when the viral burst size is not too big, while the dynamics is similar to the system without innate immunity when the viral burst size is big. link: http://identifiers.org/pubmed/29379572

Parameters:

Name Description
c = 0.48 1 Reaction: y => ; z, Rate Law: tumor_microenvironment*c*y*z
e = 0.2 1 Reaction: v =>, Rate Law: tumor_microenvironment*e*v
m = 0.6 1 Reaction: => z; y, Rate Law: tumor_microenvironment*m*y*z
a = 0.11 1 Reaction: x + v => y, Rate Law: tumor_microenvironment*a*x*v
d = 0.16 1 Reaction: v => ; z, Rate Law: tumor_microenvironment*d*v*z
n = 0.036 1 Reaction: z =>, Rate Law: tumor_microenvironment*n*z
r = 0.36 1 Reaction: => x, Rate Law: tumor_microenvironment*r*x
b = 9.0 1 Reaction: => v; y, Rate Law: tumor_microenvironment*b*y

States:

Name Description
v [Oncolytic Virus]
x [neoplastic cell]
z [Effector Immune Cell]
y [neoplastic cell]

Observables: none

Phillips2003 - The Mechanism of Ras GTPase Activation by NeurofibrominA mathematical model for Ras-GTP activation by neu…

Individual rate constants have been determined for each step of the Ras.GTP hydrolysis mechanism, activated by neurofibromin. Fluorescence intensity and anisotropy stopped-flow measurements used the fluorescent GTP analogue, mantGTP (2'(3')-O-(N-methylanthraniloyl)GTP), to determine rate constants for binding and release of neurofibromin. Quenched flow measurements provided the kinetics of the hydrolytic cleavage step. The fluorescent phosphate sensor, MDCC-PBP was used to measure phosphate release kinetics. Phosphate-water oxygen exchange, using (18)O-substituted GTP and inorganic phosphate (P(i)), was used to determine the extent of reversal of the hydrolysis step and of P(i) binding. The data show that neurofibromin and P(i) dissociate from the NF1.Ras.GDP.P(i) complex with identical kinetics, which are 3-fold slower than the preceding cleavage step. A model is presented in which the P(i) release is associated with the change of Ras from "GTP" to "GDP" conformation. In this model, the conformation change on P(i) release causes the large change in affinity of neurofibromin, which then dissociates rapidly. link: http://identifiers.org/pubmed/12667087

Parameters:

Name Description
kf=1.02102E-11; kb=1.15192E-13 Reaction: RasGTP_minus_NF1_star_ => RasGDP_minus_NF1_Pi, Rate Law: geometry*(kf*RasGTP_minus_NF1_star_-kb*RasGDP_minus_NF1_Pi)/geometry
kb=2.8798E-12; kf=2.18865E-10 Reaction: RasGTP_minus_NF1 => RasGTP_minus_NF1_star_, Rate Law: geometry*(kf*RasGTP_minus_NF1-kb*RasGTP_minus_NF1_star_)/geometry
kb=5.65482E-17; kf=2.0944E-11 Reaction: RasGDP_minus_NF1_Pi => Pi + RasGDP_NF1, Rate Law: geometry*(kf*RasGDP_minus_NF1_Pi-kb*Pi*RasGDP_NF1)/geometry
kf=6.28318E-13; kb=3.3301E-12 Reaction: RasGTP + NF1 => RasGTP_minus_NF1, Rate Law: geometry*(kf*RasGTP*NF1-kb*RasGTP_minus_NF1)/geometry
kb=6.28318E-13; kf=2.43474E-11 Reaction: RasGDP_NF1 => RasGDP + NF1, Rate Law: geometry*(kf*RasGDP_NF1-kb*RasGDP*NF1)/geometry

States:

Name Description
RasGDP [GDP; 43873]
RasGDP minus NF1 Pi [GDP; inorganic phosphate; K08052; 43873]
RasGDP NF1 [K08052; GDP; 43873]
Pi [inorganic phosphate]
NF1 [K08052]
RasGTP [GTP; 43873]
RasGTP minus NF1 [K08052; GTP; 43873]
RasGTP minus NF1 star [K08052; GTP; 43873]

Observables: none

This a model from the article: A quantitative model of sleep-wake dynamics based on the physiology of the brainstem as…

A quantitative, physiology-based model of the ascending arousal system is developed, using continuum neuronal population modeling, which involves averaging properties such as firing rates across neurons in each population. The model includes the ventrolateral preoptic area (VLPO), where circadian and homeostatic drives enter the system, the monoaminergic and cholinergic nuclei of the ascending arousal system, and their interconnections. The human sleep-wake cycle is governed by the activities of these nuclei, which modulate the behavioral state of the brain via diffuse neuromodulatory projections. The model parameters are not free since they correspond to physiological observables. Approximate parameter bounds are obtained by requiring consistency with physiological and behavioral measures, and the model replicates the human sleep-wake cycle, with physiologically reasonable voltages and firing rates. Mutual inhibition between the wake-promoting monoaminergic group and sleep-promoting VLPO causes ;;flip-flop'' behavior, with most time spent in 2 stable steady states corresponding to wake and sleep, with transitions between them on a timescale of a few minutes. The model predicts hysteresis in the sleep-wake cycle, with a region of bistability of the wake and sleep states. Reducing the monoaminergic-VLPO mutual inhibition results in a smaller hysteresis loop. This makes the model more prone to wake-sleep transitions in both directions and makes the states less distinguishable, as in narcolepsy. The model behavior is robust across the constrained parameter ranges, but with sufficient flexibility to describe a wide range of observed phenomena. link: http://identifiers.org/pubmed/17440218

Parameters:

Name Description
chi = 10.8; Qm = 4.74258731775668; mu = 3.6 Reaction: => Somnogen_level_H, Rate Law: COMpartment*(mu*Qm-Somnogen_level_H)/chi
Qm = 4.74258731775668; tau_v = 10.0; D = -10.7; v_vm = -1.9 Reaction: => Ventrolateral_preopticarea__VLPO__voltage, Rate Law: COMpartment*((v_vm*Qm+D)-Ventrolateral_preopticarea__VLPO__voltage)/(tau_v/3600)
v_mv = -1.9; Qv = 0.127101626308136; v_maQao = 1.0; tau_m = 10.0 Reaction: => Monoaminergic__MA__voltage, Rate Law: COMpartment*((v_maQao+v_mv*Qv)-Monoaminergic__MA__voltage)/(tau_m/3600)

States:

Name Description
Ventrolateral preopticarea VLPO voltage [OMIT_0027571; OMIT_0026787; Signal; C70813]
Somnogen level H [C207]
Monoaminergic MA voltage [C70813; OMIT_0026787; C73238; C62025; Signal; C2321]

Observables: none

This a model from the article: Sleep deprivation in a quantitative physiologically based model of the ascending arousa…

A physiologically based quantitative model of the human ascending arousal system is used to study sleep deprivation after being calibrated on a small set of experimentally based criteria. The model includes the sleep-wake switch of mutual inhibition between nuclei which use monoaminergic neuromodulators, and the ventrolateral preoptic area. The system is driven by the circadian rhythm and sleep homeostasis. We use a small number of experimentally derived criteria to calibrate the model for sleep deprivation, then investigate model predictions for other experiments, demonstrating the scope of application. Calibration gives an improved parameter set, in which the form of the homeostatic drive is better constrained, and its weighting relative to the circadian drive is increased. Within the newly constrained parameter ranges, the model predicts repayment of sleep debt consistent with experiment in both quantity and distribution, asymptoting to a maximum repayment for very long deprivations. Recovery is found to depend on circadian phase, and the model predicts that it is most efficient to recover during normal sleeping phases of the circadian cycle, in terms of the amount of recovery sleep required. The form of the homeostatic drive suggests that periods of wake during recovery from sleep deprivation are phases of relative recovery, in the sense that the homeostatic drive continues to converge toward baseline levels. This undermines the concept of sleep debt, and is in agreement with experimentally restricted recovery protocols. Finally, we compare our model to the two-process model, and demonstrate the power of physiologically based modeling by correctly predicting sleep latency times following deprivation from experimental data. link: http://identifiers.org/pubmed/18805427

Parameters: none

States: none

Observables: none

This a model from the article: Sleep deprivation in a quantitative physiologically based model of the ascending arousa…

A physiologically based quantitative model of the human ascending arousal system is used to study sleep deprivation after being calibrated on a small set of experimentally based criteria. The model includes the sleep-wake switch of mutual inhibition between nuclei which use monoaminergic neuromodulators, and the ventrolateral preoptic area. The system is driven by the circadian rhythm and sleep homeostasis. We use a small number of experimentally derived criteria to calibrate the model for sleep deprivation, then investigate model predictions for other experiments, demonstrating the scope of application. Calibration gives an improved parameter set, in which the form of the homeostatic drive is better constrained, and its weighting relative to the circadian drive is increased. Within the newly constrained parameter ranges, the model predicts repayment of sleep debt consistent with experiment in both quantity and distribution, asymptoting to a maximum repayment for very long deprivations. Recovery is found to depend on circadian phase, and the model predicts that it is most efficient to recover during normal sleeping phases of the circadian cycle, in terms of the amount of recovery sleep required. The form of the homeostatic drive suggests that periods of wake during recovery from sleep deprivation are phases of relative recovery, in the sense that the homeostatic drive continues to converge toward baseline levels. This undermines the concept of sleep debt, and is in agreement with experimentally restricted recovery protocols. Finally, we compare our model to the two-process model, and demonstrate the power of physiologically based modeling by correctly predicting sleep latency times following deprivation from experimental data. link: http://identifiers.org/pubmed/18805427

Parameters: none

States: none

Observables: none

Phosphatase activities on PI(3,4,5)P3 and PI(3,4)P2This model describes the action of various phosphatases on PI(3,4,5)P…

The PI3K signaling pathway regulates cell growth and movement and is heavily mutated in cancer. Class I PI3Ks synthesize the lipid messenger PI(3,4,5)P3. PI(3,4,5)P3 can be dephosphorylated by 3- or 5-phosphatases, the latter producing PI(3,4)P2. The PTEN tumor suppressor is thought to function primarily as a PI(3,4,5)P3 3-phosphatase, limiting activation of this pathway. Here we show that PTEN also functions as a PI(3,4)P2 3-phosphatase, both in vitro and in vivo. PTEN is a major PI(3,4)P2 phosphatase in Mcf10a cytosol, and loss of PTEN and INPP4B, a known PI(3,4)P2 4-phosphatase, leads to synergistic accumulation of PI(3,4)P2, which correlated with increased invadopodia in epidermal growth factor (EGF)-stimulated cells. PTEN deletion increased PI(3,4)P2 levels in a mouse model of prostate cancer, and it inversely correlated with PI(3,4)P2 levels across several EGF-stimulated prostate and breast cancer lines. These results point to a role for PI(3,4)P2 in the phenotype caused by loss-of-function mutations or deletions in PTEN. link: http://identifiers.org/doi/10.1016/j.molcel.2017.09.024

Parameters: none

States: none

Observables: none

BIOMD0000000257 @ v0.0.1

This is the self maintaining metabolism model described in the article: A Simple Self-Maintaining Metabolic System:…

A living organism must not only organize itself from within; it must also maintain its organization in the face of changes in its environment and degradation of its components. We show here that a simple (M,R)-system consisting of three interlocking catalytic cycles, with every catalyst produced by the system itself, can both establish a non-trivial steady state and maintain this despite continuous loss of the catalysts by irreversible degradation. As long as at least one catalyst is present at a sufficient concentration in the initial state, the others can be produced and maintained. The system shows bistability, because if the amount of catalyst in the initial state is insufficient to reach the non-trivial steady state the system collapses to a trivial steady state in which all fluxes are zero. It is also robust, because if one catalyst is catastrophically lost when the system is in steady state it can recreate the same state. There are three elementary flux modes, but none of them is an enzyme-maintaining mode, the entire network being necessary to maintain the two catalysts. link: http://identifiers.org/pubmed/20700491

Parameters:

Name Description
k10r = 0.05 per_time_per_M; k10 = 0.05 per_time Reaction: STUSU => STU + SU, Rate Law: env*(k10*STUSU-k10r*STU*SU)
k2 = 10.0 per_time_per_M; k2r = 10.0 per_time Reaction: T + STUS => STUST, Rate Law: env*(k2*T*STUS-k2r*STUST)
k6r = 1.0 per_time; k6 = 1.0 per_time_per_M Reaction: U + SUST => SUSTU, Rate Law: env*(k6*U*SUST-k6r*SUSTU)
k1r = 10.0 per_time; k1 = 10.0 per_time_per_M Reaction: S + STU => STUS, Rate Law: env*(k1*S*STU-k1r*STUS)
k5 = 1.0 per_time_per_M; k5r = 1.0 per_time Reaction: SU + ST => SUST, Rate Law: env*(k5*ST*SU-k5r*SUST)
k4 = 0.3 per_time Reaction: STU =>, Rate Law: env*k4*STU
k3 = 2.0 per_time; k3r = 1.0 per_time_per_M Reaction: STUST => ST + STU, Rate Law: env*(k3*STUST-k3r*ST*STU)
k9 = 0.1 per_time_per_M; k9r = 0.05 per_time Reaction: U + STUS => STUSU, Rate Law: env*(k9*U*STUS-k9r*STUSU)
k11 = NaN per_time Reaction: ST =>, Rate Law: env*k11*ST
k8 = NaN per_time Reaction: SU =>, Rate Law: env*k8*SU
k7 = 0.1 per_time; k7r = 0.1 per_time_per_M Reaction: SUSTU => STU + SU, Rate Law: env*(k7*SUSTU-k7r*STU*SU)

States:

Name Description
STUST STUST
T T
SUST SUST
SU SU
ST ST
S S
U U
STUSU STUSU
SUSTU SUSTU
STUS STUS
STU STU

Observables: none

Pinchuck2010 - Genome-scale metabolic network of Shewanella oneidensis (iSO783)This model is described in the article:…

Shewanellae are gram-negative facultatively anaerobic metal-reducing bacteria commonly found in chemically (i.e., redox) stratified environments. Occupying such niches requires the ability to rapidly acclimate to changes in electron donor/acceptor type and availability; hence, the ability to compete and thrive in such environments must ultimately be reflected in the organization and utilization of electron transfer networks, as well as central and peripheral carbon metabolism. To understand how Shewanella oneidensis MR-1 utilizes its resources, the metabolic network was reconstructed. The resulting network consists of 774 reactions, 783 genes, and 634 unique metabolites and contains biosynthesis pathways for all cell constituents. Using constraint-based modeling, we investigated aerobic growth of S. oneidensis MR-1 on numerous carbon sources. To achieve this, we (i) used experimental data to formulate a biomass equation and estimate cellular ATP requirements, (ii) developed an approach to identify cycles (such as futile cycles and circulations), (iii) classified how reaction usage affects cellular growth, (iv) predicted cellular biomass yields on different carbon sources and compared model predictions to experimental measurements, and (v) used experimental results to refine metabolic fluxes for growth on lactate. The results revealed that aerobic lactate-grown cells of S. oneidensis MR-1 used less efficient enzymes to couple electron transport to proton motive force generation, and possibly operated at least one futile cycle involving malic enzymes. Several examples are provided whereby model predictions were validated by experimental data, in particular the role of serine hydroxymethyltransferase and glycine cleavage system in the metabolism of one-carbon units, and growth on different sources of carbon and energy. This work illustrates how integration of computational and experimental efforts facilitates the understanding of microbial metabolism at a systems level. link: http://identifiers.org/pubmed/20589080

Parameters: none

States: none

Observables: none

Pitkanen2014 - Metabolic reconstruction of Ashbya gossypii using CoReCoThis model was reconstructed with the CoReCo meth…

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375

Parameters: none

States: none

Observables: none

Pitkanen2014 - Metabolic reconstruction of Aspergillus clavatus using CoReCoThis model was reconstructed with the CoReCo…

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375

Parameters: none

States: none

Observables: none

Pitkanen2014 - Metabolic reconstruction of Aspergillus fumigatus using CoReCoThis model was reconstructed with the CoReC…

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375

Parameters: none

States: none

Observables: none

Pitkanen2014 - Metabolic reconstruction of Aspergillus nidulans using CoReCoThis model was reconstructed with the CoReCo…

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375

Parameters: none

States: none

Observables: none

Pitkanen2014 - Metabolic reconstruction of Aspergillus niger using CoReCoThis model was reconstructed with the CoReCo me…

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375

Parameters: none

States: none

Observables: none

Pitkanen2014 - Metabolic reconstruction of Aspergillus oryzae using CoReCoThis model was reconstructed with the CoReCo m…

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375

Parameters: none

States: none

Observables: none

Pitkanen2014 - Metabolic reconstruction of Aspergillus terreus using CoReCoThis model was reconstructed with the CoReCo…

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375

Parameters: none

States: none

Observables: none

Pitkanen2014 - Metabolic reconstruction of Batrachochytrium dendrobatidis using CoReCoThis model was reconstructed with…

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375

Parameters: none

States: none

Observables: none

Pitkanen2014 - Metabolic reconstruction of Botrytis cinerea using CoReCoThis model was reconstructed with the CoReCo met…

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375

Parameters: none

States: none

Observables: none

Pitkanen2014 - Metabolic reconstruction of Candida albicans using CoReCoThis model was reconstructed with the CoReCo met…

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375

Parameters: none

States: none

Observables: none

Pitkanen2014 - Metabolic reconstruction of Candida glabrata using CoReCoThis model was reconstructed with the CoReCo met…

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375

Parameters: none

States: none

Observables: none

Pitkanen2014 - Metabolic reconstruction of Candida lusitaniae using CoReCoThis model was reconstructed with the CoReCo m…

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375

Parameters: none

States: none

Observables: none

Pitkanen2014 - Metabolic reconstruction of Candida tropicalis using CoReCoThis model was reconstructed with the CoReCo m…

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375

Parameters: none

States: none

Observables: none

Pitkanen2014 - Metabolic reconstruction of Chaetomium globosum using CoReCoThis model was reconstructed with the CoReCo…

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375

Parameters: none

States: none

Observables: none

Pitkanen2014 - Metabolic reconstruction of Coccidioides immitis using CoReCoThis model was reconstructed with the CoReCo…

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375

Parameters: none

States: none

Observables: none

Pitkanen2014 - Metabolic reconstruction of Coprinus cinereus using CoReCoThis model was reconstructed with the CoReCo me…

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375

Parameters: none

States: none

Observables: none

Pitkanen2014 - Metabolic reconstruction of Cryptococcus neoformans using CoReCoThis model was reconstructed with the CoR…

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375

Parameters: none

States: none

Observables: none

Pitkanen2014 - Metabolic reconstruction of Debaryomyces hansenii using CoReCoThis model was reconstructed with the CoReC…

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375

Parameters: none

States: none

Observables: none

Pitkanen2014 - Metabolic reconstruction of Encephalitozoon cuniculi using CoReCoThis model was reconstructed with the Co…

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375

Parameters: none

States: none

Observables: none

Pitkanen2014 - Metabolic reconstruction of Fusarium graminearum using CoReCoThis model was reconstructed with the CoReCo…

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375

Parameters: none

States: none

Observables: none

Pitkanen2014 - Metabolic reconstruction of Fusarium oxysporum using CoReCoThis model was reconstructed with the CoReCo m…

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375

Parameters: none

States: none

Observables: none

Pitkanen2014 - Metabolic reconstruction of Fusarium verticillioides using CoReCoThis model was reconstructed with the Co…

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375

Parameters: none

States: none

Observables: none

Pitkanen2014 - Metabolic reconstruction of Histoplasma capsulatum using CoReCoThis model was reconstructed with the CoRe…

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375

Parameters: none

States: none

Observables: none

Pitkanen2014 - Metabolic reconstruction of Kluyveromyces lactis using CoReCoThis model was reconstructed with the CoReCo…

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375

Parameters: none

States: none

Observables: none

Pitkanen2014 - Metabolic reconstruction of Laccaria bicolor using CoReCoThis model was reconstructed with the CoReCo met…

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375

Parameters: none

States: none

Observables: none

Pitkanen2014 - Metabolic reconstruction of Lodderomyces elongisporus using CoReCoThis model was reconstructed with the C…

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375

Parameters: none

States: none

Observables: none

Pitkanen2014 - Metabolic reconstruction of Magnaporthe grisea using CoReCoThis model was reconstructed with the CoReCo m…

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375

Parameters: none

States: none

Observables: none

Pitkanen2014 - Metabolic reconstruction of Mycosphaerella graminicola using CoReCoThis model was reconstructed with the…

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375

Parameters: none

States: none

Observables: none

Pitkanen2014 - Metabolic reconstruction of Nectria haematococca using CoReCoThis model was reconstructed with the CoReCo…

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375

Parameters: none

States: none

Observables: none

Pitkanen2014 - Metabolic reconstruction of Neosartorya fischeri using CoReCoThis model was reconstructed with the CoReCo…

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375

Parameters: none

States: none

Observables: none

Pitkanen2014 - Metabolic reconstruction of Neurospora crassa using CoReCoThis model was reconstructed with the CoReCo me…

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375

Parameters: none

States: none

Observables: none

Pitkanen2014 - Metabolic reconstruction of Phaeosphaeria nodorum using CoReCoThis model was reconstructed with the CoReC…

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375

Parameters: none

States: none

Observables: none

Pitkanen2014 - Metabolic reconstruction of Phanerochaete chrysosporium using CoReCoThis model was reconstructed with the…

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375

Parameters: none

States: none

Observables: none

Pitkanen2014 - Metabolic reconstruction of Phycomyces blakesleeanus using CoReCoThis model was reconstructed with the Co…

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375

Parameters: none

States: none

Observables: none

Pitkanen2014 - Metabolic reconstruction of Pichia guilliermondii using CoReCoThis model was reconstructed with the CoReC…

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375

Parameters: none

States: none

Observables: none

Pitkanen2014 - Metabolic reconstruction of Pichia pastoris using CoReCoThis model was reconstructed with the CoReCo meth…

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375

Parameters: none

States: none

Observables: none

Pitkanen2014 - Metabolic reconstruction of Pichia stipitis using CoReCoThis model was reconstructed with the CoReCo meth…

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375

Parameters: none

States: none

Observables: none

Pitkanen2014 - Metabolic reconstruction of Postia placenta using CoReCoThis model was reconstructed with the CoReCo meth…

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375

Parameters: none

States: none

Observables: none

Pitkanen2014 - Metabolic reconstruction of Puccinia graminis using CoReCoThis model was reconstructed with the CoReCo me…

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375

Parameters: none

States: none

Observables: none

Pitkanen2014 - Metabolic reconstruction of Rhizopus oryzae using CoReCoThis model was reconstructed with the CoReCo meth…

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375

Parameters: none

States: none

Observables: none

Pitkanen2014 - Metabolic reconstruction of Saccharomyces cerevisiae using CoReCoThis model was reconstructed with the Co…

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375

Parameters: none

States: none

Observables: none

Pitkanen2014 - Metabolic reconstruction of Schizosaccharomyces japonicus using CoReCoThis model was reconstructed with t…

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375

Parameters: none

States: none

Observables: none

Pitkanen2014 - Metabolic reconstruction of Schizosaccharomyces pombe using CoReCoThis model was reconstructed with the C…

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375

Parameters: none

States: none

Observables: none

Pitkanen2014 - Metabolic reconstruction of Sclerotinia sclerotiorum using CoReCoThis model was reconstructed with the Co…

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375

Parameters: none

States: none

Observables: none

Pitkanen2014 - Metabolic reconstruction of Sporobolomyces roseus using CoReCoThis model was reconstructed with the CoReC…

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375

Parameters: none

States: none

Observables: none

Pitkanen2014 - Metabolic reconstruction of Trichoderma reesei using CoReCoThis model was reconstructed with the CoReCo m…

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375

Parameters: none

States: none

Observables: none

Pitkanen2014 - Metabolic reconstruction of Uncinocarpus reesii using CoReCoThis model was reconstructed with the CoReCo…

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375

Parameters: none

States: none

Observables: none

Pitkanen2014 - Metabolic reconstruction of Ustilago maydis using CoReCoThis model was reconstructed with the CoReCo meth…

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375

Parameters: none

States: none

Observables: none

Pitkanen2014 - Metabolic reconstruction of Yarrowia lipolytica using CoReCoThis model was reconstructed with the CoReCo…

We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/. link: http://identifiers.org/pubmed/24516375

Parameters: none

States: none

Observables: none

BIOMD0000000304 @ v0.0.1

This a model from the article: Bifurcation and resonance in a model for bursting nerve cells. Plant RE J Math Biol…

In this paper we consider a model for the phenomenon of bursting in nerve cells. Experimental evidence indicates that this phenomenon is due to the interaction of multiple conductances with very different kinetics, and the model incorporates this evidence. As a parameter is varied the model undergoes a transition between two oscillatory waveforms; a corresponding transition is observed experimentally. After establishing the periodicity of the subcritical oscillatory solution, the nature of the transition is studied. It is found to be a resonance bifurcation, with the solution branching at the critical point to another periodic solution of the same period. Using this result a comparison is made between the model and experimental observations. The model is found to predict and allow an interpretation of these observations. link: http://identifiers.org/pubmed/7252375

Parameters:

Name Description
tau_n = NaN; n_infinity = NaN Reaction: n1 = (n_infinity-n1)/tau_n, Rate Law: (n_infinity-n1)/tau_n
tau_h = NaN; h_infinity = NaN Reaction: h1 = (h_infinity-h1)/tau_h, Rate Law: (h_infinity-h1)/tau_h
f = 3.0E-4; V_Ca = 140.0; K_c = 0.0085 Reaction: c = f*(K_c*x1*(V_Ca-V_membrane)-c), Rate Law: f*(K_c*x1*(V_Ca-V_membrane)-c)
i_Na = NaN; i_K_Ca = NaN; i_K = NaN; i_Ca = NaN; i_L = NaN Reaction: V_membrane = i_Na+i_Ca+i_K+i_K_Ca+i_L, Rate Law: i_Na+i_Ca+i_K+i_K_Ca+i_L
x_infinity = NaN; tau_x = 235.0 Reaction: x1 = (x_infinity-x1)/tau_x, Rate Law: (x_infinity-x1)/tau_x

States:

Name Description
h1 [sodium(1+)]
x1 [calcium(2+)]
c [calcium(2+)]
V membrane [membrane potential]
n1 [potassium(1+)]

Observables: none

MODEL1007060000 @ v0.0.1

This is the genome-scale metabolic network of Plasmodium falciparum described in the article: Reconstruction and flux-…

Genome-scale metabolic reconstructions can serve as important tools for hypothesis generation and high-throughput data integration. Here, we present a metabolic network reconstruction and flux-balance analysis (FBA) of Plasmodium falciparum, the primary agent of malaria. The compartmentalized metabolic network accounts for 1001 reactions and 616 metabolites. Enzyme-gene associations were established for 366 genes and 75% of all enzymatic reactions. Compared with other microbes, the P. falciparum metabolic network contains a relatively high number of essential genes, suggesting little redundancy of the parasite metabolism. The model was able to reproduce phenotypes of experimental gene knockout and drug inhibition assays with up to 90% accuracy. Moreover, using constraints based on gene-expression data, the model was able to predict the direction of concentration changes for external metabolites with 70% accuracy. Using FBA of the reconstructed network, we identified 40 enzymatic drug targets (i.e. in silico essential genes), with no or very low sequence identity to human proteins. To demonstrate that the model can be used to make clinically relevant predictions, we experimentally tested one of the identified drug targets, nicotinate mononucleotide adenylyltransferase, using a recently discovered small-molecule inhibitor. link: http://identifiers.org/pubmed/20823846

Parameters: none

States: none

Observables: none

MODEL1807190001 @ v0.0.1

Mathematical model of platelet intracellular signaling network

Blood platelets need to undergo activation to carry out their function of stopping bleeding. Different activation degrees lead to a stepped hierarchy of responses: ability to aggregate, granule release, and, in a fraction of platelets, phosphatidylserine (PS) exposure. This suggests the existence of decision-making mechanisms in the platelet intracellular signaling network. To identify and investigate them, we developed a computational model of PAR1-stimulated platelet signal transduction that included a minimal set of major players in the calcium signaling network. The model comprised three intracellular compartments: cytosol, dense tubular system (DTS) and mitochondria and extracellular space. Computer simulations showed that the stable resting state of platelets is maintained via a balance between calcium pumps and leaks through the DTS and plasma membranes. Stimulation of PAR1 induced oscillations in the cytosolic calcium concentrations, in good agreement with experimental observations. Further increase in the agonist level activated the mitochondrial uniporter leading to calcium uptake by mitochondria, which caused the collapse of mitochondrial membrane potential in a fraction of platelets leading to the PS exposure. The formation of this subpopulation was shown to be a stochastic process determined by the small number of activated PAR1 receptors and by heterogeneity in the number of ion pumps. These results demonstrate how a gradual increase of the activation degree can be converted into a stepped response hierarchy ultimately leading to formation of two distinct subpopulations from an initially homogeneous population. link: http://identifiers.org/pubmed/25627921

Parameters: none

States: none

Observables: none

Mathematical model of intrinsic pathway activation consisting of XIIa, kallikrein and HMWKa.

A mathematical model of contact activation of blood coagulation was developed and analysed. The model variables are concentrations of factor XIIa, kallikrein and activated high-molecular-weight kininogen. Concentrations of active factors were shown to depend on the activating signal value in a hysteretic manner. Within a range of relatively small signals, two (activated and non-activated) stable states coexist (bistability). Signals of the natural environment (surfaces of endothelial and blood cells) seem to be in the range of bistability; therefore, contact activation that persists for a short time can induce a transition of the system to the activated state, and, correspondingly, the formation of a clot. The system cannot return to the initial state, which is characterized by low activation levels, until the activating signals decrease significantly below those present in the circulation. link: http://identifiers.org/doi/10.1006/jtbi.1997.0584

Parameters: none

States: none

Observables: none

BIOMD0000000273 @ v0.0.1

This a model from the article: Data assimilation constrains new connections and components in a complex, eukaryotic…

Circadian clocks generate 24-h rhythms that are entrained by the day/night cycle. Clock circuits include several light inputs and interlocked feedback loops, with complex dynamics. Multiple biological components can contribute to each part of the circuit in higher organisms. Mechanistic models with morning, evening and central feedback loops have provided a heuristic framework for the clock in plants, but were based on transcriptional control. Here, we model observed, post-transcriptional and post-translational regulation and constrain many parameter values based on experimental data. The model's feedback circuit is revised and now includes PSEUDO-RESPONSE REGULATOR 7 (PRR7) and ZEITLUPE. The revised model matches data in varying environments and mutants, and gains robustness to parameter variation. Our results suggest that the activation of important morning-expressed genes follows their release from a night inhibitor (NI). Experiments inspired by the new model support the predicted NI function and show that the PRR5 gene contributes to the NI. The multiple PRR genes of Arabidopsis uncouple events in the late night from light-driven responses in the day, increasing the flexibility of rhythmic regulation. link: http://identifiers.org/pubmed/20865009

Parameters:

Name Description
m6 = 0.25; m7 = 0.5; D = 0.5; p5 = 1.0; m8 = 0.1; L = 0.5 Reaction: cT => ; cZG, cZTL, Rate Law: def*((m6*L+m7*D)*cT*(p5*cZTL+cZG)+m8*cT)/def
g9 = 0.3; n7 = 0.2; i = 3.0; g8 = 0.14; q3 = 2.9; h = 2.0; n4 = 0.0; L = 0.5 Reaction: => cP9_m; cL, cP, cT, Rate Law: def*(L*q3*cP+(n4*L+n7*cL^i/(cL^i+g9^i))*g8^h/(cT^h+g8^h))/def
m16 = 0.5 Reaction: cNI_m =>, Rate Law: def*m16*cNI_m/def
p10 = 0.36 Reaction: => cNI; cNI_m, Rate Law: def*p10*cNI_m/def
m4 = 0.2 Reaction: cLm =>, Rate Law: def*m4*cLm/def
m11 = 1.0; L = 0.5 Reaction: cP =>, Rate Law: def*m11*cP*L/def
D = 0.5; m26 = 0.14; m25 = 0.28; L = 0.5 Reaction: cTm =>, Rate Law: def*(m25*L+m26*D)*cTm/def
D = 0.5; p2 = 0.27; p1 = 0.4; L = 0.5 Reaction: => cL; cL_m, Rate Law: def*cL_m*(p1*L+p2*D)/def
m10 = 0.3 Reaction: cY =>, Rate Law: def*m10*cY/def
m9 = 1.0 Reaction: cY_m =>, Rate Law: def*m9*cY_m/def
p4 = 0.268 Reaction: => cT; cT_m, Rate Law: def*p4*cT_m/def
m21 = 0.2 Reaction: cZG =>, Rate Law: def*m21*cZG/def
m24 = 0.405; D = 0.5; m17 = 0.3; L = 0.5 Reaction: cNI =>, Rate Law: def*(m17*L+m24*D)*cNI/def
m18 = 1.0 Reaction: cG_m =>, Rate Law: def*m18*cG_m/def
D = 0.5; p7 = 0.3 Reaction: => cP, Rate Law: def*p7*D*(1-cP)/def
D = 0.5; m2 = 0.24; m1 = 0.54; L = 0.5 Reaction: cL_m =>, Rate Law: def*(m1*L+m2*D)*cL_m/def
p6 = 0.44 Reaction: => cY; cY_m, Rate Law: def*p6*cY_m/def
g3 = 0.4; m3 = 0.2; p3 = 0.1; c = 3.0 Reaction: cL =>, Rate Law: def*(m3*cL+p3*cL^c/(cL^c+g3^c))/def
m13 = 0.32; m22 = 2.0; D = 0.5; L = 0.5 Reaction: cP9 =>, Rate Law: def*(m13*L+m22*D)*cP9/def
n1 = 1.8; n0 = 0.4; g2 = 0.28; g1 = 0.1; q1 = 0.8; a = 2.0; b = 3.0; L = 0.5 Reaction: => cL_m; cNI, cP, cP7, cP9, cTm, Rate Law: def*(n0*L+L*q1*cP+n1*cTm^b/(cTm^b+g2^b))*g1^a/((cP9+cP7+cNI)^a+g1^a)/def
p8 = 0.7 Reaction: => cP9; cP9_m, Rate Law: def*p8*cP9_m/def
g4 = 0.91; n2 = 0.7; g5 = 0.3; e = 2.0; n3 = 0.06; d = 2.5 Reaction: => cT_m; cL, cY, Rate Law: def*(n2*cY^d/(cY^d+g4^d)+n3)*g5^e/(cL^e+g5^e)/def
n8 = 0.42; g11 = 0.7; j = 3.0; n9 = 0.26; k = 3.0; g10 = 0.7 Reaction: => cP7_m; cL, cLm, cP9, Rate Law: def*(n8*(cLm+cL)^j/((cLm+cL)^j+g10^j)+n9*cP9^k/(cP9^k+g11^k))/def
D = 0.5; p13 = 0.4; p12 = 30.0; L = 0.5 Reaction: cG + cZTL => cZG, Rate Law: def*(p12*L*cZTL*cG-p13*D*cZG)/def
m12 = 1.0 Reaction: cP9_m =>, Rate Law: def*m12*cP9_m/def
p14 = 0.45 Reaction: => cZTL, Rate Law: def*p14/def
g3 = 0.4; p3 = 0.1; c = 3.0 Reaction: => cLm; cL, Rate Law: def*p3*cL^c/(cL^c+g3^c)/def
m5 = 0.3 Reaction: cT_m =>, Rate Law: def*m5*cT_m/def
m20 = 1.2 Reaction: cZTL =>, Rate Law: def*m20*cZTL/def
g16 = 0.2; n5 = 3.4; D = 0.5; q2 = 0.5; s = 3.0; g7 = 0.18; n6 = 1.25; L = 0.5; g = 2.0 Reaction: => cY_m; cL, cP, cT, Rate Law: def*(L*q2*cP+(n5*L+n6*D)*g7^s/(cT^s+g7^s)*g16^g/(cL^g+g16^g))/def
p15 = 0.05; f = 3.0; g6 = 0.3 Reaction: => cTm; cT, Rate Law: def*p15*cT^f/(cT^f+g6^f)/def
p11 = 0.23 Reaction: => cG; cG_m, Rate Law: def*p11*cG_m/def
m19 = 0.2 Reaction: cG =>, Rate Law: def*m19*cG/def
m14 = 0.28 Reaction: cP7_m =>, Rate Law: def*m14*cP7_m/def
p9 = 0.4 Reaction: => cP7; cP7_m, Rate Law: def*p9*cP7_m/def
n10 = 0.18; g12 = 0.5; m = 2.0; n11 = 0.71; g13 = 0.6; l = 2.0 Reaction: => cNI_m; cLm, cP7, Rate Law: def*(n10*cLm^l/(cLm^l+g12^l)+n11*cP7^m/(cP7^m+g13^m))/def
g14 = 0.17; g15 = 0.4; n = 1.0; q4 = 0.6; o = 2.0; n12 = 2.3; L = 0.5 Reaction: => cG_m; cL, cP, cT, Rate Law: def*(L*q4*cP+n12*L*g15^o/(cL^o+g15^o)*g14^n/(cT^n+g14^n))/def
D = 0.5; m15 = 0.31; L = 0.5; m23 = 1.0 Reaction: cP7 =>, Rate Law: def*(m15*L+m23*D)*cP7/def

States:

Name Description
cL m [messenger RNA]
cNI [inhibitor]
cG [Protein GIGANTEA]
cP9 [Two-component response regulator-like APRR9]
cP9 m [messenger RNA]
cZTL [Adagio protein 1]
cP7 m [messenger RNA]
cNI m [inhibitor; messenger RNA]
cG m [messenger RNA]
cY [protein]
cY m [messenger RNA; RNA]
cT m [messenger RNA]
cP cP
cLm [Protein CCA1; Protein LHY; protein modification]
cP7 [Two-component response regulator-like APRR7]
cT [Two-component response regulator-like APRR1]
cZG [Protein GIGANTEA; Adagio protein 1]
cTm [Two-component response regulator-like APRR1; protein modification]
cL [Protein CCA1; Protein LHY]

Observables: none

This model is from the article: The clock gene circuit in Arabidopsis includes a repressilator with additional feedb…

Circadian clocks synchronise biological processes with the day/night cycle, using molecular mechanisms that include interlocked, transcriptional feedback loops. Recent experiments identified the evening complex (EC) as a repressor that can be essential for gene expression rhythms in plants. Integrating the EC components in this role significantly alters our mechanistic, mathematical model of the clock gene circuit. Negative autoregulation of the EC genes constitutes the clock's evening loop, replacing the hypothetical component Y. The EC explains our earlier conjecture that the morning gene Pseudo-Response Regulator 9 was repressed by an evening gene, previously identified with Timing Of CAB Expression1 (TOC1). Our computational analysis suggests that TOC1 is a repressor of the morning genes Late Elongated Hypocotyl and Circadian Clock Associated1 rather than an activator as first conceived. This removes the necessity for the unknown component X (or TOC1mod) from previous clock models. As well as matching timeseries and phase-response data, the model provides a new conceptual framework for the plant clock that includes a three-component repressilator circuit in its complex structure. link: http://identifiers.org/pubmed/22395476

Parameters:

Name Description
twilightPeriod = 0.05 3600*s; n12 = 12.5; q2 = 1.56; e = 2.0; cyclePeriod = 24.0 3600*s; photoPeriod = 12.0 3600*s; lightOffset = 0.0 3600*s; g15 = 0.4; g14 = 0.004; phase = 0.0 3600*s; lightAmplitude = 1.0 3600*s Reaction: s42 => cG_m; cEC, cL, cP, Rate Law: def*((((lightOffset+0.5*lightAmplitude*(1+tanh(cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))/twilightPeriod)))-0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-photoPeriod)/twilightPeriod)))+0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-cyclePeriod)/twilightPeriod)))*q2*cP+n12*g14/(cEC+g14)*g15^e/(cL^e+g15^e))
p16 = 0.62 Reaction: s31 => cE3; cE3_m, Rate Law: def*p16*cE3_m/def
twilightPeriod = 0.05 3600*s; cyclePeriod = 24.0 3600*s; photoPeriod = 12.0 3600*s; lightOffset = 0.0 3600*s; m27 = 0.1; p15 = 3.0; phase = 0.0 3600*s; lightAmplitude = 1.0 3600*s Reaction: cCOP1n => s40, Rate Law: def*m27*cCOP1n*(1+p15*(((lightOffset+0.5*lightAmplitude*(1+tanh(cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))/twilightPeriod)))-0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-photoPeriod)/twilightPeriod)))+0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-cyclePeriod)/twilightPeriod))))
twilightPeriod = 0.05 3600*s; cyclePeriod = 24.0 3600*s; photoPeriod = 12.0 3600*s; lightOffset = 0.0 3600*s; m11 = 1.0; phase = 0.0 3600*s; lightAmplitude = 1.0 3600*s Reaction: cP => s8, Rate Law: def*m11*cP*(((lightOffset+0.5*lightAmplitude*(1+tanh(cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))/twilightPeriod)))-0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-photoPeriod)/twilightPeriod)))+0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-cyclePeriod)/twilightPeriod)))
m16 = 0.5 Reaction: cNI_m => s18, Rate Law: def*m16*cNI_m/def
twilightPeriod = 0.05 3600*s; cyclePeriod = 24.0 3600*s; photoPeriod = 12.0 3600*s; m33 = 13.0; lightOffset = 0.0 3600*s; m31 = 0.3; phase = 0.0 3600*s; lightAmplitude = 1.0 3600*s Reaction: cCOP1d => s41, Rate Law: def*m31*(1+m33*(1-(((lightOffset+0.5*lightAmplitude*(1+tanh(cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))/twilightPeriod)))-0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-photoPeriod)/twilightPeriod)))+0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-cyclePeriod)/twilightPeriod)))))*cCOP1d
p12 = 3.4; lightOffset = 0.0 3600*s; phase = 0.0 3600*s; twilightPeriod = 0.05 3600*s; cyclePeriod = 24.0 3600*s; photoPeriod = 12.0 3600*s; p13 = 0.1; lightAmplitude = 1.0 3600*s Reaction: cG + cZTL => cZG, Rate Law: def*(p12*(((lightOffset+0.5*lightAmplitude*(1+tanh(cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))/twilightPeriod)))-0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-photoPeriod)/twilightPeriod)))+0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-cyclePeriod)/twilightPeriod)))*cZTL*cG-p13*(1-(((lightOffset+0.5*lightAmplitude*(1+tanh(cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))/twilightPeriod)))-0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-photoPeriod)/twilightPeriod)))+0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-cyclePeriod)/twilightPeriod))))*cZG)
p8 = 0.6 Reaction: s11 => cP9; cP9_m, Rate Law: def*p8*cP9_m/def
n2 = 0.64; g5 = 0.15; g4 = 0.01; e = 2.0 Reaction: s21 => cT_m; cEC, cL, Rate Law: def*n2*g4/(cEC+g4)*g5^e/(cL^e+g5^e)/def
g16 = 0.3; e = 2.0; n3 = 0.29 Reaction: s29 => cE3_m; cL, Rate Law: def*n3*g16^e/(cL^e+g16^e)/def
p23 = 0.37 Reaction: s27 => cE4; cE4_m, Rate Law: def*p23*cE4_m/def
twilightPeriod = 0.05 3600*s; p2 = 0.27; cyclePeriod = 24.0 3600*s; p1 = 0.13; photoPeriod = 12.0 3600*s; lightOffset = 0.0 3600*s; phase = 0.0 3600*s; lightAmplitude = 1.0 3600*s Reaction: s3 => cL; cL_m, Rate Law: def*cL_m*(p1*(((lightOffset+0.5*lightAmplitude*(1+tanh(cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))/twilightPeriod)))-0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-photoPeriod)/twilightPeriod)))+0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-cyclePeriod)/twilightPeriod)))+p2)
p3 = 0.1; c = 2.0; g3 = 0.6 Reaction: s5 => cLm; cL, Rate Law: def*p3*cL^c/(cL^c+g3^c)/def
n5 = 0.23 Reaction: s38 => cCOP1c, Rate Law: def*n5/def
p11 = 0.51 Reaction: s44 => cG; cG_m, Rate Law: def*p11*cG_m/def
p17 = 4.8 Reaction: cE3 + cG => cEG, Rate Law: def*p17*cE3*cG/def
p27 = 0.8 Reaction: s36 => cLUX; cLUX_m, Rate Law: def*p27*cLUX_m/def
m20 = 0.6 Reaction: cZTL => s47, Rate Law: def*m20*cZTL/def
twilightPeriod = 0.05 3600*s; cyclePeriod = 24.0 3600*s; photoPeriod = 12.0 3600*s; lightOffset = 0.0 3600*s; m24 = 0.1; phase = 0.0 3600*s; m17 = 0.5; lightAmplitude = 1.0 3600*s Reaction: cNI => s20, Rate Law: def*(m17+m24*(1-(((lightOffset+0.5*lightAmplitude*(1+tanh(cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))/twilightPeriod)))-0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-photoPeriod)/twilightPeriod)))+0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-cyclePeriod)/twilightPeriod)))))*cNI
m14 = 0.4 Reaction: cP7_m => s14, Rate Law: def*m14*cP7_m/def
p9 = 0.8 Reaction: s15 => cP7; cP7_m, Rate Law: def*p9*cP7_m/def
m12 = 1.0 Reaction: cP9_m => s10, Rate Law: def*m12*cP9_m/def
m19 = 0.2; p26 = 0.3; p28 = 2.0; m30 = 3.0; m29 = 5.0; p25 = 8.0; m37 = 0.8; p29 = 0.1; m36 = 0.1; p17 = 4.8; p21 = 1.0 Reaction: cE3n => s33; cCOP1d, cCOP1n, cE4, cG, cLUX, Rate Law: def*(((m29*cE3n*cCOP1n+m30*cE3n*cCOP1d+p25*cE4*cE3n)-p21*p25*cE4*cE3n/(p26*cLUX+p21+m37*cCOP1d+m36*cCOP1n))+p17*cE3n*p28*cG/(p29+m19+p17*cE3n))/def
m5 = 0.3 Reaction: cT_m => s22, Rate Law: def*m5*cT_m/def
twilightPeriod = 0.05 3600*s; a = 2.0; cyclePeriod = 24.0 3600*s; n1 = 2.6; photoPeriod = 12.0 3600*s; lightOffset = 0.0 3600*s; g1 = 0.1; q1 = 1.2; phase = 0.0 3600*s; lightAmplitude = 1.0 3600*s Reaction: s1 => cL_m; cNI, cP, cP7, cP9, cT, Rate Law: def*((((lightOffset+0.5*lightAmplitude*(1+tanh(cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))/twilightPeriod)))-0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-photoPeriod)/twilightPeriod)))+0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-cyclePeriod)/twilightPeriod)))*q1*cP+n1*g1^a/((cP9+cP7+cNI+cT)^a+g1^a))
m21 = 0.08 Reaction: cZG => s48, Rate Law: def*m21*cZG/def
m26 = 0.5 Reaction: cE3_m => s30, Rate Law: def*m26*cE3_m/def
m3 = 0.2; p3 = 0.1; c = 2.0; g3 = 0.6 Reaction: cL => s4, Rate Law: def*(m3*cL+p3*cL^c/(cL^c+g3^c))/def
p4 = 0.56 Reaction: s23 => cT; cT_m, Rate Law: def*p4*cT_m/def
p25 = 8.0; m37 = 0.8; m36 = 0.1; p26 = 0.3; m39 = 0.3; p21 = 1.0 Reaction: cLUX => s37; cCOP1d, cCOP1n, cE3n, cE4, Rate Law: def*(m39*cLUX+p26*cLUX*p25*cE4*cE3n/(p26*cLUX+p21+m37*cCOP1d+m36*cCOP1n))/def
m4 = 0.2 Reaction: cLm => s6, Rate Law: def*m4*cLm/def
twilightPeriod = 0.05 3600*s; g9 = 0.3; n7 = 0.2; e = 2.0; cyclePeriod = 24.0 3600*s; q3 = 2.8; g8 = 0.01; photoPeriod = 12.0 3600*s; lightOffset = 0.0 3600*s; phase = 0.0 3600*s; lightAmplitude = 1.0 3600*s; n4 = 0.07 Reaction: s9 => cP9_m; cEC, cL, cP, Rate Law: def*((((lightOffset+0.5*lightAmplitude*(1+tanh(cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))/twilightPeriod)))-0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-photoPeriod)/twilightPeriod)))+0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-cyclePeriod)/twilightPeriod)))*q3*cP+(n4+n7*cL^e/(cL^e+g9^e))*g8/(cEC+g8))
p14 = 0.14 Reaction: s46 => cZTL, Rate Law: def*p14/def
m32 = 0.2; m19 = 0.2; m10 = 1.0; m36 = 0.1; p17 = 4.8; d = 2.0; p24 = 10.0; lightOffset = 0.0 3600*s; p18 = 4.0; g7 = 0.6; m37 = 0.8; m9 = 1.1; phase = 0.0 3600*s; twilightPeriod = 0.05 3600*s; cyclePeriod = 24.0 3600*s; p29 = 0.1; p31 = 0.1; photoPeriod = 12.0 3600*s; p28 = 2.0; lightAmplitude = 1.0 3600*s Reaction: cEC => s51; cCOP1d, cCOP1n, cE3n, cEG, cG, Rate Law: def*(m36*cCOP1n*cEC+m37*cCOP1d*cEC+m32*cEC*(1+p24*(((lightOffset+0.5*lightAmplitude*(1+tanh(cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))/twilightPeriod)))-0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-photoPeriod)/twilightPeriod)))+0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-cyclePeriod)/twilightPeriod)))*(p28*cG/(p29+m19+p17*cE3n)+(p18*cEG+p17*cE3n*p28*cG/(p29+m19+p17*cE3n))/(m9*cCOP1n+m10*cCOP1d+p31))^d/((p28*cG/(p29+m19+p17*cE3n)+(p18*cEG+p17*cE3n*p28*cG/(p29+m19+p17*cE3n))/(m9*cCOP1n+m10*cCOP1d+p31))^d+g7^d)))
m34 = 0.6 Reaction: cE4_m => s26, Rate Law: def*m34*cE4_m/def
p25 = 8.0; m37 = 0.8; m36 = 0.1; p26 = 0.3; p21 = 1.0; m35 = 0.3 Reaction: cE4 => s28; cCOP1d, cCOP1n, cE3n, cLUX, Rate Law: def*((m35*cE4+p25*cE4*cE3n)-p21*p25*cE4*cE3n/(p26*cLUX+p21+m37*cCOP1d+m36*cCOP1n))/def
n10 = 0.4; g12 = 0.2; n11 = 0.6; b = 2.0; e = 2.0; g13 = 1.0 Reaction: s17 => cNI_m; cLm, cP7, Rate Law: def*(n10*cLm^e/(cLm^e+g12^e)+n11*cP7^b/(cP7^b+g13^b))/def
twilightPeriod = 0.05 3600*s; cyclePeriod = 24.0 3600*s; photoPeriod = 12.0 3600*s; m23 = 1.8; lightOffset = 0.0 3600*s; m15 = 0.7; phase = 0.0 3600*s; lightAmplitude = 1.0 3600*s Reaction: cP7 => s16, Rate Law: def*(m15+m23*(1-(((lightOffset+0.5*lightAmplitude*(1+tanh(cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))/twilightPeriod)))-0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-photoPeriod)/twilightPeriod)))+0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-cyclePeriod)/twilightPeriod)))))*cP7
twilightPeriod = 0.05 3600*s; m22 = 0.1; cyclePeriod = 24.0 3600*s; m13 = 0.32; photoPeriod = 12.0 3600*s; lightOffset = 0.0 3600*s; phase = 0.0 3600*s; lightAmplitude = 1.0 3600*s Reaction: cP9 => s12, Rate Law: def*(m13+m22*(1-(((lightOffset+0.5*lightAmplitude*(1+tanh(cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))/twilightPeriod)))-0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-photoPeriod)/twilightPeriod)))+0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-cyclePeriod)/twilightPeriod)))))*cP9
m18 = 3.4 Reaction: cG_m => s43, Rate Law: def*m18*cG_m/def
m19 = 0.2; p29 = 0.1; p17 = 4.8; p28 = 2.0 Reaction: cG => s45; cE3n, Rate Law: def*((m19*cG+p28*cG)-p29*p28*cG/(p29+m19+p17*cE3n))/def
twilightPeriod = 0.05 3600*s; p5 = 4.0; cyclePeriod = 24.0 3600*s; m8 = 0.4; photoPeriod = 12.0 3600*s; lightOffset = 0.0 3600*s; m6 = 0.3; m7 = 0.7; phase = 0.0 3600*s; lightAmplitude = 1.0 3600*s Reaction: cT => s24; cZG, cZTL, Rate Law: def*((m6+m7*(1-(((lightOffset+0.5*lightAmplitude*(1+tanh(cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))/twilightPeriod)))-0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-photoPeriod)/twilightPeriod)))+0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-cyclePeriod)/twilightPeriod)))))*cT*(p5*cZTL+cZG)+m8*cT)
twilightPeriod = 0.05 3600*s; cyclePeriod = 24.0 3600*s; photoPeriod = 12.0 3600*s; lightOffset = 0.0 3600*s; p7 = 0.3; phase = 0.0 3600*s; lightAmplitude = 1.0 3600*s Reaction: s7 => cP, Rate Law: def*p7*(1-(((lightOffset+0.5*lightAmplitude*(1+tanh(cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))/twilightPeriod)))-0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-photoPeriod)/twilightPeriod)))+0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-cyclePeriod)/twilightPeriod))))*(1-cP)
m9 = 1.1 Reaction: cE3 => s32; cCOP1c, Rate Law: def*m9*cE3*cCOP1c/def
twilightPeriod = 0.05 3600*s; n14 = 0.1; cyclePeriod = 24.0 3600*s; photoPeriod = 12.0 3600*s; lightOffset = 0.0 3600*s; n6 = 20.0; phase = 0.0 3600*s; lightAmplitude = 1.0 3600*s Reaction: cCOP1n => cCOP1d; cP, Rate Law: def*(n6*(((lightOffset+0.5*lightAmplitude*(1+tanh(cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))/twilightPeriod)))-0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-photoPeriod)/twilightPeriod)))+0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-cyclePeriod)/twilightPeriod)))*cP*cCOP1n+n14*cCOP1n)
twilightPeriod = 0.05 3600*s; m2 = 0.24; cyclePeriod = 24.0 3600*s; photoPeriod = 12.0 3600*s; lightOffset = 0.0 3600*s; m1 = 0.54; phase = 0.0 3600*s; lightAmplitude = 1.0 3600*s Reaction: cL_m => s2, Rate Law: def*(m2+(m1-m2)*(((lightOffset+0.5*lightAmplitude*(1+tanh(cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))/twilightPeriod)))-0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-photoPeriod)/twilightPeriod)))+0.5*lightAmplitude*(1+tanh((cyclePeriod*((time+phase)/cyclePeriod-floor(floor(time+phase)/cyclePeriod))-cyclePeriod)/twilightPeriod))))*cL_m
m19 = 0.2; p18 = 4.0; p28 = 2.0; m10 = 1.0; p29 = 0.1; m9 = 1.1; p17 = 4.8; p31 = 0.1 Reaction: cEG => s49; cCOP1c, cCOP1d, cCOP1n, cE3n, cG, Rate Law: def*((m9*cEG*cCOP1c+p18*cEG)-p31*(p18*cEG+p17*cE3n*p28*cG/(p29+m19+p17*cE3n))/(m9*cCOP1n+m10*cCOP1d+p31))/def
p10 = 0.54 Reaction: s19 => cNI; cNI_m, Rate Law: def*p10*cNI_m/def
p20 = 0.1; p19 = 1.0 Reaction: cE3 => cE3n, Rate Law: def*(p19*cE3-p20*cE3n)/def
p25 = 8.0; m37 = 0.8; m36 = 0.1; p26 = 0.3; p21 = 1.0 Reaction: s50 => cEC; cCOP1d, cCOP1n, cE3n, cE4, cLUX, Rate Law: def*p26*cLUX*p25*cE4*cE3n/(p26*cLUX+p21+m37*cCOP1d+m36*cCOP1n)/def
n13 = 1.3; g2 = 0.01; e = 2.0; g6 = 0.3 Reaction: s25 => cE4_m; cEC, cL, Rate Law: def*n13*g2/(cEC+g2)*g6^e/(cL^e+g6^e)/def
g11 = 0.7; n9 = 0.2; f = 2.0; g10 = 0.5; n8 = 0.5; e = 2.0 Reaction: s13 => cP7_m; cL, cLm, cP9, Rate Law: def*(n8*(cLm+cL)^e/((cLm+cL)^e+g10^e)+n9*cP9^f/(cP9^f+g11^f))/def

States:

Name Description
cE4 [Protein EARLY FLOWERING 4]
cNI [Two-component response regulator-like APRR5]
cLUX [Homeodomain-like superfamily protein]
s5 s5
s40 s40
cP9 m [Two-component response regulator-like APRR9; messenger RNA]
s37 s37
s44 s44
s31 s31
cNI m [Two-component response regulator-like APRR3; messenger RNA]
cEG [Protein GIGANTEA; Protein EARLY FLOWERING 3]
s10 s10
s34 s34
s38 s38
s36 s36
s6 s6
s46 s46
s11 s11
cP [obsolete protein]
s45 s45
cZG [Protein GIGANTEA; Adagio protein 1]
s1 s1
cG [Protein GIGANTEA]
cE3 [Protein EARLY FLOWERING 3]
s17 s17
s41 s41
cP7 m [Two-component response regulator-like APRR7; messenger RNA]
s25 s25
s2 s2
s49 s49
s33 s33
cCOP1c [E3 ubiquitin-protein ligase COP1; cytoplasm]
cT m [Two-component response regulator-like APRR1; messenger RNA]
s28 s28
cLm [Protein LHY; Protein CCA1; CCO:U0000010]
cL [Protein LHY; Protein CCA1]
s35 s35
s24 s24
cP9 [Two-component response regulator-like APRR9]
s7 s7
cZTL [Adagio protein 1]
s43 s43
cCOP1n [E3 ubiquitin-protein ligase COP1; nucleus]
s47 s47
cE4 m [Protein EARLY FLOWERING 4; messenger RNA]
cG m [Protein GIGANTEA; messenger RNA]
s32 s32
s22 s22
cCOP1d [E3 ubiquitin-protein ligase COP1; nucleus]
cE3n [Protein EARLY FLOWERING 3; nucleus]
s51 s51
cP7 [Two-component response regulator-like APRR7]
s3 s3
cE3 m [Protein EARLY FLOWERING 3; messenger RNA]
s48 s48
cEC [Protein EARLY FLOWERING 3; Protein EARLY FLOWERING 4; Homeodomain-like superfamily protein]
cL m [Protein CCA1; Protein LHY; messenger RNA]
s12 s12
s4 s4
cLUX m [Homeodomain-like superfamily protein; messenger RNA]
s30 s30
s26 s26
s42 s42
s39 s39
cT [Two-component response regulator-like APRR1]
s29 s29
s27 s27

Observables: none

Pokhilko2013 - TOC1 signalling in Arabidopsis circadian clockIn this model, Pokhilko et al. has incorporated the negat…

24-hour biological clocks are intimately connected to the cellular signalling network, which complicates the analysis of clock mechanisms. The transcriptional regulator TOC1 (TIMING OF CAB EXPRESSION 1) is a founding component of the gene circuit in the plant circadian clock. Recent results show that TOC1 suppresses transcription of multiple target genes within the clock circuit, far beyond its previously-described regulation of the morning transcription factors LHY (LATE ELONGATED HYPOCOTYL) and CCA1 (CIRCADIAN CLOCK ASSOCIATED 1). It is unclear how this pervasive effect of TOC1 affects the dynamics of the clock and its outputs. TOC1 also appears to function in a nested feedback loop that includes signalling by the plant hormone Abscisic Acid (ABA), which is upregulated by abiotic stresses, such as drought. ABA treatments both alter TOC1 levels and affect the clock's timing behaviour. Conversely, the clock rhythmically modulates physiological processes induced by ABA, such as the closing of stomata in the leaf epidermis. In order to understand the dynamics of the clock and its outputs under changing environmental conditions, the reciprocal interactions between the clock and other signalling pathways must be integrated.We extended the mathematical model of the plant clock gene circuit by incorporating the repression of multiple clock genes by TOC1, observed experimentally. The revised model more accurately matches the data on the clock's molecular profiles and timing behaviour, explaining the clock's responses in TOC1 over-expression and toc1 mutant plants. A simplified representation of ABA signalling allowed us to investigate the interactions of ABA and circadian pathways. Increased ABA levels lengthen the free-running period of the clock, consistent with the experimental data. Adding stomatal closure to the model, as a key ABA- and clock-regulated downstream process allowed to describe TOC1 effects on the rhythmic gating of stomatal closure.The integrated model of the circadian clock circuit and ABA-regulated environmental sensing allowed us to explain multiple experimental observations on the timing and stomatal responses to genetic and environmental perturbations. These results crystallise a new role of TOC1 as an environmental sensor, which both affects the pace of the central oscillator and modulates the kinetics of downstream processes. link: http://identifiers.org/pubmed/23506153

Parameters:

Name Description
p17 = 17.0 Reaction: cE3 + cG => cEG; cE3, cG, Rate Law: def*p17*cE3*cG/def
n13 = 2.0; parameter_7 = 2.0; parameter_3 = 0.4; g2 = 0.01; e = 2.0; g6 = 0.3 Reaction: => cLUX_m; cT, cEC, cL, cEC, cL, cT, Rate Law: def*parameter_3^parameter_7/(parameter_3^parameter_7+cT^parameter_7)*n13*g2/(cEC+g2)*g6^e/(cL^e+g6^e)/def
p16 = 0.62 Reaction: => cE3; cE3_m, cE3_m, Rate Law: def*p16*cE3_m/def
m16 = 0.5 Reaction: cNI_m => ; cNI_m, Rate Law: def*m16*cNI_m/def
parameter_14 = 0.5; n2 = 0.35; g5 = 0.2; parameter_11 = 2.0; g4 = 0.006; e = 2.0 Reaction: => cT_m; cL, species_3, cEC, cEC, cL, species_3, Rate Law: def*n2/(1+(cL/(g5*(1+(species_3/parameter_14)^parameter_11)))^e)*g4/(cEC+g4)/def
m29 = 0.3 Reaction: species_4 => ; species_4, Rate Law: default*m29*species_4/def
m7 = 0.1; p5 = 1.0; m6 = 0.2; m8 = 0.5; L = 0.5 Reaction: cT => ; cZTL, cZG, cT, cZG, cZTL, Rate Law: def*((m6+m7*(1-L))*cT*(p5*cZTL+cZG)+m8*cT)
parameter_29 = 1.0; parameter_28 = 0.2; parameter_9 = 2.0; parameter_18 = 1.0; parameter_16 = 0.2 Reaction: => species_2; species_1, species_1, Rate Law: default*parameter_28*parameter_16^parameter_9/((0.5*((parameter_29+species_1+parameter_18)-((parameter_29+species_1+parameter_18)^2-4*parameter_29*species_1)^(1/2)))^parameter_9+parameter_16^parameter_9)/def
m11 = 1.0; L = 0.5 Reaction: cP => ; cP, Rate Law: def*m11*cP*L
m33 = 13.0; m31 = 0.1; L = 0.5 Reaction: cCOP1d => ; cCOP1d, Rate Law: def*m31*(1+m33*(1-L))*cCOP1d
p8 = 0.6 Reaction: => cP9; cP9_m, cP9_m, Rate Law: def*p8*cP9_m/def
p17 = 17.0; p29 = 0.1; m19 = 0.9; p28 = 2.0 Reaction: cG => ; cE3n, cE3n, cG, Rate Law: def*((m19*cG+p28*cG)-p29*p28*cG/(p29+m19+p17*cE3n))/def
m30 = 1.0 Reaction: species_3 => ; species_2, species_2, species_3, Rate Law: default*m30*species_3*species_2/def
parameter_7 = 2.0; g12 = 0.1; n10 = 0.3; e = 2.0; n11 = 0.6; b = 2.0; parameter_12 = 0.6; g13 = 1.0 Reaction: => cNI_m; cT, cLm, cP7, cLm, cP7, cT, Rate Law: def*parameter_12^parameter_7/(parameter_12^parameter_7+cT^parameter_7)*(n10*cLm^e/(cLm^e+g12^e)+n11*cP7^b/(cP7^b+g13^b))/def
m32 = 0.2; parameter_26 = 0.1; m19 = 0.9; m10 = 0.1; p29 = 0.1; L = 0.5; d = 2.0; p17 = 17.0; p24 = 11.0; p18 = 4.0; p28 = 2.0; m9 = 0.2; g7 = 1.0 Reaction: cEC => ; cCOP1n, cCOP1d, cG, cE3n, cEG, cCOP1d, cCOP1n, cE3n, cEC, cEG, cG, Rate Law: def*(m10*cCOP1n*cEC+m9*cCOP1d*cEC+m32*cEC*(1+p24*L*(p28*cG/(p29+m19+p17*cE3n)+(p18*cEG+p17*cE3n*p28*cG/(p29+m19+p17*cE3n))/(m10*cCOP1n+m9*cCOP1d+parameter_26))^d/((p28*cG/(p29+m19+p17*cE3n)+(p18*cEG+p17*cE3n*p28*cG/(p29+m19+p17*cE3n))/(m10*cCOP1n+m9*cCOP1d+parameter_26))^d+g7^d)))
g16 = 0.3; e = 2.0; n3 = 0.29 Reaction: => cE3_m; cL, cL, Rate Law: def*n3*g16^e/(cL^e+g16^e)/def
p3 = 0.1; c = 2.0; g3 = 0.6 Reaction: => cLm; cL, cL, Rate Law: def*p3*cL^c/(cL^c+g3^c)/def
p23 = 0.37 Reaction: => cE4; cE4_m, cE4_m, Rate Law: def*p23*cE4_m/def
g14 = 0.02; parameter_7 = 2.0; q2 = 1.56; g15 = 0.4; e = 2.0; n12 = 9.0; parameter_1 = 0.6; L = 0.5 Reaction: => cG_m; cT, cP, cEC, cL, cEC, cL, cP, cT, Rate Law: def*parameter_1^parameter_7/(parameter_1^parameter_7+cT^parameter_7)*(L*q2*cP+n12*g14/(cEC+g14)*g15^e/(cL^e+g15^e))
p27 = 0.8 Reaction: => cLUX; cLUX_m, cLUX_m, Rate Law: def*p27*cLUX_m/def
m20 = 0.6 Reaction: cZTL => ; cZTL, Rate Law: def*m20*cZTL/def
p4 = 0.5 Reaction: => cT; cT_m, cT_m, Rate Law: def*p4*cT_m/def
p17 = 17.0; p26 = 0.3; m19 = 0.9; p28 = 2.0; m10 = 0.1; p29 = 0.1; m9 = 0.2; p21 = 1.0; p25 = 2.0 Reaction: cE3n => ; cCOP1n, cCOP1d, cE4, cLUX, cG, cE3n, cCOP1d, cCOP1n, cE3n, cE4, cG, cLUX, Rate Law: def*(((m10*cE3n*cCOP1n+m9*cE3n*cCOP1d+p25*cE4*cE3n)-p21*p25*cE4*cE3n/(p26*cLUX+p21+m9*cCOP1d+m10*cCOP1n))+p17*cE3n*p28*cG/(p29+m19+p17*cE3n))/def
m14 = 0.4 Reaction: cP7_m => ; cP7_m, Rate Law: def*m14*cP7_m/def
p9 = 0.8 Reaction: => cP7; cP7_m, cP7_m, Rate Law: def*p9*cP7_m/def
m12 = 1.0 Reaction: cP9_m => ; cP9_m, Rate Law: def*m12*cP9_m/def
n4 = 0.04; n7 = 0.1; g9 = 0.3; parameter_7 = 2.0; e = 2.0; g8 = 0.04; parameter_2 = 0.4; q3 = 3.0; L = 0.5 Reaction: => cP9_m; cP, cL, cEC, cT, cEC, cL, cP, cT, Rate Law: def*parameter_2^parameter_7/(parameter_2^parameter_7+cT^parameter_7)*(L*q3*cP+(n4+n7*cL^e/(cL^e+g9^e))*g8/(cEC+g8))
m5 = 0.3 Reaction: cT_m => ; cT_m, Rate Law: def*m5*cT_m/def
p11 = 0.5 Reaction: => cG; cG_m, cG_m, Rate Law: def*p11*cG_m/def
m37 = 0.4 Reaction: species_1 => ; species_1, Rate Law: default*m37*species_1/def
m21 = 0.08 Reaction: cZG => ; cZG, Rate Law: def*m21*cZG/def
m24 = 0.5; m17 = 0.5; L = 0.5 Reaction: cNI => ; cNI, Rate Law: def*(m17+m24*(1-L))*cNI
p1 = 0.13; p2 = 0.27; L = 0.5 Reaction: => cL; cL_m, cL_m, Rate Law: def*cL_m*(p1*L+p2)
m10 = 0.1; m9 = 0.2; p26 = 0.3; p21 = 1.0; m35 = 0.3; p25 = 2.0 Reaction: cE4 => ; cE3n, cLUX, cCOP1d, cCOP1n, cCOP1d, cCOP1n, cE3n, cE4, cLUX, Rate Law: def*((m35*cE4+p25*cE4*cE3n)-p21*p25*cE4*cE3n/(p26*cLUX+p21+m9*cCOP1d+m10*cCOP1n))/def
m26 = 0.5 Reaction: cE3_m => ; cE3_m, Rate Law: def*m26*cE3_m/def
m3 = 0.2; p3 = 0.1; c = 2.0; g3 = 0.6 Reaction: cL => ; cL, Rate Law: def*(m3*cL+p3*cL^c/(cL^c+g3^c))/def
parameter_27 = 0.1 Reaction: => species_3, Rate Law: default*parameter_27/def
m23 = 0.5; m15 = 0.7; L = 0.5 Reaction: cP7 => ; cP7, Rate Law: def*(m15+m23*(1-L))*cP7
p12 = 10.0; L = 0.5; p13 = 0.1 Reaction: cG + cZTL => cZG; cG, cZG, cZTL, Rate Law: def*(p12*L*cZTL*cG-p13*(1-L)*cZG)
p6 = 0.2 Reaction: cCOP1c => cCOP1n; cCOP1c, Rate Law: def*p6*cCOP1c/def
m4 = 0.2 Reaction: cLm => ; cLm, Rate Law: def*m4*cLm/def
p14 = 0.14 Reaction: => cZTL, Rate Law: def*p14/def
m34 = 0.6 Reaction: cLUX_m => ; cLUX_m, Rate Law: def*m34*cLUX_m/def
m10 = 0.1; m9 = 0.2; p26 = 0.3; p21 = 1.0; p25 = 2.0 Reaction: => cEC; cLUX, cE4, cE3n, cCOP1d, cCOP1n, cCOP1d, cCOP1n, cE3n, cE4, cLUX, Rate Law: def*p26*cLUX*p25*cE4*cE3n/(p26*cLUX+p21+m9*cCOP1d+m10*cCOP1n)/def
m10 = 0.1; m9 = 0.2; m36 = 0.3; p26 = 0.3; p21 = 1.0; p25 = 2.0 Reaction: cLUX => ; cE4, cE3n, cCOP1d, cCOP1n, cCOP1d, cCOP1n, cE3n, cE4, cLUX, Rate Law: def*(m36*cLUX+p26*cLUX*p25*cE4*cE3n/(p26*cLUX+p21+m9*cCOP1d+m10*cCOP1n))/def
g11 = 0.7; parameter_7 = 2.0; g10 = 0.5; n9 = 0.6; e = 2.0; f = 2.0; n8 = 0.5; parameter_6 = 0.1 Reaction: => cP7_m; cLm, cL, cP9, cT, cL, cLm, cP9, cT, Rate Law: def*parameter_6^parameter_7/(parameter_6^parameter_7+cT^parameter_7)*(n8*(cLm+cL)^e/((cLm+cL)^e+g10^e)+n9*cP9^f/(cP9^f+g11^f))/def
parameter_13 = 0.3; parameter_7 = 2.0; parameter_24 = 0.5; e = 2.0; parameter_17 = 0.1 Reaction: => species_1; cT, cL, cL, cT, Rate Law: default*parameter_13^parameter_7/(parameter_13^parameter_7+cT^parameter_7)*parameter_24*cL^e/(cL^e+parameter_17^e)/def
m18 = 3.4 Reaction: cG_m => ; cG_m, Rate Law: def*m18*cG_m/def
m9 = 0.2 Reaction: cE3 => ; cCOP1c, cCOP1c, cE3, Rate Law: def*m9*cE3*cCOP1c/def
m27 = 0.1; p15 = 2.0; L = 0.5 Reaction: cCOP1c => ; cCOP1c, Rate Law: def*m27*cCOP1c*(1+p15*L)
m13 = 0.32; m22 = 0.1; L = 0.5 Reaction: cP9 => ; cP9, Rate Law: def*(m13+m22*(1-L))*cP9
m2 = 0.24; m1 = 0.54; L = 0.5 Reaction: cL_m => ; cL_m, Rate Law: def*(m2+(m1-m2)*L)*cL_m
parameter_20 = 0.2 Reaction: species_2 => ; species_2, Rate Law: default*parameter_20*species_2/def
a = 2.0; n1 = 2.6; g1 = 0.1; q1 = 1.0; L = 0.5 Reaction: => cL_m; cP, cP9, cP7, cNI, cT, cNI, cP, cP7, cP9, cT, Rate Law: def*(L*q1*cP+n1*g1^a/((cP9+cP7+cNI+cT)^a+g1^a))
n14 = 0.1; n6 = 20.0; L = 0.5 Reaction: cCOP1n => cCOP1d; cP, cCOP1n, cP, Rate Law: def*(n6*L*cP*cCOP1n+n14*cCOP1n)
n5 = 0.4 Reaction: => cCOP1c, Rate Law: def*n5/def
parameter_10 = 2.0; parameter_21 = 0.5; parameter_15 = 0.3; parameter_25 = 0.2; L = 0.5 Reaction: => species_4; species_4, species_3, species_3, species_4, Rate Law: default*(parameter_25+parameter_21*L)*(1-species_4)*parameter_15^parameter_10/(parameter_15^parameter_10+species_3^parameter_10)/def
parameter_7 = 2.0; parameter_8 = 2.0; e = 2.0; g6 = 0.3; parameter_4 = 0.03; parameter_5 = 0.4 Reaction: => cE4_m; cT, cEC, cL, cEC, cL, cT, Rate Law: def*parameter_5^parameter_7/(parameter_5^parameter_7+cT^parameter_7)*parameter_8*parameter_4/(cEC+parameter_4)*g6^e/(cL^e+g6^e)/def
p17 = 17.0; parameter_26 = 0.1; p18 = 4.0; m19 = 0.9; p28 = 2.0; m10 = 0.1; p29 = 0.1; m9 = 0.2 Reaction: cEG => ; cCOP1c, cE3n, cG, cCOP1n, cCOP1d, cCOP1c, cCOP1d, cCOP1n, cE3n, cEG, cG, Rate Law: def*((m10*cEG*cCOP1c+p18*cEG)-parameter_26*(p18*cEG+p17*cE3n*p28*cG/(p29+m19+p17*cE3n))/(m10*cCOP1n+m9*cCOP1d+parameter_26))/def
p10 = 0.54 Reaction: => cNI; cNI_m, cNI_m, Rate Law: def*p10*cNI_m/def
p20 = 0.1; p19 = 1.0 Reaction: cE3 => cE3n; cE3, cE3n, Rate Law: def*(p19*cE3-p20*cE3n)/def
p7 = 0.3; L = 0.5 Reaction: => cP; cP, Rate Law: def*p7*(1-L)*(1-cP)

States:

Name Description
cE4 [Protein EARLY FLOWERING 4]
cNI [Two-component response regulator-like APRR5]
cLUX [Homeodomain-like superfamily protein]
cP9 [Two-component response regulator-like APRR9]
cP9 m [Two-component response regulator-like APRR9; messenger RNA]
cZTL [Adagio protein 1]
species 1 [Magnesium-chelatase subunit ChlH, chloroplastic; messenger RNA]
species 4 cs
cCOP1n [E3 ubiquitin-protein ligase COP1; nucleus]
cNI m [Two-component response regulator-like APRR3; messenger RNA]
cEG [Protein GIGANTEA; Protein EARLY FLOWERING 3]
cG m [Protein GIGANTEA; messenger RNA]
cE4 m [Protein EARLY FLOWERING 4; messenger RNA]
cCOP1d [E3 ubiquitin-protein ligase COP1; nucleus]
cP [GO:0003575]
cE3n [Protein EARLY FLOWERING 3; nucleus]
cP7 [Two-component response regulator-like APRR7]
cZG [Protein GIGANTEA; Adagio protein 1]
cE3 m [Protein EARLY FLOWERING 3; messenger RNA]
species 2 [Protein phosphatase 2C 16]
cL m [Protein CCA1; Protein LHY; messenger RNA]
cG [Protein GIGANTEA]
cE3 [Protein EARLY FLOWERING 3]
cEC [Protein EARLY FLOWERING 3; Protein EARLY FLOWERING 4; Homeodomain-like superfamily protein]
cP7 m [Two-component response regulator-like APRR7; messenger RNA]
cLUX m [Homeodomain-like superfamily protein; messenger RNA]
cT m [Two-component response regulator-like APRR1; messenger RNA]
cCOP1c [E3 ubiquitin-protein ligase COP1; cytoplasm]
species 3 [Serine/threonine-protein kinase SRK2E]
cLm [Protein LHY; Protein CCA1; CCO:U0000010]
cT [Two-component response regulator-like APRR1]
cL [Protein LHY; Protein CCA1]

Observables: none

Poliquin2013 - Energy Deregulations in Parkinson's DiseaseEncoded non-curated model. Issues: - Fluxes, reactions, param…

Parkinson's disease (PD) is a multifactorial disease known to result from a variety of factors. Although age is the principal risk factor, other etiological mechanisms have been identified, including gene mutations and exposure to toxins. Deregulation of energy metabolism, mostly through the loss of complex I efficiency, is involved in disease progression in both the genetic and sporadic forms of the disease. In this study, we investigated energy deregulation in the cerebral tissue of animal models (genetic and toxin induced) of PD using an approach that combines metabolomics and mathematical modelling. In a first step, quantitative measurements of energy-related metabolites in mouse brain slices revealed most affected pathways. A genetic model of PD, the Park2 knockout, was compared to the effect of CCCP, a mitochondrial uncoupler [corrected]. Model simulated and experimental results revealed a significant and sustained decrease in ATP after CCCP exposure, but not in the genetic mice model. In support to data analysis, a mathematical model of the relevant metabolic pathways was developed and calibrated onto experimental data. In this work, we show that a short-term stress response in nucleotide scavenging is most probably induced by the toxin exposure. In turn, the robustness of energy-related pathways in the model explains how genetic perturbations, at least in young animals, are not sufficient to induce significant changes at the metabolite level. link: http://identifiers.org/pubmed/23935941

Parameters: none

States: none

Observables: none

The cell-cycle oscillator includes an essential negative-feedback loop: Cdc2 activates the anaphase-promoting complex (A…

The cell-cycle oscillator includes an essential negative-feedback loop: Cdc2 activates the anaphase-promoting complex (APC), which leads to cyclin destruction and Cdc2 inactivation. Under some circumstances, a negative-feedback loop is sufficient to generate sustained oscillations. However, the Cdc2/APC system also includes positive-feedback loops, whose functional importance we now assess. We show that short-circuiting positive feedback makes the oscillations in Cdc2 activity faster, less temporally abrupt, and damped. This compromises the activation of cyclin destruction and interferes with mitotic exit and DNA replication. This work demonstrates a systems-level role for positive-feedback loops in the embryonic cell cycle and provides an example of how oscillations can emerge out of combinations of subcircuits whose individual behaviors are not oscillatory. This work also underscores the fundamental similarity of cell-cycle oscillations in embryos to repetitive action potentials in pacemaker neurons, with both systems relying on a combination of negative and positive-feedback loops. link: http://identifiers.org/pubmed/16122424

Parameters: none

States: none

Observables: none

BIOMD0000000013 @ v0.0.1

This a model from the article: Applications of metabolic modelling to plant metabolism. Poolman MG ,Assmus HE, F…

In this paper some of the general concepts underpinning the computer modelling of metabolic systems are introduced. The difference between kinetic and structural modelling is emphasized, and the more important techniques from both, along with the physiological implications, are described. These approaches are then illustrated by descriptions of other work, in which they have been applied to models of the Calvin cycle, sucrose metabolism in sugar cane, and starch metabolism in potatoes. link: http://identifiers.org/pubmed/15073223

Parameters:

Name Description
PGI_v=5.0E8; q14=2.3 Reaction: F6P_ch => G6P_ch, Rate Law: PGI_v*chloroplast*(F6P_ch-G6P_ch/q14)
q15=0.058; PGM_v=5.0E8 Reaction: G6P_ch => G1P_ch, Rate Law: PGM_v*chloroplast*(G6P_ch-G1P_ch/q15)
F_TKL_v=5.0E8; q7=0.084 Reaction: GAP_ch + F6P_ch => X5P_ch + E4P_ch, Rate Law: chloroplast*F_TKL_v*(F6P_ch*GAP_ch-E4P_ch*X5P_ch/q7)
Light_on = 1.0; FBPase_ch_KiF6P=0.7; FBPase_ch_km=0.03; FBPase_ch_KiPi=12.0; FBPase_ch_vm=200.0 Reaction: FBP_ch => F6P_ch + Pi_ch, Rate Law: Light_on*FBPase_ch_vm*FBP_ch*chloroplast/(FBP_ch+FBPase_ch_km*(1+F6P_ch/FBPase_ch_KiF6P+Pi_ch/FBPase_ch_KiPi))
q3=1.6E7; Light_on = 1.0; G3Pdh_v=5.0E8 Reaction: x_NADPH_ch + BPGA_ch + x_Proton_ch => x_NADP_ch + GAP_ch + Pi_ch, Rate Law: Light_on*G3Pdh_v*chloroplast*(BPGA_ch*x_NADPH_ch*x_Proton_ch-x_NADP_ch*GAP_ch*Pi_ch/q3)
q10=0.85; G_TKL_v=5.0E8 Reaction: S7P_ch + GAP_ch => R5P_ch + X5P_ch, Rate Law: chloroplast*G_TKL_v*(GAP_ch*S7P_ch-X5P_ch*R5P_ch/q10)
StPase_Vm=40.0; StPase_kiG1P=0.05; StPase_km=0.1 Reaction: x_Starch_ch + Pi_ch => G1P_ch, Rate Law: StPase_Vm*Pi_ch*chloroplast/(Pi_ch+StPase_km*(1+G1P_ch/StPase_kiG1P))
Ru5Pk_ch_KiPi=4.0; Ru5Pk_ch_KiADP1=2.5; Light_on = 1.0; Ru5Pk_ch_KiADP2=0.4; Ru5Pk_ch_vm=10000.0; Ru5Pk_ch_KiPGA=2.0; Ru5Pk_ch_km1=0.05; Ru5Pk_ch_KiRuBP=0.7; Ru5Pk_ch_km2=0.05 Reaction: Ru5P_ch + ATP_ch => RuBP_ch + ADP_ch; PGA_ch, Pi_ch, Rate Law: Light_on*Ru5Pk_ch_vm*Ru5P_ch*chloroplast*ATP_ch/((Ru5P_ch+Ru5Pk_ch_km1*(1+PGA_ch/Ru5Pk_ch_KiPGA+RuBP_ch/Ru5Pk_ch_KiRuBP+Pi_ch/Ru5Pk_ch_KiPi))*(ATP_ch*(1+ADP_ch/Ru5Pk_ch_KiADP1)+Ru5Pk_ch_km2*(1+ADP_ch/Ru5Pk_ch_KiADP2)))
q4=22.0; TPI_v=5.0E8 Reaction: GAP_ch => DHAP_ch, Rate Law: chloroplast*TPI_v*(GAP_ch-DHAP_ch/q4)
R5Piso_v=5.0E8; q11=0.4 Reaction: R5P_ch => Ru5P_ch, Rate Law: R5Piso_v*chloroplast*(R5P_ch-Ru5P_ch/q11)
Light_on = 1.0; Rbco_KiFBP=0.04; Rbco_KiNADPH=0.07; Rbco_KiPGA=0.84; Rbco_vm=340.0; Rbco_KiSBP=0.075; Rbco_km=0.02; Rbco_KiPi=0.9 Reaction: RuBP_ch + x_CO2 => PGA_ch; FBP_ch, SBP_ch, Pi_ch, x_NADPH_ch, Rate Law: Light_on*Rbco_vm*RuBP_ch*chloroplast/(RuBP_ch+Rbco_km*(1+PGA_ch/Rbco_KiPGA+FBP_ch/Rbco_KiFBP+SBP_ch/Rbco_KiSBP+Pi_ch/Rbco_KiPi+x_NADPH_ch/Rbco_KiNADPH))
TP_Piap_vm=250.0; TP_Piap_kPGA_ch=0.25; TP_Piap_kDHAP_ch=0.077; TP_Piap_kPi_ch=0.63; TP_Piap_kGAP_ch=0.075; TP_Piap_kPi_cyt=0.74 Reaction: x_Pi_cyt + GAP_ch => x_GAP_cyt + Pi_ch; PGA_ch, DHAP_ch, Rate Law: TP_Piap_vm*GAP_ch*chloroplast/(TP_Piap_kGAP_ch*(1+(1+TP_Piap_kPi_cyt/x_Pi_cyt)*(Pi_ch/TP_Piap_kPi_ch+PGA_ch/TP_Piap_kPGA_ch+DHAP_ch/TP_Piap_kDHAP_ch+GAP_ch/TP_Piap_kGAP_ch)))
E_Aldo_v=5.0E8; q8=13.0 Reaction: DHAP_ch + E4P_ch => SBP_ch, Rate Law: chloroplast*E_Aldo_v*(E4P_ch*DHAP_ch-SBP_ch/q8)
q12=0.67; X5Pepi_v=5.0E8 Reaction: X5P_ch => Ru5P_ch, Rate Law: chloroplast*X5Pepi_v*(X5P_ch-Ru5P_ch/q12)
Light_on = 1.0; SBPase_ch_km=0.013; SBPase_ch_vm=40.0; SBPase_ch_KiPi=12.0 Reaction: SBP_ch => Pi_ch + S7P_ch, Rate Law: Light_on*SBPase_ch_vm*SBP_ch*chloroplast/(SBP_ch+SBPase_ch_km*(1+Pi_ch/SBPase_ch_KiPi))
q2=3.1E-4; PGK_v=5.0E8; Light_on = 1.0 Reaction: PGA_ch + ATP_ch => BPGA_ch + ADP_ch, Rate Law: Light_on*PGK_v*chloroplast*(PGA_ch*ATP_ch-BPGA_ch*ADP_ch/q2)
stsyn_ch_km1=0.08; stsyn_ch_Ki=10.0; stsyn_ch_ka2=0.02; stsyn_ch_ka1=0.1; stsyn_ch_ka3=0.02; StSyn_vm=40.0; stsyn_ch_km2=0.08 Reaction: ATP_ch + G1P_ch => x_Starch_ch + ADP_ch + Pi_ch; PGA_ch, F6P_ch, FBP_ch, Rate Law: StSyn_vm*G1P_ch*ATP_ch*chloroplast/((G1P_ch+stsyn_ch_km1)*(1+ADP_ch/stsyn_ch_Ki)*(ATP_ch+stsyn_ch_km2)+stsyn_ch_km2*Pi_ch/(stsyn_ch_ka1*PGA_ch)+stsyn_ch_ka2*F6P_ch+stsyn_ch_ka3*FBP_ch)
q5=7.1; F_Aldo_v=5.0E8 Reaction: GAP_ch + DHAP_ch => FBP_ch, Rate Law: F_Aldo_v*chloroplast*(DHAP_ch*GAP_ch-FBP_ch/q5)
TP_Piap_vm=250.0; PGA_xpMult=0.75; TP_Piap_kPGA_ch=0.25; TP_Piap_kDHAP_ch=0.077; TP_Piap_kPi_ch=0.63; TP_Piap_kGAP_ch=0.075; TP_Piap_kPi_cyt=0.74 Reaction: x_Pi_cyt + PGA_ch => x_PGA_cyt + Pi_ch; DHAP_ch, GAP_ch, Rate Law: PGA_xpMult*TP_Piap_vm*PGA_ch*chloroplast/(TP_Piap_kPGA_ch*(1+(1+TP_Piap_kPi_cyt/x_Pi_cyt)*(Pi_ch/TP_Piap_kPi_ch+PGA_ch/TP_Piap_kPGA_ch+DHAP_ch/TP_Piap_kDHAP_ch+GAP_ch/TP_Piap_kGAP_ch)))
LR_kmPi=0.3; Light_on = 1.0; LR_kmADP=0.014; LR_vm=3500.0 Reaction: Pi_ch + ADP_ch => ATP_ch, Rate Law: Light_on*LR_vm*ADP_ch*Pi_ch*chloroplast/((ADP_ch+LR_kmADP)*(Pi_ch+LR_kmPi))

States:

Name Description
E4P ch [D-erythrose 4-phosphate; D-Erythrose 4-phosphate]
DHAP ch [dihydroxyacetone phosphate; Glycerone phosphate]
PGA ch [3-Phospho-D-glycerate]
x NADPH ch [NADPH; NADPH]
x PGA cyt [3-Phospho-D-glycerate]
x DHAP cyt [dihydroxyacetone phosphate; Glycerone phosphate]
R5P ch [aldehydo-D-ribose 5-phosphate; D-Ribose 5-phosphate]
ADP ch [ADP; ADP]
FBP ch [beta-D-fructofuranose 1,6-bisphosphate; beta-D-Fructose 1,6-bisphosphate]
Pi ch [phosphate(3-); Orthophosphate]
S7P ch [sedoheptulose 7-phosphate; Sedoheptulose 7-phosphate]
Ru5P ch [D-ribulose 5-phosphate; D-Ribulose 5-phosphate]
x Pi cyt [phosphate(3-); Orthophosphate]
GAP ch [glyceraldehyde 3-phosphate; D-Glyceraldehyde 3-phosphate]
RuBP ch [D-Ribulose 1,5-bisphosphate]
ATP ch [ATP; ATP]
x Starch ch [Starch]
BPGA ch [3-Phospho-D-glyceroyl phosphate]
x Proton ch [proton]
x GAP cyt [glyceraldehyde 3-phosphate; Glyceraldehyde 3-phosphate]
x NADP ch [NADP(+); NADP+]
G6P ch [D-glucose 6-phosphate; alpha-D-Glucose 6-phosphate]
F6P ch [beta-D-fructofuranose 6-phosphate(2-)]
X5P ch [D-xylulose 5-phosphate; D-Xylulose 5-phosphate]
x CO2 [carbon dioxide; CO2]
G1P ch [alpha-D-glucose 1-phosphate(2-); D-Glucose 1-phosphate]
SBP ch [sedoheptulose 1,7-bisphosphate; Sedoheptulose 1,7-bisphosphate]

Observables: none

MODEL3618435756 @ v0.0.1

This is the full scale model of the Arabidopsis metabolic network described in the article: A Genome-scale Metabolic M…

We describe the construction and analysis of a genome-scale metabolic model of Arabidopsis (Arabidopsis thaliana) primarily derived from the annotations in the Aracyc database. We used techniques based on linear programming to demonstrate the following: (1) that the model is capable of producing biomass components (amino acids, nucleotides, lipid, starch, and cellulose) in the proportions observed experimentally in a heterotrophic suspension culture; (2) that approximately only 15% of the available reactions are needed for this purpose and that the size of this network is comparable to estimates of minimal network size for other organisms; (3) that reactions may be grouped according to the changes in flux resulting from a hypothetical stimulus (in this case demand for ATP) and that this allows the identification of potential metabolic modules; and (4) that total ATP demand for growth and maintenance can be inferred and that this is consistent with previous estimates in prokaryotes and yeast. link: http://identifiers.org/pubmed/19755544

Parameters: none

States: none

Observables: none

MODEL3618487388 @ v0.0.1

This is the reduced model of the Arabidopsis metabolic network described in the article: A Genome-scale Metabolic Mode…

We describe the construction and analysis of a genome-scale metabolic model of Arabidopsis (Arabidopsis thaliana) primarily derived from the annotations in the Aracyc database. We used techniques based on linear programming to demonstrate the following: (1) that the model is capable of producing biomass components (amino acids, nucleotides, lipid, starch, and cellulose) in the proportions observed experimentally in a heterotrophic suspension culture; (2) that approximately only 15% of the available reactions are needed for this purpose and that the size of this network is comparable to estimates of minimal network size for other organisms; (3) that reactions may be grouped according to the changes in flux resulting from a hypothetical stimulus (in this case demand for ATP) and that this allows the identification of potential metabolic modules; and (4) that total ATP demand for growth and maintenance can be inferred and that this is consistent with previous estimates in prokaryotes and yeast. link: http://identifiers.org/pubmed/19755544

Parameters: none

States: none

Observables: none

MODEL8684444027 @ v0.0.1

This a model from the article: Mathematical model for the androgenic regulation of the prostate in intact and castrate…

The testicular-hypothalamic-pituitary axis regulates male reproductive system functions. Understanding these regulatory mechanisms is important for assessing the reproductive effects of environmental and pharmaceutical androgenic and antiandrogenic compounds. A mathematical model for the dynamics of androgenic synthesis, transport, metabolism, and regulation of the adult rodent ventral prostate was developed on the basis of a model by Barton and Anderson (1997). The model describes the systemic and local kinetics of testosterone (T), 5alpha-dihydrotestosterone (DHT), and luteinizing hormone (LH), with metabolism of T to DHT by 5alpha-reductase in liver and prostate. Also included are feedback loops for the positive regulation of T synthesis by LH and negative regulation of LH by T and DHT. The model simulates maintenance of the prostate as a function of hormone concentrations and androgen receptor (AR)-mediated signal transduction. The regulatory processes involved in prostate size and function include cell proliferation, apoptosis, fluid production, and 5alpha-reductase activity. Each process is controlled through the occupancy of a representative gene by androgen-AR dimers. The model simulates prostate dynamics for intact, castrated, and intravenous T-injected rats. After calibration, the model accurately captures the castration-induced regression of the prostate compared with experimental data that show that the prostate regresses to approximately 17 and 5% of its intact weight at 14 and 30 days postcastration, respectively. The model also accurately predicts serum T and AR levels following castration compared with data. This model provides a framework for quantifying the kinetics and effects of environmental and pharmaceutical endocrine active compounds on the prostate. link: http://identifiers.org/pubmed/16757547

Parameters: none

States: none

Observables: none

This is a basic mathematical model describing the dynamics of three cell lines (normal host cells, leukemic host cells a…

In this paper a basic mathematical model is introduced to describe the dynamics of three cell lines after allogeneic stem cell transplantation: normal host cells, leukemic host cells and donor cells. Their evolution is one of competitive type and depends upon kinetic and cellcell interaction parameters. Numerical simulations prove that the evolution can ultimately lead either to the normal hematopoietic state achieved by the expansion of the donor cells and the elimination of the host cells, or to the leukemic hematopoietic state characterized by the proliferation of the cancer line and the suppression of the other cell lines. One state or the other is reached depending on cellcell interactions (anti-host, anti-leukemia and anti-graft effects) and initial cell concentrations at transplantation. The model also provides a theoretical basis for the control of post-transplant evolution aimed at the achievement of normal hematopoiesis. link: http://identifiers.org/doi/10.1142/S1793524511001684

Parameters:

Name Description
C = 0.01 Reaction: y =>, Rate Law: compartment*C*y
epsilon = 1.0; A = 0.45; B = 2.2E-8; G = 2.0 Reaction: => y; x, z, Rate Law: compartment*A/(1+B*(x+y+z))*(x+y+epsilon)/(x+y+epsilon+G*z)*y
epsilon = 1.0; b = 2.2E-8; h = 2.0; a = 0.23 Reaction: => z; x, y, Rate Law: compartment*a/(1+b*(x+y+z))*(1-h*(x+y)/(z+epsilon+h*(x+y)))*z
epsilon = 1.0; b = 2.2E-8; a = 0.23; g = 2.0 Reaction: => x; y, z, Rate Law: compartment*a/(1+b*(x+y+z))*(x+y+epsilon)/(x+y+epsilon+g*z)*x
c = 0.01 Reaction: z =>, Rate Law: compartment*c*z

States:

Name Description
x [hematopoietic stem cell; bone marrow]
z [hematopoietic stem cell; bone marrow]
y [leukemic stem cell; bone marrow]

Observables: none

MODEL8683876463 @ v0.0.1

This a model from the article: Simulation study of cellular electric properties in heart failure Priebe L, Beuckelma…

Patients with severe heart failure are at high risk of sudden cardiac death. In the majority of these patients, sudden cardiac death is thought to be due to ventricular tachyarrhythmias. Alterations of the electric properties of single myocytes in heart failure may favor the occurrence of ventricular arrhythmias in these patients by inducing early or delayed afterdepolarizations. Mathematical models of the cellular action potential and its underlying ionic currents could help to elucidate possible arrhythmogenic mechanisms on a cellular level. In the present study, selected ionic currents based on human data are incorporated into a model of the ventricular action potential for the purpose of studying the cellular electrophysiological consequences of heart failure. Ionic currents that are not yet sufficiently characterized in human ventricular myocytes are adopted from the action potential model developed by Luo and Rudy (LR model). The main results obtained from this model are as follows: The action potential in ventricular myocytes from failing hearts is longer than in nonfailing control hearts. The major underlying mechanisms for this prolongation are the enhanced activity of the Na+-Ca2+ exchanger, the slowed diastolic decay of the [Ca2+]i transient, and the reduction of the inwardly rectifying K+ current and the Na+-K+ pump current in myocytes of failing hearts. Furthermore, the fast and slow components of the delayed rectifier K+ current (I(Kr) and I(Ks), respectively) are of utmost importance in determining repolarization of the human ventricular action potential. In contrast, the influence of the transient outward K+ current on APD is only small in both cell groups. Inhibition of I(Kr) promotes the development of early afterdepolarizations in failing, but not nonfailing, myocytes. Furthermore, spontaneous Ca2+ release from the sarcoplasmic reticulum triggers a premature action potential only in failing myocytes. This model of the ventricular action potential and its alterations in heart failure is intended to serve as a tool for investigating the effects of therapeutic interventions on the electric excitability of the human ventricular myocardium. link: http://identifiers.org/pubmed/9633920

Parameters: none

States: none

Observables: none

BIOMD0000000172 @ v0.0.1

from: **Schemes of fluc control in a model of Saccharomyces cerevisiae glycolysis ** **Pritchard, L and Kell, DB**Eu…

We used parameter scanning to emulate changes to the limiting rate for steps in a fitted model of glucose-derepressed yeast glycolysis. Three flux-control regimes were observed, two of which were under the dominant control of hexose transport, in accordance with various experimental studies and other model predictions. A third control regime in which phosphofructokinase exerted dominant glycolytic flux control was also found, but it appeared to be physiologically unreachable by this model, and all realistically obtainable flux control regimes featured hexose transport as a step involving high flux control. link: http://identifiers.org/pubmed/12180966

Parameters:

Name Description
k_19=21.4 Reaction: NAD + AcAld => NADH + Succinate, Rate Law: cell*k_19*AcAld
k1_15=45.0; k2_15=100.0 Reaction: ADP => ATP + AMP, Rate Law: cell*(k1_15*ADP*ADP-k2_15*ATP*AMP)
Kp2g_9=0.08; Kp3g_9=1.2; Keq_9=0.19; Vmax_9=2585.0 Reaction: P3G => P2G, Rate Law: cell*Vmax_9*(P3G/Kp3g_9-P2G/(Kp3g_9*Keq_9))/(1+P3G/Kp3g_9+P2G/Kp2g_9)
KGLYCOGEN_17=6.0 Reaction: ATP + G6P => ADP + Glycogen, Rate Law: cell*KGLYCOGEN_17
Ktrehalose_18=2.4 Reaction: ATP + G6P => ADP + Trehalose, Rate Law: cell*Ktrehalose_18
Keq_3=0.29; Kf6p_3=0.3; Vmax_3=1056.0; Kg6p_3=1.4 Reaction: G6P => F6P, Rate Law: cell*Vmax_3*(G6P/Kg6p_3-F6P/(Kg6p_3*Keq_3))/(1+G6P/Kg6p_3+F6P/Kf6p_3)
L0_4=0.66; Kf16_4=0.111; Kamp_4=0.0995; Camp_4=0.0845; Vmax_4=110.0; Cf16_4=0.397; Katp_4=0.71; Kiatp_4=0.65; Kf6p_4=0.1; Ciatp_4=100.0; Catp_4=3.0; Kf26_4=6.82E-4; Cf26_4=0.0174; gR_4=5.12 Reaction: ATP + F6P => ADP + F16bP; AMP, F26bP, Rate Law: cell*Vmax_4*gR_4*F6P/Kf6p_4*ATP/Katp_4*(1+F6P/Kf6p_4+ATP/Katp_4+gR_4*F6P/Kf6p_4*ATP/Katp_4)/((1+F6P/Kf6p_4+ATP/Katp_4+gR_4*F6P/Kf6p_4*ATP/Katp_4)^2+L0_4*((1+Ciatp_4*ATP/Kiatp_4)/(1+ATP/Kiatp_4))^2*((1+Camp_4*AMP/Kamp_4)/(1+AMP/Kamp_4))^2*((1+Cf26_4*F26bP/Kf26_4+Cf16_4*F16bP/Kf16_4)/(1+F26bP/Kf26_4+F16bP/Kf16_4))^2*(1+Catp_4*ATP/Katp_4)^2)
Kgap_5=2.4; Kf16bp_5=0.3; Kdhap_5=2.0; Kigap_5=10.0; Vmax_5=94.69; Keq_5=0.069 Reaction: F16bP => DHAP + GAP, Rate Law: cell*Vmax_5*(F16bP/Kf16bp_5-DHAP*GAP/(Kf16bp_5*Keq_5))/(1+F16bP/Kf16bp_5+DHAP/Kdhap_5+GAP/Kgap_5+F16bP*GAP/(Kf16bp_5*Kigap_5)+DHAP*GAP/(Kdhap_5*Kgap_5))
Katp_8=0.3; Kp3g_8=0.53; Keq_8=3200.0; Kadp_8=0.2; Vmax_8=1288.0; Kbpg_8=0.003 Reaction: ADP + BPG => ATP + P3G, Rate Law: cell*Vmax_8*(Keq_8*BPG*ADP-P3G*ATP)/(Kp3g_8*Katp_8)/((1+BPG/Kbpg_8+P3G/Kp3g_8)*(1+ADP/Kadp_8+ATP/Katp_8))
Keq_11=6500.0; Kpyr_11=21.0; Kadp_11=0.53; Vmax_11=1000.0; Kpep_11=0.14; Katp_11=1.5 Reaction: ADP + PEP => ATP + PYR, Rate Law: cell*Vmax_11*(PEP*ADP/(Kpep_11*Kadp_11)-PYR*ATP/(Kpep_11*Kadp_11*Keq_11))/((1+PEP/Kpep_11+PYR/Kpyr_11)*(1+ADP/Kadp_11+ATP/Katp_11))
Kiacald_13=1.1; Kinad_13=0.92; Keq_13=6.9E-5; Kinadh_13=0.031; Kacald_13=1.11; Kietoh_13=90.0; Knadh_13=0.11; Ketoh_13=17.0; Vmax_13=209.5; Knad_13=0.17 Reaction: NAD + EtOH => NADH + AcAld, Rate Law: cell*Vmax_13*(EtOH*NAD/(Ketoh_13*Kinad_13)-AcAld*NADH/(Ketoh_13*Kinad_13*Keq_13))/(1+NAD/Kinad_13+EtOH*Knad_13/(Kinad_13*Ketoh_13)+AcAld*Knadh_13/(Kinadh_13*Kacald_13)+NADH/Kinadh_13+EtOH*NAD/(Kinad_13*Ketoh_13)+NAD*AcAld*Knadh_13/(Kinad_13*Kinadh_13*Kacald_13)+EtOH*NADH*Knad_13/(Kinad_13*Kinadh_13*Ketoh_13)+AcAld*NADH/(Kacald_13*Kinadh_13)+EtOH*NAD*AcAld/(Kinad_13*Kiacald_13*Ketoh_13)+EtOH*AcAld*NADH/(Kietoh_13*Kinadh_13*Kacald_13))
Vmax_10=201.6; Kpep_10=0.5; Kp2g_10=0.04; Keq_10=6.7 Reaction: P2G => PEP, Rate Law: cell*Vmax_10*(P2G/Kp2g_10-PEP/(Kp2g_10*Keq_10))/(1+P2G/Kp2g_10+PEP/Kpep_10)
Katpase_14=39.5 Reaction: ATP => ADP, Rate Law: cell*Katpase_14*ATP
Kglc_1=1.1918; Ki_1=0.91; Vmax_1=97.24 Reaction: GLCo => GLCi, Rate Law: Vmax_1*(GLCo-GLCi)/Kglc_1/(1+(GLCo+GLCi)/Kglc_1+Ki_1*GLCo*GLCi/Kglc_1^2)
k2_6=1.0E7; k1_6=450000.0 Reaction: DHAP => GAP, Rate Law: cell*(k1_6*DHAP-k2_6*GAP)
Kadp_2=0.23; Katp_2=0.15; Kg6p_2=30.0; Kglc_2=0.08; Keq_2=2000.0; Vmax_2=236.7 Reaction: GLCi + ATP => G6P + ADP, Rate Law: cell*Vmax_2*(GLCi*ATP/(Kglc_2*Katp_2)-G6P*ADP/(Kglc_2*Katp_2*Keq_2))/((1+GLCi/Kglc_2+G6P/Kg6p_2)*(1+ATP/Katp_2+ADP/Kadp_2))
C_7=1.0; Vmaxf_7=1152.0; Knadh_7=0.06; Vmaxr_7=6719.0; Knad_7=0.09; Kgap_7=0.21; Kbpg_7=0.0098 Reaction: GAP + NAD => BPG + NADH, Rate Law: cell*C_7*(Vmaxf_7*GAP*NAD/(Kgap_7*Knad_7)-Vmaxr_7*BPG*NADH/(Kbpg_7*Knadh_7))/((1+GAP/Kgap_7+BPG/Kbpg_7)*(1+NAD/Knad_7+NADH/Knadh_7))
Keq_16=4300.0; Kdhap_16=0.4; Kglycerol_16=1.0; Knadh_16=0.023; Vmax_16=47.11; Knad_16=0.93 Reaction: DHAP + NADH => NAD + Glycerol, Rate Law: cell*Vmax_16*(DHAP/Kdhap_16*NADH/Knadh_16-Glycerol/Kdhap_16*NAD/Knadh_16*1/Keq_16)/((1+DHAP/Kdhap_16+Glycerol/Kglycerol_16)*(1+NADH/Knadh_16+NAD/Knad_16))
Vmax_12=857.8; nH_12=1.9; Kpyr_12=4.33 Reaction: PYR => AcAld + CO2, Rate Law: cell*Vmax_12*(PYR/Kpyr_12)^nH_12/(1+(PYR/Kpyr_12)^nH_12)

States:

Name Description
ATP [ATP; ATP]
Trehalose [alpha,alpha-trehalose; alpha,alpha-Trehalose]
F16bP [beta-D-fructofuranose 1,6-bisphosphate; beta-D-Fructose 1,6-bisphosphate]
AMP [AMP; AMP]
DHAP [dihydroxyacetone phosphate; Glycerone phosphate]
GLCi [D-glucopyranose; D-Glucose]
P2G [2-phospho-D-glyceric acid; 2-Phospho-D-glycerate]
P3G [3-phospho-D-glyceric acid; 3-Phospho-D-glycerate]
Succinate [succinate(2-); Succinate]
GLCo [D-glucopyranose; D-Glucose]
AcAld [acetaldehyde; Acetaldehyde]
PYR [pyruvate; Pyruvate]
NADH [NADH; NADH]
EtOH [ethanol; Ethanol]
BPG [3-phospho-D-glyceroyl dihydrogen phosphate; 3-Phospho-D-glyceroyl phosphate]
F6P [beta-D-fructofuranose 6-phosphate; beta-D-Fructose 6-phosphate]
CO2 [carbon dioxide; CO2]
Glycerol [glycerol; Glycerol]
GAP [D-glyceraldehyde 3-phosphate; D-Glyceraldehyde 3-phosphate]
G6P [alpha-D-glucose 6-phosphate; alpha-D-Glucose 6-phosphate]
Glycogen [glycogen; Glycogen]
NAD [NAD(+); NAD+]
ADP [ADP; ADP]
PEP [phosphoenolpyruvate; Phosphoenolpyruvate]

Observables: none

Pritchard2014 - plant-microbe interaction[](http://www.researchgate.net/publication/269416257_Phosphoproteomic_analyses_…

link: http://identifiers.org/pubmed/25382065

Parameters:

Name Description
V=0.1; Ki=0.1; Km=0.1 Reaction: E => E_int; Callose, E, Callose, Rate Law: V*E/(Km+E+Km*Callose/Ki)
k1=0.1 Reaction: Path => PAMP + Path; Path, Rate Law: k1*Path
k1=0.1; k2=0.1 Reaction: R + E_int => R_0; R, E_int, R_0, Rate Law: Cell*(k1*R*E_int-k2*R_0)

States:

Name Description
PAMP PAMP
PRR PRR*
Path bulk Path_bulk
Path Path
R 0 R*
Callose Callose
R R
E E
E int E_int
PRR 0 PRR

Observables: none

Proctor2005 - Actions of chaperones and their role in ageingThis model is described in the article: [Modelling the acti…

Many molecular chaperones are also known as heat shock proteins because they are synthesised in increased amounts after brief exposure of cells to elevated temperatures. They have many cellular functions and are involved in the folding of nascent proteins, the re-folding of denatured proteins, the prevention of protein aggregation, and assisting the targeting of proteins for degradation by the proteasome and lysosomes. They also have a role in apoptosis and are involved in modulating signals for immune and inflammatory responses. Stress-induced transcription of heat shock proteins requires the activation of heat shock factor (HSF). Under normal conditions, HSF is bound to heat shock proteins resulting in feedback repression. During stress, cellular proteins undergo denaturation and sequester heat shock proteins bound to HSF, which is then able to become transcriptionally active. The induction of heat shock proteins is impaired with age and there is also a decline in chaperone function. Aberrant/damaged proteins accumulate with age and are implicated in several important age-related conditions (e.g. Alzheimer's disease, Parkinson's disease, and cataract). Therefore, the balance between damaged proteins and available free chaperones may be greatly disturbed during ageing. We have developed a mathematical model to describe the heat shock system. The aim of the model is two-fold: to explore the heat shock system and its implications in ageing; and to demonstrate how to build a model of a biological system using our simulation system (biology of ageing e-science integration and simulation (BASIS)). link: http://identifiers.org/pubmed/15610770

Parameters:

Name Description
k14 = 0.05 Reaction: TriH + HSE => HSETriH, Rate Law: k14*HSE*TriH
k3 = 50.0 Reaction: MisP + Hsp90 => MCom, Rate Law: k3*MisP*Hsp90
k18 = 12.0 Reaction: ADP => ATP, Rate Law: k18*ADP
k15 = 0.08 Reaction: HSETriH => HSE + TriH, Rate Law: k15*HSETriH
k6 = 6.0E-7 Reaction: MisP + ATP => ADP, Rate Law: k6*MisP*ATP
k4 = 1.0E-5 Reaction: MCom => MisP + Hsp90, Rate Law: k4*MCom
k5 = 4.0E-6 Reaction: MCom + ATP => Hsp90 + NatP + ADP, Rate Law: k5*MCom*ATP
k2 = 2.0E-5 Reaction: NatP + ROS => MisP + ROS, Rate Law: k2*NatP*ROS
k13 = 0.5 Reaction: DiH => HSF1, Rate Law: k13*DiH
k17 = 8.02E-9 Reaction: Hsp90 + ATP => ADP, Rate Law: k17*Hsp90*ATP
k1 = 10.0 Reaction: source => NatP, Rate Law: k1
k8 = 500.0 Reaction: Hsp90 + HSF1 => HCom, Rate Law: k8*Hsp90*HSF1
k19 = 0.02 Reaction: ATP => ADP, Rate Law: k19*ATP
k7 = 1.0E-7 Reaction: MisP + AggP => AggP, Rate Law: k7*MisP*AggP
k20 = 0.1 Reaction: source => ROS, Rate Law: k20
k10 = 0.01 Reaction: HSF1 => DiH, Rate Law: (HSF1-1)*k10*HSF1/2
k11 = 100.0 Reaction: HSF1 + DiH => TriH, Rate Law: k11*HSF1*DiH
k12 = 0.5 Reaction: TriH => HSF1 + DiH, Rate Law: k12*TriH
k9 = 1.0 Reaction: HCom => Hsp90 + HSF1, Rate Law: k9*HCom
k21 = 0.001 Reaction: ROS =>, Rate Law: k21*ROS
k16 = 1000.0 Reaction: HSETriH => HSETriH + Hsp90, Rate Law: k16*HSETriH

States:

Name Description
DiH [protein complex; IPR000232]
ROS [reactive oxygen species]
ATP [ATP; ATP]
X X
HSETriH HSETriH
Hsp90 [IPR001404]
MisP MisP
HSF1 [IPR000232]
MCom [protein complex]
HCom [protein complex]
source source
NatP NatP
HSE HSE
ADP [ADP; ADP]
AggP AggP
TriH [protein complex; IPR000232]

Observables: none

BIOMD0000000087 @ v0.0.1

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

One of the DNA damage-response mechanisms in budding yeast is temporary cell-cycle arrest while DNA repair takes place. The DNA damage response requires the coordinated interaction between DNA repair and checkpoint pathways. Telomeres of budding yeast are capped by the Cdc13 complex. In the temperature-sensitive cdc13-1 strain, telomeres are unprotected over a specific temperature range leading to activation of the DNA damage response and subsequently cell-cycle arrest. Inactivation of cdc13-1 results in the generation of long regions of single-stranded DNA (ssDNA) and is affected by the activity of various checkpoint proteins and nucleases. This paper describes a mathematical model of how uncapped telomeres in budding yeast initiate the checkpoint pathway leading to cell-cycle arrest. The model was encoded in the Systems Biology Markup Language (SBML) and simulated using the stochastic simulation system Biology of Ageing e-Science Integration and Simulation (BASIS). Each simulation follows the time course of one mother cell keeping track of the number of cell divisions, the level of activity of each of the checkpoint proteins, the activity of nucleases and the amount of ssDNA generated. The model can be used to carry out a variety of in silico experiments in which different genes are knocked out and the results of simulation are compared to experimental data. Possible extensions to the model are also discussed. link: http://identifiers.org/pubmed/17015293

Parameters:

Name Description
k6a=5.0E-5; kalive = 1.0 Reaction: Exo1I => Exo1A, Rate Law: k6a*Exo1I*kalive
kalive = 1.0; k18a=0.001 Reaction: S + ssDNA => S, Rate Law: k18a*S*ssDNA*kalive
k8a=0.001; kalive = 1.0 Reaction: ssDNA + RPA => RPAssDNA1, Rate Law: k8a*RPA*ssDNA*kalive
k8c=100.0; kalive = 1.0 Reaction: ssDNA + RPAssDNA2 => RPAssDNA, Rate Law: k8c*RPAssDNA2*ssDNA*kalive
kc4=0.01; kalive = 1.0 Reaction: M + MCdkA + MG1on => budscar + G1 + MCdkI + MG1off, Rate Law: kc4*M*MCdkA*MG1on*kalive
kalive = 1.0; k18b=1.0E-5 Reaction: G2 + G2Moff + ssDNA => G2 + G2Moff, Rate Law: G2*G2Moff*k18b*ssDNA*kalive
k19=0.001; kalive = 1.0 Reaction: Cdc13 + Rad17Utelo + recovery => Ctelo + Rad17 + recovery, Rate Law: Cdc13*k19*Rad17Utelo*recovery*kalive
k17a=0.05; kalive = 1.0 Reaction: Mec1RPAssDNA + S => Mec1 + RPA + S + ssDNA, Rate Law: k17a*Mec1RPAssDNA*S*kalive
kalive = 1.0; k16=0.1 Reaction: Dun1A + G2Mon => Dun1A + G2Moff, Rate Law: Dun1A*G2Mon*k16*kalive
k9=100.0; kalive = 1.0 Reaction: Rad9Kin + Rad9I => Rad9Kin + Rad9A, Rate Law: k9*Rad9Kin*Rad9I*kalive
kalive = 1.0; k8d=0.004 Reaction: RPAssDNA + Mec1 => Mec1RPAssDNA, Rate Law: k8d*RPAssDNA*Mec1*kalive
kalive = 1.0; k1=5.0E-4 Reaction: Cdc13 + Utelo => Ctelo, Rate Law: k1*Cdc13*Utelo*kalive
k5=3.0E-4; kalive = 1.0 Reaction: ExoXA + Rad17Utelo => ExoXA + Rad17Utelo + ssDNA, Rate Law: k5*ExoXA*Rad17Utelo*kalive
kalive = 1.0; k7a=3.0E-5 Reaction: Utelo + Exo1A => Utelo + Exo1A + ssDNA, Rate Law: k7a*Utelo*Exo1A*kalive
k15=0.2; kalive = 1.0 Reaction: Chk1A + G2Mon => Chk1A + G2Moff, Rate Law: Chk1A*G2Mon*k15*kalive
kalive = 1.0; k6b=5.0E-4 Reaction: Exo1I + Rad24 => Exo1A + Rad24, Rate Law: k6b*Exo1I*Rad24*kalive
k14=3.3E-6; kalive = 1.0 Reaction: Dun1I + Rad53A => Dun1A + Rad53A, Rate Law: Dun1I*k14*Rad53A*kalive
kalive = 1.0; k4=0.01 Reaction: ExoXI + Rad17Utelo => ExoXA + Rad17Utelo, Rate Law: k4*ExoXI*Rad17Utelo*kalive
kalive = 1.0; k10a=0.05 Reaction: ExoXA + Rad9A => ExoXI + Rad9A, Rate Law: ExoXA*k10a*Rad9A*kalive
kc3=0.0012; kalive = 1.0 Reaction: Scyclin => sink, Rate Law: kc3*Scyclin*kalive
k7b=3.0E-5; kalive = 1.0 Reaction: Rad17Utelo + Exo1A => Rad17Utelo + Exo1A + ssDNA, Rate Law: k7b*Rad17Utelo*Exo1A*kalive
k8b=100.0; kalive = 1.0 Reaction: ssDNA + RPAssDNA1 => RPAssDNA2, Rate Law: k8b*RPAssDNA1*ssDNA*kalive
kc1=0.16; kalive = 1.0 Reaction: S => Scyclin + S, Rate Law: kc1*S*kalive
kalive = 1.0; k3=1.5E-8 Reaction: Utelo + Rad17 + Rad24 + ATP => Rad17Utelo + Rad24 + ADP, Rate Law: k3*Utelo*Rad17*Rad24*ATP*kalive/(5000+ATP)
k10b=0.05; kalive = 1.0 Reaction: ExoXA + Rad9I => ExoXI + Rad9I, Rate Law: ExoXA*k10b*Rad9I*kalive
k17b=0.05; kalive = 1.0 Reaction: G2 + G2Moff + Mec1RPAssDNA => G2 + G2Moff + Mec1 + RPA + ssDNA, Rate Law: G2*G2Moff*k17b*Mec1RPAssDNA*kalive
kalive = 1.0; k2=3.85E-4 Reaction: Ctelo => Cdc13 + Utelo, Rate Law: k2*Ctelo*kalive
k13=1.0; kalive = 1.0 Reaction: Exo1A + Rad53A => Exo1I + Rad53A, Rate Law: Exo1A*k13*Rad53A*kalive
kc2=0.01; kalive = 1.0 Reaction: G1Soff + G1 + G1CdkA => G1Son + G1 + G1CdkA, Rate Law: G1*G1CdkA*G1Soff*kc2*kalive

States:

Name Description
G2Mon [G2/M transition of mitotic cell cycle]
SG2off [obsolete regulation of transcription involved in S phase of mitotic cell cycle]
G2CdkA [nuclear cyclin-dependent protein kinase holoenzyme complex]
Rad9I [DNA repair protein RAD9]
RPAssDNA [C00271; PIRSF002091; single-stranded DNA]
Rad17 [DNA damage checkpoint control protein RAD17]
Dun1I [DNA damage response protein kinase DUN1]
MCdkI [nuclear cyclin-dependent protein kinase holoenzyme complex]
Cdc13 [Cell division control protein 13]
Rad17Utelo [DNA damage checkpoint control protein RAD17; chromosome, telomeric region]
RPAssDNA2 [C00271; PIRSF002091; single-stranded DNA]
Exo1I [Exodeoxyribonuclease 1]
M [M phase]
G2CdkI [nuclear cyclin-dependent protein kinase holoenzyme complex]
G2 [G2 phase]
G2cyclin [G2/mitotic-specific cyclin-1]
G1CdkA [nuclear cyclin-dependent protein kinase holoenzyme complex]
RPA [PIRSF002091]
ExoXA [Exodeoxyribonuclease 10]
RPAssDNA1 [C00271; PIRSF002091; single-stranded DNA]
G1Son [G1/S transition of mitotic cell cycle]
ExoXI [Exodeoxyribonuclease 10]
G1 [G1 phase]
G1CdkI [nuclear cyclin-dependent protein kinase holoenzyme complex]
MG1off MG1off
G1Soff [mitotic cell cycle checkpoint]
Ctelo [telomere cap complex; chromosome, telomeric region]
Mec1RPAssDNA [Serine/threonine-protein kinase MEC1; C00271; PIRSF002091; single-stranded DNA]
MCdkA [nuclear cyclin-dependent protein kinase holoenzyme complex]
SCdkI [nuclear cyclin-dependent protein kinase holoenzyme complex]
Utelo [chromosome, telomeric region]
Mcyclin [Meiosis-specific cyclin rem1]
Mec1 [Serine/threonine-protein kinase MEC1]
SCdkA [nuclear cyclin-dependent protein kinase holoenzyme complex]
S [mitotic S phase]
ssDNA [CHEBI:09160; C00271]
G2Moff [G2 DNA damage checkpoint]
SG2on [obsolete regulation of transcription involved in S phase of mitotic cell cycle]
ADP [ADP]
Exo1A [Exodeoxyribonuclease 1]
Scyclin [S-phase entry cyclin-5]
Rad9A [DNA repair protein RAD9]

Observables: none

Proctor2007 - Age related decline of proteolysis, ubiquitin-proteome systemThis is a stochastic model of the ubiquitin-…

The ubiquitin-proteasome system is responsible for homeostatic degradation of intact protein substrates as well as the elimination of damaged or misfolded proteins that might otherwise aggregate. During ageing there is a decline in proteasome activity and an increase in aggregated proteins. Many neurodegenerative diseases are characterised by the presence of distinctive ubiquitin-positive inclusion bodies in affected regions of the brain. These inclusions consist of insoluble, unfolded, ubiquitinated polypeptides that fail to be targeted and degraded by the proteasome. We are using a systems biology approach to try and determine the primary event in the decline in proteolytic capacity with age and whether there is in fact a vicious cycle of inhibition, with accumulating aggregates further inhibiting proteolysis, prompting accumulation of aggregates and so on. A stochastic model of the ubiquitin-proteasome system has been developed using the Systems Biology Mark-up Language (SBML). Simulations are carried out on the BASIS (Biology of Ageing e-Science Integration and Simulation) system and the model output is compared to experimental data wherein levels of ubiquitin and ubiquitinated substrates are monitored in cultured cells under various conditions. The model can be used to predict the effects of different experimental procedures such as inhibition of the proteasome or shutting down the enzyme cascade responsible for ubiquitin conjugation.The model output shows good agreement with experimental data under a number of different conditions. However, our model predicts that monomeric ubiquitin pools are always depleted under conditions of proteasome inhibition, whereas experimental data show that monomeric pools were depleted in IMR-90 cells but not in ts20 cells, suggesting that cell lines vary in their ability to replenish ubiquitin pools and there is the need to incorporate ubiquitin turnover into the model. Sensitivity analysis of the model revealed which parameters have an important effect on protein turnover and aggregation kinetics.We have developed a model of the ubiquitin-proteasome system using an iterative approach of model building and validation against experimental data. Using SBML to encode the model ensures that it can be easily modified and extended as more data become available. Important aspects to be included in subsequent models are details of ubiquitin turnover, models of autophagy, the inclusion of a pool of short-lived proteins and further details of the aggregation process. link: http://identifiers.org/pubmed/17408507

Parameters:

Name Description
k3 = 4.0E-6 Reaction: MisP => NatP + refNatP, Rate Law: k3*MisP
k61 = 1.7E-5 Reaction: MisP + E3 => E3_MisP, Rate Law: k61*MisP*E3
k1 = 0.01 Reaction: Source => NatP, Rate Law: k1*Source
k72 = 1.0E-8 Reaction: MisP_Ub3 + MisP_Ub4 => AggP, Rate Law: k72*MisP_Ub3*MisP_Ub4
k61r = 2.0E-4 Reaction: E3_MisP => MisP + E3, Rate Law: k61r*E3_MisP
k71 = 1.0E-8 Reaction: MisP => AggP, Rate Law: k71*MisP*(MisP-1)*0.5
k63 = 0.001 Reaction: E2 + E1_Ub => E2_Ub + E1, Rate Law: k63*E2*E1_Ub
k69 = 0.0 Reaction: MisP_Ub4_Proteasome + ATP => Ub + Proteasome + ADP + degUb4, Rate Law: k69*MisP_Ub4_Proteasome*ATP/(5000+ATP)
k65 = 0.01 Reaction: MisP_Ub + E2_Ub => MisP_Ub2 + E2, Rate Law: k65*MisP_Ub*E2_Ub
k2 = 2.0E-6 Reaction: NatP + ROS => MisP + ROS + totMisP, Rate Law: k2*NatP*ROS
k66 = 1.0E-5 Reaction: MisP_Ub5 + DUB => MisP_Ub4 + DUB + Ub, Rate Law: k66*MisP_Ub5*DUB
k64 = 0.001 Reaction: E2_Ub + E3_MisP => MisP_Ub + E2 + E3, Rate Law: k64*E2_Ub*E3_MisP
k67 = 1.0E-5 Reaction: MisP_Ub6 + Proteasome => MisP_Ub6_Proteasome, Rate Law: k67*MisP_Ub6*Proteasome
k62 = 2.0E-4 Reaction: E1 + Ub + ATP => E1_Ub + AMP, Rate Law: k62*E1*Ub*ATP/(5000+ATP)
k68 = 1.0E-5 Reaction: MisP_Ub4_Proteasome + DUB => MisP_Ub3 + Proteasome + Ub + DUB, Rate Law: k68*MisP_Ub4_Proteasome*DUB

States:

Name Description
MisP Ub4 MisP_Ub4
MisP Ub5 MisP_Ub5
MisP Ub7 MisP_Ub7
E3 MisP E3_MisP
MisP MisP
E1 Ub [IPR000011; IPR000626]
MisP Ub MisP_Ub
MisP Ub8 MisP_Ub8
MisP Ub6 MisP_Ub6
MisP Ub5 Proteasome MisP_Ub5_Proteasome
MisP Ub2 MisP_Ub2
MisP Ub4 Proteasome MisP_Ub4_Proteasome
MisP Ub3 MisP_Ub3
E2 Ub [IPR000626; IPR000608]
NatP NatP

Observables: none

Proctor2008 - p53/Mdm2 circuit - p53 stabilisation by ATMThis model is described in the article: [Explaining oscillatio…

In individual living cells p53 has been found to be expressed in a series of discrete pulses after DNA damage. Its negative regulator Mdm2 also demonstrates oscillatory behaviour. Attempts have been made recently to explain this behaviour by mathematical models but these have not addressed explicit molecular mechanisms. We describe two stochastic mechanistic models of the p53/Mdm2 circuit and show that sustained oscillations result directly from the key biological features, without assuming complicated mathematical functions or requiring more than one feedback loop. Each model examines a different mechanism for providing a negative feedback loop which results in p53 activation after DNA damage. The first model (ARF model) looks at the mechanism of p14ARF which sequesters Mdm2 and leads to stabilisation of p53. The second model (ATM model) examines the mechanism of ATM activation which leads to phosphorylation of both p53 and Mdm2 and increased degradation of Mdm2, which again results in p53 stabilisation. The models can readily be modified as further information becomes available, and linked to other models of cellular ageing.The ARF model is robust to changes in its parameters and predicts undamped oscillations after DNA damage so long as the signal persists. It also predicts that if there is a gradual accumulation of DNA damage, such as may occur in ageing, oscillations break out once a threshold level of damage is acquired. The ATM model requires an additional step for p53 synthesis for sustained oscillations to develop. The ATM model shows much more variability in the oscillatory behaviour and this variability is observed over a wide range of parameter values. This may account for the large variability seen in the experimental data which so far has examined ARF negative cells.The models predict more regular oscillations if ARF is present and suggest the need for further experiments in ARF positive cells to test these predictions. Our work illustrates the importance of systems biology approaches to understanding the complex role of p53 in both ageing and cancer. link: http://identifiers.org/pubmed/18706112

Parameters:

Name Description
ksynp53 = 0.006 psec Reaction: p53_mRNA => p53 + p53_mRNA + p53syn, Rate Law: ksynp53*p53_mRNA
ksynMdm2 = 4.95E-4 psec Reaction: Mdm2_mRNA => Mdm2_mRNA + Mdm2 + mdm2syn, Rate Law: ksynMdm2*Mdm2_mRNA
IR = 0.0 dGy; kdam = 0.08 molepsecpdGy Reaction: => damDNA, Rate Law: kdam*IR
krepair = 2.0E-5 psec Reaction: damDNA => Sink, Rate Law: krepair*damDNA
kdegMdm2mRNA = 1.0E-4 psec Reaction: Mdm2_mRNA => Sink + Mdm2mRNAdeg, Rate Law: kdegMdm2mRNA*Mdm2_mRNA
kproteff = 1.0 dimensionless; kdegp53 = 8.25E-4 psec Reaction: Mdm2_p53 => Mdm2 + p53deg, Rate Law: kdegp53*Mdm2_p53*kproteff
kdegATMMdm2 = 4.0E-4 psec Reaction: Mdm2_P => Sink + mdm2deg, Rate Law: kdegATMMdm2*Mdm2_P
kdephosp53 = 0.5 psec Reaction: p53_P => p53, Rate Law: kdephosp53*p53_P
kbinMdm2p53 = 0.001155 pmolpsec Reaction: p53 + Mdm2 => Mdm2_p53, Rate Law: kbinMdm2p53*p53*Mdm2
krelMdm2p53 = 1.155E-5 psec Reaction: Mdm2_p53 => p53 + Mdm2, Rate Law: krelMdm2p53*Mdm2_p53
kdephosMdm2 = 0.5 psec Reaction: Mdm2_P => Mdm2, Rate Law: kdephosMdm2*Mdm2_P
kphosMdm2 = 2.0 pmolpsec Reaction: Mdm2 + ATMA => Mdm2_P + ATMA, Rate Law: kphosMdm2*Mdm2*ATMA
kproteff = 1.0 dimensionless; kdegMdm2 = 4.33E-4 psec Reaction: Mdm2 => Sink + mdm2deg, Rate Law: kdegMdm2*Mdm2*kproteff
kinactATM = 5.0E-4 psec Reaction: ATMA => ATMI, Rate Law: kinactATM*ATMA
kphosp53 = 5.0E-4 pmolpsec Reaction: p53 + ATMA => p53_P + ATMA, Rate Law: kphosp53*p53*ATMA
kactATM = 1.0E-4 pmolpsec Reaction: damDNA + ATMI => damDNA + ATMA, Rate Law: kactATM*damDNA*ATMI
ksynp53mRNA = 0.001 psec Reaction: Source => p53_mRNA, Rate Law: ksynp53mRNA*Source
kdegp53mRNA = 1.0E-4 psec Reaction: p53_mRNA => Sink, Rate Law: kdegp53mRNA*p53_mRNA
ksynMdm2mRNA = 1.0E-4 psec Reaction: p53_P => p53_P + Mdm2_mRNA + Mdm2mRNAsyn, Rate Law: ksynMdm2mRNA*p53_P

States:

Name Description
Mdm2 P [MDM2; E3 ubiquitin-protein ligase Mdm2]
mdm2deg [proteasome-mediated ubiquitin-dependent protein catabolic process]
damDNA [deoxyribonucleic acid; cellular response to DNA damage stimulus]
p53 mRNA [messenger RNA; RNA]
Mdm2mRNAdeg [mRNA catabolic process]
mdm2syn [translation]
ATMA [Serine-protein kinase ATM]
p53 [Cellular tumor antigen p53; TP53]
p53deg [proteasome-mediated ubiquitin-dependent protein catabolic process]
totp53 totp53
Mdm2 p53 [Cellular tumor antigen p53; E3 ubiquitin-protein ligase Mdm2]
Source Source
p53 P [Cellular tumor antigen p53; TP53]
ATMI [Serine-protein kinase ATM]
p53syn [translation]
totMdm2 totMdm2
Sink Sink
Mdm2 [E3 ubiquitin-protein ligase Mdm2; MDM2]
Mdm2 mRNA [messenger RNA; RNA]
Mdm2mRNAsyn [transcription factor activity, sequence-specific DNA binding]

Observables: none

Proctor2008 - p53/Mdm2 circuit - p53 stabilisation by p14ARFThis model is described in the article: [Explaining oscilla…

In individual living cells p53 has been found to be expressed in a series of discrete pulses after DNA damage. Its negative regulator Mdm2 also demonstrates oscillatory behaviour. Attempts have been made recently to explain this behaviour by mathematical models but these have not addressed explicit molecular mechanisms. We describe two stochastic mechanistic models of the p53/Mdm2 circuit and show that sustained oscillations result directly from the key biological features, without assuming complicated mathematical functions or requiring more than one feedback loop. Each model examines a different mechanism for providing a negative feedback loop which results in p53 activation after DNA damage. The first model (ARF model) looks at the mechanism of p14ARF which sequesters Mdm2 and leads to stabilisation of p53. The second model (ATM model) examines the mechanism of ATM activation which leads to phosphorylation of both p53 and Mdm2 and increased degradation of Mdm2, which again results in p53 stabilisation. The models can readily be modified as further information becomes available, and linked to other models of cellular ageing.The ARF model is robust to changes in its parameters and predicts undamped oscillations after DNA damage so long as the signal persists. It also predicts that if there is a gradual accumulation of DNA damage, such as may occur in ageing, oscillations break out once a threshold level of damage is acquired. The ATM model requires an additional step for p53 synthesis for sustained oscillations to develop. The ATM model shows much more variability in the oscillatory behaviour and this variability is observed over a wide range of parameter values. This may account for the large variability seen in the experimental data which so far has examined ARF negative cells.The models predict more regular oscillations if ARF is present and suggest the need for further experiments in ARF positive cells to test these predictions. Our work illustrates the importance of systems biology approaches to understanding the complex role of p53 in both ageing and cancer. link: http://identifiers.org/pubmed/18706112

Parameters:

Name Description
kbinMdm2p53 = 0.001155 pmolepsec Reaction: p53 + Mdm2 => Mdm2_p53, Rate Law: kbinMdm2p53*p53*Mdm2
ksynMdm2 = 4.95E-4 psec Reaction: Mdm2_mRNA => Mdm2_mRNA + Mdm2 + mdm2syn, Rate Law: ksynMdm2*Mdm2_mRNA
IR = 0.0 dGy; kdam = 0.08 molepsecpdGy Reaction: => damDNA + totdamDNA, Rate Law: kdam*IR
kdegMdm2mRNA = 1.0E-4 psec Reaction: Mdm2_mRNA => Sink + Mdm2mRNAdeg, Rate Law: kdegMdm2mRNA*Mdm2_mRNA
krepair = 2.0E-5 psec Reaction: damDNA => Sink, Rate Law: krepair*damDNA
kproteff = 1.0 dimensionless; kdegp53 = 8.25E-4 psec Reaction: Mdm2_p53 => Mdm2 + p53deg, Rate Law: kdegp53*Mdm2_p53*kproteff
ksynp53 = 0.078 psec Reaction: Source => p53 + p53syn, Rate Law: ksynp53*Source
kproteff = 1.0 dimensionless; kdegARF = 1.0E-4 psec Reaction: ARF => Sink, Rate Law: kdegARF*ARF*kproteff
kactARF = 3.3E-5 psec Reaction: damDNA => damDNA + ARF, Rate Law: kactARF*damDNA
krelMdm2p53 = 1.155E-5 psec Reaction: Mdm2_p53 => p53 + Mdm2, Rate Law: krelMdm2p53*Mdm2_p53
kproteff = 1.0 dimensionless; kdegARFMdm2 = 0.001 psec Reaction: ARF_Mdm2 => ARF + mdm2deg, Rate Law: kdegARFMdm2*ARF_Mdm2*kproteff
kbinARFMdm2 = 0.01 pmolepsec Reaction: ARF + Mdm2 => ARF_Mdm2, Rate Law: kbinARFMdm2*ARF*Mdm2
kproteff = 1.0 dimensionless; kdegMdm2 = 4.33E-4 psec Reaction: Mdm2 => Sink + mdm2deg, Rate Law: kdegMdm2*Mdm2*kproteff
ksynMdm2mRNA = 1.0E-4 psec Reaction: p53 => p53 + Mdm2_mRNA + Mdm2mRNAsyn, Rate Law: ksynMdm2mRNA*p53

States:

Name Description
Mdm2mRNAsyn [transcription factor activity, sequence-specific DNA binding]
ARF Mdm2 [E3 ubiquitin-protein ligase Mdm2; Tumor suppressor ARF]
damDNA [deoxyribonucleic acid; cellular response to DNA damage stimulus]
Mdm2mRNAdeg [mRNA catabolic process]
mdm2syn [translation]
ARF [CDKN2A; Tumor suppressor ARF]
totp53 totp53
p53 [Cellular tumor antigen p53; TP53]
totdamDNA totdamDNA
p53deg [proteasome-mediated ubiquitin-dependent protein catabolic process]
Source Source
Mdm2 p53 [E3 ubiquitin-protein ligase Mdm2; Cellular tumor antigen p53]
p53syn [translation]
totMdm2 totMdm2
Sink Sink
Mdm2 [MDM2; E3 ubiquitin-protein ligase Mdm2]
Mdm2 mRNA [messenger RNA; RNA]
mdm2deg [proteasome-mediated ubiquitin-dependent protein catabolic process]

Observables: none

This is the model described the article: GSK3 and p53 - is there a link in Alzheimer's disease? Carole J Proctor and…

BACKGROUND: Recent evidence suggests that glycogen synthase kinase-3beta (GSK3beta) is implicated in both sporadic and familial forms of Alzheimer's disease. The transcription factor, p53 also plays a role and has been linked to an increase in tau hyperphosphorylation although the effect is indirect. There is also evidence that GSK3beta and p53 interact and that the activity of both proteins is increased as a result of this interaction. Under normal cellular conditions, p53 is kept at low levels by Mdm2 but when cells are stressed, p53 is stabilised and may then interact with GSK3beta. We propose that this interaction has an important contribution to cellular outcomes and to test this hypothesis we developed a stochastic simulation model. RESULTS: The model predicts that high levels of DNA damage leads to increased activity of p53 and GSK3beta and low levels of aggregation but if DNA damage is repaired, the aggregates are eventually cleared. The model also shows that over long periods of time, aggregates may start to form due to stochastic events leading to increased levels of ROS and damaged DNA. This is followed by increased activity of p53 and GSK3beta and a vicious cycle ensues. CONCLUSIONS: Since p53 and GSK3beta are both involved in the apoptotic pathway, and GSK3beta overactivity leads to increased levels of plaques and tangles, our model might explain the link between protein aggregation and neuronal loss in neurodegeneration. link: http://identifiers.org/pubmed/20181016

Parameters:

Name Description
kactDUBp53 = 1.0E-7 Reaction: Mdm2_p53_Ub4 + p53DUB => Mdm2_p53_Ub3 + p53DUB + Ub, Rate Law: kactDUBp53*Mdm2_p53_Ub4*p53DUB
krelMTTau = 1.0E-4 Reaction: MT_Tau => Tau, Rate Law: krelMTTau*MT_Tau
krepair = 2.0E-5 Reaction: damDNA => Sink, Rate Law: krepair*damDNA
kphosMdm2GSK3bp53 = 0.5 Reaction: Mdm2_p53_Ub4 + GSK3b_p53 => Mdm2_P1_p53_Ub4 + GSK3b_p53, Rate Law: kphosMdm2GSK3bp53*Mdm2_p53_Ub4*GSK3b_p53
kaggTauP1 = 1.0E-8 Reaction: Tau_P1 => AggTau, Rate Law: kaggTauP1*Tau_P1*(Tau_P1-1)*0.5
kaggTauP2 = 1.0E-7 Reaction: Tau_P2 => AggTau, Rate Law: kaggTauP2*Tau_P2*(Tau_P2-1)*0.5
kdephosMdm2 = 0.5 Reaction: Mdm2_P => Mdm2, Rate Law: kdephosMdm2*Mdm2_P
kdephosp53 = 0.5 Reaction: p53_P => p53, Rate Law: kdephosp53*p53_P
ksynp53mRNAAbeta = 1.0E-5 Reaction: Abeta => p53_mRNA + Abeta, Rate Law: ksynp53mRNAAbeta*Abeta
kbinTauProt = 1.925E-7 Reaction: Tau + Proteasome => Proteasome_Tau, Rate Law: kbinTauProt*Tau*Proteasome
krelGSK3bp53 = 0.002 Reaction: GSK3b_p53 => GSK3b + p53, Rate Law: krelGSK3bp53*GSK3b_p53
kdegTau20SProt = 0.01 Reaction: Proteasome_Tau => Proteasome, Rate Law: kdegTau20SProt*Proteasome_Tau
krelMdm2p53 = 1.155E-5 Reaction: Mdm2_p53 => p53 + Mdm2, Rate Law: krelMdm2p53*Mdm2_p53
kaggTau = 1.0E-8 Reaction: Tau + AggTau => AggTau, Rate Law: kaggTau*Tau*AggTau
kphosp53 = 2.0E-4 Reaction: p53 + ATMA => p53_P + ATMA, Rate Law: kphosp53*p53*ATMA
kinactATM = 5.0E-4 Reaction: ATMA => ATMI, Rate Law: kinactATM*ATMA
kgenROSAbeta = 1.0E-5 Reaction: AggAbeta => AggAbeta + ROS, Rate Law: kgenROSAbeta*AggAbeta
kinhibprot = 1.0E-5 Reaction: AggTau + Proteasome => AggTau_Proteasome, Rate Law: kinhibprot*AggTau*Proteasome
kprodAbeta = 5.0E-5 Reaction: GSK3b_p53 => Abeta + GSK3b_p53, Rate Law: kprodAbeta*GSK3b_p53
kbinGSK3bp53 = 2.0E-6 Reaction: GSK3b + p53_P => GSK3b_p53_P, Rate Law: kbinGSK3bp53*GSK3b*p53_P
kMdm2PolyUb = 0.00456 Reaction: Mdm2_Ub2 + E2_Ub => Mdm2_Ub3 + E2, Rate Law: kMdm2PolyUb*Mdm2_Ub2*E2_Ub
ksynMdm2mRNAGSK3bp53 = 7.0E-4 Reaction: GSK3b_p53_P => GSK3b_p53_P + Mdm2_mRNA, Rate Law: ksynMdm2mRNAGSK3bp53*GSK3b_p53_P
kbinProt = 2.0E-6 Reaction: Mdm2_P1_p53_Ub4 + Proteasome => p53_Ub4_Proteasome + Mdm2, Rate Law: kbinProt*Mdm2_P1_p53_Ub4*Proteasome
kphosMdm2GSK3b = 0.005 Reaction: Mdm2_p53_Ub4 + GSK3b => Mdm2_P1_p53_Ub4 + GSK3b, Rate Law: kphosMdm2GSK3b*Mdm2_p53_Ub4*GSK3b
kbinE1Ub = 2.0E-4 Reaction: E1 + Ub + ATP => E1_Ub + AMP, Rate Law: kbinE1Ub*E1*Ub*ATP/(5000+ATP)
kdegAbeta = 1.0E-4 Reaction: Abeta => Sink, Rate Law: kdegAbeta*Abeta
ksynMdm2 = 4.95E-4 Reaction: Mdm2_mRNA => Mdm2_mRNA + Mdm2, Rate Law: ksynMdm2*Mdm2_mRNA
kp53Ub = 5.0E-5 Reaction: E2_Ub + Mdm2_p53 => Mdm2_p53_Ub + E2, Rate Law: kp53Ub*E2_Ub*Mdm2_p53
kphospTauGSK3bp53 = 0.1 Reaction: GSK3b_p53_P + Tau => GSK3b_p53_P + Tau_P1, Rate Law: kphospTauGSK3bp53*GSK3b_p53_P*Tau
kp53PolyUb = 0.01 Reaction: Mdm2_p53_Ub + E2_Ub => Mdm2_p53_Ub2 + E2, Rate Law: kp53PolyUb*Mdm2_p53_Ub*E2_Ub
kproteff = 1.0; kdegp53 = 0.005 Reaction: p53_Ub4_Proteasome + ATP => Ub + Proteasome + ADP, Rate Law: kdegp53*kproteff*p53_Ub4_Proteasome*ATP/(5000+ATP)
kdephospTau = 0.01 Reaction: Tau_P1 + PP1 => Tau + PP1, Rate Law: kdephospTau*Tau_P1*PP1
ksynTau = 8.0E-5 Reaction: Source => Tau, Rate Law: ksynTau*Source
kdam = 0.08 Reaction: => damDNA; IR, Rate Law: kdam*IR
ksynMdm2mRNA = 5.0E-4 Reaction: p53_P => p53_P + Mdm2_mRNA, Rate Law: ksynMdm2mRNA*p53_P
kMdm2Ub = 4.56E-6 Reaction: Mdm2 + E2_Ub => Mdm2_Ub + E2, Rate Law: kMdm2Ub*Mdm2*E2_Ub
kactATM = 1.0E-4 Reaction: damDNA + ATMI => damDNA + ATMA, Rate Law: kactATM*damDNA*ATMI
kphospTauGSK3b = 2.0E-4 Reaction: GSK3b + Tau => GSK3b + Tau_P1, Rate Law: kphospTauGSK3b*GSK3b*Tau
kdegMdm2 = 0.01; kproteff = 1.0 Reaction: Mdm2_Ub4_Proteasome => Proteasome + Ub, Rate Law: kdegMdm2*Mdm2_Ub4_Proteasome*kproteff
kactDUBMdm2 = 1.0E-7 Reaction: Mdm2_Ub2 + Mdm2DUB => Mdm2_Ub + Mdm2DUB + Ub, Rate Law: kactDUBMdm2*Mdm2_Ub2*Mdm2DUB
kpf = 0.001 Reaction: AggAbeta + AbetaPlaque => AbetaPlaque, Rate Law: kpf*AggAbeta*AbetaPlaque
kaggAbeta = 1.0E-8 Reaction: Abeta => AggAbeta, Rate Law: kaggAbeta*Abeta*(Abeta-1)*0.5
kphosMdm2 = 2.0 Reaction: Mdm2 + ATMA => Mdm2_P + ATMA, Rate Law: kphosMdm2*Mdm2*ATMA
ksynp53mRNA = 0.001 Reaction: Source => p53_mRNA, Rate Law: ksynp53mRNA*Source
kMdm2PUb = 6.84E-6 Reaction: Mdm2_P + E2_Ub => Mdm2_P_Ub + E2, Rate Law: kMdm2PUb*Mdm2_P*E2_Ub
kdegp53mRNA = 1.0E-4 Reaction: p53_mRNA => Sink, Rate Law: kdegp53mRNA*p53_mRNA
ktangfor = 0.001 Reaction: AggTau => NFT, Rate Law: ktangfor*AggTau*(AggTau-1)*0.5
kbinMTTau = 0.1 Reaction: Tau => MT_Tau, Rate Law: kbinMTTau*Tau
ksynp53 = 0.007 Reaction: p53_mRNA => p53 + p53_mRNA, Rate Law: ksynp53*p53_mRNA
kdegMdm2mRNA = 5.0E-4 Reaction: Mdm2_mRNA => Sink, Rate Law: kdegMdm2mRNA*Mdm2_mRNA

States:

Name Description
Mdm2 P [E3 ubiquitin-protein ligase Mdm2]
AggAbeta [Amyloid beta A4 protein]
AggTau Proteasome [IPR002955; proteasome complex]
ATP [ATP]
AbetaPlaque [Amyloid beta A4 protein]
MT Tau [IPR015562]
Ub [Ubiquitin-60S ribosomal protein L40]
Proteasome Tau [IPR002955; proteasome complex]
Mdm2 P Ub3 [E3 ubiquitin-protein ligase Mdm2; Ubiquitin-60S ribosomal protein L40]
AMP [AMP; AMP]
Mdm2 Ub2 [E3 ubiquitin-protein ligase Mdm2; Ubiquitin-60S ribosomal protein L40]
p53 [Cellular tumor antigen p53]
Source Source
p53 P [Cellular tumor antigen p53]
AggAbeta Proteasome [Amyloid beta A4 protein; proteasome complex]
IR IR
E2 Ub [Ubiquitin-60S ribosomal protein L40; IPR000608]
GSK3b p53 P [Glycogen synthase kinase-3 beta; Cellular tumor antigen p53]
Abeta [Amyloid beta A4 protein]
Mdm2 [E3 ubiquitin-protein ligase Mdm2]
Mdm2DUB [IPR001394]
Tau P1 [IPR002955]
ROS [reactive oxygen species]
GSK3b p53 [Glycogen synthase kinase-3 beta; Cellular tumor antigen p53]
AggTau [IPR002955]
Tau [IPR002955]
PP1 [Serine/threonine-protein phosphatase PP1-alpha catalytic subunit]
damDNA [deoxyribonucleic acid]
Mdm2 P Ub2 [E3 ubiquitin-protein ligase Mdm2; Ubiquitin-60S ribosomal protein L40]
Mdm2 p53 Ub [E3 ubiquitin-protein ligase Mdm2; Cellular tumor antigen p53; Ubiquitin-60S ribosomal protein L40]
Mdm2 p53 Ub3 [E3 ubiquitin-protein ligase Mdm2; Cellular tumor antigen p53; Ubiquitin-60S ribosomal protein L40]
Mdm2 p53 [E3 ubiquitin-protein ligase Mdm2; Cellular tumor antigen p53]
NFT [IPR002955]
Mdm2 Ub [E3 ubiquitin-protein ligase Mdm2; Ubiquitin-60S ribosomal protein L40]
ATMI [Serine-protein kinase ATM]
Mdm2 p53 Ub4 [E3 ubiquitin-protein ligase Mdm2; Cellular tumor antigen p53; Ubiquitin-60S ribosomal protein L40]
ADP [ADP]
Tau P2 [IPR002955]
Sink Sink
Mdm2 p53 Ub2 [E3 ubiquitin-protein ligase Mdm2; Cellular tumor antigen p53; Ubiquitin-60S ribosomal protein L40]

Observables: none

BIOMD0000000293 @ v0.0.1

This a model from the article: Modelling the Role of UCH-L1 on Protein Aggregation in Age-Related Neurodegeneration.…

Overexpression of the de-ubiquitinating enzyme UCH-L1 leads to inclusion formation in response to proteasome impairment. These inclusions contain components of the ubiquitin-proteasome system and α-synuclein confirming that the ubiquitin-proteasome system plays an important role in protein aggregation. The processes involved are very complex and so we have chosen to take a systems biology approach to examine the system whereby we combine mathematical modelling with experiments in an iterative process. The experiments show that cells are very heterogeneous with respect to inclusion formation and so we use stochastic simulation. The model shows that the variability is partly due to stochastic effects but also depends on protein expression levels of UCH-L1 within cells. The model also indicates that the aggregation process can start even before any proteasome inhibition is present, but that proteasome inhibition greatly accelerates aggregation progression. This leads to less efficient protein degradation and hence more aggregation suggesting that there is a vicious cycle. However, proteasome inhibition may not necessarily be the initiating event. Our combined modelling and experimental approach show that stochastic effects play an important role in the aggregation process and could explain the variability in the age of disease onset. Furthermore, our model provides a valuable tool, as it can be easily modified and extended to incorporate new experimental data, test hypotheses and make testable predictions. link: http://identifiers.org/pubmed/20949132

Parameters:

Name Description
kpolyUb = 0.01 Reaction: E3_MisP_Ub6 + E2_Ub => E3_MisP_Ub7 + E2, Rate Law: kpolyUb*E3_MisP_Ub6*E2_Ub
kbinAggProt = 5.0E-9 Reaction: AggA1 + Proteasome => AggP_Proteasome, Rate Law: kbinAggProt*AggA1*Proteasome
kdisaggasyn3 = 6.0E-9 Reaction: AggA3 => AggA2 + asyn, Rate Law: kdisaggasyn3*AggA3
kgenROSAggP = 2.0E-5 Reaction: AggP5 => AggP5 + ROS, Rate Law: kgenROSAggP*AggP5
kdisaggasyn5 = 2.0E-9 Reaction: AggA5 => AggA4 + asyn, Rate Law: kdisaggasyn5*AggA5
kbinProt = 5.0E-6 Reaction: Parkin_asyn_dam_Ub7 + Proteasome => asyn_dam_Ub7_Proteasome + Parkin, Rate Law: kbinProt*Parkin_asyn_dam_Ub7*Proteasome
kdisagg2 = 8.0E-9 Reaction: AggP2 => AggP1 + MisP, Rate Law: kdisagg2*AggP2
kactDUB = 1.0E-4 Reaction: Parkin_asyn_dam_Ub2_DUB => Parkin_asyn_dam_Ub_DUB + Ub, Rate Law: kactDUB*Parkin_asyn_dam_Ub2_DUB
kdisaggasyn2 = 8.0E-9 Reaction: AggA2 => AggA1 + asyn, Rate Law: kdisaggasyn2*AggA2
kbinasynDUB = 2.0E-7 Reaction: Parkin_asyn_dam_Ub7 + DUB => Parkin_asyn_dam_Ub7_DUB, Rate Law: kbinasynDUB*Parkin_asyn_dam_Ub7*DUB
krelMisPE3 = 2.0E-4 Reaction: E3_MisP => MisP + E3, Rate Law: krelMisPE3*E3_MisP
kbinSUBUCHL1 = 4.0E-8 Reaction: E3SUB_SUB_misfolded_Ub2 + UCHL1 => E3SUB_SUB_misfolded_Ub2_UCHL1, Rate Law: kbinSUBUCHL1*E3SUB_SUB_misfolded_Ub2*UCHL1
kaggasyn2 = 5.0E-10 Reaction: asyn + AggA2 => AggA3, Rate Law: kaggasyn2*asyn*AggA2
kactDUBProt = 1.0E-6 Reaction: SUB_misfolded_Ub4_Proteasome + DUB => SUB_misfolded + Proteasome + Ub + DUB, Rate Law: kactDUBProt*SUB_misfolded_Ub4_Proteasome*DUB
kactProt = 0.01; kproteff = 1.0 Reaction: SUB_misfolded_Ub7_Proteasome + ATP => Ub + Proteasome + ADP, Rate Law: kactProt*SUB_misfolded_Ub7_Proteasome*kproteff*ATP/(5000+ATP)
kbinE2Ub = 0.001 Reaction: E2 + E1_Ub => E2_Ub + E1, Rate Law: kbinE2Ub*E2*E1_Ub
kigrowth2 = 5.0E-9 Reaction: E3_MisP_Ub6 + SeqAggP => SeqAggP + aggMisP + aggUb + aggE3, Rate Law: kigrowth2*E3_MisP_Ub6*SeqAggP
kremROS = 0.001 Reaction: ROS => Sink, Rate Law: kremROS*ROS
kactUchl1 = 1.0E-4 Reaction: E3SUB_SUB_misfolded_Ub3_UCHL1 => E3SUB_SUB_misfolded_Ub2_UCHL1 + Ub, Rate Law: kactUchl1*E3SUB_SUB_misfolded_Ub3_UCHL1
kubss = 0.1 Reaction: MisP => MisP + Ub + upregUb, Rate Law: kubss*MisP^6/(1500^6+MisP^6)
kbinMisPE3 = 1.0E-4 Reaction: MisP + E3 => E3_MisP, Rate Law: kbinMisPE3*MisP*E3

States:

Name Description
aggMisP aggMisP
Ub [Ubiquitin-60S ribosomal protein L40]
SeqAggP SeqAggP
aggUb [Ubiquitin-60S ribosomal protein L40]
MisP [protein]
AggP5 AggP5
AggA3 [Alpha-synuclein]
E3 MisP Ub7 [protein; Ubiquitin-60S ribosomal protein L40; IPR000569]
Parkin asyn dam Ub6 [E3 ubiquitin-protein ligase parkin; Alpha-synuclein; Ubiquitin-60S ribosomal protein L40]
aggE3 [IPR000569]
E3SUB SUB misfolded Ub3 UCHL1 [Ubiquitin carboxyl-terminal hydrolase isozyme L1; Ubiquitin-60S ribosomal protein L40; IPR000569]
AggA4 [Alpha-synuclein]
aggDUB [IPR001394]
E2 Ub [Ubiquitin-60S ribosomal protein L40; IPR000608]
E3 MisP Ub8 [protein; Ubiquitin-60S ribosomal protein L40; IPR000569]
ROS [reactive oxygen species]
Parkin asyn dam Ub7 [E3 ubiquitin-protein ligase parkin; Alpha-synuclein; Ubiquitin-60S ribosomal protein L40]
E3SUB SUB misfolded Ub2 UCHL1 [Ubiquitin carboxyl-terminal hydrolase isozyme L1; Ubiquitin-60S ribosomal protein L40; IPR000569]
Parkin asyn dam Ub DUB [E3 ubiquitin-protein ligase parkin; Alpha-synuclein; Ubiquitin-60S ribosomal protein L40]
E3 [IPR000569]
E2 [IPR000608]
AggA1 [Alpha-synuclein]
AggA5 [Alpha-synuclein]
E3 MisP Ub6 [protein; Ubiquitin-60S ribosomal protein L40; IPR000569]
Parkin asyn dam Ub8 [E3 ubiquitin-protein ligase parkin; Alpha-synuclein; Ubiquitin-60S ribosomal protein L40]
Parkin asyn dam Ub2 DUB [E3 ubiquitin-protein ligase parkin; Alpha-synuclein; Ubiquitin-60S ribosomal protein L40]
AggA2 [Alpha-synuclein]

Observables: none

This model is from the article: Modelling the Role of the Hsp70/Hsp90 System in the Maintenance of Protein Homeostas…

Neurodegeneration is an age-related disorder which is characterised by the accumulation of aggregated protein and neuronal cell death. There are many different neurodegenerative diseases which are classified according to the specific proteins involved and the regions of the brain which are affected. Despite individual differences, there are common mechanisms at the sub-cellular level leading to loss of protein homeostasis. The two central systems in protein homeostasis are the chaperone system, which promotes correct protein folding, and the cellular proteolytic system, which degrades misfolded or damaged proteins. Since these systems and their interactions are very complex, we use mathematical modelling to aid understanding of the processes involved. The model developed in this study focuses on the role of Hsp70 (IPR00103) and Hsp90 (IPR001404) chaperones in preventing both protein aggregation and cell death. Simulations were performed under three different conditions: no stress; transient stress due to an increase in reactive oxygen species; and high stress due to sustained increases in reactive oxygen species. The model predicts that protein homeostasis can be maintained during short periods of stress. However, under long periods of stress, the chaperone system becomes overwhelmed and the probability of cell death pathways being activated increases. Simulations were also run in which cell death mediated by the JNK (P45983) and p38 (Q16539) pathways was inhibited. The model predicts that inhibiting either or both of these pathways may delay cell death but does not stop the aggregation process and that eventually cells die due to aggregated protein inhibiting proteasomal function. This problem can be overcome if the sequestration of aggregated protein into inclusion bodies is enhanced. This model predicts responses to reactive oxygen species-mediated stress that are consistent with currently available experimental data. The model can be used to assess specific interventions to reduce cell death due to impaired protein homeostasis. link: http://identifiers.org/pubmed/21779370

Parameters:

Name Description
kdegHsp90 = 0.01; kalive = 1.0 Reaction: Hsp90_Proteasome + ATP => Proteasome + ADP, Rate Law: kdegHsp90*Hsp90_Proteasome*kalive*ATP/(5000+ATP)
kalive = 1.0; kdephosp38Mkp1 = 0.05 Reaction: p38_P + Mkp1_P => p38 + Mkp1_P, Rate Law: kdephosp38Mkp1*p38_P*Mkp1_P*kalive
kdegMkp1 = 0.01; kalive = 1.0 Reaction: Mkp1_Proteasome + ATP => Proteasome + ADP, Rate Law: kdegMkp1*Mkp1_Proteasome*kalive*ATP/(5000+ATP)
kalive = 1.0; kbinHspMisp = 8.0E-6 Reaction: MisP + Hsp70 => Hsp70_MisP, Rate Law: kbinHspMisp*MisP*Hsp70*kalive
kdephosJnkMkp1 = 0.05; kalive = 1.0 Reaction: Jnk_P + Mkp1_P => Jnk + Mkp1_P, Rate Law: kdephosJnkMkp1*Jnk_P*Mkp1_P*kalive
kalive = 1.0; kbinAggPProt = 1.0E-5 Reaction: AggP + Proteasome => AggP_Proteasome, Rate Law: kbinAggPProt*AggP*Proteasome*kalive
kgenROS = 0.01; kalive = 1.0 Reaction: Source => ROS, Rate Law: kgenROS*Source*kalive
kbinMisPProt = 1.0E-7; kalive = 1.0 Reaction: Hsp70_MisP + Proteasome => MisP_Proteasome + Hsp70, Rate Law: kbinMisPProt*Hsp70_MisP*Proteasome*kalive
kalive = 1.0; kbinHsp90client = 2.0E-4 Reaction: Hsp90 + Hsp90Client => Hsp90_Hsp90Client, Rate Law: kbinHsp90client*Hsp90*Hsp90Client*kalive
kalive = 1.0; kdegAkt = 0.01 Reaction: Akt_Proteasome + ATP => Proteasome + ADP, Rate Law: kdegAkt*Akt_Proteasome*kalive*ATP/(5000+ATP)
kPIdeath = 2.0E-8; kalive = 1.0 Reaction: AggP_Proteasome => AggP_Proteasome + PIDeath + CellDeath, Rate Law: kPIdeath*AggP_Proteasome*kalive
kalive = 1.0; krelHsp70Ppx = 5.0 Reaction: Hsp70_Ppx => Hsp70 + Ppx, Rate Law: krelHsp70Ppx*Hsp70_Ppx*kalive
kalive = 1.0; krelAktProt = 1.0E-8 Reaction: Akt_Proteasome => Akt + Proteasome, Rate Law: krelAktProt*Akt_Proteasome*kalive
kagg = 1.0E-8; kalive = 1.0 Reaction: MisP => AggP, Rate Law: kagg*MisP*(MisP-1)*0.5*kalive
kphosMkp1 = 0.02; kalive = 1.0 Reaction: Mkp1 + Hsp70 => Mkp1_P + Hsp70, Rate Law: kphosMkp1*Mkp1*Hsp70*kalive
kbinHsp90Prot = 1.0E-8; kalive = 1.0 Reaction: Hsp90 + Proteasome => Hsp90_Proteasome, Rate Law: kbinHsp90Prot*Hsp90*Proteasome*kalive
kalive = 1.0; kdegMisP = 0.01 Reaction: MisP_Proteasome + ATP => Proteasome + ADP, Rate Law: kdegMisP*MisP_Proteasome*kalive*ATP/(5000+ATP)
kgenROSp38 = 1.0E-4; kalive = 1.0; kp38act = 1.0 Reaction: p38_P => p38_P + ROS, Rate Law: kgenROSp38*p38_P*kalive*kp38act
kmisfold = 2.0E-6; kalive = 1.0 Reaction: NatP + ROS => MisP + ROS, Rate Law: kmisfold*NatP*ROS*kalive
kbinHsp70client = 2.0E-4; kalive = 1.0 Reaction: Hsp70 + Hsp70Client => Hsp70_Hsp70Client, Rate Law: kbinHsp70client*Hsp70*Hsp70Client*kalive
kbinHsp70Ppx = 0.2; kalive = 1.0 Reaction: Hsp70 + Ppx => Hsp70_Ppx, Rate Law: kbinHsp70Ppx*Hsp70*Ppx*kalive
ksynMkp1 = 1.0E-5; kalive = 1.0 Reaction: Source => Mkp1, Rate Law: ksynMkp1*Source*kalive
kbinHsp70Prot = 1.2E-8; kalive = 1.0 Reaction: Hsp70 + Proteasome => Hsp70_Proteasome, Rate Law: kbinHsp70Prot*Hsp70*Proteasome*kalive
krefold = 5.5E-4; kalive = 1.0 Reaction: Hsp90_MisP + ATP => Hsp90 + NatP + ADP, Rate Law: krefold*Hsp90_MisP*kalive*ATP/(5000+ATP)
kbasalsynHsp70 = 0.008; kalive = 1.0 Reaction: Source => Hsp70, Rate Law: kbasalsynHsp70*kalive
kalive = 1.0; kphosp38 = 0.02 Reaction: ROS + p38 => ROS + p38_P, Rate Law: kphosp38*ROS*p38*kalive
kphosHsf1 = 0.03; kalive = 1.0 Reaction: Hsf1_Hsf1_Hsf1 + Pkc => Hsf1_Hsf1_Hsf1_P + Pkc, Rate Law: kphosHsf1*Hsf1_Hsf1_Hsf1*Pkc*kalive
kbinMkp1Prot = 9.6E-9; kalive = 1.0 Reaction: Mkp1 + Proteasome => Mkp1_Proteasome, Rate Law: kbinMkp1Prot*Mkp1*Proteasome*kalive
kalive = 1.0; kgenROSAggP = 1.0E-6 Reaction: AggP => AggP + ROS, Rate Law: kgenROSAggP*AggP*kalive
kphosJnk = 0.02; kalive = 1.0 Reaction: ROS + Jnk => ROS + Jnk_P, Rate Law: kphosJnk*Jnk*ROS*kalive
kbinAktProt = 6.0E-8; kalive = 1.0 Reaction: Akt_CHIP_Hsp90 + Proteasome => Akt_Proteasome + CHIP + Hsp90, Rate Law: kbinAktProt*Akt_CHIP_Hsp90*Proteasome*kalive
kalive = 1.0; kp38death = 1.5E-7; kp38act = 1.0 Reaction: p38_P => p38_P + p38Death + CellDeath, Rate Law: kp38death*p38_P*kalive*kp38act
kalive = 1.0; kdamHsp = 1.0E-8 Reaction: Hsp70 + ROS => Hsp70_dam + ROS, Rate Law: kdamHsp*Hsp70*ROS*kalive
kbinHsf1Hsp90 = 0.02; kalive = 1.0 Reaction: Hsp90 + Hsf1 => Hsf1_Hsp90, Rate Law: kbinHsf1Hsp90*Hsp90*Hsf1*kalive
krelHsp70client = 5.0; kalive = 1.0 Reaction: Hsp70_Hsp70Client => Hsp70 + Hsp70Client, Rate Law: krelHsp70client*Hsp70_Hsp70Client*kalive
kremROS = 0.001; kalive = 1.0 Reaction: ROS => Sink, Rate Law: kremROS*ROS*kalive
kalive = 1.0; kJnkdeath = 1.5E-7 Reaction: Jnk_P => Jnk_P + JNKDeath + CellDeath, Rate Law: kJnkdeath*Jnk_P*kalive
krelHsf1Hsp90 = 0.5; kalive = 1.0 Reaction: Hsf1_Hsp90 => Hsp90 + Hsf1, Rate Law: krelHsf1Hsp90*Hsf1_Hsp90*kalive
kdegHsp70 = 0.01; kalive = 1.0 Reaction: Hsp70_Proteasome + ATP => Proteasome + ADP, Rate Law: kdegHsp70*Hsp70_Proteasome*kalive*ATP/(5000+ATP)
kdephosHsf1 = 0.01; kalive = 1.0 Reaction: Hsf1_Hsf1_Hsf1_P + Hsp70_Ppx => Hsf1_Hsf1_Hsf1 + Hsp70_Ppx, Rate Law: kdephosHsf1*Hsf1_Hsf1_Hsf1_P*Hsp70_Ppx*kalive
kupregHsp = 0.2; kalive = 1.0 Reaction: HSEHsp70_Hsf1_Hsf1_Hsf1_P => HSEHsp70_Hsf1_Hsf1_Hsf1_P + Hsp70, Rate Law: kupregHsp*HSEHsp70_Hsf1_Hsf1_Hsf1_P*kalive
kalive = 1.0; kdephosMkp1 = 0.001 Reaction: Mkp1_P + ROS => Mkp1 + ROS, Rate Law: kdephosMkp1*Mkp1_P*ROS*kalive
kseqagg = 7.0E-7; kalive = 1.0 Reaction: SeqAggP + MisP => SeqAggP, Rate Law: kseqagg*SeqAggP*MisP*kalive
krelHsp90client = 5.0; kalive = 1.0 Reaction: Hsp90_Hsp90Client => Hsp90 + Hsp90Client, Rate Law: krelHsp90client*Hsp90_Hsp90Client*kalive
kalive = 1.0; krelHspMisp = 8.0E-5 Reaction: Hsp90_MisP => MisP + Hsp90, Rate Law: krelHspMisp*Hsp90_MisP*kalive

States:

Name Description
Proteasome [proteasome complex]
Mkp1 [Dual specificity protein phosphatase 1]
ATP [ATP]
Jnk [Mitogen-activated protein kinase 8]
Jnk P [Mitogen-activated protein kinase 8; Phosphoprotein]
ROS [reactive oxygen species]
AggP Proteasome [protein; proteasome complex]
p38 P [Mitogen-activated protein kinase 14; Phosphoprotein]
Hsp90 [IPR001404]
p38 [Mitogen-activated protein kinase 14]
SeqAggP [protein]
MisP [protein]
Mkp1 P [Dual specificity protein phosphatase 1; Phosphoprotein]
Hsp70 [IPR001023]
Pkc [IPR012233]
MisP Proteasome [protein; proteasome complex]
Ppx [protein serine/threonine phosphatase complex]
NatP [protein]
Hsp70 Ppx [IPR001023; protein serine/threonine phosphatase complex]
AggP [protein]
Mkp1 Proteasome [Dual specificity protein phosphatase 1; proteasome complex]

Observables: none

Proctor2012 - Amyloid-beta aggregationThis model supports the current thinking that levels of dimers are important in in…

Alzheimer's disease (AD) is the most frequently diagnosed neurodegenerative disorder affecting humans, with advanced age being the most prominent risk factor for developing AD. Despite intense research efforts aimed at elucidating the precise molecular underpinnings of AD, a definitive answer is still lacking. In recent years, consensus has grown that dimerisation of the polypeptide amyloid-beta (Aß), particularly Aß₄₂, plays a crucial role in the neuropathology that characterise AD-affected post-mortem brains, including the large-scale accumulation of fibrils, also referred to as senile plaques. This has led to the realistic hope that targeting Aß₄₂ immunotherapeutically could drastically reduce plaque burden in the ageing brain, thus delaying AD onset or symptom progression. Stochastic modelling is a useful tool for increasing understanding of the processes underlying complex systems-affecting disorders such as AD, providing a rapid and inexpensive strategy for testing putative new therapies. In light of the tool's utility, we developed computer simulation models to examine Aß₄₂ turnover and its aggregation in detail and to test the effect of immunization against Aß dimers.Our model demonstrates for the first time that even a slight decrease in the clearance rate of Aß₄₂ monomers is sufficient to increase the chance of dimers forming, which could act as instigators of protofibril and fibril formation, resulting in increased plaque levels. As the process is slow and levels of Aβ are normally low, stochastic effects are important. Our model predicts that reducing the rate of dimerisation leads to a significant reduction in plaque levels and delays onset of plaque formation. The model was used to test the effect of an antibody mediated immunological response. Our results showed that plaque levels were reduced compared to conditions where antibodies are not present.Our model supports the current thinking that levels of dimers are important in initiating the aggregation process. Although substantial knowledge exists regarding the process, no therapeutic intervention is on offer that reliably decreases disease burden in AD patients. Computer modelling could serve as one of a number of tools to examine both the validity of reliable biomarkers and aid the discovery of successful intervention strategies. link: http://identifiers.org/pubmed/22748062

Parameters:

Name Description
kdimer = 1.1783E-7 Reaction: Abeta => AbDim; Abeta, Rate Law: kdimer*Abeta*(Abeta-1)*0.5
kpf = 2.785E-6 Reaction: AbDim => AbP; AbDim, Rate Law: kpf*AbDim*(AbDim-1)*0.5
kdegNep = 1.8E-10 Reaction: Nep => Sink; Nep, Rate Law: kdegNep*Nep
kdedimer = 8.4655E-6 Reaction: AbDim => Abeta; AbDim, Rate Law: kdedimer*AbDim
kprod = 1.86E-5 Reaction: Source => Abeta; Source, Rate Law: kprod*Source
kdisagg = 5.4357E-5 Reaction: AbP => Abeta; AbP, Rate Law: kdisagg*AbP
kdeg = 2.1E-5 Reaction: Abeta + Nep => Sink + Nep; Abeta, Nep, Rate Law: kdeg*Abeta*Nep*0.001
kpg = 0.00574; kpghalf = 4.0 Reaction: Abeta + AbP => AbP; Abeta, AbP, Rate Law: kpg*Abeta*AbP^2/(kpghalf^2+AbP^2)

States:

Name Description
AbP [amyloid-beta; amyloid plaque]
Source AbetaPlaque
Nep [Neprilysin]
Abeta [amyloid-beta]
Sink AbetaPlaque
AbDim [amyloid-beta; protein complex]

Observables: none

Proctor2013 - Cartilage breakdown, interventions to reduce collagen releaseThe molecular pathways involved in cartilage…

Objective. To use a novel computational approach to examine the molecular pathways involved in cartilage breakdown and to use computer simulation to test possible interventions to reduce collagen release. Methods. We constructed a computational model of the relevant molecular pathways using the Systems Biology Markup Language (SBML), a computer-readable format of a biochemical network. The model was constructed using our experimental data showing that interleukin-1 (IL-1) and oncostatin M (OSM) act synergistically to up-regulate collagenase protein and activity and initiate cartilage collagen breakdown. Simulations were performed in the COPASI software package. Results. The model predicted that simulated inhibition of c-Jun N-terminal kinase (JNK) or p38 mitogen-activated protein kinase, and over-expression of tissue inhibitor of metalloproteinases 3 (TIMP-3) led to a reduction in collagen release. Over-expression of TIMP-1 was much less effective than TIMP-3 and led to a delay, rather than a reduction, in collagen release. Simulated interventions of receptor antagonists and inhibition of Janus kinase 1 (JAK1), the first kinase in the OSM pathway, were ineffective. So, importantly, the model predicts that it is more effective to intervene at targets which are downstream, such as the JNK pathway, rather than close to the cytokine signal. In vitro experiments confirmed the effectiveness of JNK inhibition. Conclusion. Our study shows the value of computer modelling as a tool for examining possible interventions to reduce cartilage collagen breakdown. The model predicts interventions that either prevent transcription or inhibit activity of collagenases are promising strategies and should be investigated further in an experimental setting. © 2013 American College of Rheumatology. link: http://identifiers.org/pubmed/24285357

Parameters:

Name Description
kdegMKP1 = 1.0E-4 Reaction: MKP1 => Sink; MKP1, Rate Law: kdegMKP1*MKP1
kdegADAMTS4 = 5.0E-5 Reaction: ADAMTS4 => Sink; ADAMTS4, Rate Law: kdegADAMTS4*ADAMTS4
ksynDUSP16 = 0.005; kAP1activity = 1.0 Reaction: cFos_cJun => cFos_cJun + DUSP16; cFos_cJun, Rate Law: ksynDUSP16*cFos_cJun*kAP1activity
ksynMMP1 = 1.5E-4 Reaction: MMP1_mRNA => MMP1_mRNA + proMMP1; MMP1_mRNA, Rate Law: ksynMMP1*MMP1_mRNA
ksyncFosmRNAStat3 = 0.05 Reaction: STAT3_P_nuc => STAT3_P_nuc + cFos_mRNA; STAT3_P_nuc, Rate Law: ksyncFosmRNAStat3*STAT3_P_nuc
kdephosJNKDUSP16 = 0.001 Reaction: JNK_P + DUSP16 => JNK + DUSP16; JNK_P, DUSP16, Rate Law: kdephosJNKDUSP16*JNK_P*DUSP16
ksynTIMP3mRNAStat3 = 4.0E-5; kAP1activity = 1.0 Reaction: STAT3_P_nuc => STAT3_P_nuc + TIMP3_mRNA; STAT3_P_nuc, Rate Law: ksynTIMP3mRNAStat3*STAT3_P_nuc*kAP1activity
kdephoscFosDUSP16 = 1.0E-4 Reaction: cFos_P + DUSP16 => cFos + DUSP16; cFos_P, DUSP16, Rate Law: kdephoscFosDUSP16*cFos_P*DUSP16
krelTRAF6PP4 = 1.0E-6 Reaction: TRAF6_PP4 => TRAF6 + PP4; TRAF6_PP4, Rate Law: krelTRAF6PP4*TRAF6_PP4
ksynPTPRT = 1.0E-4 Reaction: STAT3_P_nuc => STAT3_P_nuc + PTPRT; STAT3_P_nuc, Rate Law: ksynPTPRT*STAT3_P_nuc
kcyt2nucSTAT3 = 0.001 Reaction: STAT3_P_cyt => STAT3_P_nuc; STAT3_P_cyt, Rate Law: kcyt2nucSTAT3*STAT3_P_cyt
ksynSOCS3 = 0.001 Reaction: SOCS3_mRNA => SOCS3_mRNA + SOCS3; SOCS3_mRNA, Rate Law: ksynSOCS3*SOCS3_mRNA
kphosSTAT3 = 0.005 Reaction: STAT3_cyt + JAK1_P => STAT3_P_cyt + JAK1_P; STAT3_cyt, JAK1_P, Rate Law: kphosSTAT3*STAT3_cyt*JAK1_P
kbinTRAF6 = 1.0E-5 Reaction: IL1_IL1R_IRAK2 + TRAF6 => IL1_IL1R + IRAK2_TRAF6; IL1_IL1R_IRAK2, TRAF6, Rate Law: kbinTRAF6*IL1_IL1R_IRAK2*TRAF6
kdegDUSP16 = 1.3E-4 Reaction: DUSP16 => Sink; DUSP16, Rate Law: kdegDUSP16*DUSP16
ksynbasalTIMP3mRNA = 2.8E-4 Reaction: Source => TIMP3_mRNA; Source, Rate Law: ksynbasalTIMP3mRNA*Source
kdegcJun = 1.3E-4 Reaction: cJun => Sink; cJun, Rate Law: kdegcJun*cJun
krelMMP1 = 0.001 Reaction: MMP1_TIMP1 => MMP1 + TIMP1; MMP1_TIMP1, Rate Law: krelMMP1*MMP1_TIMP1
kdephosSTAT3nucPTPRT = 5.0E-4 Reaction: STAT3_P_nuc + PTPRT => STAT3_nuc + PTPRT; STAT3_P_nuc, PTPRT, Rate Law: kdephosSTAT3nucPTPRT*STAT3_P_nuc*PTPRT
krelADAMTS4TIMP1 = 0.001 Reaction: ADAMTS4_TIMP1 => ADAMTS4 + TIMP1; ADAMTS4_TIMP1, Rate Law: krelADAMTS4TIMP1*ADAMTS4_TIMP1
ksynTIMP1mRNAStat3 = 4.0E-5 Reaction: STAT3_P_nuc + TIMP1_DNA => STAT3_P_nuc + TIMP1_DNA + TIMP1_mRNA; STAT3_P_nuc, TIMP1_DNA, Rate Law: ksynTIMP1mRNAStat3*STAT3_P_nuc*TIMP1_DNA
kdephosSTAT3nuc = 1.0E-7 Reaction: STAT3_P_nuc => STAT3_nuc; STAT3_P_nuc, Rate Law: kdephosSTAT3nuc*STAT3_P_nuc
ksynSOCS3mRNA = 0.006 Reaction: STAT3_P_nuc => STAT3_P_nuc + SOCS3_mRNA; STAT3_P_nuc, Rate Law: ksynSOCS3mRNA*STAT3_P_nuc
ksynDUSP16cJun = 2.0E-4 Reaction: cJun_dimer => cJun_dimer + DUSP16; cJun_dimer, Rate Law: ksynDUSP16cJun*cJun_dimer
ksynADAMTS4 = 5.0E-4 Reaction: ADAMTS4_mRNA => ADAMTS4_mRNA + ADAMTS4; ADAMTS4_mRNA, Rate Law: ksynADAMTS4*ADAMTS4_mRNA
kphoscJun = 1.0E-4 Reaction: cJun + JNK_P => cJun_P + JNK_P; cJun, JNK_P, Rate Law: kphoscJun*cJun*JNK_P
kdegAggrecan = 3.0E-8 Reaction: Aggrecan_Collagen2 + ADAMTS4 => ADAMTS4 + Collagen2 + AggFrag; Aggrecan_Collagen2, ADAMTS4, Rate Law: kdegAggrecan*Aggrecan_Collagen2*ADAMTS4
kdephosp38 = 0.001 Reaction: p38_P => p38; p38_P, Rate Law: kdephosp38*p38_P
ksynTIMP1 = 2.0E-4 Reaction: TIMP1_mRNA => TIMP1_mRNA + TIMP1; TIMP1_mRNA, Rate Law: ksynTIMP1*TIMP1_mRNA
kdegPTPRT = 5.0E-5 Reaction: PTPRT => Sink; PTPRT, Rate Law: kdegPTPRT*PTPRT
kdegSOCS3mRNA = 4.0E-4 Reaction: SOCS3_mRNA => Sink; SOCS3_mRNA, Rate Law: kdegSOCS3mRNA*SOCS3_mRNA
ksynbasalTIMP1mRNA = 1.4E-4 Reaction: TIMP1_DNA => TIMP1_mRNA + TIMP1_DNA; TIMP1_DNA, Rate Law: ksynbasalTIMP1mRNA*TIMP1_DNA
kdegTIMP3 = 2.0E-5 Reaction: TIMP3 => Sink; TIMP3, Rate Law: kdegTIMP3*TIMP3
kinhibADAMTS4TIMP1 = 3.0E-6 Reaction: ADAMTS4 + TIMP1 => ADAMTS4_TIMP1; ADAMTS4, TIMP1, Rate Law: kinhibADAMTS4TIMP1*ADAMTS4*TIMP1
kdegMMP13mRNA = 6.4E-6 Reaction: MMP13_mRNA => Sink; MMP13_mRNA, Rate Law: kdegMMP13mRNA*MMP13_mRNA
ksyncJunmRNAcJun = 0.005 Reaction: cJun_dimer => cJun_mRNA + cJun_dimer; cJun_dimer, Rate Law: ksyncJunmRNAcJun*cJun_dimer
kphosJAK1 = 1.0E-5 Reaction: JAK1 + OSM_OSMR => JAK1_P + OSM_OSMR; JAK1, OSM_OSMR, Rate Law: kphosJAK1*JAK1*OSM_OSMR
kdegTIMP1mRNA = 1.4E-5 Reaction: TIMP1_mRNA => Sink; TIMP1_mRNA, Rate Law: kdegTIMP1mRNA*TIMP1_mRNA
kdegTIMP3mRNA = 1.4E-5 Reaction: TIMP3_mRNA => Sink; TIMP3_mRNA, Rate Law: kdegTIMP3mRNA*TIMP3_mRNA
kinhibMMP1TIMP3 = 1.0E-8 Reaction: MMP1 + TIMP3 => MMP1_TIMP3; MMP1, TIMP3, Rate Law: kinhibMMP1TIMP3*MMP1*TIMP3
kdephosSTAT3 = 1.0E-5 Reaction: STAT3_P_cyt => STAT3_cyt; STAT3_P_cyt, Rate Law: kdephosSTAT3*STAT3_P_cyt
ksyncJunmRNA = 0.0125; kAP1activity = 1.0 Reaction: cFos_cJun => cJun_mRNA + cFos_cJun; cFos_cJun, Rate Law: ksyncJunmRNA*cFos_cJun*kAP1activity
kdephosSTAT3PTPRT = 8.0E-4 Reaction: STAT3_P_cyt + PTPRT => STAT3_cyt + PTPRT; STAT3_P_cyt, PTPRT, Rate Law: kdephosSTAT3PTPRT*STAT3_P_cyt*PTPRT
kdegMMP13 = 1.0E-6 Reaction: MMP13 => Sink; MMP13, Rate Law: kdegMMP13*MMP13
ksynPP4cJun = 2.0E-4 Reaction: cJun_dimer => cJun_dimer + PP4; cJun_dimer, Rate Law: ksynPP4cJun*cJun_dimer
ksynMMP1mRNAcJun = 2.0E-4 Reaction: cJun_dimer => cJun_dimer + MMP1_mRNA; cJun_dimer, Rate Law: ksynMMP1mRNAcJun*cJun_dimer
kinhibTRAF6 = 0.5 Reaction: TRAF6 + PP4 => TRAF6_PP4; TRAF6, PP4, Rate Law: kinhibTRAF6*TRAF6*PP4
kinhibADAMTS4TIMP3 = 5.0E-4 Reaction: TIMP3 + ADAMTS4 => ADAMTS4_TIMP3; TIMP3, ADAMTS4, Rate Law: kinhibADAMTS4TIMP3*TIMP3*ADAMTS4
kphosJNK = 1.0E-4 Reaction: JNK + IRAK2_TRAF6 => JNK_P + IRAK2_TRAF6; JNK, IRAK2_TRAF6, Rate Law: kphosJNK*JNK*IRAK2_TRAF6
kdegcJunmRNA = 0.003 Reaction: cJun_mRNA => Sink; cJun_mRNA, Rate Law: kdegcJunmRNA*cJun_mRNA
knuc2cytSTAT3 = 0.001 Reaction: STAT3_nuc => STAT3_cyt; STAT3_nuc, Rate Law: knuc2cytSTAT3*STAT3_nuc
ksynMMP1mRNA = 0.005; kAP1activity = 1.0 Reaction: cFos_cJun => cFos_cJun + MMP1_mRNA; cFos_cJun, Rate Law: ksynMMP1mRNA*cFos_cJun*kAP1activity
kdephosJAK1PTPRT = 0.004 Reaction: JAK1_P + PTPRT => JAK1 + PTPRT; JAK1_P, PTPRT, Rate Law: kdephosJAK1PTPRT*JAK1_P*PTPRT
ksynbasalcJunmRNA = 0.015 Reaction: Source => cJun_mRNA; Source, Rate Law: ksynbasalcJunmRNA*Source
kdephoscJun = 0.01 Reaction: cJun_P => cJun; cJun_P, Rate Law: kdephoscJun*cJun_P
kdephosJAK1 = 4.0E-4 Reaction: JAK1_P => JAK1; JAK1_P, Rate Law: kdephosJAK1*JAK1_P
kdegMMP1 = 1.0E-6 Reaction: MMP1 => Sink; MMP1, Rate Law: kdegMMP1*MMP1
kdegCollagen2mmp1 = 5.0E-12 Reaction: Collagen2 + MMP1 => MMP1 + ColFrag; Collagen2, MMP1, Rate Law: kdegCollagen2mmp1*Collagen2*MMP1
kbinSOCS3OSMR = 0.005 Reaction: SOCS3 + OSMR => OSMR_SOCS3; SOCS3, OSMR, Rate Law: kbinSOCS3OSMR*SOCS3*OSMR
kdephosp38MKP1 = 1.0E-5 Reaction: p38_P + MKP1 => p38 + MKP1; p38_P, MKP1, Rate Law: kdephosp38MKP1*p38_P*MKP1
ksynMMP13mRNA = 5.0E-4; kAP1activity = 1.0 Reaction: cFos_cJun => cFos_cJun + MMP13_mRNA; cFos_cJun, Rate Law: ksynMMP13mRNA*cFos_cJun*kAP1activity
ksynTIMP1mRNA = 5.0E-7; kAP1activity = 1.0 Reaction: cFos_cJun + TIMP1_DNA => cFos_cJun + TIMP1_mRNA + TIMP1_DNA; cFos_cJun, TIMP1_DNA, Rate Law: ksynTIMP1mRNA*cFos_cJun*TIMP1_DNA*kAP1activity
kdegPP4 = 1.0E-4 Reaction: PP4 => Sink; PP4, Rate Law: kdegPP4*PP4
kdephosJNK = 0.001 Reaction: JNK_P => JNK; JNK_P, Rate Law: kdephosJNK*JNK_P
kdegMMP1mRNA = 6.4E-6 Reaction: MMP1_mRNA => Sink; MMP1_mRNA, Rate Law: kdegMMP1mRNA*MMP1_mRNA
ksynTIMP3mRNA = 5.0E-7; kAP1activity = 1.0 Reaction: cFos_cJun => cFos_cJun + TIMP3_mRNA; cFos_cJun, Rate Law: ksynTIMP3mRNA*cFos_cJun*kAP1activity
kAP1activity = 1.0; ksyncFosmRNA = 5.0E-6 Reaction: cFos_cJun => cFos_cJun + cFos_mRNA; cFos_cJun, Rate Law: ksyncFosmRNA*cFos_cJun*kAP1activity
krelADAMTS4TIMP3 = 0.001 Reaction: ADAMTS4_TIMP3 => ADAMTS4 + TIMP3; ADAMTS4_TIMP3, Rate Law: krelADAMTS4TIMP3*ADAMTS4_TIMP3
kdegSOCS3 = 8.0E-4 Reaction: SOCS3 => Sink; SOCS3, Rate Law: kdegSOCS3*SOCS3
ksynTIMP3 = 4.0E-4 Reaction: TIMP3_mRNA => TIMP3_mRNA + TIMP3; TIMP3_mRNA, Rate Law: ksynTIMP3*TIMP3_mRNA

States:

Name Description
Aggrecan Collagen2 [Collagen alpha-1(II) chain; AggrecanAggrecan core protein]
cFos [Proto-oncogene c-Fos]
cJun [Transcription factor AP-1]
cJun mRNA [Transcription factor AP-1; JUN]
cFos mRNA [Proto-oncogene c-Fos; FOS]
p38 P [Mitogen-activated protein kinase 11; 3842]
TRAF6 PP4 [Serine/threonine-protein phosphatase 4 catalytic subunit; TNF receptor-associated factor 6]
ColFrag [Collagen alpha-1(II) chain]
ADAMTS4 [A disintegrin and metalloproteinase with thrombospondin motifs 4]
TIMP1 mRNA [TIMP1; Metalloproteinase inhibitor 1]
MKP1 [Dual specificity protein phosphatase 1]
STAT3 cyt [cytoplasm; Signal transducer and activator of transcription 3]
JAK1 [Tyrosine-protein kinase JAK1]
DUSP16 [Dual specificity protein phosphatase 16]
MMP13 mRNA [MMP13; Collagenase 3]
STAT3 nuc [Signal transducer and activator of transcription 3; nucleus]
JNK P [Mitogen-activated protein kinase 8; 3842]
STAT3 P nuc [Signal transducer and activator of transcription 3; 3842; nucleus]
SOCS3 [Suppressor of cytokine signaling 3]
ADAMTS4 TIMP1 [Metalloproteinase inhibitor 1; A disintegrin and metalloproteinase with thrombospondin motifs 4]
TIMP3 mRNA [TIMP3; Metalloproteinase inhibitor 3]
SOCS3 mRNA [SOCS3; Suppressor of cytokine signaling 3]
STAT3 P cyt [cytoplasm; Signal transducer and activator of transcription 3; 3842]
p38 [Mitogen-activated protein kinase 11]
IRAK2 TRAF6 [TNF receptor-associated factor 6; Interleukin-1 receptor-associated kinase-like 2]
IRAK2 TRAF6 PP4 [Serine/threonine-protein phosphatase 4 catalytic subunit; Interleukin-1 receptor-associated kinase-like 2; TNF receptor-associated factor 6]
ADAMTS4 TIMP3 [Metalloproteinase inhibitor 1; A disintegrin and metalloproteinase with thrombospondin motifs 4]
proMMP1 [Interstitial collagenase]
TIMP1 DNA [deoxyribonucleic acid; Metalloproteinase inhibitor 1]
JAK1 P [Tyrosine-protein kinase JAK1; 3842]
MMP1 [Interstitial collagenase]
Sink Sink
MMP1 mRNA [Interstitial collagenase; MMP1]
TRAF6 [TNF receptor-associated factor 6]
PP4 [Serine/threonine-protein phosphatase 4 catalytic subunit]

Observables: none

Proctor2013 - Effect of Aβ immunisation in Alzheimer's disease (deterministic version)Extension of a previously publishe…

Progress in the development of therapeutic interventions to treat or slow the progression of Alzheimer's disease has been hampered by lack of efficacy and unforeseen side effects in human clinical trials. This setback highlights the need for new approaches for pre-clinical testing of possible interventions. Systems modelling is becoming increasingly recognised as a valuable tool for investigating molecular and cellular mechanisms involved in ageing and age-related diseases. However, there is still a lack of awareness of modelling approaches in many areas of biomedical research. We previously developed a stochastic computer model to examine some of the key pathways involved in the aggregation of amyloid-beta (Aβ) and the micro-tubular binding protein tau. Here we show how we extended this model to include the main processes involved in passive and active immunisation against Aβ and then demonstrate the effects of this intervention on soluble Aβ, plaques, phosphorylated tau and tangles. The model predicts that immunisation leads to clearance of plaques but only results in small reductions in levels of soluble Aβ, phosphorylated tau and tangles. The behaviour of this model is supported by neuropathological observations in Alzheimer patients immunised against Aβ. Since, soluble Aβ, phosphorylated tau and tangles more closely correlate with cognitive decline than plaques, our model suggests that immunotherapy against Aβ may not be effective unless it is performed very early in the disease process or combined with other therapies. link: http://identifiers.org/pubmed/24098635

Parameters:

Name Description
kdisaggAbeta2 = 1.0E-6 Reaction: AbetaPlaque + antiAb => AbetaDimer + antiAb + disaggPlaque2; antiAb, AbetaPlaque, Rate Law: kdisaggAbeta2*antiAb*AbetaPlaque
kactDUBp53 = 1.0E-7 Reaction: Mdm2_p53_Ub + p53DUB => Mdm2_p53 + p53DUB + Ub; Mdm2_p53_Ub, p53DUB, Rate Law: kactDUBp53*Mdm2_p53_Ub*p53DUB
kremROS = 7.0E-5 Reaction: ROS => Sink; ROS, Rate Law: kremROS*ROS
kinactglia2 = 5.0E-6 Reaction: GliaM2 => GliaM1; GliaM2, Rate Law: kinactglia2*GliaM2
kprodAbeta = 1.86E-5 Reaction: Source => Abeta; Source, Rate Law: kprodAbeta*Source
krelMTTau = 1.0E-4 Reaction: MT_Tau => Tau; MT_Tau, Rate Law: krelMTTau*MT_Tau
krepair = 2.0E-5 Reaction: damDNA => Sink; damDNA, Rate Law: krepair*damDNA
kinhibprot = 1.0E-7 Reaction: AbetaDimer + Proteasome => AggAbeta_Proteasome; AbetaDimer, Proteasome, Rate Law: kinhibprot*AbetaDimer*Proteasome
kbinAbantiAb = 1.0E-6 Reaction: AbetaDimer + antiAb => AbetaDimer_antiAb; AbetaDimer, antiAb, Rate Law: kbinAbantiAb*AbetaDimer*antiAb
kactglia1 = 6.0E-7 Reaction: GliaM1 + AbetaPlaque => GliaM2 + AbetaPlaque; GliaM1, AbetaPlaque, Rate Law: kactglia1*GliaM1*AbetaPlaque
kaggTauP1 = 1.0E-8 Reaction: Tau_P1 => AggTau; Tau_P1, Rate Law: kaggTauP1*Tau_P1^2*0.5
kaggTauP2 = 1.0E-7 Reaction: Tau_P2 => AggTau; Tau_P2, Rate Law: kaggTauP2*Tau_P2^2*0.5
kdephosMdm2 = 0.5 Reaction: Mdm2_P => Mdm2; Mdm2_P, Rate Law: kdephosMdm2*Mdm2_P
kdephosp53 = 0.5 Reaction: p53_P => p53; p53_P, Rate Law: kdephosp53*p53_P
kbinMdm2p53 = 0.001155 Reaction: p53 + Mdm2 => Mdm2_p53; p53, Mdm2, Rate Law: kbinMdm2p53*p53*Mdm2
krelGSK3bp53 = 0.002 Reaction: GSK3b_p53 => GSK3b + p53; GSK3b_p53, Rate Law: krelGSK3bp53*GSK3b_p53
kdegTau20SProt = 0.01 Reaction: Proteasome_Tau => Proteasome; Proteasome_Tau, Rate Law: kdegTau20SProt*Proteasome_Tau
krelMdm2p53 = 1.155E-5 Reaction: Mdm2_p53 => p53 + Mdm2; Mdm2_p53, Rate Law: krelMdm2p53*Mdm2_p53
kdisaggAbeta = 1.0E-6 Reaction: AbetaDimer => Abeta; AbetaDimer, Rate Law: kdisaggAbeta*AbetaDimer
kaggTau = 1.0E-8 Reaction: Tau => AggTau; Tau, Rate Law: kaggTau*Tau^2*0.5
kinactATM = 5.0E-4 Reaction: ATMA => ATMI; ATMA, Rate Law: kinactATM*ATMA
kdisaggAbeta1 = 2.0E-4 Reaction: AbetaPlaque => AbetaDimer + disaggPlaque1; AbetaPlaque, Rate Law: kdisaggAbeta1*AbetaPlaque
kdegAbetaGlia = 0.005 Reaction: AbetaPlaque_GliaA => GliaA + degAbetaGlia; AbetaPlaque_GliaA, Rate Law: kdegAbetaGlia*AbetaPlaque_GliaA
kbinGSK3bp53 = 2.0E-6 Reaction: GSK3b + p53 => GSK3b_p53; GSK3b, p53, Rate Law: kbinGSK3bp53*GSK3b*p53
kgenROSGlia = 1.0E-5 Reaction: AbetaPlaque_GliaA => AbetaPlaque_GliaA + ROS; AbetaPlaque_GliaA, Rate Law: kgenROSGlia*AbetaPlaque_GliaA
kMdm2PolyUb = 0.00456 Reaction: Mdm2_Ub2 + E2_Ub => Mdm2_Ub3 + E2; Mdm2_Ub2, E2_Ub, Rate Law: kMdm2PolyUb*Mdm2_Ub2*E2_Ub
ksynMdm2mRNAGSK3bp53 = 7.0E-4 Reaction: GSK3b_p53 => GSK3b_p53 + Mdm2_mRNA; GSK3b_p53, Rate Law: ksynMdm2mRNAGSK3bp53*GSK3b_p53
kbinProt = 2.0E-6 Reaction: Mdm2_Ub4 + Proteasome => Mdm2_Ub4_Proteasome; Mdm2_Ub4, Proteasome, Rate Law: kbinProt*Mdm2_Ub4*Proteasome
kphosMdm2GSK3b = 0.005 Reaction: Mdm2_p53_Ub4 + GSK3b => Mdm2_P1_p53_Ub4 + GSK3b; Mdm2_p53_Ub4, GSK3b, Rate Law: kphosMdm2GSK3b*Mdm2_p53_Ub4*GSK3b
kbinE1Ub = 2.0E-4 Reaction: E1 + Ub + ATP => E1_Ub + AMP; E1, Ub, ATP, Rate Law: kbinE1Ub*E1*Ub*ATP/(5000+ATP)
kpghalf = 10.0; kpg = 0.15 Reaction: AbetaDimer + AbetaPlaque => AbetaPlaque; AbetaDimer, AbetaPlaque, Rate Law: kpg*AbetaDimer*AbetaPlaque^2/(kpghalf^2+AbetaPlaque^2)
kaggAbeta = 3.0E-6 Reaction: Abeta => AbetaDimer; Abeta, Rate Law: kaggAbeta*Abeta^2*0.5
krelAbetaGlia = 5.0E-5 Reaction: AbetaPlaque_GliaA => AbetaPlaque + GliaA; AbetaPlaque_GliaA, Rate Law: krelAbetaGlia*AbetaPlaque_GliaA
kp53Ub = 5.0E-5 Reaction: E2_Ub + Mdm2_p53 => Mdm2_p53_Ub + E2; E2_Ub, Mdm2_p53, Rate Law: kp53Ub*E2_Ub*Mdm2_p53
kbinAbetaGlia = 1.0E-5 Reaction: AbetaPlaque + GliaA => AbetaPlaque_GliaA; AbetaPlaque, GliaA, Rate Law: kbinAbetaGlia*AbetaPlaque*GliaA
kp53PolyUb = 0.01 Reaction: Mdm2_p53_Ub2 + E2_Ub => Mdm2_p53_Ub3 + E2; Mdm2_p53_Ub2, E2_Ub, Rate Law: kp53PolyUb*Mdm2_p53_Ub2*E2_Ub
kdamROS = 1.0E-5 Reaction: ROS => ROS + damDNA; ROS, Rate Law: kdamROS*ROS
kdam = 0.08 Reaction: IR => IR + damDNA; IR, Rate Law: kdam*IR
ksynMdm2mRNA = 5.0E-4 Reaction: p53_P => p53_P + Mdm2_mRNA; p53_P, Rate Law: ksynMdm2mRNA*p53_P
kgenROSPlaque = 1.0E-5 Reaction: AbetaPlaque => AbetaPlaque + ROS; AbetaPlaque, Rate Law: kgenROSPlaque*AbetaPlaque
kactATM = 1.0E-4 Reaction: damDNA + ATMI => damDNA + ATMA; damDNA, ATMI, Rate Law: kactATM*damDNA*ATMI
kphospTauGSK3b = 2.0E-4 Reaction: GSK3b + Tau => GSK3b + Tau_P1; GSK3b, Tau, Rate Law: kphospTauGSK3b*GSK3b*Tau
kdegMdm2 = 0.01; kproteff = 1.0 Reaction: Mdm2_Ub4_Proteasome => Proteasome + Ub; Mdm2_Ub4_Proteasome, Rate Law: kdegMdm2*Mdm2_Ub4_Proteasome*kproteff
kactDUBMdm2 = 1.0E-7 Reaction: Mdm2_Ub + Mdm2DUB => Mdm2 + Mdm2DUB + Ub; Mdm2_Ub, Mdm2DUB, Rate Law: kactDUBMdm2*Mdm2_Ub*Mdm2DUB
kdegAbeta = 1.5E-5 Reaction: Abeta_antiAb => antiAb; Abeta_antiAb, Rate Law: 10*kdegAbeta*Abeta_antiAb
kgenROSAbeta = 2.0E-5 Reaction: AggAbeta_Proteasome => AggAbeta_Proteasome + ROS; AggAbeta_Proteasome, Rate Law: kgenROSAbeta*AggAbeta_Proteasome
kphosMdm2 = 2.0 Reaction: Mdm2 + ATMA => Mdm2_P + ATMA; Mdm2, ATMA, Rate Law: kphosMdm2*Mdm2*ATMA
kactglia2 = 6.0E-7 Reaction: GliaM2 + antiAb => GliaA + antiAb; GliaM2, antiAb, Rate Law: kactglia2*GliaM2*antiAb
kMdm2PUb = 6.84E-6 Reaction: Mdm2_P + E2_Ub => Mdm2_P_Ub + E2; Mdm2_P, E2_Ub, Rate Law: kMdm2PUb*Mdm2_P*E2_Ub
kinactglia1 = 5.0E-6 Reaction: GliaA => GliaM2; GliaA, Rate Law: kinactglia1*GliaA
kdegp53mRNA = 1.0E-4 Reaction: p53_mRNA => Sink; p53_mRNA, Rate Law: kdegp53mRNA*p53_mRNA
ktangfor = 0.001 Reaction: AggTau => NFT; AggTau, Rate Law: ktangfor*AggTau^2*0.5
ksynp53 = 0.007 Reaction: p53_mRNA => p53 + p53_mRNA; p53_mRNA, Rate Law: ksynp53*p53_mRNA
kbinMTTau = 0.1 Reaction: Tau => MT_Tau; Tau, Rate Law: kbinMTTau*Tau

States:

Name Description
Mdm2 P [E3 ubiquitin-protein ligase Mdm2; phosphoprotein]
antiAb [Immunoglobulin]
Mdm2 p53 Ub2 [Cellular tumor antigen p53; E3 ubiquitin-protein ligase Mdm2; Polyubiquitin-B]
AbetaPlaque [Amyloid beta A4 protein; urn:miriam:sbo:SBO%3A0000543]
MT Tau [IPR015562]
Ub [Polyubiquitin-B]
AMP [AMP]
p53 [Cellular tumor antigen p53]
disaggPlaque2 disaggPlaque2
Mdm2 Ub2 [E3 ubiquitin-protein ligase Mdm2; Polyubiquitin-B]
Source Source
GliaA [microglial cell]
p53 P [Cellular tumor antigen p53; phosphoprotein]
IR IR
Mdm2 P Ub [Polyubiquitin-B; E3 ubiquitin-protein ligase Mdm2; phosphoprotein]
Abeta [Amyloid beta A4 protein]
Mdm2 [E3 ubiquitin-protein ligase Mdm2]
GliaM2 [microglial cell]
ROS [reactive oxygen species]
Proteasome [proteasome complex]
GSK3b p53 [Cellular tumor antigen p53; Glycogen synthase kinase-3 beta]
Mdm2 Ub3 [Polyubiquitin-B; E3 ubiquitin-protein ligase Mdm2]
AbetaDimer [Amyloid beta A4 protein]
damDNA [deoxyribonucleic acid]
AbetaDimer antiAb [Amyloid beta A4 protein; Immunoglobulin]
Mdm2 P Ub2 [Polyubiquitin-B; E3 ubiquitin-protein ligase Mdm2; phosphoprotein]
p53 mRNA [Cellular tumor antigen p53]
GliaM1 [microglial cell]
AggTau [IPR002955; urn:miriam:sbo:SBO%3A0000543]
ATMA [Serine-protein kinase ATM; urn:miriam:pato:PATO%3A000234]
Mdm2 Ub4 [Polyubiquitin-B; E3 ubiquitin-protein ligase Mdm2]
Mdm2 p53 [E3 ubiquitin-protein ligase Mdm2; Cellular tumor antigen p53]
AbetaPlaque GliaA [Amyloid beta A4 protein; microglial cell; urn:miriam:sbo:SBO%3A0000543]
Abeta antiAb [Amyloid beta A4 protein; Immunoglobulin]
GSK3b [Glycogen synthase kinase-3 beta]
degAbetaGlia degAbetaGlia
Sink Sink
Mdm2 P Ub4 Proteasome [Polyubiquitin-B; E3 ubiquitin-protein ligase Mdm2; proteasome complex; phosphoprotein]
Mdm2 Ub4 Proteasome [Polyubiquitin-B; E3 ubiquitin-protein ligase Mdm2; proteasome complex]
Mdm2 mRNA [E3 ubiquitin-protein ligase Mdm2]

Observables: none

Proctor2013 - Effect of Aβ immunisation in Alzheimer's disease (stochastic version)Extension of a previously published s…

Progress in the development of therapeutic interventions to treat or slow the progression of Alzheimer's disease has been hampered by lack of efficacy and unforeseen side effects in human clinical trials. This setback highlights the need for new approaches for pre-clinical testing of possible interventions. Systems modelling is becoming increasingly recognised as a valuable tool for investigating molecular and cellular mechanisms involved in ageing and age-related diseases. However, there is still a lack of awareness of modelling approaches in many areas of biomedical research. We previously developed a stochastic computer model to examine some of the key pathways involved in the aggregation of amyloid-beta (Aβ) and the micro-tubular binding protein tau. Here we show how we extended this model to include the main processes involved in passive and active immunisation against Aβ and then demonstrate the effects of this intervention on soluble Aβ, plaques, phosphorylated tau and tangles. The model predicts that immunisation leads to clearance of plaques but only results in small reductions in levels of soluble Aβ, phosphorylated tau and tangles. The behaviour of this model is supported by neuropathological observations in Alzheimer patients immunised against Aβ. Since, soluble Aβ, phosphorylated tau and tangles more closely correlate with cognitive decline than plaques, our model suggests that immunotherapy against Aβ may not be effective unless it is performed very early in the disease process or combined with other therapies. link: http://identifiers.org/pubmed/24098635

Parameters:

Name Description
kdisaggAbeta2 = 1.0E-6 Reaction: AbetaPlaque + antiAb => AbetaDimer + antiAb + disaggPlaque2; antiAb, AbetaPlaque, Rate Law: kdisaggAbeta2*antiAb*AbetaPlaque
kremROS = 7.0E-5 Reaction: ROS => Sink; ROS, Rate Law: kremROS*ROS
kinactglia2 = 5.0E-6 Reaction: GliaM1 => GliaI; GliaM1, Rate Law: kinactglia2*GliaM1
krelMTTau = 1.0E-4 Reaction: MT_Tau => Tau; MT_Tau, Rate Law: krelMTTau*MT_Tau
krepair = 2.0E-5 Reaction: damDNA => Sink; damDNA, Rate Law: krepair*damDNA
kinhibprot = 1.0E-7 Reaction: AggTau + Proteasome => AggTau_Proteasome; AggTau, Proteasome, Rate Law: kinhibprot*AggTau*Proteasome
kbinAbantiAb = 1.0E-6 Reaction: AbetaDimer + antiAb => AbetaDimer_antiAb; AbetaDimer, antiAb, Rate Law: kbinAbantiAb*AbetaDimer*antiAb
kactglia1 = 6.0E-7 Reaction: GliaI + AbetaPlaque => GliaM1 + AbetaPlaque; GliaI, AbetaPlaque, Rate Law: kactglia1*GliaI*AbetaPlaque
kphosMdm2GSK3bp53 = 0.5 Reaction: Mdm2_p53_Ub4 + GSK3b_p53_P => Mdm2_P1_p53_Ub4 + GSK3b_p53_P; Mdm2_p53_Ub4, GSK3b_p53_P, Rate Law: kphosMdm2GSK3bp53*Mdm2_p53_Ub4*GSK3b_p53_P
kaggTauP1 = 1.0E-8 Reaction: Tau_P1 => AggTau; Tau_P1, Rate Law: kaggTauP1*Tau_P1*(Tau_P1-1)*0.5
kaggTauP2 = 1.0E-7 Reaction: Tau_P2 => AggTau; Tau_P2, Rate Law: kaggTauP2*Tau_P2*(Tau_P2-1)*0.5
kdephosp53 = 0.5 Reaction: p53_P => p53; p53_P, Rate Law: kdephosp53*p53_P
kbinMdm2p53 = 0.001155 Reaction: p53 + Mdm2 => Mdm2_p53; p53, Mdm2, Rate Law: kbinMdm2p53*p53*Mdm2
kbinTauProt = 1.925E-7 Reaction: Tau + Proteasome => Proteasome_Tau; Tau, Proteasome, Rate Law: kbinTauProt*Tau*Proteasome
krelGSK3bp53 = 0.002 Reaction: GSK3b_p53_P => GSK3b + p53_P; GSK3b_p53_P, Rate Law: krelGSK3bp53*GSK3b_p53_P
kdegTau20SProt = 0.01 Reaction: Proteasome_Tau => Proteasome; Proteasome_Tau, Rate Law: kdegTau20SProt*Proteasome_Tau
krelMdm2p53 = 1.155E-5 Reaction: Mdm2_p53 => p53 + Mdm2; Mdm2_p53, Rate Law: krelMdm2p53*Mdm2_p53
kaggTau = 1.0E-8 Reaction: Tau => AggTau; Tau, Rate Law: kaggTau*Tau*(Tau-1)*0.5
kdisaggAbeta = 1.0E-6 Reaction: AbetaDimer => Abeta; AbetaDimer, Rate Law: kdisaggAbeta*AbetaDimer
kphosp53 = 2.0E-4 Reaction: p53 + ATMA => p53_P + ATMA; p53, ATMA, Rate Law: kphosp53*p53*ATMA
kinactATM = 5.0E-4 Reaction: ATMA => ATMI; ATMA, Rate Law: kinactATM*ATMA
kdisaggAbeta1 = 2.0E-4 Reaction: AbetaPlaque => AbetaDimer + disaggPlaque1; AbetaPlaque, Rate Law: kdisaggAbeta1*AbetaPlaque
kdegAbetaGlia = 0.005 Reaction: AbetaPlaque_GliaA => GliaA + degAbetaGlia; AbetaPlaque_GliaA, Rate Law: kdegAbetaGlia*AbetaPlaque_GliaA
kprodAbeta2 = 1.86E-5 Reaction: GSK3b_p53_P => Abeta + GSK3b_p53_P; GSK3b_p53_P, Rate Law: kprodAbeta2*GSK3b_p53_P
kbinGSK3bp53 = 2.0E-6 Reaction: GSK3b + p53_P => GSK3b_p53_P; GSK3b, p53_P, Rate Law: kbinGSK3bp53*GSK3b*p53_P
kgenROSGlia = 1.0E-5 Reaction: AbetaPlaque_GliaA => AbetaPlaque_GliaA + ROS; AbetaPlaque_GliaA, Rate Law: kgenROSGlia*AbetaPlaque_GliaA
kMdm2PolyUb = 0.00456 Reaction: Mdm2_Ub + E2_Ub => Mdm2_Ub2 + E2; Mdm2_Ub, E2_Ub, Rate Law: kMdm2PolyUb*Mdm2_Ub*E2_Ub
ksynMdm2mRNAGSK3bp53 = 7.0E-4 Reaction: GSK3b_p53 => GSK3b_p53 + Mdm2_mRNA; GSK3b_p53, Rate Law: ksynMdm2mRNAGSK3bp53*GSK3b_p53
kbinProt = 2.0E-6 Reaction: Mdm2_P1_p53_Ub4 + Proteasome => p53_Ub4_Proteasome + Mdm2; Mdm2_P1_p53_Ub4, Proteasome, Rate Law: kbinProt*Mdm2_P1_p53_Ub4*Proteasome
kbinE1Ub = 2.0E-4 Reaction: E1 + Ub + ATP => E1_Ub + AMP; E1, Ub, ATP, Rate Law: kbinE1Ub*E1*Ub*ATP/(5000+ATP)
kpghalf = 10.0; kpg = 0.15 Reaction: AbetaDimer + AbetaPlaque => AbetaPlaque; AbetaDimer, AbetaPlaque, Rate Law: kpg*AbetaDimer*AbetaPlaque^2/(kpghalf^2+AbetaPlaque^2)
ksynMdm2 = 4.95E-4 Reaction: Mdm2_mRNA => Mdm2_mRNA + Mdm2; Mdm2_mRNA, Rate Law: ksynMdm2*Mdm2_mRNA
krelAbetaGlia = 5.0E-5 Reaction: AbetaPlaque_GliaA => AbetaPlaque + GliaA; AbetaPlaque_GliaA, Rate Law: krelAbetaGlia*AbetaPlaque_GliaA
kaggAbeta = 3.0E-6 Reaction: Abeta => AbetaDimer; Abeta, Rate Law: kaggAbeta*Abeta*(Abeta-1)*0.5
kbinAbetaGlia = 1.0E-5 Reaction: AbetaPlaque + GliaA => AbetaPlaque_GliaA; AbetaPlaque, GliaA, Rate Law: kbinAbetaGlia*AbetaPlaque*GliaA
kphospTauGSK3bp53 = 0.1 Reaction: GSK3b_p53 + Tau_P1 => GSK3b_p53 + Tau_P2; GSK3b_p53, Tau_P1, Rate Law: kphospTauGSK3bp53*GSK3b_p53*Tau_P1
kpf = 0.2 Reaction: AbetaDimer => AbetaPlaque; AbetaDimer, Rate Law: kpf*AbetaDimer*(AbetaDimer-1)*0.5
kproteff = 1.0; kdegp53 = 0.005 Reaction: p53_Ub4_Proteasome + ATP => Ub + Proteasome + ADP; p53_Ub4_Proteasome, ATP, Rate Law: kdegp53*kproteff*p53_Ub4_Proteasome*ATP/(5000+ATP)
kdephospTau = 0.01 Reaction: Tau_P1 + PP1 => Tau + PP1; Tau_P1, PP1, Rate Law: kdephospTau*Tau_P1*PP1
ksynTau = 8.0E-5 Reaction: Source => Tau; Source, Rate Law: ksynTau*Source
kdamROS = 1.0E-5 Reaction: ROS => ROS + damDNA; ROS, Rate Law: kdamROS*ROS
kdam = 0.08 Reaction: IR => IR + damDNA; IR, Rate Law: kdam*IR
ksynMdm2mRNA = 5.0E-4 Reaction: p53_P => p53_P + Mdm2_mRNA; p53_P, Rate Law: ksynMdm2mRNA*p53_P
kgenROSPlaque = 1.0E-5 Reaction: AbetaPlaque => AbetaPlaque + ROS; AbetaPlaque, Rate Law: kgenROSPlaque*AbetaPlaque
kMdm2Ub = 4.56E-6 Reaction: Mdm2 + E2_Ub => Mdm2_Ub + E2; Mdm2, E2_Ub, Rate Law: kMdm2Ub*Mdm2*E2_Ub
kphospTauGSK3b = 2.0E-4 Reaction: GSK3b + Tau_P1 => GSK3b + Tau_P2; GSK3b, Tau_P1, Rate Law: kphospTauGSK3b*GSK3b*Tau_P1
kdegAntiAb = 2.75E-6 Reaction: antiAb => Sink; antiAb, Rate Law: kdegAntiAb*antiAb
kdegMdm2 = 0.01; kproteff = 1.0 Reaction: Mdm2_Ub4_Proteasome => Proteasome + Ub; Mdm2_Ub4_Proteasome, Rate Law: kdegMdm2*Mdm2_Ub4_Proteasome*kproteff
kactATM = 1.0E-4 Reaction: damDNA + ATMI => damDNA + ATMA; damDNA, ATMI, Rate Law: kactATM*damDNA*ATMI
kactDUBMdm2 = 1.0E-7 Reaction: Mdm2_Ub + Mdm2DUB => Mdm2 + Mdm2DUB + Ub; Mdm2_Ub, Mdm2DUB, Rate Law: kactDUBMdm2*Mdm2_Ub*Mdm2DUB
kdegAbeta = 1.5E-5 Reaction: AbetaDimer_antiAb => antiAb; AbetaDimer_antiAb, Rate Law: 10*kdegAbeta*AbetaDimer_antiAb
kgenROSAbeta = 2.0E-5 Reaction: Abeta => Abeta + ROS; Abeta, Rate Law: kgenROSAbeta*Abeta
kphosMdm2 = 2.0 Reaction: Mdm2 + ATMA => Mdm2_P + ATMA; Mdm2, ATMA, Rate Law: kphosMdm2*Mdm2*ATMA
ksynp53mRNA = 0.001 Reaction: Source => p53_mRNA; Source, Rate Law: ksynp53mRNA*Source
kdegp53mRNA = 1.0E-4 Reaction: p53_mRNA => Sink; p53_mRNA, Rate Law: kdegp53mRNA*p53_mRNA
ktangfor = 0.001 Reaction: AggTau => NFT; AggTau, Rate Law: ktangfor*AggTau*(AggTau-1)*0.5
ksynp53 = 0.007 Reaction: p53_mRNA => p53 + p53_mRNA; p53_mRNA, Rate Law: ksynp53*p53_mRNA
kbinMTTau = 0.1 Reaction: Tau => MT_Tau; Tau, Rate Law: kbinMTTau*Tau
kdegMdm2mRNA = 5.0E-4 Reaction: Mdm2_mRNA => Sink; Mdm2_mRNA, Rate Law: kdegMdm2mRNA*Mdm2_mRNA

States:

Name Description
Mdm2 P [E3 ubiquitin-protein ligase Mdm2; phosphoprotein]
AggTau Proteasome [urn:miriam:sbo:SBO%3A0000543; IPR002955; proteasome complex]
MT Tau [IPR002955]
Proteasome Tau [IPR002955; proteasome complex]
AMP [AMP]
p53 [Cellular tumor antigen p53]
Mdm2 Ub2 [E3 ubiquitin-protein ligase Mdm2; Polyubiquitin-B]
Source Source
p53 P [Cellular tumor antigen p53; phosphoprotein]
IR IR
E1 [IPR000011]
GSK3b p53 P [Cellular tumor antigen p53; Glycogen synthase kinase-3 beta; phosphoprotein]
Abeta [Amyloid beta A4 protein]
Mdm2 [E3 ubiquitin-protein ligase Mdm2]
ROS [reactive oxygen species]
Tau P1 [IPR002955]
Proteasome [proteasome complex]
damDNA [deoxyribonucleic acid]
AggTau [urn:miriam:sbo:SBO%3A0000543; IPR002955]
AbetaDimer [Amyloid beta A4 protein]
AbetaDimer antiAb [Amyloid beta A4 protein; Immunoglobulin]
GliaM1 [microglial cell]
p53 mRNA [Cellular tumor antigen p53]
PP1 [Serine/threonine-protein phosphatase PP1-alpha catalytic subunit]
ATMA [Serine-protein kinase ATM; active]
AbetaPlaque GliaA [Amyloid beta A4 protein; microglial cell; urn:miriam:sbo:SBO%3A0000543]
Mdm2 Ub [E3 ubiquitin-protein ligase Mdm2; Polyubiquitin-B]
ATMI [Serine-protein kinase ATM; inactive]
ADP [ADP]
Tau P2 [IPR002955]
GliaI [microglial cell]
Sink Sink
Mdm2 mRNA [E3 ubiquitin-protein ligase Mdm2]

Observables: none

Proctor2016 - Circadian rhythm of PTH and the dynamics of signaling molecules on bone remodelingThis model is described…

Bone remodeling is the continuous process of bone resorption by osteoclasts and bone formation by osteoblasts, in order to maintain homeostasis. The activity of osteoclasts and osteoblasts is regulated by a network of signaling pathways, including Wnt, parathyroid hormone (PTH), RANK ligand/osteoprotegrin, and TGF-β, in response to stimuli, such as mechanical loading. During aging there is a gradual loss of bone mass due to dysregulation of signaling pathways. This may be due to a decline in physical activity with age and/or changes in hormones and other signaling molecules. In particular, hormones, such as PTH, have a circadian rhythm, which may be disrupted in aging. Due to the complexity of the molecular and cellular networks involved in bone remodeling, several mathematical models have been proposed to aid understanding of the processes involved. However, to date, there are no models, which explicitly consider the effects of mechanical loading, the circadian rhythm of PTH, and the dynamics of signaling molecules on bone remodeling. Therefore, we have constructed a network model of the system using a modular approach, which will allow further modifications as required in future research. The model was used to simulate the effects of mechanical loading and also the effects of different interventions, such as continuous or intermittent administration of PTH. Our model predicts that the absence of regular mechanical loading and/or an impaired PTH circadian rhythm leads to a gradual decrease in bone mass over time, which can be restored by simulated interventions and that the effectiveness of some interventions may depend on their timing. link: http://identifiers.org/pubmed/27379013

Parameters:

Name Description
kdegSost = 0.004 Reaction: Sost => Sink; Sost, Rate Law: kdegSost*Sost
ksecRANKLbyOcyI = 1.0E-7 Reaction: Ocy_I => Ocy_I + RANKL; Ocy_I, Rate Law: ksecRANKLbyOcyI*Ocy_I
kdiffHSC = 5.5E-5 Reaction: HSC + MCSF => HSC + MCSF + Ocl_p; HSC, MCSF, Rate Law: kdiffHSC*HSC*MCSF^2/(50^2+MCSF^2)
kbinOclpRANKL = 0.001 Reaction: RANKL + Ocl_p => Ocl_p_RANKL; Ocl_p, RANKL, Rate Law: kbinOclpRANKL*Ocl_p*RANKL
ksecMCSFbyObp = 1.0E-5 Reaction: Ob_p => Ob_p + MCSF; Ob_p, Rate Law: ksecMCSFbyObp*Ob_p
ksecRANKLbyObpTgfb = 4.0E-6 Reaction: Ob_p_Tgfb_A => Ob_p_Tgfb_A + RANKL; Ob_p_Tgfb_A, Rate Law: ksecRANKLbyObpTgfb*Ob_p_Tgfb_A
kdiffMSC = 6.5E-4 Reaction: MSC + Wnt_A => MSC + Wnt_A + Ob_pro; MSC, Wnt_A, Rate Law: kdiffMSC*MSC*Wnt_A^2/(50^2+Wnt_A^2)
kbinBaxBcl2 = 0.01 Reaction: Bax + Bcl2 => Bax_Bcl2; Bax, Bcl2, Rate Law: kbinBaxBcl2*Bax*Bcl2
krelCrebRunx2 = 0.01 Reaction: CREB_Runx2 => CREB_P + Runx2; CREB_Runx2, Rate Law: krelCrebRunx2*CREB_Runx2
krelObmPTH = 0.005 Reaction: Ob_m_PTH => Ob_m + PTH; Ob_m_PTH, Rate Law: krelObmPTH*Ob_m_PTH
ksecMCSFbyObpro = 1.0E-5 Reaction: Ob_pro => Ob_pro + MCSF; Ob_pro, Rate Law: ksecMCSFbyObpro*Ob_pro
kactWnt = 0.03 Reaction: Wnt_I => Wnt_A; Wnt_I, Rate Law: kactWnt*Wnt_I
ksecRANKLbyObp = 3.0E-6 Reaction: Ob_p => Ob_p + RANKL; Ob_p, Rate Law: ksecRANKLbyObp*Ob_p
kdegBone = 6.5E-9 Reaction: Ocl_m + Bone => Ocl_m; Ocl_m, Bone, Rate Law: kdegBone*Ocl_m*Bone
ksecRANKLbyObm = 1.0E-7 Reaction: Ob_m => Ob_m + RANKL; Ob_m, Rate Law: ksecRANKLbyObm*Ob_m
kdiffObP = 1.0E-4 Reaction: Ob_p => Ob_m; Ob_p, Rate Law: kdiffObP*Ob_p
kmatOb = 2.0E-9 Reaction: Ob_m => Ocy_I; Ob_m, Rate Law: kmatOb*Ob_m
kdeathOb = 2.4E-4 Reaction: Ob_m_PTH + Bax => Bax + PTH; Ob_m_PTH, Bax, Rate Law: kdeathOb*Ob_m_PTH*Bax^2/(50^2+Bax^2)
kformBone = 3.07E-6 Reaction: Ob_m_PTH => Ob_m_PTH + Bone + newbone; Ob_m_PTH, Rate Law: kformBone*Ob_m_PTH
ksecRANKLbyObpro = 7.0E-6 Reaction: Ob_pro => Ob_pro + RANKL; Ob_pro, Rate Law: ksecRANKLbyObpro*Ob_pro
kactCreb = 0.009 Reaction: Ob_m_PTH + CREB => Ob_m_PTH + CREB_P; CREB, Ob_m_PTH, Rate Law: kactCreb*CREB*Ob_m_PTH^2/(100^2+Ob_m_PTH^2)
ksynPTH = 0.02 Reaction: Source => PTH; Source, Rate Law: ksynPTH*Source
ksecRANKLbyOcy = 1.0E-6 Reaction: Ocy_A => Ocy_A + RANKL; Ocy_A, Rate Law: ksecRANKLbyOcy*Ocy_A
kdeathOcy = 1.0E-8 Reaction: Ocy_I => Sink; Ocy_I, Rate Law: kdeathOcy*Ocy_I
kdiffObproTgfb = 0.05 Reaction: Ob_pro + Tgfb_A => Ob_p + Tgfb_A; Ob_pro, Tgfb_A, Rate Law: kdiffObproTgfb*Ob_pro*Tgfb_A^2/(50^2+Tgfb_A^2)
krelOclpRANKL = 0.001 Reaction: Ocl_p_RANKL => Ocl_p + RANKL; Ocl_p_RANKL, Rate Law: krelOclpRANKL*Ocl_p_RANKL
kdeathOclp = 1.0E-5 Reaction: Ocl_p => Sink; Ocl_p, Rate Law: kdeathOclp*Ocl_p
kdegRANKL = 3.0E-5 Reaction: RANKL => Sink; RANKL, Rate Law: kdegRANKL*RANKL
kinactCreb = 1.0E-4 Reaction: CREB_P => CREB; CREB_P, Rate Law: kinactCreb*CREB_P
krelOcyPTH = 0.005 Reaction: Ocy_I_PTH => Ocy_I + PTH; Ocy_I_PTH, Rate Law: krelOcyPTH*Ocy_I_PTH
kdegRunx2PTH = 0.003 Reaction: Ob_m_PTH + Runx2 => Ob_m_PTH; Runx2, Ob_m_PTH, Rate Law: kdegRunx2PTH*Runx2*Ob_m_PTH
ksecRANKLbyObmPTH = 1.0E-6 Reaction: Ob_m_PTH => Ob_m_PTH + RANKL; Ob_m_PTH, Rate Law: ksecRANKLbyObmPTH*Ob_m_PTH
ksecTgfb = 5.0E-5 Reaction: Ob_m => Ob_m + Tgfb_I; Ob_m, Rate Law: ksecTgfb*Ob_m
kinhibRANKL = 0.001 Reaction: OPG + RANKL => OPG_RANKL; OPG, RANKL, Rate Law: kinhibRANKL*OPG*RANKL
ksynX = 0.01157 Reaction: Source => X; Source, Rate Law: ksynX*Source
krelBaxBcl2 = 0.5 Reaction: Bax_Bcl2 => Bax + Bcl2; Bax_Bcl2, Rate Law: krelBaxBcl2*Bax_Bcl2
kdegPTH = 0.002 Reaction: PTH => Sink; PTH, Rate Law: kdegPTH*PTH
kdegMCSF = 1.0E-4 Reaction: MCSF => Sink; MCSF, Rate Law: kdegMCSF*MCSF
kbinObpTgfb = 2.0E-4 Reaction: Ob_p + Tgfb_A => Ob_p_Tgfb_A; Ob_p, Tgfb_A, Rate Law: kbinObpTgfb*Ob_p*Tgfb_A
ksynRunx2 = 0.005 Reaction: Source => Runx2; Source, Rate Law: ksynRunx2*Source
kdeathOcl = 6.5E-5 Reaction: Ocl_m => Sink; Ocl_m, Rate Law: kdeathOcl*Ocl_m
kdegTgfb = 5.0E-5 Reaction: Tgfb_A => Sink; Tgfb_A, Rate Law: kdegTgfb*Tgfb_A
kactWntPth = 0.001 Reaction: Wnt_I + Ob_m_PTH => Wnt_A + Ob_m_PTH; Wnt_I, Ob_m_PTH, Rate Law: kactWntPth*Wnt_I*Ob_m_PTH
ksecMCSFbyMSC = 1.0E-5 Reaction: MSC => MSC + MCSF; MSC, Rate Law: ksecMCSFbyMSC*MSC
kdegOPG = 4.0E-6 Reaction: OPG => Sink; OPG, Rate Law: kdegOPG*OPG
krelRANKL = 0.001 Reaction: OPG_RANKL => OPG + RANKL; OPG_RANKL, Rate Law: krelRANKL*OPG_RANKL
kbinCrebRunx2 = 0.01 Reaction: CREB_P + Runx2 => CREB_Runx2; CREB_P, Runx2, Rate Law: kbinCrebRunx2*CREB_P*Runx2
kdegOPGRANKL = 1.0E-5 Reaction: OPG_RANKL => Sink; OPG_RANKL, Rate Law: kdegOPGRANKL*OPG_RANKL
kinactWnt = 0.8 Reaction: Wnt_A + Sost => Wnt_I + Sost; Wnt_A, Sost, Rate Law: kinactWnt*Wnt_A*Sost^2/(50^2+Sost^2)
kactTgfb = 2.0E-7 Reaction: Tgfb_I + Ocl_m => Tgfb_A + Ocl_m; Tgfb_I, Ocl_m, Rate Law: kactTgfb*Tgfb_I*Ocl_m
kdegRunx2 = 1.0E-4 Reaction: Runx2 => Sink; Runx2, Rate Law: kdegRunx2*Runx2
ksecOPGbyObp = 2.0E-6 Reaction: Ob_p => Ob_p + OPG; Ob_p, Rate Law: ksecOPGbyObp*Ob_p
kmatObTgfb = 1.0E-8 Reaction: Ob_m + Tgfb_A => Ocy_I + Tgfb_A; Ob_m, Tgfb_A, Rate Law: kmatObTgfb*Ob_m*Tgfb_A^2/(50^2+Tgfb_A^2)
kdegTgfbPTH = 1.7E-5 Reaction: Tgfb_A + Ob_m_PTH => Ob_m_PTH; Tgfb_A, Ob_m_PTH, Rate Law: kdegTgfbPTH*Tgfb_A*Ob_m_PTH
ksecMCSFbyObm = 1.0E-5 Reaction: Ob_m_PTH => Ob_m_PTH + MCSF; Ob_m_PTH, Rate Law: ksecMCSFbyObm*Ob_m_PTH
kdegBcl2 = 0.0025 Reaction: Bcl2 => Sink; Bcl2, Rate Law: kdegBcl2*Bcl2
kbinObpPTH = 3.0E-4 Reaction: Ob_p + PTH => Ob_p_PTH; Ob_p, PTH, Rate Law: kbinObpPTH*Ob_p*PTH^2/(100^2+PTH^2)
kbinObmPTH = 0.02 Reaction: Ob_m + PTH => Ob_m_PTH; Ob_m, PTH, Rate Law: kbinObmPTH*Ob_m*PTH^2/(100^2+PTH^2)
ksynBcl2 = 0.005 Reaction: CREB_Runx2 => CREB_Runx2 + Bcl2; CREB_Runx2, Rate Law: ksynBcl2*CREB_Runx2
krelObpPTH = 0.005 Reaction: Ob_p_PTH => Ob_p + PTH; Ob_p_PTH, Rate Law: krelObpPTH*Ob_p_PTH
ksecRANKLbyMSC = 1.0E-6 Reaction: MSC => MSC + RANKL; MSC, Rate Law: ksecRANKLbyMSC*MSC
kunload = 3.5E-4 Reaction: LOAD => Sink; LOAD, Rate Law: kunload*LOAD
ksecSost = 7.5E-4 Reaction: Ocy_I => Ocy_I + Sost; Ocy_I, Rate Law: ksecSost*Ocy_I
ksecRANKLbyObpPTH = 2.0E-5 Reaction: Ob_p_PTH => Ob_p_PTH + RANKL; Ob_p_PTH, Rate Law: ksecRANKLbyObpPTH*Ob_p_PTH

States:

Name Description
Sost [Sclerostin]
CREB Runx2 [Cyclic AMP-responsive element-binding protein 1; Runt-related transcription factor 2]
Bone Bone
Ob p [preosteoblast]
Ob m [terminally differentiated osteoblast]
Wnt I [Proto-oncogene Wnt-1]
PTH [Parathyroid hormone]
MSC [mesenchymal stem cell]
HSC [bone marrow hematopoietic cell]
Bax [Apoptosis regulator BAX]
newbone newbone
Source Source
Tgfb I [Transforming growth factor beta-1]
CREB P [Cyclic AMP-responsive element-binding protein 1; phosphoprotein]
Bcl2 [Apoptosis regulator Bcl-2]
CREB [Cyclic AMP-responsive element-binding protein 1]
X X
Ob p PTH [Parathyroid hormone; preosteoblast]
Ocy I PTH [Parathyroid hormone; osteocyte]
Runx2 [Runt-related transcription factor 2]
Ob m PTH [Parathyroid hormone; terminally differentiated osteoblast]
Ob p Tgfb A [Transforming growth factor beta-1; preosteoblast]
Wnt A [Proto-oncogene Wnt-1; TGF-beta 1 isoform 1 cleaved 1]
LOAD LOAD
Ob pro [non-terminally differentiated osteoblast]
RANKL [Tumor necrosis factor ligand superfamily member 11]
Sink Sink
Bax Bcl2 [Apoptosis regulator Bcl-2; Apoptosis regulator BAX]
MCSF [Macrophage colony-stimulating factor 1]

Observables: none

Proctor2017 - Identifying microRNA for muscle regeneration during ageing (Mir181_in_muscle)This model is described in th…

MicroRNAs (miRNAs) regulate gene expression through interactions with target sites within mRNAs, leading to enhanced degradation of the mRNA or inhibition of translation. Skeletal muscle expresses many different miRNAs with important roles in adulthood myogenesis (regeneration) and myofibre hypertrophy and atrophy, processes associated with muscle ageing. However, the large number of miRNAs and their targets mean that a complex network of pathways exists, making it difficult to predict the effect of selected miRNAs on age-related muscle wasting. Computational modelling has the potential to aid this process as it is possible to combine models of individual miRNA:target interactions to form an integrated network. As yet, no models of these interactions in muscle exist. We created the first model of miRNA:target interactions in myogenesis based on experimental evidence of individual miRNAs which were next validated and used to make testable predictions. Our model confirms that miRNAs regulate key interactions during myogenesis and can act by promoting the switch between quiescent/proliferating/differentiating myoblasts and by maintaining the differentiation process. We propose that a threshold level of miR-1 acts in the initial switch to differentiation, with miR-181 keeping the switch on and miR-378 maintaining the differentiation and miR-143 inhibiting myogenesis. link: http://identifiers.org/doi/10.1038/s41598-017-12538-6

Parameters: none

States: none

Observables: none

Proctor2017 - Identifying microRNA for muscle regeneration during ageing (Mirs_in_muscle)This model is described in the…

MicroRNAs (miRNAs) regulate gene expression through interactions with target sites within mRNAs, leading to enhanced degradation of the mRNA or inhibition of translation. Skeletal muscle expresses many different miRNAs with important roles in adulthood myogenesis (regeneration) and myofibre hypertrophy and atrophy, processes associated with muscle ageing. However, the large number of miRNAs and their targets mean that a complex network of pathways exists, making it difficult to predict the effect of selected miRNAs on age-related muscle wasting. Computational modelling has the potential to aid this process as it is possible to combine models of individual miRNA:target interactions to form an integrated network. As yet, no models of these interactions in muscle exist. We created the first model of miRNA:target interactions in myogenesis based on experimental evidence of individual miRNAs which were next validated and used to make testable predictions. Our model confirms that miRNAs regulate key interactions during myogenesis and can act by promoting the switch between quiescent/proliferating/differentiating myoblasts and by maintaining the differentiation process. We propose that a threshold level of miR-1 acts in the initial switch to differentiation, with miR-181 keeping the switch on and miR-378 maintaining the differentiation and miR-143 inhibiting myogenesis. link: http://identifiers.org/doi/10.1038/s41598-017-12538-6

Parameters: none

States: none

Observables: none

Proctor2017 - Identifying microRNA for muscle regeneration during ageing (Mir1_in_muscle)This model is described in the…

MicroRNAs (miRNAs) regulate gene expression through interactions with target sites within mRNAs, leading to enhanced degradation of the mRNA or inhibition of translation. Skeletal muscle expresses many different miRNAs with important roles in adulthood myogenesis (regeneration) and myofibre hypertrophy and atrophy, processes associated with muscle ageing. However, the large number of miRNAs and their targets mean that a complex network of pathways exists, making it difficult to predict the effect of selected miRNAs on age-related muscle wasting. Computational modelling has the potential to aid this process as it is possible to combine models of individual miRNA:target interactions to form an integrated network. As yet, no models of these interactions in muscle exist. We created the first model of miRNA:target interactions in myogenesis based on experimental evidence of individual miRNAs which were next validated and used to make testable predictions. Our model confirms that miRNAs regulate key interactions during myogenesis and can act by promoting the switch between quiescent/proliferating/differentiating myoblasts and by maintaining the differentiation process. We propose that a threshold level of miR-1 acts in the initial switch to differentiation, with miR-181 keeping the switch on and miR-378 maintaining the differentiation and miR-143 inhibiting myogenesis. link: http://identifiers.org/doi/10.1038/s41598-017-12538-6

Parameters: none

States: none

Observables: none

Proctor2017 - Identifying microRNA for muscle regeneration during ageing (Mir143_in_muscle)This model is described in th…

MicroRNAs (miRNAs) regulate gene expression through interactions with target sites within mRNAs, leading to enhanced degradation of the mRNA or inhibition of translation. Skeletal muscle expresses many different miRNAs with important roles in adulthood myogenesis (regeneration) and myofibre hypertrophy and atrophy, processes associated with muscle ageing. However, the large number of miRNAs and their targets mean that a complex network of pathways exists, making it difficult to predict the effect of selected miRNAs on age-related muscle wasting. Computational modelling has the potential to aid this process as it is possible to combine models of individual miRNA:target interactions to form an integrated network. As yet, no models of these interactions in muscle exist. We created the first model of miRNA:target interactions in myogenesis based on experimental evidence of individual miRNAs which were next validated and used to make testable predictions. Our model confirms that miRNAs regulate key interactions during myogenesis and can act by promoting the switch between quiescent/proliferating/differentiating myoblasts and by maintaining the differentiation process. We propose that a threshold level of miR-1 acts in the initial switch to differentiation, with miR-181 keeping the switch on and miR-378 maintaining the differentiation and miR-143 inhibiting myogenesis. link: http://identifiers.org/doi/10.1038/s41598-017-12538-6

Parameters: none

States: none

Observables: none

Proctor2017 - Identifying microRNA for muscle regeneration during ageing (Mir378_in_muscle)This model is described in th…

MicroRNAs (miRNAs) regulate gene expression through interactions with target sites within mRNAs, leading to enhanced degradation of the mRNA or inhibition of translation. Skeletal muscle expresses many different miRNAs with important roles in adulthood myogenesis (regeneration) and myofibre hypertrophy and atrophy, processes associated with muscle ageing. However, the large number of miRNAs and their targets mean that a complex network of pathways exists, making it difficult to predict the effect of selected miRNAs on age-related muscle wasting. Computational modelling has the potential to aid this process as it is possible to combine models of individual miRNA:target interactions to form an integrated network. As yet, no models of these interactions in muscle exist. We created the first model of miRNA:target interactions in myogenesis based on experimental evidence of individual miRNAs which were next validated and used to make testable predictions. Our model confirms that miRNAs regulate key interactions during myogenesis and can act by promoting the switch between quiescent/proliferating/differentiating myoblasts and by maintaining the differentiation process. We propose that a threshold level of miR-1 acts in the initial switch to differentiation, with miR-181 keeping the switch on and miR-378 maintaining the differentiation and miR-143 inhibiting myogenesis. link: http://identifiers.org/doi/10.1038/s41598-017-12538-6

Parameters: none

States: none

Observables: none

Proctor2017- Role of microRNAs in osteoarthritis (miR140 in osteoarthritis)This model is described in the article: [Com…

The aim of this study was to show how computational models can be used to increase our understanding of the role of microRNAs in osteoarthritis (OA) using miR-140 as an example. Bioinformatics analysis and experimental results from the literature were used to create and calibrate models of gene regulatory networks in OA involving miR-140 along with key regulators such as NF-κB, SMAD3, and RUNX2. The individual models were created with the modelling standard, Systems Biology Markup Language, and integrated to examine the overall effect of miR-140 on cartilage homeostasis. Down-regulation of miR-140 may have either detrimental or protective effects for cartilage, indicating that the role of miR-140 is complex. Studies of individual networks in isolation may therefore lead to different conclusions. This indicated the need to combine the five chosen individual networks involving miR-140 into an integrated model. This model suggests that the overall effect of miR-140 is to change the response to an IL-1 stimulus from a prolonged increase in matrix degrading enzymes to a pulse-like response so that cartilage degradation is temporary. Our current model can easily be modified and extended as more experimental data become available about the role of miR-140 in OA. In addition, networks of other microRNAs that are important in OA could be incorporated. A fully integrated model could not only aid our understanding of the mechanisms of microRNAs in ageing cartilage but could also provide a useful tool to investigate the effect of potential interventions to prevent cartilage loss. link: http://identifiers.org/pubmed/29095952

Parameters: none

States: none

Observables: none

Proctor2017- Role of microRNAs in osteoarthritis (Mir140-IGFBP5 incoherent feed forward)This model is described in the a…

The aim of this study was to show how computational models can be used to increase our understanding of the role of microRNAs in osteoarthritis (OA) using miR-140 as an example. Bioinformatics analysis and experimental results from the literature were used to create and calibrate models of gene regulatory networks in OA involving miR-140 along with key regulators such as NF-κB, SMAD3, and RUNX2. The individual models were created with the modelling standard, Systems Biology Markup Language, and integrated to examine the overall effect of miR-140 on cartilage homeostasis. Down-regulation of miR-140 may have either detrimental or protective effects for cartilage, indicating that the role of miR-140 is complex. Studies of individual networks in isolation may therefore lead to different conclusions. This indicated the need to combine the five chosen individual networks involving miR-140 into an integrated model. This model suggests that the overall effect of miR-140 is to change the response to an IL-1 stimulus from a prolonged increase in matrix degrading enzymes to a pulse-like response so that cartilage degradation is temporary. Our current model can easily be modified and extended as more experimental data become available about the role of miR-140 in OA. In addition, networks of other microRNAs that are important in OA could be incorporated. A fully integrated model could not only aid our understanding of the mechanisms of microRNAs in ageing cartilage but could also provide a useful tool to investigate the effect of potential interventions to prevent cartilage loss. link: http://identifiers.org/pubmed/29095952

Parameters: none

States: none

Observables: none

Proctor2017- Role of microRNAs in osteoarthritis (miR140-IL1 coherent feed forward)This model is described in the articl…

The aim of this study was to show how computational models can be used to increase our understanding of the role of microRNAs in osteoarthritis (OA) using miR-140 as an example. Bioinformatics analysis and experimental results from the literature were used to create and calibrate models of gene regulatory networks in OA involving miR-140 along with key regulators such as NF-κB, SMAD3, and RUNX2. The individual models were created with the modelling standard, Systems Biology Markup Language, and integrated to examine the overall effect of miR-140 on cartilage homeostasis. Down-regulation of miR-140 may have either detrimental or protective effects for cartilage, indicating that the role of miR-140 is complex. Studies of individual networks in isolation may therefore lead to different conclusions. This indicated the need to combine the five chosen individual networks involving miR-140 into an integrated model. This model suggests that the overall effect of miR-140 is to change the response to an IL-1 stimulus from a prolonged increase in matrix degrading enzymes to a pulse-like response so that cartilage degradation is temporary. Our current model can easily be modified and extended as more experimental data become available about the role of miR-140 in OA. In addition, networks of other microRNAs that are important in OA could be incorporated. A fully integrated model could not only aid our understanding of the mechanisms of microRNAs in ageing cartilage but could also provide a useful tool to investigate the effect of potential interventions to prevent cartilage loss. link: http://identifiers.org/pubmed/29095952

Parameters: none

States: none

Observables: none

Proctor2017- Role of microRNAs in osteoarthritis (miR140-IL1 incoherent feed forward)This model is described in the arti…

The aim of this study was to show how computational models can be used to increase our understanding of the role of microRNAs in osteoarthritis (OA) using miR-140 as an example. Bioinformatics analysis and experimental results from the literature were used to create and calibrate models of gene regulatory networks in OA involving miR-140 along with key regulators such as NF-κB, SMAD3, and RUNX2. The individual models were created with the modelling standard, Systems Biology Markup Language, and integrated to examine the overall effect of miR-140 on cartilage homeostasis. Down-regulation of miR-140 may have either detrimental or protective effects for cartilage, indicating that the role of miR-140 is complex. Studies of individual networks in isolation may therefore lead to different conclusions. This indicated the need to combine the five chosen individual networks involving miR-140 into an integrated model. This model suggests that the overall effect of miR-140 is to change the response to an IL-1 stimulus from a prolonged increase in matrix degrading enzymes to a pulse-like response so that cartilage degradation is temporary. Our current model can easily be modified and extended as more experimental data become available about the role of miR-140 in OA. In addition, networks of other microRNAs that are important in OA could be incorporated. A fully integrated model could not only aid our understanding of the mechanisms of microRNAs in ageing cartilage but could also provide a useful tool to investigate the effect of potential interventions to prevent cartilage loss. link: http://identifiers.org/pubmed/29095952

Parameters: none

States: none

Observables: none

Proctor2017- Role of microRNAs in osteoarthritis (miR140-SMAD3 double negative feedback)This model is described in the a…

The aim of this study was to show how computational models can be used to increase our understanding of the role of microRNAs in osteoarthritis (OA) using miR-140 as an example. Bioinformatics analysis and experimental results from the literature were used to create and calibrate models of gene regulatory networks in OA involving miR-140 along with key regulators such as NF-κB, SMAD3, and RUNX2. The individual models were created with the modelling standard, Systems Biology Markup Language, and integrated to examine the overall effect of miR-140 on cartilage homeostasis. Down-regulation of miR-140 may have either detrimental or protective effects for cartilage, indicating that the role of miR-140 is complex. Studies of individual networks in isolation may therefore lead to different conclusions. This indicated the need to combine the five chosen individual networks involving miR-140 into an integrated model. This model suggests that the overall effect of miR-140 is to change the response to an IL-1 stimulus from a prolonged increase in matrix degrading enzymes to a pulse-like response so that cartilage degradation is temporary. Our current model can easily be modified and extended as more experimental data become available about the role of miR-140 in OA. In addition, networks of other microRNAs that are important in OA could be incorporated. A fully integrated model could not only aid our understanding of the mechanisms of microRNAs in ageing cartilage but could also provide a useful tool to investigate the effect of potential interventions to prevent cartilage loss. link: http://identifiers.org/pubmed/29095952

Parameters: none

States: none

Observables: none

Proctor2017- Role of microRNAs in osteoarthritis (miR140-SOX9 incoherent feed forward)This model is described in the art…

The aim of this study was to show how computational models can be used to increase our understanding of the role of microRNAs in osteoarthritis (OA) using miR-140 as an example. Bioinformatics analysis and experimental results from the literature were used to create and calibrate models of gene regulatory networks in OA involving miR-140 along with key regulators such as NF-κB, SMAD3, and RUNX2. The individual models were created with the modelling standard, Systems Biology Markup Language, and integrated to examine the overall effect of miR-140 on cartilage homeostasis. Down-regulation of miR-140 may have either detrimental or protective effects for cartilage, indicating that the role of miR-140 is complex. Studies of individual networks in isolation may therefore lead to different conclusions. This indicated the need to combine the five chosen individual networks involving miR-140 into an integrated model. This model suggests that the overall effect of miR-140 is to change the response to an IL-1 stimulus from a prolonged increase in matrix degrading enzymes to a pulse-like response so that cartilage degradation is temporary. Our current model can easily be modified and extended as more experimental data become available about the role of miR-140 in OA. In addition, networks of other microRNAs that are important in OA could be incorporated. A fully integrated model could not only aid our understanding of the mechanisms of microRNAs in ageing cartilage but could also provide a useful tool to investigate the effect of potential interventions to prevent cartilage loss. link: http://identifiers.org/pubmed/29095952

Parameters: none

States: none

Observables: none

Proctor2017- Role of microRNAs in osteoarthritis (Negative Feedback By MicroRNA with Delay)This model is described in th…

The aim of this study was to show how computational models can be used to increase our understanding of the role of microRNAs in osteoarthritis (OA) using miR-140 as an example. Bioinformatics analysis and experimental results from the literature were used to create and calibrate models of gene regulatory networks in OA involving miR-140 along with key regulators such as NF-κB, SMAD3, and RUNX2. The individual models were created with the modelling standard, Systems Biology Markup Language, and integrated to examine the overall effect of miR-140 on cartilage homeostasis. Down-regulation of miR-140 may have either detrimental or protective effects for cartilage, indicating that the role of miR-140 is complex. Studies of individual networks in isolation may therefore lead to different conclusions. This indicated the need to combine the five chosen individual networks involving miR-140 into an integrated model. This model suggests that the overall effect of miR-140 is to change the response to an IL-1 stimulus from a prolonged increase in matrix degrading enzymes to a pulse-like response so that cartilage degradation is temporary. Our current model can easily be modified and extended as more experimental data become available about the role of miR-140 in OA. In addition, networks of other microRNAs that are important in OA could be incorporated. A fully integrated model could not only aid our understanding of the mechanisms of microRNAs in ageing cartilage but could also provide a useful tool to investigate the effect of potential interventions to prevent cartilage loss. link: http://identifiers.org/pubmed/29095952

Parameters: none

States: none

Observables: none

Proctor2017- Role of microRNAs in osteoarthritis (Negative Feedback By MicroRNA)This model is described in the article:…

The aim of this study was to show how computational models can be used to increase our understanding of the role of microRNAs in osteoarthritis (OA) using miR-140 as an example. Bioinformatics analysis and experimental results from the literature were used to create and calibrate models of gene regulatory networks in OA involving miR-140 along with key regulators such as NF-κB, SMAD3, and RUNX2. The individual models were created with the modelling standard, Systems Biology Markup Language, and integrated to examine the overall effect of miR-140 on cartilage homeostasis. Down-regulation of miR-140 may have either detrimental or protective effects for cartilage, indicating that the role of miR-140 is complex. Studies of individual networks in isolation may therefore lead to different conclusions. This indicated the need to combine the five chosen individual networks involving miR-140 into an integrated model. This model suggests that the overall effect of miR-140 is to change the response to an IL-1 stimulus from a prolonged increase in matrix degrading enzymes to a pulse-like response so that cartilage degradation is temporary. Our current model can easily be modified and extended as more experimental data become available about the role of miR-140 in OA. In addition, networks of other microRNAs that are important in OA could be incorporated. A fully integrated model could not only aid our understanding of the mechanisms of microRNAs in ageing cartilage but could also provide a useful tool to investigate the effect of potential interventions to prevent cartilage loss. link: http://identifiers.org/pubmed/29095952

Parameters:

Name Description
kbinTF1miRgene = 0.005 Reaction: miR_gene + TF1 => miR_gene_TF1, Rate Law: cell*kbinTF1miRgene*miR_gene*cell*TF1*cell/cell
krelTF1miRgene = 5.0 Reaction: miR_gene_TF1 => miR_gene + TF1, Rate Law: cell*krelTF1miRgene*miR_gene_TF1*cell/cell
ksynMiR = 5.0 Reaction: miR_gene_TF1 => miR_gene_TF1 + miR, Rate Law: cell*ksynMiR*miR_gene_TF1*cell/cell
ksynTF1mRNA = 10.0 Reaction: Signal => Signal + TF1_mRNA, Rate Law: cell*ksynTF1mRNA*Signal*cell/cell
kdegMiR = 0.008 Reaction: miR => Sink, Rate Law: cell*kdegMiR*miR*cell/cell
ksynTF1 = 0.05 Reaction: TF1_mRNA => TF1_mRNA + TF1, Rate Law: cell*ksynTF1*TF1_mRNA*cell/cell
kdegTF1 = 0.005 Reaction: TF1 => Sink, Rate Law: cell*kdegTF1*TF1*cell/cell
kdegTF1mRNA = 1.0E-4 Reaction: TF1_mRNA => Sink, Rate Law: cell*kdegTF1mRNA*TF1_mRNA*cell/cell
kdegTF1mRNAbyMiR = 0.001 Reaction: TF1_mRNA + miR => miR, Rate Law: cell*kdegTF1mRNAbyMiR*TF1_mRNA*cell*miR*cell/cell

States:

Name Description
miR gene TF1 miR_gene_TF1
TF1 mRNA TF1_mRNA
miR miR
Sink Sink
miR gene miR_gene
TF1 TF1
Signal Signal

Observables: none

Proctor2017- Role of microRNAs in osteoarthritis (Positive Feedback By Micro RNA)This model is described in the article:…

The aim of this study was to show how computational models can be used to increase our understanding of the role of microRNAs in osteoarthritis (OA) using miR-140 as an example. Bioinformatics analysis and experimental results from the literature were used to create and calibrate models of gene regulatory networks in OA involving miR-140 along with key regulators such as NF-κB, SMAD3, and RUNX2. The individual models were created with the modelling standard, Systems Biology Markup Language, and integrated to examine the overall effect of miR-140 on cartilage homeostasis. Down-regulation of miR-140 may have either detrimental or protective effects for cartilage, indicating that the role of miR-140 is complex. Studies of individual networks in isolation may therefore lead to different conclusions. This indicated the need to combine the five chosen individual networks involving miR-140 into an integrated model. This model suggests that the overall effect of miR-140 is to change the response to an IL-1 stimulus from a prolonged increase in matrix degrading enzymes to a pulse-like response so that cartilage degradation is temporary. Our current model can easily be modified and extended as more experimental data become available about the role of miR-140 in OA. In addition, networks of other microRNAs that are important in OA could be incorporated. A fully integrated model could not only aid our understanding of the mechanisms of microRNAs in ageing cartilage but could also provide a useful tool to investigate the effect of potential interventions to prevent cartilage loss. link: http://identifiers.org/pubmed/29095952

Parameters:

Name Description
krelTF2miRgene = 0.001 Reaction: miR_gene_TF2 => miR_gene + TF2, Rate Law: cell*krelTF2miRgene*miR_gene_TF2*cell/cell
kdegTF1 = 1.0E-5 Reaction: TF1 => Sink, Rate Law: cell*kdegTF1*TF1*cell/cell
kbinTF1miRgene = 0.002 Reaction: miR_gene + TF1 => miR_gene_TF1, Rate Law: cell*kbinTF1miRgene*miR_gene*cell*TF1*cell/cell
kdegTF1mRNA = 1.0E-4 Reaction: TF1_mRNA => Sink, Rate Law: cell*kdegTF1mRNA*TF1_mRNA*cell/cell
ksynMiR = 0.2 Reaction: miR_gene_TF2 => miR_gene_TF2 + miR, Rate Law: cell*ksynMiR*miR_gene_TF2*cell/cell
krelTF1miRgene = 0.001 Reaction: miR_gene_TF1 => miR_gene + TF1, Rate Law: cell*krelTF1miRgene*miR_gene_TF1*cell/cell
kbinTF2miRgene = 1.0E-4 Reaction: miR_gene + TF2 => miR_gene_TF2, Rate Law: cell*kbinTF2miRgene*miR_gene*cell*TF2*cell/cell
kdegMiR = 4.0E-4 Reaction: miR => Sink, Rate Law: cell*kdegMiR*miR*cell/cell
kdegTF1mRNAbyMiR = 1.0E-6 Reaction: TF1_mRNA + miR => miR, Rate Law: cell*kdegTF1mRNAbyMiR*TF1_mRNA*cell*miR*cell/cell
ksynTF1mRNA = 0.01 Reaction: Signal => Signal + TF1_mRNA, Rate Law: cell*ksynTF1mRNA*Signal*cell/cell
ksynTF1 = 3.0E-4 Reaction: TF1_mRNA => TF1_mRNA + TF1, Rate Law: cell*ksynTF1*TF1_mRNA*cell/cell

States:

Name Description
miR gene TF1 miR_gene_TF1
miR gene TF2 miR_gene_TF2
TF2 TF2
TF1 mRNA TF1_mRNA
miR miR
miR gene miR_gene
Sink Sink
Signal Signal
TF1 TF1

Observables: none

Proctor2017- Role of microRNAs in osteoarthritis (Positive Feedforward Coherent By MicroRNA)This model is described in t…

The aim of this study was to show how computational models can be used to increase our understanding of the role of microRNAs in osteoarthritis (OA) using miR-140 as an example. Bioinformatics analysis and experimental results from the literature were used to create and calibrate models of gene regulatory networks in OA involving miR-140 along with key regulators such as NF-κB, SMAD3, and RUNX2. The individual models were created with the modelling standard, Systems Biology Markup Language, and integrated to examine the overall effect of miR-140 on cartilage homeostasis. Down-regulation of miR-140 may have either detrimental or protective effects for cartilage, indicating that the role of miR-140 is complex. Studies of individual networks in isolation may therefore lead to different conclusions. This indicated the need to combine the five chosen individual networks involving miR-140 into an integrated model. This model suggests that the overall effect of miR-140 is to change the response to an IL-1 stimulus from a prolonged increase in matrix degrading enzymes to a pulse-like response so that cartilage degradation is temporary. Our current model can easily be modified and extended as more experimental data become available about the role of miR-140 in OA. In addition, networks of other microRNAs that are important in OA could be incorporated. A fully integrated model could not only aid our understanding of the mechanisms of microRNAs in ageing cartilage but could also provide a useful tool to investigate the effect of potential interventions to prevent cartilage loss. link: http://identifiers.org/pubmed/29095952

Parameters: none

States: none

Observables: none

Proctor2017- Role of microRNAs in osteoarthritis (Positive Feedforward Incoherent By MicroRNA)This model is described in…

The aim of this study was to show how computational models can be used to increase our understanding of the role of microRNAs in osteoarthritis (OA) using miR-140 as an example. Bioinformatics analysis and experimental results from the literature were used to create and calibrate models of gene regulatory networks in OA involving miR-140 along with key regulators such as NF-κB, SMAD3, and RUNX2. The individual models were created with the modelling standard, Systems Biology Markup Language, and integrated to examine the overall effect of miR-140 on cartilage homeostasis. Down-regulation of miR-140 may have either detrimental or protective effects for cartilage, indicating that the role of miR-140 is complex. Studies of individual networks in isolation may therefore lead to different conclusions. This indicated the need to combine the five chosen individual networks involving miR-140 into an integrated model. This model suggests that the overall effect of miR-140 is to change the response to an IL-1 stimulus from a prolonged increase in matrix degrading enzymes to a pulse-like response so that cartilage degradation is temporary. Our current model can easily be modified and extended as more experimental data become available about the role of miR-140 in OA. In addition, networks of other microRNAs that are important in OA could be incorporated. A fully integrated model could not only aid our understanding of the mechanisms of microRNAs in ageing cartilage but could also provide a useful tool to investigate the effect of potential interventions to prevent cartilage loss. link: http://identifiers.org/pubmed/29095952

Parameters:

Name Description
kdegTF1targetmRNAbyMiR = 5.0E-5 1/ (mol *s) Reaction: TF1target_mRNA + miR => Sink + miR, Rate Law: cell*kdegTF1targetmRNAbyMiR*TF1target_mRNA*cell*miR*cell/cell
kdegMiR = 4.0E-4 1/s Reaction: miR => Sink, Rate Law: cell*kdegMiR*miR*cell/cell
ksynTF1targetmRNA = 0.004 1/s Reaction: TF1 => TF1 + TF1target_mRNA, Rate Law: cell*ksynTF1targetmRNA*TF1*cell/cell
ksynMiR = 2.0E-4 1/s Reaction: TF1 => TF1 + miR, Rate Law: cell*ksynMiR*TF1*cell/cell
kdegTF1targetmRNA = 0.001 1/s Reaction: TF1target_mRNA => Sink, Rate Law: cell*kdegTF1targetmRNA*TF1target_mRNA*cell/cell

States:

Name Description
miR [C25966]
Sink Sink
TF1 [1,4-beta-D-Mannooligosaccharide]
TF1target mRNA TF1target_mRNA

Observables: none

Puchalka2008 - Genome-scale metabolic network of Pseudomonas putida (iJP815)This model is described in the article: [Ge…

A cornerstone of biotechnology is the use of microorganisms for the efficient production of chemicals and the elimination of harmful waste. Pseudomonas putida is an archetype of such microbes due to its metabolic versatility, stress resistance, amenability to genetic modifications, and vast potential for environmental and industrial applications. To address both the elucidation of the metabolic wiring in P. putida and its uses in biocatalysis, in particular for the production of non-growth-related biochemicals, we developed and present here a genome-scale constraint-based model of the metabolism of P. putida KT2440. Network reconstruction and flux balance analysis (FBA) enabled definition of the structure of the metabolic network, identification of knowledge gaps, and pin-pointing of essential metabolic functions, facilitating thereby the refinement of gene annotations. FBA and flux variability analysis were used to analyze the properties, potential, and limits of the model. These analyses allowed identification, under various conditions, of key features of metabolism such as growth yield, resource distribution, network robustness, and gene essentiality. The model was validated with data from continuous cell cultures, high-throughput phenotyping data, (13)C-measurement of internal flux distributions, and specifically generated knock-out mutants. Auxotrophy was correctly predicted in 75% of the cases. These systematic analyses revealed that the metabolic network structure is the main factor determining the accuracy of predictions, whereas biomass composition has negligible influence. Finally, we drew on the model to devise metabolic engineering strategies to improve production of polyhydroxyalkanoates, a class of biotechnologically useful compounds whose synthesis is not coupled to cell survival. The solidly validated model yields valuable insights into genotype-phenotype relationships and provides a sound framework to explore this versatile bacterium and to capitalize on its vast biotechnological potential. link: http://identifiers.org/pubmed/18974823

Parameters: none

States: none

Observables: none

Puri2010 - Mathematical Modeling for the Pathogenesis of Alzheimer's DiseasePuri2010 - Mathematical Modeling for the Pat…

Despite extensive research, the pathogenesis of neurodegenerative Alzheimer's disease (AD) still eludes our comprehension. This is largely due to complex and dynamic cross-talks that occur among multiple cell types throughout the aging process. We present a mathematical model that helps define critical components of AD pathogenesis based on differential rate equations that represent the known cross-talks involving microglia, astroglia, neurons, and amyloid-β (Aβ). We demonstrate that the inflammatory activation of microglia serves as a key node for progressive neurodegeneration. Our analysis reveals that targeting microglia may hold potential promise in the prevention and treatment of AD. link: http://identifiers.org/pubmed/21179474

Parameters: none

States: none

Observables: none

MODEL7980735163 @ v0.0.1

This a model from the article: Ionic current model of a hypoglossal motoneuron. Purvis LK, Butera RJ. J Neurophysiol…

We have developed a single-compartment, electrophysiological, hypoglossal motoneuron (HM) model based primarily on experimental data from neonatal rat HMs. The model is able to reproduce the fine features of the HM action potential: the fast afterhyperpolarization, the afterdepolarization, and the medium-duration afterhyperpolarization (mAHP). The model also reproduces the repetitive firing properties seen in neonatal HMs and replicates the neuron's response to pharmacological experiments. The model was used to study the role of specific ionic currents in HM firing and how variations in the densities of these currents may account for age-dependent changes in excitability seen in HMs. By varying the density of a fast inactivating calcium current, the model alternates between accelerating and adapting firing patterns. Modeling the age-dependent increase in H current density accounts for the decrease in mAHP duration observed experimentally, but does not fully account for the decrease in input resistance. An increase in the density of the voltage-dependent potassium currents and the H current is required to account for the decrease in input resistance. These changes also account for the age-dependent decrease in action potential duration. link: http://identifiers.org/pubmed/15653786

Parameters: none

States: none

Observables: none

Mathematical model

To quantify how various molecular mechanisms are integrated to maintain platelet homeostasis and allow responsiveness to adenosine diphosphate (ADP), we developed a computational model of the human platelet. Existing kinetic information for 77 reactions, 132 fixed kinetic rate constants, and 70 species was combined with electrochemical calculations, measurements of platelet ultrastructure, novel experimental results, and published single-cell data. The model accurately predicted: (1) steady-state resting concentrations for intracellular calcium, inositol 1,4,5-trisphosphate, diacylglycerol, phosphatidic acid, phosphatidylinositol, phosphatidylinositol phosphate, and phosphatidylinositol 4,5-bisphosphate; (2) transient increases in intracellular calcium, inositol 1,4,5-trisphosphate, and G(q)-GTP in response to ADP; and (3) the volume of the platelet dense tubular system. A more stringent test of the model involved stochastic simulation of individual platelets, which display an asynchronous calcium spiking behavior in response to ADP. Simulations accurately reproduced the broad frequency distribution of measured spiking events and demonstrated that asynchronous spiking was a consequence of stochastic fluctuations resulting from the small volume of the platelet. The model also provided insights into possible mechanisms of negative-feedback signaling, the relative potency of platelet agonists, and cell-to-cell variation across platelet populations. This integrative approach to platelet biology offers a novel and complementary strategy to traditional reductionist methods. link: http://identifiers.org/pubmed/18596227

Parameters: none

States: none

Observables: none

Q


BIOMD0000000544 @ v0.0.1

Qi2013 - IL-6 and IFN crosstalk modelThis model [[BIOMD0000000544]](http://www.ebi.ac.uk/biomodels-main/BIOMD0000000544…

BACKGROUND: Interferon-gamma (IFN-gamma) and interleukin-6 (IL-6) are multifunctional cytokines that regulate immune responses, cell proliferation, and tumour development and progression, which frequently have functionally opposing roles. The cellular responses to both cytokines are activated via the Janus kinase/signal transducer and activator of transcription (JAK/STAT) pathway. During the past 10 years, the crosstalk mechanism between the IFN-gamma and IL-6 pathways has been studied widely and several biological hypotheses have been proposed, but the kinetics and detailed crosstalk mechanism remain unclear. RESULTS: Using established mathematical models and new experimental observations of the crosstalk between the IFN-gamma and IL-6 pathways, we constructed a new crosstalk model that considers three possible crosstalk levels: (1) the competition between STAT1 and STAT3 for common receptor docking sites; (2) the mutual negative regulation between SOCS1 and SOCS3; and (3) the negative regulatory effects of the formation of STAT1/3 heterodimers. A number of simulations were tested to explore the consequences of cross-regulation between the two pathways. The simulation results agreed well with the experimental data, thereby demonstrating the effectiveness and correctness of the model. CONCLUSION: In this study, we developed a crosstalk model of the IFN-gamma and IL-6 pathways to theoretically investigate their cross-regulation mechanism. The simulation experiments showed the importance of the three crosstalk levels between the two pathways. In particular, the unbalanced competition between STAT1 and STAT3 for IFNR and gp130 led to preferential activation of IFN-gamma and IL-6, while at the same time the formation of STAT1/3 heterodimers enhanced preferential signal transduction by sequestering a fraction of the activated STATs. The model provided a good explanation of the experimental observations and provided insights that may inform further research to facilitate a better understanding of the cross-regulation mechanism between the two pathways. link: http://identifiers.org/pubmed/23384097

Parameters:

Name Description
parameter_94 = 0.064; parameter_93 = 0.03 Reaction: species_35 + species_36 => species_46; species_35, species_36, species_46, Rate Law: compartment_1*(parameter_93*species_35*species_36-parameter_94*species_46)
parameter_48 = 0.005 Reaction: species_25 => species_24 + species_29; species_25, Rate Law: c3*parameter_48*species_25
parameter_153 = 0.2; parameter_152 = 0.001 Reaction: species_95 + species_24 => species_94; species_24, species_94, species_95, Rate Law: c2*(parameter_152*species_24*species_95-parameter_153*species_94)
parameter_221 = 0.001; parameter_222 = 7.99942179 Reaction: species_82 + species_11 => s118; species_82, species_11, s118, Rate Law: compartment_1*(parameter_221*species_82*species_11-parameter_222*s118)
parameter_123 = 0.3 Reaction: species_66 => species_64 + species_59; species_66, Rate Law: compartment_1*parameter_123*species_66
parameter_145 = 0.003 Reaction: species_88 => species_81 + species_108; species_88, Rate Law: compartment_1*parameter_145*species_88
parameter_120 = 0.27 Reaction: species_65 => species_64 + species_61; species_65, Rate Law: compartment_1*parameter_120*species_65
parameter_109 = 2.5E-4; parameter_110 = 0.5 Reaction: species_53 + species_57 => species_58; species_53, species_57, species_58, Rate Law: compartment_1*(parameter_109*species_53*species_57-parameter_110*species_58)
parameter_51 = 0.05 Reaction: species_28 => species_11; species_28, Rate Law: parameter_51*species_28
parameter_166 = 0.003 Reaction: species_101 => species_91 + species_20; species_101, Rate Law: compartment_1*parameter_166*species_101
parameter_155 = 0.005 Reaction: species_94 => species_96 + species_24; species_94, Rate Law: c2*parameter_155*species_94
parameter_150 = 0.2; parameter_149 = 2.0E-7 Reaction: species_84 + species_85 => species_91; species_84, species_85, species_91, Rate Law: compartment_1*(parameter_149*species_84*species_85-parameter_150*species_91)
parameter_161 = 0.1; parameter_160 = 0.02 Reaction: species_99 + species_82 => species_100; species_99, species_82, species_100, Rate Law: compartment_1*(parameter_160*species_99*species_82-parameter_161*species_100)
parameter_129 = 0.1; parameter_130 = 0.05 Reaction: species_5 + species_107 => species_78; species_5, species_107, species_78, Rate Law: compartment_1*(parameter_129*species_5*species_107-parameter_130*species_78)
parameter_241 = 0.2; parameter_240 = 0.001 Reaction: s122 + species_24 => s126; s122, species_24, s126, Rate Law: parameter_240*s122*species_24-parameter_241*s126
parameter_238 = 0.001; parameter_239 = 0.2 Reaction: species_20 + s120 => s135; species_20, s120, s135, Rate Law: compartment_1*(parameter_238*species_20*s120-parameter_239*s135)
parameter_61 = 6.0; parameter_62 = 0.06 Reaction: species_16 => species_33; species_16, species_33, Rate Law: compartment_1*(parameter_61*species_16-parameter_62*species_33)
parameter_126 = 0.0388 Reaction: species_75 => species_74; species_75, Rate Law: compartment_1*parameter_126*species_75
parameter_175 = 0.8; parameter_174 = 0.008 Reaction: species_84 + species_100 => species_104; species_84, species_100, species_104, Rate Law: compartment_1*(parameter_174*species_84*species_100-parameter_175*species_104)
parameter_100 = 0.011; parameter_101 = 0.001833 Reaction: species_44 + species_51 => species_52; species_44, species_51, species_52, Rate Law: compartment_1*(parameter_100*species_44*species_51-parameter_101*species_52)
parameter_177 = 0.2; parameter_176 = 0.001 Reaction: species_108 + species_104 => species_105; species_108, species_104, species_105, Rate Law: compartment_1*(parameter_176*species_108*species_104-parameter_177*species_105)
parameter_88 = 9.0E-4; parameter_87 = 0.3 Reaction: species_33 => species_9 + species_48; species_33, species_9, species_48, Rate Law: compartment_1*(parameter_87*species_33-parameter_88*species_9*species_48)
parameter_40 = 0.005 Reaction: species_14 => species_23; species_14, Rate Law: parameter_40*species_14
parameter_131 = 0.02; parameter_132 = 0.02 Reaction: species_79 + species_78 => species_80; species_79, species_78, species_80, Rate Law: parameter_131*species_79*species_78-parameter_132*species_80
parameter_99 = 1.0 Reaction: species_50 => species_41 + species_49; species_50, Rate Law: compartment_1*parameter_99*species_50
parameter_243 = 0.0015 Reaction: s135 => species_85 + species_11 + species_20; s135, Rate Law: compartment_1*parameter_243*s135
parameter_244 = 0.0025 Reaction: s126 => species_26 + species_24 + species_96; s126, Rate Law: parameter_244*s126
parameter_96 = 0.0429; parameter_95 = 0.03 Reaction: species_33 + species_47 => species_37; species_33, species_47, species_37, Rate Law: compartment_1*(parameter_95*species_33*species_47-parameter_96*species_37)
parameter_85 = 1.7; parameter_86 = 340.0 Reaction: species_48 => species_108; species_48, Rate Law: compartment_1*parameter_85*species_48/(parameter_86+species_48)
parameter_53 = 400.0; parameter_52 = 0.01 Reaction: => species_30; species_23, species_23, Rate Law: c3*parameter_52*species_23/(parameter_53+species_23)
parameter_224 = 5.09534E-4; parameter_225 = 4.982769238 Reaction: species_12 + species_82 => s119; species_12, species_82, s119, Rate Law: compartment_1*(parameter_224*species_12*species_82-parameter_225*s119)
parameter_178 = 0.003 Reaction: species_105 => species_99 + species_81 + species_84 + species_108; species_105, Rate Law: compartment_1*parameter_178*species_105
parameter_97 = 0.0717; parameter_98 = 0.2 Reaction: species_49 + species_44 => species_50; species_49, species_44, species_50, Rate Law: compartment_1*(parameter_97*species_49*species_44-parameter_98*species_50)
parameter_63 = 0.01; parameter_64 = 0.55 Reaction: species_33 + species_32 => species_34; species_33, species_32, species_34, Rate Law: compartment_1*(parameter_63*species_33*species_32-parameter_64*species_34)
parameter_159 = 0.01 Reaction: => species_99; species_98, species_98, Rate Law: compartment_1*parameter_159*species_98
parameter_128 = 9.0E-4; parameter_127 = 0.9854 Reaction: species_75 => species_76; species_75, species_76, Rate Law: compartment_1*(parameter_127*species_75^2-parameter_128*species_76)
parameter_83 = 0.0015; parameter_84 = 0.0045 Reaction: species_47 => species_32 + species_35; species_47, species_32, species_35, Rate Law: compartment_1*(parameter_83*species_47-parameter_84*species_32*species_35)
parameter_236 = 0.1; parameter_235 = 0.02 Reaction: species_26 + species_95 => s122; species_26, species_95, s122, Rate Law: parameter_235*species_26*species_95-parameter_236*s122
parameter_82 = 0.021; parameter_81 = 0.3 Reaction: species_46 => species_47 + species_48; species_46, species_47, species_48, Rate Law: compartment_1*(parameter_81*species_46-parameter_82*species_47*species_48)
parameter_231 = 0.001; parameter_232 = 400.0 Reaction: => species_30; species_92, species_92, Rate Law: c3*parameter_231*species_92/(parameter_232+species_92)
parameter_139 = 0.005; parameter_140 = 0.5 Reaction: species_82 + species_85 => species_86; species_82, species_85, species_86, Rate Law: compartment_1*(parameter_139*species_82*species_85-parameter_140*species_86)
parameter_89 = 0.01; parameter_90 = 0.55 Reaction: species_32 + species_48 => species_36; species_32, species_48, species_36, Rate Law: compartment_1*(parameter_89*species_32*species_48-parameter_90*species_36)
parameter_148 = 0.003 Reaction: species_90 => species_84 + species_20; species_90, Rate Law: compartment_1*parameter_148*species_90
parameter_158 = 0.001 Reaction: species_97 => species_98; species_97, Rate Law: parameter_158*species_97
parameter_49 = 0.2; parameter_50 = 2.0E-7 Reaction: species_29 => species_26 + species_28; species_29, species_26, species_28, Rate Law: c3*(parameter_49*species_29-parameter_50*species_26*species_28)
parameter_122 = 0.5; parameter_121 = 0.005 Reaction: species_61 + species_64 => species_66; species_61, species_64, species_66, Rate Law: compartment_1*(parameter_121*species_61*species_64-parameter_122*species_66)
parameter_138 = 0.4 Reaction: species_83 => species_82 + species_85; species_83, Rate Law: compartment_1*parameter_138*species_83
parameter_44 = 0.2; parameter_43 = 0.001 Reaction: species_24 + species_26 => species_27; species_24, species_26, species_27, Rate Law: c3*(parameter_43*species_24*species_26-parameter_44*species_27)
parameter_146 = 0.001; parameter_147 = 0.2 Reaction: species_85 + species_20 => species_90; species_85, species_20, species_90, Rate Law: compartment_1*(parameter_146*species_85*species_20-parameter_147*species_90)
parameter_35 = 0.001; parameter_36 = 0.2 Reaction: species_14 + species_20 => species_22; species_14, species_20, species_22, Rate Law: compartment_1*(parameter_35*species_14*species_20-parameter_36*species_22)
parameter_242 = 0.0015 Reaction: s135 => species_20 + species_12 + species_84; s135, Rate Law: compartment_1*parameter_242*s135
parameter_133 = 0.04; parameter_134 = 0.2 Reaction: species_80 => species_81; species_80, species_81, Rate Law: compartment_1*(parameter_133*species_80^2-parameter_134*species_81)
parameter_143 = 0.001; parameter_144 = 0.2 Reaction: species_82 + species_108 => species_88; species_82, species_108, species_88, Rate Law: compartment_1*(parameter_143*species_82*species_108-parameter_144*species_88)
parameter_58 = 5.0E-4 Reaction: species_31 => ; species_31, Rate Law: compartment_1*parameter_58*species_31
parameter_162 = 5.0E-4 Reaction: species_98 => ; species_98, Rate Law: compartment_1*parameter_162*species_98
parameter_169 = 0.001; parameter_170 = 0.2 Reaction: species_92 + species_24 => species_102; species_102, species_24, species_92, Rate Law: c2*(parameter_169*species_24*species_92-parameter_170*species_102)
parameter_245 = 0.0025 Reaction: s126 => species_95 + species_28 + species_24; s126, Rate Law: parameter_245*s126
parameter_223 = 3.999994653 Reaction: s118 => species_12 + species_82; s118, Rate Law: compartment_1*parameter_223*s118
parameter_135 = 0.005 Reaction: species_81 => species_82; species_81, Rate Law: compartment_1*parameter_135*species_81
parameter_137 = 0.8; parameter_136 = 0.008 Reaction: species_82 + species_84 => species_83; species_82, species_84, species_83, Rate Law: compartment_1*(parameter_136*species_82*species_84-parameter_137*species_83)
parameter_165 = 0.2; parameter_164 = 0.001 Reaction: species_87 + species_20 => species_101; species_87, species_20, species_101, Rate Law: compartment_1*(parameter_164*species_87*species_20-parameter_165*species_101)
parameter_54 = 0.001 Reaction: species_30 => species_31; species_30, Rate Law: parameter_54*species_30
parameter_56 = 5.0; parameter_57 = 0.1 Reaction: species_9 + species_19 => species_15; species_9, species_19, species_15, Rate Law: compartment_1*(parameter_56*species_9*species_19-parameter_57*species_15)
parameter_163 = 5.0E-4 Reaction: species_99 => ; species_99, Rate Law: compartment_1*parameter_163*species_99
parameter_32 = 0.001; parameter_33 = 0.2 Reaction: species_12 + species_20 => species_21; species_12, species_20, species_21, Rate Law: compartment_1*(parameter_32*species_12*species_20-parameter_33*species_21)
parameter_125 = 20000.0; parameter_124 = 0.2335 Reaction: species_74 => species_75; species_63, species_63, species_74, Rate Law: compartment_1*parameter_124*species_63*species_74/(species_74+parameter_125)
parameter_119 = 0.6; parameter_118 = 0.014 Reaction: species_63 + species_64 => species_65; species_63, species_64, species_65, Rate Law: compartment_1*(parameter_118*species_63*species_64-parameter_119*species_65)
parameter_111 = 0.058 Reaction: species_58 => species_57 + species_51; species_58, Rate Law: compartment_1*parameter_111*species_58
parameter_179 = 5.0E-4 Reaction: species_105 => species_99 + species_106; species_105, Rate Law: compartment_1*parameter_179*species_105

States:

Name Description
species 100 [Tyrosine-protein kinase JAK2; Interferon gamma; Interferon gamma receptor 1; Suppressor of cytokine signaling 1; SBO:0000286; phosphorylated]
species 98 [Suppressor of cytokine signaling 1; SBO:0000278]
species 20 [Serine/threonine-protein phosphatase PP1-alpha catalytic subunit]
species 91 [Signal transducer and activator of transcription 1-alpha/beta; Signal transducer and activator of transcription 1-alpha/beta; phosphorylated]
species 47 [Growth factor receptor-bound protein 2; Son of sevenless homolog 1]
species 66 [Mitogen-activated protein kinase 1; Serine/threonine-protein phosphatase 2A catalytic subunit alpha isoform; phosphorylated]
species 21 [Serine/threonine-protein phosphatase PP1-alpha catalytic subunit; Signal transducer and activator of transcription 3; phosphorylated]
species 57 [Serine/threonine-protein phosphatase 2A catalytic subunit alpha isoform]
species 15 [Interleukin-6 receptor subunit alpha; Interleukin-6; Tyrosine-protein kinase JAK1; Interleukin-6 receptor subunit beta; Suppressor of cytokine signaling 3; SBO:0000286]
species 83 [Interferon gamma; Tyrosine-protein kinase JAK2; Interferon gamma receptor 1; Signal transducer and activator of transcription 1-alpha/beta; SBO:0000286; phosphorylated]
species 33 [Interleukin-6; Interleukin-6 receptor subunit alpha; Interleukin-6 receptor subunit beta; Tyrosine-protein kinase JAK1; Tyrosine-protein phosphatase non-receptor type 11; SBO:0000286; phosphorylated]
species 64 [Serine/threonine-protein phosphatase 2A catalytic subunit alpha isoform]
species 24 [Serine/threonine-protein phosphatase 2A catalytic subunit alpha isoform]
species 78 [Interferon gamma receptor 1; Tyrosine-protein kinase JAK2]
species 58 [Serine/threonine-protein phosphatase 2A catalytic subunit alpha isoform; Dual specificity mitogen-activated protein kinase kinase 1; phosphorylated]
species 48 [Tyrosine-protein phosphatase non-receptor type 11; phosphorylated]
species 76 [CCAAT/enhancer-binding protein beta; active]
s126 [Signal transducer and activator of transcription 1-alpha/beta; Signal transducer and activator of transcription 3; Serine/threonine-protein phosphatase 2A catalytic subunit alpha isoform; phosphorylated]
species 99 [Suppressor of cytokine signaling 1]
species 101 [Serine/threonine-protein phosphatase PP1-alpha catalytic subunit; Signal transducer and activator of transcription 1-alpha/beta; SBO:0000286; phosphorylated]
species 65 [Mitogen-activated protein kinase 1; Serine/threonine-protein phosphatase 2A catalytic subunit alpha isoform; phosphorylated]
species 50 [Serine/threonine-protein phosphatase PP1-alpha catalytic subunit; RAF proto-oncogene serine/threonine-protein kinase]
species 27 [Serine/threonine-protein phosphatase 2A catalytic subunit alpha isoform; Signal transducer and activator of transcription 3; phosphorylated]
species 63 [Mitogen-activated protein kinase 1]
s135 [Signal transducer and activator of transcription 3; Signal transducer and activator of transcription 1-alpha/beta; Serine/threonine-protein phosphatase PP1-alpha catalytic subunit; phosphorylated]
species 31 [Suppressor of cytokine signaling 3; SBO:0000278]
species 51 [Dual specificity mitogen-activated protein kinase kinase 1]
species 104 [Interferon gamma receptor 1; Interferon gamma; Tyrosine-protein kinase JAK2; Signal transducer and activator of transcription 1-alpha/beta; Suppressor of cytokine signaling 1; SBO:0000286; phosphorylated]
species 28 [Signal transducer and activator of transcription 3]
species 75 [CCAAT/enhancer-binding protein beta; active]
species 84 [Signal transducer and activator of transcription 1-alpha/beta]
species 29 [Signal transducer and activator of transcription 3; Signal transducer and activator of transcription 3; phosphorylated]
species 32 [Growth factor receptor-bound protein 2]
species 30 [Suppressor of cytokine signaling 3; SBO:0000278]
species 49 [Serine/threonine-protein phosphatase PP1-alpha catalytic subunit]
species 74 [CCAAT/enhancer-binding protein beta]
species 81 [Interferon gamma; Tyrosine-protein kinase JAK2; Interferon gamma receptor 1; SBO:0000286]
species 14 [SBO:0000608; phosphorylated; Signal transducer and activator of transcription 3]
species 82 [Interferon gamma; Tyrosine-protein kinase JAK2; Interferon gamma receptor 1; SBO:0000286; phosphorylated]
species 80 [Tyrosine-protein kinase JAK2; Interferon gamma receptor 1; Interferon gamma]
species 46 [Tyrosine-protein phosphatase non-receptor type 11; Son of sevenless homolog 1; Growth factor receptor-bound protein 2]
species 26 [Signal transducer and activator of transcription 3; phosphorylated]
species 90 [Serine/threonine-protein phosphatase PP1-alpha catalytic subunit; Signal transducer and activator of transcription 1-alpha/beta; phosphorylated]

Observables: none

Qi2013 - IL-6 and IFN crosstalk model (non-competitive)This model [[BIOMD0000000543]](http://www.ebi.ac.uk/biomodels-ma…

BACKGROUND: Interferon-gamma (IFN-gamma) and interleukin-6 (IL-6) are multifunctional cytokines that regulate immune responses, cell proliferation, and tumour development and progression, which frequently have functionally opposing roles. The cellular responses to both cytokines are activated via the Janus kinase/signal transducer and activator of transcription (JAK/STAT) pathway. During the past 10 years, the crosstalk mechanism between the IFN-gamma and IL-6 pathways has been studied widely and several biological hypotheses have been proposed, but the kinetics and detailed crosstalk mechanism remain unclear. RESULTS: Using established mathematical models and new experimental observations of the crosstalk between the IFN-gamma and IL-6 pathways, we constructed a new crosstalk model that considers three possible crosstalk levels: (1) the competition between STAT1 and STAT3 for common receptor docking sites; (2) the mutual negative regulation between SOCS1 and SOCS3; and (3) the negative regulatory effects of the formation of STAT1/3 heterodimers. A number of simulations were tested to explore the consequences of cross-regulation between the two pathways. The simulation results agreed well with the experimental data, thereby demonstrating the effectiveness and correctness of the model. CONCLUSION: In this study, we developed a crosstalk model of the IFN-gamma and IL-6 pathways to theoretically investigate their cross-regulation mechanism. The simulation experiments showed the importance of the three crosstalk levels between the two pathways. In particular, the unbalanced competition between STAT1 and STAT3 for IFNR and gp130 led to preferential activation of IFN-gamma and IL-6, while at the same time the formation of STAT1/3 heterodimers enhanced preferential signal transduction by sequestering a fraction of the activated STATs. The model provided a good explanation of the experimental observations and provided insights that may inform further research to facilitate a better understanding of the cross-regulation mechanism between the two pathways. link: http://identifiers.org/pubmed/23384097

Parameters:

Name Description
parameter_68 = 1.3; parameter_67 = 0.015 Reaction: species_38 + species_37 => species_39; species_38, species_37, species_39, Rate Law: compartment_1*(parameter_67*species_38*species_37-parameter_68*species_39)
parameter_48 = 0.005 Reaction: species_25 => species_24 + species_29; species_25, Rate Law: c3*parameter_48*species_25
parameter_153 = 0.2; parameter_152 = 0.001 Reaction: species_95 + species_24 => species_94; species_24, species_94, species_95, Rate Law: c2*(parameter_152*species_24*species_95-parameter_153*species_94)
parameter_233 = 0.02; parameter_234 = 0.1 Reaction: species_12 + species_85 => s120; species_12, species_85, s120, Rate Law: compartment_1*(parameter_233*species_12*species_85-parameter_234*s120)
parameter_123 = 0.3 Reaction: species_66 => species_64 + species_59; species_66, Rate Law: compartment_1*parameter_123*species_66
parameter_120 = 0.27 Reaction: species_65 => species_64 + species_61; species_65, Rate Law: compartment_1*parameter_120*species_65
parameter_166 = 0.003 Reaction: species_101 => species_91 + species_20; species_101, Rate Law: compartment_1*parameter_166*species_101
parameter_69 = 0.5; parameter_70 = 1.0E-4 Reaction: species_39 => species_40 + species_37; species_39, species_40, species_37, Rate Law: compartment_1*(parameter_69*species_39-parameter_70*species_40*species_37)
parameter_51 = 0.05 Reaction: species_28 => species_11; species_28, Rate Law: parameter_51*species_28
parameter_155 = 0.005 Reaction: species_94 => species_96 + species_24; species_94, Rate Law: c2*parameter_155*species_94
parameter_150 = 0.2; parameter_149 = 2.0E-7 Reaction: species_84 + species_85 => species_91; species_84, species_85, species_91, Rate Law: compartment_1*(parameter_149*species_84*species_85-parameter_150*species_91)
parameter_65 = 0.01; parameter_66 = 0.0214 Reaction: species_35 + species_34 => species_37; species_35, species_34, species_37, Rate Law: compartment_1*(parameter_65*species_35*species_34-parameter_66*species_37)
parameter_241 = 0.2; parameter_240 = 0.001 Reaction: s122 + species_24 => s126; s122, species_24, s126, Rate Law: parameter_240*s122*species_24-parameter_241*s126
parameter_238 = 0.001; parameter_239 = 0.2 Reaction: species_20 + s120 => s135; species_20, s120, s135, Rate Law: compartment_1*(parameter_238*species_20*s120-parameter_239*s135)
parameter_126 = 0.0388 Reaction: species_75 => species_74; species_75, Rate Law: compartment_1*parameter_126*species_75
parameter_22 = 2.0; parameter_21 = 0.002 Reaction: species_10 + species_84 => species_17; species_10, species_84, species_17, Rate Law: parameter_21*species_10*species_84-parameter_22*species_17
parameter_79 = 0.47; parameter_80 = 2.45E-4 Reaction: species_37 => species_46 + species_9; species_37, species_46, species_9, Rate Law: compartment_1*(parameter_79*species_37-parameter_80*species_46*species_9)
parameter_1 = 0.1; parameter_2 = 0.05 Reaction: species_2 + species_1 => species_3; species_2, species_1, species_3, Rate Law: parameter_1*species_2*species_1-parameter_2*species_3
parameter_23 = 0.008; parameter_24 = 0.8 Reaction: species_67 + species_11 => species_17; species_67, species_11, species_17, Rate Law: parameter_23*species_67*species_11-parameter_24*species_17
parameter_131 = 0.02; parameter_132 = 0.02 Reaction: species_79 + species_78 => species_80; species_79, species_78, species_80, Rate Law: parameter_131*species_79*species_78-parameter_132*species_80
parameter_99 = 1.0 Reaction: species_50 => species_41 + species_49; species_50, Rate Law: compartment_1*parameter_99*species_50
parameter_243 = 0.0015 Reaction: s135 => species_85 + species_11 + species_20; s135, Rate Law: compartment_1*parameter_243*s135
parameter_72 = 0.0053; parameter_71 = 0.001 Reaction: species_40 + species_41 => species_42; species_40, species_41, species_42, Rate Law: compartment_1*(parameter_71*species_40*species_41-parameter_72*species_42)
parameter_8 = 0.8; parameter_7 = 0.008 Reaction: s118 + species_84 => species_68; s118, species_84, species_68, Rate Law: compartment_1*(parameter_7*s118*species_84-parameter_8*species_68)
parameter_244 = 0.0025 Reaction: s126 => species_26 + species_24 + species_96; s126, Rate Law: parameter_244*s126
parameter_14 = 0.008; parameter_15 = 0.8 Reaction: species_9 + species_11 => species_10; species_9, species_11, species_10, Rate Law: compartment_1*(parameter_14*species_9*species_11-parameter_15*species_10)
parameter_96 = 0.0429; parameter_95 = 0.03 Reaction: species_33 + species_47 => species_37; species_33, species_47, species_37, Rate Law: compartment_1*(parameter_95*species_33*species_47-parameter_96*species_37)
parameter_74 = 7.0E-4; parameter_73 = 1.0 Reaction: species_42 => species_43 + species_44; species_42, species_43, species_44, Rate Law: compartment_1*(parameter_73*species_42-parameter_74*species_43*species_44)
parameter_229 = 0.005; parameter_230 = 0.5 Reaction: species_85 + species_9 => s139; species_85, species_9, s139, Rate Law: compartment_1*(parameter_229*species_85*species_9-parameter_230*s139)
parameter_53 = 400.0; parameter_52 = 0.01 Reaction: => species_30; species_23, species_23, Rate Law: c3*parameter_52*species_23/(parameter_53+species_23)
parameter_168 = 0.5; parameter_167 = 0.005 Reaction: species_95 => species_92; species_95, species_92, Rate Law: c2*(parameter_167*species_95^2-parameter_168*species_92)
parameter_221 = 0.002; parameter_222 = 2.0 Reaction: species_82 + species_11 => s118; species_82, species_11, s118, Rate Law: compartment_1*(parameter_221*species_82*species_11-parameter_222*s118)
parameter_159 = 0.01 Reaction: => species_99; species_98, species_98, Rate Law: compartment_1*parameter_159*species_98
parameter_37 = 0.003 Reaction: species_22 => species_18 + species_20; species_22, Rate Law: compartment_1*parameter_37*species_22
parameter_25 = 0.2 Reaction: species_17 => species_10 + species_85; species_17, Rate Law: parameter_25*species_17
parameter_223 = 0.2 Reaction: s118 => species_12 + species_82; s118, Rate Law: compartment_1*parameter_223*s118
parameter_83 = 0.0015; parameter_84 = 0.0045 Reaction: species_47 => species_32 + species_35; species_47, species_32, species_35, Rate Law: compartment_1*(parameter_83*species_47-parameter_84*species_32*species_35)
parameter_236 = 0.1; parameter_235 = 0.02 Reaction: species_26 + species_95 => s122; species_26, species_95, s122, Rate Law: parameter_235*species_26*species_95-parameter_236*s122
parameter_10 = 2.0; parameter_9 = 0.002 Reaction: species_83 + species_11 => species_68; species_83, species_11, species_68, Rate Law: compartment_1*(parameter_9*species_83*species_11-parameter_10*species_68)
parameter_38 = 2.0E-7; parameter_39 = 0.2 Reaction: species_11 + species_12 => species_18; species_11, species_12, species_18, Rate Law: compartment_1*(parameter_38*species_11*species_12-parameter_39*species_18)
parameter_237 = 0.005 Reaction: s120 => s122; s120, Rate Law: compartment_1*parameter_237*s120
parameter_139 = 0.005; parameter_140 = 0.5 Reaction: species_82 + species_85 => species_86; species_82, species_85, species_86, Rate Law: compartment_1*(parameter_139*species_82*species_85-parameter_140*species_86)
parameter_89 = 0.01; parameter_90 = 0.55 Reaction: species_32 + species_48 => species_36; species_32, species_48, species_36, Rate Law: compartment_1*(parameter_89*species_32*species_48-parameter_90*species_36)
parameter_26 = 0.4 Reaction: species_17 => species_67 + species_12; species_17, Rate Law: parameter_26*species_17
parameter_158 = 0.001 Reaction: species_97 => species_98; species_97, Rate Law: parameter_158*species_97
parameter_49 = 0.2; parameter_50 = 2.0E-7 Reaction: species_29 => species_26 + species_28; species_29, species_26, species_28, Rate Law: c3*(parameter_49*species_29-parameter_50*species_26*species_28)
parameter_19 = 0.4 Reaction: species_68 => s118 + species_85; species_68, Rate Law: compartment_1*parameter_19*species_68
parameter_122 = 0.5; parameter_121 = 0.005 Reaction: species_61 + species_64 => species_66; species_61, species_64, species_66, Rate Law: compartment_1*(parameter_121*species_61*species_64-parameter_122*species_66)
parameter_138 = 0.4 Reaction: species_83 => species_82 + species_85; species_83, Rate Law: compartment_1*parameter_138*species_83
parameter_44 = 0.2; parameter_43 = 0.001 Reaction: species_24 + species_26 => species_27; species_24, species_26, species_27, Rate Law: c3*(parameter_43*species_24*species_26-parameter_44*species_27)
parameter_151 = 0.005 Reaction: species_87 => species_92; species_87, Rate Law: parameter_151*species_87
parameter_35 = 0.001; parameter_36 = 0.2 Reaction: species_14 + species_20 => species_22; species_14, species_20, species_22, Rate Law: compartment_1*(parameter_35*species_14*species_20-parameter_36*species_22)
parameter_20 = 0.2 Reaction: species_68 => species_83 + species_12; species_68, Rate Law: compartment_1*parameter_20*species_68
parameter_34 = 0.003 Reaction: species_21 => species_11 + species_20; species_21, Rate Law: compartment_1*parameter_34*species_21
parameter_78 = 2.2E-4; parameter_77 = 0.023 Reaction: species_45 => species_37 + species_38; species_45, species_37, species_38, Rate Law: compartment_1*(parameter_77*species_45-parameter_78*species_37*species_38)
parameter_242 = 0.0015 Reaction: s135 => species_20 + species_12 + species_84; s135, Rate Law: compartment_1*parameter_242*s135
parameter_224 = 0.005; parameter_225 = 0.5 Reaction: species_12 + species_82 => s119; species_12, species_82, s119, Rate Law: compartment_1*(parameter_224*species_12*species_82-parameter_225*s119)
parameter_162 = 5.0E-4 Reaction: species_98 => ; species_98, Rate Law: compartment_1*parameter_162*species_98
parameter_245 = 0.0025 Reaction: s126 => species_95 + species_28 + species_24; s126, Rate Law: parameter_245*s126
parameter_169 = 0.001; parameter_170 = 0.2 Reaction: species_92 + species_24 => species_102; species_102, species_24, species_92, Rate Law: c2*(parameter_169*species_24*species_92-parameter_170*species_102)
parameter_76 = 0.4; parameter_75 = 0.0079 Reaction: species_37 + species_43 => species_45; species_37, species_43, species_45, Rate Law: compartment_1*(parameter_75*species_37*species_43-parameter_76*species_45)
parameter_165 = 0.2; parameter_164 = 0.001 Reaction: species_87 + species_20 => species_101; species_87, species_20, species_101, Rate Law: compartment_1*(parameter_164*species_87*species_20-parameter_165*species_101)
parameter_142 = 0.1; parameter_141 = 0.02 Reaction: species_85 => species_87; species_85, species_87, Rate Law: compartment_1*(parameter_141*species_85^2-parameter_142*species_87)
parameter_56 = 5.0; parameter_57 = 0.1 Reaction: species_9 + species_19 => species_15; species_9, species_19, species_15, Rate Law: compartment_1*(parameter_56*species_9*species_19-parameter_57*species_15)
parameter_45 = 0.005 Reaction: species_27 => species_28 + species_24; species_27, Rate Law: c3*parameter_45*species_27
parameter_228 = 0.2 Reaction: s138 => species_9 + species_85; s138, Rate Law: compartment_1*parameter_228*s138
parameter_125 = 20000.0; parameter_124 = 0.2335 Reaction: species_74 => species_75; species_63, species_63, species_74, Rate Law: compartment_1*parameter_124*species_63*species_74/(species_74+parameter_125)
parameter_94 = 0.064; parameter_93 = 0.03 Reaction: species_35 + species_36 => species_46; species_35, species_36, species_46, Rate Law: compartment_1*(parameter_93*species_35*species_36-parameter_94*species_46)

States:

Name Description
species 67 [Interleukin-6; Interleukin-6 receptor subunit alpha; Tyrosine-protein kinase JAK1; Interleukin-6 receptor subunit beta; SBO:0000286; Signal transducer and activator of transcription 1-alpha/beta]
species 27 [Serine/threonine-protein phosphatase 2A catalytic subunit alpha isoform; Signal transducer and activator of transcription 3; phosphorylated]
species 36 [Growth factor receptor-bound protein 2; Tyrosine-protein phosphatase non-receptor type 11]
species 98 [Suppressor of cytokine signaling 1; SBO:0000278]
species 1 [Interleukin-6]
species 20 [Serine/threonine-protein phosphatase PP1-alpha catalytic subunit]
species 28 [Signal transducer and activator of transcription 3]
s120 [Signal transducer and activator of transcription 3; Signal transducer and activator of transcription 1-alpha/beta; SBO:0000607; phosphorylated]
s122 [Signal transducer and activator of transcription 3; Signal transducer and activator of transcription 1-alpha/beta; SBO:0000607; phosphorylated]
species 75 [CCAAT/enhancer-binding protein beta; active]
species 91 [Signal transducer and activator of transcription 1-alpha/beta; Signal transducer and activator of transcription 1-alpha/beta; phosphorylated]
species 79 [Interferon gamma]
species 92 [SBO:0000608; phosphorylated; Signal transducer and activator of transcription 1-alpha/beta]
species 39 [Interleukin-6 receptor subunit alpha; Interleukin-6; Tyrosine-protein kinase JAK1; Interleukin-6 receptor subunit beta; SBO:0000286; Son of sevenless homolog 1; Tyrosine-protein phosphatase non-receptor type 11; Growth factor receptor-bound protein 2; GTPase HRas]
species 68 [Interferon gamma receptor 1; Interferon gamma; Tyrosine-protein kinase JAK2; SBO:0000286; phosphorylated; Signal transducer and activator of transcription 1-alpha/beta; Signal transducer and activator of transcription 3]
species 66 [Mitogen-activated protein kinase 1; Serine/threonine-protein phosphatase 2A catalytic subunit alpha isoform; phosphorylated]
species 21 [Serine/threonine-protein phosphatase PP1-alpha catalytic subunit; Signal transducer and activator of transcription 3; phosphorylated]
species 32 [Growth factor receptor-bound protein 2]
species 29 [Signal transducer and activator of transcription 3; Signal transducer and activator of transcription 3; phosphorylated]
species 30 [Suppressor of cytokine signaling 3; SBO:0000278]
species 17 [Interleukin-6; Interleukin-6 receptor subunit beta; Interleukin-6 receptor subunit alpha; Tyrosine-protein kinase JAK1; SBO:0000286; Signal transducer and activator of transcription 1-alpha/beta; Signal transducer and activator of transcription 3]
species 12 [Signal transducer and activator of transcription 3; phosphorylated]
species 15 [Interleukin-6 receptor subunit alpha; Interleukin-6; Tyrosine-protein kinase JAK1; Interleukin-6 receptor subunit beta; SBO:0000286; Suppressor of cytokine signaling 3]
species 94 [Signal transducer and activator of transcription 1-alpha/beta; phosphorylated; Serine/threonine-protein phosphatase 2A catalytic subunit alpha isoform]
s118 [Interferon gamma; Interferon gamma receptor 1; Tyrosine-protein kinase JAK2; SBO:0000286; Signal transducer and activator of transcription 3]
s119 [Interferon gamma receptor 1; Interferon gamma; Tyrosine-protein kinase JAK2; SBO:0000286; phosphorylated; Signal transducer and activator of transcription 3]
species 37 [Interleukin-6 receptor subunit alpha; Interleukin-6; Interleukin-6 receptor subunit beta; Tyrosine-protein kinase JAK1; SBO:0000286; Son of sevenless homolog 1; Tyrosine-protein phosphatase non-receptor type 11; Growth factor receptor-bound protein 2]
species 38 [GTPase HRas; inactive]
species 42 [RAF proto-oncogene serine/threonine-protein kinase; GTPase HRas]
species 74 [CCAAT/enhancer-binding protein beta]
species 64 [Serine/threonine-protein phosphatase 2A catalytic subunit alpha isoform]
species 11 [Signal transducer and activator of transcription 3]
species 85 [Signal transducer and activator of transcription 1-alpha/beta; phosphorylated]
species 95 [Signal transducer and activator of transcription 1-alpha/beta; phosphorylated]
species 43 [GTPase HRas; phosphorylated; active]
species 22 [Signal transducer and activator of transcription 3; Serine/threonine-protein phosphatase PP1-alpha catalytic subunit; SBO:0000608; phosphorylated]
species 82 [Interferon gamma; Tyrosine-protein kinase JAK2; Interferon gamma receptor 1; SBO:0000286; phosphorylated]
s126 [Signal transducer and activator of transcription 1-alpha/beta; Signal transducer and activator of transcription 3; Serine/threonine-protein phosphatase 2A catalytic subunit alpha isoform; phosphorylated]
species 41 [RAF proto-oncogene serine/threonine-protein kinase]
species 99 [Suppressor of cytokine signaling 1]
species 26 [Signal transducer and activator of transcription 3; phosphorylated]
species 40 [GTPase HRas; active]

Observables: none

MODEL1108260015 @ v0.0.1

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

The paper described a limited part of the coagulation pathway, and in particular the inhibitory effects of activated protein C in the context of thrombin production. This is a computational modeling study with various assumption made of kinetic rates laws and their summation. The level of complexity and assumed parameters makes conclusions uncertain. However, an interesting outcome is that kinetic reaction rates may show oscillation behavior under particular, high levels of protein C feedback inhibition. The model would defy quantitative practical use, but could have predictive value as a qualitative descriptor of coagulation. link: http://identifiers.org/pubmed/15121060

Parameters: none

States: none

Observables: none

MODEL6185511733 @ v0.0.1

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

Physicochemical models of signaling pathways are characterized by high levels of structural and parametric uncertainty, reflecting both incomplete knowledge about signal transduction and the intrinsic variability of cellular processes. As a result, these models try to predict the dynamics of systems with tens or even hundreds of free parameters. At this level of uncertainty, model analysis should emphasize statistics of systems-level properties, rather than the detailed structure of solutions or boundaries separating different dynamic regimes. Based on the combination of random parameter search and continuation algorithms, we developed a methodology for the statistical analysis of mechanistic signaling models. In applying it to the well-studied MAPK cascade model, we discovered a large region of oscillations and explained their emergence from single-stage bistability. The surprising abundance of strongly nonlinear (oscillatory and bistable) input/output maps revealed by our analysis may be one of the reasons why the MAPK cascade in vivo is embedded in more complex regulatory structures. We argue that this type of analysis should accompany nonlinear multiparameter studies of stationary as well as transient features in network dynamics. link: http://identifiers.org/pubmed/17907797

Parameters: none

States: none

Observables: none

MODEL6185746832 @ v0.0.1

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

Physicochemical models of signaling pathways are characterized by high levels of structural and parametric uncertainty, reflecting both incomplete knowledge about signal transduction and the intrinsic variability of cellular processes. As a result, these models try to predict the dynamics of systems with tens or even hundreds of free parameters. At this level of uncertainty, model analysis should emphasize statistics of systems-level properties, rather than the detailed structure of solutions or boundaries separating different dynamic regimes. Based on the combination of random parameter search and continuation algorithms, we developed a methodology for the statistical analysis of mechanistic signaling models. In applying it to the well-studied MAPK cascade model, we discovered a large region of oscillations and explained their emergence from single-stage bistability. The surprising abundance of strongly nonlinear (oscillatory and bistable) input/output maps revealed by our analysis may be one of the reasons why the MAPK cascade in vivo is embedded in more complex regulatory structures. We argue that this type of analysis should accompany nonlinear multiparameter studies of stationary as well as transient features in network dynamics. link: http://identifiers.org/pubmed/17907797

Parameters: none

States: none

Observables: none

Qosa2014 - Mechanistic modeling that describes Aβ clearance across BBBQosa2014 - Mechanistic modeling that describes Aβ…

Alzheimer's disease (AD) has a characteristic hallmark of amyloid-β (Aβ) accumulation in the brain. This accumulation of Aβ has been related to its faulty cerebral clearance. Indeed, preclinical studies that used mice to investigate Aβ clearance showed that efflux across blood-brain barrier (BBB) and brain degradation mediate efficient Aβ clearance. However, the contribution of each process to Aβ clearance remains unclear. Moreover, it is still uncertain how species differences between mouse and human could affect Aβ clearance. Here, a modified form of the brain efflux index method was used to estimate the contribution of BBB and brain degradation to Aβ clearance from the brain of wild type mice. We estimated that 62% of intracerebrally injected (125)I-Aβ40 is cleared across BBB while 38% is cleared by brain degradation. Furthermore, in vitro and in silico studies were performed to compare Aβ clearance between mouse and human BBB models. Kinetic studies for Aβ40 disposition in bEnd3 and hCMEC/D3 cells, representative in vitro mouse and human BBB models, respectively, demonstrated 30-fold higher rate of (125)I-Aβ40 uptake and 15-fold higher rate of degradation by bEnd3 compared to hCMEC/D3 cells. Expression studies showed both cells to express different levels of P-glycoprotein and RAGE, while LRP1 levels were comparable. Finally, we established a mechanistic model, which could successfully predict cellular levels of (125)I-Aβ40 and the rate of each process. Established mechanistic model suggested significantly higher rates of Aβ uptake and degradation in bEnd3 cells as rationale for the observed differences in (125)I-Aβ40 disposition between mouse and human BBB models. In conclusion, current study demonstrates the important role of BBB in the clearance of Aβ from the brain. Moreover, it provides insight into the differences between mouse and human BBB with regards to Aβ clearance and offer, for the first time, a mathematical model that describes Aβ clearance across BBB. link: http://identifiers.org/pubmed/24467845

Parameters: none

States: none

Observables: none

BIOMD0000000110 @ v0.0.1

This model is from the article: Dynamics of the cell cycle: checkpoints, sizers, and timers. Qu Z, MacLellan WR…

We have developed a generic mathematical model of a cell cycle signaling network in higher eukaryotes that can be used to simulate both the G1/S and G2/M transitions. In our model, the positive feedback facilitated by CDC25 and wee1 causes bistability in cyclin-dependent kinase activity, whereas the negative feedback facilitated by SKP2 or anaphase-promoting-complex turns this bistable behavior into limit cycle behavior. The cell cycle checkpoint is a Hopf bifurcation point. These behaviors are coordinated by growth and division to maintain normal cell cycle and size homeostasis. This model successfully reproduces sizer, timer, and the restriction point features of the eukaryotic cell cycle, in addition to other experimental findings. link: http://identifiers.org/pubmed/14645053

Parameters:

Name Description
k4 = 30.0; k3 = 30.0 Reaction: y => x1; c, Rate Law: cell*(k3*c*y-x1*k4)
k16u = 25.0; k16 = 2.0 Reaction: ixp => x, Rate Law: cell*k16*k16u*ixp
bi = 0.1; ci = 1.0; ai = 10.0 Reaction: ix => ixp; x, Rate Law: cell*((bi+ci*x)*ix-ai*ixp)
k1 = 300.0 Reaction: => y, Rate Law: k1*cell
k11 = 1.0 Reaction: w0 =>, Rate Law: cell*w0*k11
k2 = 5.0; k2u = 50.0 Reaction: y => ; u, Rate Law: cell*(k2+k2u*u)*y
k10 = 10.0 Reaction: => w0, Rate Law: k10*cell
bw = 0.1; cw = 1.0; aw = 10.0 Reaction: w0 => w1; x, Rate Law: cell*((bw+cw*x)*w0-aw*w1)
a = 4.0; Tau = 25.0 Reaction: => u; x, Rate Law: cell*x^2/(a^2+x^2)/Tau
cz = 1.0; bz = 0.1; az = 10.0 Reaction: z0 => z1; x, Rate Law: cell*((bz+cz*x)*z0-z1*az)
k7u = 0.0; k7 = 10.0 Reaction: x => ; u, Rate Law: cell*(k7+k7u*u)*x
Tau = 25.0 Reaction: u =>, Rate Law: cell*u/Tau
k14 = 1.0; k15 = 1.0 Reaction: i + x => ix, Rate Law: (k14*x*i-k15*ix)*cell
k5 = 0.1; k6 = 1.0 Reaction: x => x1; z2, w0, Rate Law: cell*((k6+w0)*x-(k5+z2)*x1)
k13 = 1.0 Reaction: i =>, Rate Law: cell*k13*i
k12 = 0.0 Reaction: => i, Rate Law: k12*cell
k9 = 1.0 Reaction: z0 =>, Rate Law: cell*k9*z0
k8 = 100.0 Reaction: => z0, Rate Law: cell*k8

States:

Name Description
ix [IPR003175; IPR006670; cyclin-dependent protein kinase holoenzyme complex]
i [IPR003175]
c [cyclin-dependent protein kinase holoenzyme complex]
z1 [Cell division control protein 25]
x [IPR006670; cyclin-dependent protein kinase holoenzyme complex]
z0 [Cell division control protein 25]
w1 [Wee1-like protein kinase]
x1 [IPR006670; cyclin-dependent protein kinase holoenzyme complex]
totalCyclin [IPR006670]
ixp [IPR003175; IPR006670; cyclin-dependent protein kinase holoenzyme complex]
u [IPR001810; anaphase-promoting complex]
z2 [Cell division control protein 25]
w0 [Wee1-like protein kinase]
y [IPR006670]

Observables: none

Quek2008 - Genome-scale metabolic network of Mus musculusThis model is described in the article: [On the reconstruction…

Genome-scale metabolic modeling is a systems-based approach that attempts to capture the metabolic complexity of the whole cell, for the purpose of gaining insight into metabolic function and regulation. This is achieved by organizing the metabolic components and their corresponding interactions into a single context. The reconstruction process is a challenging and laborious task, especially during the stage of manual curation. For the mouse genome-scale metabolic model, however, we were able to rapidly reconstruct a compartmentalized model from well-curated metabolic databases online. The prototype model was comprehensive. Apart from minor compound naming and compartmentalization issues, only nine additional reactions without gene associations were added during model curation before the model was able to simulate growth in silico. Further curation led to a metabolic model that consists of 1399 genes mapped to 1757 reactions, with a total of 2037 reactions compartmentalized into the cytoplasm and mitochondria, capable of reproducing metabolic functions inferred from literatures. The reconstruction is made more tractable by developing a formal system to update the model against online databases. Effectively, we can focus our curation efforts into establishing better model annotations and gene-protein-reaction associations within the core metabolism, while relying on genome and proteome databases to build new annotations for peripheral pathways, which may bear less relevance to our modeling interest. link: http://identifiers.org/pubmed/19425150

Parameters: none

States: none

Observables: none

Quek2014 - Metabolic flux analysis of HEK cell culture using Recon 2 (reduced version of Recon 2)This model is described…

A representative stoichiometric model is essential to perform metabolic flux analysis (MFA) using experimentally measured consumption (or production) rates as constraints. For Human Embryonic Kidney (HEK) cell culture, there is the opportunity to use an extremely well-curated and annotated human genome-scale model Recon 2 for MFA. Performing MFA using Recon 2 without any modification would have implied that cells have access to all functionality encoded by the genome, which is not realistic. The majority of intracellular fluxes are poorly determined as only extracellular exchange rates are measured. This is compounded by the fact that there is no suitable metabolic objective function to suppress non-specific fluxes. We devised a heuristic to systematically reduce Recon 2 to emphasize flux through core metabolic reactions. This implies that cells would engage these dominant metabolic pathways to grow, and any significant changes in gross metabolic phenotypes would have invoked changes in these pathways. The reduced metabolic model becomes a functionalized version of Recon 2 used for identifying significant metabolic changes in cells by flux analysis. link: http://identifiers.org/pubmed/24907410

Parameters: none

States: none

Observables: none

Mathematical model of mitotic exit in budding yeast.

After anaphase, the high mitotic cyclin-dependent kinase (Cdk) activity is downregulated to promote exit from mitosis. To this end, in the budding yeast S. cerevisiae, the Cdk counteracting phosphatase Cdc14 is activated. In metaphase, Cdc14 is kept inactive in the nucleolus by its inhibitor Net1. During anaphase, Cdk- and Polo-dependent phosphorylation of Net1 is thought to release active Cdc14. How Net1 is phosphorylated specifically in anaphase, when mitotic kinase activity starts to decline, has remained unexplained. Here, we show that PP2A(Cdc55) phosphatase keeps Net1 underphosphorylated in metaphase. The sister chromatid-separating protease separase, activated at anaphase onset, interacts with and downregulates PP2A(Cdc55), thereby facilitating Cdk-dependent Net1 phosphorylation. PP2A(Cdc55) downregulation also promotes phosphorylation of Bfa1, contributing to activation of the "mitotic exit network" that sustains Cdc14 as Cdk activity declines. These findings allow us to present a new quantitative model for mitotic exit in budding yeast. link: http://identifiers.org/pubmed/16713564

Parameters: none

States: none

Observables: none

This model is from the article: Downregulation of PP2A(Cdc55) phosphatase by separase initiates mitotic exit in buddin…

After anaphase, the high mitotic cyclin-dependent kinase (Cdk) activity is downregulated to promote exit from mitosis. To this end, in the budding yeast S. cerevisiae, the Cdk counteracting phosphatase Cdc14 is activated. In metaphase, Cdc14 is kept inactive in the nucleolus by its inhibitor Net1. During anaphase, Cdk- and Polo-dependent phosphorylation of Net1 is thought to release active Cdc14. How Net1 is phosphorylated specifically in anaphase, when mitotic kinase activity starts to decline, has remained unexplained. Here, we show that PP2A(Cdc55) phosphatase keeps Net1 underphosphorylated in metaphase. The sister chromatid-separating protease separase, activated at anaphase onset, interacts with and downregulates PP2A(Cdc55), thereby facilitating Cdk-dependent Net1 phosphorylation. PP2A(Cdc55) downregulation also promotes phosphorylation of Bfa1, contributing to activation of the "mitotic exit network" that sustains Cdc14 as Cdk activity declines. These findings allow us to present a new quantitative model for mitotic exit in budding yeast. link: http://identifiers.org/pubmed/16713564

Parameters:

Name Description
kd = 0.45; Jnet = 0.2; kad = 0.1 Reaction: Net1P => Net1; Cdc14, Clb2, PP2A, Rate Law: (kad*Cdc14+kd*PP2A)*Net1P/(Jnet+Net1P)
kssecurin = 0.03 Reaction: AA => securinT + securin, Rate Law: kssecurin
ldnet = 1.0 Reaction: Net1Cdc14 => Net1, Rate Law: ldnet*Net1Cdc14
kadpolo = 0.25; kdpolo = 0.01 Reaction: Polo => degr; Cdh1, Rate Law: (kdpolo+kadpolo*Cdh1)*Polo
Jpolo = 0.25; kipolo = 0.1 Reaction: Polo => Polo_i, Rate Law: kipolo*Polo/(Jpolo+Polo)
PP2AT = 1.0; kpp = 0.1; ki = 20.0 Reaction: PP2A = (1+kpp*ki*separase)/(1+ki*separase)*PP2AT, Rate Law: missing
kdsecurin = 0.05; kadsecurin = 2.0 Reaction: securinT + securin => degr; Cdc20, Rate Law: (kdsecurin+kadsecurin*Cdc20)*securinT
kaicdc15 = 0.2; kicdc15 = 0.0; Jcdc15 = 0.2; Cdk = NaN Reaction: Cdc15 => Cdc15_i, Rate Law: (kicdc15+kaicdc15*Cdk)*Cdc15/(Jcdc15+Cdc15)
Cdh1T = 1.0; Jcdh = 0.0015; kadcdh = 1.0; kdcdh = 0.01 Reaction: Cdh1_i => Cdh1; Cdc14, Rate Law: (kdcdh+kadcdh*Cdc14)*(Cdh1T-Cdh1)/((Jcdh+Cdh1T)-Cdh1)
kitem = 0.1; kaitem = 1.0; Jtem1 = 0.005 Reaction: Tem1 => Tem1_i; PP2A, Rate Law: (kitem+kaitem*PP2A)*Tem1/(Jtem1+Tem1)
kdcdc20 = 0.05; kadcdc20 = 2.0 Reaction: Cdc20 => degr; Cdh1, Rate Law: (kdcdc20+kadcdc20*Cdh1)*Cdc20
lamen = 10.0 Reaction: AA => MEN; Tem1, Cdc15, Rate Law: lamen*(Tem1-MEN)*(Cdc15-MEN)
kp = 0.4; Jnet = 0.2; kap = 2.0; Cdk = NaN Reaction: Net1Cdc14 => Net1P; MEN, Net1, Clb2, Rate Law: (kp*Cdk+kap*MEN)*Net1Cdc14/(Jnet+Net1+Net1Cdc14)
Cdc14T = 0.5 Reaction: Cdc14 = Cdc14T-Net1Cdc14, Rate Law: missing
ksclb2 = 0.03 Reaction: AA => Clb2, Rate Law: ksclb2
Net1T = 1.0 Reaction: Net1P = (Net1T-Net1)-Net1Cdc14, Rate Law: missing
Tem1T = 1.0 Reaction: Tem1_i = Tem1T-Tem1, Rate Law: missing
kaacdc15 = 0.5; Jcdc15 = 0.2; kacdc15 = 0.02; Cdc15T = 1.0 Reaction: Cdc15_i => Cdc15; Cdc14, Rate Law: (kacdc15+kaacdc15*Cdc14)*(Cdc15T-Cdc15)/((Jcdc15+Cdc15T)-Cdc15)
kadclb2 = 0.2; kaadclb2 = 2.0; kdclb2 = 0.03 Reaction: Clb2 => degr; Cdc20, Cdh1, Rate Law: (kdclb2+kadclb2*Cdc20+kaadclb2*Cdh1)*Clb2
kscdc20 = 0.015 Reaction: AA => Cdc20, Rate Law: kscdc20
ksseparase = 0.001 Reaction: AA => separaseT + separase, Rate Law: ksseparase
lanet = 500.0 Reaction: Net1 => Net1Cdc14; Cdc14, Rate Law: lanet*Net1*Cdc14
ldmen = 0.1 Reaction: MEN => degr, Rate Law: ldmen*MEN
kaapolo = 0.5; Jpolo = 0.25; kapolo = 0.0; Cdk = NaN Reaction: Polo_i => Polo; PoloT, Rate Law: (kapolo+kaapolo*Cdk)*(PoloT-Polo)/((Jpolo+PoloT)-Polo)
kspolo = 0.01 Reaction: AA => PoloT + Polo_i, Rate Law: kspolo
Jcdh = 0.0015; Cdk = NaN; kapcdh = 1.0 Reaction: Cdh1 => Cdh1_i, Rate Law: kapcdh*Cdk*Cdh1/(Jcdh+Cdh1)
kaatem = 0.5; katem = 0.0; Tem1T = 1.0; Jtem1 = 0.005 Reaction: Tem1_i => Tem1; Polo, Rate Law: (katem+kaatem*Polo)*(Tem1T-Tem1)/((Jtem1+Tem1T)-Tem1)
ldsecurin = 1.0; lasecurin = 500.0 Reaction: securin + separase => securinseparase, Rate Law: lasecurin*securin*separase-ldsecurin*securinseparase
Cdc15T = 1.0 Reaction: Cdc15_i = Cdc15T-Cdc15, Rate Law: missing
kdseparase = 0.004 Reaction: separaseT + separase => degr, Rate Law: kdseparase*separaseT
Cdh1T = 1.0 Reaction: Cdh1_i = Cdh1T-Cdh1, Rate Law: missing

States:

Name Description
Polo [Cell cycle serine/threonine-protein kinase CDC5/MSD2]
securinT [Securin]
Polo i [Cell cycle serine/threonine-protein kinase CDC5/MSD2]
MEN [Protein TEM1; Cell division control protein 15]
Tem1 [Protein TEM1]
Tem1 i [Protein TEM1]
Net1 [Nucleolar protein NET1]
degr degr
Cdh1 i [APC/C activator protein CDH1]
separaseT [Separin]
Clb2 [G2/mitotic-specific cyclin-2]
separase [Separin]
Cdc15 [Cell division control protein 15]
Cdc14 [Tyrosine-protein phosphatase CDC14]
securinseparase [Securin; Separin]
Net1Cdc14 [Nucleolar protein NET1; Tyrosine-protein phosphatase CDC14]
securin [Securin]
PP2A [Protein phosphatase PP2A regulatory subunit B]
PoloT [Cell cycle serine/threonine-protein kinase CDC5/MSD2]
Net1P [Nucleolar protein NET1; Phosphoprotein]
AA [alpha-amino acid]
Cdh1 [APC/C activator protein CDH1]
Cdc20 [APC/C activator protein CDC20]
Cdc15 i [Cell division control protein 15]

Observables: none

R


This model is from the article: Epidemics of panic during a bioterrorist attack--a mathematical model. Radosavljevic…

A bioterrorist attacks usually cause epidemics of panic in a targeted population. We have presented epidemiologic aspect of this phenomenon as a three-component model–host, information on an attack and social network. We have proposed a mathematical model of panic and counter-measures as the function of time in a population exposed to a bioterrorist attack. The model comprises ordinary differential equations and graphically presented combinations of the equations parameters. Clinically, we have presented a model through a sequence of psychic conditions and disorders initiated by an act of bioterrorism. This model might be helpful for an attacked community to timely and properly apply counter-measures and to minimize human mental suffering during a bioterrorist attack. link: http://identifiers.org/pubmed/19423234

Parameters:

Name Description
delta = 1.0; gamma = 0.0 Reaction: P = ((-gamma)+delta*S)*P, Rate Law: ((-gamma)+delta*S)*P
alpha = 6.0; C = 10.0; beta = 2.8 Reaction: S = (alpha*(1-S/C)-beta*P)*S, Rate Law: (alpha*(1-S/C)-beta*P)*S

States:

Name Description
S panic_intensity
P protection+prevention_intensity

Observables: none

Radulescu2008 - NF-κB hierarchy ℳ(16,34,46)This is a model of NF-κB pathway functioning from hierarchy of models of decr…

BACKGROUND: Cellular processes such as metabolism, decision making in development and differentiation, signalling, etc., can be modeled as large networks of biochemical reactions. In order to understand the functioning of these systems, there is a strong need for general model reduction techniques allowing to simplify models without loosing their main properties. In systems biology we also need to compare models or to couple them as parts of larger models. In these situations reduction to a common level of complexity is needed. RESULTS: We propose a systematic treatment of model reduction of multiscale biochemical networks. First, we consider linear kinetic models, which appear as "pseudo-monomolecular" subsystems of multiscale nonlinear reaction networks. For such linear models, we propose a reduction algorithm which is based on a generalized theory of the limiting step that we have developed in 1. Second, for non-linear systems we develop an algorithm based on dominant solutions of quasi-stationarity equations. For oscillating systems, quasi-stationarity and averaging are combined to eliminate time scales much faster and much slower than the period of the oscillations. In all cases, we obtain robust simplifications and also identify the critical parameters of the model. The methods are demonstrated for simple examples and for a more complex model of NF-kappaB pathway. CONCLUSION: Our approach allows critical parameter identification and produces hierarchies of models. Hierarchical modeling is important in "middle-out" approaches when there is need to zoom in and out several levels of complexity. Critical parameter identification is an important issue in systems biology with potential applications to biological control and therapeutics. Our approach also deals naturally with the presence of multiple time scales, which is a general property of systems biology models. link: http://identifiers.org/pubmed/18854041

Parameters: none

States: none

Observables: none

MODEL7743212613 @ v0.0.1

# NFkB model M(5,8,12) - minimal model This is a model of NFkB pathway functioning from hierarchy of models of decreasi…

BACKGROUND: Cellular processes such as metabolism, decision making in development and differentiation, signalling, etc., can be modeled as large networks of biochemical reactions. In order to understand the functioning of these systems, there is a strong need for general model reduction techniques allowing to simplify models without loosing their main properties. In systems biology we also need to compare models or to couple them as parts of larger models. In these situations reduction to a common level of complexity is needed. RESULTS: We propose a systematic treatment of model reduction of multiscale biochemical networks. First, we consider linear kinetic models, which appear as "pseudo-monomolecular" subsystems of multiscale nonlinear reaction networks. For such linear models, we propose a reduction algorithm which is based on a generalized theory of the limiting step that we have developed in 1. Second, for non-linear systems we develop an algorithm based on dominant solutions of quasi-stationarity equations. For oscillating systems, quasi-stationarity and averaging are combined to eliminate time scales much faster and much slower than the period of the oscillations. In all cases, we obtain robust simplifications and also identify the critical parameters of the model. The methods are demonstrated for simple examples and for a more complex model of NF-kappaB pathway. CONCLUSION: Our approach allows critical parameter identification and produces hierarchies of models. Hierarchical modeling is important in "middle-out" approaches when there is need to zoom in and out several levels of complexity. Critical parameter identification is an important issue in systems biology with potential applications to biological control and therapeutics. Our approach also deals naturally with the presence of multiple time scales, which is a general property of systems biology models. link: http://identifiers.org/pubmed/18854041

Parameters: none

States: none

Observables: none

MODEL7743315447 @ v0.0.1

# NFkB model M(6,10,15) This is a model of NFkB pathway functioning from hierarchy of models of decreasing complexity,…

BACKGROUND: Cellular processes such as metabolism, decision making in development and differentiation, signalling, etc., can be modeled as large networks of biochemical reactions. In order to understand the functioning of these systems, there is a strong need for general model reduction techniques allowing to simplify models without loosing their main properties. In systems biology we also need to compare models or to couple them as parts of larger models. In these situations reduction to a common level of complexity is needed. RESULTS: We propose a systematic treatment of model reduction of multiscale biochemical networks. First, we consider linear kinetic models, which appear as "pseudo-monomolecular" subsystems of multiscale nonlinear reaction networks. For such linear models, we propose a reduction algorithm which is based on a generalized theory of the limiting step that we have developed in 1. Second, for non-linear systems we develop an algorithm based on dominant solutions of quasi-stationarity equations. For oscillating systems, quasi-stationarity and averaging are combined to eliminate time scales much faster and much slower than the period of the oscillations. In all cases, we obtain robust simplifications and also identify the critical parameters of the model. The methods are demonstrated for simple examples and for a more complex model of NF-kappaB pathway. CONCLUSION: Our approach allows critical parameter identification and produces hierarchies of models. Hierarchical modeling is important in "middle-out" approaches when there is need to zoom in and out several levels of complexity. Critical parameter identification is an important issue in systems biology with potential applications to biological control and therapeutics. Our approach also deals naturally with the presence of multiple time scales, which is a general property of systems biology models. link: http://identifiers.org/pubmed/18854041

Parameters: none

States: none

Observables: none

MODEL7743358405 @ v0.0.1

# NFkB model M(8,12,19) This is a model of NFkB pathway functioning from hierarchy of models of decreasing complexity,…

BACKGROUND: Cellular processes such as metabolism, decision making in development and differentiation, signalling, etc., can be modeled as large networks of biochemical reactions. In order to understand the functioning of these systems, there is a strong need for general model reduction techniques allowing to simplify models without loosing their main properties. In systems biology we also need to compare models or to couple them as parts of larger models. In these situations reduction to a common level of complexity is needed. RESULTS: We propose a systematic treatment of model reduction of multiscale biochemical networks. First, we consider linear kinetic models, which appear as "pseudo-monomolecular" subsystems of multiscale nonlinear reaction networks. For such linear models, we propose a reduction algorithm which is based on a generalized theory of the limiting step that we have developed in 1. Second, for non-linear systems we develop an algorithm based on dominant solutions of quasi-stationarity equations. For oscillating systems, quasi-stationarity and averaging are combined to eliminate time scales much faster and much slower than the period of the oscillations. In all cases, we obtain robust simplifications and also identify the critical parameters of the model. The methods are demonstrated for simple examples and for a more complex model of NF-kappaB pathway. CONCLUSION: Our approach allows critical parameter identification and produces hierarchies of models. Hierarchical modeling is important in "middle-out" approaches when there is need to zoom in and out several levels of complexity. Critical parameter identification is an important issue in systems biology with potential applications to biological control and therapeutics. Our approach also deals naturally with the presence of multiple time scales, which is a general property of systems biology models. link: http://identifiers.org/pubmed/18854041

Parameters: none

States: none

Observables: none

# NFkB model M(14,25,28) - Lipniacky's NFkB model This is a model of NFkB pathway functioning from hierarchy of models…

BACKGROUND: Cellular processes such as metabolism, decision making in development and differentiation, signalling, etc., can be modeled as large networks of biochemical reactions. In order to understand the functioning of these systems, there is a strong need for general model reduction techniques allowing to simplify models without loosing their main properties. In systems biology we also need to compare models or to couple them as parts of larger models. In these situations reduction to a common level of complexity is needed. RESULTS: We propose a systematic treatment of model reduction of multiscale biochemical networks. First, we consider linear kinetic models, which appear as "pseudo-monomolecular" subsystems of multiscale nonlinear reaction networks. For such linear models, we propose a reduction algorithm which is based on a generalized theory of the limiting step that we have developed in 1. Second, for non-linear systems we develop an algorithm based on dominant solutions of quasi-stationarity equations. For oscillating systems, quasi-stationarity and averaging are combined to eliminate time scales much faster and much slower than the period of the oscillations. In all cases, we obtain robust simplifications and also identify the critical parameters of the model. The methods are demonstrated for simple examples and for a more complex model of NF-kappaB pathway. CONCLUSION: Our approach allows critical parameter identification and produces hierarchies of models. Hierarchical modeling is important in "middle-out" approaches when there is need to zoom in and out several levels of complexity. Critical parameter identification is an important issue in systems biology with potential applications to biological control and therapeutics. Our approach also deals naturally with the presence of multiple time scales, which is a general property of systems biology models. link: http://identifiers.org/pubmed/18854041

Parameters:

Name Description
kr13 = 0.0; kf13 = 18.4 Reaction: s160 + s161 => s135, Rate Law: kf13*s161*s160-kr13*s135
k8 = 0.1 Reaction: s139 => s132, Rate Law: k8*s139
kf28 = 0.01; kr28 = 0.0 Reaction: s159 => s135, Rate Law: kf28*s159-kr28*s135
k3 = 2.5E-6 Reaction: s150 => s133, Rate Law: k3
k10 = 0.1 Reaction: s152 => s161 + s132, Rate Law: k10*s152
k19 = 0.0; k20 = 5.0E-7 Reaction: s126 => s127; s164, Rate Law: k19+k20*s164
k6 = 1.25E-4 Reaction: s132 => s134, Rate Law: k6*s132
k21 = 1.0E-4 Reaction: s160 => s122, Rate Law: k21*s160
kr23 = 5.0E-4; kf23 = 0.001 Reaction: s160 => s167, Rate Law: kf23*s160-kr23*s167
k27 = 4.0E-4 Reaction: s125 => s124, Rate Law: k27*s125
k11 = 1.25E-4 Reaction: s130 => s129, Rate Law: k11*s130
k9 = 1.0 Reaction: s132 + s135 => s152, Rate Law: k9*s132*s135
k17 = 4.0E-4 Reaction: s127 => s153, Rate Law: k17*s127
k26 = 5.0E-7 Reaction: s121 => s125; s164, Rate Law: k26*s164
k2 = 1.25E-4 Reaction: s133 => s131, Rate Law: k2*s133
kr14 = 0.0; kf14 = 18.4 Reaction: s164 + s167 => s159, Rate Law: kf14*s164*s167-kr14*s159
k7 = 0.2 Reaction: s160 + s132 => s139, Rate Law: k7*s132*s160
k5 = 0.0015; k4 = 0.1 Reaction: s132 => s130; s128, Rate Law: k5*s132+k4*s132*s128
k22 = 0.5 Reaction: s125 => s160 + s125, Rate Law: k22*s125
k18 = 3.0E-4 Reaction: s128 => s154, Rate Law: k18*s128
k1 = 0.0025 Reaction: s133 => s132, Rate Law: k1*s133
k16 = 0.5 Reaction: s127 => s128 + s127, Rate Law: k16*s127
k12 = 2.0E-5 Reaction: s135 => s161; s132, Rate Law: k12*s135
kf15 = 0.0025; kr15 = 0.0 Reaction: s161 => s164, Rate Law: kf15*s161-kr15*s164

States:

Name Description
s150 [Inhibitor of nuclear factor kappa-B kinase subunit alpha; Inhibitor of nuclear factor kappa-B kinase subunit beta; NF-kappa-B essential modulator]
s124 sa12_degraded
s135 [Nuclear factor NF-kappa-B p105 subunit; NF-kappa-B inhibitor alpha]
s159 [Nuclear factor NF-kappa-B p105 subunit; NF-kappa-B inhibitor alpha]
s121 [NF-kappa-B inhibitor alpha]
s153 sa96_degraded
s122 sa13_degraded
s128 [Tumor necrosis factor alpha-induced protein 3]
s132 [Inhibitor of nuclear factor kappa-B kinase subunit beta; Inhibitor of nuclear factor kappa-B kinase subunit alpha; NF-kappa-B essential modulator]
s167 [NF-kappa-B inhibitor alpha]
s160 [NF-kappa-B inhibitor alpha]
s127 [Tnfaip3-201]
s129 sa444_degraded
s152 [Nuclear factor NF-kappa-B p105 subunit; NF-kappa-B inhibitor alpha; Inhibitor of nuclear factor kappa-B kinase subunit alpha; Inhibitor of nuclear factor kappa-B kinase subunit beta; NF-kappa-B essential modulator]
s134 sa20_degraded
s154 sa97_degraded
s130 [Inhibitor of nuclear factor kappa-B kinase subunit alpha; Inhibitor of nuclear factor kappa-B kinase subunit beta; NF-kappa-B essential modulator]
s164 [Nuclear factor NF-kappa-B p105 subunit]
s131 sa19_degraded
s139 [NF-kappa-B inhibitor alpha; Inhibitor of nuclear factor kappa-B kinase subunit alpha; Inhibitor of nuclear factor kappa-B kinase subunit beta; NF-kappa-B essential modulator]
s133 [Inhibitor of nuclear factor kappa-B kinase subunit alpha; Inhibitor of nuclear factor kappa-B kinase subunit beta; NF-kappa-B essential modulator]
s161 [Nuclear factor NF-kappa-B p105 subunit]
s126 [Tumor necrosis factor alpha-induced protein 3]
s125 [Nfkbia-201]

Observables: none

MODEL7743444866 @ v0.0.1

# NFkB model M(14,25,33) This is a model of NFkB pathway functioning from hierarchy of models of decreasing complexity,…

BACKGROUND: Cellular processes such as metabolism, decision making in development and differentiation, signalling, etc., can be modeled as large networks of biochemical reactions. In order to understand the functioning of these systems, there is a strong need for general model reduction techniques allowing to simplify models without loosing their main properties. In systems biology we also need to compare models or to couple them as parts of larger models. In these situations reduction to a common level of complexity is needed. RESULTS: We propose a systematic treatment of model reduction of multiscale biochemical networks. First, we consider linear kinetic models, which appear as "pseudo-monomolecular" subsystems of multiscale nonlinear reaction networks. For such linear models, we propose a reduction algorithm which is based on a generalized theory of the limiting step that we have developed in 1. Second, for non-linear systems we develop an algorithm based on dominant solutions of quasi-stationarity equations. For oscillating systems, quasi-stationarity and averaging are combined to eliminate time scales much faster and much slower than the period of the oscillations. In all cases, we obtain robust simplifications and also identify the critical parameters of the model. The methods are demonstrated for simple examples and for a more complex model of NF-kappaB pathway. CONCLUSION: Our approach allows critical parameter identification and produces hierarchies of models. Hierarchical modeling is important in "middle-out" approaches when there is need to zoom in and out several levels of complexity. Critical parameter identification is an important issue in systems biology with potential applications to biological control and therapeutics. Our approach also deals naturally with the presence of multiple time scales, which is a general property of systems biology models. link: http://identifiers.org/pubmed/18854041

Parameters: none

States: none

Observables: none

MODEL7743528808 @ v0.0.1

# NFkB model M(14,30,41) This is a model of NFkB pathway functioning from hierarchy of models of decreasing complexity,…

BACKGROUND: Cellular processes such as metabolism, decision making in development and differentiation, signalling, etc., can be modeled as large networks of biochemical reactions. In order to understand the functioning of these systems, there is a strong need for general model reduction techniques allowing to simplify models without loosing their main properties. In systems biology we also need to compare models or to couple them as parts of larger models. In these situations reduction to a common level of complexity is needed. RESULTS: We propose a systematic treatment of model reduction of multiscale biochemical networks. First, we consider linear kinetic models, which appear as "pseudo-monomolecular" subsystems of multiscale nonlinear reaction networks. For such linear models, we propose a reduction algorithm which is based on a generalized theory of the limiting step that we have developed in 1. Second, for non-linear systems we develop an algorithm based on dominant solutions of quasi-stationarity equations. For oscillating systems, quasi-stationarity and averaging are combined to eliminate time scales much faster and much slower than the period of the oscillations. In all cases, we obtain robust simplifications and also identify the critical parameters of the model. The methods are demonstrated for simple examples and for a more complex model of NF-kappaB pathway. CONCLUSION: Our approach allows critical parameter identification and produces hierarchies of models. Hierarchical modeling is important in "middle-out" approaches when there is need to zoom in and out several levels of complexity. Critical parameter identification is an important issue in systems biology with potential applications to biological control and therapeutics. Our approach also deals naturally with the presence of multiple time scales, which is a general property of systems biology models. link: http://identifiers.org/pubmed/18854041

Parameters: none

States: none

Observables: none

MODEL7743608569 @ v0.0.1

# NFkB model M(24,45,62) This is a model of NFkB pathway functioning from hierarchy of models of decreasing complexity,…

BACKGROUND: Cellular processes such as metabolism, decision making in development and differentiation, signalling, etc., can be modeled as large networks of biochemical reactions. In order to understand the functioning of these systems, there is a strong need for general model reduction techniques allowing to simplify models without loosing their main properties. In systems biology we also need to compare models or to couple them as parts of larger models. In these situations reduction to a common level of complexity is needed. RESULTS: We propose a systematic treatment of model reduction of multiscale biochemical networks. First, we consider linear kinetic models, which appear as "pseudo-monomolecular" subsystems of multiscale nonlinear reaction networks. For such linear models, we propose a reduction algorithm which is based on a generalized theory of the limiting step that we have developed in 1. Second, for non-linear systems we develop an algorithm based on dominant solutions of quasi-stationarity equations. For oscillating systems, quasi-stationarity and averaging are combined to eliminate time scales much faster and much slower than the period of the oscillations. In all cases, we obtain robust simplifications and also identify the critical parameters of the model. The methods are demonstrated for simple examples and for a more complex model of NF-kappaB pathway. CONCLUSION: Our approach allows critical parameter identification and produces hierarchies of models. Hierarchical modeling is important in "middle-out" approaches when there is need to zoom in and out several levels of complexity. Critical parameter identification is an important issue in systems biology with potential applications to biological control and therapeutics. Our approach also deals naturally with the presence of multiple time scales, which is a general property of systems biology models. link: http://identifiers.org/pubmed/18854041

Parameters: none

States: none

Observables: none

MODEL7743631122 @ v0.0.1

# NFkB model M(34,60,82) This is a model of NFkB pathway functioning from hierarchy of models of decreasing complexity,…

BACKGROUND: Cellular processes such as metabolism, decision making in development and differentiation, signalling, etc., can be modeled as large networks of biochemical reactions. In order to understand the functioning of these systems, there is a strong need for general model reduction techniques allowing to simplify models without loosing their main properties. In systems biology we also need to compare models or to couple them as parts of larger models. In these situations reduction to a common level of complexity is needed. RESULTS: We propose a systematic treatment of model reduction of multiscale biochemical networks. First, we consider linear kinetic models, which appear as "pseudo-monomolecular" subsystems of multiscale nonlinear reaction networks. For such linear models, we propose a reduction algorithm which is based on a generalized theory of the limiting step that we have developed in 1. Second, for non-linear systems we develop an algorithm based on dominant solutions of quasi-stationarity equations. For oscillating systems, quasi-stationarity and averaging are combined to eliminate time scales much faster and much slower than the period of the oscillations. In all cases, we obtain robust simplifications and also identify the critical parameters of the model. The methods are demonstrated for simple examples and for a more complex model of NF-kappaB pathway. CONCLUSION: Our approach allows critical parameter identification and produces hierarchies of models. Hierarchical modeling is important in "middle-out" approaches when there is need to zoom in and out several levels of complexity. Critical parameter identification is an important issue in systems biology with potential applications to biological control and therapeutics. Our approach also deals naturally with the presence of multiple time scales, which is a general property of systems biology models. link: http://identifiers.org/pubmed/18854041

Parameters: none

States: none

Observables: none

BIOMD0000000227 @ v0.0.1

# NFkB model M(39,65,90) - most complex model This is a model of NFkB pathway functioning from hierarchy of models of d…

BACKGROUND: Cellular processes such as metabolism, decision making in development and differentiation, signalling, etc., can be modeled as large networks of biochemical reactions. In order to understand the functioning of these systems, there is a strong need for general model reduction techniques allowing to simplify models without loosing their main properties. In systems biology we also need to compare models or to couple them as parts of larger models. In these situations reduction to a common level of complexity is needed. RESULTS: We propose a systematic treatment of model reduction of multiscale biochemical networks. First, we consider linear kinetic models, which appear as "pseudo-monomolecular" subsystems of multiscale nonlinear reaction networks. For such linear models, we propose a reduction algorithm which is based on a generalized theory of the limiting step that we have developed in 1. Second, for non-linear systems we develop an algorithm based on dominant solutions of quasi-stationarity equations. For oscillating systems, quasi-stationarity and averaging are combined to eliminate time scales much faster and much slower than the period of the oscillations. In all cases, we obtain robust simplifications and also identify the critical parameters of the model. The methods are demonstrated for simple examples and for a more complex model of NF-kappaB pathway. CONCLUSION: Our approach allows critical parameter identification and produces hierarchies of models. Hierarchical modeling is important in "middle-out" approaches when there is need to zoom in and out several levels of complexity. Critical parameter identification is an important issue in systems biology with potential applications to biological control and therapeutics. Our approach also deals naturally with the presence of multiple time scales, which is a general property of systems biology models. link: http://identifiers.org/pubmed/18854041

Parameters:

Name Description
k8 = 0.1 Reaction: s191 => s132, Rate Law: k8*s191
k37 = 5.0E-5 Reaction: s113 => s112, Rate Law: k37*s113
kf59 = 0.0038; kr59 = 8.0E-13 Reaction: s213 => s205 + s192, Rate Law: kf59*s213-kr59*s192*s205
k10 = 0.1 Reaction: s189 => s190 + s132, Rate Law: k10*s189
k53 = 2.0E-4 Reaction: s190 => s156, Rate Law: k53*s190
kr23 = 5.0E-4; kf23 = 0.001 Reaction: s123 => s93, Rate Law: kf23*s123-kr23*s93
k45 = 0.0053 Reaction: s117 => s119 + s117, Rate Law: k45*s117
kf52 = 0.003; kr52 = 0.001 Reaction: s114 + s119 => s190, Rate Law: kf52*s114*s119-kr52*s190
k7 = 0.24 Reaction: s123 + s132 => s191, Rate Law: k7*s132*s123
k39 = 1.3E-4 Reaction: s110 => s114, Rate Law: k39*s110
kf57 = 18.4; kr57 = 0.055 Reaction: s93 + s212 => s213, Rate Law: kf57*s93*s212-kr57*s213
kr56 = 4.8E-4; kf56 = 0.62 Reaction: s195 + s206 => s214, Rate Law: kf56*s195*s206-kr56*s214
kf66 = 18.4; kr66 = 0.055 Reaction: s200 + s93 => s201, Rate Law: kf66*s93*s200-kr66*s201
k43 = 0.1; k42 = 5.0E-4 Reaction: s160 => s165; s193, s194, Rate Law: k42*s193+k43*s194
k72 = 2.0E-4 Reaction: s192 => s158, Rate Law: k72*s192
k5 = 0.0015; k4 = 0.1 Reaction: s132 => s130; s128, Rate Law: k5*s132+k4*s132*s128
kf64 = 0.62; kr64 = 4.8E-4 Reaction: s199 + s195 => s200, Rate Law: kf64*s195*s199-kr64*s200
k47 = 6.4E-5 Reaction: s119 => s120, Rate Law: k47*s119
kr14 = 0.0; kf14 = 0.5 Reaction: s195 + s93 => s192, Rate Law: kf14*s195*s93-kr14*s192
k22 = 0.5 Reaction: s178 => s123 + s178, Rate Law: k22*s178
k18 = 3.0E-4 Reaction: s128 => s154, Rate Law: k18*s128
k61 = 0.06; k49 = 5.0E-4; k50 = 0.02; k62 = 0.6 Reaction: s209 => s185; s214, s212, s205, s206, Rate Law: k49*s205+k50*s206+k62*s214+k61*s212
k12 = 2.0E-5 Reaction: s188 => s190, Rate Law: k12*s188
k33 = 5.0E-4; k70 = 0.06; k69 = 0.006; k34 = 0.1 Reaction: s170 => s173; s200, s199, s198, s196, Rate Law: k33*s198+k34*s199+k69*s196+k70*s200
k36 = 0.0041 Reaction: s113 => s110 + s113, Rate Law: k36*s113
k9 = 1.2 Reaction: s132 + s188 => s189, Rate Law: k9*s132*s188
k51 = 0.025 Reaction: s185 => s178, Rate Law: k51*s185
kf28 = 0.01; kr28 = 0.0 Reaction: s192 => s188, Rate Law: kf28*s192-kr28*s188
k19 = 0.0; k20 = 5.0E-7 Reaction: s126 => s127; s195, Rate Law: k19+k20*s195
kr13 = 0.0; kf13 = 0.5 Reaction: s123 + s190 => s188, Rate Law: kf13*s190*s123-kr13*s188
k6 = 1.25E-4 Reaction: s132 => s134, Rate Law: k6*s132
k21 = 1.0E-4 Reaction: s123 => s122, Rate Law: k21*s123
k1 = 0.0 Reaction: s133 => s132, Rate Law: k1*s133
k71 = 2.0E-4 Reaction: s188 => s157, Rate Law: k71*s188
k46 = 5.0E-5 Reaction: s117 => s118, Rate Law: k46*s117
k38 = 6.0E-5 Reaction: s110 => s109, Rate Law: k38*s110
k27 = 4.0E-4 Reaction: s178 => s124, Rate Law: k27*s178
k11 = 1.25E-4 Reaction: s130 => s129, Rate Law: k11*s130
kf32 = 10.0; kr32 = 1.0E-4 Reaction: s22 + s198 => s199, Rate Law: kf32*s198*s22-kr32*s199
kr58 = 0.055; kf58 = 18.4 Reaction: s93 + s214 => s215, Rate Law: kf58*s93*s214-kr58*s215
kr68 = 8.0E-13; kf68 = 0.0038 Reaction: s201 => s199 + s192, Rate Law: kf68*s201-kr68*s192*s199
kr65 = 0.055; kf65 = 18.4 Reaction: s196 + s93 => s197, Rate Law: kf65*s93*s196-kr65*s197
kr67 = 8.0E-13; kf67 = 0.0038 Reaction: s197 => s198 + s192, Rate Law: kf67*s197-kr67*s192*s198
k17 = 4.0E-4 Reaction: s127 => s153, Rate Law: k17*s127
k40 = 6.4E-5 Reaction: s114 => s111, Rate Law: k40*s114
k2 = 1.25E-4 Reaction: s133 => s131, Rate Law: k2*s133
kr55 = 4.8E-4; kf55 = 0.62 Reaction: s195 + s205 => s212, Rate Law: kf55*s195*s205-kr55*s212
kr63 = 4.8E-4; kf63 = 0.62 Reaction: s195 + s198 => s196, Rate Law: kf63*s195*s198-kr63*s196
kr41 = 1.0E-4; kf41 = 10.0 Reaction: s193 + s36 => s194, Rate Law: kf41*s36*s193-kr41*s194
k44 = 0.016 Reaction: s165 => s117, Rate Law: k44*s165
kr48 = 1.0E-4; kf48 = 10.0 Reaction: s65 + s205 => s206, Rate Law: kf48*s65*s205-kr48*s206
k16 = 0.5 Reaction: s127 => s128 + s127, Rate Law: k16*s127
k3 = 1.0E-5 Reaction: s150 => s133, Rate Law: k3
k35 = 0.01 Reaction: s173 => s113, Rate Law: k35*s173
k54 = 2.0E-4 Reaction: s195 => s108, Rate Law: k54*s195

States:

Name Description
s113 [Nfkb1-201]
s213 PromIkBa:RNAP3:p50p65:IkBa
s122 sa13_degraded
s128 [Tumor necrosis factor alpha-induced protein 3]
s36 [Transcription factor RelB]
s197 Promp105:RNAP1:p50p65:IkBa
s178 [Nfkbia-201]
s198 Promp105:RNAP
s189 [Nuclear factor NF-kappa-B p105 subunit; NF-kappa-B inhibitor alpha; Inhibitor of nuclear factor kappa-B kinase subunit alpha; Inhibitor of nuclear factor kappa-B kinase subunit beta; NF-kappa-B essential modulator; Transcription factor p65]
s160 InactivePRaseonp65
s127 [Tnfaip3-201]
s134 sa20_degraded
s129 sa444_degraded
s192 [Nuclear factor NF-kappa-B p105 subunit; Transcription factor p65; NF-kappa-B inhibitor alpha]
s119 [Transcription factor p65]
s118 sa8_degraded
s205 PromIkBa:RNAP3
s131 sa19_degraded
s133 [Inhibitor of nuclear factor kappa-B kinase subunit alpha; Inhibitor of nuclear factor kappa-B kinase subunit beta; NF-kappa-B essential modulator]
s126 [Tumor necrosis factor alpha-induced protein 3]
s193 Promp65:RNAP2
s111 sa438_degraded
s112 sa3_degraded
s124 sa12_degraded
s156 csa21_degraded
s109 sa4_degraded
s214 IkBa:RNAP3:FTAz:p50p65
s93 [NF-kappa-B inhibitor alpha]
s117 [Rela-201]
s120 sa9_degraded
s165 ActivePRaseonp65
s132 [Inhibitor of nuclear factor kappa-B kinase subunit alpha; Inhibitor of nuclear factor kappa-B kinase subunit beta; NF-kappa-B essential modulator]
s206 PromIkBa:RNAP3:FTAz
s22 [Transcription factor RelB]
s185 ActivePRaseonIkB_alpha
s199 Promp105:RNAP:FTAX
s170 InactivePRaseonp105
s130 [Inhibitor of nuclear factor kappa-B kinase subunit alpha; Inhibitor of nuclear factor kappa-B kinase subunit beta; NF-kappa-B essential modulator]
s215 PromIkBa:RNAP3:FTAz:p50p65:IkB_alpha
s195 [Nuclear factor NF-kappa-B p105 subunit; Transcription factor p65]
s188 [Nuclear factor NF-kappa-B p105 subunit; NF-kappa-B inhibitor alpha; Transcription factor p65]
s108 csa17_degraded
s123 [NF-kappa-B inhibitor alpha]
s201 Promp105:RNAP1:FTAx:p50p65:IkBa
s114 [Nuclear factor NF-kappa-B p105 subunit]
s173 ActivePRaseonp105
s190 [Nuclear factor NF-kappa-B p105 subunit; Transcription factor p65]
s65 [Transcription factor RelB]
s110 [Nuclear factor NF-kappa-B p105 subunit]
s200 Promp105:RNAP1:FTAx:p50p65
s158 csa9_degraded

Observables: none

Raghunathan2009 - Genome-scale metabolic network of Salmonella typhimurium (iRR1083)This model is described in the artic…

BACKGROUND: Infections with Salmonella cause significant morbidity and mortality worldwide. Replication of Salmonella typhimurium inside its host cell is a model system for studying the pathogenesis of intracellular bacterial infections. Genome-scale modeling of bacterial metabolic networks provides a powerful tool to identify and analyze pathways required for successful intracellular replication during host-pathogen interaction. RESULTS: We have developed and validated a genome-scale metabolic network of Salmonella typhimurium LT2 (iRR1083). This model accounts for 1,083 genes that encode proteins catalyzing 1,087 unique metabolic and transport reactions in the bacterium. We employed flux balance analysis and in silico gene essentiality analysis to investigate growth under a wide range of conditions that mimic in vitro and host cell environments. Gene expression profiling of S. typhimurium isolated from macrophage cell lines was used to constrain the model to predict metabolic pathways that are likely to be operational during infection. CONCLUSION: Our analysis suggests that there is a robust minimal set of metabolic pathways that is required for successful replication of Salmonella inside the host cell. This model also serves as platform for the integration of high-throughput data. Its computational power allows identification of networked metabolic pathways and generation of hypotheses about metabolism during infection, which might be used for the rational design of novel antibiotics or vaccine strains. link: http://identifiers.org/pubmed/19356237

Parameters: none

States: none

Observables: none

Raghunathan2010 - Genome-scale metabolic network of Francisella tularensis (iRS605)This model is described in the articl…

BACKGROUND: Francisella tularensis is a prototypic example of a pathogen for which few experimental datasets exist, but for which copious high-throughout data are becoming available because of its re-emerging significance as biothreat agent. The virulence of Francisella tularensis depends on its growth capabilities within a defined environmental niche of the host cell. RESULTS: We reconstructed the metabolism of Francisella as a stoichiometric matrix. This systems biology approach demonstrated that changes in carbohydrate utilization and amino acid metabolism play a pivotal role in growth, acid resistance, and energy homeostasis during infection with Francisella. We also show how varying the expression of certain metabolic genes in different environments efficiently controls the metabolic capacity of F. tularensis. Selective gene-expression analysis showed modulation of sugar catabolism by switching from oxidative metabolism (TCA cycle) in the initial stages of infection to fatty acid oxidation and gluconeogenesis later on. Computational analysis with constraints derived from experimental data revealed a limited set of metabolic genes that are operational during infection. CONCLUSIONS: This integrated systems approach provides an important tool to understand the pathogenesis of an ill-characterized biothreat agent and to identify potential novel drug targets when rapid target identification is required should such microbes be intentionally released or become epidemic. link: http://identifiers.org/pubmed/20731870

Parameters: none

States: none

Observables: none

BIOMD0000000313 @ v0.0.1

This is the model of IL13 induced signalling in MedB-1 cell described in the article: **Dynamic Mathematical Modeling o…

Primary mediastinal B-cell lymphoma (PMBL) and classical Hodgkin lymphoma (cHL) share a frequent constitutive activation of JAK (Janus kinase)/STAT signaling pathway. Because of complex, nonlinear relations within the pathway, key dynamic properties remained to be identified to predict possible strategies for intervention. We report the development of dynamic pathway models based on quantitative data collected on signaling components of JAK/STAT pathway in two lymphoma-derived cell lines, MedB-1 and L1236, representative of PMBL and cHL, respectively. We show that the amounts of STAT5 and STAT6 are higher whereas those of SHP1 are lower in the two lymphoma cell lines than in normal B cells. Distinctively, L1236 cells harbor more JAK2 and less SHP1 molecules per cell than MedB-1 or control cells. In both lymphoma cell lines, we observe interleukin-13 (IL13)-induced activation of IL4 receptor α, JAK2, and STAT5, but not of STAT6. Genome-wide, 11 early and 16 sustained genes are upregulated by IL13 in both lymphoma cell lines. Specifically, the known STAT-inducible negative regulators CISH and SOCS3 are upregulated within 2 hours in MedB-1 but not in L1236 cells. On the basis of this detailed quantitative information, we established two mathematical models, MedB-1 and L1236 model, able to describe the respective experimental data. Most of the model parameters are identifiable and therefore the models are predictive. Sensitivity analysis of the model identifies six possible therapeutic targets able to reduce gene expression levels in L1236 cells and three in MedB-1. We experimentally confirm reduction in target gene expression in response to inhibition of STAT5 phosphorylation, thereby validating one of the predicted targets. link: http://identifiers.org/pubmed/21127196

Parameters:

Name Description
pSTAT5_dephosphorylation = 3.43392E-4 Reaction: pSTAT5 => STAT5; SHP1, Rate Law: pSTAT5_dephosphorylation*pSTAT5*SHP1*cell
Kon_IL13Rec = 0.00341992 Reaction: Rec => IL13_Rec; IL13, Rate Law: Kon_IL13Rec*IL13*Rec*cell
STAT5_phosphorylation = 0.0382596 Reaction: STAT5 => pSTAT5; pJAK2, Rate Law: STAT5_phosphorylation*STAT5*pJAK2*cell
SOCS3_degradation = 0.0429186 Reaction: SOCS3 =>, Rate Law: SOCS3_degradation*SOCS3*cell
DecoyR_binding = 1.24391E-4 Reaction: DecoyR => IL13_DecoyR; IL13, Rate Law: DecoyR_binding*IL13*DecoyR*cell
Rec_intern = 0.103346 Reaction: Rec => Rec_i, Rate Law: Rec_intern*Rec*cell
CD274mRNA_production = 8.21752E-5 Reaction: => CD274mRNA; pSTAT5, Rate Law: pSTAT5*CD274mRNA_production*cell
pJAK2_dephosphorylation = 6.21906E-4 Reaction: pJAK2 => JAK2; SHP1, Rate Law: pJAK2_dephosphorylation*pJAK2*SHP1*cell
pRec_degradation = 0.172928 Reaction: p_IL13_Rec_i =>, Rate Law: pRec_degradation*p_IL13_Rec_i*cell
SOCS3_accumulation = 3.70803; SOCS3_translation = 11.9086 Reaction: => SOCS3; SOCS3mRNA, Rate Law: SOCS3mRNA*SOCS3_translation/(SOCS3_accumulation+SOCS3mRNA)*cell
SOCS3mRNA_production = 0.00215826 Reaction: => SOCS3mRNA; pSTAT5, Rate Law: pSTAT5*SOCS3mRNA_production*cell
pRec_intern = 0.15254 Reaction: p_IL13_Rec => p_IL13_Rec_i, Rate Law: pRec_intern*p_IL13_Rec*cell
Rec_recycle = 0.00135598 Reaction: Rec_i => Rec, Rate Law: Rec_recycle*Rec_i*cell
IL13stimulation = 1.0 ng_per_ml Reaction: IL13 = 2.265*IL13stimulation, Rate Law: missing
Rec_phosphorylation = 999.631 Reaction: IL13_Rec => p_IL13_Rec; pJAK2, Rate Law: Rec_phosphorylation*IL13_Rec*pJAK2*cell
JAK2_phosphorylation = 0.157057; JAK2_p_inhibition = 0.0168268 Reaction: JAK2 => pJAK2; IL13_Rec, SOCS3, Rate Law: JAK2_phosphorylation*IL13_Rec*JAK2/(1+JAK2_p_inhibition*SOCS3)*cell

States:

Name Description
p IL13 Rec [MOD:00048; Interleukin-13; Interleukin-4 receptor subunit alpha; Interleukin-13 receptor subunit alpha-1; Phosphoprotein; Non-receptor tyrosine-protein kinase TYK2; interleukin-4 receptor complex; urn:miriam:mod:MOD%3A00048]
SOCS3 [Suppressor of cytokine signaling 3]
IL13 DecoyR [Interleukin-13; Interleukin-13 receptor subunit alpha-2]
SOCS3mRNA [messenger RNA; RNA; Suppressor of cytokine signaling 3]
pSTAT5 [MOD:00048; Signal transducer and activator of transcription 5B; urn:miriam:mod:MOD%3A00048; Signal transducer and activator of transcription 5A; Phosphoprotein]
IL13 [Interleukin-13; interleukin-13 receptor binding]
Rec i [Non-receptor tyrosine-protein kinase TYK2; interleukin-4 receptor complex; Interleukin-13 receptor subunit alpha-1; receptor internalization; Interleukin-4 receptor subunit alpha]
CD274mRNA [messenger RNA; RNA; T-cell surface glycoprotein CD3 zeta chain]
STAT5 [Signal transducer and activator of transcription 5A; Signal transducer and activator of transcription 5B]
p IL13 Rec i [MOD:00048; Interleukin-13; Non-receptor tyrosine-protein kinase TYK2; urn:miriam:mod:MOD%3A00048; interleukin-4 receptor complex; Interleukin-4 receptor subunit alpha; Interleukin-13 receptor subunit alpha-1; receptor internalization]
IL13 Rec [Interleukin-13; Interleukin-13 receptor subunit alpha-1; Interleukin-4 receptor subunit alpha; Non-receptor tyrosine-protein kinase TYK2; interleukin-4 receptor complex]
DecoyR [Interleukin-13 receptor subunit alpha-2]
Rec [interleukin-4 receptor complex; Non-receptor tyrosine-protein kinase TYK2; Interleukin-4 receptor subunit alpha; Interleukin-13 receptor subunit alpha-1; interleukin-13 binding]
pJAK2 [MOD:00048; Tyrosine-protein kinase JAK2; Phosphoprotein; urn:miriam:mod:MOD%3A00048]
JAK2 [Tyrosine-protein kinase JAK2]

Observables: none

BIOMD0000000314 @ v0.0.1

This is the model of IL13 induced signalling in L1236 cells described in the article: **Dynamic Mathematical Modeling…

Primary mediastinal B-cell lymphoma (PMBL) and classical Hodgkin lymphoma (cHL) share a frequent constitutive activation of JAK (Janus kinase)/STAT signaling pathway. Because of complex, nonlinear relations within the pathway, key dynamic properties remained to be identified to predict possible strategies for intervention. We report the development of dynamic pathway models based on quantitative data collected on signaling components of JAK/STAT pathway in two lymphoma-derived cell lines, MedB-1 and L1236, representative of PMBL and cHL, respectively. We show that the amounts of STAT5 and STAT6 are higher whereas those of SHP1 are lower in the two lymphoma cell lines than in normal B cells. Distinctively, L1236 cells harbor more JAK2 and less SHP1 molecules per cell than MedB-1 or control cells. In both lymphoma cell lines, we observe interleukin-13 (IL13)-induced activation of IL4 receptor α, JAK2, and STAT5, but not of STAT6. Genome-wide, 11 early and 16 sustained genes are upregulated by IL13 in both lymphoma cell lines. Specifically, the known STAT-inducible negative regulators CISH and SOCS3 are upregulated within 2 hours in MedB-1 but not in L1236 cells. On the basis of this detailed quantitative information, we established two mathematical models, MedB-1 and L1236 model, able to describe the respective experimental data. Most of the model parameters are identifiable and therefore the models are predictive. Sensitivity analysis of the model identifies six possible therapeutic targets able to reduce gene expression levels in L1236 cells and three in MedB-1. We experimentally confirm reduction in target gene expression in response to inhibition of STAT5 phosphorylation, thereby validating one of the predicted targets. link: http://identifiers.org/pubmed/21127196

Parameters:

Name Description
Rec_phosphorylation = 9.07541 Reaction: IL13_Rec => p_IL13_Rec; pJAK2, Rate Law: Rec_phosphorylation*IL13_Rec*pJAK2*cell
pSTAT5_dephosphorylation = 0.0116389 Reaction: pSTAT5 => STAT5; SHP1, Rate Law: pSTAT5_dephosphorylation*pSTAT5*SHP1*cell
CD274mRNA_production = 0.0115928 Reaction: => CD274mRNA; pSTAT5, Rate Law: pSTAT5*CD274mRNA_production*cell
Kon_IL13Rec = 0.00174087 Reaction: Rec => IL13_Rec; IL13, Rate Law: Kon_IL13Rec*IL13*Rec*cell
pRec_degradation = 0.417538 Reaction: p_IL13_Rec_i =>, Rate Law: pRec_degradation*p_IL13_Rec_i*cell
pJAK2_dephosphorylation = 0.0981611 Reaction: pJAK2 => JAK2; SHP1, Rate Law: pJAK2_dephosphorylation*pJAK2*SHP1*cell
JAK2_phosphorylation = 0.300019 Reaction: JAK2 => pJAK2; IL13_Rec, Rate Law: JAK2_phosphorylation*JAK2*IL13_Rec*cell
Rec_recycle = 0.0039243 Reaction: Rec_i => Rec, Rate Law: Rec_recycle*Rec_i*cell
pRec_intern = 0.324132 Reaction: p_IL13_Rec => p_IL13_Rec_i, Rate Law: pRec_intern*p_IL13_Rec*cell
IL13stimulation = 1.0 ng_per_ml Reaction: IL13 = 3.776*IL13stimulation, Rate Law: missing
STAT5_phosphorylation = 0.00426767 Reaction: STAT5 => pSTAT5; pJAK2, Rate Law: STAT5_phosphorylation*STAT5*pJAK2*cell
Rec_intern = 0.259686 Reaction: Rec => Rec_i, Rate Law: Rec_intern*Rec*cell

States:

Name Description
p IL13 Rec [Non-receptor tyrosine-protein kinase TYK2; Interleukin-13; interleukin-4 receptor complex; urn:miriam:mod:MOD%3A00048; Phosphoprotein; Interleukin-13 receptor subunit alpha-1; Interleukin-4 receptor subunit alpha]
pSTAT5 [Signal transducer and activator of transcription 5A; Signal transducer and activator of transcription 5B; Phosphoprotein; urn:miriam:mod:MOD%3A00048]
IL13 [Interleukin-13; interleukin-13 receptor binding]
Rec i [Non-receptor tyrosine-protein kinase TYK2; interleukin-4 receptor complex; Interleukin-4 receptor subunit alpha; Interleukin-13 receptor subunit alpha-1; receptor internalization]
CD274mRNA [messenger RNA; RNA; T-cell surface glycoprotein CD3 zeta chain]
STAT5 [Signal transducer and activator of transcription 5B; Signal transducer and activator of transcription 5A]
p IL13 Rec i [urn:miriam:mod:MOD%3A00048; interleukin-4 receptor complex; Interleukin-13 receptor subunit alpha-1; Interleukin-4 receptor subunit alpha; Non-receptor tyrosine-protein kinase TYK2; Interleukin-13; receptor internalization]
IL13 Rec [Non-receptor tyrosine-protein kinase TYK2; Interleukin-13; interleukin-4 receptor complex; Interleukin-4 receptor subunit alpha; Interleukin-13 receptor subunit alpha-1]
Rec [interleukin-4 receptor complex; Non-receptor tyrosine-protein kinase TYK2; Interleukin-13 receptor subunit alpha-1; Interleukin-4 receptor subunit alpha; interleukin-13 binding]
pJAK2 [Tyrosine-protein kinase JAK2; Phosphoprotein; urn:miriam:mod:MOD%3A00048]
JAK2 [Tyrosine-protein kinase JAK2]

Observables: none

BIOMD0000000247 @ v0.0.1

This is the model with unfitted parameters described in the article **Dynamic rerouting of the carbohydrate flux is k…

Eukaryotic cells have evolved various response mechanisms to counteract the deleterious consequences of oxidative stress. Among these processes, metabolic alterations seem to play an important role.We recently discovered that yeast cells with reduced activity of the key glycolytic enzyme triosephosphate isomerase exhibit an increased resistance to the thiol-oxidizing reagent diamide. Here we show that this phenotype is conserved in Caenorhabditis elegans and that the underlying mechanism is based on a redirection of the metabolic flux from glycolysis to the pentose phosphate pathway, altering the redox equilibrium of the cytoplasmic NADP(H) pool. Remarkably, another key glycolytic enzyme, glyceraldehyde-3-phosphate dehydrogenase (GAPDH), is known to be inactivated in response to various oxidant treatments, and we show that this provokes a similar redirection of the metabolic flux.The naturally occurring inactivation of GAPDH functions as a metabolic switch for rerouting the carbohydrate flux to counteract oxidative stress. As a consequence, altering the homoeostasis of cytoplasmic metabolites is a fundamental mechanism for balancing the redox state of eukaryotic cells under stress conditions. link: http://identifiers.org/pubmed/18154684

Parameters:

Name Description
VmALD=322.258 mMpermin; KeqTPI=0.045 dimensionless; KeqALD=0.069 dimensionless; KmALDDHAP=2.4 mM; KmALDGAPi=10.0 mM; KmALDF16P=0.3 mM; KmALDGAP=2.0 mM Reaction: F16P => DHAP + GA3P, Rate Law: cytoplasm*VmALD*F16P/KmALDF16P*(1-DHAP*GA3P/(F16P*KeqALD))/(1+F16P/KmALDF16P+DHAP/KmALDDHAP+GA3P/KmALDGAP+F16P*GA3P/(KmALDF16P*KmALDGAPi)+DHAP*GA3P/(KmALDDHAP*KmALDGAP))
KmEry4P=0.09 mM; KmGA3P=2.1 mM; VmTransk2f=3.2 mMpermin; KmXyl5P=0.16 mM; KmF6P=1.1 mM; VmTransk2r=43.0 mMpermin Reaction: Xyl5P + Erythrose4P => GA3P + F6P, Rate Law: cytoplasm*(VmTransk2f*Erythrose4P*Xyl5P/(KmEry4P*KmXyl5P)-VmTransk2r*F6P*GA3P/(KmF6P*KmGA3P))/((1+Xyl5P/KmXyl5P+GA3P/KmGA3P)*(1+Erythrose4P/KmEry4P+F6P/KmF6P))
VmG6PDH=4.0 mMpermin; KmG6P=0.04 mM; KmNADP=0.02 mM; KiNADPH=0.017 mM Reaction: G6P + NADP => D6PGluconoLactone + NADPH; NADPH, Rate Law: cytoplasm*VmG6PDH*G6P*NADP/(KmG6P*KmNADP)/((1+G6P/KmG6P+NADPH/KiNADPH)*(1+NADP/KmNADP))
VmPPIf=3458.0 mMpermin; KmRibu5P=1.6 mM; KmRibo5P=1.6 mM; VmPPIr=3458.0 mMpermin Reaction: Ribulose5P => Ribose5P, Rate Law: cytoplasm*(VmPPIf*Ribulose5P/KmRibu5P-VmPPIr*Ribose5P/KmRibo5P)/(1+Ribulose5P/KmRibu5P+Ribose5P/KmRibo5P)
KeqENO=6.7 dimensionless; KmENOP2G=0.04 mM; KmENOPEP=0.5 mM; VmENO=365.806 mMpermin Reaction: P2G => PEP, Rate Law: cytoplasm*VmENO/KmENOP2G*(P2G-PEP/KeqENO)/(1+P2G/KmENOP2G+PEP/KmENOPEP)
KeqAK=0.45 dimensionless; KATPASE=39.5 permin; SUMAXP = 4.1 Reaction: P => X, Rate Law: cytoplasm*KATPASE*(((P-4*KeqAK*P)-SUMAXP)+(((P^2-4*KeqAK*P^2)-2*P*SUMAXP)+8*KeqAK*P*SUMAXP+SUMAXP^2)^0.5)/(2-8*KeqAK)
KmPGMP3G=1.2 mM; KeqPGM=0.19 dimensionless; VmPGM=2525.81 mMpermin; KmPGMP2G=0.08 mM Reaction: P3G => P2G, Rate Law: cytoplasm*VmPGM/KmPGMP3G*(P3G-P2G/KeqPGM)/(1+P3G/KmPGMP3G+P2G/KmPGMP2G)
VmGAPDHr=6549.68 mMpermin; VmGAPDHf=1184.52 mMpermin; k_rel_GAPDH = 1.0 dimensionless; KeqTPI=0.045 dimensionless; KmGAPDHNAD=0.09 mM; KeqGAPDH=0.005 dimensionless; KmGAPDHBPG=0.0098 mM; KmGAPDHGAP=0.21 mM; KmGAPDHNADH=0.06 mM Reaction: GA3P + NAD => BPG + NADH, Rate Law: cytoplasm*k_rel_GAPDH*VmGAPDHf*GA3P*NAD/(KmGAPDHGAP*KmGAPDHNAD)*(1-BPG*NADH/(GA3P*NAD*KeqGAPDH))/((1+GA3P/KmGAPDHGAP+BPG/KmGAPDHBPG)*(1+NAD/KmGAPDHNAD+NADH/KmGAPDHNADH))
KmADHNAD=0.17 mM; KiADHETOH=90.0 mM; KiADHNADH=0.031 mM; KiADHACE=1.1 mM; KmADHETOH=17.0 mM; KeqADH=6.9E-5 dimensionless; KmADHNADH=0.11 mM; KiADHNAD=0.92 mM; VmADH=810.0 mMpermin; KmADHACE=1.11 mM Reaction: ACE + NADH => ETOH + NAD, Rate Law: cytoplasm*(-VmADH/(KiADHNAD*KmADHETOH)*(NAD*ETOH-NADH*ACE/KeqADH)/(1+NAD/KiADHNAD+KmADHNAD*ETOH/(KiADHNAD*KmADHETOH)+KmADHNADH*ACE/(KiADHNADH*KmADHACE)+NADH/KiADHNADH+NAD*ETOH/(KiADHNAD*KmADHETOH)+KmADHNADH*NAD*ACE/(KiADHNAD*KiADHNADH*KmADHACE)+KmADHNAD*ETOH*NADH/(KiADHNAD*KmADHETOH*KiADHNADH)+NADH*ACE/(KiADHNADH*KmADHACE)+NAD*ETOH*ACE/(KiADHNAD*KmADHETOH*KiADHACE)+ETOH*NADH*ACE/(KiADHETOH*KiADHNADH*KmADHACE)))
Km6PGL=0.8 mM; Vm6PGL=4.0 mMpermin Reaction: D6PGluconoLactone => D6PGluconate, Rate Law: cytoplasm*Vm6PGL*D6PGluconoLactone/(Km6PGL+D6PGluconoLactone)
KSUCC=21.4 permin Reaction: ACE + NAD => NADH + SUCC, Rate Law: cytoplasm*KSUCC*ACE
kNADPH=2.0 permin Reaction: NADPH => NADP, Rate Law: cytoplasm*kNADPH*NADPH
CPFKATP=3.0 dimensionless; CPFKF16BP=0.397 dimensionless; CPFKF26BP=0.0174 dimensionless; VmPFK=182.903 mMpermin; L0=0.66 dimensionless; SUMAXP = 4.1; KmPFKF6P=0.1 mM; KeqAK=0.45 dimensionless; KPFKF26BP=6.82E-4 mM; CPFKAMP=0.0845 dimensionless; KPFKAMP=0.0995 mM; CiPFKATP=100.0 dimensionless; KPFKF16BP=0.111 mM; KmPFKATP=0.71 mM; gR=5.12 dimensionless; KiPFKATP=0.65 mM Reaction: F6P + P => F16P; F26BP, Rate Law: cytoplasm*gR*VmPFK*F6P*((((-SUMAXP)+P)-4*KeqAK*P)+(((SUMAXP^2-2*SUMAXP*P)+8*KeqAK*SUMAXP*P+P^2)-4*KeqAK*P^2)^0.5)*(1+F6P/KmPFKF6P+((((-SUMAXP)+P)-4*KeqAK*P)+(((SUMAXP^2-2*SUMAXP*P)+8*KeqAK*SUMAXP*P+P^2)-4*KeqAK*P^2)^0.5)/((2-8*KeqAK)*KmPFKATP)+gR*F6P*((((-SUMAXP)+P)-4*KeqAK*P)+(((SUMAXP^2-2*SUMAXP*P)+8*KeqAK*SUMAXP*P+P^2)-4*KeqAK*P^2)^0.5)/((2-8*KeqAK)*KmPFKATP*KmPFKF6P))/((2-8*KeqAK)*KmPFKATP*KmPFKF6P*(L0*(1+CPFKF26BP*F26BP/KPFKF26BP+CPFKF16BP*F16P/KPFKF16BP)^2*(1+2*CPFKAMP*KeqAK*(SUMAXP-(((SUMAXP^2-2*SUMAXP*P)+8*KeqAK*SUMAXP*P+P^2)-4*KeqAK*P^2)^0.5)^2/((-1+4*KeqAK)*KPFKAMP*(((SUMAXP-P)+4*KeqAK*P)-(((SUMAXP^2-2*SUMAXP*P)+8*KeqAK*SUMAXP*P+P^2)-4*KeqAK*P^2)^0.5)))^2*(1+CiPFKATP*((((-SUMAXP)+P)-4*KeqAK*P)+(((SUMAXP^2-2*SUMAXP*P)+8*KeqAK*SUMAXP*P+P^2)-4*KeqAK*P^2)^0.5)/((2-8*KeqAK)*KiPFKATP))^2*(1+CPFKATP*((((-SUMAXP)+P)-4*KeqAK*P)+(((SUMAXP^2-2*SUMAXP*P)+8*KeqAK*SUMAXP*P+P^2)-4*KeqAK*P^2)^0.5)/((2-8*KeqAK)*KmPFKATP))^2/((1+F26BP/KPFKF26BP+F16P/KPFKF16BP)^2*(1+2*KeqAK*(SUMAXP-(((SUMAXP^2-2*SUMAXP*P)+8*KeqAK*SUMAXP*P+P^2)-4*KeqAK*P^2)^0.5)^2/((-1+4*KeqAK)*KPFKAMP*(((SUMAXP-P)+4*KeqAK*P)-(((SUMAXP^2-2*SUMAXP*P)+8*KeqAK*SUMAXP*P+P^2)-4*KeqAK*P^2)^0.5)))^2*(1+((((-SUMAXP)+P)-4*KeqAK*P)+(((SUMAXP^2-2*SUMAXP*P)+8*KeqAK*SUMAXP*P+P^2)-4*KeqAK*P^2)^0.5)/((2-8*KeqAK)*KiPFKATP))^2)+(1+F6P/KmPFKF6P+((((-SUMAXP)+P)-4*KeqAK*P)+(((SUMAXP^2-2*SUMAXP*P)+8*KeqAK*SUMAXP*P+P^2)-4*KeqAK*P^2)^0.5)/((2-8*KeqAK)*KmPFKATP)+gR*F6P*((((-SUMAXP)+P)-4*KeqAK*P)+(((SUMAXP^2-2*SUMAXP*P)+8*KeqAK*SUMAXP*P+P^2)-4*KeqAK*P^2)^0.5)/((2-8*KeqAK)*KmPFKATP*KmPFKF6P))^2))
VmTransaldf=55.0 mMpermin; KmF6P=0.32 mM; KmGA3P=0.22 mM; VmTransaldr=10.0 mMpermin; KmSeduhept=0.18 mM; KmEry4P=0.018 mM Reaction: Seduhept7P + GA3P => F6P + Erythrose4P, Rate Law: cytoplasm*(VmTransaldf*GA3P*Seduhept7P/(KmGA3P*KmSeduhept)-VmTransaldr*F6P*Erythrose4P/(KmF6P*KmEry4P))/((1+GA3P/KmGA3P+F6P/KmF6P)*(1+Seduhept7P/KmSeduhept+Erythrose4P/KmEry4P))
KmG3PDHGLY=1.0 mM; KeqTPI=0.045 dimensionless; KeqG3PDH=4300.0 dimensionless; KmG3PDHDHAP=0.4 mM; KmG3PDHNADH=0.023 mM; KmG3PDHNAD=0.93 mM; VmG3PDH=70.15 mMpermin Reaction: DHAP + NADH => GLY + NAD, Rate Law: cytoplasm*VmG3PDH*((-GLY*NAD/KeqG3PDH)+NADH*DHAP/(1+KeqTPI))/(KmG3PDHDHAP*KmG3PDHNADH*(1+NAD/KmG3PDHNAD+NADH/KmG3PDHNADH)*(1+GLY/KmG3PDHGLY+DHAP/((1+KeqTPI)*KmG3PDHDHAP)))
VmGluDH=4.0 mMpermin; KmGluconate=0.02 mM; KmNADP=0.03 mM; KiNADPH=0.03 mM Reaction: D6PGluconate + NADP => Ribulose5P + NADPH; NADPH, Rate Law: cytoplasm*VmGluDH*D6PGluconate*NADP/(KmGluconate*KmNADP)/((1+D6PGluconate/KmGluconate+NADPH/KiNADPH)*(1+NADP/KmNADP))
VmGA3P=555.0 mMpermin; KmDHAP=1.23 mM; k_rel_TPI = 1.0 dimensionless; KmGA3P=1.27 mM; VmDHAP=10900.0 mMpermin Reaction: GA3P => DHAP, Rate Law: cytoplasm*k_rel_TPI*(VmDHAP*GA3P/KmGA3P-VmGA3P*DHAP/KmDHAP)/(1+GA3P/KmGA3P+DHAP/KmDHAP)
KmXyl=1.5 mM; KmRibu5P=1.5 mM; VmR5PIr=1039.0 mMpermin; VmR5PIf=1039.0 mMpermin Reaction: Ribulose5P => Xyl5P, Rate Law: cytoplasm*(VmR5PIf*Ribulose5P/KmRibu5P-VmR5PIr*Xyl5P/KmXyl)/(1+Ribulose5P/KmRibu5P+Xyl5P/KmXyl)
VmGLT=97.264 mMpermin; KeqGLT=1.0 mM; KmGLTGLCo=1.1918 mM; KmGLTGLCi=1.1918 mM Reaction: GLCo => GLCi, Rate Law: cytoplasm*VmGLT*(GLCo-GLCi/KeqGLT)/(KmGLTGLCo*(1+GLCo/KmGLTGLCo+GLCi/KmGLTGLCi+0.91*GLCo*GLCi/(KmGLTGLCi*KmGLTGLCo)))
KeqAK=0.45 dimensionless; KmPGKBPG=0.003 mM; KeqPGK=3200.0 dimensionless; KmPGKADP=0.2 mM; KmPGKATP=0.3 mM; VmPGK=1306.45 mMpermin; KmPGKP3G=0.53 mM; SUMAXP = 4.1 Reaction: BPG => P3G + P, Rate Law: cytoplasm*VmPGK*(KeqPGK*BPG*(SUMAXP-(((SUMAXP^2-2*SUMAXP*P)+8*KeqAK*SUMAXP*P+P^2)-4*KeqAK*P^2)^0.5)/(1-4*KeqAK)-((((-SUMAXP)+P)-4*KeqAK*P)+(((SUMAXP^2-2*SUMAXP*P)+8*KeqAK*SUMAXP*P+P^2)-4*KeqAK*P^2)^0.5)*P3G/(2-8*KeqAK))/(KmPGKATP*KmPGKP3G*(1+(SUMAXP-(((SUMAXP^2-2*SUMAXP*P)+8*KeqAK*SUMAXP*P+P^2)-4*KeqAK*P^2)^0.5)/((1-4*KeqAK)*KmPGKADP)+((((-SUMAXP)+P)-4*KeqAK*P)+(((SUMAXP^2-2*SUMAXP*P)+8*KeqAK*SUMAXP*P+P^2)-4*KeqAK*P^2)^0.5)/((2-8*KeqAK)*KmPGKATP))*(1+BPG/KmPGKBPG+P3G/KmPGKP3G))
VmPYK=1088.71 mMpermin; KeqAK=0.45 dimensionless; KmPYKADP=0.53 mM; KmPYKPEP=0.14 mM; KmPYKATP=1.5 mM; KeqPYK=6500.0 dimensionless; KmPYKPYR=21.0 mM; SUMAXP = 4.1 Reaction: PEP => PYR + P, Rate Law: cytoplasm*VmPYK/(KmPYKPEP*KmPYKADP)*(PEP*(SUMAXP-(((P^2-4*KeqAK*P^2)-2*P*SUMAXP)+8*KeqAK*P*SUMAXP+SUMAXP^2)^0.5)/(1-4*KeqAK)-PYR*(((P-4*KeqAK*P)-SUMAXP)+(((P^2-4*KeqAK*P^2)-2*P*SUMAXP)+8*KeqAK*P*SUMAXP+SUMAXP^2)^0.5)/(2-8*KeqAK)/KeqPYK)/((1+PEP/KmPYKPEP+PYR/KmPYKPYR)*(1+(((P-4*KeqAK*P)-SUMAXP)+(((P^2-4*KeqAK*P^2)-2*P*SUMAXP)+8*KeqAK*P*SUMAXP+SUMAXP^2)^0.5)/(2-8*KeqAK)/KmPYKATP+(SUMAXP-(((P^2-4*KeqAK*P^2)-2*P*SUMAXP)+8*KeqAK*P*SUMAXP+SUMAXP^2)^0.5)/(1-4*KeqAK)/KmPYKADP))
VmPDC=174.194 mMpermin; KmPDCPYR=4.33 mM; nPDC=1.9 dimensionless Reaction: PYR => ACE + CO2, Rate Law: cytoplasm*VmPDC*PYR^nPDC/KmPDCPYR^nPDC/(1+PYR^nPDC/KmPDCPYR^nPDC)
KmPGIF6P=0.3 mM; KeqPGI=0.314 dimensionless; VmPGI=339.677 mMpermin; KmPGIG6P=1.4 mM Reaction: G6P => F6P, Rate Law: cytoplasm*VmPGI/KmPGIG6P*(G6P-F6P/KeqPGI)/(1+G6P/KmPGIG6P+F6P/KmPGIF6P)
KmSeduhept=0.15 mM; KmXyl5P=0.15 mM; VmTransk1f=4.0 mMpermin; KmRibose5P=0.1 mM; KmGA3P=0.1 mM; VmTransk1r=2.0 mMpermin Reaction: Ribose5P + Xyl5P => GA3P + Seduhept7P, Rate Law: cytoplasm*(VmTransk1f*Ribose5P*Xyl5P/(KmRibose5P*KmXyl5P)-VmTransk1r*GA3P*Seduhept7P/(KmGA3P*KmSeduhept))/((1+Ribose5P/KmRibose5P+GA3P/KmGA3P)*(1+Xyl5P/KmXyl5P+Seduhept7P/KmSeduhept))
KeqAK=0.45 dimensionless; KmGLKADP=0.23 mM; KmGLKGLCi=0.08 mM; VmGLK=226.452 mMpermin; KmGLKG6P=30.0 mM; KeqGLK=3800.0 dimensionless; KmGLKATP=0.15 mM; SUMAXP = 4.1 Reaction: GLCi + P => G6P, Rate Law: cytoplasm*VmGLK*((-G6P*(SUMAXP-(((SUMAXP^2-2*SUMAXP*P)+8*KeqAK*SUMAXP*P+P^2)-4*KeqAK*P^2)^0.5)/((1-4*KeqAK)*KeqGLK))+GLCi*((((-SUMAXP)+P)-4*KeqAK*P)+(((SUMAXP^2-2*SUMAXP*P)+8*KeqAK*SUMAXP*P+P^2)-4*KeqAK*P^2)^0.5)/(2-8*KeqAK))/(KmGLKATP*KmGLKGLCi*(1+G6P/KmGLKG6P+GLCi/KmGLKGLCi)*(1+(SUMAXP-(((SUMAXP^2-2*SUMAXP*P)+8*KeqAK*SUMAXP*P+P^2)-4*KeqAK*P^2)^0.5)/((1-4*KeqAK)*KmGLKADP)+((((-SUMAXP)+P)-4*KeqAK*P)+(((SUMAXP^2-2*SUMAXP*P)+8*KeqAK*SUMAXP*P+P^2)-4*KeqAK*P^2)^0.5)/((2-8*KeqAK)*KmGLKATP)))

States:

Name Description
Seduhept7P [sedoheptulose 7-phosphate]
P [ADP; ATP; ADP; ADP; ATP]
GLY [glycerol; Glycerol]
DHAP [dihydroxyacetone phosphate]
F16P [keto-D-fructose 1,6-bisphosphate; D-Fructose 1,6-bisphosphate]
NADPH [NADPH]
Xyl5P [D-xylulose 5-phosphate]
GLCi [glucose; C00293]
P2G [2-phospho-D-glyceric acid; 2-Phospho-D-glycerate]
Ribulose5P [D-ribulose 5-phosphate]
P3G [3-phospho-D-glyceric acid; 3-Phospho-D-glycerate]
GLCo [glucose; C00293]
Ribose5P [aldehydo-D-ribose 5-phosphate]
NADH [NADH; NADH]
PYR [pyruvate; Pyruvate]
X X
NADP [NADP(+)]
Erythrose4P [D-erythrose 4-phosphate]
GA3P [glyceraldehyde 3-phosphate]
SUCC [succinate(2-)]
BPG [3-phospho-D-glyceroyl dihydrogen phosphate; 3-Phospho-D-glyceroyl phosphate]
F6P [keto-D-fructose 6-phosphate; beta-D-Fructose 6-phosphate]
CO2 [carbon dioxide; CO2]
G6P [alpha-D-glucose 6-phosphate; alpha-D-Glucose 6-phosphate]
D6PGluconoLactone [6-O-phosphono-D-glucono-1,5-lactone]
D6PGluconate [6-phospho-D-gluconate]
PEP [phosphoenolpyruvate; Phosphoenolpyruvate]
NAD [NAD(+); NAD+]
ETOH [ethanol; Ethanol]
ACE [acetaldehyde; Acetaldehyde]

Observables: none

MODEL8568434338 @ v0.0.1

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

Mycobacterium tuberculosis is the focus of several investigations for design of newer drugs, as tuberculosis remains a major epidemic despite the availability of several drugs and a vaccine. Mycobacteria owe many of their unique qualities to mycolic acids, which are known to be important for their growth, survival, and pathogenicity. Mycolic acid biosynthesis has therefore been the focus of a number of biochemical and genetic studies. It also turns out to be the pathway inhibited by front-line anti-tubercular drugs such as isoniazid and ethionamide. Recent years have seen the emergence of systems-based methodologies that can be used to study microbial metabolism. Here, we seek to apply insights from flux balance analyses of the mycolic acid pathway (MAP) for the identification of anti-tubercular drug targets. We present a comprehensive model of mycolic acid synthesis in the pathogen M. tuberculosis involving 197 metabolites participating in 219 reactions catalysed by 28 proteins. Flux balance analysis (FBA) has been performed on the MAP model, which has provided insights into the metabolic capabilities of the pathway. In silico systematic gene deletions and inhibition of InhA by isoniazid, studied here, provide clues about proteins essential for the pathway and hence lead to a rational identification of possible drug targets. Feasibility studies using sequence analysis of the M. tuberculosis H37Rv and human proteomes indicate that, apart from the known InhA, potential targets for anti-tubercular drug design are AccD3, Fas, FabH, Pks13, DesA1/2, and DesA3. Proteins identified as essential by FBA correlate well with those previously identified experimentally through transposon site hybridisation mutagenesis. This study demonstrates the application of FBA for rational identification of potential anti-tubercular drug targets, which can indeed be a general strategy in drug design. The targets, chosen based on the critical points in the pathway, form a ready shortlist for experimental testing. link: http://identifiers.org/pubmed/16261191

Parameters: none

States: none

Observables: none

This is a mathematical model provides a platform for investigating the efficacy of dendritic cell vaccines during cancer…

Therapeutic protocols in immunotherapy are usually proposed following the intuition and experience of the therapist. In order to deduce such protocols mathematical modeling, optimal control and simulations are used instead of the therapist's experience. Clinical efficacy of dendritic cell (DC) vaccines to cancer treatment is still unclear, since dendritic cells face several obstacles in the host environment, such as immunosuppression and poor transference to the lymph nodes reducing the vaccine effect. In view of that, we have created a mathematical murine model to measure the effects of dendritic cell injections admitting such obstacles. In addition, the model considers a therapy given by bolus injections of small duration as opposed to a continual dose. Doses timing defines the therapeutic protocols, which in turn are improved to minimize the tumor mass by an optimal control algorithm. We intend to supplement therapist's experience and intuition in the protocol's implementation. Experimental results made on mice infected with melanoma with and without therapy agree with the model. It is shown that the dendritic cells' percentage that manages to reach the lymph nodes has a crucial impact on the therapy outcome. This suggests that efforts in finding better methods to deliver DC vaccines should be pursued. link: http://identifiers.org/pubmed/28912828

Parameters: none

States: none

Observables: none

Rantasalo2015-Synthetic_expresion_modulator_constitutiveSTF_B42 This model is part of a family of models describing a m…

This work describes the development and characterization of a modular synthetic expression system that provides a broad range of adjustable and predictable expression levels in S. cerevisiae. The system works as a fixed-gain transcription amplifier, where the input signal is transferred via a synthetic transcription factor (sTF) onto a synthetic promoter, containing a defined core promoter, generating a transcription output signal. The system activation is based on the bacterial LexA-DNA-binding domain, a set of modified, modular LexA-binding sites and a selection of transcription activation domains. We show both experimentally and computationally that the tuning of the system is achieved through the selection of three separate modules, each of which enables an adjustable output signal: 1) the transcription-activation domain of the sTF, 2) the binding-site modules in the output promoter, and 3) the core promoter modules which define the transcription initiation site in the output promoter. The system has a novel bidirectional architecture that enables generation of compact, yet versatile expression modules for multiple genes with highly diversified expression levels ranging from negligible to very strong using one synthetic transcription factor. In contrast to most existing modular gene expression regulation systems, the present system is independent from externally added compounds. Furthermore, the established system was minimally affected by the several tested growth conditions. These features suggest that it can be highly useful in large scale biotechnology applications. link: http://identifiers.org/doi/10.1371/journal.pone.0148320

Parameters: none

States: none

Observables: none

Rantasalo2015-Synthetic_expresion_modulator_constitutiveSTF_VP16 This model is part of a family of models describing a…

This work describes the development and characterization of a modular synthetic expression system that provides a broad range of adjustable and predictable expression levels in S. cerevisiae. The system works as a fixed-gain transcription amplifier, where the input signal is transferred via a synthetic transcription factor (sTF) onto a synthetic promoter, containing a defined core promoter, generating a transcription output signal. The system activation is based on the bacterial LexA-DNA-binding domain, a set of modified, modular LexA-binding sites and a selection of transcription activation domains. We show both experimentally and computationally that the tuning of the system is achieved through the selection of three separate modules, each of which enables an adjustable output signal: 1) the transcription-activation domain of the sTF, 2) the binding-site modules in the output promoter, and 3) the core promoter modules which define the transcription initiation site in the output promoter. The system has a novel bidirectional architecture that enables generation of compact, yet versatile expression modules for multiple genes with highly diversified expression levels ranging from negligible to very strong using one synthetic transcription factor. In contrast to most existing modular gene expression regulation systems, the present system is independent from externally added compounds. Furthermore, the established system was minimally affected by the several tested growth conditions. These features suggest that it can be highly useful in large scale biotechnology applications. link: http://identifiers.org/doi/10.1371/journal.pone.0148320

Parameters: none

States: none

Observables: none

Rantasalo2015-Synthetic_expresion_modulator_constitutiveSTF_VP16_pBID2-EDcorePromoter This model is part of a family of…

This work describes the development and characterization of a modular synthetic expression system that provides a broad range of adjustable and predictable expression levels in S. cerevisiae. The system works as a fixed-gain transcription amplifier, where the input signal is transferred via a synthetic transcription factor (sTF) onto a synthetic promoter, containing a defined core promoter, generating a transcription output signal. The system activation is based on the bacterial LexA-DNA-binding domain, a set of modified, modular LexA-binding sites and a selection of transcription activation domains. We show both experimentally and computationally that the tuning of the system is achieved through the selection of three separate modules, each of which enables an adjustable output signal: 1) the transcription-activation domain of the sTF, 2) the binding-site modules in the output promoter, and 3) the core promoter modules which define the transcription initiation site in the output promoter. The system has a novel bidirectional architecture that enables generation of compact, yet versatile expression modules for multiple genes with highly diversified expression levels ranging from negligible to very strong using one synthetic transcription factor. In contrast to most existing modular gene expression regulation systems, the present system is independent from externally added compounds. Furthermore, the established system was minimally affected by the several tested growth conditions. These features suggest that it can be highly useful in large scale biotechnology applications. link: http://identifiers.org/doi/10.1371/journal.pone.0148320

Parameters: none

States: none

Observables: none

Rantasalo2015-Synthetic_expresion_modulator_inducedSTF_B42 This model is part of a family of models describing a modula…

This work describes the development and characterization of a modular synthetic expression system that provides a broad range of adjustable and predictable expression levels in S. cerevisiae. The system works as a fixed-gain transcription amplifier, where the input signal is transferred via a synthetic transcription factor (sTF) onto a synthetic promoter, containing a defined core promoter, generating a transcription output signal. The system activation is based on the bacterial LexA-DNA-binding domain, a set of modified, modular LexA-binding sites and a selection of transcription activation domains. We show both experimentally and computationally that the tuning of the system is achieved through the selection of three separate modules, each of which enables an adjustable output signal: 1) the transcription-activation domain of the sTF, 2) the binding-site modules in the output promoter, and 3) the core promoter modules which define the transcription initiation site in the output promoter. The system has a novel bidirectional architecture that enables generation of compact, yet versatile expression modules for multiple genes with highly diversified expression levels ranging from negligible to very strong using one synthetic transcription factor. In contrast to most existing modular gene expression regulation systems, the present system is independent from externally added compounds. Furthermore, the established system was minimally affected by the several tested growth conditions. These features suggest that it can be highly useful in large scale biotechnology applications. link: http://identifiers.org/doi/10.1371/journal.pone.0148320

Parameters: none

States: none

Observables: none

Rantasalo2015-Synthetic_expresion_modulator_inducedSTF_VP16 This model is part of a family of models describing a modul…

This work describes the development and characterization of a modular synthetic expression system that provides a broad range of adjustable and predictable expression levels in S. cerevisiae. The system works as a fixed-gain transcription amplifier, where the input signal is transferred via a synthetic transcription factor (sTF) onto a synthetic promoter, containing a defined core promoter, generating a transcription output signal. The system activation is based on the bacterial LexA-DNA-binding domain, a set of modified, modular LexA-binding sites and a selection of transcription activation domains. We show both experimentally and computationally that the tuning of the system is achieved through the selection of three separate modules, each of which enables an adjustable output signal: 1) the transcription-activation domain of the sTF, 2) the binding-site modules in the output promoter, and 3) the core promoter modules which define the transcription initiation site in the output promoter. The system has a novel bidirectional architecture that enables generation of compact, yet versatile expression modules for multiple genes with highly diversified expression levels ranging from negligible to very strong using one synthetic transcription factor. In contrast to most existing modular gene expression regulation systems, the present system is independent from externally added compounds. Furthermore, the established system was minimally affected by the several tested growth conditions. These features suggest that it can be highly useful in large scale biotechnology applications. link: http://identifiers.org/doi/10.1371/journal.pone.0148320

Parameters: none

States: none

Observables: none

This represents the reduced version of the "time course model" of Van Eunen et al (2013): Biochemical competition makes…

BACKGROUND: In this paper we propose a model reduction method for biochemical reaction networks governed by a variety of reversible and irreversible enzyme kinetic rate laws, including reversible Michaelis-Menten and Hill kinetics. The method proceeds by a stepwise reduction in the number of complexes, defined as the left and right-hand sides of the reactions in the network. It is based on the Kron reduction of the weighted Laplacian matrix, which describes the graph structure of the complexes and reactions in the network. It does not rely on prior knowledge of the dynamic behaviour of the network and hence can be automated, as we demonstrate. The reduced network has fewer complexes, reactions, variables and parameters as compared to the original network, and yet the behaviour of a preselected set of significant metabolites in the reduced network resembles that of the original network. Moreover the reduced network largely retains the structure and kinetics of the original model. RESULTS: We apply our method to a yeast glycolysis model and a rat liver fatty acid beta-oxidation model. When the number of state variables in the yeast model is reduced from 12 to 7, the difference between metabolite concentrations in the reduced and the full model, averaged over time and species, is only 8%. Likewise, when the number of state variables in the rat-liver beta-oxidation model is reduced from 42 to 29, the difference between the reduced model and the full model is 7.5%. CONCLUSIONS: The method has improved our understanding of the dynamics of the two networks. We found that, contrary to the general disposition, the first few metabolites which were deleted from the network during our stepwise reduction approach, are not those with the shortest convergence times. It shows that our reduction approach performs differently from other approaches that are based on time-scale separation. The method can be used to facilitate fitting of the parameters or to embed a detailed model of interest in a more coarse-grained yet realistic environment. link: http://identifiers.org/pubmed/24885656

Parameters:

Name Description
Kmcpt2C10AcylCarMAT = 51.0; Keqcpt2 = 2.22; Kmcpt2C12AcylCarMAT = 51.0; Kmcpt2C16AcylCoAMAT = 38.0; Vcpt2 = 0.391; Kmcpt2C12AcylCoAMAT = 38.0; Kmcpt2C10AcylCoAMAT = 38.0; sfcpt2C12=0.95; Kmcpt2C16AcylCarMAT = 51.0; Kmcpt2C14AcylCoAMAT = 38.0; Kmcpt2C14AcylCarMAT = 51.0; Kmcpt2CoAMAT = 30.0; Kmcpt2C6AcylCoAMAT = 1000.0; Kmcpt2C4AcylCoAMAT = 1000000.0; Kmcpt2C8AcylCoAMAT = 38.0; Kmcpt2C8AcylCarMAT = 51.0; Kmcpt2C4AcylCarMAT = 51.0; Kmcpt2C6AcylCarMAT = 51.0; Kmcpt2CarMAT = 350.0 Reaction: C12AcylCarMAT => C12AcylCoAMAT; C16AcylCarMAT, C14AcylCarMAT, C10AcylCarMAT, C8AcylCarMAT, C6AcylCarMAT, C4AcylCarMAT, CoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, CarMAT, C12AcylCarMAT, C12AcylCoAMAT, Rate Law: VMAT*sfcpt2C12*Vcpt2*(C12AcylCarMAT*CoAMAT/(Kmcpt2C12AcylCarMAT*Kmcpt2CoAMAT)-C12AcylCoAMAT*CarMAT/(Kmcpt2C12AcylCarMAT*Kmcpt2CoAMAT*Keqcpt2))/((1+C12AcylCarMAT/Kmcpt2C12AcylCarMAT+C12AcylCoAMAT/Kmcpt2C12AcylCoAMAT+C16AcylCarMAT/Kmcpt2C16AcylCarMAT+C16AcylCoAMAT/Kmcpt2C16AcylCoAMAT+C14AcylCarMAT/Kmcpt2C14AcylCarMAT+C14AcylCoAMAT/Kmcpt2C14AcylCoAMAT+C10AcylCarMAT/Kmcpt2C10AcylCarMAT+C10AcylCoAMAT/Kmcpt2C10AcylCoAMAT+C8AcylCarMAT/Kmcpt2C8AcylCarMAT+C8AcylCoAMAT/Kmcpt2C8AcylCoAMAT+C6AcylCarMAT/Kmcpt2C6AcylCarMAT+C6AcylCoAMAT/Kmcpt2C6AcylCoAMAT+C4AcylCarMAT/Kmcpt2C4AcylCarMAT+C4AcylCoAMAT/Kmcpt2C4AcylCoAMAT)*(1+CoAMAT/Kmcpt2CoAMAT+CarMAT/Kmcpt2CarMAT))/VMAT
KmlcadC10EnoylCoAMAT = 1.08; KmlcadC14AcylCoAMAT = 7.4; Keqlcad = 6.0; KmlcadFADH = 24.2; sflcadC12=0.9; KmlcadC12AcylCoAMAT = 9.0; KmlcadFAD = 0.12; KmlcadC12EnoylCoAMAT = 1.08; KmlcadC10AcylCoAMAT = 24.3; KmlcadC16EnoylCoAMAT = 1.08; Vlcad = 0.01; KmlcadC16AcylCoAMAT = 2.5; KmlcadC8AcylCoAMAT = 123.0; KmlcadC8EnoylCoAMAT = 1.08; KmlcadC14EnoylCoAMAT = 1.08 Reaction: C12AcylCoAMAT => C12EnoylCoAMAT + FADHMAT; C16AcylCoAMAT, C14AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, FADtMAT, C14EnoylCoAMAT, C16EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, C12AcylCoAMAT, FADHMAT, Rate Law: VMAT*sflcadC12*Vlcad*(C12AcylCoAMAT*(FADtMAT-FADHMAT)/(KmlcadC12AcylCoAMAT*KmlcadFAD)-C14EnoylCoAMAT*FADHMAT/(KmlcadC12AcylCoAMAT*KmlcadFAD*Keqlcad))/((1+C12AcylCoAMAT/KmlcadC12AcylCoAMAT+C14EnoylCoAMAT/KmlcadC12EnoylCoAMAT+C16AcylCoAMAT/KmlcadC16AcylCoAMAT+C16EnoylCoAMAT/KmlcadC16EnoylCoAMAT+C14AcylCoAMAT/KmlcadC14AcylCoAMAT+C14EnoylCoAMAT/KmlcadC14EnoylCoAMAT+C10AcylCoAMAT/KmlcadC10AcylCoAMAT+C10EnoylCoAMAT/KmlcadC10EnoylCoAMAT+C8AcylCoAMAT/KmlcadC8AcylCoAMAT+C8EnoylCoAMAT/KmlcadC8EnoylCoAMAT)*(1+(FADtMAT-FADHMAT)/KmlcadFAD+FADHMAT/KmlcadFADH))/VMAT
Keqmckat = 1051.0; KmmckatC4AcylCoAMAT = 13.83; Vmckat = 0.377; KmmckatC8KetoacylCoAMAT = 3.2; KmmckatCoAMAT = 26.6; KmmckatC16KetoacylCoAMAT = 1.1; KmmckatC6KetoacylCoAMAT = 6.7; KmmckatC16AcylCoAMAT = 13.83; KmmckatC10AcylCoAMAT = 13.83; KmmckatC8AcylCoAMAT = 13.83; KmmckatC14KetoacylCoAMAT = 1.2; KmmckatC12KetoacylCoAMAT = 1.3; sfmckatC4=0.49; KmmckatAcetylCoAMAT = 30.0; KmmckatC12AcylCoAMAT = 13.83; KmmckatC6AcylCoAMAT = 13.83; KmmckatC10KetoacylCoAMAT = 2.1; KmmckatC4AcetoacylCoAMAT = 12.4; KmmckatC14AcylCoAMAT = 13.83 Reaction: C4AcetoacylCoAMAT => AcetylCoAMAT; C16KetoacylCoAMAT, C14KetoacylCoAMAT, C12KetoacylCoAMAT, C10KetoacylCoAMAT, C8KetoacylCoAMAT, C6KetoacylCoAMAT, CoAMAT, C4AcylCoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, AcetylCoAMAT, C4AcetoacylCoAMAT, Rate Law: VMAT*sfmckatC4*Vmckat*(C4AcetoacylCoAMAT*CoAMAT/(KmmckatC4AcetoacylCoAMAT*KmmckatCoAMAT)-AcetylCoAMAT*AcetylCoAMAT/(KmmckatC4AcetoacylCoAMAT*KmmckatCoAMAT*Keqmckat))/((1+C4AcetoacylCoAMAT/KmmckatC4AcetoacylCoAMAT+C4AcylCoAMAT/KmmckatC4AcylCoAMAT+C16KetoacylCoAMAT/KmmckatC16KetoacylCoAMAT+C16AcylCoAMAT/KmmckatC16AcylCoAMAT+C14KetoacylCoAMAT/KmmckatC14KetoacylCoAMAT+C14AcylCoAMAT/KmmckatC14AcylCoAMAT+C12KetoacylCoAMAT/KmmckatC12KetoacylCoAMAT+C12AcylCoAMAT/KmmckatC12AcylCoAMAT+C10KetoacylCoAMAT/KmmckatC10KetoacylCoAMAT+C10AcylCoAMAT/KmmckatC10AcylCoAMAT+C8KetoacylCoAMAT/KmmckatC8KetoacylCoAMAT+C8AcylCoAMAT/KmmckatC8AcylCoAMAT+C6KetoacylCoAMAT/KmmckatC6KetoacylCoAMAT+C6AcylCoAMAT/KmmckatC6AcylCoAMAT+AcetylCoAMAT/KmmckatAcetylCoAMAT)*(1+CoAMAT/KmmckatCoAMAT+AcetylCoAMAT/KmmckatAcetylCoAMAT))/VMAT
KmmtpC6AcylCoAMAT = 13.83; sfmtpC12=0.81; Keqmtp = 0.71; KmmtpC14EnoylCoAMAT = 25.0; KmmtpC10AcylCoAMAT = 13.83; KmmtpC12AcylCoAMAT = 13.83; KmmtpAcetylCoAMAT = 30.0; KmmtpC8AcylCoAMAT = 13.83; KmmtpC16EnoylCoAMAT = 25.0; KmmtpC14AcylCoAMAT = 13.83; KmmtpC10EnoylCoAMAT = 25.0; KicrotC4AcetoacylCoA = 1.6; KmmtpCoAMAT = 30.0; Vmtp = 2.84; KmmtpC12EnoylCoAMAT = 25.0; KmmtpNADMAT = 60.0; KmmtpC16AcylCoAMAT = 13.83; KmmtpC8EnoylCoAMAT = 25.0; KmmtpNADHMAT = 50.0 Reaction: C12EnoylCoAMAT => C10AcylCoAMAT + AcetylCoAMAT + NADHMAT; C16EnoylCoAMAT, C14EnoylCoAMAT, C10EnoylCoAMAT, C8EnoylCoAMAT, NADtMAT, CoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcetoacylCoAMAT, AcetylCoAMAT, C10AcylCoAMAT, C12EnoylCoAMAT, NADHMAT, Rate Law: VMAT*sfmtpC12*Vmtp*(C12EnoylCoAMAT*(NADtMAT-NADHMAT)*CoAMAT/(KmmtpC12EnoylCoAMAT*KmmtpNADMAT*KmmtpCoAMAT)-C10AcylCoAMAT*NADHMAT*AcetylCoAMAT/(KmmtpC12EnoylCoAMAT*KmmtpNADMAT*KmmtpCoAMAT*Keqmtp))/((1+C12EnoylCoAMAT/KmmtpC12EnoylCoAMAT+C10AcylCoAMAT/KmmtpC10AcylCoAMAT+C16EnoylCoAMAT/KmmtpC16EnoylCoAMAT+C16AcylCoAMAT/KmmtpC16AcylCoAMAT+C14EnoylCoAMAT/KmmtpC14EnoylCoAMAT+C14AcylCoAMAT/KmmtpC14AcylCoAMAT+C10EnoylCoAMAT/KmmtpC10EnoylCoAMAT+C12AcylCoAMAT/KmmtpC12AcylCoAMAT+C8EnoylCoAMAT/KmmtpC8EnoylCoAMAT+C8AcylCoAMAT/KmmtpC8AcylCoAMAT+C6AcylCoAMAT/KmmtpC6AcylCoAMAT+C4AcetoacylCoAMAT/KicrotC4AcetoacylCoA)*(1+(NADtMAT-NADHMAT)/KmmtpNADMAT+NADHMAT/KmmtpNADHMAT)*(1+CoAMAT/KmmtpCoAMAT+AcetylCoAMAT/KmmtpAcetylCoAMAT))/VMAT
KmmcadC12EnoylCoAMAT = 1.08; KmmcadC8AcylCoAMAT = 4.0; KmmcadC6EnoylCoAMAT = 1.08; KmmcadC12AcylCoAMAT = 5.7; KmmcadC6AcylCoAMAT = 9.4; KmmcadC4AcylCoAMAT = 135.0; Vmcad = 0.081; Keqmcad = 6.0; KmmcadFADH = 24.2; KmmcadC10AcylCoAMAT = 5.4; KmmcadFAD = 0.12; KmmcadC10EnoylCoAMAT = 1.08; KmmcadC4EnoylCoAMAT = 1.08; sfmcadC10=0.8; KmmcadC8EnoylCoAMAT = 1.08 Reaction: C10AcylCoAMAT => C10EnoylCoAMAT + FADHMAT; C12AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, FADtMAT, C12EnoylCoAMAT, C8EnoylCoAMAT, C6EnoylCoAMAT, C4EnoylCoAMAT, C10AcylCoAMAT, C10EnoylCoAMAT, FADHMAT, Rate Law: VMAT*sfmcadC10*Vmcad*(C10AcylCoAMAT*(FADtMAT-FADHMAT)/(KmmcadC10AcylCoAMAT*KmmcadFAD)-C10EnoylCoAMAT*FADHMAT/(KmmcadC10AcylCoAMAT*KmmcadFAD*Keqmcad))/((1+C10AcylCoAMAT/KmmcadC10AcylCoAMAT+C10EnoylCoAMAT/KmmcadC10EnoylCoAMAT+C12AcylCoAMAT/KmmcadC12AcylCoAMAT+C12EnoylCoAMAT/KmmcadC12EnoylCoAMAT+C8AcylCoAMAT/KmmcadC8AcylCoAMAT+C8EnoylCoAMAT/KmmcadC8EnoylCoAMAT+C6AcylCoAMAT/KmmcadC6AcylCoAMAT+C6EnoylCoAMAT/KmmcadC6EnoylCoAMAT+C4AcylCoAMAT/KmmcadC4AcylCoAMAT+C4EnoylCoAMAT/KmmcadC4EnoylCoAMAT)*(1+(FADtMAT-FADHMAT)/KmmcadFAD+FADHMAT/KmmcadFADH))/VMAT
KmmtpC6AcylCoAMAT = 13.83; Keqmtp = 0.71; KmmtpC14EnoylCoAMAT = 25.0; KmmtpC10AcylCoAMAT = 13.83; sfmtpC8=0.34; KmmtpC12AcylCoAMAT = 13.83; KmmtpAcetylCoAMAT = 30.0; KmmtpC8AcylCoAMAT = 13.83; KmmtpC16EnoylCoAMAT = 25.0; KmmtpC14AcylCoAMAT = 13.83; KmmtpC10EnoylCoAMAT = 25.0; KicrotC4AcetoacylCoA = 1.6; KmmtpCoAMAT = 30.0; Vmtp = 2.84; KmmtpC12EnoylCoAMAT = 25.0; KmmtpNADMAT = 60.0; KmmtpC16AcylCoAMAT = 13.83; KmmtpC8EnoylCoAMAT = 25.0; KmmtpNADHMAT = 50.0 Reaction: C8EnoylCoAMAT => C6AcylCoAMAT + AcetylCoAMAT + NADHMAT; C16EnoylCoAMAT, C14EnoylCoAMAT, C12EnoylCoAMAT, C10EnoylCoAMAT, NADtMAT, CoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C4AcetoacylCoAMAT, AcetylCoAMAT, C6AcylCoAMAT, C8EnoylCoAMAT, NADHMAT, Rate Law: VMAT*sfmtpC8*Vmtp*(C8EnoylCoAMAT*(NADtMAT-NADHMAT)*CoAMAT/(KmmtpC8EnoylCoAMAT*KmmtpNADMAT*KmmtpCoAMAT)-C6AcylCoAMAT*NADHMAT*AcetylCoAMAT/(KmmtpC8EnoylCoAMAT*KmmtpNADMAT*KmmtpCoAMAT*Keqmtp))/((1+C8EnoylCoAMAT/KmmtpC8EnoylCoAMAT+C6AcylCoAMAT/KmmtpC6AcylCoAMAT+C16EnoylCoAMAT/KmmtpC16EnoylCoAMAT+C16AcylCoAMAT/KmmtpC16AcylCoAMAT+C14EnoylCoAMAT/KmmtpC14EnoylCoAMAT+C14AcylCoAMAT/KmmtpC14AcylCoAMAT+C12EnoylCoAMAT/KmmtpC12EnoylCoAMAT+C12AcylCoAMAT/KmmtpC12AcylCoAMAT+C10EnoylCoAMAT/KmmtpC10EnoylCoAMAT+C10AcylCoAMAT/KmmtpC10AcylCoAMAT+C8AcylCoAMAT/KmmtpC8AcylCoAMAT+C4AcetoacylCoAMAT/KicrotC4AcetoacylCoA)*(1+(NADtMAT-NADHMAT)/KmmtpNADMAT+NADHMAT/KmmtpNADHMAT)*(1+CoAMAT/KmmtpCoAMAT+AcetylCoAMAT/KmmtpAcetylCoAMAT))/VMAT
Kmcpt2C10AcylCarMAT = 51.0; Keqcpt2 = 2.22; Kmcpt2C12AcylCarMAT = 51.0; Kmcpt2C16AcylCoAMAT = 38.0; Vcpt2 = 0.391; Kmcpt2C12AcylCoAMAT = 38.0; Kmcpt2C10AcylCoAMAT = 38.0; Kmcpt2C16AcylCarMAT = 51.0; Kmcpt2C14AcylCoAMAT = 38.0; Kmcpt2C14AcylCarMAT = 51.0; Kmcpt2CoAMAT = 30.0; Kmcpt2C6AcylCoAMAT = 1000.0; Kmcpt2C4AcylCoAMAT = 1000000.0; Kmcpt2C8AcylCoAMAT = 38.0; sfcpt2C8=0.35; Kmcpt2C8AcylCarMAT = 51.0; Kmcpt2C4AcylCarMAT = 51.0; Kmcpt2C6AcylCarMAT = 51.0; Kmcpt2CarMAT = 350.0 Reaction: C8AcylCarMAT => C8AcylCoAMAT; C16AcylCarMAT, C14AcylCarMAT, C12AcylCarMAT, C10AcylCarMAT, C6AcylCarMAT, C4AcylCarMAT, CoAMAT, C16AcylCoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, CarMAT, C8AcylCarMAT, C8AcylCoAMAT, Rate Law: VMAT*sfcpt2C8*Vcpt2*(C8AcylCarMAT*CoAMAT/(Kmcpt2C8AcylCarMAT*Kmcpt2CoAMAT)-C8AcylCoAMAT*CarMAT/(Kmcpt2C8AcylCarMAT*Kmcpt2CoAMAT*Keqcpt2))/((1+C8AcylCarMAT/Kmcpt2C8AcylCarMAT+C8AcylCoAMAT/Kmcpt2C8AcylCoAMAT+C16AcylCarMAT/Kmcpt2C16AcylCarMAT+C16AcylCoAMAT/Kmcpt2C16AcylCoAMAT+C14AcylCarMAT/Kmcpt2C14AcylCarMAT+C14AcylCoAMAT/Kmcpt2C14AcylCoAMAT+C12AcylCarMAT/Kmcpt2C12AcylCarMAT+C12AcylCoAMAT/Kmcpt2C12AcylCoAMAT+C10AcylCarMAT/Kmcpt2C10AcylCarMAT+C10AcylCoAMAT/Kmcpt2C10AcylCoAMAT+C6AcylCarMAT/Kmcpt2C6AcylCarMAT+C6AcylCoAMAT/Kmcpt2C6AcylCoAMAT+C4AcylCarMAT/Kmcpt2C4AcylCarMAT+C4AcylCoAMAT/Kmcpt2C4AcylCoAMAT)*(1+CoAMAT/Kmcpt2CoAMAT+CarMAT/Kmcpt2CarMAT))/VMAT
KmcactCarMAT = 130.0; KmcactCarCYT = 130.0; KicactC16AcylCarCYT=56.0; KmcactC16AcylCarMAT=15.0; KmcactC16AcylCarCYT=15.0; Vfcact = 0.42; Keqcact = 1.0; KicactCarCYT = 200.0; Vrcact = 0.42 Reaction: C16AcylCarCYT => C16AcylCarMAT; CarMAT, CarCYT, C16AcylCarCYT, C16AcylCarMAT, Rate Law: Vfcact*(C16AcylCarCYT*CarMAT-C16AcylCarMAT*CarCYT/Keqcact)/(C16AcylCarCYT*CarMAT+KmcactCarMAT*C16AcylCarCYT+KmcactC16AcylCarCYT*CarMAT*(1+CarCYT/KicactCarCYT)+Vfcact/(Vrcact*Keqcact)*(KmcactCarCYT*C16AcylCarMAT*(1+C16AcylCarCYT/KicactC16AcylCarCYT)+CarCYT*(KmcactC16AcylCarMAT+C16AcylCarMAT)))
Kmcpt2C10AcylCarMAT = 51.0; Keqcpt2 = 2.22; Kmcpt2C12AcylCarMAT = 51.0; Kmcpt2C16AcylCoAMAT = 38.0; Vcpt2 = 0.391; Kmcpt2C12AcylCoAMAT = 38.0; Kmcpt2C10AcylCoAMAT = 38.0; Kmcpt2C16AcylCarMAT = 51.0; Kmcpt2C14AcylCoAMAT = 38.0; Kmcpt2C14AcylCarMAT = 51.0; Kmcpt2CoAMAT = 30.0; Kmcpt2C6AcylCoAMAT = 1000.0; sfcpt2C16=0.85; Kmcpt2C4AcylCoAMAT = 1000000.0; Kmcpt2C8AcylCoAMAT = 38.0; Kmcpt2C8AcylCarMAT = 51.0; Kmcpt2C4AcylCarMAT = 51.0; Kmcpt2C6AcylCarMAT = 51.0; Kmcpt2CarMAT = 350.0 Reaction: C16AcylCarMAT => C16AcylCoAMAT; C14AcylCarMAT, C12AcylCarMAT, C10AcylCarMAT, C8AcylCarMAT, C6AcylCarMAT, C4AcylCarMAT, CoAMAT, C14AcylCoAMAT, C12AcylCoAMAT, C10AcylCoAMAT, C8AcylCoAMAT, C6AcylCoAMAT, C4AcylCoAMAT, CarMAT, C16AcylCarMAT, C16AcylCoAMAT, Rate Law: VMAT*sfcpt2C16*Vcpt2*(C16AcylCarMAT*CoAMAT/(Kmcpt2C16AcylCarMAT*Kmcpt2CoAMAT)-C16AcylCoAMAT*CarMAT/(Kmcpt2C16AcylCarMAT*Kmcpt2CoAMAT*Keqcpt2))/((1+C16AcylCarMAT/Kmcpt2C16AcylCarMAT+C16AcylCoAMAT/Kmcpt2C16AcylCoAMAT+C14AcylCarMAT/Kmcpt2C14Acy