SBMLBioModels: C - F

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C


MODEL1012220003 @ v0.0.1

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

The mammalian target of rapamycin (mTOR) is a central regulator of cell growth and proliferation. mTOR signaling is frequently dysregulated in oncogenic cells, and thus an attractive target for anticancer therapy. Using CellDesigner, a modeling support software for graphical notation, we present herein a comprehensive map of the mTOR signaling network, which includes 964 species connected by 777 reactions. The map complies with both the systems biology markup language (SBML) and graphical notation (SBGN) for computational analysis and graphical representation, respectively. As captured in the mTOR map, we review and discuss our current understanding of the mTOR signaling network and highlight the impact of mTOR feedback and crosstalk regulations on drug-based cancer therapy. This map is available on the Payao platform, a Web 2.0 based community-wide interactive process for creating more accurate and information-rich databases. Thus, this comprehensive map of the mTOR network will serve as a tool to facilitate systems-level study of up-to-date mTOR network components and signaling events toward the discovery of novel regulatory processes and therapeutic strategies for cancer. link: http://identifiers.org/pubmed/21179025

Parameters: none

States: none

Observables: none

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

The mammalian target of rapamycin (mTOR) is a central regulator of cell growth and proliferation. mTOR signaling is frequently dysregulated in oncogenic cells, and thus an attractive target for anticancer therapy. Using CellDesigner, a modeling support software for graphical notation, we present herein a comprehensive map of the mTOR signaling network, which includes 964 species connected by 777 reactions. The map complies with both the systems biology markup language (SBML) and graphical notation (SBGN) for computational analysis and graphical representation, respectively. As captured in the mTOR map, we review and discuss our current understanding of the mTOR signaling network and highlight the impact of mTOR feedback and crosstalk regulations on drug-based cancer therapy. This map is available on the Payao platform, a Web 2.0 based community-wide interactive process for creating more accurate and information-rich databases. Thus, this comprehensive map of the mTOR network will serve as a tool to facilitate systems-level study of up-to-date mTOR network components and signaling events toward the discovery of novel regulatory processes and therapeutic strategies for cancer. link: http://identifiers.org/pubmed/21179025

Parameters: none

States: none

Observables: none

Caspeta2012 - Genome-scale metabolic network of Pichia pastoris (iLC915)This model is described in the article: [Genome…

BACKGROUND: Pichia stipitis and Pichia pastoris have long been investigated due to their native abilities to metabolize every sugar from lignocellulose and to modulate methanol consumption, respectively. The latter has been driving the production of several recombinant proteins. As a result, significant advances in their biochemical knowledge, as well as in genetic engineering and fermentation methods have been generated. The release of their genome sequences has allowed systems level research. RESULTS: In this work, genome-scale metabolic models (GEMs) of P. stipitis (iSS884) and P. pastoris (iLC915) were reconstructed. iSS884 includes 1332 reactions, 922 metabolites, and 4 compartments. iLC915 contains 1423 reactions, 899 metabolites, and 7 compartments. Compared with the previous GEMs of P. pastoris, PpaMBEL1254 and iPP668, iLC915 contains more genes and metabolic functions, as well as improved predictive capabilities. Simulations of physiological responses for the growth of both yeasts on selected carbon sources using iSS884 and iLC915 closely reproduced the experimental data. Additionally, the iSS884 model was used to predict ethanol production from xylose at different oxygen uptake rates. Simulations with iLC915 closely reproduced the effect of oxygen uptake rate on physiological states of P. pastoris expressing a recombinant protein. The potential of P. stipitis for the conversion of xylose and glucose into ethanol using reactors in series, and of P. pastoris to produce recombinant proteins using mixtures of methanol and glycerol or sorbitol are also discussed. CONCLUSIONS: In conclusion the first GEM of P. stipitis (iSS884) was reconstructed and validated. The expanded version of the P. pastoris GEM, iLC915, is more complete and has improved capabilities over the existing models. Both GEMs are useful frameworks to explore the versatility of these yeasts and to capitalize on their biotechnological potentials. link: http://identifiers.org/pubmed/22472172

Parameters: none

States: none

Observables: none

Caspeta2012 - Genome-scale metabolic network of Pichia stipitis (iSS884)This model is described in the article: [Genome…

BACKGROUND: Pichia stipitis and Pichia pastoris have long been investigated due to their native abilities to metabolize every sugar from lignocellulose and to modulate methanol consumption, respectively. The latter has been driving the production of several recombinant proteins. As a result, significant advances in their biochemical knowledge, as well as in genetic engineering and fermentation methods have been generated. The release of their genome sequences has allowed systems level research. RESULTS: In this work, genome-scale metabolic models (GEMs) of P. stipitis (iSS884) and P. pastoris (iLC915) were reconstructed. iSS884 includes 1332 reactions, 922 metabolites, and 4 compartments. iLC915 contains 1423 reactions, 899 metabolites, and 7 compartments. Compared with the previous GEMs of P. pastoris, PpaMBEL1254 and iPP668, iLC915 contains more genes and metabolic functions, as well as improved predictive capabilities. Simulations of physiological responses for the growth of both yeasts on selected carbon sources using iSS884 and iLC915 closely reproduced the experimental data. Additionally, the iSS884 model was used to predict ethanol production from xylose at different oxygen uptake rates. Simulations with iLC915 closely reproduced the effect of oxygen uptake rate on physiological states of P. pastoris expressing a recombinant protein. The potential of P. stipitis for the conversion of xylose and glucose into ethanol using reactors in series, and of P. pastoris to produce recombinant proteins using mixtures of methanol and glycerol or sorbitol are also discussed. CONCLUSIONS: In conclusion the first GEM of P. stipitis (iSS884) was reconstructed and validated. The expanded version of the P. pastoris GEM, iLC915, is more complete and has improved capabilities over the existing models. Both GEMs are useful frameworks to explore the versatility of these yeasts and to capitalize on their biotechnological potentials. link: http://identifiers.org/pubmed/22472172

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkane…

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes. link: http://identifiers.org/pubmed/27895706

Parameters: none

States: none

Observables: none

This model is from the article: A dynamical model of the spindle position checkpoint Ayse Koca Caydasi, Maiko Lohel,…

The orientation of the mitotic spindle with respect to the polarity axis is crucial for the accuracy of asymmetric cell division. In budding yeast, a surveillance mechanism called the spindle position checkpoint (SPOC) prevents exit from mitosis when the mitotic spindle fails to align along the mother-to-daughter polarity axis. SPOC arrest relies upon inhibition of the GTPase Tem1 by the GTPase-activating protein (GAP) complex Bfa1-Bub2. Importantly, reactions signaling mitotic exit take place at yeast centrosomes (named spindle pole bodies, SPBs) and the GAP complex also promotes SPB localization of Tem1. Yet, whether the regulation of Tem1 by Bfa1-Bub2 takes place only at the SPBs remains elusive. Here, we present a quantitative analysis of Bfa1-Bub2 and Tem1 localization at the SPBs. Based on the measured SPB-bound protein levels, we introduce a dynamical model of the SPOC that describes the regulation of Bfa1 and Tem1. Our model suggests that Bfa1 interacts with Tem1 in the cytoplasm as well as at the SPBs to provide efficient Tem1 inhibition. link: http://identifiers.org/pubmed/22580890

Parameters: none

States: none

Observables: none

Caydasi2012 - Regulation of Tem1 by the GAP complex in spindle position cell cycle checkpoint - Ubiquitous association m…

The orientation of the mitotic spindle with respect to the polarity axis is crucial for the accuracy of asymmetric cell division. In budding yeast, a surveillance mechanism called the spindle position checkpoint (SPOC) prevents exit from mitosis when the mitotic spindle fails to align along the mother-to-daughter polarity axis. SPOC arrest relies upon inhibition of the GTPase Tem1 by the GTPase-activating protein (GAP) complex Bfa1-Bub2. Importantly, reactions signaling mitotic exit take place at yeast centrosomes (named spindle pole bodies, SPBs) and the GAP complex also promotes SPB localization of Tem1. Yet, whether the regulation of Tem1 by Bfa1-Bub2 takes place only at the SPBs remains elusive. Here, we present a quantitative analysis of Bfa1-Bub2 and Tem1 localization at the SPBs. Based on the measured SPB-bound protein levels, we introduce a dynamical model of the SPOC that describes the regulation of Bfa1 and Tem1. Our model suggests that Bfa1 interacts with Tem1 in the cytoplasm as well as at the SPBs to provide efficient Tem1 inhibition. link: http://identifiers.org/pubmed/22580890

Parameters:

Name Description
koffBT = 0.183 1/s; konB5T = 7000000.0 l/(mol*s) Reaction: Tem1GTP + B_Bfa1P5 => B_Bfa1P5_Tem1GTP, Rate Law: c3*(konB5T*B_Bfa1P5*Tem1GTP-koffBT*B_Bfa1P5_Tem1GTP)
konB4T = 3.65E7 l/(mol*s); koffBT = 0.183 1/s Reaction: Tem1GDP + B_Bfa1P4 => B_Bfa1P4_Tem1GDP, Rate Law: c3*(konB4T*B_Bfa1P4*Tem1GDP-koffBT*B_Bfa1P4_Tem1GDP)
kfKin4Cyto = 0.09 1/s; u = 1.0 1 Reaction: Bfa1_Tem1GDP => Bfa1P4_Tem1GDP, Rate Law: c2*u*kfKin4Cyto*Bfa1_Tem1GDP
krKin4 = 0.0251 1/s Reaction: Bfa1P4_Tem1GTP => Bfa1_Tem1GTP, Rate Law: c2*krKin4*Bfa1P4_Tem1GTP
koffB4 = 0.0365 1/s; konB4 = 20000.0 l/(mol*s) Reaction: Bfa1P4_Tem1GDP + SPB_B => B_Bfa1P4_Tem1GDP, Rate Law: c3*(konB4*SPB_B*Bfa1P4_Tem1GDP-koffB4*B_Bfa1P4_Tem1GDP)
khydBT = 2.0 1/s Reaction: Bfa1_Tem1GTP => Bfa1_Tem1GDP, Rate Law: c2*khydBT*Bfa1_Tem1GTP
alpha = 1.0 1; koffBT = 0.183 1/s; konBT = 3.65E7 l/(mol*s) Reaction: Bfa1 + Tem1GDP => Bfa1_Tem1GDP, Rate Law: c2*(alpha*konBT*Bfa1*Tem1GDP-koffBT*Bfa1_Tem1GDP)
kfKin4 = 1000.0 1/s; u = 1.0 1 Reaction: B_Bfa1 => B_Bfa1P4, Rate Law: c3*u*kfKin4*B_Bfa1
konB4T = 3.65E7 l/(mol*s); alpha = 1.0 1; koffBT = 0.183 1/s Reaction: Bfa1P4 + Tem1GTP => Bfa1P4_Tem1GTP, Rate Law: c2*(alpha*konB4T*Bfa1P4*Tem1GTP-koffBT*Bfa1P4_Tem1GTP)
krCdc5 = 0.01 1/s; u = 1.0 1 Reaction: Bfa1P5_Tem1GDP => Bfa1_Tem1GDP, Rate Law: c2*u*krCdc5*Bfa1P5_Tem1GDP
avogadro = 6.0221415E23 Reaction: Active_Tem1_at_the_SPB = (T_Tem1GTP+B_Bfa1_Tem1GTP+B_Bfa1P4_Tem1GTP+B_Bfa1P5_Tem1GTP)*c3*avogadro, Rate Law: missing
khydB4T = 2.0 1/s Reaction: Bfa1P4_Tem1GTP => Bfa1P4_Tem1GDP, Rate Law: c2*khydB4T*Bfa1P4_Tem1GTP
alpha = 1.0 1; koffBT = 0.183 1/s; konB5T = 7000000.0 l/(mol*s) Reaction: Bfa1P5 + Tem1GTP => Bfa1P5_Tem1GTP, Rate Law: c2*(alpha*konB5T*Bfa1P5*Tem1GTP-koffBT*Bfa1P5_Tem1GTP)
khyd = 0.00224 1/s Reaction: Bfa1P5_Tem1GTP => Bfa1P5_Tem1GDP, Rate Law: c2*khyd*Bfa1P5_Tem1GTP
konB = 1250000.0 l/(mol*s); koffB = 0.0012 1/s Reaction: Bfa1P5_Tem1GTP + SPB_B => B_Bfa1P5_Tem1GTP, Rate Law: c3*(konB*SPB_B*Bfa1P5_Tem1GTP-koffB*B_Bfa1P5_Tem1GTP)
knex = 0.0136 1/s Reaction: Tem1GDP => Tem1GTP, Rate Law: c2*knex*Tem1GDP
kfCdc5 = 1.0 1/s Reaction: B_Bfa1 => B_Bfa1P5, Rate Law: c3*kfCdc5*B_Bfa1
koffBT = 0.183 1/s; konBT = 3.65E7 l/(mol*s) Reaction: Tem1GTP + B_Bfa1 => B_Bfa1_Tem1GTP, Rate Law: c3*(konBT*B_Bfa1*Tem1GTP-koffBT*B_Bfa1_Tem1GTP)
konT = 1900000.0 l/(mol*s); koffT = 0.183 1/s Reaction: Tem1GTP + SPB_T => T_Tem1GTP, Rate Law: c3*(konT*SPB_T*Tem1GTP-koffT*T_Tem1GTP)
avogadro = 6.0221415E23; q = 1.0 1 Reaction: Active_Bfa1_at_the_SPB = (q*(B_Bfa1+B_Bfa1_Tem1GTP+B_Bfa1_Tem1GDP)+B_Bfa1P4+B_Bfa1P4_Tem1GTP+B_Bfa1P4_Tem1GDP)*c3*avogadro, Rate Law: missing

States:

Name Description
B Bfa1 [Mitotic check point protein BFA1; binding site]
Active Tem1 at the SPB [Protein TEM1; active]
Bfa1P5 Tem1GDP [GDP; Mitotic check point protein BFA1; Protein TEM1; increased phosphorylation]
Bfa1P5 Tem1GTP [Mitotic check point protein BFA1; GTP; Protein TEM1; increased phosphorylation]
B Bfa1P4 Tem1GDP [binding site; GDP; Protein TEM1; Mitotic check point protein BFA1; phosphorylated]
Inactive Bfa1 at the SPB [Mitotic check point protein BFA1; inactive]
B Bfa1P4 [Mitotic check point protein BFA1; binding site; phosphorylated]
B Bfa1P4 Tem1GTP [Protein TEM1; GTP; binding site; Mitotic check point protein BFA1; phosphorylated]
Bfa1 Tem1GDP [Protein TEM1; GDP; Mitotic check point protein BFA1]
Inactive Bfa1 in the cytosol [Mitotic check point protein BFA1; inactive]
Bfa1P5 [Mitotic check point protein BFA1; increased phosphorylation]
SPB T [binding site]
Tem1GTP [Protein TEM1; GTP]
SPB B [binding site]
Bfa1P4 [Mitotic check point protein BFA1; phosphorylated]
Bfa1 [Mitotic check point protein BFA1]
B Bfa1P5 Tem1GDP [Mitotic check point protein BFA1; binding site; GDP; Protein TEM1; phosphorylated]
B Bfa1P5 Tem1GTP [GTP; Mitotic check point protein BFA1; binding site; Protein TEM1; phosphorylated]
Active Bfa1 at the Cytosol [Mitotic check point protein BFA1; active]
Bfa1P4 Tem1GDP [Protein TEM1; Mitotic check point protein BFA1; GDP; phosphorylated]
B Bfa1P5 [Mitotic check point protein BFA1; binding site; phosphorylated]
Bfa1P4 Tem1GTP [Mitotic check point protein BFA1; GTP; Protein TEM1; phosphorylated]
Active Tem1 in the Cytosol [Protein TEM1; active]
B Bfa1 Tem1GTP [GTP; binding site; Protein TEM1; Mitotic check point protein BFA1]
Tem1GDP [GDP; Protein TEM1]
Inactive Tem1 in the cytosol [Protein TEM1; inactive]
Total Bfa1 in the Cytosol [Mitotic check point protein BFA1]
Total Tem1 in the Cytosol [Protein TEM1]
Total Bfa1 at the SPB [Mitotic check point protein BFA1]
Active Bfa1 at the SPB [Mitotic check point protein BFA1; active]
Total Tem1 at the SPB [Protein TEM1]
Bfa1 Tem1GTP [GTP; Mitotic check point protein BFA1; Protein TEM1]
Inactive Tem1 at the SPB [Protein TEM1; inactive]

Observables: none

Regulation of Tem1 by the GAP complex in Spindle Position Checkpoint - Ubiquitous inactiveThis model is described in the…

The orientation of the mitotic spindle with respect to the polarity axis is crucial for the accuracy of asymmetric cell division. In budding yeast, a surveillance mechanism called the spindle position checkpoint (SPOC) prevents exit from mitosis when the mitotic spindle fails to align along the mother-to-daughter polarity axis. SPOC arrest relies upon inhibition of the GTPase Tem1 by the GTPase-activating protein (GAP) complex Bfa1-Bub2. Importantly, reactions signaling mitotic exit take place at yeast centrosomes (named spindle pole bodies, SPBs) and the GAP complex also promotes SPB localization of Tem1. Yet, whether the regulation of Tem1 by Bfa1-Bub2 takes place only at the SPBs remains elusive. Here, we present a quantitative analysis of Bfa1-Bub2 and Tem1 localization at the SPBs. Based on the measured SPB-bound protein levels, we introduce a dynamical model of the SPOC that describes the regulation of Bfa1 and Tem1. Our model suggests that Bfa1 interacts with Tem1 in the cytoplasm as well as at the SPBs to provide efficient Tem1 inhibition. link: http://identifiers.org/pubmed/22580890

Parameters:

Name Description
koffBT = 0.183 1/s; konB5T = 7000000.0 l/(mol*s) Reaction: Tem1GDP + B_Bfa1P5 => B_Bfa1P5_Tem1GDP, Rate Law: c3*(konB5T*B_Bfa1P5*Tem1GDP-koffBT*B_Bfa1P5_Tem1GDP)
konB4T = 3.65E7 l/(mol*s); koffBT = 0.183 1/s Reaction: Tem1GTP + B_Bfa1P4 => B_Bfa1P4_Tem1GTP, Rate Law: c3*(konB4T*B_Bfa1P4*Tem1GTP-koffBT*B_Bfa1P4_Tem1GTP)
kfKin4Cyto = 0.09 1/s; u = 1.0 1 Reaction: Bfa1_Tem1GTP => Bfa1P4_Tem1GTP, Rate Law: c2*u*kfKin4Cyto*Bfa1_Tem1GTP
krKin4 = 0.0251 1/s Reaction: Bfa1P4_Tem1GTP => Bfa1_Tem1GTP, Rate Law: c2*krKin4*Bfa1P4_Tem1GTP
koffB4 = 0.0365 1/s; konB4 = 20000.0 l/(mol*s) Reaction: Bfa1P4 + SPB_B => B_Bfa1P4, Rate Law: c3*(konB4*SPB_B*Bfa1P4-koffB4*B_Bfa1P4)
konB4T = 3.65E7 l/(mol*s); alpha = 1.0 1; koffBT = 0.183 1/s Reaction: Bfa1P4 + Tem1GDP => Bfa1P4_Tem1GDP, Rate Law: c2*(alpha*konB4T*Bfa1P4*Tem1GDP-koffBT*Bfa1P4_Tem1GDP)
alpha = 1.0 1; koffBT = 0.183 1/s; konBT = 3.65E7 l/(mol*s) Reaction: Bfa1 + Tem1GDP => Bfa1_Tem1GDP, Rate Law: c2*(alpha*konBT*Bfa1*Tem1GDP-koffBT*Bfa1_Tem1GDP)
kfKin4 = 1000.0 1/s; u = 1.0 1 Reaction: B_Bfa1 => B_Bfa1P4, Rate Law: c3*u*kfKin4*B_Bfa1
krCdc5 = 0.01 1/s; u = 1.0 1 Reaction: Bfa1P5_Tem1GDP => Bfa1_Tem1GDP, Rate Law: c2*u*krCdc5*Bfa1P5_Tem1GDP
avogadro = 6.0221415E23; q = 0.0 1 Reaction: Active_Bfa1_at_the_Cytosol = (q*(Bfa1+Bfa1_Tem1GTP+Bfa1_Tem1GDP)+Bfa1P4+Bfa1P4_Tem1GTP+Bfa1P4_Tem1GDP)*c2*avogadro, Rate Law: missing
avogadro = 6.0221415E23 Reaction: Active_Tem1_in_the_Cytosol = (Tem1GTP+Bfa1_Tem1GTP+Bfa1P4_Tem1GTP+Bfa1P5_Tem1GTP)*c2*avogadro, Rate Law: missing
khydB4T = 2.0 1/s Reaction: Bfa1P4_Tem1GTP => Bfa1P4_Tem1GDP, Rate Law: c2*khydB4T*Bfa1P4_Tem1GTP
alpha = 1.0 1; koffBT = 0.183 1/s; konB5T = 7000000.0 l/(mol*s) Reaction: Bfa1P5 + Tem1GTP => Bfa1P5_Tem1GTP, Rate Law: c2*(alpha*konB5T*Bfa1P5*Tem1GTP-koffBT*Bfa1P5_Tem1GTP)
khyd = 0.00224 1/s Reaction: T_Tem1GTP => T_Tem1GDP, Rate Law: c3*khyd*T_Tem1GTP
konB = 1250000.0 l/(mol*s); koffB = 0.0012 1/s Reaction: SPB_B + Bfa1 => B_Bfa1, Rate Law: c3*(konB*SPB_B*Bfa1-koffB*B_Bfa1)
khydBT = 0.00224 1/s Reaction: Bfa1_Tem1GTP => Bfa1_Tem1GDP, Rate Law: c2*khydBT*Bfa1_Tem1GTP
koffBT = 0.183 1/s; konBT = 3.65E7 l/(mol*s) Reaction: Tem1GDP + B_Bfa1 => B_Bfa1_Tem1GDP, Rate Law: c3*(konBT*B_Bfa1*Tem1GDP-koffBT*B_Bfa1_Tem1GDP)
kfCdc5 = 1.0 1/s Reaction: B_Bfa1 => B_Bfa1P5, Rate Law: c3*kfCdc5*B_Bfa1
knex = 0.0136 1/s Reaction: T_Tem1GDP => T_Tem1GTP, Rate Law: c3*knex*T_Tem1GDP
konT = 1900000.0 l/(mol*s); koffT = 0.183 1/s Reaction: Tem1GTP + SPB_T => T_Tem1GTP, Rate Law: c3*(konT*SPB_T*Tem1GTP-koffT*T_Tem1GTP)

States:

Name Description
Bfa1 Tem1GTP [Mitotic check point protein BFA1; GTP; Protein TEM1]
Active Tem1 at the SPB [Protein TEM1; active]
Bfa1P5 Tem1GDP [Mitotic check point protein BFA1; increased phosphorylation; Protein TEM1; GDP]
Bfa1P5 Tem1GTP [Protein TEM1; increased phosphorylation; GTP; Mitotic check point protein BFA1]
B Bfa1P4 [Mitotic check point protein BFA1; phosphorylated; urn:miriam:sbo:SBO%3A0000494]
Inactive Bfa1 at the SPB [Mitotic check point protein BFA1; inactive]
B Bfa1P4 Tem1GDP [urn:miriam:sbo:SBO%3A0000494; phosphorylated; GDP; Protein TEM1; Mitotic check point protein BFA1]
B Bfa1P4 Tem1GTP [urn:miriam:sbo:SBO%3A0000494; phosphorylated; Protein TEM1; Mitotic check point protein BFA1; GTP]
Bfa1 Tem1GDP [Protein TEM1; GDP; Mitotic check point protein BFA1]
Inactive Bfa1 in the cytosol [Mitotic check point protein BFA1; inactive]
Bfa1P5 [Mitotic check point protein BFA1; increased phosphorylation]
SPB T [urn:miriam:sbo:SBO%3A0000494]
B Bfa1 Tem1GDP [urn:miriam:sbo:SBO%3A0000494; Mitotic check point protein BFA1; GDP; Protein TEM1]
Tem1GTP [GTP; Protein TEM1]
SPB B [urn:miriam:sbo:SBO%3A0000494]
Bfa1P4 [Mitotic check point protein BFA1; phosphorylated]
Bfa1 [Mitotic check point protein BFA1]
B Bfa1P5 Tem1GTP [urn:miriam:sbo:SBO%3A0000494; phosphorylated; GTP; Protein TEM1; Mitotic check point protein BFA1]
B Bfa1P5 Tem1GDP [GDP; phosphorylated; Mitotic check point protein BFA1; urn:miriam:sbo:SBO%3A0000494; Protein TEM1]
Active Bfa1 at the Cytosol [Mitotic check point protein BFA1; active]
Bfa1P4 Tem1GDP [GDP; phosphorylated; Protein TEM1; Mitotic check point protein BFA1]
B Bfa1P5 [Mitotic check point protein BFA1; phosphorylated; urn:miriam:sbo:SBO%3A0000494]
Bfa1P4 Tem1GTP [Protein TEM1; phosphorylated; GTP; Mitotic check point protein BFA1]
B Bfa1 Tem1GTP [Mitotic check point protein BFA1; GTP; Protein TEM1; urn:miriam:sbo:SBO%3A0000494]
Tem1GDP [Protein TEM1; GDP]
Active Tem1 in the Cytosol [Protein TEM1; active]
Inactive Tem1 in the cytosol [Protein TEM1; inactive]
T Tem1GDP [Protein TEM1; urn:miriam:sbo:SBO%3A0000494; GDP]
Total Bfa1 in the Cytosol [Mitotic check point protein BFA1]
Total Tem1 in the Cytosol [Protein TEM1]
Total Bfa1 at the SPB [Mitotic check point protein BFA1]
T Tem1GTP [Protein TEM1; GTP; urn:miriam:sbo:SBO%3A0000494]
Active Bfa1 at the SPB [Mitotic check point protein BFA1; active]
Total Tem1 at the SPB [Protein TEM1]
B Bfa1 [Mitotic check point protein BFA1; urn:miriam:sbo:SBO%3A0000494]
Inactive Tem1 at the SPB [Protein TEM1; inactive]

Observables: none

Mathematical model of blood coagulation with platelet activation. Model includes factor XII, factor VIIIa fragments, mei…

The introduction of general-purpose Graphics Processing Units (GPUs) is boosting scientific applications in Bioinformatics, Systems Biology, and Computational Biology. In these fields, the use of high-performance computing solutions is motivated by the need of performing large numbers of in silico analysis to study the behavior of biological systems in different conditions, which necessitate a computing power that usually overtakes the capability of standard desktop computers. In this work we present coagSODA, a CUDA-powered computational tool that was purposely developed for the analysis of a large mechanistic model of the blood coagulation cascade (BCC), defined according to both mass-action kinetics and Hill functions. coagSODA allows the execution of parallel simulations of the dynamics of the BCC by automatically deriving the system of ordinary differential equations and then exploiting the numerical integration algorithm LSODA. We present the biological results achieved with a massive exploration of perturbed conditions of the BCC, carried out with one-dimensional and bi-dimensional parameter sweep analysis, and show that GPU-accelerated parallel simulations of this model can increase the computational performances up to a 181× speedup compared to the corresponding sequential simulations. link: http://identifiers.org/doi/10.1155/2014/863298

Parameters: none

States: none

Observables: none

Cellière2011 - Plasticity of TGF-β SignallingTransforming growth factor beta (TGF-β) signalling has been implicated as a…

BACKGROUND: The family of TGF-β ligands is large and its members are involved in many different signaling processes. These signaling processes strongly differ in type with TGF-β ligands eliciting both sustained or transient responses. Members of the TGF-β family can also act as morphogen and cellular responses would then be expected to provide a direct read-out of the extracellular ligand concentration. A number of different models have been proposed to reconcile these different behaviours. We were interested to define the set of minimal modifications that are required to change the type of signal processing in the TGF-β signaling network. RESULTS: To define the key aspects for signaling plasticity we focused on the core of the TGF-β signaling network. With the help of a parameter screen we identified ranges of kinetic parameters and protein concentrations that give rise to transient, sustained, or oscillatory responses to constant stimuli, as well as those parameter ranges that enable a proportional response to time-varying ligand concentrations (as expected in the read-out of morphogens). A combination of a strong negative feedback and fast shuttling to the nucleus biases signaling to a transient rather than a sustained response, while oscillations were obtained if ligand binding to the receptor is weak and the turn-over of the I-Smad is fast. A proportional read-out required inefficient receptor activation in addition to a low affinity of receptor-ligand binding. We find that targeted modification of single parameters suffices to alter the response type. The intensity of a constant signal (i.e. the ligand concentration), on the other hand, affected only the strength but not the type of the response. CONCLUSIONS: The architecture of the TGF-β pathway enables the observed signaling plasticity. The observed range of signaling outputs to TGF-β ligand in different cell types and under different conditions can be explained with differences in cellular protein concentrations and with changes in effective rate constants due to cross-talk with other signaling pathways. It will be interesting to uncover the exact cellular differences as well as the details of the cross-talks in future work. link: http://identifiers.org/pubmed/22051045

Parameters:

Name Description
k19 = 4.12E-4 Reaction: I_Smad =>, Rate Law: c*k19*I_Smad
k13 = 0.00164 Reaction: Smad_P_N => Smad_N, Rate Law: n*k13*Smad_P_N
k10 = 5.12E-8 Reaction: Smad_P + CoSmad => Smad_P_CoSmad, Rate Law: c*k10*Smad_P*CoSmad
k7 = 9.35E-6 Reaction: Smad => Smad_P; TGFb_TGFbR_P, Smad, Rate Law: c*k7*Smad*TGFb_TGFbR_P
k3 = 0.324 Reaction: TGFb_TGFbR => TGFb_TGFbR_P, Rate Law: c*k3*TGFb_TGFbR
k5 = 5.49E-4 Reaction: TGFb_TGFbR_P + I_Smad => I_Smad_TGFb_TGFbR_P, Rate Law: c*k5*TGFb_TGFbR_P*I_Smad
k11 = 0.00923 Reaction: Smad_P_Smad_P => Smad_P, Rate Law: c*k11*Smad_P_Smad_P
k1 = 0.00446 Reaction: TGFb_TGFbR => TGFbR, Rate Law: c*k1*TGFb_TGFbR
k8 = 0.0104; k12 = 0.0513 Reaction: Smad_P_Smad_P => Smad_P_Smad_P_N, Rate Law: k12*k8*Smad_P_Smad_P
k6 = 1.29E-5 Reaction: I_Smad_TGFb_TGFbR_P => TGFb_TGFbR + I_Smad, Rate Law: c*k6*I_Smad_TGFb_TGFbR_P
k2 = 4.39E-6 Reaction: TGFbR + TGFb => TGFb_TGFbR, Rate Law: k2*TGFbR*TGFb
k14 = 0.038; k15 = 28.52; h = 2.06 Reaction: => I_Smad_mRNA1; Smad_P_CoSmad_N, Rate Law: n*k14*Smad_P_CoSmad_N^h/(Smad_P_CoSmad_N^h+k15^h)
k4 = 0.00192 Reaction: TGFb_TGFbR_P => TGFb_TGFbR, Rate Law: c*k4*TGFb_TGFbR_P
k17 = 8.05E-5 Reaction: I_Smad_mRNA2 =>, Rate Law: c*k17*I_Smad_mRNA2
k18 = 0.0434 Reaction: => I_Smad; I_Smad_mRNA2, Rate Law: c*k18*I_Smad_mRNA2
k9 = 7.5E-4 Reaction: Smad_N => Smad, Rate Law: k9*Smad_N
k8 = 0.0104 Reaction: Smad => Smad_N, Rate Law: k8*Smad
k16 = 0.0214 Reaction: I_Smad_mRNA1 => I_Smad_mRNA2, Rate Law: k16*I_Smad_mRNA1

States:

Name Description
TGFb [TGF-beta receptor type-1]
Smad P CoSmad [IPR008984; Mothers against decapentaplegic homolog 4]
Smad P Smad P N [IPR008984]
TGFb TGFbR P [IPR000333; IPR016319]
Smad P CoSmad N [IPR008984; Mothers against decapentaplegic homolog 4]
CoSmad [IPR008984]
I Smad mRNA2 [IPR008984]
I Smad TGFb TGFbR P [IPR017855; IPR016319; IPR000333]
Smad P [IPR008984]
Smad P N [IPR008984; Mothers against decapentaplegic homolog 4]
I Smad mRNA1 [IPR008984]
Smad P Smad P [IPR008984]
TGFbR [IPR000333]
Smad [IPR008984]
Smad N [IPR008984]
CoSmad N [IPR008984]
I Smad [IPR008984]
TGFb TGFbR [IPR000333; IPR016319]

Observables: none

Cetinkaya2017 - Engineering targets for Komagataella phaffiiThis model is described in the article: [Metabolic modellin…

Genome-scale metabolic models are valuable tools for the design of novel strains of industrial microorganisms, such as Komagataella phaffii (syn. Pichia pastoris). However, as is the case for many industrial microbes, there is no executable metabolic model for K. phaffiii that confirms to current standards by providing the metabolite and reactions IDs, to facilitate model extension and reuse, and gene-reaction associations to enable identification of targets for genetic manipulation. In order to remedy this deficiency, we decided to reconstruct the genome-scale metabolic model of K. phaffii by reconciling the extant models and performing extensive manual curation in order to construct an executable model (Kp.1.0) that conforms to current standards. We then used this model to study the effect of biomass composition on the predictive success of the model. Twelve different biomass compositions obtained from published empirical data obtained under a range of growth conditions were employed in this investigation. We found that the success of Kp1.0 in predicting both gene essentiality and growth characteristics was relatively unaffected by biomass composition. However, we found that biomass composition had a profound effect on the distribution of the fluxes involved in lipid, DNA and steroid biosynthetic processes, cellular alcohol metabolic process and oxidation-reduction process. Further, we investigated the effect of biomass composition on the identification of suitable target genes for strain development. The analyses revealed that around 40% of the predictions of the effect of gene overexpression or deletion changed depending on the representation of biomass composition in the model. Considering the robustness of the in silico flux distributions to the changing biomass representations enables better interpretation of experimental results, reduces the risk of wrong target identification, and so both speeds and improves the process of directed strain development. link: http://identifiers.org/doi/10.1002/bit.26380

Parameters: none

States: none

Observables: none

This is a reinvestigation of a previous model depicting cancer remission. It involves application of mathematical tools…

The mathematical model depicting cancer remission as presented by Banerjee and Sarkar1 is reinvestigated here. Mathematical tools from control theory have been used to analyze and determine how an optimal external treatment of Adaptive Cellular Immunotherapy and interleukin-2 can result in more effective remission of malignant tumors while minimizing any adverse affect on the immune response. link: http://identifiers.org/doi/10.1142/S0218339010003226

Parameters:

Name Description
d_1 = 0.0412 Reaction: N_CTL =>, Rate Law: compartment*d_1*N_CTL
mu_2 = 0.0 Reaction: => N_CTL; N_CTL, Z_THL, Rate Law: compartment*mu_2*N_CTL*Z_THL
mu_1 = 0.05 Reaction: M_Tumor_Cells =>, Rate Law: compartment*mu_1*M_Tumor_Cells
alpha_2 = 3.422E-10 Reaction: N_CTL => ; M_Tumor_Cells, Rate Law: compartment*alpha_2*M_Tumor_Cells*N_CTL
r_2 = 0.0245; k_2 = 1.0E7 Reaction: => Z_THL; Z_THL, Rate Law: compartment*r_2*Z_THL*(1-Z_THL/k_2)
k_1 = 5000000.0; r_1 = 0.18 Reaction: => M_Tumor_Cells; M_Tumor_Cells, Rate Law: compartment*r_1*M_Tumor_Cells*(1-M_Tumor_Cells/k_1)
alpha_1 = 1.101E-7 Reaction: M_Tumor_Cells => ; M_Tumor_Cells, N_CTL, Rate Law: compartment*alpha_1*M_Tumor_Cells*N_CTL

States:

Name Description
Z THL [helper T cell]
M Tumor Cells [Neoplastic Cell]
N CTL [cytotoxic T cell]

Observables: none

BIOMD0000000120 @ v0.0.1

The model reproduces Fig 3a of the paper. Please note that the authors mention that they used a value of 2 for n, n bein…

The specificity and sensitivity of T-cell recognition is vital to the immune response. Ligand engagement with the T-cell receptor (TCR) results in the activation of a complex sequence of signalling events, both on the cell membrane and intracellularly. Feedback is an integral part of these signalling pathways, yet is often ignored in standard accounts of T-cell signalling. Here we show, using a mathematical model, that these feedback loops can explain the ability of the TCR to discriminate between ligands with high specificity and sensitivity, as well as provide a mechanism for sustained signalling. The model also explains the recent counter-intuitive observation that endogenous 'null' ligands can significantly enhance T-cell signalling. Finally, the model may provide an archetype for receptor switching based on kinase-phosphatase switches, and thus be of interest to the wider signalling community. link: http://identifiers.org/pubmed/15255048

Parameters:

Name Description
k2 = 0.01 sec_inv Reaction: phosphatase_inactive => phosphatase_active, Rate Law: k2*phosphatase_inactive
d2 = 0.0 sec_inv Reaction: phosphatase_active =>, Rate Law: d2*phosphatase_active
k1 = 0.01 sec_inv Reaction: lck_inactive => lck_active, Rate Law: k1*lck_inactive
r_l = 0.0 items_per_time Reaction: => lck_inactive, Rate Law: r_l
n1 = 1.0 per_sec_per_item Reaction: lck_active => lck_inactive; phosphatase_active, Rate Law: n1*lck_active*phosphatase_active
d1 = 0.15 sec_inv Reaction: lck_active =>, Rate Law: d1*lck_active
m2 = 1.0 per_sec_per_item Reaction: phosphatase_inactive => phosphatase_active; lck_active, Rate Law: m2*lck_active*phosphatase_inactive
n2 = 0.02 sec_inv Reaction: phosphatase_active => phosphatase_inactive, Rate Law: n2*phosphatase_active
d0 = 0.15 sec_inv Reaction: lck_inactive =>, Rate Law: d0*lck_inactive
m1 = 1.0; n = 1.95 dimensionless Reaction: lck_inactive => lck_active, Rate Law: m1*lck_active^n*lck_inactive

States:

Name Description
lck total [Tyrosine-protein kinase Lck]
phosphatase active [Tyrosine-protein phosphatase non-receptor type 6]
lck active [Tyrosine-protein kinase Lck]
lck inactive [Tyrosine-protein kinase Lck]
phosphatase inactive [Tyrosine-protein phosphatase non-receptor type 6]

Observables: none

BIOMD0000000283 @ v0.0.1

Default parameter values are those in the right hand panel of Fig 12. The other panels may be obtained by setting X to 1…

Under the narrow range of experimental conditions, and at a temperature of approximately 25 degrees, the following data were obtained. 1. The equilibrium constant of peroxidase and hydrogen peroxide has a minimum value of 2 x 10(-8). 2. The velocity constant for the formation of peroxidase-H2O2 Complex I is 1.2 x 10(7) liter mole-1 sec.-1, +/- 0.4 x 10(7). 3. The velocity constant for the reversible breakdown of peroxidase-H2O2 Complex I is a negligible factor in the enzyme-substrate kinetics and is calculated to be less than 0.2 sec.-1. 4. The velocity constant, k3, for the enzymatic breakdown of peroxidase-H2O2 Complex I varies from nearly zero to higher than 5 sec.-1, depending upon the acceptor and its concentration. The quotient of k3 and the leucomalachite green concentration is 3.0 x 10(4) liter mole-1 sec.-1. For ascorbic acid this has a value of 1.8 x 10(5) liter mole-1 sec.-1. 5. For a particular acceptor concentration, k3 is determined solely from the enzyme-substrate kinetics and is found to be 4.2 sec.-1. 6. For the same conditions, k3 is determined from a simple relationship derived from mathematical solutions of the Michaelis theory and is found to be 5.2 sec.-1. 7. For the same conditions, k3 is determined from the over-all enzyme action and is found to be 5.1 sec.-1. 8. The Michaelis constant determined from kinetic data alone is found to be 0.44 x 10(-6). 9. The Michaelis constant determined from steady state measurements is found to be 0.41 x 10(-6). 10. The Michaelis constant determined from measurement of the overall enzyme reaction is found to be 0.50 x 10(-6). 11. The kinetics of the enzyme-substrate compound closely agree with mathematical solutions of an extension of the Michaelis theory obtained for experimental values of concentrations and reaction velocity constants. 12. The adequacy of the criteria by which experiment and theory were correlated has been examined critically and the mathematical solutions have been found to be sensitive to variations in the experimental conditions. 13. The critical features of the enzyme-substrate kinetics are Pmax, and curve shape, rather than t1/2. t1/2 serves as a simple measure of dx/dt. 14. A second order combination of enzyme and substrate to form the enzyme-substrate compound, followed by a first order breakdown of the compound, describes the activity of peroxidase for a particular acceptor concentration. 15. The kinetic data indicate a bimolecular combination of acceptor and enzyme-substrate compound. link: http://identifiers.org/pubmed/10218104

Parameters:

Name Description
K3 = 0.5 dimensionless Reaction: P => E + Q, Rate Law: cell*K3*P
K2 = 0.0 dimensionless Reaction: X + E => P, Rate Law: cell*(E*X-K2*P)

States:

Name Description
Q [water]
X [hydrogen peroxide; Hydrogen peroxide]
P [hydrogen peroxide; IPR000823]
E [Peroxidase C1A; Peroxidase C1C; Peroxidase C1B; IPR000823]

Observables: none

BIOMD0000000282 @ v0.0.1

This model is described in the article: **The mechanism of catalase action. II. Electric analog computer studies.** Br…

link: http://identifiers.org/pubmed/14953444

Parameters:

Name Description
k2 = 0.0 per second; k1 = 11.0 per micromolar per second Reaction: e + x => p, Rate Law: cell*(k1*e*x-k2*p)
k4_prime = 16.6 per micromolar per second Reaction: p + x => e + p1, Rate Law: cell*k4_prime*p*x
k4 = 0.72 per micromolar per second Reaction: p + a => e + p2, Rate Law: cell*k4*p*a

States:

Name Description
e [catalase activity; Catalase; IPR002226; Catalase]
x [hydrogen peroxide; Hydrogen peroxide]
p2 [water; carbonyl compound]
p1 [water; dioxygen]
a [primary alcohol; secondary alcohol]
p [hydrogen peroxide; IPR002226]

Observables: none

BIOMD0000000281 @ v0.0.1

This model is described inthe article: **Metabolic control mechanisms. 5. A solution for the equations representing in…

link: http://identifiers.org/pubmed/13692276

Parameters:

Name Description
k=5.0E9 per molar per second Reaction: PGA + ADP => TP1 + PYR, Rate Law: cell*1E-6*k*PGA*ADP
k=1.0E7 per molar per second Reaction: LAC + DPN => PYR + DPH, Rate Law: cell*1E-6*k*LAC*DPN
k=1.2E8 per molar per second Reaction: XSI => XI; DBP, Rate Law: cell*1E-6*k*XSI*DBP
k=4000000.0 per molar per second Reaction: TP2 => TP1; DBP, Rate Law: cell*1E-6*k*TP2*DBP
k=2000000.0 per second Reaction: PPP => ADP + PUE + PID, Rate Law: cell*1E-6*k*PPP
k=1.0E10 per molar per second Reaction: ENG + TP1 => ADP + GLP + ENZ, Rate Law: cell*1E-6*k*ENG*TP1
k=2.0E9 per molar per second Reaction: DHA + DPH => AGP + DPN, Rate Law: cell*1E-6*k*DHA*DPH
k=6.0E11 per molar per second Reaction: GAP + MOD => MOB + DPH, Rate Law: cell*1E-6*k*GAP*MOD
k=8.0E7 per molar per second Reaction: AGP + DPN => DHA + DPH, Rate Law: cell*1E-6*k*AGP*DPN
k=5.0E8 per molar per second Reaction: PYR + DPH => LAC + DPN, Rate Law: cell*1E-6*k*PYR*DPH
k=6.0E9 per molar per second Reaction: MOX + DPN => MOD, Rate Law: cell*1E-6*k*MOX*DPN
k=100000.0 per second Reaction: GPP => GAP + DHA, Rate Law: cell*1E-6*k*GPP
k=7.5E12 per molar squared per second Reaction: DIH + XI + OXY => XSI + DIN, Rate Law: cell*1E-6*k*DIH*XI*OXY
k=1.5E10 per molar per second Reaction: XSP + ADP => TP2 + XI, Rate Law: cell*1E-6*k*XSP*ADP
k=4.0E10 per molar per second Reaction: ETG + TP1 => GPP + ETZ + ADP, Rate Law: cell*1E-6*k*ETG*TP1
k=2.0E7 per molar per second Reaction: PYR + DIN => DIH, Rate Law: cell*1E-6*k*PYR*DIN
k=4.0E8 per molar per second Reaction: MOB + PID => DGA + MOX, Rate Law: cell*1E-6*k*MOB*PID
k=3.0E9 per molar per second Reaction: GLU + ENZ => ENG, Rate Law: cell*1E-6*k*GLU*ENZ

States:

Name Description
ENG [glucose; IPR001312]
DPN [NAD(+); NAD+]
PID [phosphate(3-); Orthophosphate]
TP1 [ATP; ATP]
MOB [D-glyceraldehyde 3-phosphate; IPR006424; glyceraldehyde-3-phosphate dehydrogenase (NAD+) (phosphorylating) activity]
GPP [beta-D-Fructose 1,6-bisphosphate; beta-D-fructofuranose 1,6-bisphosphate]
DGA [3-Phospho-D-glyceroyl phosphate; 3-phospho-D-glyceroyl dihydrogen phosphate]
PGA [3-Phospho-D-glycerate; 3-phospho-D-glyceric acid]
GLP [D-glucopyranose 6-phosphate; D-Glucose 6-phosphate]
ETZ [IPR022463; 1-phosphofructokinase activity]
MOX [Glyceraldehyde-3-phosphate dehydrogenase; Glyceraldehyde-3-phosphate dehydrogenase; IPR006424; glyceraldehyde-3-phosphate dehydrogenase (NAD+) (phosphorylating) activity]
DHA [Glycerone phosphate; dihydroxyacetone phosphate]
TP2 [ATP; ATP]
MOD [NAD(+); IPR006424]
XSP [mitochondrial respiratory chain; proton-transporting ATP synthase complex]
PYR [Pyruvate; pyruvic acid]
DPH [NADH; NADH]
PPP [ATP; protein polypeptide chain; ATPase activity]
DIN [NAD+; NAD(+)]
AGP [sn-Glycerol 1-phosphate; sn-glycerol 1-phosphate]
DIH [NADH; NADH]
XSI [proton-transporting ATP synthase complex; mitochondrial respiratory chain]
OXY [Oxygen; dioxygen]
GAP [D-glyceraldehyde 3-phosphate; D-Glyceraldehyde 3-phosphate]
ETG [D-glucopyranose 6-phosphate; IPR022463; 1-phosphofructokinase activity]
LAC [(S)-Lactate; (S)-lactic acid]
ENZ [IPR001312; hexokinase activity]
XI [proton-transporting ATP synthase complex; mitochondrial respiratory chain]
ADP [ADP; ADP]
GLU [C00293; glucose]
PUE [Protein; protein polypeptide chain]

Observables: none

Chang2008 - ERK activation, hallucinogenic drugs mediated signalling through serotonin receptorsThis model is described…

Through a multidisciplinary approach involving experimental and computational studies, we address quantitative aspects of signaling mechanisms triggered in the cell by the receptor targets of hallucinogenic drugs, the serotonin 5-HT2A receptors. To reveal the properties of the signaling pathways, and the way in which responses elicited through these receptors alone and in combination with other serotonin receptors' subtypes (the 5-HT1AR), we developed a detailed mathematical model of receptor-mediated ERK1/2 activation in cells expressing the 5-HT1A and 5-HT2A subtypes individually, and together. In parallel, we measured experimentally the activation of ERK1/2 by the action of selective agonists on these receptors expressed in HEK293 cells. We show here that the 5-HT1AR agonist Xaliproden HCl elicited transient activation of ERK1/2 by phosphorylation, whereas 5-HT2AR activation by TCB-2 led to higher, and more sustained responses. The 5-HT2AR response dominated the MAPK signaling pathway when co-expressed with 5-HT1AR, and diminution of the response by the 5-HT2AR antagonist Ketanserin could not be rescued by the 5-HT1AR agonist. Computational simulations produced qualitative results in good agreement with these experimental data, and parameter optimization made this agreement quantitative. In silico simulation experiments suggest that the deletion of the positive regulators PKC in the 5-HT2AR pathway, or PLA2 in the combined 5-HT1A/2AR model greatly decreased the basal level of active ERK1/2. Deletion of negative regulators of MKP and PP2A in 5-HT1AR and 5-HT2AR models was found to have even stronger effects. Under various parameter sets, simulation results implied that the extent of constitutive activity in a particular tissue and the specific drug efficacy properties may determine the distinct dynamics of the 5-HT receptor-mediated ERK1/2 activation pathways. Thus, the mathematical models are useful exploratory tools in the ongoing efforts to establish a mechanistic understanding and an experimentally testable representation of hallucinogen-specific signaling in the cellular machinery, and can be refined with quantitative, function-related information. link: http://identifiers.org/pubmed/18762202

Parameters: none

States: none

Observables: none

MODEL1011080004 @ v0.0.1

This is the reduced kidney metabolic network described in the article Drug off-target effects predicted using structur…

Recent advances in structural bioinformatics have enabled the prediction of protein-drug off-targets based on their ligand binding sites. Concurrent developments in systems biology allow for prediction of the functional effects of system perturbations using large-scale network models. Integration of these two capabilities provides a framework for evaluating metabolic drug response phenotypes in silico. This combined approach was applied to investigate the hypertensive side effect of the cholesteryl ester transfer protein inhibitor torcetrapib in the context of human renal function. A metabolic kidney model was generated in which to simulate drug treatment. Causal drug off-targets were predicted that have previously been observed to impact renal function in gene-deficient patients and may play a role in the adverse side effects observed in clinical trials. Genetic risk factors for drug treatment were also predicted that correspond to both characterized and unknown renal metabolic disorders as well as cryptic genetic deficiencies that are not expected to exhibit a renal disorder phenotype except under drug treatment. This study represents a novel integration of structural and systems biology and a first step towards computational systems medicine. The methodology introduced herein has important implications for drug development and personalized medicine. link: http://identifiers.org/pubmed/20957118

Parameters: none

States: none

Observables: none

This model is from the article: Metabolic network reconstruction of Chlamydomonas offers insight into light-driven alg…

Metabolic network reconstruction encompasses existing knowledge about an organism's metabolism and genome annotation, providing a platform for omics data analysis and phenotype prediction. The model alga Chlamydomonas reinhardtii is employed to study diverse biological processes from photosynthesis to phototaxis. Recent heightened interest in this species results from an international movement to develop algal biofuels. Integrating biological and optical data, we reconstructed a genome-scale metabolic network for this alga and devised a novel light-modeling approach that enables quantitative growth prediction for a given light source, resolving wavelength and photon flux. We experimentally verified transcripts accounted for in the network and physiologically validated model function through simulation and generation of new experimental growth data, providing high confidence in network contents and predictive applications. The network offers insight into algal metabolism and potential for genetic engineering and efficient light source design, a pioneering resource for studying light-driven metabolism and quantitative systems biology. link: http://identifiers.org/pubmed/21811229

Parameters: none

States: none

Observables: none

Chaouiya2013 - EGF and TNFalpha mediated signalling pathwayThis model is described in the article: [SBML qualitative mo…

Qualitative frameworks, especially those based on the logical discrete formalism, are increasingly used to model regulatory and signalling networks. A major advantage of these frameworks is that they do not require precise quantitative data, and that they are well-suited for studies of large networks. While numerous groups have developed specific computational tools that provide original methods to analyse qualitative models, a standard format to exchange qualitative models has been missing.We present the Systems Biology Markup Language (SBML) Qualitative Models Package ("qual"), an extension of the SBML Level 3 standard designed for computer representation of qualitative models of biological networks. We demonstrate the interoperability of models via SBML qual through the analysis of a specific signalling network by three independent software tools. Furthermore, the collective effort to define the SBML qual format paved the way for the development of LogicalModel, an open-source model library, which will facilitate the adoption of the format as well as the collaborative development of algorithms to analyse qualitative models.SBML qual allows the exchange of qualitative models among a number of complementary software tools. SBML qual has the potential to promote collaborative work on the development of novel computational approaches, as well as on the specification and the analysis of comprehensive qualitative models of regulatory and signalling networks. link: http://identifiers.org/pubmed/24321545

Parameters: none

States: none

Observables: none

Mixed immunotherapy and chemotherapy of tumors: feedback design and model updating schemes. Chareyron S1, Alamir M. Auth…

In this paper, a recently developed model governing the cancer growth on a cell population level with combination of immune and chemotherapy is used to develop a reactive (feedback) mixed treatment strategy. The feedback design proposed here is based on nonlinear constrained model predictive control together with an adaptation scheme that enables the effects of unavoidable modeling uncertainties to be compensated. The effectiveness of the proposed strategy is shown under realistic human data showing the advantage of treatment in feedback form as well as the relevance of the adaptation strategy in handling uncertainties and modeling errors. A new treatment strategy defined by an original optimal control problem formulation is also proposed. This new formulation shows particularly interesting possibilities since it may lead to tumor regression under better health indicator profile. link: http://identifiers.org/pubmed/18655792

Parameters: none

States: none

Observables: none

BIOMD0000000066 @ v0.0.1

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

A computer simulation of the threonine-synthesis pathway in Escherichia coli Tir-8 has been developed based on our previous measurements of the kinetics of the pathway enzymes under near-physiological conditions. The model successfully simulates the main features of the time courses of threonine synthesis previously observed in a cell-free extract without alteration of the experimentally determined parameters, although improved quantitative fits can be obtained with small parameter adjustments. At the concentrations of enzymes, precursors and products present in cells, the model predicts a threonine-synthesis flux close to that required to support cell growth. Furthermore, the first two enzymes operate close to equilibrium, providing an example of a near-equilibrium feedback-inhibited enzyme. The predicted flux control coefficients of the pathway enzymes under physiological conditions show that the control of flux is shared between the first three enzymes: aspartate kinase, aspartate semialdehyde dehydrogenase and homoserine dehydrogenase, with no single activity dominating the control. The response of the model to the external metabolites shows that the sharing of control between the three enzymes holds across a wide range of conditions, but that the pathway flux is sensitive to the aspartate concentration. When the model was embedded in a larger model to simulate the variable demands for threonine at different growth rates, it showed the accumulation of free threonine that is typical of the Tir-8 strain at low growth rates. At low growth rates, the control of threonine flux remains largely with the pathway enzymes. As an example of the predictive power of the model, we studied the consequences of over-expressing different enzymes in the pathway. link: http://identifiers.org/pubmed/11368770

Parameters:

Name Description
vm5=0.0434 mM_per_min; k5hsp=0.31 mM Reaction: hsp => thr + phos, Rate Law: compartment*vm5*hsp/(hsp+k5hsp)
katpase=4.1E-5 millimole_per_mg_per_min; prot=202.0 mg_per_litre Reaction: atp => adp + phos, Rate Law: compartment*prot*katpase
knadph=5.4E-6 litre_per_mg_per_min; prot=202.0 mg_per_litre Reaction: nadph => nadp, Rate Law: compartment*prot*knadph*nadph
k13atp=0.22 mM; k13aspp=0.017 mM; k1lys=0.391 mM; k1aspp=0.017 mM; k1thr=0.167 mM; k13=0.32 mM; k1atp=0.98 mM; keqak=6.4E-4 dimensionless; vm13=0.0722 mM_per_min; nak3=2.8 dimensionless; nak1=4.09 dimensionless; k13adp=0.25 mM; alpha=2.47 dimensionless; k1adp=0.25 mM; lys=0.46 mM; vm11=0.15 mM_per_min; k11=0.97 mM Reaction: atp + asp => aspp + adp; thr, Rate Law: compartment*(vm11*(asp*atp-aspp*adp/keqak)/((k11*(1+(thr/k1thr)^nak1)/(1+(thr/(alpha*k1thr))^nak1)+k11*aspp/k1aspp+asp)*(k1atp*(1+adp/k1adp)+atp))+vm13*(asp*atp-aspp*adp/keqak)/((1+(lys/k1lys)^nak3)*(k13*(1+aspp/k13aspp)+asp)*(k13atp*(1+adp/k13adp)+atp)))
k3thr=0.097 mM; k3hs=3.39 mM; alpha3=3.93 dimensionless; k3nadph=0.037 mM; k3eq=3162.27766016838 dimensionless; k3nadp=0.067 mM; vm3f=1.001 mM_per_min; nhdh1=1.41 dimensionless; k3asa=0.24 mM Reaction: nadph + asa => hs + nadp; asp, thr, Rate Law: compartment*vm3f*(asa*nadph-hs*nadp/k3eq)/((1+(thr/k3thr)^nhdh1)/(1+(thr/(alpha3*k3thr))^nhdh1)*(k3asa+asa+hs*k3asa/k3hs)*(k3nadph*(1+nadp/k3nadp)+nadph))
k4lys=9.45 mM; k4thr=1.09 mM; k4atp=0.072 mM; k4ihs=4.7 mM; vm4f=0.1 mM_per_min; k4hs=0.11 mM; k4iatp=4.35 mM; lys=0.46 mM Reaction: hs + atp => hsp + adp; thr, Rate Law: compartment*vm4f*hs*atp/((1+lys/k4lys)*(atp+k4atp*(1+hs/k4ihs))*(hs+k4hs*(1+thr/k4thr)*(1+atp/k4iatp)))
k2p=10.0 mM; k2nadph=0.029 mM; k2nadp=0.144 mM; k2asa=0.11 mM; k2eq=56.4150334574039 dimensionless; k2aspp=0.022 mM; vm2f=0.1812 mM_per_min Reaction: nadph + aspp => nadp + phos + asa, Rate Law: compartment*vm2f*(aspp*nadph-asa*nadp*phos/k2eq)/((k2aspp*(1+asa/k2asa)*(1+phos/k2p)+aspp)*(k2nadph*(1+nadp/k2nadp)+nadph))

States:

Name Description
nadph [NADPH; NADPH]
hsp [O-phospho-L-homoserine; O-Phospho-L-homoserine]
asa [L-aspartic 4-semialdehyde; L-Aspartate 4-semialdehyde]
hs [L-homoserine; L-Homoserine]
atp [ATP; ATP]
asp [L-aspartic acid; L-Aspartate]
thr [L-threonine; L-Threonine]
adp [ADP; ADP]
aspp [4-phospho-L-aspartic acid; 4-Phospho-L-aspartate]
nadp [NADP(+); NADP+]
phos [phosphate(3-); Orthophosphate]

Observables: none

BIOMD0000000051 @ v0.0.1

The model reproduces Figures 4,5 and 6 of the publication. The analytical functions for cometabolites Catp, Camp, Cnadph…

Application of metabolic engineering principles to the rational design of microbial production processes crucially depends on the ability to describe quantitatively the systemic behavior of the central carbon metabolism to redirect carbon fluxes to the product-forming pathways. Despite the importance for several production processes, development of an essential dynamic model for central carbon metabolism of Escherichia coli has been severely hampered by the current lack of kinetic information on the dynamics of the metabolic reactions. Here we present the design and experimental validation of such a dynamic model, which, for the first time, links the sugar transport system (i.e., phosphotransferase system [PTS]) with the reactions of glycolysis and the pentose-phosphate pathway. Experimental observations of intracellular concentrations of metabolites and cometabolites at transient conditions are used to validate the structure of the model and to estimate the kinetic parameters. Further analysis of the detailed characteristics of the system offers the possibility of studying important questions regarding the stability and control of metabolic fluxes. link: http://identifiers.org/pubmed/17590932

Parameters:

Name Description
KGAPDHgap=0.683 milli Molar; cnad = 1.47 milli Molar; KGAPDHnad=0.252 milli Molar; KGAPDHnadh=1.09 milli Molar; cnadh = 0.1 milli Molar; KGAPDHpgp=1.04E-5 milli Molar; rmaxGAPDH=921.5942861 mM per second; KGAPDHeq=0.63 dimensionless Reaction: cgap => cpgp, Rate Law: cytosol*rmaxGAPDH*(cgap*cnad-cpgp*cnadh/KGAPDHeq)/((KGAPDHgap*(1+cpgp/KGAPDHpgp)+cgap)*(KGAPDHnad*(1+cnadh/KGAPDHnadh)+cnad))
VALDOblf=2.0 dimensionless; kALDOgapinh=0.6 milli Molar; kALDOeq=0.144 milli Molar; kALDOfdp=1.75 milli Molar; rmaxALDO=17.41464425 mM per second; kALDOgap=0.088 milli Molar; kALDOdhap=0.088 milli Molar Reaction: cfdp => cdhap + cgap, Rate Law: cytosol*rmaxALDO*(cfdp-cgap*cdhap/kALDOeq)/(kALDOfdp+cfdp+kALDOgap*cdhap/(kALDOeq*VALDOblf)+kALDOdhap*cgap/(kALDOeq*VALDOblf)+cfdp*cgap/kALDOgapinh+cgap*cdhap/(VALDOblf*kALDOeq))
catp = 4.27 milli Molar; rmaxG1PAT=0.007525458026 mM per second; KG1PATg1p=3.2 milli Molar; KG1PATfdp=0.119 milli Molar; KG1PATatp=4.42 milli Molar; nG1PATfdp=1.2 milli Molar Reaction: cg1p => ; cfdp, Rate Law: cytosol*rmaxG1PAT*cg1p*catp*(1+(cfdp/KG1PATfdp)^nG1PATfdp)/((KG1PATatp+catp)*(KG1PATg1p+cg1p))
rmaxR5PI=4.83841193 second inverse; KR5PIeq=4.0 dimensionless Reaction: cribu5p => crib5p, Rate Law: cytosol*rmaxR5PI*(cribu5p-crib5p/KR5PIeq)
cnadph = 0.062 milli Molar; KG6PDHnadp=0.0246 milli Molar; rmaxG6PDH=1.380196955 mM per second; KG6PDHg6p=14.4 milli Molar; cnadp = 0.195 milli Molar; KG6PDHnadphg6pinh=6.43 milli Molar; KG6PDHnadphnadpinh=0.01 milli Molar Reaction: cg6p => cpg, Rate Law: cytosol*rmaxG6PDH*cg6p*cnadp/((cg6p+KG6PDHg6p)*(1+cnadph/KG6PDHnadphg6pinh)*(KG6PDHnadp*(1+cnadph/KG6PDHnadphnadpinh)+cnadp))
KG3PDHdhap=1.0 milli Molar; rmaxG3PDH=0.01162042696 mM per second Reaction: cdhap =>, Rate Law: cytosol*rmaxG3PDH*cdhap/(KG3PDHdhap+cdhap)
rmaxTA=10.87164108 per mM per second; KTAeq=1.05 dimensionless Reaction: cgap + csed7p => cf6p + ce4p, Rate Law: cytosol*rmaxTA*(cgap*csed7p-ce4p*cf6p/KTAeq)
KENOpep=0.135 milli Molar; KENOpg2=0.1 milli Molar; KENOeq=6.73 milli Molar; rmaxENO=330.4476151 mM per second Reaction: cpg2 => cpep, Rate Law: cytosol*rmaxENO*(cpg2-cpep/KENOeq)/(KENOpg2*(1+cpep/KENOpep)+cpg2)
KPFKf6ps=0.325 milli Molar; KPFKampa=19.1 milli Molar; nPFK=11.1 dimensionless; catp = 4.27 milli Molar; KPFKadpb=3.89 milli Molar; KPFKampb=3.2 milli Molar; KPFKadpc=4.14 milli Molar; KPFKatps=0.123 milli Molar; cadp = 0.595 milli Molar; rmaxPFK=1840.584747 mM per second; camp = 0.955 milli Molar; KPFKpep=3.26 milli Molar; KPFKadpa=128.0 milli Molar; LPFK=5629067.0 dimensionless Reaction: cf6p => cfdp; cpep, Rate Law: cytosol*rmaxPFK*catp*cf6p/((catp+KPFKatps*(1+cadp/KPFKadpc))*(cf6p+KPFKf6ps*(1+cpep/KPFKpep+cadp/KPFKadpb+camp/KPFKampb)/(1+cadp/KPFKadpa+camp/KPFKampa))*(1+LPFK/(1+cf6p*(1+cadp/KPFKadpa+camp/KPFKampa)/(KPFKf6ps*(1+cpep/KPFKpep+cadp/KPFKadpb+camp/KPFKampb)))^nPFK))
kTISeq=1.39 dimensionless; kTISdhap=2.8 milli Molar; rmaxTIS=68.67474392 mM per second; kTISgap=0.3 milli Molar Reaction: cdhap => cgap, Rate Law: cytosol*rmaxTIS*(cdhap-cgap/kTISeq)/(kTISdhap*(1+cgap/kTISgap)+cdhap)
KPTSa2=0.01 milli Molar; nPTSg6p=3.66 dimensionless; rmaxPTS=7829.78 mM per second; KPTSa3=245.3 dimensionless; KPTSa1=3082.3 milli Molar; KPTSg6p=2.15 milli Molar Reaction: cglcex + cpep => cg6p + cpyr, Rate Law: extracellular*rmaxPTS*cglcex*cpep/cpyr/((KPTSa1+KPTSa2*cpep/cpyr+KPTSa3*cglcex+cglcex*cpep/cpyr)*(1+cg6p^nPTSg6p/KPTSg6p))
KPGKeq=1934.4 dimensionless; KPGKadp=0.185 milli Molar; catp = 4.27 milli Molar; KPGKatp=0.653 milli Molar; cadp = 0.595 milli Molar; rmaxPGK=3021.773771 mM per second; KPGKpg3=0.473 milli Molar; KPGKpgp=0.0468 milli Molar Reaction: cpgp => cpg3, Rate Law: cytosol*rmaxPGK*(cadp*cpgp-catp*cpg3/KPGKeq)/((KPGKadp*(1+catp/KPGKatp)+cadp)*(KPGKpgp*(1+cpg3/KPGKpg3)+cpgp))
rmaxMetSynth=0.0022627 mM per second Reaction: => cpyr, Rate Law: cytosol*rmaxMetSynth
rmaxMurSynth=4.3711E-4 mM per second Reaction: cf6p =>, Rate Law: cytosol*rmaxMurSynth
KRu5Peq=1.4 dimensionless; rmaxRu5P=6.739029475 second inverse Reaction: cribu5p => cxyl5p, Rate Law: cytosol*rmaxRu5P*(cribu5p-cxyl5p/KRu5Peq)
npepCxylasefdp=4.21 dimensionless; rmaxpepCxylase=0.1070205858 mM per second; KpepCxylasefdp=0.7 milli Molar; KpepCxylasepep=4.07 milli Molar Reaction: cpep => ; cfdp, Rate Law: cytosol*rmaxpepCxylase*cpep*(1+(cfdp/KpepCxylasefdp)^npepCxylasefdp)/(KpepCxylasepep+cpep)
rmaxTrpSynth=0.001037 mM per second Reaction: => cpyr + cgap, Rate Law: cytosol*rmaxTrpSynth
Dil=2.78E-5 second inverse; cfeed=110.96 milli Molar Reaction: => cglcex, Rate Law: extracellular*Dil*(cfeed-cglcex)
KPGluMupg3=0.2 milli Molar; KPGluMueq=0.188 dimensionless; KPGluMupg2=0.369 milli Molar; rmaxPGluMu=89.04965407 mM per second Reaction: cpg3 => cpg2, Rate Law: cytosol*rmaxPGluMu*(cpg3-cpg2/KPGluMueq)/(KPGluMupg3*(1+cpg2/KPGluMupg2)+cpg3)
mu=2.78E-5 second inverse Reaction: cg6p =>, Rate Law: cytosol*mu*cg6p
rmaxTKb=86.55855855 per mM per second; KTKbeq=10.0 dimensionless Reaction: ce4p + cxyl5p => cgap + cf6p, Rate Law: cytosol*rmaxTKb*(cxyl5p*ce4p-cf6p*cgap/KTKbeq)
LPK=1000.0 dimensionless; KPKatp=22.5 milli Molar; catp = 4.27 milli Molar; KPKpep=0.31 milli Molar; cadp = 0.595 milli Molar; rmaxPK=0.06113150238 mM per second; camp = 0.955 milli Molar; KPKfdp=0.19 milli Molar; KPKadp=0.26 milli Molar; KPKamp=0.2 milli Molar; nPK=4.0 dimensionless Reaction: cpep => cpyr; cfdp, Rate Law: cytosol*rmaxPK*cpep*(cpep/KPKpep+1)^(nPK-1)*cadp/(KPKpep*(LPK*((1+catp/KPKatp)/(cfdp/KPKfdp+camp/KPKamp+1))^nPK+(cpep/KPKpep+1)^nPK)*(cadp+KPKadp))
KPGIf6p=0.266 milli Molar; KPGIf6ppginh=0.2 milli Molar; KPGIg6ppginh=0.2 milli Molar; KPGIg6p=2.9 milli Molar; KPGIeq=0.1725 dimensionless; rmaxPGI=650.9878687 mM per second Reaction: cg6p => cf6p; cpg, Rate Law: cytosol*rmaxPGI*(cg6p-cf6p/KPGIeq)/(KPGIg6p*(1+cf6p/(KPGIf6p*(1+cpg/KPGIf6ppginh))+cpg/KPGIg6ppginh)+cg6p)
KSynth1pep=1.0 milli Molar; rmaxSynth1=0.01953897003 mM per second Reaction: cpep =>, Rate Law: cytosol*rmaxSynth1*cpep/(KSynth1pep+cpep)
KDAHPSpep=0.0053 milli Molar; KDAHPSe4p=0.035 milli Molar; nDAHPSpep=2.2 dimensionless; rmaxDAHPS=0.1079531227 mM per second; nDAHPSe4p=2.6 dimensionless Reaction: ce4p + cpep =>, Rate Law: cytosol*rmaxDAHPS*ce4p^nDAHPSe4p*cpep^nDAHPSpep/((KDAHPSe4p+ce4p^nDAHPSe4p)*(KDAHPSpep+cpep^nDAHPSpep))
KPDHpyr=1159.0 milli Molar; rmaxPDH=6.059531017 mM per second; nPDH=3.68 dimensionless Reaction: cpyr =>, Rate Law: cytosol*rmaxPDH*cpyr^nPDH/(KPDHpyr+cpyr^nPDH)
KSynth2pyr=1.0 milli Molar; rmaxSynth2=0.07361855055 mM per second Reaction: cpyr =>, Rate Law: cytosol*rmaxSynth2*cpyr/(KSynth2pyr+cpyr)
rmaxTKa=9.473384783 per mM per second; KTKaeq=1.2 dimensionless Reaction: crib5p + cxyl5p => cgap + csed7p, Rate Law: cytosol*rmaxTKa*(crib5p*cxyl5p-csed7p*cgap/KTKaeq)
rmaxSerSynth=0.025712107 mM per second; KSerSynthpg3=1.0 milli Molar Reaction: cpg3 =>, Rate Law: cytosol*rmaxSerSynth*cpg3/(KSerSynthpg3+cpg3)
cnadph = 0.062 milli Molar; KPGDHpg=37.5 milli Molar; KPGDHatpinh=208.0 milli Molar; catp = 4.27 milli Molar; rmaxPGDH=16.23235977 mM per second; KPGDHnadp=0.0506 milli Molar; KPGDHnadphinh=0.0138 milli Molar; cnadp = 0.195 milli Molar Reaction: cpg => cribu5p, Rate Law: cytosol*rmaxPGDH*cpg*cnadp/((cpg+KPGDHpg)*(cnadp+KPGDHnadp*(1+cnadph/KPGDHnadphinh)*(1+catp/KPGDHatpinh)))
rmaxRPPK=0.01290045226 mM per second; KRPPKrib5p=0.1 milli Molar Reaction: crib5p =>, Rate Law: cytosol*rmaxRPPK*crib5p/(KRPPKrib5p+crib5p)
KPGMeq=0.196 dimensionless; rmaxPGM=0.8398242773 mM per second; KPGMg6p=1.038 milli Molar; KPGMg1p=0.0136 milli Molar Reaction: cg6p => cg1p, Rate Law: cytosol*rmaxPGM*(cg6p-cg1p/KPGMeq)/(KPGMg6p*(1+cg1p/KPGMg1p)+cg6p)

States:

Name Description
crib5p [aldehydo-D-ribose 5-phosphate; D-Ribose 5-phosphate]
cpg2 [2-phospho-D-glyceric acid; 2-Phospho-D-glycerate]
cglcex [D-glucopyranose; D-Glucose; D-glucopyranose; glucose; C00293]
cpg [6-phospho-D-gluconate; 6-Phospho-D-gluconate]
cdhap [dihydroxyacetone phosphate; Glycerone phosphate]
cg1p [D-glucopyranose 1-phosphate; D-Glucose 1-phosphate]
cpep [phosphoenolpyruvate; Phosphoenolpyruvate]
cpg3 [3-phospho-D-glyceric acid; 3-Phospho-D-glycerate]
cg6p [alpha-D-glucose 6-phosphate; alpha-D-Glucose 6-phosphate]
cpyr [pyruvate; Pyruvate]
ce4p [D-erythrose 4-phosphate; D-Erythrose 4-phosphate]
cribu5p [D-ribulose 5-phosphate; D-Ribulose 5-phosphate]
csed7p [sedoheptulose 7-phosphate; C00281]
cf6p [beta-D-Fructose 6-phosphate; beta-D-fructofuranose 6-phosphate; D-Fructose 6-phosphate; keto-D-fructose 6-phosphate; keto-D-fructose 6-phosphate]
cfdp [keto-D-fructose 1,6-bisphosphate; D-Fructose 1,6-bisphosphate]
cpgp [3-phospho-D-glyceroyl dihydrogen phosphate; 3-Phospho-D-glyceroyl phosphate]
cgap [glyceraldehyde 3-phosphate; Glyceraldehyde 3-phosphate]
cxyl5p [D-xylulose 5-phosphate; D-Xylulose 5-phosphate]

Observables: none

MODEL1108260014 @ v0.0.1

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

Blood function defines bleeding and clotting risks and dictates approaches for clinical intervention. Independent of adding exogenous tissue factor (TF), human blood treated in vitro with corn trypsin inhibitor (CTI, to block Factor XIIa) will generate thrombin after an initiation time (T(i)) of 1 to 2 hours (depending on donor), while activation of platelets with the GPVI-activator convulxin reduces T(i) to ∼20 minutes. Since current kinetic models fail to generate thrombin in the absence of added TF, we implemented a Platelet-Plasma ODE model accounting for: the Hockin-Mann protease reaction network, thrombin-dependent display of platelet phosphatidylserine, VIIa function on activated platelets, XIIa and XIa generation and function, competitive thrombin substrates (fluorogenic detector and fibrinogen), and thrombin consumption during fibrin polymerization. The kinetic model consisting of 76 ordinary differential equations (76 species, 57 reactions, 105 kinetic parameters) predicted the clotting of resting and convulxin-activated human blood as well as predicted T(i) of human blood under 50 different initial conditions that titrated increasing levels of TF, Xa, Va, XIa, IXa, and VIIa. Experiments with combined anti-XI and anti-XII antibodies prevented thrombin production, demonstrating that a leak of XIIa past saturating amounts of CTI (and not "blood-borne TF" alone) was responsible for in vitro initiation without added TF. Clotting was not blocked by antibodies used individually against TF, VII/VIIa, P-selectin, GPIb, protein disulfide isomerase, cathepsin G, nor blocked by the ribosome inhibitor puromycin, the Clk1 kinase inhibitor Tg003, or inhibited VIIa (VIIai). This is the first model to predict the observed behavior of CTI-treated human blood, either resting or stimulated with platelet activators. CTI-treated human blood will clot in vitro due to the combined activity of XIIa and XIa, a process enhanced by platelet activators and which proceeds in the absence of any evidence for kinetically significant blood borne tissue factor. link: http://identifiers.org/pubmed/20941387

Parameters: none

States: none

Observables: none

Chavali2008 - Genome-scale metabolic network of Leishmania major (iAC560)This model is described in the article: [Syste…

Systems analyses have facilitated the characterization of metabolic networks of several organisms. We have reconstructed the metabolic network of Leishmania major, a poorly characterized organism that causes cutaneous leishmaniasis in mammalian hosts. This network reconstruction accounts for 560 genes, 1112 reactions, 1101 metabolites and 8 unique subcellular localizations. Using a systems-based approach, we hypothesized a comprehensive set of lethal single and double gene deletions, some of which were validated using published data with approximately 70% accuracy. Additionally, we generated hypothetical annotations to dozens of previously uncharacterized genes in the L. major genome and proposed a minimal medium for growth. We further demonstrated the utility of a network reconstruction with two proof-of-concept examples that yielded insight into robustness of the network in the presence of enzymatic inhibitors and delineation of promastigote/amastigote stage-specific metabolism. This reconstruction and the associated network analyses of L. major is the first of its kind for a protozoan. It can serve as a tool for clarifying discrepancies between data sources, generating hypotheses that can be experimentally validated and identifying ideal therapeutic targets. link: http://identifiers.org/pubmed/18364711

Parameters: none

States: none

Observables: none

Chavez2009 - a core regulatory network of OCT4 in human embryonic stem cellsA core OCT4-regulated network has been ident…

BACKGROUND: The transcription factor OCT4 is highly expressed in pluripotent embryonic stem cells which are derived from the inner cell mass of mammalian blastocysts. Pluripotency and self renewal are controlled by a transcription regulatory network governed by the transcription factors OCT4, SOX2 and NANOG. Recent studies on reprogramming somatic cells to induced pluripotent stem cells highlight OCT4 as a key regulator of pluripotency. RESULTS: We have carried out an integrated analysis of high-throughput data (ChIP-on-chip and RNAi experiments along with promoter sequence analysis of putative target genes) and identified a core OCT4 regulatory network in human embryonic stem cells consisting of 33 target genes. Enrichment analysis with these target genes revealed that this integrative analysis increases the functional information content by factors of 1.3 - 4.7 compared to the individual studies. In order to identify potential regulatory co-factors of OCT4, we performed a de novo motif analysis. In addition to known validated OCT4 motifs we obtained binding sites similar to motifs recognized by further regulators of pluripotency and development; e.g. the heterodimer of the transcription factors C-MYC and MAX, a prerequisite for C-MYC transcriptional activity that leads to cell growth and proliferation. CONCLUSION: Our analysis shows how heterogeneous functional information can be integrated in order to reconstruct gene regulatory networks. As a test case we identified a core OCT4-regulated network that is important for the analysis of stem cell characteristics and cellular differentiation. Functional information is largely enriched using different experimental results. The de novo motif discovery identified well-known regulators closely connected to the OCT4 network as well as potential new regulators of pluripotency and differentiation. These results provide the basis for further targeted functional studies. link: http://identifiers.org/pubmed/19604364

Parameters: none

States: none

Observables: none

BIOMD0000000378 @ v0.0.1

This a model from the article: Effects of extracellular calcium on electrical bursting and intracellular and luminal…

The extracellular calcium concentration has interesting effects on bursting of pancreatic beta-cells. The mechanism underlying the extracellular Ca2+ effect is not well understood. By incorporating a low-threshold transient inward current to the store-operated bursting model of Chay, this paper elucidates the role of the extracellular Ca2+ concentration in influencing electrical activity, intracellular Ca2+ concentration, and the luminal Ca2+ concentration in the intracellular Ca2+ store. The possibility that this inward current is a carbachol-sensitive and TTX-insensitive Na+ current discovered by others is discussed. In addition, this paper explains how these three variables respond when various pharmacological agents are applied to the store-operated model. link: http://identifiers.org/pubmed/9284334

Parameters:

Name Description
d_infinity = 0.00344187186519272; tau_d = 0.0234265674250627 Reaction: d = (d_infinity-d)/tau_d, Rate Law: (d_infinity-d)/tau_d
Cm = 1.0; i_Ca = -24.1248530333721; i_NS = -6.24107017458029; i_fast = -96.6401171990526; i_K_dr = 25.014877991785; i_K_Ca = 46.2079655277309; i_NaL = -35.502438; i_K_ATP = 73.31708 Reaction: V_membrane = (-(i_K_dr+i_K_Ca+i_K_ATP+i_fast+i_Ca+i_NS+i_NaL))/Cm, Rate Law: (-(i_K_dr+i_K_Ca+i_K_ATP+i_fast+i_Ca+i_NS+i_NaL))/Cm
tau_n = 0.0313553515963197; n_infinity = 0.189546217642834 Reaction: n = (n_infinity-n)/tau_n, Rate Law: (n_infinity-n)/tau_n
tau_h = 0.0320623804770684; h_infinity = 0.201042499324815 Reaction: h = (h_infinity-h)/tau_h, Rate Law: (h_infinity-h)/tau_h
k_rel = 0.2; k_pump = 30.0 Reaction: Ca_lum = (-k_rel)*(Ca_lum-Ca_i_cytosolic_calcium)+k_pump*Ca_i_cytosolic_calcium, Rate Law: (-k_rel)*(Ca_lum-Ca_i_cytosolic_calcium)+k_pump*Ca_i_cytosolic_calcium
k_Ca = 7.0; i_Ca = -24.1248530333721; k_rel = 0.2; k_pump = 30.0; omega = 0.2 Reaction: Ca_i_cytosolic_calcium = k_rel*(Ca_lum-Ca_i_cytosolic_calcium)-(omega*i_Ca+k_Ca*Ca_i_cytosolic_calcium+k_pump*Ca_i_cytosolic_calcium), Rate Law: k_rel*(Ca_lum-Ca_i_cytosolic_calcium)-(omega*i_Ca+k_Ca*Ca_i_cytosolic_calcium+k_pump*Ca_i_cytosolic_calcium)

States:

Name Description
d [gated channel activity]
Ca i cytosolic calcium [calcium(2+)]
Ca lum [calcium(2+)]
V membrane [membrane potential]
h [gated channel activity]
n [delayed rectifier potassium channel activity]

Observables: none

BIOMD0000000675 @ v0.0.1

This a model from the article: Kinetic analysis of a molecular model of the budding yeast cell cycle. Chen KC, Csika…

The molecular machinery of cell cycle control is known in more detail for budding yeast, Saccharomyces cerevisiae, than for any other eukaryotic organism. In recent years, many elegant experiments on budding yeast have dissected the roles of cyclin molecules (Cln1-3 and Clb1-6) in coordinating the events of DNA synthesis, bud emergence, spindle formation, nuclear division, and cell separation. These experimental clues suggest a mechanism for the principal molecular interactions controlling cyclin synthesis and degradation. Using standard techniques of biochemical kinetics, we convert the mechanism into a set of differential equations, which describe the time courses of three major classes of cyclin-dependent kinase activities. Model in hand, we examine the molecular events controlling "Start" (the commitment step to a new round of chromosome replication, bud formation, and mitosis) and "Finish" (the transition from metaphase to anaphase, when sister chromatids are pulled apart and the bud separates from the mother cell) in wild-type cells and 50 mutants. The model accounts for many details of the physiology, biochemistry, and genetics of cell cycle control in budding yeast. link: http://identifiers.org/pubmed/10637314

Parameters:

Name Description
Vd_b2 = 2.023494; kd1_c1 = 0.01; kas_b2 = 50.0; Jd2_c1 = 0.05; kdi_b2 = 0.05; Vd2_c1 = 0.0306448922911362 Reaction: Clb2_Sic1 = kas_b2*Clb2*Sic1-Clb2_Sic1*(kdi_b2+Vd_b2+kd1_c1+Vd2_c1/(Jd2_c1+Sic1_T)), Rate Law: kas_b2*Clb2*Sic1-Clb2_Sic1*(kdi_b2+Vd_b2+kd1_c1+Vd2_c1/(Jd2_c1+Sic1_T))
ka_t1_ = 2.0; Vi_t1 = 0.118613853471055; ka_t1 = 0.04; Ji_t1 = 0.05; Ja_t1 = 0.05 Reaction: Hct1 = (ka_t1+ka_t1_*Cdc20)*(Hct1_T-Hct1)/((Ja_t1+Hct1_T)-Hct1)-Vi_t1*Hct1/(Ji_t1+Hct1), Rate Law: (ka_t1+ka_t1_*Cdc20)*(Hct1_T-Hct1)/((Ja_t1+Hct1_T)-Hct1)-Vi_t1*Hct1/(Ji_t1+Hct1)
mass = 0.6608 Reaction: Bck2 = Bck2_0*mass, Rate Law: missing
ki_sbf_ = 6.0; Va_sbf = 0.310772953639202; ki_sbf = 0.5; Ji_sbf = 0.01; Ja_sbf = 0.01 Reaction: SBF = 2*Va_sbf*Ji_sbf/(((ki_sbf+ki_sbf_*Clb2+Va_sbf*Ji_sbf+(ki_sbf+ki_sbf_*Clb2)*Ja_sbf)-Va_sbf)+(((ki_sbf+ki_sbf_*Clb2+Va_sbf*Ji_sbf+(ki_sbf+ki_sbf_*Clb2)*Ja_sbf)-Va_sbf)^2-4*Va_sbf*Ji_sbf*((ki_sbf+ki_sbf_*Clb2)-Va_sbf))^(1/2)), Rate Law: missing
Ji_mcm = 1.0; ka_mcm = 1.0; Ja_mcm = 1.0; ki_mcm = 0.15 Reaction: Mcm1 = 2*ka_mcm*Clb2*Ji_mcm/(((ki_mcm+ka_mcm*Clb2*Ji_mcm+ki_mcm*Ja_mcm)-ka_mcm*Clb2)+(((ki_mcm+ka_mcm*Clb2*Ji_mcm+ki_mcm*Ja_mcm)-ka_mcm*Clb2)^2-4*(ki_mcm-ka_mcm*Clb2)*ka_mcm*Clb2*Ji_mcm)^(1/2)), Rate Law: missing
kd_n2 = 0.1; ks_n2_ = 0.05; ks_n2 = 0.0; mass = 0.6608 Reaction: Cln2 = mass*(ks_n2+ks_n2_*SBF)-kd_n2*Cln2, Rate Law: mass*(ks_n2+ks_n2_*SBF)-kd_n2*Cln2
Vd_b5 = 0.2712; kd1_c1 = 0.01; kas_b5 = 50.0; Jd2_c1 = 0.05; kdi_b5 = 0.05; Vd2_c1 = 0.0306448922911362 Reaction: Clb5_Sic1 = kas_b5*Clb5*Sic1-Clb5_Sic1*(kdi_b5+Vd_b5+kd1_c1+Vd2_c1/(Jd2_c1+Sic1_T)), Rate Law: kas_b5*Clb5*Sic1-Clb5_Sic1*(kdi_b5+Vd_b5+kd1_c1+Vd2_c1/(Jd2_c1+Sic1_T))
kd1_c1 = 0.01; ks_c1 = 0.02; Jd2_c1 = 0.05; ks_c1_ = 0.1; Vd2_c1 = 0.0306448922911362 Reaction: Sic1_T = (ks_c1+ks_c1_*Swi5)-Sic1_T*(kd1_c1+Vd2_c1/(Jd2_c1+Sic1_T)), Rate Law: (ks_c1+ks_c1_*Swi5)-Sic1_T*(kd1_c1+Vd2_c1/(Jd2_c1+Sic1_T))
Vd_b2 = 2.023494; ks_b2 = 0.002; ks_b2_ = 0.05; mass = 0.6608 Reaction: Clb2_T = mass*(ks_b2+ks_b2_*Mcm1)-Vd_b2*Clb2_T, Rate Law: mass*(ks_b2+ks_b2_*Mcm1)-Vd_b2*Clb2_T
kd_20 = 0.08; ks_20_ = 0.06; ks_20 = 0.005 Reaction: Cdc20_T = (ks_20+ks_20_*Clb2)-kd_20*Cdc20_T, Rate Law: (ks_20+ks_20_*Clb2)-kd_20*Cdc20_T
Jn3 = 6.0; Dn3 = 1.0; mass = 0.6608 Reaction: Cln3 = Cln3_max*Dn3*mass/(Jn3+Dn3*mass), Rate Law: missing
ka_swi = 1.0; Ja_swi = 0.1; ki_swi_ = 0.2; Ji_swi = 0.1; ki_swi = 0.3 Reaction: Swi5 = 2*ka_swi*Cdc20*Ji_swi/(((ki_swi+ki_swi_*Clb2+ka_swi*Cdc20*Ji_swi+(ki_swi+ki_swi_*Clb2)*Ja_swi)-ka_swi*Cdc20)+(((ki_swi+ki_swi_*Clb2+ka_swi*Cdc20*Ji_swi+(ki_swi+ki_swi_*Clb2)*Ja_swi)-ka_swi*Cdc20)^2-4*((ki_swi+ki_swi_*Clb2)-ka_swi*Cdc20)*ka_swi*Cdc20*Ji_swi)^(1/2)), Rate Law: missing
kd_20 = 0.08; ka_20 = 1.0; Vi_20 = 0.1 Reaction: Cdc20 = ka_20*(Cdc20_T-Cdc20)-Cdc20*(Vi_20+kd_20), Rate Law: ka_20*(Cdc20_T-Cdc20)-Cdc20*(Vi_20+kd_20)
ks_b5 = 0.006; ks_b5_ = 0.02; Vd_b5 = 0.2712; mass = 0.6608 Reaction: Clb5_T = mass*(ks_b5+ks_b5_*MBF)-Vd_b5*Clb5_T, Rate Law: mass*(ks_b5+ks_b5_*MBF)-Vd_b5*Clb5_T

States:

Name Description
Clb2 T [G2/mitotic-specific cyclin-2]
Sic1 T [Protein SIC1]
Cln2 [G1/S-specific cyclin CLN2]
Cln3 [G1/S-specific cyclin CLN3]
Hct1 [APC/C activator protein CDH1]
Clb5 T [S-phase entry cyclin-5]
Clb5 [S-phase entry cyclin-5]
Mcm1 [Pheromone receptor transcription factor]
Swi5 [Transcriptional factor SWI5]
Bck2 [Protein BCK2]
Sic1 [Protein SIC1]
SBF [DNA-binding protein RAP1; SBF transcription complex]
Clb2 Sic1 [Protein SIC1; G2/mitotic-specific cyclin-2]
Clb5 Sic1 [S-phase entry cyclin-5; Protein SIC1]
Cdc20 [APC/C activator protein CDC20]
Clb2 [G2/mitotic-specific cyclin-2]
Cdc20 T [APC/C activator protein CDC20]
MBF [Multiprotein-bridging factor 1]

Observables: none

BIOMD0000000056 @ v0.0.1

Chen2004 - Cell Cycle RegulationThis is a hypothetical model of cell cycle that describes the molecular mechanism for re…

The adaptive responses of a living cell to internal and external signals are controlled by networks of proteins whose interactions are so complex that the functional integration of the network cannot be comprehended by intuitive reasoning alone. Mathematical modeling, based on biochemical rate equations, provides a rigorous and reliable tool for unraveling the complexities of molecular regulatory networks. The budding yeast cell cycle is a challenging test case for this approach, because the control system is known in exquisite detail and its function is constrained by the phenotypic properties of >100 genetically engineered strains. We show that a mathematical model built on a consensus picture of this control system is largely successful in explaining the phenotypes of mutants described so far. A few inconsistencies between the model and experiments indicate aspects of the mechanism that require revision. In addition, the model allows one to frame and critique hypotheses about how the division cycle is regulated in wild-type and mutant cells, to predict the phenotypes of new mutant combinations, and to estimate the effective values of biochemical rate constants that are difficult to measure directly in vivo. link: http://identifiers.org/pubmed/15169868

Parameters:

Name Description
IET = 1.0 Reaction: IE = IET-IEP, Rate Law: missing
Jatem = 0.1 Reaction: TEM1GDP => TEM1GTP; LTE1, Rate Law: 1*TEM1GDP*LTE1/(Jatem+TEM1GDP)
Vppc1 = NaN Reaction: C5P => C5, Rate Law: Vppc1*C5P
kd14 = 0.1 Reaction: RENT => NET1, Rate Law: kd14*RENT
kd3f6 = 1.0 Reaction: F2P => CLB2, Rate Law: kd3f6*F2P
Vdb2 = NaN Reaction: F2 => CDC6, Rate Law: Vdb2*F2
Jacdh = 0.03; Vacdh = NaN Reaction: CDH1i => CDH1, Rate Law: 1*CDH1i*Vacdh/(Jacdh+CDH1i)
Vicdh = NaN; Jicdh = 0.03 Reaction: CDH1 => CDH1i, Rate Law: 1*CDH1*Vicdh/(Jicdh+CDH1)
kasrentp = 1.0 Reaction: CDC14 + NET1P => RENTP, Rate Law: kasrentp*CDC14*NET1P
ksb5_p_p = 0.005; ksb5_p = 8.0E-4 Reaction: => CLB5; SBF, MASS, Rate Law: (ksb5_p+ksb5_p_p*SBF)*MASS
kd3c1 = 1.0 Reaction: C5P => CLB5, Rate Law: kd3c1*C5P
kasb5 = 50.0 Reaction: CLB5 + SIC1 => C5, Rate Law: kasb5*CLB5*SIC1
kdswi = 0.08 Reaction: SWI5 =>, Rate Law: kdswi*SWI5
kdif5 = 0.01 Reaction: F5 => CLB5 + CDC6, Rate Law: kdif5*F5
kdif2 = 0.5 Reaction: F2 => CLB2 + CDC6, Rate Law: kdif2*F2
Vppnet = NaN Reaction: NET1P => NET1, Rate Law: Vppnet*NET1P
kdib2 = 0.05 Reaction: C2 => CLB2 + SIC1, Rate Law: kdib2*C2
ksspn = 0.1; Jspn = 0.14 Reaction: => SPN; CLB2, Rate Law: ksspn*CLB2/(Jspn+CLB2)
kasb2 = 50.0 Reaction: CLB2 + SIC1 => C2, Rate Law: kasb2*CLB2*SIC1
ksswi_p_p = 0.08; ksswi_p = 0.005 Reaction: => SWI5; MCM1, Rate Law: ksswi_p+ksswi_p_p*MCM1
ks14 = 0.2 Reaction: => CDC14, Rate Law: ks14
kdspn = 0.06 Reaction: SPN =>, Rate Law: kdspn*SPN
b0 = 0.054 Reaction: BCK2 = b0*MASS, Rate Law: missing
Vdppx = NaN Reaction: PPX =>, Rate Law: Vdppx*PPX
kiswi = 0.05 Reaction: SWI5 => SWI5P; CLB2, Rate Law: kiswi*CLB2*SWI5
kdib5 = 0.06 Reaction: C5 => CLB5 + SIC1, Rate Law: kdib5*C5
ksc1_p = 0.012; ksc1_p_p = 0.12 Reaction: => SIC1; SWI5, Rate Law: ksc1_p+ksc1_p_p*SWI5
Vdpds = NaN Reaction: PDS1 =>, Rate Law: Vdpds*PDS1
ks2pds_p_p = 0.055; ks1pds_p_p = 0.03; kspds_p = 0.0 Reaction: => PDS1; SBF, MCM1, Rate Law: kspds_p+ks1pds_p_p*SBF+ks2pds_p_p*MCM1
Vppf6 = NaN Reaction: F5P => F5, Rate Law: Vppf6*F5P
Vkpnet = NaN Reaction: NET1 => NET1P, Rate Law: Vkpnet*NET1
ks20_p = 0.006; ks20_p_p = 0.6 Reaction: => CDC20i; MCM1, Rate Law: ks20_p+ks20_p_p*MCM1
Vdb5 = NaN Reaction: F5P => CDC6P, Rate Law: Vdb5*F5P
kdiesp = 0.5 Reaction: PE => PDS1 + ESP1, Rate Law: kdiesp*PE
Vkpc1 = NaN Reaction: C5 => C5P, Rate Law: Vkpc1*C5
ka20_p = 0.05; ka20_p_p = 0.2 Reaction: CDC20i => CDC20; IEP, Rate Law: (ka20_p+ka20_p_p*IEP)*CDC20i
kdirentp = 2.0 Reaction: RENTP => CDC14 + NET1P, Rate Law: kdirentp*RENTP
Vkpf6 = NaN Reaction: F5 => F5P, Rate Law: Vkpf6*F5
ki15 = 0.5 Reaction: CDC15 => CDC15i, Rate Law: ki15*CDC15
ksf6_p = 0.024; ksf6_p_p_p = 0.004; ksf6_p_p = 0.12 Reaction: => CDC6; SWI5, SBF, Rate Law: ksf6_p+ksf6_p_p*SWI5+ksf6_p_p_p*SBF
ksb2_p_p = 0.04; ksb2_p = 0.001 Reaction: => CLB2; MCM1, MASS, Rate Law: (ksb2_p+ksb2_p_p*MCM1)*MASS
kdbud = 0.06 Reaction: BUD =>, Rate Law: kdbud*BUD
kasf2 = 15.0 Reaction: CLB2 + CDC6 => F2, Rate Law: kasf2*CLB2*CDC6
Vaiep = NaN; Jaiep = 0.1 Reaction: IE => IEP, Rate Law: 1*IE*Vaiep/(Jaiep+IE)
kd20 = 0.3 Reaction: CDC20 =>, Rate Law: kd20*CDC20
kscdh = 0.01 Reaction: => CDH1, Rate Law: kscdh
kasf5 = 0.01 Reaction: CLB5 + CDC6 => F5, Rate Law: kasf5*CLB5*CDC6
kdcdh = 0.01 Reaction: CDH1i =>, Rate Law: kdcdh*CDH1i
Jitem = 0.1 Reaction: TEM1GTP => TEM1GDP; BUB2, Rate Law: 1*TEM1GTP*BUB2/(Jitem+TEM1GTP)
kdnet = 0.03 Reaction: NET1 =>, Rate Law: kdnet*NET1
kaswi = 2.0 Reaction: SWI5P => SWI5; CDC14, Rate Law: kaswi*CDC14*SWI5P
ksnet = 0.084 Reaction: => NET1, Rate Law: ksnet
k=1.0 Reaction: CDC20 => CDC20i; MAD2, Rate Law: k*MAD2*CDC20
ka15p = 0.001; ka15_p = 0.002; ka15_p_p = 1.0 Reaction: CDC15i => CDC15; TEM1GDP, TEM1GTP, CDC14, Rate Law: (ka15_p*TEM1GDP+ka15_p_p*TEM1GTP+ka15p*CDC14)*CDC15i
TEM1T = 1.0 Reaction: TEM1GDP = TEM1T-TEM1GTP, Rate Law: missing
Jiiep = 0.1; kiiep = 0.15 Reaction: IEP => IE, Rate Law: kiiep*IEP*1/(Jiiep+IEP)

States:

Name Description
CDC15i [Cell division control protein 15]
TEM1GTP [Protein TEM1]
BCK2 [Protein BCK2]
SPN [CCO:P0000392]
CDH1 [APC/C activator protein CDH1]
CDH1i [APC/C activator protein CDH1]
CDC15 [Cell division control protein 15]
CKIT [Protein SIC1; Cell division control protein 6]
F5P [Cell division control protein 6; S-phase entry cyclin-5; S-phase entry cyclin-6]
NET1P [Nucleolar protein NET1]
CDC20i [APC/C activator protein CDC20]
C5P [Protein SIC1; S-phase entry cyclin-6; S-phase entry cyclin-5]
CLB2 [G2/mitotic-specific cyclin-2; G2/mitotic-specific cyclin-1]
CDC20 [APC/C activator protein CDC20]
C2 [Protein SIC1; G2/mitotic-specific cyclin-2; G2/mitotic-specific cyclin-1]
CDC6 [Cell division control protein 6]
CDC6T [Cell division control protein 6]
IEP [anaphase-promoting complex]
C5 [Protein SIC1; S-phase entry cyclin-6; S-phase entry cyclin-5]
SWI5 [Transcriptional factor SWI5]
F5 [S-phase entry cyclin-6; S-phase entry cyclin-5; Cell division control protein 6]
TEM1GDP [Protein TEM1]
CLB2T [G2/mitotic-specific cyclin-2; G2/mitotic-specific cyclin-1]
CDC6P [Cell division control protein 6]
CLB5T [S-phase entry cyclin-6; S-phase entry cyclin-5]
SIC1P [Protein SIC1]
PDS1 [Securin]
SIC1T [Protein SIC1]
IE [anaphase-promoting complex]
CDC14 [Tyrosine-protein phosphatase CDC14]
BUD [CCO:C0000485]
PPX [Exopolyphosphatase]
RENTP [RENT complex; NAD-dependent histone deacetylase SIR2; Nucleolar protein NET1; Tyrosine-protein phosphatase CDC14]
SIC1 [Protein SIC1]
CLB5 [S-phase entry cyclin-6; S-phase entry cyclin-5]
SWI5P [Transcriptional factor SWI5]
NET1 [Nucleolar protein NET1]
F2 [Cell division control protein 6; G2/mitotic-specific cyclin-1; G2/mitotic-specific cyclin-2]

Observables: none

Chen2006 - Nitric Oxide Release from Endothelial CellsThis model is described in the article: [Theoretical analysis of…

Vascular endothelium expressing endothelial nitric oxide synthase (eNOS) produces nitric oxide (NO), which has a number of important physiological functions in the microvasculature. The rate of NO production by the endothelium is a critical determinant of NO distribution in the vascular wall. We have analyzed the biochemical pathways of NO synthesis and formulated a model to estimate NO production by the microvascular endothelium under physiological conditions. The model quantifies the NO produced by eNOS based on the kinetics of NO synthesis and the availability of eNOS and its intracellular substrates. The predicted NO production from microvessels was in the range of 0.005-0.1 microM/s. This range of predicted values is in agreement with some experimental values but is much lower than other rates previously measured or estimated from experimental data with the help of mathematical modeling. Paradoxical discrepancies between the model predictions and previously reported results based on experimental measurements of NO concentration in the vicinity of the arteriolar wall suggest that NO can also be released through eNOS-independent mechanisms, such as catalysis by neuronal NOS (nNOS). We also used our model to test the sensitivity of NO production to substrate availability, eNOS concentration, and potential rate-limiting factors. The results indicated that the predicted low level of NO production can be attributed primarily to a low expression of eNOS in the microvascular endothelial cells. link: http://identifiers.org/pubmed/16864000

Parameters:

Name Description
k10_prime = 89.9 Reaction: Fe3__O2__NOHA => Fe2__NOHA, Rate Law: Endothelium*k10_prime*Fe3__O2__NOHA
k14 = 53.9 Reaction: Fe3__NO => Fe3__enos + NO, Rate Law: Endothelium*k14*Fe3__NO
S = 0.0 Reaction: => Arg, Rate Law: Endothelium*S
k11 = 29.4 Reaction: Fe3__O2__NOHA => Fe3__NO + Citrulline, Rate Law: Endothelium*k11*Fe3__O2__NOHA
k2 = 0.91 Reaction: Fe3__enos => Fe2, Rate Law: Endothelium*k2*Fe3__enos
k8_prime = 0.1; k8 = 0.1 Reaction: Fe3__NOHA => Fe3__enos + NOHA, Rate Law: Endothelium*(k8*Fe3__NOHA-k8_prime*Fe3__enos*NOHA)
k5_prime = 98.0 Reaction: Fe3__O2__Arg => Fe2__Arg, Rate Law: Endothelium*k5_prime*Fe3__O2__Arg
k9_prime = 1.89; k9 = 11.4 Reaction: Fe2__NOHA => Fe2 + NOHA, Rate Law: Endothelium*(k9*Fe2__NOHA-k9_prime*Fe2*NOHA)
k1 = 0.1; k1_prime = 0.1 Reaction: Arg + Fe3__enos => Fe3__Arg, Rate Law: Endothelium*(k1*Arg*Fe3__enos-k1_prime*Fe3__Arg)
k10 = 3.33 Reaction: Fe2__NOHA => Fe3__O2__NOHA; O2, Rate Law: Endothelium*k10*O2*Fe2__NOHA
k6 = 12.6 Reaction: Fe3__O2__Arg => Fe3__NOHA, Rate Law: Endothelium*k6*Fe3__O2__Arg
k7 = 0.91 Reaction: Fe3__NOHA => Fe2__NOHA, Rate Law: Endothelium*k7*Fe3__NOHA
k3 = 0.91 Reaction: Fe3__Arg => Fe2__Arg, Rate Law: Endothelium*k3*Fe3__Arg
k12 = 0.91 Reaction: Fe3__NO => Fe2__NO, Rate Law: Endothelium*k12*Fe3__NO
k13 = 0.033 Reaction: Fe2__NO => Fe3__enos; O2, Rate Law: Endothelium*k13*O2*Fe2__NO
k5 = 2.58 Reaction: Fe2__Arg => Fe3__O2__Arg; O2, Rate Law: Endothelium*k5*O2*Fe2__Arg
k4_prime = 11.4; k4 = 1.89 Reaction: Arg + Fe2 => Fe2__Arg, Rate Law: Endothelium*(k4*Arg*Fe2-k4_prime*Fe2__Arg)

States:

Name Description
Fe2 NO [iron(2+); nitric oxide]
NO [nitric oxide]
Fe3 enos [iron(3+)]
Citrulline [citrulline]
Fe2 Arg [arginine; iron(2+)]
Fe2 NOHA [iron(2+); hydroxyarginine]
NOHA [hydroxyarginine]
Fe3 O2 Arg [dioxygen; arginine; iron(3+)]
Fe2 [iron(2+)]
Fe3 NO [iron(3+); nitric oxide]
Fe3 O2 NOHA [dioxygen; iron(3+); hydroxyarginine]
Fe3 Arg [iron(3+); arginine]
Arg [arginine]
Fe3 NOHA [iron(3+); hydroxyarginine]

Observables: none

MODEL0491251823 @ v0.0.1

This a model from the article: Vascular and perivascular nitric oxide release and transport: biochemical pathways of n…

Nitric oxide (NO) derived from nitric oxide synthase (NOS) is an important paracrine effector that maintains vascular tone. The release of NO mediated by NOS isozymes under various O(2) conditions critically determines the NO bioavailability in tissues. Because of experimental difficulties, there has been no direct information on how enzymatic NO production and distribution change around arterioles under various oxygen conditions. In this study, we used computational models based on the analysis of biochemical pathways of enzymatic NO synthesis and the availability of NOS isozymes to quantify the NO production by neuronal NOS (NOS1) and endothelial NOS (NOS3). We compared the catalytic activities of NOS1 and NOS3 and their sensitivities to the concentration of substrate O(2). Based on the NO release rates predicted from kinetic models, the geometric distribution of NO sources, and mass balance analysis, we predicted the NO concentration profiles around an arteriole under various O(2) conditions. The results indicated that NOS1-catalyzed NO production was significantly more sensitive to ambient O(2) concentration than that catalyzed by NOS3. Also, the high sensitivity of NOS1 catalytic activity to O(2) was associated with significantly reduced NO production and therefore NO concentrations, upon hypoxia. Moreover, the major source determining the distribution of NO was NOS1, which was abundantly expressed in the nerve fibers and mast cells close to arterioles, rather than NOS3, which was expressed in the endothelium. Finally, the perivascular NO concentration predicted by the models under conditions of normoxia was paradoxically at least an order of magnitude lower than a number of experimental measurements, suggesting a higher abundance of NOS1 or NOS3 and/or the existence of other enzymatic or nonenzymatic sources of NO in the microvasculature. link: http://identifiers.org/pubmed/17320763

Parameters: none

States: none

Observables: none

BIOMD0000000255 @ v0.0.1

This is A431 IERMv1.0 model described in the article Input-output behavior of ErbB signaling pathways as revealed by a…

The ErbB signaling pathways, which regulate diverse physiological responses such as cell survival, proliferation and motility, have been subjected to extensive molecular analysis. Nonetheless, it remains poorly understood how different ligands induce different responses and how this is affected by oncogenic mutations. To quantify signal flow through ErbB-activated pathways we have constructed, trained and analyzed a mass action model of immediate-early signaling involving ErbB1-4 receptors (EGFR, HER2/Neu2, ErbB3 and ErbB4), and the MAPK and PI3K/Akt cascades. We find that parameter sensitivity is strongly dependent on the feature (e.g. ERK or Akt activation) or condition (e.g. EGF or heregulin stimulation) under examination and that this context dependence is informative with respect to mechanisms of signal propagation. Modeling predicts log-linear amplification so that significant ERK and Akt activation is observed at ligand concentrations far below the K(d) for receptor binding. However, MAPK and Akt modules isolated from the ErbB model continue to exhibit switch-like responses. Thus, key system-wide features of ErbB signaling arise from nonlinear interaction among signaling elements, the properties of which appear quite different in context and in isolation. link: http://identifiers.org/pubmed/19156131

Parameters:

Name Description
k8 = 5.91474E-7; kd8 = 0.2 Reaction: c14 + c336 => c344, Rate Law: k8*c14*c336-kd8*c344
k60c = 5.2E-4; kd60 = 0.0 Reaction: c349 => c86, Rate Law: k60c*c349-kd60*c86
kd123 = 0.177828; k123 = 0.0 Reaction: c336 + c105 => c139, Rate Law: k123*c336*c105-kd123*c139
k18 = 2.5E-5; kd18 = 1.3 Reaction: c26 + c317 => c320, Rate Law: k18*c26*c317-kd18*c320
k5 = 0.0; kd5b = 0.0080833 Reaction: c9 + c320 => c319, Rate Law: k5*c9*c320-kd5b*c319
k37 = 1.5E-6; kd37 = 0.3 Reaction: c341 + c40 => c351, Rate Law: k37*c341*c40-kd37*c351
kd36 = 0.0; k36 = 0.005 Reaction: c40 => c31, Rate Law: k36*c40-kd36*c31
k17 = 1.67E-5; kd17 = 0.06 Reaction: c24 + c314 => c317, Rate Law: k17*c24*c314-kd17*c317
kd122 = 1.0; k122 = 1.8704E-8 Reaction: c550 + c105 => c555, Rate Law: k122*c550*c105-kd122*c555
k2b = 3.73632E-8; kd2b = 0.016 Reaction: c499 + c141 => c492, Rate Law: k2b*c499*c141-kd2b*c492
kd33 = 0.2; k33 = 3.5E-5 Reaction: c40 + c30 => c38, Rate Law: k33*c40*c30-kd33*c38
kd2 = 0.16; k2 = 7.44622E-6 Reaction: c3 + c499 => c500, Rate Law: k2*c3*c499-kd2*c500
kd6b = 0.0; k6b = 0.0 Reaction: c347 => c349, Rate Law: k6b*c347-kd6b*c349
k6 = 0.013; kd6 = 5.0E-5 Reaction: c32 => c63, Rate Law: k6*c32-kd6*c63
k32 = 4.0E-7; kd32 = 0.1 Reaction: c293 + c38 => c305, Rate Law: k32*c293*c38-kd32*c305
k60 = 0.00266742; kd60 = 0.0 Reaction: c17 => c86, Rate Law: k60*c17-kd60*c86
kd111 = 6.57; k111 = 0.0 Reaction: c83 + c490 => c475, Rate Law: k111*c83*c490-kd111*c475
k34 = 7.5E-6; kd34 = 0.03 Reaction: c293 + c30 => c317, Rate Law: k34*c293*c30-kd34*c317
k1 = 0.0; kd1 = 0.033 Reaction: c1 + c286 => c499, Rate Law: k1*c1*c286-kd1*c499
kd23 = 0.06; k23 = 6.0 Reaction: c32 => c33, Rate Law: k23*c32-kd23*c33
kd103 = 0.016; k103 = 8.36983E-9 Reaction: c87 + c332 => c336, Rate Law: k103*c87*c332-kd103*c336
k60b = 0.0471248; kd60 = 0.0 Reaction: c323 => c86, Rate Law: k60b*c323-kd60*c86
k16 = 1.67E-5; kd24 = 0.55 Reaction: c22 + c299 => c302, Rate Law: k16*c22*c299-kd24*c302
kd1d = 0.1; k1d = 518.0 Reaction: c117 + c1 => c336, Rate Law: k1d*c117*c1-kd1d*c336
kd97 = 0.015; k97 = 1000000.0 Reaction: c531 + c285 => c286, Rate Law: k97*c531*c285-kd97*c286
kd19 = 0.5; k19 = 1.667E-7 Reaction: c69 + c317 => c320, Rate Law: k19*c69*c317-kd19*c320
kd35 = 0.0015; k35 = 7.5E-6 Reaction: c24 + c22 => c30, Rate Law: k35*c24*c22-kd35*c30
k65 = 0.0; kd65 = 0.2 Reaction: c83 + c420 => c98, Rate Law: k65*c83*c420-kd65*c98
k20 = 1.1068E-5; kd20 = 0.4 Reaction: c317 + c71 => c323, Rate Law: k20*c317*c71-kd20*c323
k110 = 3.33E-4; kd110 = 0.1 Reaction: c83 + c446 => c437, Rate Law: k110*c83*c446-kd110*c437
kd41 = 0.0429; k41 = 5.0E-5 Reaction: c30 + c299 => c305, Rate Law: k41*c30*c299-kd41*c305
k21 = 3.67E-7; kd21 = 0.23 Reaction: c317 + c26 => c323, Rate Law: k21*c317*c26-kd21*c323
k64 = 1.67E-5; kd64 = 0.3 Reaction: c83 + c24 => c102, Rate Law: k64*c83*c24-kd64*c102
k22 = 1.39338E-7; kd22 = 0.1 Reaction: c31 + c17 => c63, Rate Law: k22*c31*c17-kd22*c63
kd123h = 0.1; k123h = 0.0 Reaction: c5 + c105 => c556, Rate Law: k123h*c5*c105-kd123h*c556
k22 = 1.39338E-7; kd22b = 0.1 Reaction: c31 + c341 => c347, Rate Law: k22*c31*c341-kd22b*c347
kd63 = 0.275; k16 = 1.67E-5 Reaction: c293 + c22 => c314, Rate Law: k16*c293*c22-kd63*c314
kd7 = 1.38E-4; k7 = 5.0E-5 Reaction: c336 => c338, Rate Law: k7*c336-kd7*c338
kd25 = 0.0214; k25 = 1.67E-5 Reaction: c24 + c302 => c305, Rate Law: k25*c24*c302-kd25*c305

States:

Name Description
c349 (ErbB3:ErbB2)_P:GAP:Shc
c286 [Epidermal growth factor receptor]
c17 2(EGF:ErbB1)_P:GAP
c33 2(EGF:ErbB1)_P:GAP:(Shc_P)
c499 [Pro-epidermal growth factor; Epidermal growth factor receptor]
c302 2(ErbB2)_P:GAP:(Shc_P):Grb2
c83 (ERK_PP)_i
c105 ATP 1.2e9
c125 [Pro-epidermal growth factor; Epidermal growth factor receptor]
c336 [Receptor tyrosine-protein kinase erbB-4; Receptor tyrosine-protein kinase erbB-2]
c314 2(ErbB2)_P:GAP:Grb2
c311 2(ErbB2)_P:GAP:(Shc_P):Grb2:Sos:(Ras:GTP)
c31 [SHC-transforming protein 2; 605217]
c347 (ErbB3:ErbB2)_P:GAP:Shc
c353 (ErbB3:ErbB2)_P:GAP:(Shc_P)
c351 (ErbB3:ErbB2)_P:GAP:(Shc_P)
c117 [Receptor tyrosine-protein kinase erbB-2; Receptor tyrosine-protein kinase erbB-4]
c344 (ErbB4:ErbB2)_P:GAP
c341 (ErbB3:ErbB2)_P:GAP
c64 2(EGF:ErbB1)_P:GAP:(Shc_P)
c320 2(ErbB2)_P:GAP:Grb2:Sos:(Ras:GDP)
c348 (ErbB4:ErbB2)_P:GAP:Shc
c40 (Shc_P)
c305 2(ErbB2)_P:GAP:(Shc_P):Grb2:Sos
c308 2(ErbB2)_P:GAP:(Shc_P):Grb2:Sos:(Ras:GDP)
c32 2(EGF:ErbB1)_P:GAP:Shc
c323 2(ErbB2)_P:GAP:Grb2:Sos:(Ras:GTP)
c317 2(ErbB2)_P:GAP:Grb2:Sos
c63 2(EGF:ErbB1)_P:GAP:Shc
c343 (ErbB3:ErbB2)_P:GAP
c346 (ErbB4:ErbB2)_P:GAP
c22 [Growth factor receptor-bound protein 2; 108355]
c123 [Pro-epidermal growth factor; Epidermal growth factor receptor]
c124 [Pro-epidermal growth factor; Epidermal growth factor receptor]
c350 (ErbB4:ErbB2)_P:GAP:Shc

Observables: none

The paper describes a model of tumor invasion to bone marrow. Created by COPASI 4.26 (Build 213) This model is des…

The invasion of a new species into an established ecosystem can be directly compared to the steps involved in cancer metastasis. Cancer must grow in a primary site, extravasate and survive in the circulation to then intravasate into target organ (invasive species survival in transport). Cancer cells often lay dormant at their metastatic site for a long period of time (lag period for invasive species) before proliferating (invasive spread). Proliferation in the new site has an impact on the target organ microenvironment (ecological impact) and eventually the human host (biosphere impact).Tilman has described mathematical equations for the competition between invasive species in a structured habitat. These equations were adapted to study the invasion of cancer cells into the bone marrow microenvironment as a structured habitat. A large proportion of solid tumor metastases are bone metastases, known to usurp hematopoietic stem cells (HSC) homing pathways to establish footholds in the bone marrow. This required accounting for the fact that this is the natural home of hematopoietic stem cells and that they already occupy this structured space. The adapted Tilman model of invasion dynamics is especially valuable for modeling the lag period or dormancy of cancer cells.The Tilman equations for modeling the invasion of two species into a defined space have been modified to study the invasion of cancer cells into the bone marrow microenvironment. These modified equations allow a more flexible way to model the space competition between the two cell species. The ability to model initial density, metastatic seeding into the bone marrow and growth once the cells are present, and movement of cells out of the bone marrow niche and apoptosis of cells are all aspects of the adapted equations. These equations are currently being applied to clinical data sets for verification and further refinement of the models. link: http://identifiers.org/pubmed/21967667

Parameters:

Name Description
b1 = 0.2 1 Reaction: => H, Rate Law: bone_marrow*b1*H*(1-H)
u1 = 0.1 1 Reaction: H =>, Rate Law: bone_marrow*u1*H
u2 = 0.1 1 Reaction: T =>, Rate Law: bone_marrow*u2*T
b2 = 0.8 1 Reaction: => T; H, Rate Law: bone_marrow*b2*T*((1-T)-H)

States:

Name Description
T [malignant cell]
H [hematopoietic stem cell]

Observables: none

The paper describes a model of tumor invasion to bone marrow. Created by COPASI 4.26 (Build 213) This model is des…

The invasion of a new species into an established ecosystem can be directly compared to the steps involved in cancer metastasis. Cancer must grow in a primary site, extravasate and survive in the circulation to then intravasate into target organ (invasive species survival in transport). Cancer cells often lay dormant at their metastatic site for a long period of time (lag period for invasive species) before proliferating (invasive spread). Proliferation in the new site has an impact on the target organ microenvironment (ecological impact) and eventually the human host (biosphere impact).Tilman has described mathematical equations for the competition between invasive species in a structured habitat. These equations were adapted to study the invasion of cancer cells into the bone marrow microenvironment as a structured habitat. A large proportion of solid tumor metastases are bone metastases, known to usurp hematopoietic stem cells (HSC) homing pathways to establish footholds in the bone marrow. This required accounting for the fact that this is the natural home of hematopoietic stem cells and that they already occupy this structured space. The adapted Tilman model of invasion dynamics is especially valuable for modeling the lag period or dormancy of cancer cells.The Tilman equations for modeling the invasion of two species into a defined space have been modified to study the invasion of cancer cells into the bone marrow microenvironment. These modified equations allow a more flexible way to model the space competition between the two cell species. The ability to model initial density, metastatic seeding into the bone marrow and growth once the cells are present, and movement of cells out of the bone marrow niche and apoptosis of cells are all aspects of the adapted equations. These equations are currently being applied to clinical data sets for verification and further refinement of the models. link: http://identifiers.org/pubmed/21967667

Parameters:

Name Description
u1 = 0.1 1 Reaction: T =>, Rate Law: bone_marrow*u1*T
v = 0.1 1; b2 = 0.8 1 Reaction: => H; T, Rate Law: bone_marrow*b2*H*((1-H)-(1-v)*T)
u2 = 0.1 1 Reaction: H =>, Rate Law: bone_marrow*u2*H
k = 0.9 1; b1 = 0.2 1 Reaction: H => ; T, Rate Law: bone_marrow*b1*H*T*k

States:

Name Description
T [malignant cell]
H [hematopoietic stem cell]

Observables: none

BIOMD0000000202 @ v0.0.1

The model reproduces the plots in Figures 1 and 2. Note that the units of the time scale "A" are not right in the paper,…

A mathematical model is proposed to illustrate the activation of STIM1 (stromal interaction molecule 1) protein, the assembly and activation of calcium-release activated calcium (CRAC) channels in T cells. In combination with De Young-Keizer-Li-Rinzel model, we successfully reproduce a sustained Ca(2+) oscillation in cytoplasm. Our results reveal that Ca(2+) oscillation dynamics in cytoplasm can be significantly affected by the way how the Orai1 CRAC channel are assembled and activated. A low sustained Ca(2+) influx is observed through the CRAC channels across the plasma membrane. In particular, our model shows that a tetrameric channel complex can effectively regulate the total quantity of the channels and the ratio of the active channels to the total channels, and a period of Ca(2+) oscillation about 29 s is in agreement with published experimental data. The bifurcation analyses illustrate the different dynamic properties between our mixed Ca(2+) feedback model and the single positive or negative feedback models. link: http://identifiers.org/pubmed/18538916

Parameters:

Name Description
k_a = 4.0 s_1 Reaction: => S2a; S2, Rate Law: ER*k_a*S2
kod = 1.0 s_1 Reaction: O_o => Oc, Rate Law: PM*kod*O_o
k_i = 6.0 s_1 Reaction: S2a =>, Rate Law: ER*k_i*S2a
Ca_ec = 1500.0 uM; k_soc = 2.3 uM_1_s_1; V_PMleak = 5.0E-7 s_1 Reaction: => Ca_Cyt; O_o, Rate Law: Cytoplasm*(k_soc*O_o+V_PMleak)*(Ca_ec-Ca_Cyt)
kdo = 0.6 s_1 Reaction: O_o =>, Rate Law: PM*kdo*O_o
kop = 0.5 s_1; l_hill = 1.0 dimensionless; Ko = 0.2 uM Reaction: Oc => O_o; S2a, Rate Law: PM*kop*S2a^l_hill*Oc/(Ko^l_hill+S2a^l_hill)
K1 = 5.0 uM; St = 0.6 uM Reaction: S2 = K1^2/(Ca_ER^2+K1^2)*(St-S2a), Rate Law: missing
kdc = 0.5 s_1 Reaction: Oc =>, Rate Law: PM*kdc*Oc
q = 2.0 dimensionless; V_PMCA = 1.0 uM_s_1; K_PMCA = 0.45 uM Reaction: Ca_Cyt =>, Rate Law: Cytoplasm*V_PMCA*Ca_Cyt^q/(K_PMCA^q+Ca_Cyt^q)
Vs4 = 0.25 uM_s_1; K2 = 0.14 uM Reaction: => S4; S2, Rate Law: ER*Vs4*S2^2/(S2^2+K2^2)
kd_oligo = 0.8 s_1 Reaction: S4 =>, Rate Law: ER*kd_oligo*S4
K_PLC = 0.12 uM; V_PLC = 0.5 uM_s_1 Reaction: => IP3_Cyt; Ca_Cyt, Rate Law: Cytoplasm*V_PLC*Ca_Cyt^2/(K_PLC^2+Ca_Cyt^2)
K_SERCA = 0.15 uM; V_SERCA = 1.0 uM_s_1; p = 2.0 dimensionless Reaction: Ca_Cyt => Ca_ER, Rate Law: Cytoplasm*V_SERCA*Ca_Cyt^p/(K_SERCA^p+Ca_Cyt^p)
L = 9.3E-4 s_1; Ki = 1.0 uM; Ka = 0.4 uM; h = 0.0 dimensionless; P_IP3R = 66.6 s_1 Reaction: Ca_ER => Ca_Cyt; IP3_Cyt, Rate Law: Cytoplasm*(L+P_IP3R*IP3_Cyt^3*Ca_Cyt^3*h^3/((IP3_Cyt+Ki)^3*(Ca_Cyt+Ka)^3))*(Ca_ER-Ca_Cyt)
n_hill = 3.0 dimensionless; Kc = 2.0E-5 uM; Vcp = 1.8E-4 uM_s_1 Reaction: => Oc; Orai1, Rate Law: PM*Vcp*Orai1^n_hill/(Kc^n_hill+Orai1^n_hill)
r_hill = 4.0 dimensionless; Orai1_t = 0.001 uM Reaction: Orai1 = Orai1_t-(r_hill*Oc+r_hill*O_o), Rate Law: missing
kdeg = 0.5 s_1; K_deg = 0.1 uM Reaction: IP3_Cyt => ; Ca_Cyt, Rate Law: Cytoplasm*kdeg*Ca_Cyt^2/(K_deg^2+Ca_Cyt^2)*IP3_Cyt

States:

Name Description
S2a [Stromal interaction molecule 1]
O o O_o
S4 [Stromal interaction molecule 1]
Orai1 [Calcium release-activated calcium channel protein 1]
Ca Cyt [calcium(2+); Calcium cation]
S2 [Stromal interaction molecule 1]
Ca ER [calcium(2+); Calcium cation]
IP3 Cyt [1D-myo-inositol 1,4,5-trisphosphate; D-myo-Inositol 1,4,5-trisphosphate]
Oc Oc

Observables: none

Chickarmane2006 - Stem cell switch irreversibleKinetic modeling approach of the transcriptional dynamics of the embryoni…

Recent ChIP experiments of human and mouse embryonic stem cells have elucidated the architecture of the transcriptional regulatory circuitry responsible for cell determination, which involves the transcription factors OCT4, SOX2, and NANOG. In addition to regulating each other through feedback loops, these genes also regulate downstream target genes involved in the maintenance and differentiation of embryonic stem cells. A search for the OCT4-SOX2-NANOG network motif in other species reveals that it is unique to mammals. With a kinetic modeling approach, we ascribe function to the observed OCT4-SOX2-NANOG network by making plausible assumptions about the interactions between the transcription factors at the gene promoter binding sites and RNA polymerase (RNAP), at each of the three genes as well as at the target genes. We identify a bistable switch in the network, which arises due to several positive feedback loops, and is switched on/off by input environmental signals. The switch stabilizes the expression levels of the three genes, and through their regulatory roles on the downstream target genes, leads to a binary decision: when OCT4, SOX2, and NANOG are expressed and the switch is on, the self-renewal genes are on and the differentiation genes are off. The opposite holds when the switch is off. The model is extremely robust to parameter changes. In addition to providing a self-consistent picture of the transcriptional circuit, the model generates several predictions. Increasing the binding strength of NANOG to OCT4 and SOX2, or increasing its basal transcriptional rate, leads to an irreversible bistable switch: the switch remains on even when the activating signal is removed. Hence, the stem cell can be manipulated to be self-renewing without the requirement of input signals. We also suggest tests that could discriminate between a variety of feedforward regulation architectures of the target genes by OCT4, SOX2, and NANOG. link: http://identifiers.org/pubmed/16978048

Parameters:

Name Description
c1 = 1.0; f = 1000.0; d3 = 0.001; eta3 = 1.0E-4; c2 = 0.01; d2 = 0.001; c3 = 0.5; d1 = 0.0011 Reaction: SOX2_Gene => SOX2; A, OCT4_SOX2, NANOG, Rate Law: (eta3+c1*A+c2*OCT4_SOX2+c3*OCT4_SOX2*NANOG)/(1+eta3/f+d1*A+d2*OCT4_SOX2+d3*OCT4_SOX2*NANOG)
a2 = 0.01; b2 = 0.001; a3 = 0.5; f = 1000.0; b3 = 0.001; a1 = 1.0; eta1 = 1.0E-4; b1 = 0.0011 Reaction: OCT4_Gene => OCT4; A, OCT4_SOX2, NANOG, Rate Law: (eta1+a1*A+a2*OCT4_SOX2+a3*OCT4_SOX2*NANOG)/(1+eta1/f+b1*A+b2*OCT4_SOX2+b3*OCT4_SOX2*NANOG)
gamma4 = 0.01 Reaction: Protein => degradation, Rate Law: gamma4*Protein
h1 = 0.0011; g1 = 0.1; f2 = 0.001; h2 = 1.0; eta7 = 1.0E-4 Reaction: targetGene => Protein; OCT4_SOX2, NANOG, Rate Law: (g1*OCT4_SOX2+eta7)/(1+eta7/f2+h1*OCT4_SOX2+h2*OCT4_SOX2*NANOG)
f3 = 0.05; f = 1000.0; e1 = 0.01; f1 = 0.001; e2 = 0.1; f2 = 0.001; eta5 = 1.0E-4 Reaction: NANOG_Gene => NANOG; OCT4_SOX2, p53, Rate Law: (eta5+e1*OCT4_SOX2+e2*OCT4_SOX2*NANOG)/(1+eta5/f+f2*OCT4_SOX2+f1*OCT4_SOX2*NANOG+f3*p53)
gamma2 = 1.0 Reaction: NANOG => degradation, Rate Law: gamma2*NANOG
k2c = 0.001; k1c = 0.05 Reaction: OCT4 + SOX2 => OCT4_SOX2, Rate Law: k1c*OCT4*SOX2-k2c*OCT4_SOX2
gamma1 = 1.0 Reaction: OCT4 => degradation, Rate Law: gamma1*OCT4
k3c = 5.0 Reaction: OCT4_SOX2 => degradation, Rate Law: k3c*OCT4_SOX2
gamma3 = 1.0 Reaction: SOX2 => degradation, Rate Law: gamma3*SOX2

States:

Name Description
OCT4 Gene [POU5F1; POU domain, class 5, transcription factor 1]
SOX2 Gene [QSOX2; Sulfhydryl oxidase 2]
targetGene targetGene
NANOG [Putative homeobox protein NANOG2]
NANOG Gene [Putative homeobox protein NANOG2; NANOGP1]
SOX2 [Sulfhydryl oxidase 2]
OCT4 [POU domain, class 5, transcription factor 1]
Protein Protein
degradation degradation
OCT4 SOX2 [POU domain, class 5, transcription factor 1; Sulfhydryl oxidase 2]

Observables: none

Chickarmane2006 - Stem cell switch reversibleKinetic modeling approach of the transcriptional dynamics of the embryonic…

Recent ChIP experiments of human and mouse embryonic stem cells have elucidated the architecture of the transcriptional regulatory circuitry responsible for cell determination, which involves the transcription factors OCT4, SOX2, and NANOG. In addition to regulating each other through feedback loops, these genes also regulate downstream target genes involved in the maintenance and differentiation of embryonic stem cells. A search for the OCT4-SOX2-NANOG network motif in other species reveals that it is unique to mammals. With a kinetic modeling approach, we ascribe function to the observed OCT4-SOX2-NANOG network by making plausible assumptions about the interactions between the transcription factors at the gene promoter binding sites and RNA polymerase (RNAP), at each of the three genes as well as at the target genes. We identify a bistable switch in the network, which arises due to several positive feedback loops, and is switched on/off by input environmental signals. The switch stabilizes the expression levels of the three genes, and through their regulatory roles on the downstream target genes, leads to a binary decision: when OCT4, SOX2, and NANOG are expressed and the switch is on, the self-renewal genes are on and the differentiation genes are off. The opposite holds when the switch is off. The model is extremely robust to parameter changes. In addition to providing a self-consistent picture of the transcriptional circuit, the model generates several predictions. Increasing the binding strength of NANOG to OCT4 and SOX2, or increasing its basal transcriptional rate, leads to an irreversible bistable switch: the switch remains on even when the activating signal is removed. Hence, the stem cell can be manipulated to be self-renewing without the requirement of input signals. We also suggest tests that could discriminate between a variety of feedforward regulation architectures of the target genes by OCT4, SOX2, and NANOG. link: http://identifiers.org/pubmed/16978048

Parameters:

Name Description
f = 1000.0; f2 = 9.95E-4; f1 = 0.001; e1 = 0.005; e2 = 0.1; f3 = 0.01; eta5 = 1.0E-4 Reaction: NANOG_Gene => NANOG; OCT4_SOX2, p53, Rate Law: (eta5+e1*OCT4_SOX2+e2*OCT4_SOX2*NANOG)/(1+eta5/f+f2*OCT4_SOX2+f1*OCT4_SOX2*NANOG+f3*p53)
gamma4 = 0.01 Reaction: Protein => degradation, Rate Law: gamma4*Protein
a2 = 0.01; b2 = 0.001; b3 = 7.0E-4; a3 = 0.2; f = 1000.0; a1 = 1.0; eta1 = 1.0E-4; b1 = 0.0011 Reaction: OCT4_Gene => OCT4; A, OCT4_SOX2, NANOG, Rate Law: (eta1+a1*A+a2*OCT4_SOX2+a3*OCT4_SOX2*NANOG)/(1+eta1/f+b1*A+b2*OCT4_SOX2+b3*OCT4_SOX2*NANOG)
f = 1000.0; g1 = 0.1; h1 = 0.0019; h2 = 0.05; eta7 = 1.0E-4 Reaction: targetGene => Protein; OCT4_SOX2, NANOG, Rate Law: (g1*OCT4_SOX2+eta7)/(1+eta7/f+h1*OCT4_SOX2+h2*OCT4_SOX2*NANOG)
c1 = 1.0; f = 1000.0; eta3 = 1.0E-4; c2 = 0.01; c3 = 0.2; d2 = 0.001; d3 = 7.0E-4; d1 = 0.0011 Reaction: SOX2_Gene => SOX2; A, OCT4_SOX2, NANOG, Rate Law: (eta3+c1*A+c2*OCT4_SOX2+c3*OCT4_SOX2*NANOG)/(1+eta3/f+d1*A+d2*OCT4_SOX2+d3*OCT4_SOX2*NANOG)
gamma2 = 1.0 Reaction: NANOG => degradation, Rate Law: gamma2*NANOG
k2c = 0.001; k1c = 0.05 Reaction: OCT4 + SOX2 => OCT4_SOX2, Rate Law: k1c*OCT4*SOX2-k2c*OCT4_SOX2
gamma1 = 1.0 Reaction: OCT4 => degradation, Rate Law: gamma1*OCT4
k3c = 5.0 Reaction: OCT4_SOX2 => degradation, Rate Law: k3c*OCT4_SOX2
gamma3 = 1.0 Reaction: SOX2 => degradation, Rate Law: gamma3*SOX2

States:

Name Description
OCT4 Gene [POU5F1; POU domain, class 5, transcription factor 1]
SOX2 Gene [QSOX2; Sulfhydryl oxidase 2]
OCT4 [POU domain, class 5, transcription factor 1]
targetGene targetGene
NANOG Gene [NANOGP1; Putative homeobox protein NANOG2]
SOX2 [Sulfhydryl oxidase 2]
NANOG [Putative homeobox protein NANOG2]
Protein Protein
degradation degradation
OCT4 SOX2 [POU domain, class 5, transcription factor 1; Sulfhydryl oxidase 2]

Observables: none

Chickarmane2008 - Stem cell lineage - NANOG GATA-6 switchIn this work, a dynamical model of lineage determination based…

Recent studies have associated the transcription factors, Oct4, Sox2 and Nanog as parts of a self-regulating network which is responsible for maintaining embryonic stem cell properties: self renewal and pluripotency. In addition, mutual antagonism between two of these and other master regulators have been shown to regulate lineage determination. In particular, an excess of Cdx2 over Oct4 determines the trophectoderm lineage whereas an excess of Gata-6 over Nanog determines differentiation into the endoderm lineage. Also, under/over-expression studies of the master regulator Oct4 have revealed that some self-renewal/pluripotency as well as differentiation genes are expressed in a biphasic manner with respect to the concentration of Oct4.We construct a dynamical model of a minimalistic network, extracted from ChIP-on-chip and microarray data as well as literature studies. The model is based upon differential equations and makes two plausible assumptions; activation of Gata-6 by Oct4 and repression of Nanog by an Oct4-Gata-6 heterodimer. With these assumptions, the results of simulations successfully describe the biphasic behavior as well as lineage commitment. The model also predicts that reprogramming the network from a differentiated state, in particular the endoderm state, into a stem cell state, is best achieved by over-expressing Nanog, rather than by suppression of differentiation genes such as Gata-6.The computational model provides a mechanistic understanding of how different lineages arise from the dynamics of the underlying regulatory network. It provides a framework to explore strategies of reprogramming a cell from a differentiated state to a stem cell state through directed perturbations. Such an approach is highly relevant to regenerative medicine since it allows for a rapid search over the host of possibilities for reprogramming to a stem cell state. link: http://identifiers.org/pubmed/18941526

Parameters:

Name Description
i2 = 0.1; j1 = 0.1; i1 = 0.1; j0 = 0.1; i0 = 0.001 Reaction: GCNF_Gene => GCNF; CDX2, GATA6, Rate Law: (i0+i1*CDX2+i2*GATA6)/(1+j0*CDX2+j1*GATA6)
c1 = 0.05; d1 = 0.05; d2 = 0.0125; d3 = 0.05; c2 = 0.0125 Reaction: GATA6_Gene => GATA6; OCT4_SOX2, NANOG, Rate Law: (c1*OCT4_SOX2+c2*GATA6)/(1+d1*OCT4_SOX2+d2*GATA6+d3*NANOG)
gamman = 0.01 Reaction: NANOG => degradation, Rate Law: gamman*NANOG
gamma1 = 0.1 Reaction: OCT4 => degradation, Rate Law: gamma1*OCT4
a0 = 0.001; a2 = 0.0125; b1 = 0.02; a1 = 0.02; b3 = 0.03; a3 = 0.025; b5 = 10.0; b0 = 1.0; b4 = 10.0; b2 = 0.0125 Reaction: OCT4_Gene => OCT4; A, SOX2, NANOG, CDX2, GCNF, Rate Law: (a0+a1*A+a2*OCT4*SOX2+a3*OCT4*SOX2*NANOG)/(1+b0*A+b1*OCT4+b2*OCT4*SOX2+b3*OCT4*SOX2*NANOG+b4*CDX2*OCT4+b5*GCNF)
c1 = 0.05; d1 = 0.05; d2 = 0.0125; d0 = 0.001; c2 = 0.0125; c0 = 0.001 Reaction: SOX2_Gene => SOX2; OCT4, NANOG, Rate Law: (c0+c1*OCT4*SOX2+c2*OCT4*SOX2*NANOG)/(1+d0*OCT4+d1*OCT4*SOX2+d2*OCT4*SOX2*NANOG)
gammag = 0.01 Reaction: GATA6 => degradation, Rate Law: gammag*GATA6
a2 = 0.0125; b1 = 0.02; a1 = 0.02; b3 = 0.03; b2 = 0.0125 Reaction: NANOG_Gene => NANOG; OCT4_SOX2, GATA6, Rate Law: (a1*OCT4_SOX2+a2*OCT4_SOX2*NANOG)/(1+b1*OCT4_SOX2+b2*OCT4_SOX2*NANOG+b3*OCT4_SOX2*GATA6)
gamma5 = 0.1 Reaction: GCNF => degradation, Rate Law: gamma5*GCNF
gamma4 = 0.1 Reaction: CDX2 => degradation, Rate Law: gamma4*CDX2
gamma2 = 0.1 Reaction: SOX2 => degradation, Rate Law: gamma2*SOX2
g0 = 0.001; h0 = 2.0; g1 = 2.0; h1 = 5.0 Reaction: CDX2_Gene => CDX2; OCT4, Rate Law: (g0+g1*CDX2)/(1+h0*CDX2+h1*CDX2*OCT4)

States:

Name Description
GCNF [Nuclear receptor subfamily 6 group A member 1]
CDX2 Gene [CDX2; Homeobox protein CDX-2]
GATA6 [Transcription factor GATA-6]
SOX2 Gene [QSOX2; Sulfhydryl oxidase 2]
GCNF Gene [NR6A1; Nuclear receptor subfamily 6 group A member 1]
NANOG Gene [NANOGP1; Putative homeobox protein NANOG2]
SOX2 [Sulfhydryl oxidase 2]
CDX2 [Homeobox protein CDX-2]
OCT4 Gene [POU5F1; POU domain, class 5, transcription factor 1]
GATA6 Gene [GATA6; Transcription factor GATA-6]
OCT4 [POU domain, class 5, transcription factor 1]
NANOG [Putative homeobox protein NANOG2]
degradation degradation

Observables: none

Chickarmane2008 - Stem cell lineage determinationIn this work, a dynamical model of lineage determination based upon a…

Recent studies have associated the transcription factors, Oct4, Sox2 and Nanog as parts of a self-regulating network which is responsible for maintaining embryonic stem cell properties: self renewal and pluripotency. In addition, mutual antagonism between two of these and other master regulators have been shown to regulate lineage determination. In particular, an excess of Cdx2 over Oct4 determines the trophectoderm lineage whereas an excess of Gata-6 over Nanog determines differentiation into the endoderm lineage. Also, under/over-expression studies of the master regulator Oct4 have revealed that some self-renewal/pluripotency as well as differentiation genes are expressed in a biphasic manner with respect to the concentration of Oct4.We construct a dynamical model of a minimalistic network, extracted from ChIP-on-chip and microarray data as well as literature studies. The model is based upon differential equations and makes two plausible assumptions; activation of Gata-6 by Oct4 and repression of Nanog by an Oct4-Gata-6 heterodimer. With these assumptions, the results of simulations successfully describe the biphasic behavior as well as lineage commitment. The model also predicts that reprogramming the network from a differentiated state, in particular the endoderm state, into a stem cell state, is best achieved by over-expressing Nanog, rather than by suppression of differentiation genes such as Gata-6.The computational model provides a mechanistic understanding of how different lineages arise from the dynamics of the underlying regulatory network. It provides a framework to explore strategies of reprogramming a cell from a differentiated state to a stem cell state through directed perturbations. Such an approach is highly relevant to regenerative medicine since it allows for a rapid search over the host of possibilities for reprogramming to a stem cell state. link: http://identifiers.org/pubmed/18941526

Parameters:

Name Description
d1 = 0.005; c1 = 0.005; d2 = 0.025; d0 = 0.001; c2 = 0.025; c0 = 0.001 Reaction: SOX2_Gene => SOX2; OCT4, NANOG, Rate Law: (c0+c1*OCT4*SOX2+c2*OCT4*SOX2*NANOG)/(1+d0*OCT4+d1*OCT4*SOX2+d2*OCT4*SOX2*NANOG)
i2 = 0.1; j1 = 0.1; i1 = 0.1; j0 = 0.1; i0 = 0.001 Reaction: GCNF_Gene => GCNF; CDX2, GATA6, Rate Law: (i0+i1*CDX2+i2*GATA6)/(1+j0*CDX2+j1*GATA6)
gamma1 = 0.1 Reaction: OCT4 => degradation, Rate Law: gamma1*OCT4
e1 = 0.1; e3 = 1.0; f0 = 0.001; f1 = 0.1; e2 = 0.1; f2 = 0.1; e0 = 0.001; f3 = 10.0; f4 = 1.0 Reaction: NANOG_Gene => NANOG; OCT4, SOX2, GATA6, SN, Rate Law: (e0+e1*OCT4*SOX2+e2*OCT4*SOX2*NANOG+e3*SN)/(1+f0*OCT4+f1*OCT4*SOX2+f2*OCT4*SOX2*NANOG+f3*OCT4*GATA6+f4*SN)
gamma3 = 0.1 Reaction: NANOG => degradation, Rate Law: gamma3*NANOG
gammag = 0.1 Reaction: GATA6 => degradation, Rate Law: gammag*GATA6
gamma5 = 0.1 Reaction: GCNF => degradation, Rate Law: gamma5*GCNF
gamma4 = 0.1 Reaction: CDX2 => degradation, Rate Law: gamma4*CDX2
a0 = 0.001; b1 = 0.001; b2 = 0.005; b3 = 0.025; a3 = 0.025; b5 = 10.0; a1 = 1.0; b0 = 1.0; b4 = 10.0; a2 = 0.005 Reaction: OCT4_Gene => OCT4; A, SOX2, NANOG, CDX2, GCNF, Rate Law: (a0+a1*A+a2*OCT4*SOX2+a3*OCT4*SOX2*NANOG)/(1+b0*A+b1*OCT4+b2*OCT4*SOX2+b3*OCT4*SOX2*NANOG+b4*CDX2*OCT4+b5*GCNF)
gamma2 = 0.1 Reaction: SOX2 => degradation, Rate Law: gamma2*SOX2
q2 = 15.0; p2 = 2.5E-4; p0 = 0.1; q1 = 2.5E-4; q0 = 1.0; p1 = 1.0; q3 = 10.0 Reaction: GATA6_Gene => GATA6; OCT4, NANOG, SG, Rate Law: (p0+p1*OCT4+p2*GATA6)/(1+q0*OCT4+q1*GATA6+q2*NANOG+q3*SG)
g0 = 0.001; h0 = 2.0; g1 = 2.0; h1 = 5.0 Reaction: CDX2_Gene => CDX2; OCT4, Rate Law: (g0+g1*CDX2)/(1+h0*CDX2+h1*CDX2*OCT4)

States:

Name Description
GCNF [Nuclear receptor subfamily 6 group A member 1]
CDX2 Gene [CDX2; Homeobox protein CDX-2]
GATA6 [Transcription factor GATA-6]
SOX2 Gene [QSOX2; Sulfhydryl oxidase 2]
GCNF Gene [NR6A1; Nuclear receptor subfamily 6 group A member 1]
NANOG Gene [NANOGP1; Putative homeobox protein NANOG2]
SOX2 [Sulfhydryl oxidase 2]
CDX2 [Homeobox protein CDX-2]
OCT4 Gene [POU5F1; POU domain, class 5, transcription factor 1]
GATA6 Gene [GATA6; Transcription factor GATA-6]
OCT4 [POU domain, class 5, transcription factor 1]
NANOG [Putative homeobox protein NANOG2]
degradation degradation

Observables: none

MODEL2003180003 @ v0.0.1

mathematical approach to model the protein interactions regulating the transition from the G1 phase to the phase of DNA…

Cell cycle duration and phase transition times are not fixed, even within homogeneous cell populations growing under optimal environmental conditions. We investigate G(1) phase variability from the molecular point of view and propose a mathematical approach to model the protein interactions regulating the transition from the G(1) phase to the phase of DNA synthesis. The mathematical model has some connections with flow cytometry experimental data. link: http://identifiers.org/pubmed/11965250

Parameters: none

States: none

Observables: none

Mathematical model of malaria transmission for low and high transmission rates.

We perform sensitivity analyses on a mathematical model of malaria transmission to determine the relative importance of model parameters to disease transmission and prevalence. We compile two sets of baseline parameter values: one for areas of high transmission and one for low transmission. We compute sensitivity indices of the reproductive number (which measures initial disease transmission) and the endemic equilibrium point (which measures disease prevalence) to the parameters at the baseline values. We find that in areas of low transmission, the reproductive number and the equilibrium proportion of infectious humans are most sensitive to the mosquito biting rate. In areas of high transmission, the reproductive number is again most sensitive to the mosquito biting rate, but the equilibrium proportion of infectious humans is most sensitive to the human recovery rate. This suggests strategies that target the mosquito biting rate (such as the use of insecticide-treated bed nets and indoor residual spraying) and those that target the human recovery rate (such as the prompt diagnosis and treatment of infectious individuals) can be successful in controlling malaria. link: http://identifiers.org/pubmed/18293044

Parameters:

Name Description
v_h = 0.1 Reaction: Exposed_Human => Infected_Human, Rate Law: Human*v_h*Exposed_Human
N_h = 623.0; Psi_h = 5.5E-5 Reaction: => Susceptible_Human, Rate Law: Human*Psi_h*N_h
lambda_v = 2.93379660870939E-4 Reaction: Susceptible_Mosquito => Exposed_Mosquito, Rate Law: Mosquito*lambda_v*Susceptible_Mosquito
v_v = 0.083 Reaction: Exposed_Mosquito => Infected_Mosquito, Rate Law: Mosquito*v_v*Exposed_Mosquito
lambda_h = 4.48218926330601E-5 Reaction: Susceptible_Human => Exposed_Human, Rate Law: Human*lambda_h*Susceptible_Human
rho_h = 0.0027 Reaction: Recovered => Susceptible_Human, Rate Law: Human*rho_h*Recovered
gamma_h = 0.0035 Reaction: Infected_Human => Recovered, Rate Law: Human*gamma_h*Infected_Human
f_h = 1.334E-4 Reaction: Infected_Human =>, Rate Law: Human*f_h*Infected_Human
Capital_lambda_h = 0.041 Reaction: => Susceptible_Human, Rate Law: Human*Capital_lambda_h
delta_h = 1.8E-5 Reaction: Infected_Human =>, Rate Law: Human*delta_h*Infected_Human
Psi_v = 0.13; N_v = 2435.0 Reaction: => Susceptible_Mosquito, Rate Law: Mosquito*Psi_v*N_v
f_v = 0.1304 Reaction: Infected_Mosquito =>, Rate Law: Mosquito*f_v*Infected_Mosquito

States:

Name Description
Infected Human [Infection; Homo sapiens]
Exposed Mosquito [Disease Transmission; C123547]
Susceptible Human [0005461]
Recovered [Recovery]
Susceptible Mosquito [0005461]
Infected Mosquito [Infection; C123547]
Exposed Human [C156623; Homo sapiens]

Observables: none

Mathematical model for Rift Valley Fever transmission between cattle and mosquitoes without infectious eggs.

We present two ordinary differential equation models for Rift Valley fever (RVF) transmission in cattle and mosquitoes. We extend existing models for vector-borne diseases to include an asymptomatic host class and vertical transmission in vectors. We define the basic reproductive number, ℛ(0), and analyse the existence and stability of equilibrium points. We compute sensitivity indices of ℛ(0) and a reactivity index (that measures epidemicity) to parameters for baseline wet and dry season values. ℛ(0) is most sensitive to the mosquito biting and death rates. The reactivity index is most sensitive to the mosquito biting rate and the infectivity of hosts to vectors. Numerical simulations show that even with low equilibrium prevalence, increases in mosquito densities through higher rainfall, in the presence of vertical transmission, can result in large epidemics. This suggests that vertical transmission is an important factor in the size and persistence of RVF epidemics. link: http://identifiers.org/pubmed/23098257

Parameters:

Name Description
gamma_h = 0.25; u_h = 4.5662100456621E-4; gamma_tilde_h = 0.25 Reaction: R_h = (gamma_h*I_h+gamma_tilde_h*A_h)-u_h*R_h, Rate Law: (gamma_h*I_h+gamma_tilde_h*A_h)-u_h*R_h
lambda_h = 5.143359375E-5; theta_h = 0.4; u_h = 4.5662100456621E-4; gamma_tilde_h = 0.25 Reaction: A_h = theta_h*lambda_h*S_h-(u_h+gamma_tilde_h)*A_h, Rate Law: theta_h*lambda_h*S_h-(u_h+gamma_tilde_h)*A_h
u_v = 0.05; lambda_v = 0.0; N_v = 20000.0; M0 = 20000.0; psi_v = 0.1 Reaction: S_v = ((N_v-psi_v*I_v)/N_v*u_v*M0-lambda_v*S_v)-u_v*S_v, Rate Law: ((N_v-psi_v*I_v)/N_v*u_v*M0-lambda_v*S_v)-u_v*S_v
u_v = 0.05; N_v = 20000.0; M0 = 20000.0; v_v = 0.0714285714285714; psi_v = 0.1 Reaction: I_v = (psi_v*I_v/N_v*u_v*M0+v_v*E_v)-u_v*I_v, Rate Law: (psi_v*I_v/N_v*u_v*M0+v_v*E_v)-u_v*I_v
u_v = 0.05; lambda_v = 0.0; v_v = 0.0714285714285714 Reaction: E_v = lambda_v*S_v-(u_v+v_v)*E_v, Rate Law: lambda_v*S_v-(u_v+v_v)*E_v
C0 = 1000.0; lambda_h = 5.143359375E-5; u_h = 4.5662100456621E-4 Reaction: S_h = (u_h*C0-lambda_h*S_h)-u_h*S_h, Rate Law: (u_h*C0-lambda_h*S_h)-u_h*S_h
delta_h = 0.1; gamma_h = 0.25; lambda_h = 5.143359375E-5; theta_h = 0.4; u_h = 4.5662100456621E-4 Reaction: I_h = (1-theta_h)*lambda_h*S_h-(u_h+gamma_h+delta_h)*I_h, Rate Law: (1-theta_h)*lambda_h*S_h-(u_h+gamma_h+delta_h)*I_h

States:

Name Description
I v [0000460; 0004757]
S h [C66819; 0003748]
A h [C3833; Infection]
E v [0003748; PATO:0002425]
S v [C66819; 0004757]
R h [0003748; Recovered or Resolved]
I h [0003748; 0000460]

Observables: none

This ODE model is a representation of the two compartment macronutrient partition model that Chow and Hall outlined in t…

An imbalance between energy intake and energy expenditure will lead to a change in body weight (mass) and body composition (fat and lean masses). A quantitative understanding of the processes involved, which currently remains lacking, will be useful in determining the etiology and treatment of obesity and other conditions resulting from prolonged energy imbalance. Here, we show that a mathematical model of the macronutrient flux balances can capture the long-term dynamics of human weight change; all previous models are special cases of this model. We show that the generic dynamic behavior of body composition for a clamped diet can be divided into two classes. In the first class, the body composition and mass are determined uniquely. In the second class, the body composition can exist at an infinite number of possible states. Surprisingly, perturbations of dietary energy intake or energy expenditure can give identical responses in both model classes, and existing data are insufficient to distinguish between these two possibilities. Nevertheless, this distinction has important implications for the efficacy of clinical interventions that alter body composition and mass. link: http://identifiers.org/pubmed/18369435

Parameters:

Name Description
Energy_Expenditure_Rate = 11.05; p___Ratio = 0.038480263286012; Psy = 0.00102051309738594; rho_F = 39.5; Intake_Rate = 9.2 Reaction: Fat_Mass = ((1-p___Ratio)*(Intake_Rate-Energy_Expenditure_Rate)-Psy)/rho_F, Rate Law: ((1-p___Ratio)*(Intake_Rate-Energy_Expenditure_Rate)-Psy)/rho_F
Energy_Expenditure_Rate = 11.05; rho_L = 7.6; p___Ratio = 0.038480263286012; Psy = 0.00102051309738594; Intake_Rate = 9.2 Reaction: Lean_Mass = (p___Ratio*(Intake_Rate-Energy_Expenditure_Rate)+Psy)/rho_L, Rate Law: (p___Ratio*(Intake_Rate-Energy_Expenditure_Rate)+Psy)/rho_L

States:

Name Description
Lean Mass [C71258; C81328]
Body Mass [C81328]
Fat Mass [C158256; C81328]

Observables: none

This is a mathematical model describing competition between an artificially induced tumor and the adaptive immune system…

We present a model of competition between an artificially induced tumor and the adaptive immune system based on the use of an autonomous system of ordinary differential equations (ODE). The aim of this work is to reproduce experimental results which find two possible outcomes depending on the initial quantities of the tumor and the adaptive immune cells. The ODE system is positively invariant and its solutions are bounded. The linear stability analysis of the fixed points of the model yields two groups of solutions depending on the initial conditions. In the first one, the immune system wins against the tumor cells, so the cancer disappears (elimination). In the second one, the cancer keeps on growing (escape). These results are coherent with experimental results which show these two possibilities, so the model reproduces the macroscopic behavior of the experiments. From the model some conclusions on the underlying competitive behavior can be derived. link: http://identifiers.org/doi/10.1142/S0218339011004111

Parameters:

Name Description
a = 0.0625 Reaction: => x_Cancer, Rate Law: compartment*a*x_Cancer
d = 0.03125 Reaction: => y_Immune_System, Rate Law: compartment*d*y_Immune_System
c = 0.03125 Reaction: x_Cancer =>, Rate Law: compartment*c*x_Cancer^2
e = 0.0859375 Reaction: y_Immune_System => ; x_Cancer, Rate Law: compartment*e*x_Cancer*y_Immune_System
b = 0.125 Reaction: x_Cancer => ; y_Immune_System, Rate Law: compartment*b*x_Cancer*y_Immune_System
f = 0.03125 Reaction: y_Immune_System =>, Rate Law: compartment*f*y_Immune_System^2

States:

Name Description
y Immune System [Immune Cell]
x Cancer [neoplastic cell]

Observables: none

Chung2010 - Genome-scale metabolic network of Pichia pastoris (iPP668)This model is described in the article: [Genome-s…

Pichia pastoris has been recognized as an effective host for recombinant protein production. A number of studies have been reported for improving this expression system. However, its physiology and cellular metabolism still remained largely uncharacterized. Thus, it is highly desirable to establish a systems biotechnological framework, in which a comprehensive in silico model of P. pastoris can be employed together with high throughput experimental data analysis, for better understanding of the methylotrophic yeast's metabolism.A fully compartmentalized metabolic model of P. pastoris (iPP668), composed of 1,361 reactions and 1,177 metabolites, was reconstructed based on its genome annotation and biochemical information. The constraints-based flux analysis was then used to predict achievable growth rate which is consistent with the cellular phenotype of P. pastoris observed during chemostat experiments. Subsequent in silico analysis further explored the effect of various carbon sources on cell growth, revealing sorbitol as a promising candidate for culturing recombinant P. pastoris strains producing heterologous proteins. Interestingly, methanol consumption yields a high regeneration rate of reducing equivalents which is substantial for the synthesis of valuable pharmaceutical precursors. Hence, as a case study, we examined the applicability of P. pastoris system to whole-cell biotransformation and also identified relevant metabolic engineering targets that have been experimentally verified.The genome-scale metabolic model characterizes the cellular physiology of P. pastoris, thus allowing us to gain valuable insights into the metabolism of methylotrophic yeast and devise possible strategies for strain improvement through in silico simulations. This computational approach, combined with synthetic biology techniques, potentially forms a basis for rational analysis and design of P. pastoris metabolic network to enhance humanized glycoprotein production. link: http://identifiers.org/pubmed/20594333

Parameters: none

States: none

Observables: none

Ciliberto2003 - CyclinE / Cdk2 timer in the cell cycle of Xenopus laevis embryoThis model is described in the article:…

Early cell cycles of Xenopus laevis embryos are characterized by rapid oscillations in the activity of two cyclin-dependent kinases. Cdk1 activity peaks at mitosis, driven by periodic degradation of cyclins A and B. In contrast, Cdk2 activity oscillates twice per cell cycle, despite a constant level of its partner, cyclin E. Cyclin E degrades at a fixed time after fertilization, normally corresponding to the midblastula transition. Based on published data and new experiments, we constructed a mathematical model in which: (1) oscillations in Cdk2 activity depend upon changes in phosphorylation, (2) Cdk2 participates in a negative feedback loop with the inhibitory kinase Wee1; (3) cyclin E is cooperatively removed from the oscillatory system; and (4) removed cyclin E is degraded by a pathway activated by cyclin E/Cdk2 itself. The model's predictions about embryos injected with Xic1, a stoichiometric inhibitor of cyclin E/Cdk2, were experimentally validated. link: http://identifiers.org/pubmed/12914904

Parameters:

Name Description
kon = 0.02 1/s; phi = 0.0390625 1 Reaction: Cdk2_CycE => Cdk2_CycErem, Rate Law: compartment*kon*phi*Cdk2_CycE
kwee = 1.5 1/s Reaction: Cdk2_CycE => PCdk2_CycE; Wee1_a, Rate Law: compartment*kwee*Wee1_a*Cdk2_CycE
kwinact = 1.5 1/s; kwact = 0.75 1/s; Jwact = 0.01; Jwinact = 0.01 1 Reaction: => Wee1_a; Kin_a, Wee1_total, Rate Law: compartment*(kwact*(Wee1_total-Wee1_a)/((Jwact+Wee1_total)-Wee1_a)-kwinact*Kin_a*Wee1_a/(Jwinact+Wee1_a))
kxdeg = 0.01 1/s Reaction: Xic_Cdk2_CycErem => Cdk2_CycErem, Rate Law: compartment*kxdeg*Xic_Cdk2_CycErem
kedeg = 0.017 1/s Reaction: Xic_PCdk2_CycErem => Xicrem; Deg_a, Rate Law: compartment*kedeg*Xic_PCdk2_CycErem*Deg_a
Heav = 0.0 1; kdact = 0.023 1/s Reaction: => Deg_a, Rate Law: compartment*kdact*Heav
koff = 1.0E-4 1/s Reaction: PCdk2_CycErem => PCdk2_CycE, Rate Law: compartment*koff*PCdk2_CycErem
kassoc = 0.1 1/s Reaction: Xic + Cdk2_CycErem => Xic_Cdk2_CycErem, Rate Law: compartment*kassoc*Xic*Cdk2_CycErem
kdissoc = 0.001 1/s Reaction: Xic_Cdk2_CycErem => Xic + Cdk2_CycErem, Rate Law: compartment*kdissoc*Xic_Cdk2_CycErem
k25A = 0.1 1/s Reaction: PCdk2_CycE => Cdk2_CycE, Rate Law: compartment*k25A*PCdk2_CycE
Jiinact = 0.01; kiinact = 0.6 1/s; kiact = 0.15 1/s; Jiact = 0.01 Reaction: Cdk2_CycE => Kin_a + Cdk2_CycE; Cdk2_CycE, Rate Law: compartment*(kiact*(1-Kin_a)/((Jiact+1)-Kin_a)-kiinact*Cdk2_CycE*Kin_a/(Jiinact+Kin_a))

States:

Name Description
PCdk2 CycE [G1/S-specific cyclin-E1; cyclin E1-CDK2 complex; Cyclin-dependent kinase 2]
Xic Cdk2 CycE [G1/S-specific cyclin-E1; protein-containing complex; Cyclin-dependent kinase 2; NP_001108275.1]
PCdk2 CycErem [G1/S-specific cyclin-E1; cyclin E1-CDK2 complex; inactive; Cyclin-dependent kinase 2; phosphorylated]
Cdk2 CycE [G1/S-specific cyclin-E1; cyclin E1-CDK2 complex; Cyclin-dependent kinase 2]
Xicrem [NP_001108275.1; inactive]
Kin a [Kinase]
Xic Cdk2 CycErem [NP_001108275.1; protein-containing complex; inactive; Cyclin-dependent kinase 2; G1/S-specific cyclin-E1]
Xic total [NP_001108275.1]
Xic PCdk2 CycE [G1/S-specific cyclin-E1; protein-containing complex; phosphorylated; Cyclin-dependent kinase 2; NP_001108275.1]
Cyc total [G1/S-specific cyclin-E1]
Deg a [G1/S-specific cyclin-E1; Protein Degradation Process]
Cdk2 CycErem [Cyclin-dependent kinase 2; cyclin E1-CDK2 complex; inactive; G1/S-specific cyclin-E1]
Wee1 a [Wee1-like protein kinase 1-B; urn:miriam:pato:PATO_0002354]
Xic PCdk2 CycErem [Cyclin-dependent kinase 2; protein-containing complex; phosphorylated; G1/S-specific cyclin-E1; inactive; NP_001108275.1]
Xic [NP_001108275.1]

Observables: none

BIOMD0000000297 @ v0.0.1

This a model from the article: Mathematical model of the morphogenesis checkpoint in budding yeast. Ciliberto A, Nov…

The morphogenesis checkpoint in budding yeast delays progression through the cell cycle in response to stimuli that prevent bud formation. Central to the checkpoint mechanism is Swe1 kinase: normally inactive, its activation halts cell cycle progression in G2. We propose a molecular network for Swe1 control, based on published observations of budding yeast and analogous control signals in fission yeast. The proposed Swe1 network is merged with a model of cyclin-dependent kinase regulation, converted into a set of differential equations and studied by numerical simulation. The simulations accurately reproduce the phenotypes of a dozen checkpoint mutants. Among other predictions, the model attributes a new role to Hsl1, a kinase known to play a role in Swe1 degradation: Hsl1 must also be indirectly responsible for potent inhibition of Swe1 activity. The model supports the idea that the morphogenesis checkpoint, like other checkpoints, raises the cell size threshold for progression from one phase of the cell cycle to the next. link: http://identifiers.org/pubmed/14691135

Parameters:

Name Description
kssic = 0.1 Reaction: => Sic, Rate Law: kssic
ksbud = 0.1 Reaction: => BE; Cln, Rate Law: ksbud*Cln
kdcln = 0.1 Reaction: Cln =>, Rate Law: kdcln*Cln
kdclb_tripleprime = 0.1; kdclb_prime = 0.015; kdclb_doubleprime = 1.0 Reaction: Trim => Sic; Cdh1, Cdc20a, Rate Law: Trim*(kdclb_doubleprime*Cdh1+kdclb_tripleprime*Cdc20a+kdclb_prime)
mu = 0.005 Reaction: => mass, Rate Law: mu*mass
jicdc20 = 0.001; kicdc20 = 0.25 Reaction: Cdc20a => Cdc20, Rate Law: Cdc20a*kicdc20/(jicdc20+Cdc20a)
kscdc20_doubleprime = 0.3; kscdc20_prime = 0.005; jscdc20 = 0.3 Reaction: => Cdc20; Clb, Rate Law: kscdc20_prime+kscdc20_doubleprime*Clb^4/(jscdc20^4+Clb^4)
Jawee = 0.05; Vawee = 0.3 Reaction: PSwe1 => Swe1, Rate Law: PSwe1*Vawee/(Jawee+PSwe1)
kdswe_prime = 0.007 Reaction: Swe1 =>, Rate Law: kdswe_prime*Swe1
jacdc20 = 0.001; kacdc20 = 1.0 Reaction: Cdc20 => Cdc20a; IE, Rate Law: kacdc20*Cdc20*IE/(jacdc20+Cdc20)
kdbud = 0.1 Reaction: BE =>, Rate Law: kdbud*BE
kdsic = 0.01; kdsic_prime = 1.0; kdsic_doubleprime = 3.0 Reaction: Trim => Clb; Cln, Rate Law: Trim*(kdsic_prime*Cln+kdsic_doubleprime*Clb+kdsic)
kdswe_doubleprime = 0.05 Reaction: PSwe1M =>, Rate Law: kdswe_doubleprime*PSwe1M
kmih = 0.0 Reaction: PTrim => Trim, Rate Law: PTrim*kmih
Vamih = 1.0; Jamih = 0.1; Mih1 = 0.0 Reaction: => Mih1a; Clb, Rate Law: Vamih*Mih1*Clb/(Jamih+Mih1)
kimcm = 0.15; jimcm = 0.1 Reaction: Mcm =>, Rate Law: Mcm*kimcm/(jimcm+Mcm)
kass = 300.0 Reaction: Sic + Clb => Trim, Rate Law: kass*Sic*Clb
jimih = 0.1; Vimih = 0.3 Reaction: Mih1a =>, Rate Law: Mih1a*Vimih/(jimih+Mih1a)
BUD = 0.0; khsl1 = 1.0 Reaction: Swe1 => Swe1M, Rate Law: khsl1*BUD*Swe1
Kacdh_doubleprime = 10.0; jacdh = 0.01; Cdh1in = 0.0; Kacdh_prime = 1.0 Reaction: => Cdh1; Cdc20a, Rate Law: Cdh1in*(Kacdh_prime+Kacdh_doubleprime*Cdc20a)/(jacdh+Cdh1in)
Viwee = 1.0; Jiwee = 0.05 Reaction: Swe1M => PSwe1M; Clb, Rate Law: Viwee*Swe1M*Clb/(Jiwee+Swe1M)
ksswe = 0.0025 Reaction: => Swe1; SBF, Rate Law: ksswe*SBF
kisbf_doubleprime = 2.0; kisbf_prime = 1.0; jisbf = 0.01 Reaction: SBF => ; Clb, Rate Law: SBF*(kisbf_prime+kisbf_doubleprime*Clb)/(jisbf+SBF)
kssweC = 0.0 Reaction: => Swe1, Rate Law: kssweC
kscln = 0.1 Reaction: => Cln; SBF, Rate Law: kscln*SBF
SBFin = 0.0; kasbf_doubleprime = 0.0; jasbf = 0.01; kasbf_prime = 1.0 Reaction: => SBF; mass, Cln, Rate Law: SBFin*(kasbf_prime*mass+kasbf_doubleprime*Cln)/(jasbf+SBFin)
kswe = 0.0 Reaction: Clb => PClb, Rate Law: kswe*Clb
Jm = 10.0; ksclb = 0.015; eps = 0.5 Reaction: => Clb; mass, Mcm, Rate Law: ksclb*mass*Jm*(eps+Mcm)/(mass+Jm)
kdcdc20 = 0.1 Reaction: Cdc20a =>, Rate Law: kdcdc20*Cdc20a
Mcmin = 0.0; jamcm = 0.1; kamcm = 1.0 Reaction: => Mcm; Clb, Rate Law: Mcmin*Clb*kamcm/(jamcm+Mcmin)
kdiss = 0.1 Reaction: Trim => Sic + Clb, Rate Law: kdiss*Trim
IEin = 0.0; kaie = 0.1; jaie = 0.01 Reaction: => IE; Clb, Rate Law: kaie*IEin*Clb/(jaie+IEin)
khsl1r = 0.01 Reaction: Swe1M => Swe1, Rate Law: khsl1r*Swe1M
jiie = 0.01; kiie = 0.04 Reaction: IE =>, Rate Law: IE*kiie/(jiie+IE)
jicdh = 0.01; kicdh = 35.0; kicdh_prime = 2.0 Reaction: Cdh1 => ; Clb, Cln, Rate Law: Cdh1*(kicdh*Clb+kicdh_prime*Cln)/(jicdh+Cdh1)

States:

Name Description
PSwe1M [Mitosis inhibitor protein kinase SWE1]
Mih1a [M-phase inducer phosphatase]
Trim [Protein SIC1; G2/mitotic-specific cyclin-2; Pre-mRNA-splicing factor ATP-dependent RNA helicase-like protein cdc28]
Cln [G1/S-specific cyclin CLN2; G1/S-specific cyclin CLN1]
Clb [G2/mitotic-specific cyclin-2]
Cdc20a [APC/C activator protein CDC20]
PSwe1 [Mitosis inhibitor protein kinase SWE1]
PTrim [G2/mitotic-specific cyclin-2; Protein SIC1; Pre-mRNA-splicing factor ATP-dependent RNA helicase-like protein cdc28]
BE BE
Mcm [Pheromone receptor transcription factor]
IE Intermediary Enzyme
SBF [G1-specific transcription factors activator MSA1]
Swe1M [Mitosis inhibitor protein kinase SWE1]
mass mass
Swe1 [Mitosis inhibitor protein kinase SWE1]
Cdh1 [APC/C activator protein CDH1]
PClb [G2/mitotic-specific cyclin-2]
Cdc20 [APC/C activator protein CDC20]
Sic [Protein SIC1]

Observables: none

MODEL0913285268 @ v0.0.1

This a model from the article: Mathematical model of the morphogenesis checkpoint in budding yeast. Ciliberto A, Nov…

The morphogenesis checkpoint in budding yeast delays progression through the cell cycle in response to stimuli that prevent bud formation. Central to the checkpoint mechanism is Swe1 kinase: normally inactive, its activation halts cell cycle progression in G2. We propose a molecular network for Swe1 control, based on published observations of budding yeast and analogous control signals in fission yeast. The proposed Swe1 network is merged with a model of cyclin-dependent kinase regulation, converted into a set of differential equations and studied by numerical simulation. The simulations accurately reproduce the phenotypes of a dozen checkpoint mutants. Among other predictions, the model attributes a new role to Hsl1, a kinase known to play a role in Swe1 degradation: Hsl1 must also be indirectly responsible for potent inhibition of Swe1 activity. The model supports the idea that the morphogenesis checkpoint, like other checkpoints, raises the cell size threshold for progression from one phase of the cell cycle to the next. link: http://identifiers.org/pubmed/14691135

Parameters: none

States: none

Observables: none

Its a mathematial model studying steady state and oscialltions in p53-MDM2 network triggered by IR induced DNA Damage.

p53 is activated in response to events compromising the genetic integrity of a cell. Recent data show that p53 activity does not increase steadily with genetic damage but rather fluctuates in an oscillatory fashion. Theoretical studies suggest that oscillations can arise from a combination of positive and negative feedbacks or from a long negative feedback loop alone. Both negative and positive feedbacks are present in the p53/Mdm2 network, but it is not known what roles they play in the oscillatory response to DNA damage. We developed a mathematical model of p53 oscillations based on positive and negative feedbacks in the p53/Mdm2 network. According to the model, the system reacts to DNA damage by moving from a stable steady state into a region of stable limit cycles. Oscillations in the model are born with large amplitude, which guarantees an all-or-none response to damage. As p53 oscillates, damage is repaired and the system moves back to a stable steady state with low p53 activity. The model reproduces experimental data in quantitative detail. We suggest new experiments for dissecting the contributions of negative and positive feedbacks to the generation of oscillations. link: http://identifiers.org/pubmed/15725723

Parameters: none

States: none

Observables: none

BIOMD0000000121 @ v0.0.1

This model is according to the paper *Cellular consequences of HEGR mutations in the long QT syndrome: precursors to sud…

A variety of mutations in HERG, the major subunit of the rapidly activating component of the cardiac delayed rectifier I(Kr), have been found to underlie the congenital Long-QT syndrome, LQT2. LQT2 may give rise to severe arrhythmogenic phenotypes leading to sudden cardiac death.We attempt to elucidate the mechanisms by which heterogeneous LQT2 genotypes can lead to prolongation of the action potential duration (APD) and consequently the QT interval on the ECG.We develop Markovian models of wild-type (WT) and mutant I(Kr) channels and incorporate these models into a comprehensive model of the cardiac ventricular cell.Using this virtual transgenic cell model, we describe the effects of HERG mutations on the cardiac ventricular action potential (AP) and provide insight into the mechanism by which each defect results in a net loss of repolarizing current and prolongation of APD.This study demonstrates which mutations can prolong APD sufficiently to generate early afterdepolarizations (EADs), which may trigger life-threatening arrhythmias. The severity of the phenotype is shown to depend on the specific kinetic changes and how they affect I(Kr) during the time course of the action potential. Clarifying how defects in HERG can lead to impaired cellular electrophysiology can improve our understanding of the link between channel structure and cellular function. link: http://identifiers.org/pubmed/11334834

Parameters:

Name Description
ain = 2.172; bin = 1.077 Reaction: c2 => c1, Rate Law: (ain*c2-bin*c1)*cell
ai = NaN; bi = NaN Reaction: o => i, Rate Law: (o*bi-ai*i)*cell
b = NaN; a = NaN Reaction: c3 => c2, Rate Law: (a*c3-b*c2)*cell
bb = NaN; aa = NaN Reaction: c1 => o, Rate Law: (c1*aa-bb*o)*cell
u = NaN; aa = NaN Reaction: c1 => i, Rate Law: (aa*c1-u*i)*cell
v = -40.0; vk = NaN; Gk = NaN Reaction: ik = Gk*o*(v-vk), Rate Law: missing

States:

Name Description
c2 [IPR003967; voltage-gated potassium channel complex]
c1 [IPR003967; voltage-gated potassium channel complex]
c3 [IPR003967; voltage-gated potassium channel complex]
o [IPR003967; voltage-gated potassium channel complex]
ik cardiac delayed rectifier current
i [IPR003967; voltage-gated potassium channel complex]

Observables: none

BIOMD0000000126 @ v0.0.1

The model is according to the paper *Na+ Channel Mutation That Causes Both Brugada and Long-QT Syndrome Phenotypes: A Si…

Complex physiological interactions determine the functional consequences of gene abnormalities and make mechanistic interpretation of phenotypes extremely difficult. A recent example is a single mutation in the C terminus of the cardiac Na(+) channel, 1795insD. The mutation causes two distinct clinical syndromes, long QT (LQT) and Brugada, leading to life-threatening cardiac arrhythmias. Coexistence of these syndromes is seemingly paradoxical; LQT is associated with enhanced Na(+) channel function, and Brugada with reduced function.Using a computational approach, we demonstrate that the 1795insD mutation exerts variable effects depending on the myocardial substrate. We develop Markov models of the wild-type and 1795insD cardiac Na(+) channels. By incorporating the models into a virtual transgenic cell, we elucidate the mechanism by which 1795insD differentially disrupts cellular electrical behavior in epicardial and midmyocardial cell types. We provide a cellular mechanistic basis for the ECG abnormalities observed in patients carrying the 1795insD gene mutation.We demonstrate that the 1795insD mutation can cause both LQT and Brugada syndromes through interaction with the heterogeneous myocardium in a rate-dependent manner. The results highlight the complexity and multiplicity of genotype-phenotype relationships, and the usefulness of computational approaches in establishing a mechanistic link between genetic defects and functional abnormalities. link: http://identifiers.org/pubmed/11889015

Parameters:

Name Description
a11 = NaN; b11 = NaN Reaction: IC3 => IC2, Rate Law: cell*(IC3*a11-IC2*b11)
a4 = NaN; b4 = NaN Reaction: IF => IM1, Rate Law: cell*(IF*a4-IM1*b4)
b12 = NaN; a12 = NaN Reaction: IC2 => IF, Rate Law: cell*(IC2*a12-IF*b12)
b5 = NaN; a5 = NaN Reaction: IM1 => IM2, Rate Law: cell*(IM1*a5-IM2*b5)
a2 = NaN; b2 = NaN Reaction: IF => O, Rate Law: cell*(IF*b2-a2*O)
a3 = NaN; b3 = NaN Reaction: C2 => IC2, Rate Law: cell*(C2*b3-IC2*a3)
a13 = NaN; b13 = NaN Reaction: O => C1, Rate Law: cell*((-C1)*a13+O*b13)

States:

Name Description
IC3 [IPR001696; voltage-gated sodium channel complex]
IC2 [IPR001696; voltage-gated sodium channel complex]
IM2 [IPR001696; voltage-gated sodium channel complex]
C1 [IPR001696; voltage-gated sodium channel complex]
IM1 [IPR001696; voltage-gated sodium channel complex]
C2 [IPR001696; voltage-gated sodium channel complex]
C3 [IPR001696; voltage-gated sodium channel complex]
IF [IPR001696; voltage-gated sodium channel complex]
O [IPR001696; voltage-gated sodium channel complex]

Observables: none

Claret2009 - Predicting phase III overall survival in colorectal cancerThis model is described in the article: [Model-b…

PURPOSE: We developed a drug-disease simulation model to predict antitumor response and overall survival in phase III studies from longitudinal tumor size data in phase II trials. METHODS: We developed a longitudinal exposure-response tumor-growth inhibition (TGI) model of drug effect (and resistance) using phase II data of capecitabine (n = 34) and historical phase III data of fluorouracil (FU; n = 252) in colorectal cancer (CRC); and we developed a parametric survival model that related change in tumor size and patient characteristics to survival time using historical phase III data (n = 245). The models were validated in simulation of antitumor response and survival in an independent phase III study (n = 1,000 replicates) of capecitabine versus FU in CRC. RESULTS: The TGI model provided a good fit of longitudinal tumor size data. A lognormal distribution best described the survival time, and baseline tumor size and change in tumor size from baseline at week 7 were predictors (P < .00001). Predicted change of tumor size and survival time distributions in the phase III study for both capecitabine and FU were consistent with observed values, for example, 431 days (90% prediction interval, 362 to 514 days) versus 401 days observed for survival in the capecitabine arm. A modest survival improvement of 39 days (90% prediction interval, -21 to 110 days) versus 35 days observed was predicted for capecitabine. CONCLUSION: The modeling framework successfully predicted survival in a phase III trial on the basis of capecitabine phase II data in CRC. It is a useful tool to support end-of-phase II decisions and design of phase III studies. link: http://identifiers.org/pubmed/19636014

Parameters: none

States: none

Observables: none

Clarke2000 - One-hit model of cell death in neuronal degenerationsThis one-hit model fits different neuronal-death assoc…

In genetic disorders associated with premature neuronal death, symptoms may not appear for years or decades. This delay in clinical onset is often assumed to reflect the occurrence of age-dependent cumulative damage. For example, it has been suggested that oxidative stress disrupts metabolism in neurological degenerative disorders by the cumulative damage of essential macromolecules. A prediction of the cumulative damage hypothesis is that the probability of cell death will increase over time. Here we show in contrast that the kinetics of neuronal death in 12 models of photoreceptor degeneration, hippocampal neurons undergoing excitotoxic cell death, a mouse model of cerebellar degeneration and Parkinson's and Huntington's diseases are all exponential and better explained by mathematical models in which the risk of cell death remains constant or decreases exponentially with age. These kinetics argue against the cumulative damage hypothesis; instead, the time of death of any neuron is random. Our findings are most simply accommodated by a 'one-hit' biochemical model in which mutation imposes a mutant steady state on the neuron and a single event randomly initiates cell death. This model appears to be common to many forms of neurodegeneration and has implications for therapeutic strategies. link: http://identifiers.org/pubmed/10910361

Parameters:

Name Description
Rrom = 0.103; ONLrom_0 = 40.947; Murom = 0.0708 Reaction: ONLrom = ONLrom_0*exp((exp((-Rrom)*time)-1)*Murom/Rrom)*100/ONLrom_0, Rate Law: missing
Mupcd = 0.223 Reaction: ONLpcd = (-Mupcd)*ONLpcd, Rate Law: (-Mupcd)*ONLpcd
Munr = 0.278 Reaction: ONLnr = (-Munr)*ONLnr, Rate Law: (-Munr)*ONLnr

States:

Name Description
ONLrom [outer nuclear layer]
ONLpcd [outer nuclear layer]
ONLnr [outer nuclear layer]

Observables: none

BIOMD0000000112 @ v0.0.1

The model reproduces the temporal evolution of four variables depicted in Fig 2a. The solution is generated for median p…

Transforming growth factor-beta (TGFbeta) signalling is an important regulator of cellular growth and differentiation. The principal intracellular mediators of TGFbeta signalling are the Smad proteins, which upon TGFbeta stimulation accumulate in the nucleus and regulate the transcription of target genes. To investigate the mechanisms of Smad nuclear accumulation, we developed a simple mathematical model of canonical Smad signalling. The model was built using both published data and our experimentally determined cellular Smad concentrations (isoforms 2, 3 and 4). We found in mink lung epithelial cells that Smad2 (8.5-12 x 10(4) molecules cell(-1)) was present in similar amounts to Smad4 (9.3-12 x 10(4) molecules cell(-1)), whereas both were in excess of Smad3 (1.1-2.0 x 10(4) molecules cell(-1)). Variation of the model parameters and statistical analysis showed that Smad nuclear accumulation is most sensitive to parameters affecting the rates of R-Smad phosphorylation and dephosphorylation and Smad complex formation/ dissociation in the nucleus. Deleting Smad4 from the model revealed that rate-limiting phospho-R-Smad dephosphorylation could be an important mechanism for Smad nuclear accumulation. Furthermore, we observed that binding factors constitutively localised to the nucleus do not efficiently mediate Smad nuclear accumulation, if dephosphorylation is rapid. We therefore conclude that an imbalance in the rates of R-Smad phosphorylation and dephosphorylation is likely an important mechanism of Smad nuclear accumulation during TGFbeta signalling. link: http://identifiers.org/pubmed/17186703

Parameters:

Name Description
k6d=0.0492 min_inv; k6a=1.44E-4 per item per min Reaction: R_smad_P_smad4_nuc => smad4_nuc + R_smad_P_nuc, Rate Law: k6d*R_smad_P_smad4_nuc-k6a*smad4_nuc*R_smad_P_nuc
k3=16.6 min_inv Reaction: R_smad_P_smad4_cyt => R_smad_P_smad4_nuc, Rate Law: k3*R_smad_P_smad4_cyt
k5nc=5.63 min_inv; k5cn=0.563 min_inv Reaction: R_smad_nuc => R_smad_cyt, Rate Law: k5nc*R_smad_nuc-k5cn*R_smad_cyt
k2d=0.0399 min_inv; k2a=6.5E-5 per item per min Reaction: R_smad_P_cyt + smad4_cyt => R_smad_P_smad4_cyt, Rate Law: k2a*R_smad_P_cyt*smad4_cyt-k2d*R_smad_P_smad4_cyt
k4cn=0.00497 min_inv; k4nc=0.783 min_inv Reaction: smad4_nuc => smad4_cyt, Rate Law: k4nc*smad4_nuc-k4cn*smad4_cyt
K7=8950.0 item; Vmax7=17100.0 items per min Reaction: R_smad_P_nuc => R_smad_nuc + Pi, Rate Law: Vmax7*R_smad_P_nuc/(K7+R_smad_P_nuc)
KCAT=3.51 min_inv; K1=289000.0 item Reaction: R_smad_cyt => R_smad_P_cyt; receptor, Rate Law: KCAT*receptor*R_smad_cyt/(K1+R_smad_cyt)

States:

Name Description
receptor [TGF-beta receptor type-1; TGF-beta receptor type-2]
smad4 cyt [Mothers against decapentaplegic homolog 4]
R smad P cyt [Mothers against decapentaplegic homolog 2]
R smad P nuc [Mothers against decapentaplegic homolog 2]
R smad P smad4 nuc [Mothers against decapentaplegic homolog 2; Mothers against decapentaplegic homolog 4]
R smad cyt [Mothers against decapentaplegic homolog 2]
R smad nuc [Mothers against decapentaplegic homolog 2]
smad4 nuc [Mothers against decapentaplegic homolog 4]
Pi [phosphate(3-); Orthophosphate]
R smad P smad4 cyt [Mothers against decapentaplegic homolog 4; Mothers against decapentaplegic homolog 2]

Observables: none

BIOMD0000000554 @ v0.0.1

Cloutier2009 - Brain Energy Metabolism This model was taken from the  [CellMLrepository](http://www.cellml.org/models)…

An integrative, systems approach to the modelling of brain energy metabolism is presented. Mechanisms such as glutamate cycling between neurons and astrocytes and glycogen storage in astrocytes have been implemented. A unique feature of the model is its calibration using in vivo data of brain glucose and lactate from freely moving rats under various stimuli. The model has been used to perform simulated perturbation experiments that show that glycogen breakdown in astrocytes is significantly activated during sensory (tail pinch) stimulation. This mechanism provides an additional input of energy substrate during high consumption phases. By way of validation, data from the perfusion of 50 microM propranolol in the rat brain was compared with the model outputs. Propranolol affects the glucose dynamics during stimulation, and this was accurately reproduced in the model by a reduction in the glycogen breakdown in astrocytes. The model's predictive capacity was verified by using data from a sensory stimulation (restraint) that was not used for model calibration. Finally, a sensitivity analysis was conducted on the model parameters, this showed that the control of energy metabolism and transport processes are critical in the metabolic behaviour of cerebral tissue. link: http://identifiers.org/pubmed/19396534

Parameters:

Name Description
Vn_ldh = -0.001026864256; Vn_mito = 0.0129174754920542; Vn_pk = 0.0120203036981555 Reaction: PYRn = Vn_pk-(Vn_ldh+Vn_mito), Rate Law: Vn_pk-(Vn_ldh+Vn_mito)
Vn_pgi = 0.00600284722882977; Vn_hk = 0.00600093047858717 Reaction: G6Pn = Vn_hk-Vn_pgi, Rate Law: Vn_hk-Vn_pgi
Vn_pgk = 0.012002606302138; Vn_pfk = 0.00599809710207478 Reaction: GAPn = 2*Vn_pfk-Vn_pgk, Rate Law: 2*Vn_pfk-Vn_pgk
Vg_hk = 0.00455613617326311; Vg_glyp = 3.51571428571429E-5; Vg_pgi = 0.00451935700191414; Vg_glys = 9.08171994158688E-5 Reaction: G6Pg = (Vg_hk+Vg_glyp)-(Vg_pgi+Vg_glys), Rate Law: (Vg_hk+Vg_glyp)-(Vg_pgi+Vg_glys)
Vg_pfk = 0.00450657384340637; Vg_pgk = 0.0090457605321121 Reaction: GAPg = 2*Vg_pfk-Vg_pgk, Rate Law: 2*Vg_pfk-Vg_pgk
Vg_glyp = 3.51571428571429E-5; Vg_glys = 9.08171994158688E-5 Reaction: GLYg = Vg_glys-Vg_glyp, Rate Law: Vg_glys-Vg_glyp
Vg_pgk = 0.0090457605321121; Vg_pk = 0.00906366080685179 Reaction: PEPg = Vg_pgk-Vg_pk, Rate Law: Vg_pgk-Vg_pk
Vn_mito = 0.0129174754920542; NAero = 3.0; Vcn_O2 = 0.0390504186958046 Reaction: O2n = Vcn_O2-NAero*Vn_mito, Rate Law: Vcn_O2-NAero*Vn_mito
Vn_ldh = -0.001026864256; Vne_LAC = -0.00101735054205471 Reaction: LACn = Vn_ldh-Vne_LAC, Rate Law: Vn_ldh-Vne_LAC
NAero = 3.0; Vcg_O2 = 0.0180867710645166; Vg_mito = 0.0060112916441682 Reaction: O2g = Vcg_O2-NAero*Vg_mito, Rate Law: Vcg_O2-NAero*Vg_mito
ATPtot = 2.379 Reaction: AMPg = ATPtot-(ATPg+ADPg), Rate Law: missing
Vn_ck = 2.93701651940294E-5 Reaction: PCrn = -Vn_ck, Rate Law: -Vn_ck
Rcg = 0.022; Vc_GLC = 0.69774545454546; Vce_GLC = 0.0154673938740423; Vcg_GLC = 0.00297412147754264; Rce = 0.0275 Reaction: GLCc = Vc_GLC-(Vce_GLC*1/Rce+Vcg_GLC*1/Rcg), Rate Law: Vc_GLC-(Vce_GLC*1/Rce+Vcg_GLC*1/Rcg)
Reg = 0.8; Veg_GLU = 0.0; Ren = 0.444444444444444; Vn_stim_GLU = 0.0 Reaction: GLUe = Vn_stim_GLU*1/Ren-Veg_GLU*1/Reg, Rate Law: Vn_stim_GLU*1/Ren-Veg_GLU*1/Reg
Vn_pgi = 0.00600284722882977; Vn_pfk = 0.00599809710207478 Reaction: F6Pn = Vn_pgi-Vn_pfk, Rate Law: Vn_pgi-Vn_pfk
Vgc_LAC = 1.46095762940601E-5; Rcg = 0.022; Vec_LAC = 0.0014407850610198; Vc_LAC = -0.0528; Rce = 0.0275 Reaction: LACc = Vc_LAC+Vec_LAC*1/Rce+Vgc_LAC*1/Rcg, Rate Law: Vc_LAC+Vec_LAC*1/Rce+Vgc_LAC*1/Rcg
Vn_pgk = 0.012002606302138; Vn_mito = 0.0129174754920542; Vn_pk = 0.0120203036981555; Vn_ATPase = 0.0488683691708698; Vn_pump = 0.158300842198194; Vn_ck = 2.93701651940294E-5; dAMP_dATPn = -0.101010798503538; nOP = 15.0; Vn_hk = 0.00600093047858717; Vn_pfk = 0.00599809710207478 Reaction: ATPn = ((Vn_pgk+Vn_pk+nOP*Vn_mito+Vn_ck)-(Vn_hk+Vn_pfk+Vn_ATPase+Vn_pump))*(1-dAMP_dATPn)^(-1), Rate Law: ((Vn_pgk+Vn_pk+nOP*Vn_mito+Vn_ck)-(Vn_hk+Vn_pfk+Vn_ATPase+Vn_pump))*(1-dAMP_dATPn)^(-1)
Vg_pgi = 0.00451935700191414; Vg_pfk = 0.00450657384340637 Reaction: F6Pg = Vg_pgi-Vg_pfk, Rate Law: Vg_pgi-Vg_pfk
Vn_pgk = 0.012002606302138; Vn_pk = 0.0120203036981555 Reaction: PEPn = Vn_pgk-Vn_pk, Rate Law: Vn_pgk-Vn_pk
Vn_ldh = -0.001026864256; Vn_pgk = 0.012002606302138; Vn_mito = 0.0129174754920542 Reaction: NADHn = Vn_pgk-(Vn_ldh+Vn_mito), Rate Law: Vn_pgk-(Vn_ldh+Vn_mito)
Vg_ldh = 0.003039015294; Vg_pgk = 0.0090457605321121; Vg_mito = 0.0060112916441682 Reaction: NADHg = Vg_pgk-(Vg_ldh+Vg_mito), Rate Law: Vg_pgk-(Vg_ldh+Vg_mito)
qak = 0.92; ATPtot = 2.379 Reaction: ADPg = ATPg/2*((-qak)+(qak^2+4*qak*(ATPtot/ATPg-1))^(1/2)), Rate Law: missing
Vc_O2 = 4.01410909090909; Rcg = 0.022; Vcn_O2 = 0.0390504186958046; Rcn = 0.01222; Vcg_O2 = 0.0180867710645166 Reaction: O2c = Vc_O2-(Vcn_O2*1/Rcn+Vcg_O2*1/Rcg), Rate Law: Vc_O2-(Vcn_O2*1/Rcn+Vcg_O2*1/Rcg)
Vg_hk = 0.00455613617326311; Vg_ck = 8.98869880248884E-5; Vg_pgk = 0.0090457605321121; Vg_pfk = 0.00450657384340637; Vg_pump = 0.0634531133946177; Vg_pk = 0.00906366080685179; Vg_gs = 0.0; nOP = 15.0; dAMP_dATPg = -0.115857415908852; Vg_ATPase = 0.035641088799643; Vg_mito = 0.0060112916441682 Reaction: ATPg = ((Vg_pgk+Vg_pk+nOP*Vg_mito+Vg_ck)-(Vg_hk+Vg_pfk+Vg_ATPase+Vg_pump+Vg_gs))*(1-dAMP_dATPg)^(-1), Rate Law: ((Vg_pgk+Vg_pk+nOP*Vg_mito+Vg_ck)-(Vg_hk+Vg_pfk+Vg_ATPase+Vg_pump+Vg_gs))*(1-dAMP_dATPg)^(-1)
Vn_leak_Na = 0.474905958264092; Vn_pump = 0.158300842198194; Vn_stim = 0.0 Reaction: NAn = (Vn_leak_Na+Vn_stim)-3*Vn_pump, Rate Law: (Vn_leak_Na+Vn_stim)-3*Vn_pump
Rng = 1.8; Vg_gs = 0.0; Vn_stim_GLU = 0.0 Reaction: GLUn = Vg_gs*1/Rng-Vn_stim_GLU, Rate Law: Vg_gs*1/Rng-Vn_stim_GLU
Reg = 0.8; Ren = 0.444444444444444; Vec_LAC = 0.0014407850610198; Vge_LAC = 0.00298013264659761; Vne_LAC = -0.00101735054205471 Reaction: LACe = (Vne_LAC*1/Ren+Vge_LAC*1/Reg)-Vec_LAC, Rate Law: (Vne_LAC*1/Ren+Vge_LAC*1/Reg)-Vec_LAC
Rcg = 0.022; Vgc_CO2 = 0.0180338749325046; Vc_CO2 = 4.01454545454546; Rcn = 0.01222; Vnc_CO2 = 0.0387524264761627 Reaction: CO2c = (Vnc_CO2*1/Rcn+Vgc_CO2*1/Rcg)-Vc_CO2, Rate Law: (Vnc_CO2*1/Rcn+Vgc_CO2*1/Rcg)-Vc_CO2
Vg_leak_Na = 0.190378997692294; Veg_GLU = 0.0; Vg_pump = 0.0634531133946177 Reaction: NAg = (Vg_leak_Na+3*Veg_GLU)-3*Vg_pump, Rate Law: (Vg_leak_Na+3*Veg_GLU)-3*Vg_pump
Vg_ldh = 0.003039015294; Vg_pk = 0.00906366080685179; Vg_mito = 0.0060112916441682 Reaction: PYRg = Vg_pk-(Vg_ldh+Vg_mito), Rate Law: Vg_pk-(Vg_ldh+Vg_mito)
Veg_GLC = 0.00158470181577655; Vg_hk = 0.00455613617326311; Vcg_GLC = 0.00297412147754264 Reaction: GLCg = (Vcg_GLC+Veg_GLC)-Vg_hk, Rate Law: (Vcg_GLC+Veg_GLC)-Vg_hk
Vg_ldh = 0.003039015294; Vgc_LAC = 1.46095762940601E-5; Vge_LAC = 0.00298013264659761 Reaction: LACg = Vg_ldh-(Vge_LAC+Vgc_LAC), Rate Law: Vg_ldh-(Vge_LAC+Vgc_LAC)
Veg_GLC = 0.00158470181577655; Reg = 0.8; Ren = 0.444444444444444; Vce_GLC = 0.0154673938740423; V_en_GLC = 0.00599865999248041 Reaction: GLCe = Vce_GLC-(Veg_GLC*1/Reg+V_en_GLC*1/Ren), Rate Law: Vce_GLC-(Veg_GLC*1/Reg+V_en_GLC*1/Ren)
V_en_GLC = 0.00599865999248041; Vn_hk = 0.00600093047858717 Reaction: GLCn = V_en_GLC-Vn_hk, Rate Law: V_en_GLC-Vn_hk
Veg_GLU = 0.0; Vg_gs = 0.0 Reaction: GLUg = Veg_GLU-Vg_gs, Rate Law: Veg_GLU-Vg_gs
Vg_ck = 8.98869880248884E-5 Reaction: PCrg = -Vg_ck, Rate Law: -Vg_ck

States:

Name Description
G6Pg [D-glucopyranose 6-phosphate]
PYRn [pyruvate]
GLCe [glucose]
GLYg [glycogen]
AMPn [AMP]
NADg [NAD(+)]
PCrg [N-phosphocreatine]
NADHn [NADH]
PEPg [phosphoenolpyruvic acid]
PCrn [N-phosphocreatine]
F6Pn [D-fructose 6-phosphate(2-)]
GAPn [glyceraldehyde 3-phosphate]
PEPn [phosphoenolpyruvate]
NAn [sodium(1+)]
LACn [(S)-lactic acid]
O2n [singlet dioxygen]
GLUn [glutamic acid]
AMPg [AMP]
PYRg [pyruvate]
GLUe [glutamic acid]
ADPn [ADP]
F6Pg [D-fructose 6-phosphate(2-)]
O2g [singlet dioxygen]
G6Pn [D-glucopyranose 6-phosphate]
NAg [sodium(1+)]
GLCc [glucose]
CO2c [carbon dioxide]
LACc [(S)-lactic acid]
GLUg [glutamic acid]
GLCn [glucose]
ADPg [ADP]
ATPn [ATP]
GLCg [glucose]
CRn [creatine]
ATPg [ATP]
NADn [NAD(+)]
NADHg [NADH]
O2c [singlet dioxygen]
LACg [(S)-lactic acid]
CRg [creatine]
LACe [(S)-lactic acid]
GAPg [glyceraldehyde 3-phosphate]

Observables: none

MODEL1006230010 @ v0.0.1

This a model from the article: The control systems structures of energy metabolism. Cloutier M, Wellstead P. J R Soc…

The biochemical regulation of energy metabolism (EM) allows cells to modulate their energetic output depending on available substrates and requirements. To this end, numerous biomolecular mechanisms exist that allow the sensing of the energetic state and corresponding adjustment of enzymatic reaction rates. This regulation is known to induce dynamic systems properties such as oscillations or perfect adaptation. Although the various mechanisms of energy regulation have been studied in detail from many angles at the experimental and theoretical levels, no framework is available for the systematic analysis of EM from a control systems perspective. In this study, we have used principles well known in control to clarify the basic system features that govern EM. The major result is a subdivision of the biomolecular mechanisms of energy regulation in terms of widely used engineering control mechanisms: proportional, integral, derivative control, and structures: feedback, cascade and feed-forward control. Evidence for each mechanism and structure is demonstrated and the implications for systems properties are shown through simulations. As the equivalence between biological systems and control components presented here is generic, it is also hypothesized that our work could eventually have an applicability that is much wider than the focus of the current study. link: http://identifiers.org/pubmed/19828503

Parameters: none

States: none

Observables: none

MODEL1006230016 @ v0.0.1

This a model from the article: The control systems structures of energy metabolism. Cloutier M, Wellstead P. J R Soc…

The biochemical regulation of energy metabolism (EM) allows cells to modulate their energetic output depending on available substrates and requirements. To this end, numerous biomolecular mechanisms exist that allow the sensing of the energetic state and corresponding adjustment of enzymatic reaction rates. This regulation is known to induce dynamic systems properties such as oscillations or perfect adaptation. Although the various mechanisms of energy regulation have been studied in detail from many angles at the experimental and theoretical levels, no framework is available for the systematic analysis of EM from a control systems perspective. In this study, we have used principles well known in control to clarify the basic system features that govern EM. The major result is a subdivision of the biomolecular mechanisms of energy regulation in terms of widely used engineering control mechanisms: proportional, integral, derivative control, and structures: feedback, cascade and feed-forward control. Evidence for each mechanism and structure is demonstrated and the implications for systems properties are shown through simulations. As the equivalence between biological systems and control components presented here is generic, it is also hypothesized that our work could eventually have an applicability that is much wider than the focus of the current study. link: http://identifiers.org/pubmed/19828503

Parameters: none

States: none

Observables: none

MODEL1006230068 @ v0.0.1

This a model from the article: The control systems structures of energy metabolism. Cloutier M, Wellstead P. J R Soc…

The biochemical regulation of energy metabolism (EM) allows cells to modulate their energetic output depending on available substrates and requirements. To this end, numerous biomolecular mechanisms exist that allow the sensing of the energetic state and corresponding adjustment of enzymatic reaction rates. This regulation is known to induce dynamic systems properties such as oscillations or perfect adaptation. Although the various mechanisms of energy regulation have been studied in detail from many angles at the experimental and theoretical levels, no framework is available for the systematic analysis of EM from a control systems perspective. In this study, we have used principles well known in control to clarify the basic system features that govern EM. The major result is a subdivision of the biomolecular mechanisms of energy regulation in terms of widely used engineering control mechanisms: proportional, integral, derivative control, and structures: feedback, cascade and feed-forward control. Evidence for each mechanism and structure is demonstrated and the implications for systems properties are shown through simulations. As the equivalence between biological systems and control components presented here is generic, it is also hypothesized that our work could eventually have an applicability that is much wider than the focus of the current study. link: http://identifiers.org/pubmed/19828503

Parameters: none

States: none

Observables: none

MODEL1006230095 @ v0.0.1

This a model from the article: The control systems structures of energy metabolism. Cloutier M, Wellstead P. J R Soc…

The biochemical regulation of energy metabolism (EM) allows cells to modulate their energetic output depending on available substrates and requirements. To this end, numerous biomolecular mechanisms exist that allow the sensing of the energetic state and corresponding adjustment of enzymatic reaction rates. This regulation is known to induce dynamic systems properties such as oscillations or perfect adaptation. Although the various mechanisms of energy regulation have been studied in detail from many angles at the experimental and theoretical levels, no framework is available for the systematic analysis of EM from a control systems perspective. In this study, we have used principles well known in control to clarify the basic system features that govern EM. The major result is a subdivision of the biomolecular mechanisms of energy regulation in terms of widely used engineering control mechanisms: proportional, integral, derivative control, and structures: feedback, cascade and feed-forward control. Evidence for each mechanism and structure is demonstrated and the implications for systems properties are shown through simulations. As the equivalence between biological systems and control components presented here is generic, it is also hypothesized that our work could eventually have an applicability that is much wider than the focus of the current study. link: http://identifiers.org/pubmed/19828503

Parameters: none

States: none

Observables: none

MODEL1006230059 @ v0.0.1

This a model from the article: The control systems structures of energy metabolism. Cloutier M, Wellstead P. J R Soc…

The biochemical regulation of energy metabolism (EM) allows cells to modulate their energetic output depending on available substrates and requirements. To this end, numerous biomolecular mechanisms exist that allow the sensing of the energetic state and corresponding adjustment of enzymatic reaction rates. This regulation is known to induce dynamic systems properties such as oscillations or perfect adaptation. Although the various mechanisms of energy regulation have been studied in detail from many angles at the experimental and theoretical levels, no framework is available for the systematic analysis of EM from a control systems perspective. In this study, we have used principles well known in control to clarify the basic system features that govern EM. The major result is a subdivision of the biomolecular mechanisms of energy regulation in terms of widely used engineering control mechanisms: proportional, integral, derivative control, and structures: feedback, cascade and feed-forward control. Evidence for each mechanism and structure is demonstrated and the implications for systems properties are shown through simulations. As the equivalence between biological systems and control components presented here is generic, it is also hypothesized that our work could eventually have an applicability that is much wider than the focus of the current study. link: http://identifiers.org/pubmed/19828503

Parameters: none

States: none

Observables: none

MODEL1006230096 @ v0.0.1

This a model from the article: The control systems structures of energy metabolism. Cloutier M, Wellstead P. J R Soc…

The biochemical regulation of energy metabolism (EM) allows cells to modulate their energetic output depending on available substrates and requirements. To this end, numerous biomolecular mechanisms exist that allow the sensing of the energetic state and corresponding adjustment of enzymatic reaction rates. This regulation is known to induce dynamic systems properties such as oscillations or perfect adaptation. Although the various mechanisms of energy regulation have been studied in detail from many angles at the experimental and theoretical levels, no framework is available for the systematic analysis of EM from a control systems perspective. In this study, we have used principles well known in control to clarify the basic system features that govern EM. The major result is a subdivision of the biomolecular mechanisms of energy regulation in terms of widely used engineering control mechanisms: proportional, integral, derivative control, and structures: feedback, cascade and feed-forward control. Evidence for each mechanism and structure is demonstrated and the implications for systems properties are shown through simulations. As the equivalence between biological systems and control components presented here is generic, it is also hypothesized that our work could eventually have an applicability that is much wider than the focus of the current study. link: http://identifiers.org/pubmed/19828503

Parameters: none

States: none

Observables: none

Cloutier2012 - Feedback motif for Parkinson's diseaseThis model is described in the article: [Feedback motif for the pa…

Previous article on the integrative modelling of Parkinson's disease (PD) described a mathematical model with properties suggesting that PD pathogenesis is associated with a feedback-induced biochemical bistability. In this article, the authors show that the dynamics of the mathematical model can be extracted and distilled into an equivalent two-state feedback motif whose stability properties are controlled by multi-factorial combinations of risk factors and genetic mutations associated with PD. Based on this finding, the authors propose a principle for PD pathogenesis in the form of the switch-like transition of a bistable feedback process from 'healthy' homeostatic levels of reactive oxygen species and the protein α-synuclein, to an alternative 'disease' state in which concentrations of both molecules are stable at the damagingly high-levels associated with PD. The bistability is analysed using the rate curves and steady-state response characteristics of the feedback motif. In particular, the authors show how a bifurcation in the feedback motif marks the pathogenic moment at which the 'healthy' state is lost and the 'disease' state is initiated. Further analysis shows how known risks (such as: age, toxins and genetic predisposition) modify the stability characteristics of the feedback motif in a way that is compatible with known features of PD, and which explain properties such as: multi-factorial causality, variability in susceptibility and severity, multi-timescale progression and the special cases of familial Parkinson's and Parkinsonian symptoms induced purely by toxic stress. link: http://identifiers.org/pubmed/22757587

Parameters:

Name Description
S1 = 0.0; k1 = 17280.0; kalphasyn = 8.5; dalphasyn = 15.0 Reaction: => ROS; alpha_syn, alpha_syn, Rate Law: Neuron*k1*(1+S1+dalphasyn*(alpha_syn/kalphasyn)^4/(1+(alpha_syn/kalphasyn)^4))
k3 = 0.168; S2_4 = 1.0 Reaction: => alpha_syn; ROS, ROS, Rate Law: Neuron*k3*ROS*S2_4
S2_4 = 1.0; k4 = 0.168 Reaction: alpha_syn => ; alpha_syn, Rate Law: Neuron*k4*alpha_syn*S2_4
S2_4 = 1.0; k2 = 17280.0 Reaction: ROS => ; ROS, Rate Law: Neuron*k2*ROS*S2_4

States:

Name Description
ROS [reactive oxygen species]
alpha syn [Alpha-synuclein]

Observables: none

Collombet2016 - Lymphoid and myeloid cell specification and transdifferentiationThis model is described in the article:…

Blood cells are derived from a common set of hematopoietic stem cells, which differentiate into more specific progenitors of the myeloid and lymphoid lineages, ultimately leading to differentiated cells. This developmental process is controlled by a complex regulatory network involving cytokines and their receptors, transcription factors, and chromatin remodelers. Using public data and data from our own molecular genetic experiments (quantitative PCR, Western blot, EMSA) or genome-wide assays (RNA-sequencing, ChIP-sequencing), we have assembled a comprehensive regulatory network encompassing the main transcription factors and signaling components involved in myeloid and lymphoid development. Focusing on B-cell and macrophage development, we defined a qualitative dynamical model recapitulating cytokine-induced differentiation of common progenitors, the effect of various reported gene knockdowns, and the reprogramming of pre-B cells into macrophages induced by the ectopic expression of specific transcription factors. The resulting network model can be used as a template for the integration of new hematopoietic differentiation and transdifferentiation data to foster our understanding of lymphoid/myeloid cell-fate decisions. link: http://identifiers.org/doi/10.1073/pnas.1610622114

Parameters: none

States: none

Observables: none

BIOMD0000000177 @ v0.0.1

This a model from the article: Increased glycolytic flux as an outcome of whole-genome duplication in yeast. Conant…

After whole-genome duplication (WGD), deletions return most loci to single copy. However, duplicate loci may survive through selection for increased dosage. Here, we show how the WGD increased copy number of some glycolytic genes could have conferred an almost immediate selective advantage to an ancestor of Saccharomyces cerevisiae, providing a rationale for the success of the WGD. We propose that the loss of other redundant genes throughout the genome resulted in incremental dosage increases for the surviving duplicated glycolytic genes. This increase gave post-WGD yeasts a growth advantage through rapid glucose fermentation; one of this lineage's many adaptations to glucose-rich environments. Our hypothesis is supported by data from enzyme kinetics and comparative genomics. Because changes in gene dosage follow directly from post-WGD deletions, dosage selection can confer an almost instantaneous benefit after WGD, unlike neofunctionalization or subfunctionalization, which require specific mutations. We also show theoretically that increased fermentative capacity is of greatest advantage when glucose resources are both large and dense, an observation potentially related to the appearance of angiosperms around the time of WGD. link: http://identifiers.org/pubmed/17667951

Parameters:

Name Description
Katp_11=1.5 mM; Vmax_11=1000.0 mMpermin; WGD_E = 0.65 dimensionless; Kadp_11=0.53 mM; Kpyr_11=21.0 mM; Kpep_11=0.14 mM; Keq_11=6500.0 dimensionless Reaction: ADP + PEP => ATP + PYR, Rate Law: cyto*Vmax_11*WGD_E*(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))
Kf26_4=6.82E-4 mM; Ciatp_4=100.0 dimensionless; Kiatp_4=0.65 mM; Vmax_4=110.0 mMpermin; Cf26_4=0.0174 dimensionless; Camp_4=0.0845 dimensionless; Kf6p_4=0.1 mM; Katp_4=0.71 mM; WGD_E = 0.65 dimensionless; Kamp_4=0.0995 mM; gR_4=5.12 dimensionless; Cf16_4=0.397 dimensionless; Kf16_4=0.111 mM; L0_4=0.66 dimensionless; Catp_4=3.0 dimensionless Reaction: ATP + F6P => ADP + F16bP; AMP, F26bP, Rate Law: cyto*Vmax_4*WGD_E*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)
Ktrehalose_18=2.4 mMpermin Reaction: ATP + G6P => ADP + Trehalose, Rate Law: cyto*Ktrehalose_18
k_19=21.4 permin Reaction: NAD + AcAld => NADH + Succinate, Rate Law: cyto*k_19*AcAld
WGD_E = 0.65 dimensionless; nH_12=1.9 dimensionless; Vmax_12=857.8 mMpermin; Kpyr_12=4.33 mM Reaction: PYR => AcAld + CO2, Rate Law: cyto*Vmax_12*WGD_E*(PYR/Kpyr_12)^nH_12/(1+(PYR/Kpyr_12)^nH_12)
Kigap_5=10.0 mM; Keq_5=0.069 mM; Kgap_5=2.4 mM; WGD_E = 0.65 dimensionless; Kdhap_5=2.0 mM; Vmax_5=94.69 mMpermin; Kf16bp_5=0.3 mM Reaction: F16bP => DHAP + GAP, Rate Law: cyto*Vmax_5*WGD_E*(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))
Ki_NADH=50.0 mM; WGD_E = 0.65 dimensionless; Vmax_PDH=379.2 mMpermin; NADX_tot=8.01 mM; K_PYR=70.0 mM; Ki_PYR=20.0 mM; K_NAD=160.0 mM Reaction: PYRmito => AcCoA + CO2mito; NAD, NADH, Rate Law: mito*WGD_E*Vmax_PDH*PYRmito*(NADX_tot-NADX_tot/(1+NAD/NADH))/(NADX_tot*Ki_PYR*K_NAD/Ki_NADH/(1+NAD/NADH)+K_PYR*(NADX_tot-NADX_tot/(1+NAD/NADH))+K_NAD*PYRmito+NADX_tot*K_NAD/Ki_NADH*PYRmito/(1+NAD/NADH)+(NADX_tot-NADX_tot/(1+NAD/NADH))*PYRmito)
k2_6=1.0E7 permin; k1_6=450000.0 permin Reaction: DHAP => GAP, Rate Law: cyto*(k1_6*DHAP-k2_6*GAP)
Keq_3=0.29 dimensionless; Kf6p_3=0.3 mM; WGD_E = 0.65 dimensionless; Kg6p_3=1.4 mM; Vmax_3=1056.0 mMpermin Reaction: G6P => F6P, Rate Law: cyto*Vmax_3*WGD_E*(G6P/Kg6p_3-F6P/(Kg6p_3*Keq_3))/(1+G6P/Kg6p_3+F6P/Kf6p_3)
Keq_8=3200.0 dimensionless; Vmax_8=1288.0 mMpermin; Kbpg_8=0.003 mM; WGD_E = 0.65 dimensionless; Kp3g_8=0.53 mM; Kadp_8=0.2 mM; Katp_8=0.3 mM Reaction: ADP + BPG => ATP + P3G, Rate Law: cyto*Vmax_8*WGD_E*(Keq_8*BPG*ADP-P3G*ATP)/(Kp3g_8*Katp_8)/((1+BPG/Kbpg_8+P3G/Kp3g_8)*(1+ADP/Kadp_8+ATP/Katp_8))
k2_15=100.0 permMpermin; k1_15=45.0 permMpermin Reaction: ADP => ATP + AMP, Rate Law: cyto*(k1_15*ADP*ADP-k2_15*ATP*AMP)
Vmax_1=97.24 mmolepermin; WGD_E = 0.65 dimensionless; Kglc_1=1.1918 mM; Ki_1=0.91 dimensionless Reaction: GLCo => GLCi, Rate Law: Vmax_1*WGD_E*(GLCo-GLCi)/Kglc_1/(1+(GLCo+GLCi)/Kglc_1+Ki_1*GLCo*GLCi/Kglc_1^2)
Knadh_7=0.06 mM; Kgap_7=0.21 mM; Vmaxr_7=6719.0 mMpermin; WGD_E = 0.65 dimensionless; C_7=1.0 dimensionless; Kbpg_7=0.0098 mM; Knad_7=0.09 mM; Vmaxf_7=1152.0 mMpermin Reaction: GAP + NAD => BPG + NADH, Rate Law: cyto*C_7*(Vmaxf_7*WGD_E*GAP*NAD/(Kgap_7*Knad_7)-Vmaxr_7*WGD_E*BPG*NADH/(Kbpg_7*Knadh_7))/((1+GAP/Kgap_7+BPG/Kbpg_7)*(1+NAD/Knad_7+NADH/Knadh_7))
Kp2g_9=0.08 mM; WGD_E = 0.65 dimensionless; Vmax_9=2585.0 mMpermin; Kp3g_9=1.2 mM; Keq_9=0.19 dimensionless Reaction: P3G => P2G, Rate Law: cyto*Vmax_9*WGD_E*(P3G/Kp3g_9-P2G/(Kp3g_9*Keq_9))/(1+P3G/Kp3g_9+P2G/Kp2g_9)
Kglc_2=0.08 mM; Kadp_2=0.23 mM; Vmax_2=236.7 mMpermin; WGD_E = 0.65 dimensionless; Keq_2=2000.0 dimensionless; Kg6p_2=30.0 mM; Katp_2=0.15 mM Reaction: GLCi + ATP => G6P + ADP, Rate Law: cyto*WGD_E*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))
Ketoh_13=17.0 mM; Knad_13=0.17 mM; Kietoh_13=90.0 mM; Kacald_13=1.11 mM; Vmax_13=209.5 mMpermin; Knadh_13=0.11 mM; WGD_E = 0.65 dimensionless; Kinad_13=0.92 mM; Keq_13=6.9E-5 dimensionless; Kiacald_13=1.1 mM; Kinadh_13=0.031 mM Reaction: NAD + EtOH => NADH + AcAld, Rate Law: cyto*Vmax_13*WGD_E*(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))
Kpep_10=0.5 mM; Kp2g_10=0.04 mM; WGD_E = 0.65 dimensionless; Vmax_10=201.6 mMpermin; Keq_10=6.7 dimensionless Reaction: P2G => PEP, Rate Law: cyto*Vmax_10*WGD_E*(P2G/Kp2g_10-PEP/(Kp2g_10*Keq_10))/(1+P2G/Kp2g_10+PEP/Kpep_10)
KGLYCOGEN_17=6.0 mMpermin Reaction: ATP + G6P => ADP + Glycogen, Rate Law: cyto*KGLYCOGEN_17
Katpase_14=39.5 permin Reaction: ATP => ADP, Rate Law: cyto*Katpase_14*ATP
k1=1.0 permin; k2=1.0 permin; t_m = 1.0 dimensionless Reaction: PYR => PYRmito, Rate Law: t_m*(k1*PYR-k2*PYRmito)
Knadh_16=0.023 mM; Kdhap_16=0.4 mM; Kglycerol_16=1.0 mM; WGD_E = 0.65 dimensionless; Vmax_16=47.11 mMpermin; Keq_16=4300.0 dimensionless; Knad_16=0.93 mM Reaction: DHAP + NADH => NAD + Glycerol, Rate Law: cyto*Vmax_16*WGD_E*(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))

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]
CO2mito [carbon dioxide; CO2]
DHAP [dihydroxyacetone phosphate; Glycerone phosphate]
GLCi [D-glucopyranose; D-Glucose]
P2G [2-phospho-D-glyceric acid; 2-Phospho-D-glycerate]
AcCoA Fru2,6-P2
Succinate [succinate(2-); Succinate]
P3G [3-phospho-D-glyceric acid; 3-Phospho-D-glycerate]
GLCo [D-glucopyranose; D-Glucose]
NADH [NADH; NADH]
PYR [pyruvate; Pyruvate]
PYRmito [pyruvate; Pyruvate]
AcAld [acetaldehyde; Acetaldehyde]
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]
G6P [alpha-D-glucose 6-phosphate; alpha-D-Glucose 6-phosphate]
GAP [D-glyceraldehyde 3-phosphate; D-Glyceraldehyde 3-phosphate]
Glycogen [glycogen; Glycogen]
PEP [phosphoenolpyruvate; Phosphoenolpyruvate]
ADP [ADP; ADP]
NAD [NAD(+); NAD+]

Observables: none

BIOMD0000000176 @ v0.0.1

This a model from the article: Increased glycolytic flux as an outcome of whole-genome duplication in yeast. Conant…

After whole-genome duplication (WGD), deletions return most loci to single copy. However, duplicate loci may survive through selection for increased dosage. Here, we show how the WGD increased copy number of some glycolytic genes could have conferred an almost immediate selective advantage to an ancestor of Saccharomyces cerevisiae, providing a rationale for the success of the WGD. We propose that the loss of other redundant genes throughout the genome resulted in incremental dosage increases for the surviving duplicated glycolytic genes. This increase gave post-WGD yeasts a growth advantage through rapid glucose fermentation; one of this lineage's many adaptations to glucose-rich environments. Our hypothesis is supported by data from enzyme kinetics and comparative genomics. Because changes in gene dosage follow directly from post-WGD deletions, dosage selection can confer an almost instantaneous benefit after WGD, unlike neofunctionalization or subfunctionalization, which require specific mutations. We also show theoretically that increased fermentative capacity is of greatest advantage when glucose resources are both large and dense, an observation potentially related to the appearance of angiosperms around the time of WGD. link: http://identifiers.org/pubmed/17667951

Parameters:

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

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]
NADH [NADH; NADH]
PYR [pyruvate; Pyruvate]
AcAld [acetaldehyde; Acetaldehyde]
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]
G6P [alpha-D-glucose 6-phosphate; alpha-D-Glucose 6-phosphate]
GAP [D-glyceraldehyde 3-phosphate; D-Glyceraldehyde 3-phosphate]
PEP [phosphoenolpyruvate; Phosphoenolpyruvate]
Glycogen [glycogen; Glycogen]
ADP [ADP; ADP]
NAD [NAD(+); NAD+]

Observables: none

MODEL4780441670 @ v0.0.1

This model features the observations of <a href = "http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&…

Free nitric oxide (NO) activates soluble guanylate cyclase (sGC), an enzyme, within both pulmonary and vascular smooth muscle. sGC catalyzes the cyclization of guanosine 5'-triphosphate to guanosine 3',5'-cyclic monophosphate (cGMP). Binding rates of NO to the ferrous heme(s) of sGC have been measured in vitro. However, a missing link in our understanding of the control mechanism of sGC by NO is a comprehensive in vivo kinetic analysis. Available literature data suggests that NO dissociation from the heme center of sGC is accelerated by its interaction with one or more cofactors in vivo. We present a working model for sGC activation and NO consumption in vivo. Our model predicts that NO influences the cGMP formation rate over a concentration range of approximately 5-100 nM (apparent Michaelis constant approximately 23 nM), with Hill coefficients between 1.1 and 1.5. The apparent reaction order for NO consumption by sGC is dependent on NO concentration, and varies between 0 and 1.5. Finally, the activation of sGC (half-life approximately 1-2 s) is much more rapid than deactivation (approximately 50 s). We conclude that control of sGC in vivo is most likely ultra-sensitive, and that activation in vivo occurs at lower NO concentrations than previously reported. link: http://identifiers.org/pubmed/11325714

Parameters: none

States: none

Observables: none

BIOMD0000000265 @ v0.0.1

This model is from the article: Restriction point control of the mammalian cell cycle via the cyclin E/Cdk2:p27 comp…

Numerous top-down kinetic models have been constructed to describe the cell cycle. These models have typically been constructed, validated and analyzed using model species (molecular intermediates and proteins) and phenotypic observations, and therefore do not focus on the individual model processes (reaction steps). We have developed a method to: (a) quantify the importance of each of the reaction steps in a kinetic model for the positioning of a switch point [i.e. the restriction point (RP)]; (b) relate this control of reaction steps to their effects on molecular species, using sensitivity and co-control analysis; and thereby (c) go beyond a correlation towards a causal relationship between molecular species and effects. The method is generic and can be applied to responses of any type, but is most useful for the analysis of dynamic and emergent responses such as switch points in the cell cycle. The strength of the analysis is illustrated for an existing mammalian cell cycle model focusing on the RP [Novak B, Tyson J (2004) J Theor Biol230, 563-579]. The reactions in the model with the highest RP control were those involved in: (a) the interplay between retinoblastoma protein and E2F transcription factor; (b) those synthesizing the delayed response genes and cyclin D/Cdk4 in response to growth signals; (c) the E2F-dependent cyclin E/Cdk2 synthesis reaction; as well as (d) p27 formation reactions. Nine of the 23 intermediates were shown to have a good correlation between their concentration control and RP control. Sensitivity and co-control analysis indicated that the strongest control of the RP is mediated via the cyclin E/Cdk2:p27 complex concentration. Any perturbation of the RP could be related to a change in the concentration of this complex; apparent effects of other molecular species were indirect and always worked through cyclin E/Cdk2:p27, indicating a causal relationship between this complex and the positioning of the RP. link: http://identifiers.org/pubmed/20015233

Parameters:

Name Description
K23a = 0.005 per hour; K23 = 1.0 per hour Reaction: var2 => var3; CYCA, CYCB, Rate Law: (K23a+K23*(CYCA+CYCB))*var2
K10 = 5.0 per hour Reaction: CD => P27, Rate Law: K10*CD
K25R = 10.0 per hour Reaction: CE => CYCE + P27, Rate Law: K25R*CE
LD = 3.3 dimensionless; LB = 5.0 dimensionless; LE = 5.0 dimensionless; LA = 3.0 dimensionless; K20 = 10.0 per hour Reaction: var4 => var1; CYCA, CYCB, CD, CYCD, CYCE, Rate Law: K20*(LA*CYCA+LB*CYCB+LD*(CD+CYCD)+LE*CYCE)*var4
PP1A = NaN dimensionless; K19=20.0 per hour; PP1T = 1.0 dimensionless; K19a=0.0 per hour Reaction: var1 => var4; CYCB, CYCA, CYCE, Rate Law: (K19*PP1A+K19a*(PP1T-PP1A))*var1
K22 = 1.0 per hour Reaction: var3 => var2, Rate Law: K22*var3
J1=0.1 dimensionless; K1a=0.1 per hour; K1=0.6 per hour; eps = 1.0 dimensionless Reaction: => CYCB, Rate Law: eps*(K1a+K1*CYCB^2/(J1^2*(1+CYCB^2/J1^2)))
J14=0.005 dimensionless; K14=2.5 per hour Reaction: CDc20 =>, Rate Law: K14*CDc20/(J14+CDc20)
K33=0.05 per hour; eps = 1.0 dimensionless Reaction: => PPX, Rate Law: eps*K33
K3=140.0 per hour; K3a=7.5 per hour; J3=0.01 dimensionless Reaction: => CDh1; CDc20, Rate Law: (K3a+K3*CDc20)*(1-CDh1)/((1+J3)-CDh1)
k15=0.25 per hour; eps = 1.0 dimensionless; J15=0.1 dimensionless Reaction: => ERG; DRG, Rate Law: eps*k15/(1+DRG^2/J15^2)
k17=10.0 per hour; eps = 1.0 dimensionless; J17=0.3 dimensionless; k17a=0.35 per hour Reaction: => DRG; ERG, Rate Law: eps*(k17*DRG^2/(J17^2*(1+DRG^2/J17^2))+k17a*ERG)
K30 = 20.0 per hour Reaction: CA => P27; CDc20, Rate Law: K30*CA*CDc20
K25 = 1000.0 per hour Reaction: CYCE + P27 => CE, Rate Law: K25*CYCE*P27
k18=10.0 per hour Reaction: DRG =>, Rate Law: k18*DRG
K26 = 10000.0 per hour Reaction: var2 + var4 => var5, Rate Law: K26*var2*var4
V4 = NaN dimensionless; J4=0.01 dimensionless Reaction: CDh1 => ; CYCA, CYCB, CYCE, Rate Law: V4*CDh1/(J4+CDh1)
K27=0.2 per hour; r31switch = 1.0 dimensionless Reaction: => GM; MASS, Rate Law: K27*MASS*r31switch
V2 = NaN dimensionless Reaction: CYCB => ; CDc20, CDh1, Rate Law: V2*CYCB
eps = 1.0 dimensionless; K7a=0.0 per hour; K7=0.6 per hour Reaction: => CYCE; var2, Rate Law: eps*(K7a+K7*var2)
k24=1000.0 per hour Reaction: CYCD + P27 => CD, Rate Law: k24*CYCD*P27
V6 = NaN dimensionless Reaction: CE => CYCE; CYCA, CYCB, Rate Law: V6*CE
J13=0.005 dimensionless; K13=5.0 per hour Reaction: => CDc20; CDc20T, IEP, Rate Law: K13*((-CDc20)+CDc20T)*IEP/((J13-CDc20)+CDc20T)
J31=0.01 dimensionless; K31=0.7 per hour Reaction: => IEP; CYCB, Rate Law: K31*CYCB*(1-IEP)/((1+J31)-IEP)
K29=0.05 per hour; eps = 1.0 dimensionless Reaction: => CYCA; MASS, var2, Rate Law: eps*K29*MASS*var2
K11=1.5 per hour; eps = 1.0 dimensionless; K11a=0.0 per hour Reaction: => CDc20T; CYCB, Rate Law: eps*(K11a+K11*CYCB)
V8 = NaN dimensionless Reaction: CE => P27; CYCB, CYCA, CYCE, Rate Law: V8*CE
J32=0.01 dimensionless; K32=1.8 per hour Reaction: IEP => ; PPX, Rate Law: K32*IEP*PPX/(J32+IEP)
K28=0.2 per hour Reaction: GM =>, Rate Law: K28*GM
K26R = 200.0 per hour Reaction: var5 => var2 + var4, Rate Law: K26R*var5
eps = 1.0 dimensionless; K5=20.0 per hour Reaction: => P27, Rate Law: eps*K5
K34=0.05 per hour Reaction: PPX =>, Rate Law: K34*PPX
MU=0.061 per hour; eps = 1.0 dimensionless Reaction: => MASS; GM, Rate Law: eps*MU*GM
K12 = 1.5 per hour Reaction: CDc20T =>, Rate Law: K12*CDc20T
k24r=10.0 per hour Reaction: CD => CYCD + P27, Rate Law: k24r*CD
k16=0.25 per hour Reaction: ERG =>, Rate Law: k16*ERG
K9=2.5 per hour; eps = 1.0 dimensionless Reaction: => CYCD; DRG, Rate Law: eps*K9*DRG

States:

Name Description
var1 [Retinoblastoma-associated protein]
ERG early-response genes
CYCB [Cyclin-dependent kinase 2; IPR015454]
var2 [Transcription factor E2F1]
var4 [Retinoblastoma-associated protein]
GM general machinery for protein synthesis
P27 [Cyclin-dependent kinase inhibitor 1B]
var3 [Transcription factor E2F1]
DRG delayed-response genes
CDc20 [APC/C activator protein CDC20]
var5 [Transcription factor E2F1; Retinoblastoma-associated protein]
MASS [cell growth]
IEP [anaphase-promoting complex]
CYCD [Cyclin-dependent kinase 2; IPR015451]
CA [Cyclin-dependent kinase inhibitor 1B; Cyclin-dependent kinase 2; IPR015453]
CE [Cyclin-dependent kinase inhibitor 1B; G1/S-specific cyclin-E1; Cyclin-dependent kinase 2]
CYCE [G1/S-specific cyclin-E1; Cyclin-dependent kinase 2]
CDc20T [APC/C activator protein CDC20]
CDh1 [Cadherin-1]
PPX [Exopolyphosphatase]
CYCA [Cyclin-dependent kinase 2; IPR015453]
var6 [Transcription factor E2F1; Retinoblastoma-associated protein]
CD [Cyclin-dependent kinase inhibitor 1B; Cyclin-dependent kinase 2; IPR015451]

Observables: none

BIOMD0000000405 @ v0.0.1

This model is from the article: Queueing up for enzymatic processing: correlated signaling through coupled degradati…

High-throughput technologies have led to the generation of complex wiring diagrams as a post-sequencing paradigm for depicting the interactions between vast and diverse cellular species. While these diagrams are useful for analyzing biological systems on a large scale, a detailed understanding of the molecular mechanisms that underlie the observed network connections is critical for the further development of systems and synthetic biology. Here, we use queueing theory to investigate how 'waiting lines' can lead to correlations between protein 'customers' that are coupled solely through a downstream set of enzymatic 'servers'. Using the E. coli ClpXP degradation machine as a model processing system, we observe significant cross-talk between two networks that are indirectly coupled through a common set of processors. We further illustrate the implications of enzymatic queueing using a synthetic biology application, in which two independent synthetic networks demonstrate synchronized behavior when common ClpXP machinery is overburdened. Our results demonstrate that such post-translational processes can lead to dynamic connections in cellular networks and may provide a mechanistic understanding of existing but currently inexplicable links. link: http://identifiers.org/pubmed/22186735

Parameters:

Name Description
parameter_3 = 10.0 Reaction: species_4 => species_5, Rate Law: compartment_1*parameter_3*species_4
parameter_5 = 0.03465735902799 Reaction: species_1 =>, Rate Law: compartment_1*parameter_5*species_1
parameter_4 = 1000.0 Reaction: species_1 + species_5 => species_3, Rate Law: compartment_1*parameter_4*species_1*species_5
parameter_1 = 500.0 Reaction: => species_1, Rate Law: compartment_1*parameter_1
parameter_2 = 500.0 Reaction: => species_2, Rate Law: compartment_1*parameter_2

States:

Name Description
species 2 [protein]
species 3 E1
species 1 [protein]
species 4 E2
species 5 E

Observables: none

BIOMD0000000400 @ v0.0.1

This a model from the article: Modeling hypertrophic IP3 transients in the cardiac myocyte. Cooling M, Hunter P, Cr…

Cardiac hypertrophy is a known risk factor for heart disease, and at the cellular level is caused by a complex interaction of signal transduction pathways. The IP3-calcineurin pathway plays an important role in stimulating the transcription factor NFAT which binds to DNA cooperatively with other hypertrophic transcription factors. Using available kinetic data, we construct a mathematical model of the IP3 signal production system after stimulation by a hypertrophic alpha-adrenergic agonist (endothelin-1) in the mouse atrial cardiac myocyte. We use a global sensitivity analysis to identify key controlling parameters with respect to the resultant IP3 transient, including the phosphorylation of cell-membrane receptors, the ligand strength and binding kinetics to precoupled (with G(alpha)GDP) receptor, and the kinetics associated with precoupling the receptors. We show that the kinetics associated with the receptor system contribute to the behavior of the system to a great extent, with precoupled receptors driving the response to extracellular ligand. Finally, by reparameterizing for a second hypertrophic alpha-adrenergic agonist, angiotensin-II, we show that differences in key receptor kinetic and membrane density parameters are sufficient to explain different observed IP3 transients in essentially the same pathway. link: http://identifiers.org/pubmed/17693463

Parameters:

Name Description
J13 = NaN; J9 = NaN; J11 = NaN Reaction: Pg = J9-(J11+J13), Rate Law: J9-(J11+J13)
J7 = NaN; J10 = NaN; J9 = NaN; J6 = NaN Reaction: Gt = J6-(J7+J9+J10), Rate Law: J6-(J7+J9+J10)
J5 = NaN Reaction: Rlgp = J5, Rate Law: J5
J7 = NaN; J13 = NaN; J2 = NaN; J3 = NaN; J12 = NaN Reaction: Gd = (J7+J13+J12)-(J2+J3), Rate Law: (J7+J13+J12)-(J2+J3)
J10 = NaN; J11 = NaN; J12 = NaN Reaction: Pcg = (J10+J11)-J12, Rate Law: (J10+J11)-J12
J10 = NaN; J8 = NaN; J12 = NaN Reaction: Pc = (J8+J12)-J10, Rate Law: (J8+J12)-J10
J13 = NaN; J9 = NaN; J8 = NaN Reaction: P = J13-(J9+J8), Rate Law: J13-(J9+J8)
Cpc = NaN; J15 = NaN; J14 = NaN; J16 = NaN Reaction: IP3 = Cpc*(J14+J15)-J16, Rate Law: Cpc*(J14+J15)-J16
J4 = NaN; J5 = NaN; J6 = NaN; J3 = NaN Reaction: Rlg = ((J3-J5)+J4)-J6, Rate Law: ((J3-J5)+J4)-J6
Cpc = NaN; J11 = NaN; J8 = NaN Reaction: Ca = Cpc*(-1)*(J8+J11), Rate Law: Cpc*(-1)*(J8+J11)
J4 = NaN; J2 = NaN Reaction: Rg = J2-J4, Rate Law: J2-J4
J1 = NaN; J2 = NaN Reaction: R = (-1)*(J1+J2), Rate Law: (-1)*(J1+J2)
J1 = NaN; J6 = NaN; J3 = NaN Reaction: Rl = (J1+J6)-J3, Rate Law: (J1+J6)-J3

States:

Name Description
Rl [G-protein coupled receptor 12; CCO:F0001633]
P [1-phosphatidylinositol 4,5-bisphosphate phosphodiesterase beta-1]
Pcg [calcium(2+); GTP; 1-phosphatidylinositol 4,5-bisphosphate phosphodiesterase beta-1; Guanine nucleotide-binding protein subunit alpha-12]
Gd [GDP; Guanine nucleotide-binding protein subunit alpha-12]
Rlgp [GDP; G-protein coupled receptor 12; Guanine nucleotide-binding protein subunit alpha-12; CCO:F0001633; urn:miriam:mod:MOD%3A00696]
Pc [calcium(2+); 1-phosphatidylinositol 4,5-bisphosphate phosphodiesterase beta-1]
IP3 [Inositol-trisphosphate 3-kinase A]
Gt [GTP; Guanine nucleotide-binding protein subunit alpha-12]
Pg [GTP; 1-phosphatidylinositol 4,5-bisphosphate phosphodiesterase beta-1; Guanine nucleotide-binding protein subunit alpha-12]
Ca [calcium(2+)]
R [G-protein coupled receptor 12]
Rlg [GDP; G-protein coupled receptor 12; Guanine nucleotide-binding protein subunit alpha-12; CCO:F0001633]
Rg [GDP; G-protein coupled receptor 12; Guanine nucleotide-binding protein subunit alpha-12]

Observables: none

This is an ordinary differential equation-based mathematical model describing the inflammatory phase of the wound healin…

The normal wound healing response is characterized by a progression from clot formation, to an inflammatory phase, to a repair phase, and finally, to remodeling. In many chronic wounds there is an extended inflammatory phase that stops this progression. In order to understand the inflammatory phase in more detail, we developed an ordinary differential equation model that accounts for two systemic mediators that are known to modulate this phase, estrogen (a protective hormone during wound healing) and cortisol (a hormone elevated after trauma that slows healing). This model describes the interactions in the wound between wound debris, pathogens, neutrophils and macrophages and the modulation of these interactions by estrogen and cortisol. A collection of parameter sets, which qualitatively match published data on the dynamics of wound healing, was chosen using Latin Hypercube Sampling. This collection of parameter sets represents normal healing in the population as a whole better than one single parameter set. Including the effects of estrogen and cortisol is a necessary step to creating a patient specific model that accounts for gender and trauma. Utilization of math modeling techniques to better understand the wound healing inflammatory phase could lead to new therapeutic strategies for the treatment of chronic wounds. This inflammatory phase model will later become the inflammatory subsystem of our full wound healing model, which includes fibroblast activity, collagen accumulation and remodeling. link: http://identifiers.org/pubmed/25446708

Parameters:

Name Description
mupt = 0.37 Reaction: Pt =>, Rate Law: compartment*mupt*Pt
mun = 1.02 Reaction: N => Pt, Rate Law: compartment*mun*N
fi2 = 0.0; kem = 4.97; E = 0.0; kpm = 34.8 Reaction: P =>, Rate Law: compartment*kpm*P*fi2*(1+kem*E)
pinf = 20.0; kpg = 14.4 Reaction: => P, Rate Law: compartment*kpg*P*(1-P/pinf)
kpb = 14.4; mub = 0.048; kbp = 0.2; sb = 0.12 Reaction: P =>, Rate Law: compartment*kpb*sb*P/(mub+kbp*P)
kpn = 35.03; ken = 5.37; E = 0.0 Reaction: P => ; N, Rate Law: compartment*kpn*P*N*(1+ken*E)
ken = 5.37; kptn = 2.03 Reaction: Pt => ; N, Rate Law: compartment*kptn*Pt*N*(1+ken*N)
mum = 0.5 Reaction: M =>, Rate Law: compartment*mum*M
smr = 0.17; mumr = 0.54; R1 = 85.91 Reaction: => M, Rate Law: compartment*smr*R1/(mumr+R1)
kptm = 3.16; fi2 = 0.0; kem = 4.97; E = 0.0 Reaction: Pt =>, Rate Law: compartment*kptm*Pt*fi2*(1+kem*E)
fi2 = 0.0; knm = 6.42 Reaction: N =>, Rate Law: compartment*knm*N*fi2
fEN = 0.265979268957992 Reaction: => N, Rate Law: compartment*fEN

States:

Name Description
M [macrophage]
P [C80324]
N [CL:0000775]
Pt [C120869]

Observables: none

Apoptotic Reaction Model is an ordinary differential equation model describing every species in the apoptotic cascade an…

Apoptosis, a form of programmed cell death central to all multicellular organisms, plays a key role during organism development and is often misregulated in cancer. Devising a single model applicable to distinct stimuli and conditions has been limited by lack of robust observables. Indeed, previous numerical models have been tailored to fit experimental datasets in restricted scenarios, failing to predict response to different stimuli. We quantified the activity of three caspases simultaneously upon intrinsic or extrinsic stimulation to assemble a comprehensive dataset. We measured and modeled the time between maximum activity of intrinsic, extrinsic and effector caspases, a robust observable of network dynamics, to create the first integrated Apoptotic Reaction Model (ARM). Observing how effector caspases reach maximum activity first irrespective of stimuli used, led us to identify and incorporate a missing feedback into a successful model for extrinsic stimulation. By simulating different recently performed experiments, we corroborated that ARM adequately describes them. This integrated model provides further insight into the indispensable feedback from effector caspase to initiator caspases. link: http://identifiers.org/doi/10.1101/2021.05.21.444824

Parameters: none

States: none

Observables: none

MODEL1801090001 @ v0.0.1

Arabidopsis thaliana is a well-established model system for the analysis of the basic physiological and metabolic pathwa…

Motivation:Arabidopsis thaliana is a well-established model system for the analysis of the basic physiological and metabolic pathways of plants. Nevertheless, the system is not yet fully understood, although many mechanisms are described, and information for many processes exists. However, the combination and interpretation of the large amount of biological data remain a big challenge, not only because data sets for metabolic paths are still incomplete. Moreover, they are often inconsistent, because they are coming from different experiments of various scales, regarding, for example, accuracy and/or significance. Here, theoretical modeling is powerful to formulate hypotheses for pathways and the dynamics of the metabolism, even if the biological data are incomplete. To develop reliable mathematical models they have to be proven for consistency. This is still a challenging task because many verification techniques fail already for middle-sized models. Consequently, new methods, like decomposition methods or reduction approaches, are developed to circumvent this problem. Methods: We present a new semi-quantitative mathematical model of the metabolism of Arabidopsis thaliana. We used the Petri net formalism to express the complex reaction system in a mathematically unique manner. To verify the model for correctness and consistency we applied concepts of network decomposition and network reduction such as transition invariants, common transition pairs, and invariant transition pairs. Results: We formulated the core metabolism of Arabidopsis thaliana based on recent knowledge from literature, including the Calvin cycle, glycolysis and citric acid cycle, glyoxylate cycle, urea cycle, sucrose synthesis, and the starch metabolism. By applying network decomposition and reduction techniques at steady-state conditions, we suggest a straightforward mathematical modeling process. We demonstrate that potential steady-state pathways exist, which provide the fixed carbon to nearly all parts of the network, especially to the citric acid cycle. There is a close cooperation of important metabolic pathways, e.g., the de novo synthesis of uridine-5-monophosphate, the γ-aminobutyric acid shunt, and the urea cycle. The presented approach extends the established methods for a feasible interpretation of biological network models, in particular of large and complex models. link: http://identifiers.org/pubmed/28713420

Parameters: none

States: none

Observables: none

IL-17A and F are critical cytokines in anti-microbial immunity but also contribute to auto-immune pathologies. Recent ev…

link: http://identifiers.org/doi/10.1186/s43556-021-00034-3

Parameters: none

States: none

Observables: none

MODEL0913145131 @ v0.0.1

This a model from the article: A quantitative model of gastric smooth muscle cellular activation. Corrias A, Buist M…

A physiologically realistic quantitative description of the electrical behavior of a gastric smooth muscle (SM) cell is presented. The model describes the response of a SM cell when activated by an electrical stimulus coming from the network of interstitial cells of Cajal (ICC) and is mediated by the activation of different ion channels species in the plasma membrane. The conductances (predominantly Ca2+ and K+) that are believed to substantially contribute to the membrane potential fluctuations during slow wave activity have been included in the model. A phenomenological description of intracellular Ca2+ dynamics has also been included because of its primary importance in regulating a number of cellular processes. In terms of shape, duration, and amplitude, the resulting simulated smooth muscle depolarizations (SMDs) are in good agreement with experimentally recordings from mammalian gastric SM in control and altered conditions. This model has also been designed to be suitable for incorporation into large scale multicellular simulations. link: http://identifiers.org/pubmed/17486452

Parameters: none

States: none

Observables: none

MODEL0913095435 @ v0.0.1

This a model from the article: Quantitative cellular description of gastric slow wave activity. Corrias A, Buist ML.…

Interstitial cells of Cajal (ICC) are responsible for the spontaneous and omnipresent electrical activity in the stomach. A quantitative description of the intracellular processes whose coordinated activity is believed to generate electrical slow waves has been developed and is presented here. In line with recent experimental evidence, the model describes how the interplay between the mitochondria and the endoplasmic reticulum in cycling intracellular Ca(2+) provides the primary regulatory signal for the initiation of the slow wave. The major ion channels that have been identified as influencing slow wave activity have been modeled according to data obtained from isolated ICC. The model has been validated by comparing the simulated profile of the slow waves with experimental recordings and shows good correspondence in terms of frequency, amplitude, and shape in both control and pharmacologically altered conditions. link: http://identifiers.org/pubmed/18276830

Parameters: none

States: none

Observables: none

Optimality of the spontaneous prophage induction rate. Cortes MG1, Krog J2, Balázsi G3. 1 Department of Applied Mathema…

Lysogens are bacterial cells that have survived after genomically incorporating the DNA of temperate bacteriophages infecting them. If an infection results in lysogeny, the lysogen continues to grow and divide normally, seemingly unaffected by the integrated viral genome known as a prophage. However, the prophage can still have an impact on the host's phenotype and overall fitness in certain environments. Additionally, the prophage within the lysogen can activate the lytic pathway via spontaneous prophage induction (SPI), killing the lysogen and releasing new progeny phages. These new phages can then lyse or lysogenize other susceptible nonlysogens, thereby impacting the competition between lysogens and nonlysogens. In a scenario with differing growth rates, it is not clear whether SPI would be beneficial or detrimental to the lysogens since it kills the host cell but also attacks nonlysogenic competitors, either lysing or lysogenizing them. Here we study the evolutionary dynamics of a mixture of lysogens and nonlysogens and derive general conditions on SPI rates for lysogens to displace nonlysogens. We show that there exists an optimal SPI rate for bacteriophage λ and explain why it is so low. We also investigate the impact of stochasticity and conclude that even at low cell numbers SPI can still provide an advantage to the lysogens. These results corroborate recent experimental studies showing that lower SPI rates are advantageous for phage-phage competition, and establish theoretical bounds on the SPI rate in terms of ecological and environmental variables associated with lysogens having a competitive advantage over their nonlysogenic counterparts. link: http://identifiers.org/pubmed/31525321

Parameters:

Name Description
g = 1.0 Reaction: => U, Rate Law: compartment*g*U
phi = 0.999899; alpha = 1.0E-7 Reaction: U => ; V, Rate Law: compartment*(alpha*U*V+phi*U)
gamma = 0.001; alpha = 1.0E-7 Reaction: V => ; L, Rate Law: compartment*(gamma*V+alpha*V*L)
r = 0.99; p = 0.3; alpha = 1.0E-7 Reaction: => L; U, V, Rate Law: compartment*(r*L+p*alpha*U*V)
delta = 1.0E-4; b = 150.0; p = 0.3; alpha = 1.0E-7 Reaction: => V; U, L, Rate Law: compartment*((1-p)*b*alpha*U*V+b*delta*L)
delta = 1.0E-4; phi = 0.999899 Reaction: L =>, Rate Law: compartment*(delta*L+phi*L)

States:

Name Description
U U
V [C14283]
L [C14283]

Observables: none

Costa2014 - Computational Model of L. lactis MetabolismThis model is described in the article: [An extended dynamic mod…

Biomedical research and biotechnological production are greatly benefiting from the results provided by the development of dynamic models of microbial metabolism. Although several kinetic models of Lactococcus lactis (a Lactic Acid Bacterium (LAB) commonly used in the dairy industry) have been developed so far, most of them are simplified and focus only on specific metabolic pathways. Therefore, the application of mathematical models in the design of an engineering strategy for the production of industrially important products by L. lactis has been very limited. In this work, we extend the existing kinetic model of L. lactis central metabolism to include industrially relevant production pathways such as mannitol and 2,3-butanediol. In this way, we expect to study the dynamics of metabolite production and make predictive simulations in L. lactis. We used a system of ordinary differential equations (ODEs) with approximate Michaelis-Menten-like kinetics for each reaction, where the parameters were estimated from multivariate time-series metabolite concentrations obtained by our team through in vivo Nuclear Magnetic Resonance (NMR). The results show that the model captures observed transient dynamics when validated under a wide range of experimental conditions. Furthermore, we analyzed the model using global perturbations, which corroborate experimental evidence about metabolic responses upon enzymatic changes. These include that mannitol production is very sensitive to lactate dehydrogenase (LDH) in the wild type (W.T.) strain, and to mannitol phosphoenolpyruvate: a phosphotransferase system (PTS(Mtl)) in a LDH mutant strain. LDH reduction has also a positive control on 2,3-butanediol levels. Furthermore, it was found that overproduction of mannitol-1-phosphate dehydrogenase (MPD) in a LDH/PTS(Mtl) deficient strain can increase the mannitol levels. The results show that this model has prediction capability over new experimental conditions and offers promising possibilities to elucidate the effect of alterations in the main metabolism of L. lactis, with application in strain optimization. link: http://identifiers.org/pubmed/24413179

Parameters:

Name Description
kiPint_Ptransport=0.561093; kmADP_Ptransport=0.192278; Vmax_Ptransport=3.59588; kmATP_Ptransport=0.523288; kmPint_Ptransport=0.30336; kmPext_Ptransport=0.749898 Reaction: Pext + ATP => Pint + ADP; Pint, ADP, ATP, Pext, Pint, Rate Law: kiPint_Ptransport/(Pint+kiPint_Ptransport)*Vmax_Ptransport*ATP/kmATP_Ptransport*Pext/kmPext_Ptransport/(((1+Pext/kmPext_Ptransport)*(1+ATP/kmATP_Ptransport)+(1+Pint/kmPint_Ptransport+(Pint/kmPint_Ptransport)^2)*(1+ADP/kmADP_Ptransport))-1)
kmG6P_PTS_Glc=0.284871; kmPEP_PTS_Glc=0.305604; Vmax_PTS_Glc=3.71082; kmGlucose_PTS_Glc=0.0485045; kmPYR_PTS_Glc=1.95993; kaPint_PTS_Glc=0.070909; kiFBP_PTS_Glc=1.16937 Reaction: Glucose + PEP => G6P + PYR; FBP, Pint, FBP, G6P, Glucose, PEP, PYR, Pint, Rate Law: Pint/(Pint+kaPint_PTS_Glc)*kiFBP_PTS_Glc/(FBP+kiFBP_PTS_Glc)*Vmax_PTS_Glc*Glucose/kmGlucose_PTS_Glc*PEP/kmPEP_PTS_Glc/(((1+Glucose/kmGlucose_PTS_Glc)*(1+PEP/kmPEP_PTS_Glc)+(1+G6P/kmG6P_PTS_Glc)*(1+PYR/kmPYR_PTS_Glc))-1)
kmAcetoin_Acetoin_transp=1.89255; kmAcetoin_Ext_Acetoin_transp=7.05248; Vmax_Acetoin_transp=1.60066 Reaction: Acetoin => Acetoin_Ext; Acetoin, Acetoin_Ext, Rate Law: Vmax_Acetoin_transp*Acetoin/kmAcetoin_Acetoin_transp/(1+Acetoin/kmAcetoin_Acetoin_transp+Acetoin_Ext/kmAcetoin_Ext_Acetoin_transp)
kmNAD_MPD=0.373149; kmMannitol1Phoshate_MPD=0.0891203; kmF6P_MPD=0.321372; Keq_MPD=200.0; kiF6P_MPD=22.0284; kmNADH_MPD=0.0303446; Vmax_MPD=1.32695 Reaction: F6P + NADH => Mannitol1Phosphate + NAD; F6P, F6P, Mannitol1Phosphate, NAD, NADH, Rate Law: compartment_1*kiF6P_MPD/(F6P+kiF6P_MPD)*(Vmax_MPD*F6P/kmF6P_MPD*NADH/kmNADH_MPD-Vmax_MPD/Keq_MPD*Mannitol1Phosphate/kmF6P_MPD*NAD/kmNADH_MPD)/(((1+F6P/kmF6P_MPD)*(1+NADH/kmNADH_MPD)+(1+Mannitol1Phosphate/kmMannitol1Phoshate_MPD)*(1+NAD/kmNAD_MPD))-1)
kmG3P_FBA=10.1058; Keq_FBA=0.056; Vmax_FBA=56.1311; kmFBP_FBA=0.300745 Reaction: FBP => G3P; FBP, G3P, Rate Law: compartment_1*(Vmax_FBA*FBP/kmFBP_FBA-Vmax_FBA/Keq_FBA*G3P^2/kmFBP_FBA)/(1+FBP/kmFBP_FBA+G3P/kmG3P_FBA+(G3P/kmG3P_FBA)^2)
kmF6P_PFK=1.01973; kmATP_PFK=0.0616607; kmADP_PFK=10.7357; kmFBP_PFK=86.8048; Vmax_PFK=18.3577 Reaction: F6P + ATP => FBP + ADP; ADP, ATP, F6P, FBP, Rate Law: compartment_1*Vmax_PFK*F6P/kmF6P_PFK*ATP/kmATP_PFK/(((1+F6P/kmF6P_PFK)*(1+ATP/kmATP_PFK)+(1+FBP/kmFBP_PFK)*(1+ADP/kmADP_PFK))-1)
Vmax_PYK=2.22404; kmPYR_PYK=96.4227; kiPint_PYK=3.70071; nPYK=3.0; kaFBP_PYK=0.0388651; kmADP_PYK=3.07711; kmATP_PYK=29.6028; kmPEP_PYK=0.330583 Reaction: PEP + ADP => PYR + ATP; FBP, Pint, ADP, ATP, FBP, PEP, PYR, Pint, Rate Law: compartment_1*FBP/(FBP+kaFBP_PYK)*kiPint_PYK^nPYK/(Pint^nPYK+kiPint_PYK^nPYK)*Vmax_PYK*ADP/kmADP_PYK*PEP/kmPEP_PYK/(((1+ADP/kmADP_PYK)*(1+PEP/kmPEP_PYK)+(1+ATP/kmATP_PYK)*(1+PYR/kmPYR_PYK))-1)
kmF6P_FBPase=1.90796; kmFBP_FBPase=0.412307; Vmax_FBPase=0.0970486; kmPint_FBPase=0.0109675 Reaction: FBP => F6P + Pint; F6P, FBP, Pint, Rate Law: compartment_1*Vmax_FBPase*FBP/kmFBP_FBPase/(FBP/kmFBP_FBPase+(1+F6P/kmF6P_FBPase)*(1+Pint/kmPint_FBPase))
kmATP_ENO=0.748238; Keq_ENO=27.55; kmADP_ENO=0.413195; Vmax_ENO=29.132; kmBPG_ENO=0.0241572; kmPEP_ENO=1.38899 Reaction: BPG + ADP => PEP + ATP; ADP, ATP, BPG, PEP, Rate Law: compartment_1*(Vmax_ENO*BPG/kmBPG_ENO*ADP/kmADP_ENO-Vmax_ENO/Keq_ENO*PEP/kmBPG_ENO*ATP/kmADP_ENO)/(((1+BPG/kmBPG_ENO)*(1+ADP/kmADP_ENO)+(1+PEP/kmPEP_ENO)*(1+ATP/kmATP_ENO))-1)
kmPYR_ALS=0.262819; Keq_ALS=900000.0; Vmax_ALS=0.354581; kmAcetoin_ALS=0.0495418 Reaction: PYR => Acetoin; Acetoin, PYR, Rate Law: compartment_1*(Vmax_ALS*(PYR/kmPYR_ALS)^2-Vmax_ALS/Keq_ALS*Acetoin/kmPYR_ALS)/((1+PYR/kmPYR_ALS+(PYR/kmPYR_ALS)^2+1+Acetoin/kmAcetoin_ALS)-1)
Vmax_BDH=2.28578; kmNAD_BDH=1.29567; kmButanediol_BDH=1.80684; Keq_BDH=1400.0; kmNADH_BDH=3.54858; kmAcetoin_BDH=5.62373 Reaction: Acetoin + NADH => Butanediol + NAD; Acetoin, Butanediol, NAD, NADH, Rate Law: (Vmax_BDH*Acetoin/kmAcetoin_BDH*NADH/kmNADH_BDH-Vmax_BDH/Keq_BDH*Butanediol/kmAcetoin_BDH*NAD/kmNADH_BDH)/(((1+Acetoin/kmAcetoin_BDH)*(1+NADH/kmNADH_BDH)+(1+Butanediol/kmButanediol_BDH)*(1+NAD/kmNAD_BDH))-1)
kmPYR_PTS_Man=0.344134; Vmax_PTS_Man=4.44903; kmMannitol1Phosphate_PTS_Man=0.362571; kmPEP_PTS_Man=2.20816; kmMannitolExt_PTS_Man=0.0127321 Reaction: Mannitol_Ext + PEP => Mannitol1Phosphate + PYR; Mannitol1Phosphate, Mannitol_Ext, PEP, PYR, Rate Law: Vmax_PTS_Man*Mannitol_Ext/kmMannitolExt_PTS_Man*PEP/kmPEP_PTS_Man/(((1+Mannitol_Ext/kmMannitolExt_PTS_Man)*(1+PEP/kmPEP_PTS_Man)+(1+Mannitol1Phosphate/kmMannitol1Phosphate_PTS_Man)*(1+PYR/kmPYR_PTS_Man))-1)
Keq_PFL=650.0; kmPYR_PFL=5.77662; kiG3P_PFL=0.511288; kmAcetCoA_PFL=7.34319; KmCoA_PFL=0.124066; kmFormate_PFL=54.2693; Vmax_PFL=0.00230934 Reaction: PYR + CoA => AcetCoA + Formate; G3P, AcetCoA, CoA, Formate, G3P, PYR, Rate Law: kiG3P_PFL/(G3P+kiG3P_PFL)*(Vmax_PFL*PYR/kmPYR_PFL*CoA/KmCoA_PFL-Vmax_PFL/Keq_PFL*AcetCoA/kmPYR_PFL*Formate/KmCoA_PFL)/(((1+PYR/kmPYR_PFL)*(1+CoA/KmCoA_PFL)+(1+AcetCoA/kmAcetCoA_PFL)*(1+Formate/kmFormate_PFL))-1)
kaFBP_LDH=0.0184011; Vmax_LDH=566.598; kmNADH_LDH=0.144443; kiPint_LDH=0.0676829; kmPYR_LDH=0.01; kmLactate_LDH=94.1203; kmNAD_LDH=3.08447 Reaction: PYR + NADH => Lactate + NAD; FBP, Pint, FBP, Lactate, NAD, NADH, PYR, Pint, Rate Law: FBP/(FBP+kaFBP_LDH)*kiPint_LDH/(Pint+kiPint_LDH)*Vmax_LDH*PYR/kmPYR_LDH*NADH/kmNADH_LDH/(((1+PYR/kmPYR_LDH)*(1+NADH/kmNADH_LDH)+(1+Lactate/kmLactate_LDH)*(1+NAD/kmNAD_LDH))-1)
kmNADH_AE=0.43127; kmAcetCoA_AE=7.38021; kmNAD_AE=1.31442; kiATP_AE=6.28119; kmCoA_AE=0.091813; kmEthanol_AE=2.28106; Vmax_AE=2.11844 Reaction: AcetCoA + NADH => Ethanol + NAD + CoA; ATP, ATP, AcetCoA, CoA, Ethanol, NAD, NADH, Rate Law: kiATP_AE/(ATP+kiATP_AE)*Vmax_AE*AcetCoA/kmAcetCoA_AE*(NADH/kmNADH_AE)^2/(((1+NADH/kmNADH_AE+(NADH/kmNADH_AE)^2)*(1+AcetCoA/kmAcetCoA_AE)+(1+Ethanol/kmEthanol_AE)*(1+CoA/kmCoA_AE)*(1+NAD/kmNAD_AE+(NAD/kmNAD_AE)^2))-1)
Vmax_ACK=3.83918; kmADP_ACK=1.17242; kmATP_ACK=14.1556; kmAcetate_ACK=0.552221; kmCoA_ACK=0.173388; kmAcetCoA_ACK=0.55824 Reaction: AcetCoA + ADP => Acetate + ATP + CoA; ADP, ATP, AcetCoA, Acetate, CoA, Rate Law: Vmax_ACK*AcetCoA/kmAcetCoA_ACK*ADP/kmADP_ACK/(((1+AcetCoA/kmAcetCoA_ACK)*(1+ADP/kmADP_ACK)+(1+Acetate/kmAcetate_ACK)*(1+ATP/kmATP_ACK)*(1+CoA/kmCoA_ACK))-1)
kmMannitol_Mannitol_transp=0.0223502; kmMannitol_Ext_Mannitol_transp=0.940662; Vmax_Mannitol_transp=1.62459 Reaction: Mannitol => Mannitol_Ext; Mannitol, Mannitol_Ext, Rate Law: Vmax_Mannitol_transp*Mannitol/kmMannitol_Mannitol_transp/(1+Mannitol/kmMannitol_Mannitol_transp+Mannitol_Ext/kmMannitol_Ext_Mannitol_transp)
kmMannitol1Phosphate_MP=3.51571; kmMannitol_MP=0.238849; Vmax_MP=3.48563 Reaction: Mannitol1Phosphate => Mannitol; Mannitol, Mannitol1Phosphate, Rate Law: compartment_1*Vmax_MP*Mannitol1Phosphate/kmMannitol1Phosphate_MP/((1+Mannitol1Phosphate/kmMannitol1Phosphate_MP+1+Mannitol/kmMannitol_MP)-1)
kmF6P_PGI=3.13894; Keq_PGI=0.43; Vmax_PGI=21.681; kmG6P_PGI=0.199409 Reaction: G6P => F6P; F6P, G6P, Rate Law: compartment_1*(Vmax_PGI*G6P/kmG6P_PGI-Vmax_PGI/Keq_PGI*F6P/kmG6P_PGI)/(1+G6P/kmG6P_PGI+F6P/kmF6P_PGI)
kmATP_ATPase=4.34159; Vmax_ATPase=3.2901; nATPase=3.0 Reaction: ATP => ADP + Pint; ATP, Rate Law: compartment_1*Vmax_ATPase*(ATP/kmATP_ATPase)^nATPase/((ATP/kmATP_ATPase)^nATPase+1)
kmPint_GAPDH=6.75302; kmNADH_GAPDH=0.643019; kmBPG_GAPDH=0.0481603; Vmax_GAPDH=30.0058; kmG3P_GAPDH=0.181788; Keq_GAPDH=7.0E-4; kmNAD_GAPDH=0.0477445 Reaction: G3P + Pint + NAD => BPG + NADH; BPG, G3P, NAD, NADH, Pint, Rate Law: compartment_1*(Vmax_GAPDH*G3P/kmG3P_GAPDH*NAD/kmNAD_GAPDH*Pint/kmPint_GAPDH-Vmax_GAPDH/Keq_GAPDH*BPG/kmG3P_GAPDH*NADH/kmNAD_GAPDH*1/kmPint_GAPDH)/(((1+G3P/kmG3P_GAPDH)*(1+Pint/kmPint_GAPDH)*(1+NAD/kmNAD_GAPDH)+(1+BPG/kmBPG_GAPDH)*(1+NADH/kmNADH_GAPDH))-1)

States:

Name Description
Glucose [glucose]
Acetoin Ext [acetoin]
ATP [ATP]
FBP [keto-D-fructose 1,6-bisphosphate]
Acetoin [acetoin]
Mannitol Ext [mannitol]
Formate [formate]
AcetCoA [acetyl-CoA]
Mannitol [mannitol]
CoA [coenzyme A]
NADH [NADH]
PYR [pyruvate]
Ethanol [ethanol]
Mannitol1Phosphate [D-mannitol 1-phosphate]
Pext [phosphate(3-)]
BPG [3-phospho-D-glyceroyl dihydrogen phosphate]
F6P [keto-D-fructose 6-phosphate]
Pint [phosphate(3-)]
G6P [alpha-D-glucose 6-phosphate]
Acetate [acetate]
Lactate [(S)-lactic acid]
PEP [phosphoenolpyruvate]
ADP [ADP]
NAD [NAD(+)]
Butanediol [butanediol]
G3P [glyceraldehyde 3-phosphate]

Observables: none

MODEL1011080002 @ v0.0.1

This is metabolic network reconstruction of Baumannia cicadellinicola described in the article Graph-Based Analysis o…

Endosymbiotic bacteria from different species can live inside cells of the same eukaryotic organism. Metabolic exchanges occur between host and bacteria but also between different endocytobionts. Since a complete genome annotation is available for both, we built the metabolic network of two endosymbiotic bacteria, Sulcia muelleri and Baumannia cicadellinicola, that live inside specific cells of the sharpshooter Homalodisca coagulata and studied the metabolic exchanges involving transfers of carbon atoms between the three. We automatically determined the set of metabolites potentially exogenously acquired (seeds) for both metabolic networks. We show that the number of seeds needed by both bacteria in the carbon metabolism is extremely reduced. Moreover, only three seeds are common to both metabolic networks, indicating that the complementarity of the two metabolisms is not only manifested in the metabolic capabilities of each bacterium, but also by their different use of the same environment. Furthermore, our results show that the carbon metabolism of S. muelleri may be completely independent of the metabolic network of B. cicadellinicola. On the contrary, the carbon metabolism of the latter appears dependent on the metabolism of S. muelleri, at least for two essential amino acids, threonine and lysine. Next, in order to define which subsets of seeds (precursor sets) are sufficient to produce the metabolites involved in a symbiotic function, we used a graph-based method, PITUFO, that we recently developed. Our results highly refine our knowledge about the complementarity between the metabolisms of the two bacteria and their host. We thus indicate seeds that appear obligatory in the synthesis of metabolites are involved in the symbiotic function. Our results suggest both B. cicadellinicola and S. muelleri may be completely independent of the metabolites provided by the co-resident endocytobiont to produce the carbon backbone of the metabolites provided to the symbiotic system (., thr and lys are only exploited by B. cicadellinicola to produce its proteins). link: http://identifiers.org/pubmed/20838465

Parameters: none

States: none

Observables: none

MODEL1011080000 @ v0.0.1

This is metabolic network reconstruction of Sulcia muelleri described in the article Graph-Based Analysis of the Meta…

Endosymbiotic bacteria from different species can live inside cells of the same eukaryotic organism. Metabolic exchanges occur between host and bacteria but also between different endocytobionts. Since a complete genome annotation is available for both, we built the metabolic network of two endosymbiotic bacteria, Sulcia muelleri and Baumannia cicadellinicola, that live inside specific cells of the sharpshooter Homalodisca coagulata and studied the metabolic exchanges involving transfers of carbon atoms between the three. We automatically determined the set of metabolites potentially exogenously acquired (seeds) for both metabolic networks. We show that the number of seeds needed by both bacteria in the carbon metabolism is extremely reduced. Moreover, only three seeds are common to both metabolic networks, indicating that the complementarity of the two metabolisms is not only manifested in the metabolic capabilities of each bacterium, but also by their different use of the same environment. Furthermore, our results show that the carbon metabolism of S. muelleri may be completely independent of the metabolic network of B. cicadellinicola. On the contrary, the carbon metabolism of the latter appears dependent on the metabolism of S. muelleri, at least for two essential amino acids, threonine and lysine. Next, in order to define which subsets of seeds (precursor sets) are sufficient to produce the metabolites involved in a symbiotic function, we used a graph-based method, PITUFO, that we recently developed. Our results highly refine our knowledge about the complementarity between the metabolisms of the two bacteria and their host. We thus indicate seeds that appear obligatory in the synthesis of metabolites are involved in the symbiotic function. Our results suggest both B. cicadellinicola and S. muelleri may be completely independent of the metabolites provided by the co-resident endocytobiont to produce the carbon backbone of the metabolites provided to the symbiotic system (., thr and lys are only exploited by B. cicadellinicola to produce its proteins). link: http://identifiers.org/pubmed/20838465

Parameters: none

States: none

Observables: none

This is a mathematical model comprised of non-linear ordinary differential equations describing the dynamic relationship…

Natural killer (NK) cells belong to the first line of host defense against infection and cancer. Cytokines, including interleukin-15 (IL-15), critically regulate NK cell activity, resulting in recognition and direct killing of transformed and infected target cells. NK cells have to adapt and respond in inflamed and often hypoxic areas. Cellular stabilization and accumulation of the transcription factor hypoxia-inducible factor-1α (HIF-1α) is a key mechanism of the cellular hypoxia response. At the same time, HIF-1α plays a critical role in both innate and adaptive immunity. While the HIF-1α hydroxylation and degradation pathway has been recently described with the help of mathematical methods, less is known concerning the mechanistic mathematical description of processes regulating the levels of HIF-1α mRNA and protein. In this work we combine mathematical modeling with experimental laboratory analysis and examine the dynamic relationship between HIF-1α mRNA, HIF-1α protein, and IL-15-mediated upstream signaling events in NK cells from human blood. We propose a system of non-linear ordinary differential equations with positive and negative feedback loops for describing the complex interplay of HIF-1α regulators. The experimental design is optimized with the help of mathematical methods, and numerical optimization techniques yield reliable parameter estimates. The mathematical model allows for the investigation and prediction of HIF-1α stabilization under different inflammatory conditions and provides a better understanding of mechanisms mediating cellular enrichment of HIF-1α. Thanks to the combination of in vitro experimental data and in silico predictions we identified the mammalian target of rapamycin (mTOR), the nuclear factor-κB (NF-κB), and the signal transducer and activator of transcription 3 (STAT3) as central regulators of HIF-1α accumulation. We hypothesize that the regulatory pathway proposed here for NK cells can be extended to other types of immune cells. Understanding the molecular mechanisms involved in the dynamic regulation of the HIF-1α pathway in immune cells is of central importance to the immune cell function and could be a promising strategy in the design of treatments for human inflammatory diseases and cancer. link: http://identifiers.org/pubmed/31681292

Parameters:

Name Description
k1 = 2.0E-5 Reaction: => y2_Akt; y1_IL_15, Rate Law: compartment*k1*y1_IL_15
d8 = 0.577 Reaction: y8_STAT3 =>, Rate Law: compartment*d8*y8_STAT3
d2 = 0.848 Reaction: y2_Akt =>, Rate Law: compartment*d2*y2_Akt
delta = 200.0; xi10 = 8.127; a11 = 4.17; K_O2 = 0.96; k12 = 0.061 Reaction: y10_HIF_1a_aOH => ; y6_HIF_1_Complex, Rate Law: compartment*k12*K_O2*y10_HIF_1a_aOH*(delta*y6_HIF_1_Complex+a11)/(xi10+y10_HIF_1a_aOH)
d1 = 0.062 Reaction: y1_IL_15 =>, Rate Law: compartment*d1*y1_IL_15
d7 = 0.914 Reaction: y7_NF_KB =>, Rate Law: compartment*d7*y7_NF_KB
d5 = 0.196 Reaction: y5_HIF_1b =>, Rate Law: compartment*d5*y5_HIF_1b
k9 = 0.753 Reaction: => y9_HIF_1a_mRNA; y7_NF_KB, Rate Law: compartment*k9*y7_NF_KB
kalpha = 1.034 Reaction: => y4_HIF_1a; y9_HIF_1a_mRNA, Rate Law: compartment*kalpha*y9_HIF_1a_mRNA
phi = 0.829; K_O2 = 0.96; rho6 = 0.99; xi4 = 15.018; D = 1.0; k10 = 421.353 Reaction: y4_HIF_1a => y10_HIF_1a_aOH, Rate Law: compartment*k10*K_O2*phi*y4_HIF_1a*(1-rho6*D)/(xi4+y4_HIF_1a)
a7 = 0.0 Reaction: => y7_NF_KB, Rate Law: compartment*a7
k7 = 2.903 Reaction: => y7_NF_KB; y1_IL_15, Rate Law: compartment*k7*y1_IL_15
k3 = 0.181 Reaction: => y9_HIF_1a_mRNA; y8_STAT3, Rate Law: compartment*k3*y8_STAT3
d6 = 0.301 Reaction: y6_HIF_1_Complex =>, Rate Law: compartment*d6*y6_HIF_1_Complex
k15 = 0.088 Reaction: => y7_NF_KB; y3_mTOR, Rate Law: compartment*k15*y3_mTOR
a2 = 0.848 Reaction: => y2_Akt, Rate Law: compartment*a2
d10 = 0.935 Reaction: y10_HIF_1a_aOH =>, Rate Law: compartment*d10*y10_HIF_1a_aOH
d4 = 0.623 Reaction: y4_HIF_1a =>, Rate Law: compartment*d4*y4_HIF_1a
a1 = 0.0 Reaction: => y1_IL_15, Rate Law: compartment*a1
d9 = 0.934 Reaction: y9_HIF_1a_mRNA =>, Rate Law: compartment*d9*y9_HIF_1a_mRNA
xi28 = 38.44; kS = 9.0E-4; n2 = 2.0 Reaction: => y2_Akt; y8_STAT3, Rate Law: compartment*kS*y8_STAT3^n2/(xi28^n2+y8_STAT3^n2)
delta = 200.0; a11 = 4.17; K_O2 = 0.96; rho6 = 0.99; D = 1.0; xi44 = 128.022; k13 = 12.152 Reaction: y4_HIF_1a => ; y6_HIF_1_Complex, Rate Law: compartment*k13*K_O2*y4_HIF_1a*(delta*y6_HIF_1_Complex+a11)*(1-rho6*D)/(xi44+y4_HIF_1a)
alpha1 = 1.163; R = 0.0; k2 = 0.307; a3 = 0.037; alpha2 = 0.386 Reaction: => y3_mTOR; y2_Akt, y6_HIF_1_Complex, Rate Law: compartment*(a3+k2*y2_Akt)*alpha1*(1-R)/(alpha2*y6_HIF_1_Complex)
k4 = 76.196 Reaction: y4_HIF_1a + y5_HIF_1b => y6_HIF_1_Complex, Rate Law: compartment*k4*y4_HIF_1a*y5_HIF_1b
k11 = 0.211 Reaction: y10_HIF_1a_aOH => y4_HIF_1a, Rate Law: compartment*k11*y10_HIF_1a_aOH
a9 = 0.0 Reaction: => y9_HIF_1a_mRNA, Rate Law: compartment*a9
d3 = 0.919 Reaction: y3_mTOR =>, Rate Law: compartment*d3*y3_mTOR
a5 = 0.211 Reaction: => y5_HIF_1b, Rate Law: compartment*a5
k5 = 75.895 Reaction: y6_HIF_1_Complex => y4_HIF_1a + y5_HIF_1b, Rate Law: compartment*k5*y6_HIF_1_Complex
k8 = 0.577; a8 = 0.0; rho4 = 0.863; D = 1.0; k6 = 25.001; S3 = 0.0; rho3 = 1.0 Reaction: => y8_STAT3; y3_mTOR, y1_IL_15, Rate Law: compartment*(a8+k8*y3_mTOR+k6*(1-rho4*D)*y1_IL_15)*(1-rho3*S3)
k14 = 16.528 Reaction: => y7_NF_KB; y6_HIF_1_Complex, Rate Law: compartment*k14*y6_HIF_1_Complex

States:

Name Description
y3 mTOR [PR:000003041]
y5 HIF 1b [C28553]
y7 NF KB [NF-kB]
y9 HIF 1a mRNA [C20214; Messenger RNA]
y1 IL 15 [Interleukin-15]
y2 Akt [C41625]
y10 HIF 1a aOH [C20214; MOD:00677]
y8 STAT3 [C28664]
y4 HIF 1a [C20214]
y6 HIF 1 Complex [C28553; C20214; Complex]

Observables: none

MODEL0913049417 @ v0.0.1

This a model from the article: Ionic mechanisms underlying human atrial action potential properties: insights from a m…

The mechanisms underlying many important properties of the human atrial action potential (AP) are poorly understood. Using specific formulations of the K+, Na+, and Ca2+ currents based on data recorded from human atrial myocytes, along with representations of pump, exchange, and background currents, we developed a mathematical model of the AP. The model AP resembles APs recorded from human atrial samples and responds to rate changes, L-type Ca2+ current blockade, Na+/Ca2+ exchanger inhibition, and variations in transient outward current amplitude in a fashion similar to experimental recordings. Rate-dependent adaptation of AP duration, an important determinant of susceptibility to atrial fibrillation, was attributable to incomplete L-type Ca2+ current recovery from inactivation and incomplete delayed rectifier current deactivation at rapid rates. Experimental observations of variable AP morphology could be accounted for by changes in transient outward current density, as suggested experimentally. We conclude that this mathematical model of the human atrial AP reproduces a variety of observed AP behaviors and provides insights into the mechanisms of clinically important AP properties. link: http://identifiers.org/pubmed/9688927

Parameters: none

States: none

Observables: none

Crespo2012 - Kinetics of Amyloid Fibril FormationThis model is described in the article: [A generic crystallization-lik…

Associated with neurodegenerative disorders such as Alzheimer, Parkinson, or prion diseases, the conversion of soluble proteins into amyloid fibrils remains poorly understood. Extensive "in vitro" measurements of protein aggregation kinetics have been reported, but no consensus mechanism has emerged until now. This contribution aims at overcoming this gap by proposing a theoretically consistent crystallization-like model (CLM) that is able to describe the classic types of amyloid fibrillization kinetics identified in our literature survey. Amyloid conversion represented as a function of time is shown to follow different curve shapes, ranging from sigmoidal to hyperbolic, according to the relative importance of the nucleation and growth steps. Using the CLM, apparently unrelated data are deconvoluted into generic mechanistic information integrating the combined influence of seeding, nucleation, growth, and fibril breakage events. It is notable that this complex assembly of interdependent events is ultimately reduced to a mathematically simple model, whose two parameters can be determined by little more than visual inspection. The good fitting results obtained for all cases confirm the CLM as a good approximation to the generalized underlying principle governing amyloid fibrillization. A perspective is presented on possible applications of the CLM during the development of new targets for amyloid disease therapeutics. link: http://identifiers.org/pubmed/22767606

Parameters:

Name Description
kb = 1.6E-10; Ka = 1.44 Reaction: alpha = 1-1/(kb*(exp(Ka*time)-1)+1), Rate Law: missing

States:

Name Description
alpha [amyloid fibril]

Observables: none

Croft2013 - GPCR-RGS interaction that compartmentalizes RGS activityThrough modelling studies, the classic quaternary co…

G protein-coupled receptors (GPCRs) can interact with regulator of G protein signaling (RGS) proteins. However, the effects of such interactions on signal transduction and their physiological relevance have been largely undetermined. Ligand-bound GPCRs initiate by promoting exchange of GDP for GTP on the Gα subunit of heterotrimeric G proteins. Signaling is terminated by hydrolysis of GTP to GDP through intrinsic GTPase activity of the Gα subunit, a reaction catalyzed by RGS proteins. Using yeast as a tool to study GPCR signaling in isolation, we define an interaction between the cognate GPCR (Mam2) and RGS (Rgs1), mapping the interaction domains. This reaction tethers Rgs1 at the plasma membrane and is essential for physiological signaling response. In vivo quantitative data inform the development of a kinetic model of the GTPase cycle, which extends previous attempts by including GPCR-RGS interactions. In vivo and in silico data confirm that GPCR-RGS interactions can impose an additional layer of regulation through mediating RGS subcellular localization to compartmentalize RGS activity within a cell, thus highlighting their importance as potential targets to modulate GPCR signaling pathways. link: http://identifiers.org/pubmed/23900842

Parameters:

Name Description
k13=5.0E-4 1/hr Reaction: RGSc => RGSm; RGSc, Rate Law: compartment*RGSc*k13
k36=50.0 1/(nM*hr) Reaction: GaGTPEffectorOFF + LRRGSm => LRRGSmGaGTPEffectorOFF; GaGTPEffectorOFF, LRRGSm, Rate Law: compartment*GaGTPEffectorOFF*LRRGSm*k36
k12=10.0 1/(nM*hr) Reaction: Effector + GaGTP => GaGTPEffector; Effector, GaGTP, Rate Law: compartment*Effector*GaGTP*k12
k32=0.5 1/hr Reaction: RRGSmGaGTP => GaGDPP + RRGSm; RRGSmGaGTP, Rate Law: compartment*RRGSmGaGTP*k32
k6=0.005 1/(nM*hr) Reaction: RRGSm + Gabg => RRGSmGabg; RRGSm, Gabg, Rate Law: compartment*RRGSm*Gabg*k6
k18=100.0 1/hr Reaction: LRRGSm => LR + RGSm; LRRGSm, Rate Law: compartment*LRRGSm*k18
k7=0.02 1/(nM*hr) Reaction: LRRGSm + Gabg => LRRGSmGabg; LRRGSm, Gabg, Rate Law: compartment*LRRGSm*Gabg*k7
k38=1000.0 1/hr Reaction: GaGDPP => GaGDP + P; GaGDPP, Rate Law: compartment*GaGDPP*k38
k29=100.0 1/(nM*hr) Reaction: GaGTP + LRRGSm => LRRGSmGaGTP; GaGTP, LRRGSm, Rate Law: compartment*GaGTP*LRRGSm*k29
k28=2.5 1/hr Reaction: RGSmGaGTP => GaGDPP + RGSc; RGSmGaGTP, Rate Law: compartment*RGSmGaGTP*k28
k27=500.0 1/(nM*hr) Reaction: GaGTP + RGSm => RGSmGaGTP; GaGTP, RGSm, Rate Law: compartment*GaGTP*RGSm*k27
k15=0.1 1/(nM*hr) Reaction: R + RGSc => RRGSm; R, RGSc, Rate Law: compartment*R*RGSc*k15
k21=0.1 1/(nM*hr) Reaction: LRGabg + RGSc => LRRGSmGabg; LRGabg, RGSc, Rate Law: compartment*LRGabg*RGSc*k21
k17=0.1 1/(nM*hr) Reaction: LR + RGSc => LRRGSm; LR, RGSc, Rate Law: compartment*LR*RGSc*k17
k34=50.0 1/(nM*hr) Reaction: GaGTPEffectorOFF + RGSm => RGSmGaGTPEffectorOFF; GaGTPEffectorOFF, RGSm, Rate Law: compartment*GaGTPEffectorOFF*RGSm*k34
k2=0.005 1/(nM*hr) Reaction: R + Gabg => RGabg; R, Gabg, Rate Law: compartment*R*Gabg*k2
k37=0.3 1/hr Reaction: LRRGSmGaGTPEffectorOFF => GaGDPP + LRRGSm + Effector; LRRGSmGaGTPEffectorOFF, Rate Law: compartment*LRRGSmGaGTPEffectorOFF*k37
k40=10.0 1/hr Reaction: P => ; P, Rate Law: compartment*P*k40
k5=0.005 1/(nM*hr) Reaction: L + RRGSm => LRRGSm; L, RRGSm, Rate Law: compartment*L*RRGSm*k5
k24=1.0E-4 1/(nM*hr) Reaction: GaGTPEffectorOFF + RGSc => RGSmGaGTPEffectorOFF; GaGTPEffectorOFF, RGSc, Rate Law: compartment*GaGTPEffectorOFF*RGSc*k24
k1=0.0025 1/(nM*hr) Reaction: L + R => LR; L, R, Rate Law: compartment*L*R*k1
k35=0.3 1/hr Reaction: RGSmGaGTPEffectorOFF => GaGDPP + RGSc + Effector; RGSmGaGTPEffectorOFF, Rate Law: compartment*RGSmGaGTPEffectorOFF*k35
ka = 1.5 1/hr Reaction: => z1; GaGTPEffector, GaGTPEffector, Rate Law: compartment*GaGTPEffector*ka
k22=60.0 1/(nM*hr) Reaction: GaGTP + RGSc => RGSmGaGTP; GaGTP, RGSc, Rate Law: compartment*GaGTP*RGSc*k22
k26=0.005 1/hr Reaction: GaGTP => GaGDPP; GaGTP, Rate Law: compartment*GaGTP*k26
k39=1000.0 1/(nM*hr) Reaction: GaGDP + Gbg => Gabg; GaGDP, Gbg, Rate Law: compartment*GaGDP*Gbg*k39
k8=0.005 1/(nM*hr) Reaction: L + RRGSmGabg => LRRGSmGabg; L, RRGSmGabg, Rate Law: compartment*L*RRGSmGabg*k8
k10=0.2 1/hr Reaction: Gabg => GaGTP + Gbg; Gabg, Rate Law: compartment*Gabg*k10
k4=0.005 1/(nM*hr) Reaction: L + RGabg => LRGabg; L, RGabg, Rate Law: compartment*L*RGabg*k4
k23=0.05 1/hr Reaction: RGSmGaGTP => GaGTP + RGSc; RGSmGaGTP, Rate Law: compartment*RGSmGaGTP*k23
k19=0.1 1/(nM*hr) Reaction: RGabg + RGSc => RRGSmGabg; RGabg, RGSc, Rate Law: compartment*RGabg*RGSc*k19
k9=50.0 1/hr Reaction: LRGabg => LR + GaGTP + Gbg; LRGabg, Rate Law: compartment*LRGabg*k9
k16=100.0 1/hr Reaction: RRGSm => R + RGSm; RRGSm, Rate Law: compartment*RRGSm*k16
k11=40.0 1/hr Reaction: LRRGSmGabg => GaGTP + Gbg + LRRGSm; LRRGSmGabg, Rate Law: compartment*LRRGSmGabg*k11
k14=0.005 1/hr Reaction: RGSm => RGSc; RGSm, Rate Law: compartment*RGSm*k14
k3=0.02 1/(nM*hr) Reaction: LR + Gabg => LRGabg; LR, Gabg, Rate Law: compartment*LR*Gabg*k3
k25=1.0 1/hr Reaction: GaGTPEffector => GaGTPEffectorOFF; GaGTPEffector, Rate Law: compartment*GaGTPEffector*k25
k31=0.5 1/(nM*hr) Reaction: GaGTP + RRGSm => RRGSmGaGTP; GaGTP, RRGSm, Rate Law: compartment*GaGTP*RRGSm*k31
k33=0.005 1/hr Reaction: GaGTPEffectorOFF => GaGDPP + Effector; GaGTPEffectorOFF, Rate Law: compartment*GaGTPEffectorOFF*k33
k30=2.5 1/hr Reaction: LRRGSmGaGTP => GaGDPP + LRRGSm; LRRGSmGaGTP, Rate Law: compartment*LRRGSmGaGTP*k30
k20=0.1 1/hr Reaction: RRGSmGabg => RGabg + RGSm; RRGSmGabg, Rate Law: compartment*RRGSmGabg*k20

States:

Name Description
RGabg [IPR000239; IPR001019; IPR001632; IPR001770]
GaGDPP [GDP; IPR001019; phosphorylated]
z1 [delay]
P [phosphate(3-)]
RGSmGaGTP [Plasma membrane; GTP; IPR000342; IPR001019]
LRGabg [IPR000239; IPR001019; IPR001632; IPR001770; SBO:0000280]
L [SBO:0000280]
Gabg [IPR001019; IPR001632; IPR001770]
LRRGSm [Plasma membrane; IPR000239; IPR000342; SBO:0000280]
LRRGSmGabg [Plasma membrane; IPR000239; IPR001019; IPR000342; IPR001632; IPR001770; SBO:0000280]
RGSc [Cytoplasm; IPR000342]
GaGTP [GTP; IPR001019]
LR [IPR000239; SBO:0000280]
RGSmGaGTPEffectorOFF [effector; GTP; IPR000342; IPR001019; inactive]
LRRGSmGaGTP [GTP; IPR000342; IPR000239; IPR001019; SBO:0000280]
RRGSm [IPR000239; IPR000342; Plasma membrane]
LRRGSmGaGTPEffectorOFF [GTP; IPR000239; IPR001019; IPR000342; SBO:0000280; effector; inactive]
RRGSmGaGTP [GTP; IPR000239; IPR000342; IPR001019]
Gbg [IPR001632; IPR001770]
z3 [delay]
GaGDP [GDP; IPR001019]
GaGTPEffector [GTP; IPR001019; effector]
RRGSmGabg [IPR000239; IPR000342; IPR001019; IPR001632; IPR001770; Plasma membrane]
RGSm [Plasma membrane; IPR000342]
Effector [effector]
R [IPR000239]
z2 [delay]
GaGTPEffectorOFF [GTP; IPR001019; inactive; effector]

Observables: none

BIOMD0000000076 @ v0.0.1

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

Glycerol, a major by-product of ethanol fermentation by Saccharomyces cerevisiae, is of significant importance to the wine, beer, and ethanol production industries. To gain a clearer understanding of and to quantify the extent to which parameters of the pathway affect glycerol flux in S. cerevisiae, a kinetic model of the glycerol synthesis pathway has been constructed. Kinetic parameters were collected from published values. Maximal enzyme activities and intracellular effector concentrations were determined experimentally. The model was validated by comparing experimental results on the rate of glycerol production to the rate calculated by the model. Values calculated by the model agreed well with those measured in independent experiments. The model also mimics the changes in the rate of glycerol synthesis at different phases of growth. Metabolic control analysis values calculated by the model indicate that the NAD(+)-dependent glycerol 3-phosphate dehydrogenase-catalyzed reaction has a flux control coefficient (C(J)v1) of approximately 0.85 and exercises the majority of the control of flux through the pathway. Response coefficients of parameter metabolites indicate that flux through the pathway is most responsive to dihydroxyacetone phosphate concentration (R(J)DHAP= 0.48 to 0.69), followed by ATP concentration (R(J)ATP = -0.21 to -0.50). Interestingly, the pathway responds weakly to NADH concentration (R(J)NADH = 0.03 to 0.08). The model indicates that the best strategy to increase flux through the pathway is not to increase enzyme activity, substrate concentration, or coenzyme concentration alone but to increase all of these parameters in conjunction with each other. link: http://identifiers.org/pubmed/12200299

Parameters:

Name Description
K2phi=1.0 mM; V2=53.0 mM_per_minute; K2g3p=3.5 mM; Phi=1.0 mM Reaction: G3P => Gly, Rate Law: compartment*V2*G3P/K2g3p/((1+G3P/K2g3p)*(1+Phi/K2phi))
Keq1=10000.0 dimensionless; K1f16bp=4.8 mM; K1nadh=0.023 mM; NADH=1.87 mM; K1dhap=0.54 mM; ADP=2.17 mM; K1nad=0.93 mM; ATP=2.37 mM; K1atp=0.73 mM; K1adp=2.0 mM; F16BP=6.01 mM; K1g3p=1.2 mM; Vf1=47.0 mM_per_minute; NAD=1.45 mM Reaction: DHAP => G3P, Rate Law: compartment*Vf1/(K1nadh*K1dhap)*(NADH*DHAP-NAD*G3P/Keq1)/((1+F16BP/K1f16bp+ATP/K1atp+ADP/K1adp)*(1+NADH/K1nadh+NAD/K1nad)*(1+DHAP/K1dhap+G3P/K1g3p))

States:

Name Description
Gly [glycerol; Glycerol]
DHAP [dihydroxyacetone phosphate; Glycerone phosphate]
G3P [sn-glycerol 3-phosphate; sn-Glycerol 3-phosphate]

Observables: none

MODEL3897771820 @ v0.0.1

This model originates from the [Cell Cycle Database](http://www.itb.cnr.it/cellcycle/) . It is described in: **Analys…

We propose a protein interaction network for the regulation of DNA synthesis and mitosis that emphasizes the universality of the regulatory system among eukaryotic cells. The idiosyncrasies of cell cycle regulation in particular organisms can be attributed, we claim, to specific settings of rate constants in the dynamic network of chemical reactions. The values of these rate constants are determined ultimately by the genetic makeup of an organism. To support these claims, we convert the reaction mechanism into a set of governing kinetic equations and provide parameter values (specific to budding yeast, fission yeast, frog eggs, and mammalian cells) that account for many curious features of cell cycle regulation in these organisms. Using one-parameter bifurcation diagrams, we show how overall cell growth drives progression through the cell cycle, how cell-size homeostasis can be achieved by two different strategies, and how mutations remodel bifurcation diagrams and create unusual cell-division phenotypes. The relation between gene dosage and phenotype can be summarized compactly in two-parameter bifurcation diagrams. Our approach provides a theoretical framework in which to understand both the universality and particularity of cell cycle regulation, and to construct, in modular fashion, increasingly complex models of the networks controlling cell growth and division. link: http://identifiers.org/pubmed/16581849

Parameters: none

States: none

Observables: none

The role of geospatial disparities in the dynamics of the COVID-19 pandemic is poorly understood. We developed a spatial…

The role of geospatial disparities in the dynamics of the COVID-19 pandemic is poorly understood. We developed a spatially-explicit mathematical model to simulate transmission dynamics of COVID-19 disease infection in relation with the uneven distribution of the healthcare capacity in Ohio, U.S. The results showed substantial spatial variation in the spread of the disease, with localized areas showing marked differences in disease attack rates. Higher COVID-19 attack rates experienced in some highly connected and urbanized areas (274 cases per 100,000 people) could substantially impact the critical health care response of these areas regardless of their potentially high healthcare capacity compared to more rural and less connected counterparts (85 cases per 100,000). Accounting for the spatially uneven disease diffusion linked to the geographical distribution of the critical care resources is essential in designing effective prevention and control programmes aimed at reducing the impact of COVID-19 pandemic. link: http://identifiers.org/pubmed/32736312

Parameters: none

States: none

Observables: none

This is a simple mathematical model describing the growth and removal of normal and leukemic haematopoietic stem cell po…

Acute myeloid leukaemia is defined by the expansion of a mutated haematopoietic stem cell clone, with the inhibition of surrounding normal clones. Haematopoiesis can be seen as an evolutionary tree, starting with one cell that undergoes several divisions during the expansion phase, afterwards losing functional cells during the aging-related contraction phase. During divisions, offspring cells acquire variations, which can be either normal or abnormal. If an abnormal variation is present in more than 25% of the final cells, a monoclonal, leukemic pattern occurs. Such a pattern develops if: (A1) The abnormal variation occurs early, during the first or second divisions; (A2) The variation confers exceptional proliferative capacity; (B) A sizable proportion of the normal clones are destroyed and a previously non-significant abnormal clone gains relative dominance over a depleted environment; (C) The abnormal variation confers relative immortality, rendering it significant during the contraction phase. Combinations of these pathways further enhance the leukemic risk of the system. A simple mathematical model is used in order to characterize normal and leukemic states and to explain the above cellular processes generating monoclonal leukemic patterns. link: http://identifiers.org/doi/10.1080/17486700902973751

Parameters:

Name Description
c = 0.1 Reaction: x_Normal_Hematopoietic_Stem_Cell =>, Rate Law: compartment*c*x_Normal_Hematopoietic_Stem_Cell
C = 0.1 Reaction: y_Leukemic_Cell =>, Rate Law: compartment*C*y_Leukemic_Cell
a = 0.3; b = 0.5 Reaction: => x_Normal_Hematopoietic_Stem_Cell; y_Leukemic_Cell, Rate Law: compartment*a*x_Normal_Hematopoietic_Stem_Cell/(1+b*(x_Normal_Hematopoietic_Stem_Cell+y_Leukemic_Cell))
B = 0.5; A = 0.3 Reaction: => y_Leukemic_Cell; x_Normal_Hematopoietic_Stem_Cell, Rate Law: compartment*A*y_Leukemic_Cell/(1+B*(x_Normal_Hematopoietic_Stem_Cell+y_Leukemic_Cell))

States:

Name Description
y Leukemic Cell [leukemic stem cell; bone marrow]
x Normal Hematopoietic Stem Cell [hematopoietic stem cell; bone marrow]

Observables: none

MODEL0913003363 @ v0.0.1

This a model from the article: Mathematical modeling of calcium homeostasis in yeast cells. Cui J, Kaandorp JA. Cell…

In this study, based on currently available experimental observations on protein level, we constructed a mathematical model to describe calcium homeostasis in normally growing yeast cells (Saccharomyces cerevisiae). Simulation results show that tightly controlled low cytosolic calcium ion level can be a natural result under the general mechanism of gene expression feedback control. The calmodulin (a sensor protein) behavior in our model cell agrees well with relevant observations in real cells. Moreover, our model can qualitatively reproduce the experimentally observed response curve of real yeast cell responding to step-like disturbance in extracellular calcium ion concentration. Further investigations show that the feedback control mechanism in our model is as robust as it is in real cells. link: http://identifiers.org/pubmed/16445978

Parameters: none

States: none

Observables: none

BACKGROUND: The zinc homeostasis system in Escherichia coli is one of the most intensively studied prokaryotic zinc home…

BACKGROUND: The zinc homeostasis system in Escherichia coli is one of the most intensively studied prokaryotic zinc homeostasis systems. Its underlying regulatory machine consists of repression on zinc influx through ZnuABC by Zur (Zn2+ uptake regulator) and activation on zinc efflux via ZntA by ZntR (a zinc-responsive regulator). Although these transcriptional regulations seem to be well characterized, and there is an abundance of detailed in vitro experimental data available, as yet there is no mathematical model to help interpret these data. To our knowledge, the work described here is the first attempt to use a mathematical model to simulate these regulatory relations and to help explain the in vitro experimental data. RESULTS: We develop a unified mathematical model consisting of 14 reactions to simulate the in vitro transcriptional response of the zinc homeostasis system in E. coli. Firstly, we simulate the in vitro Zur-DNA interaction by using two of these reactions, which are expressed as 4 ordinary differential equations (ODEs). By imposing the conservation restraints and solving the relevant steady state equations, we find that the simulated sigmoidal curve matches the corresponding experimental data. Secondly, by numerically solving the ODEs for simulating the Zur and ZntR run-off transcription experiments, and depicting the simulated concentrations of zntA and znuC transcripts as a function of free zinc concentration, we find that the simulated curves fit the corresponding in vitro experimental data. Moreover, we also perform simulations, after taking into consideration the competitive effects of ZntR with the zinc buffer, and depict the simulated concentration of zntA transcripts as a function of the total ZntR concentration, both in the presence and absence of Zn(II). The obtained simulation results are in general agreement with the corresponding experimental data. CONCLUSION: Simulation results show that our model can quantitatively reproduce the results of several of the in vitro experiments conducted by Outten CE and her colleagues. Our model provides a detailed insight into the dynamics of the regulatory system and also provides a general framework for simulating in vitro metal-binding and transcription experiments and interpreting the relevant experimental data. link: http://identifiers.org/pubmed/18950480

Parameters: none

States: none

Observables: none

MODEL1172425728 @ v0.0.1

This a model from the article: Simulating Complex Calcium-Calcineurin Signaling Network Jiangjun Cui and Jaap A. Kaa…

Understanding of processes in which calcium signaling is involved is of fundamental importance in systems biology and has many applications in medicine. In this paper we have studied the particular case of the complex calcium-calcineurin-MCIP-NFAT signaling network in cardiac myocytes, the understanding of which is critical for treatment of pathologic hypertrophy and heart failure. By including some most recent experimental findings, we constructed a computational model totally based on biochemical principles. The model can correctly predict the mutant (MCIP1−/−) behavior under different stress such as PO (pressure overload) and Caivated calcineurin) overexpression. link: http://identifiers.org/doi/10.1007/978-3-540-69389-5_14

Parameters: none

States: none

Observables: none

BIOMD0000000068 @ v0.0.1

This a model from the article: A kinetic model of the branch-point between the methionine and threonine biosynthesis p…

This work proposes a model of the metabolic branch-point between the methionine and threonine biosynthesis pathways in Arabidopsis thaliana which involves kinetic competition for phosphohomoserine between the allosteric enzyme threonine synthase and the two-substrate enzyme cystathionine gamma-synthase. Threonine synthase is activated by S-adenosylmethionine and inhibited by AMP. Cystathionine gamma-synthase condenses phosphohomoserine to cysteine via a ping-pong mechanism. Reactions are irreversible and inhibited by inorganic phosphate. The modelling procedure included an examination of the kinetic links, the determination of the operating conditions in chloroplasts and the establishment of a computer model using the enzyme rate equations. To test the model, the branch-point was reconstituted with purified enzymes. The computer model showed a partial agreement with the in vitro results. The model was subsequently improved and was then found consistent with flux partition in vitro and in vivo. Under near physiological conditions, S-adenosylmethionine, but not AMP, modulates the partition of a steady-state flux of phosphohomoserine. The computer model indicates a high sensitivity of cystathionine flux to enzyme and S-adenosylmethionine concentrations. Cystathionine flux is sensitive to modulation of threonine flux whereas the reverse is not true. The cystathionine gamma-synthase kinetic mechanism favours a low sensitivity of the fluxes to cysteine. Though sensitivity to inorganic phosphate is low, its concentration conditions the dynamics of the system. Threonine synthase and cystathionine gamma-synthase display similar kinetic efficiencies in the metabolic context considered and are first-order for the phosphohomoserine substrate. Under these conditions outflows are coordinated. link: http://identifiers.org/pubmed/14622248

Parameters:

Name Description
KmPHSER=2500.0 microM; kcat2=30.0 microM; KmCYS=460.0 microM; Ki2=2000.0 microM Reaction: Phser + Cys => Cystathionine + Phi; CGS, Rate Law: CGS*kcat2/(1+KmCYS/Cys)*Phser/(Phser+KmPHSER*(1+Phi/Ki2)/(1+KmCYS/Cys))
V0=1.0 microM_per_second Reaction: Hser => Phser, Rate Law: compartment*V0
Ki3=1000.0 microM Reaction: Phser => Thr + Phi; AdoMet, TS, Rate Law: TS*(5.9E-4+0.062*AdoMet^2.9/(32^2.9+AdoMet^2.9))*Phser/(1+Phi/Ki3)

States:

Name Description
Cys [L-cysteine; L-Cysteine]
Hser [L-homoserine; L-Homoserine]
Thr [L-threonine; L-Threonine]
Cystathionine [L-cystathionine; L-Cystathionine]
Phi [phosphate(3-); Orthophosphate]
Phser [O-phospho-L-homoserine; O-Phospho-L-homoserine]

Observables: none

BIOMD0000000212 @ v0.0.1

This a model described in the article: Understanding the regulation of aspartate metabolism using a model based on mea…

The aspartate-derived amino-acid pathway from plants is well suited for analysing the function of the allosteric network of interactions in branched pathways. For this purpose, a detailed kinetic model of the system in the plant model Arabidopsis was constructed on the basis of in vitro kinetic measurements. The data, assembled into a mathematical model, reproduce in vivo measurements and also provide non-intuitive predictions. A crucial result is the identification of allosteric interactions whose function is not to couple demand and supply but to maintain a high independence between fluxes in competing pathways. In addition, the model shows that enzyme isoforms are not functionally redundant, because they contribute unequally to the flux and its regulation. Another result is the identification of the threonine concentration as the most sensitive variable in the system, suggesting a regulatory role for threonine at a higher level of integration. link: http://identifiers.org/pubmed/19455135

Parameters:

Name Description
AKII_kreverse_app_exp=0.22 l*μmol^(-1)*s^(-1); AKII_Thr_Ki_app_exp=109.0 μmol*l^(-1); AKII_nH_exp=2.0 dimensionless; AKII_kforward_app_exp=1.35 s^(-1) Reaction: Asp => AspP; AKHSDHII, Thr, Rate Law: c1*AKHSDHII*(AKII_kforward_app_exp-AKII_kreverse_app_exp*AspP)/(1+(Thr/AKII_Thr_Ki_app_exp)^AKII_nH_exp)
AKI_kreverse_app_exp=0.15 l*μmol^(-1)*s^(-1); AKI_nH_exp=2.0 dimensionless; AKI_Thr_Ki_app_exp=124.0 μmol*l^(-1); AKI_kforward_app_exp=0.36 s^(-1) Reaction: Asp => AspP; AKHSDHI, Thr, Rate Law: c1*AKHSDHI*(AKI_kforward_app_exp-AKI_kreverse_app_exp*AspP)/(1+(Thr/AKI_Thr_Ki_app_exp)^AKI_nH_exp)
TS1_kcatmin_exp=0.42 dimensionless; TS1_nH_exp=2.0 dimensionless; TS1_AdoMet_Ka2_exp=0.5 dimensionless; TS1_AdoMet_Ka1_exp=73.0 μmol^2*l^(-2); TS1_AdoMEt_Km_no_AdoMet_exp=250.0 dimensionless; TS1_AdoMet_Ka3_exp=1.09 dimensionless; TS1_AdoMet_kcatmax_exp=3.5 dimensionless; TS1_Phosphate_Ki_exp=1000.0 μmol*l^(-1); TS1_AdoMet_Ka4_exp=140.0 μmol^2*l^(-2) Reaction: PHser => Thr; TS1, Phosphate, AdoMet, Rate Law: c1*TS1*PHser*(TS1_kcatmin_exp+TS1_AdoMet_kcatmax_exp*AdoMet^TS1_nH_exp/TS1_AdoMet_Ka1_exp)/(1+AdoMet^TS1_nH_exp/TS1_AdoMet_Ka1_exp)/(TS1_AdoMEt_Km_no_AdoMet_exp*(1+AdoMet/TS1_AdoMet_Ka2_exp)/(1+AdoMet/TS1_AdoMet_Ka3_exp)/(1+AdoMet^TS1_nH_exp/TS1_AdoMet_Ka4_exp)*(1+Phosphate/TS1_Phosphate_Ki_exp)+PHser)
AK2_kforward_app_exp=3.15 s^(-1); AK2_nH_exp=1.1 dimensionless; AK2_kreverse_app_exp=0.86 l*μmol^(-1)*s^(-1); AK2_Lys_Ki_app_exp=22.0 μmol*l^(-1) Reaction: Asp => AspP; AK2, Lys, Rate Law: c1*AK2*(AK2_kforward_app_exp-AK2_kreverse_app_exp*AspP)/(1+(Lys/AK2_Lys_Ki_app_exp)^AK2_nH_exp)
TD_nH_app_exp=3.0 dimensionless; TD_k_app_exp=0.0124 dimensionless; TD_Ile_Ki_no_Val_app_exp=30.0 dimensionless; TD_Val_Ka1_app_exp=73.0 dimensionless; TD_Val_Ka2_app_exp=615.0 μmol*l^(-1) Reaction: Thr => Ile; TD, Val, Ile, Rate Law: c1*TD*Thr*TD_k_app_exp/(1+(Ile/(TD_Ile_Ki_no_Val_app_exp+TD_Val_Ka1_app_exp*Val/(TD_Val_Ka2_app_exp+Val)))^TD_nH_app_exp)
AK1_nH_exp=2.0 dimensionless; AK1_kreverse_app_exp=1.6 l*μmol^(-1)*s^(-1); AK1_kforward_app_exp=5.65 s^(-1); AK1_Lys_Ki_app_exp=550.0 μmol*l^(-1); AK1_AdoMet_Ka_app_exp=3.5 μmol*l^(-1) Reaction: Asp => AspP; AK1, Lys, AdoMet, Rate Law: c1*AK1*(AK1_kforward_app_exp-AK1_kreverse_app_exp*AspP)/(1+(Lys/(AK1_Lys_Ki_app_exp/(1+AdoMet/AK1_AdoMet_Ka_app_exp)))^AK1_nH_exp)
THA_Thr_Km_exp=7100.0 μmol*l^(-1); THA_kcat_exp=1.7 s^(-1) Reaction: Thr => Gly; THA, Rate Law: c1*THA_kcat_exp*THA*Thr/(THA_Thr_Km_exp+Thr)
ASADH_kforward_app_exp=0.9 l*μmol^(-1)*s^(-1); ASADH_kreverse_app_exp=0.23 l*μmol^(-1)*s^(-1) Reaction: AspP => ASA; ASADH, Rate Law: c1*ASADH*(ASADH_kforward_app_exp*AspP-ASADH_kreverse_app_exp*ASA)
HSDHII_Thr_relative_inhibition_app_exp=0.75 dimensionless; HSDHII_kforward_app_exp=0.64 l*μmol^(-1)*s^(-1); HSDHII_Thr_relative_residual_activity_app_exp=0.25 dimensionless; HSDHII_Thr_Ki_app_exp=8500.0 μmol*l^(-1) Reaction: ASA => Hser; AKHSDHII, Thr, Rate Law: c1*HSDHII_kforward_app_exp*AKHSDHII*ASA*(HSDHII_Thr_relative_residual_activity_app_exp+HSDHII_Thr_relative_inhibition_app_exp/(1+Thr/HSDHII_Thr_Ki_app_exp))
DHDPS2_Lys_Ki_app_exp=33.0 μmol*l^(-1); DHDPS2_nH_exp=2.0 dimensionless; DHDPS2_k_app_exp=1.0 μmol*l^(-1) Reaction: ASA => Lys; DHDPS2, Lys, Rate Law: c1*DHDPS2_k_app_exp*DHDPS2*ASA*1/(1+(Lys/DHDPS2_Lys_Ki_app_exp)^DHDPS2_nH_exp)
V_Thr_RS = 0.43 μmol*l^(-1)*s^(-1); Thr_tRNAS_Thr_Km=100.0 μmol*l^(-1) Reaction: Thr => ThrTRNA, Rate Law: c1*V_Thr_RS*Thr/(Thr_tRNAS_Thr_Km+Thr)
DHDPS1_nH_exp=2.0 dimensionless; DHDPS1_Lys_Ki_app_exp=10.0 μmol*l^(-1); DHDPS1_k_app_exp=1.0 μmol*l^(-1) Reaction: ASA => Lys; DHDPS1, Lys, Rate Law: c1*DHDPS1_k_app_exp*DHDPS1*ASA*1/(1+(Lys/DHDPS1_Lys_Ki_app_exp)^DHDPS1_nH_exp)
LKR_Lys_Km_exp=13000.0 μmol*l^(-1); LKR_kcat_exp=3.1 s^(-1) Reaction: Lys => Sacc; LKR, Rate Law: c1*LKR_kcat_exp*LKR*Lys/(LKR_Lys_Km_exp+Lys)
HSK_Hser_app_exp=14.0 μmol*l^(-1); HSK_kcat_app_exp=2.8 s^(-1) Reaction: Hser => PHser; HSK, Rate Law: c1*HSK_kcat_app_exp*HSK*Hser/(HSK_Hser_app_exp+Hser)
HSDHI_Thr_relative_inhibition_app_exp=0.86 dimensionless; HSDHI_kforward_app_exp=0.84 l*μmol^(-1)*s^(-1); HSDHI_Thr_relative_residual_activity_app_exp=0.14 dimensionless; HSDHI_Thr_Ki_app_exp=400.0 μmol*l^(-1) Reaction: ASA => Hser; AKHSDHI, Thr, Rate Law: c1*HSDHI_kforward_app_exp*AKHSDHI*ASA*(HSDHI_Thr_relative_residual_activity_app_exp+HSDHI_Thr_relative_inhibition_app_exp/(1+Thr/HSDHI_Thr_Ki_app_exp))
CGS_kcat_exp=30.0 dimensionless; CGS_Phosphate_Ki_exp=2000.0 dimensionless; CGS_Cys_Km_exp=460.0 dimensionless; CGS_Phser_Km_exp=2500.0 dimensionless Reaction: PHser => Cysta; CGS, Cys, Phosphate, Rate Law: c1*CGS*PHser*CGS_kcat_exp/(1+CGS_Cys_Km_exp/Cys)/(CGS_Phser_Km_exp/(1+CGS_Cys_Km_exp/Cys)*(1+Phosphate/CGS_Phosphate_Ki_exp)+PHser)
Ile_tRNAS_Ile_Km=20.0 μmol*l^(-1); V_Ile_RS = 0.43 μmol*l^(-1)*s^(-1) Reaction: Ile => IleTRNA, Rate Law: c1*V_Ile_RS*Ile/(Ile_tRNAS_Ile_Km+Ile)
Lys_tRNAS_Lys_Km=25.0 μmol*l^(-1); V_Lys_RS = 0.43 μmol*l^(-1)*s^(-1) Reaction: Lys => LysTRNA, Rate Law: c1*V_Lys_RS*Lys/(Lys_tRNAS_Lys_Km+Lys)

States:

Name Description
IleTRNA [Ile-tRNA(Ile); L-Isoleucyl-tRNA(Ile)]
Gly [glycine; Glycine]
Lys [L-lysine; L-Lysine; 47205736]
Cysta [cystathionine; Cystathionine]
ASA [L-aspartate 4-semialdehyde; L-Aspartate 4-semialdehyde]
Asp [L-aspartic acid; L-Aspartate; 47205730]
Hser [L-homoserine; L-Homoserine]
Thr [L-threonine; L-Threonine]
AspP [4-phospho-L-aspartic acid; 4-Phospho-L-aspartate]
ThrTRNA [Thr-tRNA(Thr); L-Threonyl-tRNA(Thr)]
PHser [O-phospho-L-homoserine; O-Phospho-L-homoserine]
Sacc [L-saccharopine; N6-(L-1,3-Dicarboxypropyl)-L-lysine]
Ile [L-isoleucine; L-Isoleucine]
LysTRNA [Lys-tRNA(Lys); L-Lysyl-tRNA]

Observables: none

Cursons2015 - Regulation of ERK-MAPK signaling in human epidermisModel comparing the abundance of phosphorylated MAPK si…

The skin is largely comprised of keratinocytes within the interfollicular epidermis. Over approximately two weeks these cells differentiate and traverse the thickness of the skin. The stage of differentiation is therefore reflected in the positions of cells within the tissue, providing a convenient axis along which to study the signaling events that occur in situ during keratinocyte terminal differentiation, over this extended two-week timescale. The canonical ERK-MAPK signaling cascade (Raf-1, MEK-1/2 and ERK-1/2) has been implicated in controlling diverse cellular behaviors, including proliferation and differentiation. While the molecular interactions involved in signal transduction through this cascade have been well characterized in cell culture experiments, our understanding of how this sequence of events unfolds to determine cell fate within a homeostatic tissue environment has not been fully characterized.We measured the abundance of total and phosphorylated ERK-MAPK signaling proteins within interfollicular keratinocytes in transverse cross-sections of human epidermis using immunofluorescence microscopy. To investigate these data we developed a mathematical model of the signaling cascade using a normalized-Hill differential equation formalism.These data show coordinated variation in the abundance of phosphorylated ERK-MAPK components across the epidermis. Statistical analysis of these data shows that associations between phosphorylated ERK-MAPK components which correspond to canonical molecular interactions are dependent upon spatial position within the epidermis. The model demonstrates that the spatial profile of activation for ERK-MAPK signaling components across the epidermis may be maintained in a cell-autonomous fashion by an underlying spatial gradient in calcium signaling.Our data demonstrate an extended phospho-protein profile of ERK-MAPK signaling cascade components across the epidermis in situ, and statistical associations in these data indicate canonical ERK-MAPK interactions underlie this spatial profile of ERK-MAPK activation. Using mathematical modelling we have demonstrated that spatially varying calcium signaling components across the epidermis may be sufficient to maintain the spatial profile of ERK-MAPK signaling cascade components in a cell-autonomous manner. These findings may have significant implications for the wide range of cancer drugs which therapeutically target ERK-MAPK signaling components. link: http://identifiers.org/pubmed/26209520

Parameters:

Name Description
funcHillMEKToERKNuc = 0.387623644887571 1; numCytoToNucVolRatio = 2.35714285714286 1; numERKCytoToNucParam = 0.01 1; numERKNucToCytoParam = 0.01 1; numHillMax = 1.0 1; funcHillERKToERKNuc = 0.16212840948525 1; numHillTau = 1.0 1 Reaction: pERK_nuc = 1/numHillTau*((((funcHillMEKToERKNuc-funcHillERKToERKNuc)*numHillMax-pERK_nuc)-numERKCytoToNucParam*pERK_nuc)+numCytoToNucVolRatio*numERKNucToCytoParam*pERK_cyto), Rate Law: 1/numHillTau*((((funcHillMEKToERKNuc-funcHillERKToERKNuc)*numHillMax-pERK_nuc)-numERKCytoToNucParam*pERK_nuc)+numCytoToNucVolRatio*numERKNucToCytoParam*pERK_cyto)
numCytoToNucVolRatio = 2.35714285714286 1; numMEKCytoToNucParam = 0.05 1; numMEKNucToCytoParam = 0.5 1; numHillTau = 1.0 1 Reaction: pMEK_nuc = 1/numHillTau*(((-pMEK_nuc)-numMEKNucToCytoParam*pMEK_nuc)+numCytoToNucVolRatio*numMEKCytoToNucParam*pMEK_cyto), Rate Law: 1/numHillTau*(((-pMEK_nuc)-numMEKNucToCytoParam*pMEK_nuc)+numCytoToNucVolRatio*numMEKCytoToNucParam*pMEK_cyto)
funcHillMEKToERKCyto = 0.819554213811451 1; numCytoToNucVolRatio = 2.35714285714286 1; numERKCytoToNucParam = 0.01 1; numERKNucToCytoParam = 0.01 1; numHillMax = 1.0 1; numHillTau = 1.0 1 Reaction: pERK_cyto = 1/numHillTau*(((funcHillMEKToERKCyto*numHillMax-pERK_cyto)-numERKCytoToNucParam*pERK_cyto)+1/numCytoToNucVolRatio*numERKNucToCytoParam*pERK_nuc), Rate Law: 1/numHillTau*(((funcHillMEKToERKCyto*numHillMax-pERK_cyto)-numERKCytoToNucParam*pERK_cyto)+1/numCytoToNucVolRatio*numERKNucToCytoParam*pERK_nuc)
numHillMax = 1.0 1; numTotalRafInputs = 0.0 1; numHillTau = 1.0 1 Reaction: pRaf_cyto = 1/numHillTau*(numTotalRafInputs*numHillMax-pRaf_cyto), Rate Law: 1/numHillTau*(numTotalRafInputs*numHillMax-pRaf_cyto)
numCytoToNucVolRatio = 2.35714285714286 1; numHillMax = 1.0 1; numMEKCytoToNucParam = 0.05 1; numMEKNucToCytoParam = 0.5 1; numHillTau = 1.0 1; funcHillRafToMEK = 0.501898596166978 1 Reaction: pMEK_cyto = 1/numHillTau*(((funcHillRafToMEK*numHillMax-pMEK_cyto)-numMEKCytoToNucParam*pMEK_cyto)+1/numCytoToNucVolRatio*numMEKNucToCytoParam*pMEK_nuc), Rate Law: 1/numHillTau*(((funcHillRafToMEK*numHillMax-pMEK_cyto)-numMEKCytoToNucParam*pMEK_cyto)+1/numCytoToNucVolRatio*numMEKNucToCytoParam*pMEK_nuc)

States:

Name Description
pRaf cyto [RAF proto-oncogene serine/threonine-protein kinase; urn:miriam:pato:PATO_0002220]
pERK cyto [urn:miriam:pato:PATO_0002220; Mitogen-activated protein kinase 1]
pMEK nuc [Dual specificity mitogen-activated protein kinase kinase 1; urn:miriam:pato:PATO_0002220]
pERK nuc [urn:miriam:pato:PATO_0002220; Mitogen-activated protein kinase 1]
pMEK cyto [urn:miriam:pato:PATO_0002220; Dual specificity mitogen-activated protein kinase kinase 1]

Observables: none

BIOMD0000000015 @ v0.0.1

Curto1998 - purine metabolismThis is a purine metabolism model that is geared toward studies of gout. The model uses Ge…

Experimental and clinical data on purine metabolism are collated and analyzed with three mathematical models. The first model is the result of an attempt to construct a traditional kinetic model based on Michaelis-Menten rate laws. This attempt is only partially successful, since kinetic information, while extensive, is not complete, and since qualitative information is difficult to incorporate into this type of model. The data gaps necessitate the complementation of the Michaelis-Menten model with other functional forms that can incorporate different types of data. The most convenient and established representations for this purpose are rate laws formulated as power-law functions, and these are used to construct a Complemented Michaelis-Menten (CMM) model. The other two models are pure power-law-representations, one in the form of a Generalized Mass Action (GMA) system, and the other one in the form of an S-system. The first part of the paper contains a compendium of experimental data necessary for any model of purine metabolism. This is followed by the formulation of the three models and a comparative analysis. For physiological and moderately pathological perturbations in metabolites or enzymes, the results of the three models are very similar and consistent with clinical findings. This is an encouraging result since the three models have different structures and data requirements and are based on different mathematical assumptions. Significant enzyme deficiencies are not so well modeled by the S-system model. The CMM model captures the dynamics better, but judging by comparisons with clinical observations, the best model in this case is the GMA model. The model results are discussed in some detail, along with advantages and disadvantages of each modeling strategy. link: http://identifiers.org/pubmed/9664759

Parameters:

Name Description
ahx=0.003793; fhx13=1.12 Reaction: HX =>, Rate Law: ahx*HX^fhx13
aadna=3.2789; fdnap9=0.42; fdnap10=0.33 Reaction: dATP => DNA; dGTP, Rate Law: aadna*dATP^fdnap9*dGTP^fdnap10
frnan11=1.0; arnag=0.04615 Reaction: RNA => GTP, Rate Law: arnag*RNA^frnan11
aasli=66544.0; fasli3=0.99; fasli4=-0.95 Reaction: SAMP => ATP; ATP, Rate Law: aasli*SAMP^fasli3*ATP^fasli4
fxd14=0.55; axd=0.949 Reaction: Xa => UA, Rate Law: axd*Xa^fxd14
fgnuc18=-0.34; agnuc=0.2511; fgnuc8=0.9 Reaction: GTP => Gua; Pi, Rate Law: agnuc*GTP^fgnuc8*Pi^fgnuc18
apyr=1.2951; fpyr1=1.27 Reaction: PRPP =>, Rate Law: apyr*PRPP^fpyr1
fhxd13=0.65; ahxd=0.2754 Reaction: HX => Xa, Rate Law: ahxd*HX^fhxd13
agrna=409.6; frnap4=0.05; frnap8=0.13 Reaction: GTP => RNA; ATP, Rate Law: agrna*ATP^frnap4*GTP^frnap8
fgprt15=0.42; fgprt8=-1.2; agprt=361.69; fgprt1=1.2 Reaction: Gua + PRPP => GTP; GTP, Rate Law: agprt*PRPP^fgprt1*GTP^fgprt8*Gua^fgprt15
fmat5=-0.6; amat=7.2067; fmat4=0.2 Reaction: ATP => SAM; SAM, Rate Law: amat*ATP^fmat4*SAM^fmat5
aadrnr=0.0602; fadrnr10=0.87; fadrnr9=-0.3; fadrnr4=0.1 Reaction: ATP => dATP; dGTP, dATP, Rate Law: aadrnr*ATP^fadrnr4*dATP^fadrnr9*dGTP^fadrnr10
fimpd2=0.15; aimpd=1.2823; fimpd8=-0.03; fimpd7=-0.09 Reaction: IMP => XMP; GTP, XMP, Rate Law: aimpd*IMP^fimpd2*XMP^fimpd7*GTP^fimpd8
fdada9=1.0; adada=0.03333 Reaction: dATP => HX, Rate Law: adada*dATP^fdada9
agmpr=0.3005; fgmpr7=-0.76; fgmpr2=-0.15; fgmpr4=-0.07; fgmpr8=0.7 Reaction: GTP => IMP; XMP, ATP, IMP, Rate Law: agmpr*IMP^fgmpr2*ATP^fgmpr4*XMP^fgmpr7*GTP^fgmpr8
aden=5.2728; fden8=-0.2; fden1=2.0; fden4=-0.25; fden18=-0.08; fden2=-0.06 Reaction: PRPP => IMP; dGTP, IMP, ATP, GTP, Pi, Rate Law: aden*PRPP^fden1*IMP^fden2*ATP^fden4*GTP^fden8*Pi^fden18
fdnap9=0.42; fdnap10=0.33; agdna=2.2296 Reaction: dGTP => DNA; dATP, Rate Law: agdna*dATP^fdnap9*dGTP^fdnap10
fdnan12=1.0; adnag=0.001318 Reaction: DNA => dGTP, Rate Law: adnag*DNA^fdnan12
fdnan12=1.0; adnaa=0.001938 Reaction: DNA => dATP, Rate Law: adnaa*DNA^fdnan12
fhprt2=-0.89; fhprt1=1.1; fhprt13=0.48; ahprt=12.569 Reaction: HX + PRPP => IMP; IMP, Rate Law: ahprt*PRPP^fhprt1*IMP^fhprt2*HX^fhprt13
finuc2=0.8; finuc18=-0.36; ainuc=0.9135 Reaction: IMP => HX; Pi, Rate Law: ainuc*IMP^finuc2*Pi^finuc18
atrans=8.8539; ftrans5=0.33 Reaction: SAM => ATP, Rate Law: atrans*SAM^ftrans5
apolyam=0.29; fpolyam5=0.9 Reaction: SAM => Ade, Rate Law: apolyam*SAM^fpolyam5
fgdrnr10=-0.39; agdrnr=0.1199; fgdrnr8=0.4; fgdrnr9=-1.2 Reaction: GTP => dGTP; dATP, dGTP, Rate Law: agdrnr*GTP^fgdrnr8*dATP^fgdrnr9*dGTP^fgdrnr10
fgmps7=0.16; fgmps4=0.12; agmps=0.3738 Reaction: XMP => GTP; ATP, Rate Law: agmps*ATP^fgmps4*XMP^fgmps7
aada=0.001062; fada4=0.97 Reaction: ATP => HX, Rate Law: aada*ATP^fada4
fprpps1=-0.03; fprpps4=-0.45; fprpps17=0.65; fprpps18=0.7; aprpps=0.9; fprpps8=-0.04 Reaction: R5P => PRPP; ATP, GTP, Pi, PRPP, Rate Law: aprpps*PRPP^fprpps1*ATP^fprpps4*GTP^fprpps8*R5P^fprpps17*Pi^fprpps18
adgnuc=0.03333; fdgnuc10=1.0 Reaction: dGTP => Gua, Rate Law: adgnuc*dGTP^fdgnuc10
arnaa=0.06923; frnan11=1.0 Reaction: RNA => ATP, Rate Law: arnaa*RNA^frnan11
fx14=2.0; ax=0.0012 Reaction: Xa =>, Rate Law: ax*Xa^fx14
fasuc18=-0.05; fasuc4=-0.24; fasuc2=0.4; fasuc8=0.2; aasuc=3.5932 Reaction: IMP => SAMP; ATP, GTP, Pi, Rate Law: aasuc*IMP^fasuc2*ATP^fasuc4*GTP^fasuc8*Pi^fasuc18
aaprt=233.8; faprt4=-0.8; faprt1=0.5; faprt6=0.75 Reaction: PRPP + Ade => ATP; ATP, Rate Law: aaprt*PRPP^faprt1*ATP^faprt4*Ade^faprt6
aua=8.744E-5; fua16=2.21 Reaction: UA =>, Rate Law: aua*UA^fua16
agua=0.4919; fgua15=0.5 Reaction: Gua => Xa, Rate Law: agua*Gua^fgua15
aarna=614.5; frnap4=0.05; frnap8=0.13 Reaction: ATP => RNA; GTP, Rate Law: aarna*ATP^frnap4*GTP^frnap8
fampd4=0.8; fampd8=-0.03; aampd=0.02688; fampd18=-0.1 Reaction: ATP => IMP; GTP, Pi, Rate Law: aampd*ATP^fampd4*GTP^fampd8*Pi^fampd18
aade=0.01; fade6=0.55 Reaction: Ade =>, Rate Law: aade*Ade^fade6

States:

Name Description
ATP [ATP; adenosine; AMP; ADP; ADP; Adenosine; AMP; ATP; ADP]
PRPP [5-O-phosphono-alpha-D-ribofuranosyl diphosphate; 5-Phospho-alpha-D-ribose 1-diphosphate]
SAMP [N(6)-(1,2-dicarboxyethyl)-AMP; N6-(1,2-Dicarboxyethyl)-AMP]
R5P [aldehydo-D-ribose 5-phosphate; D-Ribose 5-phosphate]
SAM [S-adenosyl-L-methionine; S-Adenosyl-L-methionine]
DNA [DNA; deoxyribonucleic acid]
Xa [9H-xanthine; Xanthine]
UA [7,9-dihydro-1H-purine-2,6,8(3H)-trione; Urate]
GTP [GDP; GMP; GTP; GDP; GMP; GDP; GTP]
HX [Hypoxanthine; Deoxyinosine; Inosine; inosine; hypoxanthine; 2'-deoxyinosine]
dGTP [dGTP; dGMP; dGDP; dGTP; dGDP; dGMP; dGTP]
Ade [adenine; Adenine]
dATP [dATP; dADP; dAMP; Deoxyadenosine; dATP; dATP; dADP; 2'-deoxyadenosine; dAMP]
Gua [Guanine; Deoxyguanosine; Guanosine; 2'-deoxyuridine; guanine; 2'-deoxyguanosine]
RNA [RNA]
IMP [IMP; IMP]
XMP [5'-xanthylic acid; Xanthosine 5'-phosphate]

Observables: none

D


MODEL1603270000 @ v0.0.1

Nair2016 - Integration of calcium and dopamine signals by D1R-expressing medium-sized spiny neuronsThis model is describ…

In reward learning, the integration of NMDA-dependent calcium and dopamine by striatal projection neurons leads to potentiation of corticostriatal synapses through CaMKII/PP1 signaling. In order to elicit the CaMKII/PP1-dependent response, the calcium and dopamine inputs should arrive in temporal proximity and must follow a specific (dopamine after calcium) order. However, little is known about the cellular mechanism which enforces these temporal constraints on the signal integration. In this computational study, we propose that these temporal requirements emerge as a result of the coordinated signaling via two striatal phosphoproteins, DARPP-32 and ARPP-21. Specifically, DARPP-32-mediated signaling could implement an input-interval dependent gating function, via transient PP1 inhibition, thus enforcing the requirement for temporal proximity. Furthermore, ARPP-21 signaling could impose the additional input-order requirement of calcium and dopamine, due to its Ca2+/calmodulin sequestering property when dopamine arrives first. This highlights the possible role of phosphoproteins in the temporal aspects of striatal signal transduction. link: http://identifiers.org/pubmed/27584878

Parameters: none

States: none

Observables: none

MODEL8938094216 @ v0.0.1

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

Postsynaptic Ca2+ signals of different amplitudes and durations are able to induce either long-lasting potentiation (LPT) or depression (LTD). The bidirectional character of synaptic plasticity may result at least in part from an increased or decreased responsiveness of the glutamatergic alpha-amino-3-hydroxy-5-methylisoxazole-4-propionic acid receptor (AMPA-R) due to the modification of conductance and/or channel number, and controlled by the balance between the activities of phosphorylation and dephosphorylation pathways. AMPA-R depression can be induced by a long-lived Ca2+ signal of moderate amplitude favouring the activation of the dephosphorylation pathway, whereas a shorter but higher Ca2+ signal would induce AMPA-R potentiation resulting from the preferential activation of the phosphorylation pathway. Within the framework of a model involving calcium/calmodulin-dependent protein kinase II (CaMKII), calcineurin (PP2B) and type 1 protein phosphatase (PP1), we aimed at delineating the conditions allowing a biphasic U-shaped relationship between AMPA-R and Ca2+ signal amplitude, and thus bidirectional plasticity. Our theoretical analysis shows that such a property may be observed if the phosphorylation pathway: (i) displays higher cooperativity in its Ca2+-dependence than the dephosphorylation pathway; (ii) displays a basal Ca2+-independent activity; or (iii) is directly inhibited by the dephosphorylation pathway. Because the experimentally observed inactivation of CaMKII by PP1 accounts for this latter characteristic, we aimed at verifying whether a realistic model using reported parameters values can simulate the induction of either LTP or LTD, depending on the time and amplitude characteristics of the Ca2+ signal. Our simulations demonstrate that the experimentally observed bidirectional nature of Ca2+-dependent synaptic plasticity could be the consequence of the PP1-mediated inactivation of CaMKII. link: http://identifiers.org/pubmed/12823459

Parameters: none

States: none

Observables: none

This a model from the article: Meal simulation model of the glucose-insulin system. Dalla Man C, Rizza RA, Cobelli…

A simulation model of the glucose-insulin system in the postprandial state can be useful in several circumstances, including testing of glucose sensors, insulin infusion algorithms and decision support systems for diabetes. Here, we present a new simulation model in normal humans that describes the physiological events that occur after a meal, by employing the quantitative knowledge that has become available in recent years. Model parameters were set to fit the mean data of a large normal subject database that underwent a triple tracer meal protocol which provided quasi-model-independent estimates of major glucose and insulin fluxes, e.g., meal rate of appearance, endogenous glucose production, utilization of glucose, insulin secretion. By decomposing the system into subsystems, we have developed parametric models of each subsystem by using a forcing function strategy. Model results are shown in describing both a single meal and normal daily life (breakfast, lunch, dinner) in normal. The same strategy is also applied on a smaller database for extending the model to type 2 diabetes. link: http://identifiers.org/pubmed/17926672

Parameters:

Name Description
k_2 = 0.079; U_id = 0.748772844504839; k_1 = 0.065 Reaction: G_t = ((-U_id)+k_1*G_p)-k_2*G_t, Rate Law: ((-U_id)+k_1*G_p)-k_2*G_t
k_empt = 0.0554800817258192; k_gri = 0.0558 Reaction: Q_sto2 = (-k_empt)*Q_sto2+k_gri*Q_sto1, Rate Law: (-k_empt)*Q_sto2+k_gri*Q_sto1
m_4 = 0.194; m_1 = 0.19; m_2 = 0.484 Reaction: I_p = ((-m_2)*I_p-m_4*I_p)+m_1*I_l, Rate Law: ((-m_2)*I_p-m_4*I_p)+m_1*I_l
S = 1.8; m_1 = 0.19; m_3 = 0.276120406260733; m_2 = 0.484 Reaction: I_l = ((-m_1)*I_l-m_3*I_l)+m_2*I_p+S, Rate Law: ((-m_1)*I_l-m_3*I_l)+m_2*I_p+S
I = 25.0; k_i = 0.0079 Reaction: I_1 = (-k_i)*(I_1-I), Rate Law: (-k_i)*(I_1-I)
beta = 0.11; G_b = 95.0; G = 94.6808510638298; alpha = 0.05 Reaction: Y = (-alpha)*(Y-beta*(G-G_b)), Rate Law: (-alpha)*(Y-beta*(G-G_b))
k_gri = 0.0558 Reaction: Q_sto1 = (-k_gri)*Q_sto1, Rate Law: (-k_gri)*Q_sto1
k_abs = 0.057; k_empt = 0.0554800817258192 Reaction: Q_gut = (-k_abs)*Q_gut+k_empt*Q_sto2, Rate Law: (-k_abs)*Q_gut+k_empt*Q_sto2
k_2 = 0.079; E = 0.0; EGP = 1.87872; Ra = 0.0; k_1 = 0.065; U_ii = 1.0 Reaction: G_p = ((((EGP+Ra)-E)-U_ii)-k_1*G_p)+k_2*G_t, Rate Law: ((((EGP+Ra)-E)-U_ii)-k_1*G_p)+k_2*G_t
k_i = 0.0079 Reaction: I_d = (-k_i)*(I_d-I_1), Rate Law: (-k_i)*(I_d-I_1)
gamma = 0.5; S_po = 1.76784893617021 Reaction: I_po = (-gamma)*I_po+S_po, Rate Law: (-gamma)*I_po+S_po
I = 25.0; p_2U = 0.0331; I_b = 25.0 Reaction: X = (-p_2U)*X+p_2U*(I-I_b), Rate Law: (-p_2U)*X+p_2U*(I-I_b)

States:

Name Description
X [Insulin]
I po [Insulin]
G p [glucose]
Q sto1 [glucose]
I p [Insulin]
I 1 [Insulin]
Q gut [glucose]
Y Y
Q sto2 [glucose]
G t [glucose]
I l [Insulin]
I d I_d

Observables: none

DallePezze2012 - TSC-independent mTORC2 regulationThis model is described in the article: [A dynamic network model of m…

The kinase mammalian target of rapamycin (mTOR) exists in two multiprotein complexes (mTORC1 and mTORC2) and is a central regulator of growth and metabolism. Insulin activation of mTORC1, mediated by phosphoinositide 3-kinase (PI3K), Akt, and the inhibitory tuberous sclerosis complex 1/2 (TSC1-TSC2), initiates a negative feedback loop that ultimately inhibits PI3K. We present a data-driven dynamic insulin-mTOR network model that integrates the entire core network and used this model to investigate the less well understood mechanisms by which insulin regulates mTORC2. By analyzing the effects of perturbations targeting several levels within the network in silico and experimentally, we found that, in contrast to current hypotheses, the TSC1-TSC2 complex was not a direct or indirect (acting through the negative feedback loop) regulator of mTORC2. Although mTORC2 activation required active PI3K, this was not affected by the negative feedback loop. Therefore, we propose an mTORC2 activation pathway through a PI3K variant that is insensitive to the negative feedback loop that regulates mTORC1. This putative pathway predicts that mTORC2 would be refractory to Akt, which inhibits TSC1-TSC2, and, indeed, we found that mTORC2 was insensitive to constitutive Akt activation in several cell types. Our results suggest that a previously unknown network structure connects mTORC2 to its upstream cues and clarifies which molecular connectors contribute to mTORC2 activation. link: http://identifiers.org/pubmed/22457331

Parameters:

Name Description
k1=0.999989 Reaction: species_2 + species_6 => species_11 + species_6; species_2, species_6, Rate Law: compartment_2*k1*species_2*species_6
k1=4.50769 Reaction: species_3 + species_22 => species_4 + species_22; species_3, species_22, Rate Law: compartment_2*k1*species_3*species_22
k1=0.0253763 Reaction: species_20 + species_41 => species_21; species_20, species_41, Rate Law: compartment_1*k1*species_20*species_41
k1=0.073093 Reaction: species_9 + species_2 => species_12 + species_2; species_9, species_2, Rate Law: compartment_2*k1*species_9*species_2
k1=1.00001E-4 Reaction: species_9 + species_4 => species_10 + species_4; species_9, species_4, Rate Law: compartment_2*k1*species_9*species_4
k1=0.00812537 Reaction: species_8 => species_6; species_8, Rate Law: compartment_2*k1*species_8
k1=0.0513784 Reaction: species_11 + species_28 => species_2; species_11, species_28, Rate Law: compartment_2*k1*species_11*species_28
k1=2.32165E-4 Reaction: species_16 => species_18; species_16, Rate Law: compartment_2*k1*species_16
k1=0.0309731 Reaction: species_15 => species_20; species_15, Rate Law: compartment_1*k1*species_15
k1=7.52842 Reaction: species_4 => species_3; species_4, Rate Law: compartment_2*k1*species_4
k1=0.0239178 Reaction: species_9 + species_3 => species_10 + species_3; species_9, species_3, Rate Law: compartment_2*k1*species_9*species_3
k1=0.00573896 Reaction: species_47 + species_2 => species_17 + species_2; species_47, species_2, Rate Law: compartment_2*k1*species_47*species_2
k1=0.00627315 Reaction: species_6 + species_3 => species_8 + species_3; species_6, species_3, Rate Law: compartment_2*k1*species_6*species_3
k1=1.0 Reaction: species_42 + species_17 => species_19 + species_17; species_42, species_17, Rate Law: compartment_2*k1*species_42*species_17
k1=0.403706 Reaction: species_12 => species_9; species_12, Rate Law: compartment_2*k1*species_12
k1=0.0255714 Reaction: species_22 => species_5; species_22, Rate Law: compartment_2*k1*species_22
k1=0.0999968 Reaction: species_1 => species_42; species_1, Rate Law: compartment_2*k1*species_1
k1=1.00039E-4 Reaction: species_6 + species_4 => species_8 + species_4; species_6, species_4, Rate Law: compartment_2*k1*species_6*species_4
k1=0.699505 Reaction: species_27 + species_7 => species_3 + species_7; species_27, species_7, Rate Law: compartment_2*k1*species_27*species_7
k1=0.0318902 Reaction: species_5 + species_16 => species_22 + species_16; species_5, species_16, Rate Law: compartment_2*k1*species_5*species_16
k1=5.90372 Reaction: species_3 + species_14 => species_4 + species_14; species_3, species_14, Rate Law: compartment_2*k1*species_3*species_14
k1=0.00328283 Reaction: species_7 => species_42; species_7, Rate Law: compartment_2*k1*species_7
k1=0.00528455 Reaction: species_17 => species_47; species_17, Rate Law: compartment_2*k1*species_17
k1=1.0E-4 Reaction: species_7 + species_17 => species_19 + species_17; species_7, species_17, Rate Law: compartment_2*k1*species_7*species_17
k1=0.149328 Reaction: species_21 => species_15; species_21, Rate Law: compartment_1*k1*species_21
k1=4.0739 Reaction: species_3 => species_27; species_3, Rate Law: compartment_2*k1*species_3
k1=0.999985 Reaction: species_18 + species_21 => species_16 + species_21; species_18, species_21, Rate Law: k1*species_18*species_21
k1=0.1 Reaction: species_13 + species_21 => species_14 + species_21; species_13, species_21, Rate Law: k1*species_13*species_21
k1=0.999991 Reaction: species_10 => species_9; species_10, Rate Law: compartment_2*k1*species_10
k1=0.134664 Reaction: species_42 + species_21 => species_7 + species_21; species_42, species_21, Rate Law: k1*species_42*species_21

States:

Name Description
species 9 [Proline-rich AKT1 substrate 1]
species 27 [RAC-alpha serine/threonine-protein kinase]
species 1 Sink
species 18 [Phosphatidylinositol 3-kinase regulatory subunit alpha]
species 4 [RAC-alpha serine/threonine-protein kinase]
species 16 [Phosphatidylinositol 3-kinase regulatory subunit alpha]
species 20 [Insulin receptor]
species 28 Amino_Acids
species 47 [Ribosomal protein S6 kinase beta-1]
species 21 [Insulin receptor]
species 8 [Tuberin; Hamartin]
species 17 [Ribosomal protein S6 kinase beta-1]
species 12 [Proline-rich AKT1 substrate 1]
species 5 [Serine/threonine-protein kinase mTOR]
species 15 [Insulin receptor]
species 2 [Serine/threonine-protein kinase mTOR]
species 42 [Phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit alpha isoform; Insulin receptor substrate 1]
species 6 [Hamartin; Tuberin]
species 19 [Insulin receptor substrate 1; Phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit alpha isoform]
species 10 [Proline-rich AKT1 substrate 1]
species 11 [Serine/threonine-protein kinase mTOR]
species 14 [DNA-dependent protein kinase catalytic subunit; Serine-protein kinase ATM; Serine/threonine-protein kinase PAK 1; MAP kinase-activated protein kinase 2]
species 22 [Serine/threonine-protein kinase mTOR]
species 3 [RAC-alpha serine/threonine-protein kinase]
species 7 [Phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit alpha isoform; Insulin receptor substrate 1]
species 41 [Insulin]
species 13 [MAP kinase-activated protein kinase 2; DNA-dependent protein kinase catalytic subunit; Serine-protein kinase ATM; Serine/threonine-protein kinase PAK 1]

Observables: none

DallePazze2014 - Cellular senescene-induced mitochondrial dysfunctionThis model is described in the article: [Dynamic m…

Cellular senescence, a state of irreversible cell cycle arrest, is thought to help protect an organism from cancer, yet also contributes to ageing. The changes which occur in senescence are controlled by networks of multiple signalling and feedback pathways at the cellular level, and the interplay between these is difficult to predict and understand. To unravel the intrinsic challenges of understanding such a highly networked system, we have taken a systems biology approach to cellular senescence. We report a detailed analysis of senescence signalling via DNA damage, insulin-TOR, FoxO3a transcription factors, oxidative stress response, mitochondrial regulation and mitophagy. We show in silico and in vitro that inhibition of reactive oxygen species can prevent loss of mitochondrial membrane potential, whilst inhibition of mTOR shows a partial rescue of mitochondrial mass changes during establishment of senescence. Dual inhibition of ROS and mTOR in vitro confirmed computational model predictions that it was possible to further reduce senescence-induced mitochondrial dysfunction and DNA double-strand breaks. However, these interventions were unable to abrogate the senescence-induced mitochondrial dysfunction completely, and we identified decreased mitochondrial fission as the potential driving force for increased mitochondrial mass via prevention of mitophagy. Dynamic sensitivity analysis of the model showed the network stabilised at a new late state of cellular senescence. This was characterised by poor network sensitivity, high signalling noise, low cellular energy, high inflammation and permanent cell cycle arrest suggesting an unsatisfactory outcome for treatments aiming to delay or reverse cellular senescence at late time points. Combinatorial targeted interventions are therefore possible for intervening in the cellular pathway to senescence, but in the cases identified here, are only capable of delaying senescence onset. link: http://identifiers.org/pubmed/25166345

Parameters:

Name Description
scale_Mito_Membr_Pot_obs = 1.0 Reaction: Mito_Membr_Pot_obs = scale_Mito_Membr_Pot_obs*(Mito_membr_pot_new+Mito_membr_pot_old), Rate Law: missing
DNA_damaged_by_irradiation = 9237.72311545872 Reaction: => DNA_damage; Irradiation, Irradiation, Rate Law: Cell*DNA_damaged_by_irradiation*Irradiation
mitophagy_activ_by_FoxO3a_n_AMPK_pT172 = 1319.84219165251 Reaction: => Mitophagy; FoxO3a, AMPK_pT172, AMPK_pT172, FoxO3a, Rate Law: Cell*mitophagy_activ_by_FoxO3a_n_AMPK_pT172*FoxO3a*AMPK_pT172
IKKbeta_inactiv = 1.0 Reaction: IKKbeta => Nil; IKKbeta, Rate Law: Cell*IKKbeta_inactiv*IKKbeta
mTORC1_pS2448_dephos_by_AMPK_pT172 = 191.297262771509 Reaction: mTORC1_pS2448 => mTORC1; AMPK_pT172, AMPK_pT172, mTORC1_pS2448, Rate Law: Cell*mTORC1_pS2448_dephos_by_AMPK_pT172*mTORC1_pS2448*AMPK_pT172
mito_biogenesis_by_mTORC1_pS2448 = 0.0133620123598202 Reaction: Mito_mass_turnover => Mito_mass_new; mTORC1_pS2448, Mito_mass_turnover, mTORC1_pS2448, Rate Law: Cell*mito_biogenesis_by_mTORC1_pS2448*Mito_mass_turnover*mTORC1_pS2448
mito_membr_pot_new_dec = 1094.58423149719 Reaction: Mito_membr_pot_new => Nil; Mito_membr_pot_new, Rate Law: Cell*mito_membr_pot_new_dec*Mito_membr_pot_new
scale_Akt_pS473_obs = 1.0 Reaction: Akt_pS473_obs = scale_Akt_pS473_obs*Akt_pS473, Rate Law: missing
Akt_pS473_dephos_by_mTORC1_pS2448 = 0.114598191621279 Reaction: Akt_pS473 => Akt; mTORC1_pS2448, Akt_pS473, mTORC1_pS2448, Rate Law: Cell*Akt_pS473_dephos_by_mTORC1_pS2448*Akt_pS473*mTORC1_pS2448
scale_CDKN1A_obs = 1.0 Reaction: CDKN1A_obs = scale_CDKN1A_obs*CDKN1A, Rate Law: missing
CDKN1A_transcr_by_FoxO3a_n_DNA_damage = 0.0852182335681166 Reaction: => CDKN1A; DNA_damage, FoxO3a, DNA_damage, FoxO3a, Rate Law: Cell*CDKN1A_transcr_by_FoxO3a_n_DNA_damage*DNA_damage*FoxO3a
CDKN1B_transcr_by_FoxO3a_n_DNA_damage = 0.0920526565951487 Reaction: => CDKN1B; DNA_damage, FoxO3a, DNA_damage, FoxO3a, Rate Law: Cell*CDKN1B_transcr_by_FoxO3a_n_DNA_damage*DNA_damage*FoxO3a
Akt_S473_phos_by_insulin = 0.588783148144923 Reaction: Akt => Akt_pS473; Insulin, Akt, Insulin, Rate Law: Cell*Akt_S473_phos_by_insulin*Akt*Insulin
AMPK_pT172_dephos_by_Mito_membr_pot_old = 1.00000000000003E-6 Reaction: AMPK_pT172 => AMPK; Mito_membr_pot_old, AMPK_pT172, Mito_membr_pot_old, Rate Law: Cell*AMPK_pT172_dephos_by_Mito_membr_pot_old*AMPK_pT172*Mito_membr_pot_old
AMPK_T172_phos = 0.355183987378767 Reaction: AMPK => AMPK_pT172; AMPK, Rate Law: Cell*AMPK_T172_phos*AMPK
sen_ass_beta_gal_dec = 0.154821166783837 Reaction: SA_beta_gal => ; SA_beta_gal, Rate Law: Cell*sen_ass_beta_gal_dec*SA_beta_gal
ROS_prod_by_Mito_membr_pot_old = 772.829490967078 Reaction: => ROS; Mito_membr_pot_old, Mito_membr_pot_old, Rate Law: Cell*ROS_prod_by_Mito_membr_pot_old*Mito_membr_pot_old
mTORC1_S2448_phos_by_AA_n_Akt_pS473 = 162.471039450073 Reaction: mTORC1 => mTORC1_pS2448; Amino_Acids, Akt_pS473, Akt_pS473, Amino_Acids, mTORC1, Rate Law: Cell*mTORC1_S2448_phos_by_AA_n_Akt_pS473*mTORC1*Amino_Acids*Akt_pS473
scale_CDKN1B_obs = 1.0 Reaction: CDKN1B_obs = scale_CDKN1B_obs*CDKN1B, Rate Law: missing
mitophagy_new = 0.22465992989378 Reaction: Mito_mass_new => Mito_mass_turnover; Mitophagy, Mito_mass_new, Mitophagy, Rate Law: Cell*mitophagy_new*Mito_mass_new*Mitophagy
mitophagy_old = 0.00122607614891116 Reaction: Mito_mass_old => Mito_mass_turnover; Mitophagy, Mito_mass_old, Mitophagy, Rate Law: Cell*mitophagy_old*Mito_mass_old*Mitophagy
ROS_turnover = 3.23082321168464 Reaction: ROS => Nil; ROS, Rate Law: Cell*ROS_turnover*ROS
DNA_damaged_by_ROS = 0.118873655169353 Reaction: => DNA_damage; ROS, ROS, Rate Law: Cell*DNA_damaged_by_ROS*ROS
scale_JNK_pT183_obs = 1.0 Reaction: JNK_pT183_obs = scale_JNK_pT183_obs*JNK_pT183, Rate Law: missing
mito_membr_pot_old_inc = 0.00586017882122243 Reaction: => Mito_membr_pot_old; Mito_mass_old, Mito_mass_old, Rate Law: Cell*mito_membr_pot_old_inc*Mito_mass_old
DNA_repair = 0.325724769122274 Reaction: DNA_damage => Nil; DNA_damage, Rate Law: Cell*DNA_repair*DNA_damage
mito_dysfunction = 0.0270695257507146 Reaction: Mito_mass_new => Mito_mass_old; CDKN1A, CDKN1A, Mito_mass_new, Rate Law: Cell*mito_dysfunction*Mito_mass_new*CDKN1A
FoxO3a_phos_by_JNK_pT183 = 0.112877630496044 Reaction: FoxO3a_pS253 => FoxO3a; JNK_pT183, FoxO3a_pS253, JNK_pT183, Rate Law: Cell*FoxO3a_phos_by_JNK_pT183*FoxO3a_pS253*JNK_pT183
scale_Mitophagy_obs = 1.0 Reaction: Mitophagy_obs = scale_Mitophagy_obs*Mitophagy, Rate Law: missing
JNK_pT183_inactiv = 0.0718429173444438 Reaction: JNK_pT183 => JNK; JNK_pT183, Rate Law: Cell*JNK_pT183_inactiv*JNK_pT183
mito_membr_pot_old_dec = 0.954903499913184 Reaction: Mito_membr_pot_old => Nil; Mito_membr_pot_old, Rate Law: Cell*mito_membr_pot_old_dec*Mito_membr_pot_old
mitophagy_inactiv_by_mTORC1_pS2448 = 645.999307230137 Reaction: Mitophagy => Nil; mTORC1_pS2448, Mitophagy, mTORC1_pS2448, Rate Law: Cell*mitophagy_inactiv_by_mTORC1_pS2448*Mitophagy*mTORC1_pS2448
FoxO3a_pS253_degrad = 39.4068609318082 Reaction: FoxO3a_pS253 => Nil; FoxO3a_pS253, Rate Law: Cell*FoxO3a_pS253_degrad*FoxO3a_pS253
JNK_activ_by_ROS = 0.00502329152478409 Reaction: JNK => JNK_pT183; ROS, JNK, ROS, Rate Law: Cell*JNK_activ_by_ROS*JNK*ROS
scale_FoxO3a_pS253_obs = 1.0 Reaction: FoxO3a_pS253_obs = scale_FoxO3a_pS253_obs*FoxO3a_pS253, Rate Law: missing
mTORC1_S2448_phos_by_AA_n_IKKbeta = 1.00008996727694E-5 Reaction: mTORC1 => mTORC1_pS2448; Amino_Acids, IKKbeta, Amino_Acids, IKKbeta, mTORC1, Rate Law: Cell*mTORC1_S2448_phos_by_AA_n_IKKbeta*mTORC1*Amino_Acids*IKKbeta
mTORC1_S2448_phos_by_AA = 1.00008999860285E-6 Reaction: mTORC1 => mTORC1_pS2448; Amino_Acids, Amino_Acids, mTORC1, Rate Law: Cell*mTORC1_S2448_phos_by_AA*mTORC1*Amino_Acids
CDKN1A_inactiv_by_Akt_pS473 = 0.0667971061916905 Reaction: CDKN1A => Nil; Akt_pS473, Akt_pS473, CDKN1A, Rate Law: Cell*CDKN1A_inactiv_by_Akt_pS473*CDKN1A*Akt_pS473
mito_membr_pot_new_inc = 9882.02736076158 Reaction: => Mito_membr_pot_new; Mito_mass_new, Mito_mass_new, Rate Law: Cell*mito_membr_pot_new_inc*Mito_mass_new
FoxO3a_synthesis = 407.307409980937 Reaction: => FoxO3a, Rate Law: Cell*FoxO3a_synthesis
scale_mTOR_pS2448_obs = 1.0 Reaction: mTOR_pS2448_obs = scale_mTOR_pS2448_obs*mTORC1_pS2448, Rate Law: missing
scale_AMPK_pT172_obs = 1.0 Reaction: AMPK_pT172_obs = scale_AMPK_pT172_obs*AMPK_pT172, Rate Law: missing
sen_ass_beta_gal_inc_by_Mitophagy = 1.00000000000011E-6 Reaction: => SA_beta_gal; Mitophagy, Mitophagy, Rate Law: Cell*sen_ass_beta_gal_inc_by_Mitophagy*Mitophagy
CDKN1B_inactiv_by_Akt_pS473 = 0.0596841598127919 Reaction: CDKN1B => Nil; Akt_pS473, Akt_pS473, CDKN1B, Rate Law: Cell*CDKN1B_inactiv_by_Akt_pS473*CDKN1B*Akt_pS473
ROS_prod_by_Mito_membr_pot_new = 4.55464788075885 Reaction: => ROS; Mito_membr_pot_new, Mito_membr_pot_new, Rate Law: Cell*ROS_prod_by_Mito_membr_pot_new*Mito_membr_pot_new
scale_DNA_damage_gammaH2AX_obs = 1.0 Reaction: DNA_damage_gammaH2AX_obs = scale_DNA_damage_gammaH2AX_obs*DNA_damage, Rate Law: missing
scale_FoxO3a_total_obs = 1.0 Reaction: FoxO3a_total_obs = scale_FoxO3a_total_obs*(FoxO3a+FoxO3a_pS253), Rate Law: missing
FoxO3a_phos_by_Akt_pS473 = 6.83511123229576 Reaction: FoxO3a => FoxO3a_pS253; Akt_pS473, Akt_pS473, FoxO3a, Rate Law: Cell*FoxO3a_phos_by_Akt_pS473*FoxO3a*Akt_pS473
scale_Mito_Mass_obs = 1.0 Reaction: Mito_Mass_obs = scale_Mito_Mass_obs*(Mito_mass_new+Mito_mass_old), Rate Law: missing
IKKbeta_activ_by_ROS = 1.0 Reaction: => IKKbeta; ROS, ROS, Rate Law: Cell*IKKbeta_activ_by_ROS*ROS
mito_biogenesis_by_AMPK_pT172 = 5.8915457309741E-5 Reaction: Mito_mass_turnover => Mito_mass_new; mTORC1_pS2448, Mito_mass_turnover, mTORC1_pS2448, Rate Law: Cell*mito_biogenesis_by_AMPK_pT172*Mito_mass_turnover*mTORC1_pS2448
AMPK_pT172_dephos_by_Mito_membr_pot_new = 0.117744691539618 Reaction: AMPK_pT172 => AMPK; Mito_membr_pot_new, AMPK_pT172, Mito_membr_pot_new, Rate Law: Cell*AMPK_pT172_dephos_by_Mito_membr_pot_new*AMPK_pT172*Mito_membr_pot_new
scale_SA_beta_gal_obs = 1.0 Reaction: SA_beta_gal_obs = scale_SA_beta_gal_obs*SA_beta_gal, Rate Law: missing
sen_ass_beta_gal_inc_by_ROS = 0.0701139988718817 Reaction: => SA_beta_gal; ROS, ROS, Rate Law: Cell*sen_ass_beta_gal_inc_by_ROS*ROS
scale_ROS_obs = 1.0 Reaction: ROS_obs = scale_ROS_obs*ROS, Rate Law: missing

States:

Name Description
Mito mass turnover [mitochondrion]
mTORC1 pS2448 [TORC1 complex]
Mito membr pot new [mitochondrion]
DNA damage [deoxyribonucleic acid]
mTOR pS2448 obs mTOR_pS2448_obs
AMPK pT172 obs AMPK_pT172_obs
Mito membr pot old [mitochondrion]
CDKN1A [Cyclin-dependent kinase inhibitor 1]
Akt [RAC-alpha serine/threonine-protein kinase]
Mitophagy [autophagy of mitochondrion]
AMPK pT172 [5'-AMP-activated protein kinase subunit beta-1; 5'-AMP-activated protein kinase catalytic subunit alpha-2]
CDKN1B obs CDKN1B_obs
FoxO3a total obs FoxO3a_total_obs
ROS obs ROS_obs
IKKbeta [Inhibitor of nuclear factor kappa-B kinase subunit beta]
Mito Mass obs Mito_Mass_obs
Akt pS473 [RAC-alpha serine/threonine-protein kinase]
Nil [empty set]
Mito Membr Pot obs Mito_Membr_Pot_obs
CDKN1A obs CDKN1A_obs
CDKN1B [CDKN1B proteinCyclin-dependent kinase inhibitor 1BCyclin-dependent kinase inhibitor 1B (P27, Kip1), isoform CRA_acDNA, FLJ92816, Homo sapiens cyclin-dependent kinase inhibitor 1B (p27, Kip1)(CDKN1B), mRNA]
SA beta gal obs SA_beta_gal_obs
ROS [reactive oxygen species]
Akt pS473 obs Akt_pS473_obs
Mito mass new [mitochondrion]
Mitophagy obs Mitophagy_obs
Mito mass old [mitochondrion]
Insulin [insulin (human)]
FoxO3a pS253 [Forkhead box protein O3]
mTORC1 [TORC1 complex]
AMPK [5'-AMP-activated protein kinase subunit beta-1; 5'-AMP-activated protein kinase catalytic subunit alpha-2]
Amino Acids [amino acid]
Irradiation [SBO:0000405]
DNA damage gammaH2AX obs DNA_damage_gammaH2AX_obs
JNK pT183 [Mitogen-activated protein kinase 8]
SA beta gal [Beta-galactosidase]
FoxO3a pS253 obs FoxO3a_pS253_obs
JNK pT183 obs JNK_pT183_obs
FoxO3a [Forkhead box protein O3]
JNK [Mitogen-activated protein kinase 8]

Observables: none

DallePezze2016 - Activation of AMPK and mTOR by amino acids (Model 3)This model is as described in the Supplementary So…

Amino acids (aa) are not only building blocks for proteins, but also signalling molecules, with the mammalian target of rapamycin complex 1 (mTORC1) acting as a key mediator. However, little is known about whether aa, independently of mTORC1, activate other kinases of the mTOR signalling network. To delineate aa-stimulated mTOR network dynamics, we here combine a computational-experimental approach with text mining-enhanced quantitative proteomics. We report that AMP-activated protein kinase (AMPK), phosphatidylinositide 3-kinase (PI3K) and mTOR complex 2 (mTORC2) are acutely activated by aa-readdition in an mTORC1-independent manner. AMPK activation by aa is mediated by Ca2+/calmodulin-dependent protein kinase kinase β (CaMKKβ). In response, AMPK impinges on the autophagy regulators Unc-51-like kinase-1 (ULK1) and c-Jun. AMPK is widely recognized as an mTORC1 antagonist that is activated by starvation. We find that aa acutely activate AMPK concurrently with mTOR. We show that AMPK under aa sufficiency acts to sustain autophagy. This may be required to maintain protein homoeostasis and deliver metabolite intermediates for biosynthetic processes. link: http://identifiers.org/pubmed/27869123

Parameters:

Name Description
AMPK_pT172_dephos = 165.704 Reaction: AMPK_pT172 => AMPK, Rate Law: Cell*AMPK_pT172_dephos*AMPK_pT172
AMPK_T172_phos_by_Amino_Acids = 17.6284 Reaction: AMPK => AMPK_pT172; Amino_Acids, Rate Law: Cell*AMPK_T172_phos_by_Amino_Acids*AMPK*Amino_Acids
IRS_phos_by_p70_S6K_pT229_pT389 = 0.0863775267376444 Reaction: IRS => IRS_pS636; p70_S6K_pT229_pT389, Rate Law: Cell*IRS_phos_by_p70_S6K_pT229_pT389*IRS*p70_S6K_pT229_pT389
IRS_phos_by_Amino_Acids = 0.0331672 Reaction: IRS => IRS_p; Amino_Acids, Rate Law: Cell*IRS_phos_by_Amino_Acids*IRS*Amino_Acids
PRAS40_pS183_dephos_second = 1.88453 Reaction: PRAS40_pT246_pS183 => PRAS40_pT246, Rate Law: Cell*PRAS40_pS183_dephos_second*PRAS40_pT246_pS183
IR_beta_pY1146_dephos = 0.493514 Reaction: IR_beta_pY1146 => IR_beta_refractory, Rate Law: Cell*IR_beta_pY1146_dephos*IR_beta_pY1146
mTORC2_pS2481_dephos = 1.42511 Reaction: mTORC2_pS2481 => mTORC2, Rate Law: Cell*mTORC2_pS2481_dephos*mTORC2_pS2481
mTORC1_S2448_activation_by_Amino_Acids = 0.0156992 Reaction: mTORC1 => mTORC1_pS2448; Amino_Acids, Rate Law: Cell*mTORC1_S2448_activation_by_Amino_Acids*mTORC1*Amino_Acids
Akt_T308_phos_by_PI3K_p_PDK1_second = 7.47345 Reaction: Akt_pS473 => Akt_pT308_pS473; PI3K_p_PDK1, Rate Law: Cell*Akt_T308_phos_by_PI3K_p_PDK1_second*Akt_pS473*PI3K_p_PDK1
PRAS40_pT246_dephos_second = 11.876 Reaction: PRAS40_pT246_pS183 => PRAS40_pS183, Rate Law: Cell*PRAS40_pT246_dephos_second*PRAS40_pT246_pS183
PRAS40_pS183_dephos_first = 1.8706 Reaction: PRAS40_pS183 => PRAS40, Rate Law: Cell*PRAS40_pS183_dephos_first*PRAS40_pS183
IRS_p_phos_by_p70_S6K_pT229_pT389 = 0.338859859949792 Reaction: IRS_p => IRS_pS636; p70_S6K_pT229_pT389, Rate Law: Cell*IRS_p_phos_by_p70_S6K_pT229_pT389*IRS_p*p70_S6K_pT229_pT389
IR_beta_phos_by_Insulin = 0.0203796 Reaction: IR_beta => IR_beta_pY1146; Insulin, Rate Law: Cell*IR_beta_phos_by_Insulin*IR_beta*Insulin
p70_S6K_T229_phos_by_PI3K_p_PDK1_second = 1.00000002814509E-6 Reaction: p70_S6K_pT389 => p70_S6K_pT229_pT389; PI3K_p_PDK1, Rate Law: Cell*p70_S6K_T229_phos_by_PI3K_p_PDK1_second*p70_S6K_pT389*PI3K_p_PDK1
TSC1_TSC2_pT1462_dephos = 147.239 Reaction: TSC1_TSC2_pT1462 => TSC1_TSC2, Rate Law: Cell*TSC1_TSC2_pT1462_dephos*TSC1_TSC2_pT1462
IR_beta_ready = 323.611 Reaction: IR_beta_refractory => IR_beta, Rate Law: Cell*IR_beta_ready*IR_beta_refractory
p70_S6K_pT389_dephos_first = 1.10036057608758 Reaction: p70_S6K_pT389 => p70_S6K, Rate Law: Cell*p70_S6K_pT389_dephos_first*p70_S6K_pT389
p70_S6K_pT229_dephos_first = 1.00000012897033E-6 Reaction: p70_S6K_pT229 => p70_S6K, Rate Law: Cell*p70_S6K_pT229_dephos_first*p70_S6K_pT229
PI3K_p_PDK1_dephos = 0.18913343080532 Reaction: PI3K_p_PDK1 => PI3K_PDK1, Rate Law: Cell*PI3K_p_PDK1_dephos*PI3K_p_PDK1
Akt_T308_phos_by_PI3K_p_PDK1_first = 7.47437 Reaction: Akt => Akt_pT308; PI3K_p_PDK1, Rate Law: Cell*Akt_T308_phos_by_PI3K_p_PDK1_first*Akt*PI3K_p_PDK1
p70_S6K_pT229_dephos_second = 0.159201353240651 Reaction: p70_S6K_pT229_pT389 => p70_S6K_pT389, Rate Law: Cell*p70_S6K_pT229_dephos_second*p70_S6K_pT229_pT389
p70_S6K_T389_phos_by_mTORC1_pS2448_first = 0.00261303413778722 Reaction: p70_S6K => p70_S6K_pT389; mTORC1_pS2448, Rate Law: Cell*p70_S6K_T389_phos_by_mTORC1_pS2448_first*p70_S6K*mTORC1_pS2448
PRAS40_T246_phos_by_Akt_pT308_second = 0.279401 Reaction: PRAS40_pS183 => PRAS40_pT246_pS183; Akt_pT308, Akt_pT308_pS473, Rate Law: Cell*PRAS40_T246_phos_by_Akt_pT308_second*PRAS40_pS183*(Akt_pT308+Akt_pT308_pS473)
PRAS40_S183_phos_by_mTORC1_pS2448_second = 0.0683009 Reaction: PRAS40_pT246 => PRAS40_pT246_pS183; mTORC1_pS2448, Rate Law: Cell*PRAS40_S183_phos_by_mTORC1_pS2448_second*PRAS40_pT246*mTORC1_pS2448
mTORC2_S2481_phos_by_PI3K_variant_p = 0.120736 Reaction: mTORC2 => mTORC2_pS2481; PI3K_variant_p, Rate Law: Cell*mTORC2_S2481_phos_by_PI3K_variant_p*mTORC2*PI3K_variant_p
TSC1_TSC2_T1462_phos_by_Akt_pT308 = 1.52417 Reaction: TSC1_TSC2 => TSC1_TSC2_pT1462; Akt_pT308, Akt_pT308_pS473, Rate Law: Cell*TSC1_TSC2_T1462_phos_by_Akt_pT308*TSC1_TSC2*(Akt_pT308+Akt_pT308_pS473)
p70_S6K_T389_phos_by_mTORC1_pS2448_second = 0.110720890919343 Reaction: p70_S6K_pT229 => p70_S6K_pT229_pT389; mTORC1_pS2448, Rate Law: Cell*p70_S6K_T389_phos_by_mTORC1_pS2448_second*p70_S6K_pT229*mTORC1_pS2448
mTORC1_pS2448_dephos_by_TSC1_TSC2 = 0.00869774 Reaction: mTORC1_pS2448 => mTORC1; TSC1_TSC2, TSC1_TSC2_pS1387, Rate Law: Cell*mTORC1_pS2448_dephos_by_TSC1_TSC2*mTORC1_pS2448*(TSC1_TSC2+TSC1_TSC2_pS1387)
Akt_S473_phos_by_mTORC2_pS2481_second = 0.159093 Reaction: Akt_pT308 => Akt_pT308_pS473; mTORC2_pS2481, Rate Law: Cell*Akt_S473_phos_by_mTORC2_pS2481_second*Akt_pT308*mTORC2_pS2481
TSC1_TSC2_pS1387_dephos = 0.25319 Reaction: TSC1_TSC2_pS1387 => TSC1_TSC2, Rate Law: Cell*TSC1_TSC2_pS1387_dephos*TSC1_TSC2_pS1387
mTORC2_S2481_phos_by_Amino_Acids = 0.0268658 Reaction: mTORC2 => mTORC2_pS2481; Amino_Acids, Rate Law: Cell*mTORC2_S2481_phos_by_Amino_Acids*mTORC2*Amino_Acids
Akt_pS473_dephos_second = 0.380005 Reaction: Akt_pT308_pS473 => Akt_pT308, Rate Law: Cell*Akt_pS473_dephos_second*Akt_pT308_pS473
Akt_pT308_dephos_second = 88.9639 Reaction: Akt_pT308_pS473 => Akt_pS473, Rate Law: Cell*Akt_pT308_dephos_second*Akt_pT308_pS473
TSC1_TSC2_S1387_phos_by_AMPK_pT172 = 0.00175772 Reaction: TSC1_TSC2 => TSC1_TSC2_pS1387; AMPK_pT172, Rate Law: Cell*TSC1_TSC2_S1387_phos_by_AMPK_pT172*TSC1_TSC2*AMPK_pT172
Akt_S473_phos_by_mTORC2_pS2481_first = 1.31992E-5 Reaction: Akt => Akt_pS473; mTORC2_pS2481, Rate Law: Cell*Akt_S473_phos_by_mTORC2_pS2481_first*Akt*mTORC2_pS2481
PRAS40_T246_phos_by_Akt_pT308_first = 0.279344 Reaction: PRAS40 => PRAS40_pT246; Akt_pT308, Akt_pT308_pS473, Rate Law: Cell*PRAS40_T246_phos_by_Akt_pT308_first*PRAS40*(Akt_pT308+Akt_pT308_pS473)
PI3K_PDK1_phos_by_IRS_p = 1.87226757782201E-4 Reaction: PI3K_PDK1 => PI3K_p_PDK1; IRS_p, Rate Law: Cell*PI3K_PDK1_phos_by_IRS_p*PI3K_PDK1*IRS_p
p70_S6K_T229_phos_by_PI3K_p_PDK1_first = 0.0133520172873009 Reaction: p70_S6K => p70_S6K_pT229; PI3K_p_PDK1, Rate Law: Cell*p70_S6K_T229_phos_by_PI3K_p_PDK1_first*p70_S6K*PI3K_p_PDK1
Akt_pS473_dephos_first = 0.376999 Reaction: Akt_pS473 => Akt, Rate Law: Cell*Akt_pS473_dephos_first*Akt_pS473
IRS_pS636_turnover = 25.0 Reaction: IRS_pS636 => IRS, Rate Law: Cell*IRS_pS636_turnover*IRS_pS636
IRS_phos_by_IR_beta_pY1146 = 2.11894 Reaction: IRS => IRS_p; IR_beta_pY1146, Rate Law: Cell*IRS_phos_by_IR_beta_pY1146*IRS*IR_beta_pY1146
PRAS40_pT246_dephos_first = 11.8759 Reaction: PRAS40_pT246 => PRAS40, Rate Law: Cell*PRAS40_pT246_dephos_first*PRAS40_pT246
Akt_pT308_dephos_first = 88.9654 Reaction: Akt_pT308 => Akt, Rate Law: Cell*Akt_pT308_dephos_first*Akt_pT308
PRAS40_S183_phos_by_mTORC1_pS2448_first = 0.15881 Reaction: PRAS40 => PRAS40_pS183; mTORC1_pS2448, Rate Law: Cell*PRAS40_S183_phos_by_mTORC1_pS2448_first*PRAS40*mTORC1_pS2448
AMPK_T172_phos = 0.490602 Reaction: AMPK => AMPK_pT172; IRS_p, Rate Law: Cell*AMPK_T172_phos*AMPK*IRS_p
p70_S6K_pT389_dephos_second = 1.10215267954479 Reaction: p70_S6K_pT229_pT389 => p70_S6K_pT229, Rate Law: Cell*p70_S6K_pT389_dephos_second*p70_S6K_pT229_pT389

States:

Name Description
mTORC1 pS2448 mTORC1_pS2448
mTOR pS2448 obs mTOR_pS2448_obs
Akt pT308 pS473 Akt_pT308_pS473
IR beta [Insulin receptor]
AMPK pT172 AMPK_pT172
IRS pS636 obs IRS_pS636_obs
PRAS40 pT246 obs PRAS40_pT246_obs
p70 S6K pT389 p70_S6K_pT389
mTOR pS2481 obs mTOR_pS2481_obs
p70 S6K pT229 obs p70_S6K_pT229_obs
TSC1 TSC2 pT1462 TSC1_TSC2_pT1462
PI3K p PDK1 PI3K_p_PDK1
mTORC1 mTORC1
Amino Acids Amino_Acids
IRS pS636 IRS_pS636
PRAS40 pT246 pS183 PRAS40_pT246_pS183
TSC1 TSC2 pS1387 TSC1_TSC2_pS1387
IR beta pY1146 obs IR_beta_pY1146_obs
p70 S6K p70_S6K
AMPK pT172 obs AMPK_pT172_obs
PRAS40 PRAS40
Akt Akt
p70 S6K pT229 pT389 p70_S6K_pT229_pT389
PRAS40 pT246 PRAS40_pT246
PRAS40 pS183 PRAS40_pS183
Akt pS473 Akt_pS473
IRS p IRS_p
PI3K PDK1 PI3K_PDK1
PRAS40 pS183 obs PRAS40_pS183_obs
IR beta refractory IR_beta_refractory
Akt pS473 obs Akt_pS473_obs
mTORC2 mTORC2
IRS [Insulin receptor substrate 1]
Akt pT308 Akt_pT308
TSC1 TSC2 TSC1_TSC2
Insulin Insulin
IR beta pY1146 IR_beta_pY1146
AMPK [5'-AMP-activated protein kinase catalytic subunit alpha-1]
p70 S6K pT229 p70_S6K_pT229
p70 S6K pT389 obs p70_S6K_pT389_obs
mTORC2 pS2481 mTORC2_pS2481
TSC1 TSC2 pS1387 obs TSC1_TSC2_pS1387_obs
Akt pT308 obs Akt_pT308_obs

Observables: none

DallePezze2016 - Activation of AMPK and mTOR by amino acids (Model 1)This model is as described in Supplementary Softwar…

Amino acids (aa) are not only building blocks for proteins, but also signalling molecules, with the mammalian target of rapamycin complex 1 (mTORC1) acting as a key mediator. However, little is known about whether aa, independently of mTORC1, activate other kinases of the mTOR signalling network. To delineate aa-stimulated mTOR network dynamics, we here combine a computational-experimental approach with text mining-enhanced quantitative proteomics. We report that AMP-activated protein kinase (AMPK), phosphatidylinositide 3-kinase (PI3K) and mTOR complex 2 (mTORC2) are acutely activated by aa-readdition in an mTORC1-independent manner. AMPK activation by aa is mediated by Ca2+/calmodulin-dependent protein kinase kinase β (CaMKKβ). In response, AMPK impinges on the autophagy regulators Unc-51-like kinase-1 (ULK1) and c-Jun. AMPK is widely recognized as an mTORC1 antagonist that is activated by starvation. We find that aa acutely activate AMPK concurrently with mTOR. We show that AMPK under aa sufficiency acts to sustain autophagy. This may be required to maintain protein homoeostasis and deliver metabolite intermediates for biosynthetic processes. link: http://identifiers.org/pubmed/27869123

Parameters: none

States: none

Observables: none

DallePezze2016 - Activation of AMPK and mTOR by amino acids (Model 2)This model is as described in the Supplementary So…

Amino acids (aa) are not only building blocks for proteins, but also signalling molecules, with the mammalian target of rapamycin complex 1 (mTORC1) acting as a key mediator. However, little is known about whether aa, independently of mTORC1, activate other kinases of the mTOR signalling network. To delineate aa-stimulated mTOR network dynamics, we here combine a computational-experimental approach with text mining-enhanced quantitative proteomics. We report that AMP-activated protein kinase (AMPK), phosphatidylinositide 3-kinase (PI3K) and mTOR complex 2 (mTORC2) are acutely activated by aa-readdition in an mTORC1-independent manner. AMPK activation by aa is mediated by Ca2+/calmodulin-dependent protein kinase kinase β (CaMKKβ). In response, AMPK impinges on the autophagy regulators Unc-51-like kinase-1 (ULK1) and c-Jun. AMPK is widely recognized as an mTORC1 antagonist that is activated by starvation. We find that aa acutely activate AMPK concurrently with mTOR. We show that AMPK under aa sufficiency acts to sustain autophagy. This may be required to maintain protein homoeostasis and deliver metabolite intermediates for biosynthetic processes. link: http://identifiers.org/pubmed/27869123

Parameters: none

States: none

Observables: none

MODEL5952308332 @ v0.0.1

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2011 T…

The complexity of full-scale metabolic models is a major obstacle for their effective use in computational systems biology. The aim of model reduction is to circumvent this problem by eliminating parts of a model that are unimportant for the properties of interest. The choice of reduction method is influenced both by the type of model complexity and by the objective of the reduction; therefore, no single method is superior in all cases. In this study we present a comparative study of two different methods applied to a 20D model of yeast glycolytic oscillations. Our objective is to obtain biochemically meaningful reduced models, which reproduce the dynamic properties of the 20D model. The first method uses lumping and subsequent constrained parameter optimization. The second method is a novel approach that eliminates variables not essential for the dynamics. The applications of the two methods result in models of eight (lumping), six (elimination) and three (lumping followed by elimination) dimensions. All models have similar dynamic properties and pin-point the same interactions as being crucial for generation of the oscillations. The advantage of the novel method is that it is algorithmic, and does not require input in the form of biochemical knowledge. The lumping approach, however, is better at preserving biochemical properties, as we show through extensive analyses of the models. link: http://identifiers.org/pubmed/17010168

Parameters: none

States: none

Observables: none

Das2010 - Effect of a gamma-secretase inhibitor on Amyloid-beta dynamicsThis model is described in the article: [Modeli…

Aggregation of the small peptide amyloid beta (Aβ) into oligomers and fibrils in the brain is believed to be a precursor to Alzheimer's disease. Aβ is produced via multiple proteolytic cleavages of amyloid precursor protein (APP), mediated by the enzymes β- and γ-secretase. In this study, we examine the temporal dynamics of soluble (unaggregated) Aβ in the plasma and cerebral-spinal fluid (CSF) of rhesus monkeys treated with different oral doses of a γ-secretase inhibitor. A dose-dependent reduction of Aβ concentration was observed within hours of drug ingestion, for all doses tested. Aβ concentration in the CSF returned to its predrug level over the monitoring period. In contrast, Aβ concentration in the plasma exhibited an unexpected overshoot to as high as 200% of the predrug concentration, and this overshoot persisted as late as 72 hours post-drug ingestion. To account for these observations, we proposed and analyzed a minimal physiological model for Aβ dynamics that could fit the data. Our analysis suggests that the overshoot arises from the attenuation of an Aβ clearance mechanism, possibly due to the inhibitor. Our model predicts that the efficacy of Aβ clearance recovers to its basal (pretreatment) value with a characteristic time of >48 hours, matching the time-scale of the overshoot. These results point to the need for a more detailed investigation of soluble Aβ clearance mechanisms and their interaction with Aβ-reducing drugs. link: http://identifiers.org/pubmed/20411345

Parameters:

Name Description
k1 = 1.13; r = 0.43; deltap = 0.55; J = 0.0; l = 1.0 Reaction: P = (k1*r*C-J*r)-deltap*P*l, Rate Law: (k1*r*C-J*r)-deltap*P*l
Ki = 0.0232; k1 = 1.13; Sc = 1.16; g_t = 0.0; J = 0.0 Reaction: C = (Sc/(1+g_t/Ki)-k1*C)+J, Rate Law: (Sc/(1+g_t/Ki)-k1*C)+J

States:

Name Description
C [Amyloid beta A4 protein]
P [Amyloid beta A4 protein]

Observables: none

a simple kinetic mass-action-law-based model could be utilized to adequately describe clustering in response to activat…

The process of clustering of plasma membrane receptors in response to their agonist is the first step in signal transduction. The rate of the clustering process and the size of the clusters determine further cell responses. Here we aim to demonstrate that a simple 2-differential equation mathematical model is capable of quantitative description of the kinetics of 2D or 3D cluster formation in various processes. Three mathematical models based on mass action kinetics were considered and compared with each other by their ability to describe experimental data on GPVI or CR3 receptor clustering (2D) and albumin or platelet aggregation (3D) in response to activation. The models were able to successfully describe experimental data without losing accuracy after switching between complex and simple models. However, additional restrictions on parameter values are required to match a single set of parameters for the given experimental data. The extended clustering model captured several properties of the kinetics of cluster formation, such as the existence of only three typical steady states for this system: unclustered receptors, receptor dimers, and clusters. Therefore, a simple kinetic mass-action-law-based model could be utilized to adequately describe clustering in response to activation both in 2D and in 3D. link: http://identifiers.org/pubmed/32604803

Parameters: none

States: none

Observables: none

David2008 - Genome-scale metabolic network of Aspergillus nidulans (iHD666)This model is described in the article: [Ana…

BACKGROUND: Aspergillus nidulans is a member of a diverse group of filamentous fungi, sharing many of the properties of its close relatives with significance in the fields of medicine, agriculture and industry. Furthermore, A. nidulans has been a classical model organism for studies of development biology and gene regulation, and thus it has become one of the best-characterized filamentous fungi. It was the first Aspergillus species to have its genome sequenced, and automated gene prediction tools predicted 9,451 open reading frames (ORFs) in the genome, of which less than 10% were assigned a function. RESULTS: In this work, we have manually assigned functions to 472 orphan genes in the metabolism of A. nidulans, by using a pathway-driven approach and by employing comparative genomics tools based on sequence similarity. The central metabolism of A. nidulans, as well as biosynthetic pathways of relevant secondary metabolites, was reconstructed based on detailed metabolic reconstructions available for A. niger and Saccharomyces cerevisiae, and information on the genetics, biochemistry and physiology of A. nidulans. Thereby, it was possible to identify metabolic functions without a gene associated, and to look for candidate ORFs in the genome of A. nidulans by comparing its sequence to sequences of well-characterized genes in other species encoding the function of interest. A classification system, based on defined criteria, was developed for evaluating and selecting the ORFs among the candidates, in an objective and systematic manner. The functional assignments served as a basis to develop a mathematical model, linking 666 genes (both previously and newly annotated) to metabolic roles. The model was used to simulate metabolic behavior and additionally to integrate, analyze and interpret large-scale gene expression data concerning a study on glucose repression, thereby providing a means of upgrading the information content of experimental data and getting further insight into this phenomenon in A. nidulans. CONCLUSION: We demonstrate how pathway modeling of A. nidulans can be used as an approach to improve the functional annotation of the genome of this organism. Furthermore we show how the metabolic model establishes functional links between genes, enabling the upgrade of the information content of transcriptome data. link: http://identifiers.org/pubmed/18405346

Parameters: none

States: none

Observables: none

This is an ordinary differential equation model of the early inflammatory response during transplantion. Descriptions ar…

A mathematical model of the early inflammatory response in transplantation is formulated with ordinary differential equations. We first consider the inflammatory events associated only with the initial surgical procedure and the subsequent ischemia/reperfusion (I/R) events that cause tissue damage to the host as well as the donor graft. These events release damage-associated molecular pattern molecules (DAMPs), thereby initiating an acute inflammatory response. In simulations of this model, resolution of inflammation depends on the severity of the tissue damage caused by these events and the patient's (co)-morbidities. We augment a portion of a previously published mathematical model of acute inflammation with the inflammatory effects of T cells in the absence of antigenic allograft mismatch (but with DAMP release proportional to the degree of graft damage prior to transplant). Finally, we include the antigenic mismatch of the graft, which leads to the stimulation of potent memory T cell responses, leading to further DAMP release from the graft and concomitant increase in allograft damage. Regulatory mechanisms are also included at the final stage. Our simulations suggest that surgical injury and I/R-induced graft damage can be well-tolerated by the recipient when each is present alone, but that their combination (along with antigenic mismatch) may lead to acute rejection, as seen clinically in a subset of patients. An emergent phenomenon from our simulations is that low-level DAMP release can tolerize the recipient to a mismatched allograft, whereas different restimulation regimens resulted in an exaggerated rejection response, in agreement with published studies. We suggest that mechanistic mathematical models might serve as an adjunct for patient- or sub-group-specific predictions, simulated clinical studies, and rational design of immunosuppression. link: http://identifiers.org/doi/10.3389/fimmu.2015.00484

Parameters: none

States: none

Observables: none

deBack2012 - Lineage Specification in Pancreas DevelopmentThis model of two neighbouring pancreas precursor cells, descr…

The cell fate decision of multi-potent pancreatic progenitor cells between the exocrine and endocrine lineages is regulated by Notch signalling, mediated by cell-cell interactions. However, canonical models of Notch-mediated lateral inhibition cannot explain the scattered spatial distribution of endocrine cells and the cell-type ratio in the developing pancreas. Based on evidence from acinar-to-islet cell transdifferentiation in vitro, we propose that lateral stabilization, i.e. positive feedback between adjacent progenitor cells, acts in parallel with lateral inhibition to regulate pattern formation in the pancreas. A simple mathematical model of transcriptional regulation and cell-cell interaction reveals the existence of multi-stability of spatial patterns whose simultaneous occurrence causes scattering of endocrine cells in the presence of noise. The scattering pattern allows for control of the endocrine-to-exocrine cell-type ratio by modulation of lateral stabilization strength. These theoretical results suggest a previously unrecognized role for lateral stabilization in lineage specification, spatial patterning and cell-type ratio control in organ development. link: http://identifiers.org/pubmed/23193107

Parameters:

Name Description
k1=1.0 Reaction: species_4 => ; species_4, Rate Law: compartment_1*k1*species_4
b=21.0; theta=1.0E-4; c=1.0; n=4.0 Reaction: => species_2; species_2, species_4, species_1, species_2, species_4, species_1, Rate Law: compartment_1*(theta+b*(species_2*species_4)^n)/(theta+c*species_1^n+b*(species_2*species_4)^n)
a=1.0; theta=1.0E-4; n=4.0 Reaction: => species_3; species_1, species_1, Rate Law: compartment_1*theta/(theta+a*species_1^n)

States:

Name Description
species 2 [Pancreas transcription factor 1 subunit alpha]
species 3 [Neurogenin-3]
species 1 [Neurogenin-3]
species 4 [Pancreas transcription factor 1 subunit alpha]

Observables: none

BIOMD0000000631 @ v0.0.1

DeCaluwé2016 - Circadian ClockThis model is described in the article: [A Compact Model for the Complex Plant Circadian…

The circadian clock is an endogenous timekeeper that allows organisms to anticipate and adapt to the daily variations of their environment. The plant clock is an intricate network of interlocked feedback loops, in which transcription factors regulate each other to generate oscillations with expression peaks at specific times of the day. Over the last decade, mathematical modeling approaches have been used to understand the inner workings of the clock in the model plant Arabidopsis thaliana. Those efforts have produced a number of models of ever increasing complexity. Here, we present an alternative model that combines a low number of equations and parameters, similar to the very earliest models, with the complex network structure found in more recent ones. This simple model describes the temporal evolution of the abundance of eight clock gene mRNA/protein and captures key features of the clock on a qualitative level, namely the entrained and free-running behaviors of the wild type clock, as well as the defects found in knockout mutants (such as altered free-running periods, lack of entrainment, or changes in the expression of other clock genes). Additionally, our model produces complex responses to various light cues, such as extreme photoperiods and non-24 h environmental cycles, and can describe the control of hypocotyl growth by the clock. Our model constitutes a useful tool to probe dynamical properties of the core clock as well as clock-dependent processes. link: http://identifiers.org/pubmed/26904049

Parameters:

Name Description
K12 = 0.56; g2 = 0.12 Reaction: => hypocotyl; PIF_p, Rate Law: g2*PIF_p^2/(K12^2+PIF_p^2)
k3 = 0.56 Reaction: P51_m =>, Rate Law: k3*P51_m
K5 = 0.3; K4 = 0.23; v2A = 1.27 Reaction: => P97_m; P51_p, EL_p, Rate Law: v2A/(1+(P51_p/K4)^2+(EL_p/K5)^2)
d3D = 0.48; D = 0.0 Reaction: P51_p =>, Rate Law: d3D*D*P51_p
p5 = 0.62 Reaction: => PIF_p; PIF_m, Rate Law: p5*PIF_m
p1 = 0.76 Reaction: => CL_p; CL_m, Rate Law: p1*CL_m
k1D = 0.21; D = 0.0 Reaction: CL_m =>, Rate Law: k1D*D*CL_m
p3 = 0.64 Reaction: => P51_p; P51_m, Rate Law: p3*P51_m
K2 = 1.18; v1 = 4.58; K1 = 0.16 Reaction: => CL_m; P51_p, P97_p, Rate Law: v1/(1+(P97_p/K1)^2+(P51_p/K2)^2)
d5D = 0.52; D = 0.0 Reaction: PIF_p =>, Rate Law: d5D*D*PIF_p
d2D = 0.5; D = 0.0 Reaction: P97_p =>, Rate Law: d2D*D*P97_p
k4 = 0.57 Reaction: EL_m =>, Rate Law: k4*EL_m
L = 1.0; d2L = 0.29 Reaction: P97_p =>, Rate Law: d2L*L*P97_p
L = 1.0; d5L = 4.0 Reaction: PIF_p =>, Rate Law: d5L*L*PIF_p
K2 = 1.18; L = 1.0; v1L = 3.0; K1 = 0.16 Reaction: => CL_m; P51_p, P97_p, P, Rate Law: v1L*L*P/(1+(P97_p/K1)^2+(P51_p/K2)^2)
K8 = 0.36; L = 1.0; v4 = 1.47; K10 = 1.9; K9 = 1.9 Reaction: => EL_m; P51_p, CL_p, EL_p, Rate Law: L*v4/(1+(CL_p/K8)^2+(P51_p/K9)^2+(EL_p/K10)^2)
v3 = 1.0; K7 = 2.0; K6 = 0.46 Reaction: => P51_m; P51_p, CL_p, Rate Law: v3/(1+(CL_p/K6)^2+(P51_p/K7)^2)
d4D = 1.21; D = 0.0 Reaction: EL_p =>, Rate Law: d4D*D*EL_p
d4L = 0.38; L = 1.0 Reaction: EL_p =>, Rate Law: d4L*L*EL_p
p2 = 1.01 Reaction: => P97_p; P97_m, Rate Law: p2*P97_m
D = 0.0 Reaction: => P, Rate Law: 0.3*(1-P)*D
L = 1.0; p1L = 0.42 Reaction: => CL_p; CL_m, Rate Law: p1L*L*CL_m
p4 = 1.01 Reaction: => EL_p; EL_m, Rate Law: p4*EL_m
L = 1.0 Reaction: P =>, Rate Law: P*L
v5 = 0.1; K11 = 0.21 Reaction: => PIF_m; EL_p, Rate Law: v5/(1+(EL_p/K11)^2)
d3L = 0.78; L = 1.0 Reaction: P51_p =>, Rate Law: d3L*L*P51_p
v2L = 5.0; L = 1.0; K5 = 0.3; K4 = 0.23 Reaction: => P97_m; P51_p, EL_p, P, Rate Law: v2L*L*P/(1+(P51_p/K4)^2+(EL_p/K5)^2)
d1 = 0.68 Reaction: CL_p =>, Rate Law: d1*CL_p
g1 = 0.01 Reaction: => hypocotyl, Rate Law: g1
k2 = 0.35 Reaction: P97_m =>, Rate Law: k2*P97_m
v2B = 1.48; K5 = 0.3; K3 = 0.24; K4 = 0.23 Reaction: => P97_m; P51_p, CL_p, EL_p, Rate Law: v2B*CL_p^2/(K3^2+CL_p^2)/(1+(P51_p/K4)^2+(EL_p/K5)^2)
k5 = 0.14 Reaction: PIF_m =>, Rate Law: k5*PIF_m
L = 1.0; k1L = 0.53 Reaction: CL_m =>, Rate Law: k1L*L*CL_m

States:

Name Description
CL p [Protein LHY; Protein CCA1]
P97 p [Two-component response regulator-like APRR9; Two-component response regulator-like APRR7]
EL p [Transcription factor LUX; Protein EARLY FLOWERING 4]
EL m [823817; 818596]
P [Transcription factor PIF3; Transcription factor PIL1]
PIF m [825075; 818903]
PIF p [Transcription factor PIF5; Transcription factor PIF4]
P51 m [836259; 832518]
hypocotyl [hypocotyl]
P51 p [Two-component response regulator-like APRR1; Two-component response regulator-like APRR5]
P97 m [831793; 819292]
CL m [839341; 819296]

Observables: none

BIOMD0000000319 @ v0.0.1

This is the scaled model described in the article: **Birhythmicity, chaos, and other patterns of temporal self-organiza…

We analyze on a model biochemical system the effect of a coupling between two instability-generating mechanisms. The system considered is that of two allosteric enzymes coupled in series and activated by their respective products. In addition to simple periodic oscillations, the system can exhibit a variety of new modes of dynamic behavior; coexistence between two stable periodic regimes (birhythmicity), random oscillations (chaos), and coexistence of a stable periodic regime with a stable steady state (hard excitation) or with chaos. The relationship between these patterns of temporal self-organization is analyzed as a function of the control parameters of the model. Chaos and birhythmicity appear to be rare events in comparison with simple periodic behavior. We discuss the relevance of these results with respect to the regularity of most biological rhythms. link: http://identifiers.org/pubmed/6960354

Parameters:

Name Description
L1=5.0E8 dimensionless; sigma1=10.0 per sec Reaction: alpha => beta, Rate Law: sigma1*alpha*(1+alpha)*(1+beta)^2/(L1+(1+alpha)^2*(1+beta)^2)
v_Km1=0.45 per sec Reaction: => alpha, Rate Law: v_Km1
ks=1.99 per sec Reaction: gamma =>, Rate Law: ks*gamma
d=0.0 dimensionless; sigma2=10.0 per sec; L2=100.0 dimensionless Reaction: beta => gamma, Rate Law: sigma2*beta*(1+d*beta)*(1+gamma)^2/(L2+(1+d*beta)^2*(1+gamma)^2)

States:

Name Description
alpha alpha
gamma gamma
beta beta

Observables: none

BIOMD0000000208 @ v0.0.1

The model reproduces Fig 3 of the paper corresponding to the transition to S phase. Units have not been defined for this…

The study of the molecular mechanisms determining cellular programs of proliferation, differentiation, and apoptosis is currently attracting much attention. Recent studies have demonstrated that the system of cell-cycle control based on the transcriptional regulation of the expression of specific genes is responsible for the transition between programs. These groups of functionally connected genes from so-called gene networks characterized by numerous feedbacks and a complex behavioral dynamics. Computer simulation methods have been applied to studying the dynamics of gene networks regulating the cell cycle of vertebrates. The data on the regulation of the key genes obtained from the CYCLE-TRRD database have been used as a basis to construct gene networks of different degrees of complexity controlling the G1/S transition, one of the most important stages of the cell cycle. The behavior dynamics of the model constructed has been analyzed. Two qualitatively different functional modes of the system has been obtained. It has also been shown that the transition between these modes depends on the duration of the proliferation signal. It has also been demonstrated that the additional feedback from factor E2F to genes c-fos and c-jun, which was predicted earlier based on the computer analysis of promoters, plays an important role in the transition of the cell to the S phase. link: http://identifiers.org/pubmed/14582399

Parameters:

Name Description
phi6 = 0.1 Reaction: y6 =>, Rate Law: phi6*y6
phi1 = 0.1 Reaction: y1 =>, Rate Law: phi1*y1
k4i = 1.0 Reaction: y4 => y5, Rate Law: k4i*y4*y5
emax = 2.0; k1 = 1.0; k1_prime = 1.0; k1_double_prime = 10.0 Reaction: => y1; y2, Rate Law: emax*k1*y1/(k1*y1+(k1_prime+k1_double_prime*y1)*y2)
k4 = 0.09 Reaction: => y4; y1, Rate Law: k4*y1
k4_double_prime = 0.1 Reaction: => y4; y6, Rate Law: k4_double_prime*y6
k4a = 2.0 Reaction: y5 => y4, Rate Law: k4a*y5
phi2 = 0.01 Reaction: y2 =>, Rate Law: phi2*y2
F6 = 0.044 Reaction: => y6, Rate Law: F6
k6 = 0.0 Reaction: => y6; y1, Rate Law: k6*y1
phi4a = 0.01 Reaction: y5 =>, Rate Law: phi4a*y5
k2 = 1.0 Reaction: => y2; y1, Rate Law: k2*y1
k3 = 0.4 Reaction: y2 => y3; y5, Rate Law: k3*y2*y5
phi4i = 0.01 Reaction: y4 =>, Rate Law: phi4i*y4
phi3 = 0.1 Reaction: y3 =>, Rate Law: phi3*y3

States:

Name Description
y3 [Retinoblastoma-associated protein]
y1 [Transcription factor E2F1]
y4 [Cyclin-dependent kinase 2; G1/S-specific cyclin-E1]
y2 [Retinoblastoma-associated protein]
y6 [Transcription factor AP-1]
y5 [Cyclin-dependent kinase 2; G1/S-specific cyclin-E1]

Observables: none

Key cellular functions and developmental processes rely on cascades of GTPases. GTPases of the Rab family provide a mole…

Cut-out switch model

Membrane identity and GTPase cascades regulated by toggle and cut-out switches

Perla Del Conte-Zerial, Lutz Brusch, Jochen C Rink, Claudio Collinet, Yannis Kalaidzidis, Marino Zerial, and Andreas Deutsch: Molecular Systems Biology, 4:206, 15 July 2008, doi:10.1038/msb.2008.45

This is the cut-out switch model for the Rab5 - Rab7 transition, also referred to as model 2 in the original publication.

This model is not completely described in all details in the publication. Thanks go to Barbara Szomolay and Lutz Brusch for finding and clarifying this. According to Dr. Brusch this model represents the mechanism identified by the qualitative analysis in the article in the scenario deemed most useful by the authors. For the time-course simulations it was necessary to add a time dependency to one of the parameters, which is only verbally described in the article.

As argued in the publication the switch between early and late endosomes can be triggered by a parameter change. While with fixed parameter values each switch just converges to one steady state from its initial conditions and stays there, endosomes should switch between two different states. These changes would in reality of course depend on many different factors, such as cargo composition and amount in the specific endosome, its location and some additional cellular control mechanisms and encompass many different parameters. To keep the model simple the authors chose to add a time dependency to only one reaction - ke in the activation of RAB5 is multiplied with a term monotonously increasing over time from 0 to 1. They also hard coded a time dependence in this term, 100 minutes, to make the switch occur after several hundred minutes. As long as this modulating term remains monotonic all resulting time courses should look similar, with the switching behavior depending on the initial conditions and whether the term is increasing or decreasing. Monotonic increase is a reasonable assumption for the described mechanism of cargo accumulation.

Not explicitly described in the article:

activation of Rab5 (time): $r*ke*time/(100+time)/(1+e_{(kg-R)*kf})$ instead of $r*ke/(1+e_{(kg-R)*kf})$

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

For more information see the terms of use.

To cite BioModels Database, please use Le Novère N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

Parameters:

Name Description
kminus1=1.0 persec Reaction: r5 =>, Rate Law: endosome*kminus1*r5
K1=0.483 Mpers Reaction: => r7, Rate Law: endosome*K1
kh=0.06 persec Reaction: R5 => r5, Rate Law: endosome*kh*R5
kg=1.0 M; ke=0.021 persec; kf=3.0 lpermole Reaction: r7 => R7; R5, Rate Law: endosome*ke*r7/(1+exp((kg-R5)*kf))
kf=2.5 lpermole; ke=0.3 persec; kg=0.1 M Reaction: r5 => R5; R5, Rate Law: endosome*r5*ke*time/(100+time)/(1+exp((kg-R5)*kf))
ke=0.21 persec; kg=0.1; h=3.0 dimensionless Reaction: r7 => R7; R7, Rate Law: endosome*r7*ke*R7^h/(kg+R7^h)
kg=0.3 M; ke=0.31 persec; kf=3.0 lpermole Reaction: R5 => r5; R7, Rate Law: endosome*ke*R5/(1+exp((kg-R7)*kf))
kh=0.15 persec Reaction: R7 => r7, Rate Law: endosome*kh*R7
kminus1=0.483 persec Reaction: r7 =>, Rate Law: endosome*kminus1*r7
K1=1.0 Mpers Reaction: => r5, Rate Law: endosome*K1

States:

Name Description
r7 [GDP; Ras-related protein Rab-7a; anchored component of membrane]
r5 [GDP; Ras-related protein Rab-5A; anchored component of membrane]
R5 [GTP; Ras-related protein Rab-5A; anchored component of membrane]
R7 [GTP; Ras-related protein Rab-7a; anchored component of membrane]

Observables: none

Modified Recon2.2 where the host biomass objective function has been modified to reflect the average composition of the…

Viruses rely on their host for reproduction. Here, we made use of genomic and structural information to create a biomass function capturing the amino and nucleic acid requirements of SARSCoV- 2. Incorporating this biomass function into a stoichiometric metabolic model of the human lung cell and applying metabolic flux balance analysis, we identified host-based metabolic perturbations inhibiting SARS-CoV-2 reproduction. Our results highlight reactions in the central metabolism, as well as amino acid and nucleotide biosynthesis pathways. By incorporating host cellular maintenance into the model based on available protein expression data from human lung cells, we find thaViruses rely on their host for reproduction. Here, we made use of genomic and structural information to create a biomass function capturing the amino and nucleic acid requirements of SARSCoV- 2. Incorporating this biomass function into a stoichiometric metabolic model of the human lung cell and applying metabolic flux balance analysis, we identified host-based metabolic perturbations inhibiting SARS-CoV-2 reproduction. Our results highlight reactions in the central metabolism, as well as amino acid and nucleotide biosynthesis pathways. By incorporating host cellular maintenance into the model based on available protein expression data from human lung cells, we find that only few of these metabolic perturbations are able to selectively inhibit virus reproduction. Some of the catalysing enzymes of such reactions have demonstrated interactions with existing drugs, which can be used for experimental testing of the presented predictions using gene knockouts and RNA interference techniques. In summary, the developed computational approach offers a platform for rapid, experimentally testable generation of drug predictions against existing and emerging viruses based on their biomass requirements.t only few of these metabolic perturbations are able to selectively inhibit virus reproduction. Some of the catalysing enzymes of such reactions have demonstrated interactions with existing drugs, which can be used for experimental testing of the presented predictions using gene knockouts and RNA interference techniques. In summary, the developed computational approach offers a platform for rapid, experimentally testable generation of drug predictions against existing and emerging viruses based on their biomass requirements. link: http://identifiers.org/doi/10.26508/LSA.202000869

Parameters: none

States: none

Observables: none

BIOMD0000000326 @ v0.0.1

This a model from the article: Network-level analysis of light adaptation in rod cells under normal and altered cond…

Photoreceptor cells finely adjust their sensitivity and electrical response according to changes in light stimuli as a direct consequence of the feedback and regulation mechanisms in the phototransduction cascade. In this study, we employed a systems biology approach to develop a dynamic model of vertebrate rod phototransduction that accounts for the details of the underlying biochemistry. Following a bottom-up strategy, we first reproduced the results of a robust model developed by Hamer et al. (Vis. Neurosci., 2005, 22(4), 417), and then added a number of additional cascade reactions including: (a) explicit reactions to simulate the interaction between the activated effector and the regulator of G-protein signalling (RGS); (b) a reaction for the reformation of the G-protein from separate subunits; (c) a reaction for rhodopsin (R) reconstitution from the association of the opsin apoprotein with the 11-cis-retinal chromophore; (d) reactions for the slow activation of the cascade by opsin. The extended network structure successfully reproduced a number of experimental conditions that were inaccessible to prior models. With a single set of parameters the model was able to predict qualitative and quantitative features of rod photoresponses to light stimuli ranging over five orders of magnitude, in normal and altered conditions, including genetic manipulations of the cascade components. In particular, the model reproduced the salient dynamic features of the rod from Rpe65(-/-) animals, a well established model for Leber congenital amaurosis and vitamin A deficiency. The results of this study suggest that a systems-level approach can help to unravel the adaptation mechanisms in normal and in disease-associated conditions on a molecular basis. link: http://identifiers.org/pubmed/19756313

Parameters:

Name Description
kGshutoff = 0.05 Reaction: Ga_GTP => Ga_GDP, Rate Law: kGshutoff*Ga_GTP
kGrecyc = 2.0 Reaction: Ga_GDP + Gbg => Gt, Rate Law: kGrecyc*Gbg*Ga_GDP
kRec3 = 9.68777; Rec_wCa2 = 0.0; kRec4 = 0.610084 Reaction: RK => Rec_wCa2_RK; Ca2_free, Rate Law: kRec3*Rec_wCa2*RK-kRec4*Rec_wCa2_RK
kP1 = 0.0549715; kP1_rev = 0.0 Reaction: Ga_GTP + PDE => PDE_Ga_GTP, Rate Law: kP1*PDE*Ga_GTP-kP1_rev*PDE_Ga_GTP
kG7 = 200.0 Reaction: G_GTP => Ga_GTP + Gbg, Rate Law: kG7*G_GTP
kRK2 = 250.0; kRK1_5 = 0.0 Reaction: R5 + RK => R5_RKpre, Rate Law: kRK1_5*RK*R5-kRK2*R5_RKpre
kP3 = 1.49834E-9 Reaction: Ga_GTP + PDE_a_Ga_GTP => Ga_GTP_PDE_a_Ga_GTP, Rate Law: kP3*PDE_a_Ga_GTP*Ga_GTP
kRGS1 = 1.57E-7 Reaction: Ga_GTP_a_PDE_a_Ga_GTP + RGS => RGS_Ga_GTP_a_PDE_a_Ga_GTP, Rate Law: kRGS1*RGS*Ga_GTP_a_PDE_a_Ga_GTP
kRGS2 = 256.07 Reaction: RGS_Ga_GTP_a_PDE_a_Ga_GTP => Ga_GDP + PDE_a_Ga_GTP + RGS, Rate Law: kRGS2*RGS_Ga_GTP_a_PDE_a_Ga_GTP
kA2 = 0.00323198; kA1_2 = 0.0 Reaction: Arr + R2 => R2_Arr, Rate Law: kA1_2*Arr*R2-kA2*R2_Arr
kP4 = 21.0881 Reaction: Ga_GTP_PDE_a_Ga_GTP => Ga_GTP_a_PDE_a_Ga_GTP, Rate Law: kP4*Ga_GTP_PDE_a_Ga_GTP
kRK2 = 250.0; kRK1_0 = 0.0076429599557114 Reaction: R0 + RK => R0_RKpre, Rate Law: kRK1_0*RK*R0-kRK2*R0_RKpre
kRK3_ATP = 400.0 Reaction: R0_RKpre => R1_RKpost, Rate Law: kRK3_ATP*R0_RKpre
kRK4 = 20.0 Reaction: R2_RKpost => R2 + RK, Rate Law: kRK4*R2_RKpost
kA3 = 0.0445091 Reaction: R1_Arr => Arr + Ops, Rate Law: kA3*R1_Arr
kG1_5 = 0.0; kG2 = 2250.34 Reaction: Gt + R5 => R5_Gt, Rate Law: kG1_5*Gt*R5-kG2*R5_Gt
kG6 = 2000.0 Reaction: Ops_G_GTP => G_GTP + Ops, Rate Law: kG6*Ops_G_GTP
kOps = 6.1172E-13; kG2 = 2250.34 Reaction: Gt + Ops => Ops_Gt, Rate Law: kOps*Ops*Gt-kG2*Ops_Gt
kRK2 = 250.0; kRK1_4 = 0.0 Reaction: R4 + RK => R4_RKpre, Rate Law: kRK1_4*RK*R4-kRK2*R4_RKpre
k2 = 1.9094; k1 = 0.381529; eT = 400.0 Reaction: Ca2_free => Ca2_buff, Rate Law: k1*(eT-Ca2_buff)*Ca2_free-k2*Ca2_buff
kG2 = 2250.34; kG1_4 = 0.0 Reaction: Gt + R4 => R4_Gt, Rate Law: kG1_4*Gt*R4-kG2*R4_Gt
kRK1_3 = 0.0; kRK2 = 250.0 Reaction: R3 + RK => R3_RKpre, Rate Law: kRK1_3*RK*R3-kRK2*R3_RKpre
kG2 = 2250.34; kG1_2 = 0.0 Reaction: Gt + R2 => R2_Gt, Rate Law: kG1_2*Gt*R2-kG2*R2_Gt
kG2 = 2250.34; kG1_6 = 0.0 Reaction: Gt + R6 => R6_Gt, Rate Law: kG1_6*Gt*R6-kG2*R6_Gt
kG4_GDP = 600.0; kG3 = 2000.0 Reaction: Ops_Gt => Ops_G, Rate Law: kG3*Ops_Gt-kG4_GDP*Ops_G
kG5_GTP = 750.0 Reaction: Ops_G => Ops_G_GTP, Rate Law: kG5_GTP*Ops_G
betasub = 4.3E-4; E = 0.0; betadark = 1.2 Reaction: cGMP => ; Ga_GTP_a_PDE_a_Ga_GTP, PDE_a_Ga_GTP, Rate Law: (betadark+betasub*E)*cGMP
ktherm = 0.0238 Reaction: R0 => Ops, Rate Law: ktherm*R0
kRK2 = 250.0; kRK1_1 = 0.0 Reaction: R1 + RK => R1_RKpre, Rate Law: kRK1_1*RK*R1-kRK2*R1_RKpre
kG2 = 2250.34; kG1_3 = 0.0 Reaction: Gt + R3 => R3_Gt, Rate Law: kG1_3*Gt*R3-kG2*R3_Gt
Kc = 0.17; alfamax = 0.0; m = 2.5 Reaction: => cGMP; Ca2_free, Rate Law: alfamax/(1+(Ca2_free/Kc)^m)
kRK2 = 250.0; kRK1_6 = 0.0 Reaction: R6 + RK => R6_RKpre, Rate Law: kRK1_6*RK*R6-kRK2*R6_RKpre
kPDEshutoff = 0.033 Reaction: Ga_GTP_a_PDE_a_Ga_GTP => Ga_GDP + PDE_a_Ga_GTP, Rate Law: kPDEshutoff*Ga_GTP_a_PDE_a_Ga_GTP
gammaCa = 47.554; Ca2_0 = 0.01 Reaction: Ca2_free =>, Rate Law: gammaCa*(Ca2_free-Ca2_0)
kRK2 = 250.0; kRK1_2 = 0.0 Reaction: R2 + RK => R2_RKpre, Rate Law: kRK1_2*RK*R2-kRK2*R2_RKpre
fCa = 0.2; Jdark = 29.7778; Vcyto = 1.0; cGMPdark = 4.0; ncg = 3.0; F = 96485.3415 Reaction: => Ca2_free; cGMP, Rate Law: 1E6*fCa*Jdark/((2+fCa)*F*Vcyto)*(cGMP/cGMPdark)^ncg
kG1_0 = 3.0586111111E-5; kG2 = 2250.34 Reaction: Gt + R0 => R0_Gt, Rate Law: kG1_0*Gt*R0-kG2*R0_Gt
kA1_6 = 0.0; kA2 = 0.00323198 Reaction: Arr + R6 => R6_Arr, Rate Law: kA1_6*Arr*R6-kA2*R6_Arr
kG1_1 = 0.0; kG2 = 2250.34 Reaction: Gt + R1 => R1_Gt, Rate Law: kG1_1*Gt*R1-kG2*R1_Gt
kA1_1 = 0.0; kA2 = 0.00323198 Reaction: Arr + R1 => R1_Arr, Rate Law: kA1_1*Arr*R1-kA2*R1_Arr

States:

Name Description
RGS Ga GTP a PDE a Ga GTP [GTP; Retinal rod rhodopsin-sensitive cGMP 3',5'-cyclic phosphodiesterase subunit gamma; Guanine nucleotide-binding protein subunit alpha-12; Regulator of G-protein signaling 9]
Ga GDP [GDP; Guanine nucleotide-binding protein subunit alpha-12]
G GTP [GTP; G-protein coupled receptor 183]
R0 Gt [Rhodopsin; Transducin beta-like protein 2]
RGS [Regulator of G-protein signaling 9]
R0 RKpre [Rhodopsin kinase; Rhodopsin]
Ga GTP a PDE a Ga GTP [GTP; Guanine nucleotide-binding protein subunit alpha-12; Retinal rod rhodopsin-sensitive cGMP 3',5'-cyclic phosphodiesterase subunit gamma]
cGMP [3',5'-cyclic GMP]
Ca2 buff [calcium(2+); Calcium cation]
Ops G [G-protein coupled receptor 183; Medium-wave-sensitive opsin 1]
R6 G [G-protein coupled receptor 183; Phosphorhodopsin]
Gt [Transducin beta-like protein 2]
R6 Arr [S-arrestin; Phosphorhodopsin]
Ops G GTP [GTP; Medium-wave-sensitive opsin 1; G-protein coupled receptor 183]
R6 Gt [Transducin beta-like protein 2; Phosphorhodopsin]
R6 RKpost [Rhodopsin kinase; Phosphorhodopsin]
R0 G GTP [GTP; Rhodopsin; G-protein coupled receptor 183]
Ops [Medium-wave-sensitive opsin 1]
R0 G [Rhodopsin; G-protein coupled receptor 183]
RK [Rhodopsin kinase]
R1 [Phosphorhodopsin]
R6 G GTP [GTP; G-protein coupled receptor 183; Phosphorhodopsin]
Ga GTP [GTP; Guanine nucleotide-binding protein subunit alpha-12]
R6 RKpre [Rhodopsin kinase; Phosphorhodopsin]
Ga GTP PDE a Ga GTP [GTP; Retinal rod rhodopsin-sensitive cGMP 3',5'-cyclic phosphodiesterase subunit gamma; Guanine nucleotide-binding protein subunit alpha-12]
Arr [S-arrestin]
RGS PDE a Ga GTP [GTP; Regulator of G-protein signaling 9; Guanine nucleotide-binding protein subunit alpha-12; Retinal rod rhodopsin-sensitive cGMP 3',5'-cyclic phosphodiesterase subunit gamma]
Gbg [G-protein beta/gamma-subunit complex]
Rec wCa2 RK [calcium(2+); Recoverin; Rhodopsin kinase]
Ca2 free [calcium(2+); Calcium cation]

Observables: none

Demin2013 - PKPD behaviour - 5-Lipoxygenase inhibitorsThis model is described in the article: [Systems pharmacology mod…

Zileuton, a 5-lipoxygenase (5LO) inhibitor, displays complex pharmaokinetic (PK)-pharmacodynamic (PD) behavior. Available clinical data indicate a lack of dose-bronchodilatory response during initial treatment, with a dose response developing after ~1-2 weeks. We developed a quantitative systems pharmacology (QSP) model to understand the mechanism behind this phenomenon. The model described the release, maturation, and trafficking of eosinophils into the airways, leukotriene synthesis by the 5LO enzyme, leukotriene signaling and bronchodilation, and the PK of zileuton. The model provided a plausible explanation for the two-phase bronchodilatory effect of zileuton-the short-term bronchodilation was due to leukotriene inhibition and the long-term bronchodilation was due to inflammatory cell infiltration blockade. The model also indicated that the theoretical maximum bronchodilation of both 5LO inhibition and leukotriene receptor blockade is likely similar. QSP modeling provided interesting insights into the effects of leukotriene modulation.CPT: Pharmacometrics & Systems Pharmacology (2013) 2, e74; doi:10.1038/psp.2013.49; advance online publication 11 September 2013. link: http://identifiers.org/pubmed/24026253

Parameters:

Name Description
FLO3_b = 0.0; r1 = 0.0; FLO2_b = 0.0; Ke_ox = 99.99979 Reaction: HPETE_b => HETE_b; HPETE_b, HETE_b, Rate Law: Default*r1*(HPETE_b*FLO2_b-HETE_b*FLO3_b/Ke_ox)
k_Hn_p = 1.8E10 Reaction: => Hn_aw; EO_a_aw, EO_i_aw, EO_aw, EO_a_aw, EO_i_aw, EO_aw, Rate Law: V_AW*k_Hn_p*(EO_a_aw+EO_i_aw+EO_aw)
k_lo = 4642.68; FLO3t_aw = 0.0; K_AA = 10.74959 Reaction: AA_aw => ; AA_aw, Rate Law: Default*k_lo*AA_aw*FLO3t_aw/K_AA
FLO5HP_aw = 0.0; Ki_AA = 551.8748; k_3 = 263640.0; FLO3t_aw = 0.0; k3 = 34.0 Reaction: => HPETE_aw; AA_aw, HPETE_aw, AA_aw, Rate Law: Default*(k_3*FLO5HP_aw-k3*FLO3t_aw*HPETE_aw)*(1.0+AA_aw/Ki_AA)
R_Hn_B = 141.0; R_Hn_AW = 5130.0; Kp_Hn_AW = 3950.0; Q_AW_blf = 5.23 Reaction: Hn_aw => Hn_b; Hn_aw, Hn_b, Rate Law: Q_AW_blf*R_Hn_B*(Hn_aw*R_Hn_AW/Kp_Hn_AW-Hn_b)
Kp_LTE_AW = 0.22; Q_AW_blf = 5.23; R_LTE_B = 0.538; R_LTE_AW = 1.4 Reaction: LTE4_aw => LTE4_b; LTE4_aw, LTE4_b, Rate Law: Q_AW_blf*R_LTE_B*(LTE4_aw*R_LTE_AW/Kp_LTE_AW-LTE4_b)
ca = 10.0; kia = 0.001 Reaction: EO_a_aw => EO_aw; EO_a_aw, Rate Law: ca*V_AW*kia*EO_a_aw
k_lo = 4642.68; FLO3t_b = 0.0; K_AA = 10.74959 Reaction: AA_b => ; AA_b, Rate Law: Default*k_lo*AA_b*FLO3t_b/K_AA
fup_Hn = 0.77; k_Hn_d = 0.033 Reaction: Hn_b => ; Hn_b, Rate Law: Vd_Hn*k_Hn_d*fup_Hn*Hn_b
EC50_migr = 0.115; h_migr = 3.0; k_EO_t_baw = 0.04; Rec_occup_migr = 0.0 Reaction: EO_b => EO_aw; EO_b, Rate Law: V_B*k_EO_t_baw*EO_b*Rec_occup_migr^h_migr/(EC50_migr^h_migr+Rec_occup_migr^h_migr)
R_LTD_AW = 1.4; Q_AW_blf = 5.23; R_LTD_B = 0.538; Kp_LTD_AW = 0.22 Reaction: LTD4_aw => LTD4_b; LTD4_aw, LTD4_b, Rate Law: Q_AW_blf*R_LTD_B*(LTD4_aw*R_LTD_AW/Kp_LTD_AW-LTD4_b)
B_aw = 0.0; A_aw = 0.0; GPx = 1.6 Reaction: HPETE_aw => HETE_aw, Rate Law: Default*GPx*B_aw/A_aw
R_ZF_B = 0.533; Q_AW_blf = 5.23; R_ZF_AW = 2.96; Kp_ZF_AW = 0.204 Reaction: ZF_blood => ZF_airways; ZF_blood, ZF_airways, Rate Law: Q_AW_blf*R_ZF_B*(ZF_blood-ZF_airways*R_ZF_AW/Kp_ZF_AW)
k1_min = 1.6E-7; h_matur = 1.0; k1 = 1.0E-6; Km_1 = 2.0 Reaction: => EO_bm; IL_bm, IL_bm, Rate Law: V_BM*(k1*IL_bm^h_matur/(Km_1^h_matur+IL_bm^h_matur)+k1_min)
k_lte_el = 0.04; k_acet = 0.002703885 Reaction: LTE4_aw => ; LTE4_aw, Rate Law: Vd_AW_LTE*(k_lte_el+k_acet)*LTE4_aw
k_IL_t_bbm = 0.001; J_BM_lymfl = 4.9E-4 Reaction: IL_b => IL_bm; IL_b, IL_bm, Rate Law: k_IL_t_bbm*(IL_b-IL_bm)-J_BM_lymfl*IL_bm
Kd50 = 0.43; V_LTC_CB = 0.0 Reaction: => LTC4_b_out; LTC4_b, LTC4_b, Rate Law: Default*Kd50*LTC4_b*V_LTC_CB*1E1^6.0
A_hedh_aw = 0.0; HEDH5 = 0.5; B_hedh_aw = 0.0 Reaction: HETE_aw =>, Rate Law: Default*HEDH5*B_hedh_aw/A_hedh_aw
den_LTCs_b = 0.0; nom_LTCs_b = 0.0 Reaction: LTA4_b => LTC4_b, Rate Law: Default*nom_LTCs_b/den_LTCs_b
Kd12 = 0.007 Reaction: LTA4_aw => ; LTA4_aw, Rate Law: Default*Kd12*LTA4_aw
k_EO_m = 10.0; ca = 10.0 Reaction: EO_i_aw => EO_a_aw; EO_i_aw, Rate Law: ca*V_AW*k_EO_m*EO_i_aw
k_EO_a_d = 1.5E-4 Reaction: EO_a_aw => ; EO_a_aw, Rate Law: V_AW*k_EO_a_d*EO_a_aw
ka = 500.0; ca = 10.0; h_act = 3.0; EC50_act = 0.75; OL_b = 0.0 Reaction: EO_b => EO_i_b; EO_b, Rate Law: ca*V_B*ka*EO_b*OL_b^h_act/(EC50_act^h_act+OL_b^h_act)
fup_LT = 0.16; k_lte_el = 0.04; k_acet = 0.002703885 Reaction: LTE4_b => ; LTE4_b, Rate Law: Vd_LTE*(k_lte_el+k_acet)*fup_LT*LTE4_b
k_IL_d = 0.0046 Reaction: IL_aw => ; IL_aw, Rate Law: V_AW*k_IL_d*IL_aw
k_lta_syn = 54420.0; FLO5HP_aw = 0.0 Reaction: => LTA4_aw, Rate Law: Default*k_lta_syn*FLO5HP_aw
k_ggt = 0.1 Reaction: LTC4_aw_out => LTD4_aw; LTC4_aw_out, Rate Law: Vd_AW_LTC*k_ggt*LTC4_aw_out
Q_AW_blf = 5.23; R_LTC_B = 0.538; R_LTC_AW = 1.4; Kp_LTC_AW = 0.22 Reaction: LTC4_aw_out => LTC4_b_out; LTC4_aw_out, LTC4_b_out, Rate Law: Q_AW_blf*R_LTC_B*(LTC4_aw_out*R_LTC_AW/Kp_LTC_AW-LTC4_b_out)
k_IL_p = 16.0 Reaction: => IL_aw; EO_a_aw, EO_a_aw, Rate Law: V_AW*k_IL_p*EO_a_aw
Kd50 = 0.43 Reaction: LTC4_aw => ; LTC4_aw, Rate Law: Default*Kd50*LTC4_aw
k_EO_d = 3.0E-4 Reaction: EO_aw => ; EO_aw, Rate Law: V_AW*k_EO_d*EO_aw
k_ltc_ltd_el = 0.1 Reaction: LTC4_aw_out => ; LTC4_aw_out, Rate Law: Vd_AW_LTC*k_ltc_ltd_el*LTC4_aw_out
FLO3_aw = 0.0; r1 = 0.0; FLO2_aw = 0.0; Ke_ox = 99.99979 Reaction: HPETE_aw => HETE_aw; HPETE_aw, HETE_aw, Rate Law: Default*r1*(HPETE_aw*FLO2_aw-HETE_aw*FLO3_aw/Ke_ox)
PLA2_Ca = 0.0; PL = 110.0; Km_PLA2_APC = 20.0; Km_CoA_AA = 0.005; V_CoA = 350.0; Vmax_PLA2 = 450.0 Reaction: => AA_b; AA_b, Rate Law: Default*(Vmax_PLA2*PLA2_Ca*PL/(Km_PLA2_APC+PL)-V_CoA*AA_b/(Km_CoA_AA+AA_b))
Kd50 = 0.43; V_LTC_CAW = 0.0 Reaction: => LTC4_aw_out; LTC4_aw, LTC4_aw, Rate Law: Default*Kd50*LTC4_aw*V_LTC_CAW*1E1^6.0
HEDH5 = 0.5; B_hedh_b = 0.0; A_hedh_b = 0.0 Reaction: HETE_b =>, Rate Law: Default*HEDH5*B_hedh_b/A_hedh_b
k_lta_syn = 54420.0; FLO5HP_b = 0.0 Reaction: => LTA4_b, Rate Law: Default*k_lta_syn*FLO5HP_b
ka = 500.0; OL_aw = 0.0; ca = 10.0; h_act = 3.0; EC50_act = 0.75 Reaction: EO_aw => EO_i_aw; EO_aw, Rate Law: ca*V_AW*ka*EO_aw*OL_aw^h_act/(EC50_act^h_act+OL_aw^h_act)
k_ltc_ltd_el = 0.1; fup_LT = 0.16 Reaction: LTD4_b => ; LTD4_b, Rate Law: Vd_LTD*k_ltc_ltd_el*fup_LT*LTD4_b
k_elim_zf = 0.004 Reaction: ZF_blood => ; ZF_blood, Rate Law: Vd_ZF*k_elim_zf*ZF_blood
fup_LT = 0.16; k_dp = 0.067 Reaction: LTD4_b => LTE4_b; LTD4_b, Rate Law: Vd_LTD*k_dp*fup_LT*LTD4_b
Ki_AA = 551.8748; k_3 = 263640.0; FLO3t_b = 0.0; FLO5HP_b = 0.0; k3 = 34.0 Reaction: => HPETE_b; AA_b, HPETE_b, AA_b, Rate Law: Default*(k_3*FLO5HP_b-k3*FLO3t_b*HPETE_b)*(1.0+AA_b/Ki_AA)
k_Hn_d = 0.033 Reaction: Hn_aw => ; Hn_aw, Rate Law: Vd_AW_Hn*k_Hn_d*Hn_aw
ft_zf = 0.0; F_zf = 0.082; a = 1.0; oral = 1.0; M_ZF = 236.0; DOSE_zf = 0.0; k_abs_zf = 0.018 Reaction: ZF_intes => ZF_blood; ZF_intes, Rate Law: Default*k_abs_zf*(ZF_intes+oral*F_zf*(a*ft_zf+(1.0-a))*DOSE_zf*1E3/M_ZF)
B_b = 0.0; A_b = 0.0; GPx = 1.6 Reaction: HPETE_b => HETE_b, Rate Law: Default*GPx*B_b/A_b
J_AW_lymfl = 0.00115; k_IL_t_awb = 0.05 Reaction: IL_aw => IL_b; IL_aw, IL_b, Rate Law: k_IL_t_awb*(IL_aw-IL_b)+J_AW_lymfl*IL_aw
k_dp = 0.067 Reaction: LTD4_aw => LTE4_aw; LTD4_aw, Rate Law: Vd_AW_LTD*k_dp*LTD4_aw
k_EO_t_bmb = 0.02; ca = 10.0 Reaction: EO_bm => EO_b; EO_bm, Rate Law: ca*V_BM*k_EO_t_bmb*EO_bm
den_LTCs_aw = 0.0; nom_LTCs_aw = 0.0 Reaction: LTA4_aw => LTC4_aw, Rate Law: Default*nom_LTCs_aw/den_LTCs_aw
k_elim_ml = 0.00225 Reaction: ML_blood => ; ML_blood, Rate Law: Vd_ML*k_elim_ml*ML_blood
fup_LT = 0.16; k_ggt = 0.1 Reaction: LTC4_b_out => LTD4_b; LTC4_b_out, Rate Law: Vd_LTC*k_ggt*fup_LT*LTC4_b_out
a = 1.0; oral = 1.0; F_ml = 0.660688; k_abs_ml = 0.012; M_ML = 586.18; ft_ml = 0.0; DOSE_ml = 0.0 Reaction: ML_intes => ML_blood; ML_intes, Rate Law: Default*k_abs_ml*(ML_intes+oral*F_ml*(a*ft_ml+(1.0-a))*DOSE_ml*1E9/M_ML)

States:

Name Description
LTA4 b [5280383; blood plasma]
HETE aw [5280733; respiratory smooth muscle]
EO a aw [respiratory smooth muscle; eosinophil]
ML intes [montelukast; intestine]
IL b [Interleukin-5; blood plasma]
LTD4 aw [respiratory smooth muscle; 6435286]
EO b [blood plasma; eosinophil]
ZF airways [zileuton; respiratory smooth muscle]
LTE4 b [5280879]
EO i aw [respiratory smooth muscle; eosinophil]
LTC4 aw [5280493; respiratory smooth muscle]
AA aw [arachidonic acid; respiratory smooth muscle]
ZF intes [zileuton; intestine]
HETE b [5280733; blood plasma]
ML blood [montelukast; blood plasma]
EO bm [eosinophil; bone marrow]
HPETE aw [5280778; respiratory smooth muscle]
EO i b [blood plasma; eosinophil]
LTE4 aw [5280879; respiratory smooth muscle]
IL aw [Interleukin-5; respiratory smooth muscle]
AA b [arachidonic acid; blood plasma]
LTC4 b [5280493; blood plasma]
LTC4 b out [5280493]
HPETE b [5280778; blood plasma]
EO a b [blood plasma; eosinophil]
Hn aw [respiratory smooth muscle; histamine]
EO aw [respiratory smooth muscle; eosinophil]
LTA4 aw [5280383; respiratory smooth muscle]
Hn b [histamine; blood plasma]
LTC4 aw out [5280493]
LTD4 b [6435286]
ZF blood [zileuton; respiratory smooth muscle]
IL bm [Interleukin-5; bone marrow]

Observables: none

MODEL0912887467 @ v0.0.1

This a model from the article: A mathematical model of a rabbit sinoatrial node cell. Demir SS, Clark JW, Murphey CR…

A mathematical model for the electrophysiological responses of a rabbit sinoatrial node cell that is based on whole cell recordings from enzymatically isolated single pacemaker cells at 37 degrees C has been developed. The ion channels, Na(+)-K+ and Ca2+ pumps, and Na(+)-Ca2+ exchanger in the surface membrane (sarcolemma) are described using equations for these known currents in mammalian pacemaker cells. The extracellular environment is treated as a diffusion-limited space, and the myoplasm contains Ca(2+)-binding proteins (calmodulin and troponin). Original features of this model include 1) new equations for the hyperpolarization-activated inward current, 2) assessment of the role of the transient-type Ca2+ current during pacemaker depolarization, 3) inclusion of an Na+ current based on recent experimental data, and 4) demonstration of the possible influence of pump and exchanger currents and background currents on the pacemaker rate. This model provides acceptable fits to voltage-clamp and action potential data and can be used to seek biophysically based explanations of the electrophysiological activity in the rabbit sinoatrial node cell. link: http://identifiers.org/pubmed/8166247

Parameters: none

States: none

Observables: none

MODEL0912940495 @ v0.0.1

This a model from the article: Parasympathetic modulation of sinoatrial node pacemaker activity in rabbit heart: a uni…

We have extended our compartmental model [Am. J. Physiol. 266 (Cell Physiol. 35): C832-C852, 1994] of the single rabbit sinoatrial node (SAN) cell so that it can simulate cellular responses to bath applications of ACh and isoprenaline as well as the effects of neuronally released ACh. The model employs three different types of muscarinic receptors to explain the variety of responses observed in mammalian cardiac pacemaking cells subjected to vagal stimulation. The response of greatest interest is the ACh-sensitive change in cycle length that is not accompanied by a change in action potential duration or repolarization or hyperpolarization of the maximum diastolic potential. In this case, an ACh-sensitive K+ current is not involved. Membrane hyperpolarization occurs in response to much higher levels of vagal stimulation, and this response is also mimicked by the model. Here, an ACh-sensitive K+ current is involved. The well-known phase-resetting response of the SAN cell to single and periodically applied vagal bursts of impulses is also simulated in the presence and absence of the beta-agonist isoprenaline. Finally, the responses of the SAN cell to longer continuous trains of periodic vagal stimulation are simulated, and this can result in the complete cessation of pacemaking. Therefore, this model is 1) applicable over the full range of intensity and pattern of vagal input and 2) can offer biophysically based explanations for many of the phenomena associated with the autonomic control of cardiac pacemaking. link: http://identifiers.org/pubmed/10362707

Parameters: none

States: none

Observables: none

MODEL0912835813 @ v0.0.1

This a model from the article: A mechanistic model of ACTH-stimulated cortisol secretion. Dempsher DP, Gann DS, Phai…

Adrenal secretory rates of cortisol and arterial concentrations of adrenocorticotropin (ACTH) were measured in conscious trained dogs subjected to intravenous infusion of ACTH. To investigate the causal relation of ACTH to the secretion of cortisol, a mechanistic mathematical model based on current hypotheses of adrenocortical function was constructed and tested. It is widely believed that ACTH stimulates cortisol secretion through adenosine 3',5'-cyclic monophosphate (cAMP), which provides substrate cholesterol by activating cholesterol ester hydrolase and facilitating transport of cholesterol to the side-chain cleavage enzyme. In addition, cholesterol modulates its own synthesis by inhibiting beta-hydroxy-beta-methylglutaryl (HMG)-CoA reductase in the adrenocortical cell. These and other steps in the biosynthetic reaction sequence were described using differential equations subject to the additional constraints imposed by available measurements of intracellular quantities. The resulting model is consistent with many of the known characteristics of the canine adrenal response to ACTH. In this model, steady-state nonlinearities arise from cooperative binding of cAMP to its receptor protein and saturation of mitochondrial pregnenolone transport. The transient response is dominated by a depletable pool of intracellular free cholesterol. Other inferences based on the model are presented, and a quantifiable cellular basis for increased adrenal sensitivity to ACTH is proposed. link: http://identifiers.org/pubmed/6326602

Parameters: none

States: none

Observables: none

BIOMD0000000759 @ v0.0.1

The paper describes a model of re-polarisation of M2 and M1 macrophages and its role on cancer outcomes. Created by COP…

The anti-tumour and pro-tumour roles of Th1/Th2 immune cells and M1/M2 macrophages have been documented by numerous experimental studies. However, it is still unknown how these immune cells interact with each other to control tumour dynamics. Here, we use a mathematical model for the interactions between mouse melanoma cells, Th2/Th1 cells and M2/M1 macrophages, to investigate the unknown role of the re-polarisation between M1 and M2 macrophages on tumour growth. The results show that tumour growth is associated with a type-II immune response described by large numbers of Th2 and M2 cells. Moreover, we show that (i) the ratio k of the transition rates k12 (for the re-polarisation M1→M2) and k21 (for the re-polarisation M2→M1) is important in reducing tumour population, and (ii) the particular values of these transition rates control the delay in tumour growth and the final tumour size. We also perform a sensitivity analysis to investigate the effect of various model parameters on changes in the tumour cell population, and confirm that the ratio k alone and the ratio of M2 and M1 macrophage populations at earlier times (e.g., day 7) cannot always predict the final tumour size. link: http://identifiers.org/pubmed/26551154

Parameters:

Name Description
rh2 = 9.0E-6 1/d; bth = 1.0E8 1 Reaction: => Th2; M2, Th1, Rate Law: tumor_microenvironment*rh2*M2*Th2*(1-(Th2+Th1)/bth)
ah2 = 0.008 1/d Reaction: => Th2; M2, Rate Law: tumor_microenvironment*ah2*M2
dh1 = 0.05 1/d Reaction: Th1 =>, Rate Law: tumor_microenvironment*dh1*Th1
dm2 = 0.2 1/d Reaction: M2 =>, Rate Law: tumor_microenvironment*dm2*M2
ah1 = 0.008 1/d Reaction: => Th1; M1, Rate Law: tumor_microenvironment*ah1*M1
as = 1.0E-6 1/d; am1 = 5.0E-8 1/d; bm = 100000.0 1 Reaction: => M1; Ts, Th1, M2, Rate Law: tumor_microenvironment*(as*Ts+am1*Th1)*M1*(1-(M1+M2)/bm)
rh1 = 9.0E-6 1/d; bth = 1.0E8 1 Reaction: => Th1; M1, Th2, Rate Law: tumor_microenvironment*rh1*M1*Th1*(1-(Th1+Th2)/bth)
k21 = 4.0E-5 1/d Reaction: M2 => M1, Rate Law: tumor_microenvironment*k21*M2*M1
dmn = 2.0E-6 1/d Reaction: Tn => ; M1, Rate Law: tumor_microenvironment*dmn*M1*Tn
r = 0.565 1/d; bt = 2.0E9 1 Reaction: => Tn; Ts, Rate Law: tumor_microenvironment*r*Tn*(1-(Tn+Ts)/bt)
rmn = 1.0E-7 1/d Reaction: => Tn; M2, Rate Law: tumor_microenvironment*rmn*Tn*M2
dh2 = 0.05 1/d Reaction: Th2 =>, Rate Law: tumor_microenvironment*dh2*Th2
dm1 = 0.2 1/d Reaction: M1 =>, Rate Law: tumor_microenvironment*dm1*M1
an = 5.0E-8 1/d; bm = 100000.0 1; am2 = 5.0E-8 1/d Reaction: => M2; Tn, Th2, M1, Rate Law: tumor_microenvironment*(an*Tn+am2*Th2)*M2*(1-(M2+M1)/bm)
dms = 2.0E-6 1/d Reaction: Ts => ; M1, Rate Law: tumor_microenvironment*dms*M1*Ts
dts = 5.3E-8 1/d Reaction: Ts => ; Th1, Rate Law: tumor_microenvironment*dts*Th1*Ts
k12 = 5.0E-5 1/d Reaction: M1 => M2, Rate Law: tumor_microenvironment*k12*M1*M2
ksn = 0.1 1/d Reaction: Ts => Tn, Rate Law: tumor_microenvironment*ksn*Ts

States:

Name Description
M2 [M2 Macrophage]
M1 [M1 Macrophage]
Ts [malignant cell]
Tn [malignant cell]
Th2 [T-helper 2 cell]
Th1 [T-helper 1 cell]

Observables: none

deOliveiraDalMolin2010 - Genome-scale metabolic network of Arabidopsis thaliana (AraGEM)This model is described in the a…

Genome-scale metabolic network models have been successfully used to describe metabolism in a variety of microbial organisms as well as specific mammalian cell types and organelles. This systems-based framework enables the exploration of global phenotypic effects of gene knockouts, gene insertion, and up-regulation of gene expression. We have developed a genome-scale metabolic network model (AraGEM) covering primary metabolism for a compartmentalized plant cell based on the Arabidopsis (Arabidopsis thaliana) genome. AraGEM is a comprehensive literature-based, genome-scale metabolic reconstruction that accounts for the functions of 1,419 unique open reading frames, 1,748 metabolites, 5,253 gene-enzyme reaction-association entries, and 1,567 unique reactions compartmentalized into the cytoplasm, mitochondrion, plastid, peroxisome, and vacuole. The curation process identified 75 essential reactions with respective enzyme associations not assigned to any particular gene in the Kyoto Encyclopedia of Genes and Genomes or AraCyc. With the addition of these reactions, AraGEM describes a functional primary metabolism of Arabidopsis. The reconstructed network was transformed into an in silico metabolic flux model of plant metabolism and validated through the simulation of plant metabolic functions inferred from the literature. Using efficient resource utilization as the optimality criterion, AraGEM predicted the classical photorespiratory cycle as well as known key differences between redox metabolism in photosynthetic and nonphotosynthetic plant cells. AraGEM is a viable framework for in silico functional analysis and can be used to derive new, nontrivial hypotheses for exploring plant metabolism. link: http://identifiers.org/pubmed/20044452

Parameters: none

States: none

Observables: none

MODEL1172940336 @ v0.0.1

This a model from the article: A feedback oscillator model for circulatory autoregulation Annraoi M. De Paor, Patric…

Circulatory autregulation is the phenomenon whereby an isolated organ can maintain a constant or almost-constant blood flow rate over a range of perfusion pressures. A mathematical model is developed, based on work reported in the physiological literature, and tuned to show that autoregulation can be accomplished by pressure-induced oscillations in arteriolar radius. Various features known lo be exhibited by skeletal muscle and by stretch receptors are incorporated in the model ofsmooth muscle surrounding the arterioles. link: http://identifiers.org/doi/10.1080/00207178608933494

Parameters: none

States: none

Observables: none

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

Abstract

We present a phase-space analysis of a mathematical model of tumor growth with an immune response and chemotherapy. We prove that all orbits are bounded and must converge to one of several possible equilibrium points. Therefore, the long-term behavior of an orbit is classified according to the basin of attraction in which it starts. The addition of a drug term to the system can move the solution trajectory into a desirable basin of attraction. We show that the solutions of the model with a time-varying drug term approach the solutions of the system without the drug once traatment has stopped. We present numerical experiments in which optimal control therapy is able to drive the system into a desirable basin of attraction, whereas traditional pulsed chemotherapy is not.

Volume 37, Issue 11, June 2003, Pages 1221-1244 link: http://identifiers.org/doi/10.1016/S0895-7177(03)00133-X

Parameters:

Name Description
d2 = 1.0 Reaction: u =>, Rate Law: compartment*d2*u
v = 0.0 Reaction: => u, Rate Law: compartment*v
alpha = 0.3; s = 0.33; p = 0.01 Reaction: => I; T, Rate Law: compartment*(s+p*I*T/(alpha+T))
b1 = 1.0; r1 = 1.5 Reaction: => T, Rate Law: compartment*r1*T*(1-b1*T)
c2 = 0.5; c3 = 1.0; a2 = 0.3 Reaction: T => ; I, N, u, Rate Law: compartment*(c2*I*T+c3*T*N+a2*(1-exp(-u))*T)
d1 = 0.2; c1 = 1.0; a1 = 0.2 Reaction: I => ; T, u, Rate Law: compartment*(c1*I*T+d1*I+a1*(1-exp(-u))*I)
b2 = 1.0; r2 = 1.0 Reaction: => N, Rate Law: compartment*r2*N*(1-b2*N)
a3 = 0.1; c4 = 1.0 Reaction: N => ; T, u, Rate Law: compartment*(c4*T*N+a3*(1-exp(-u))*N)

States:

Name Description
I [C12735]
T [Neoplastic Cell]
N N
u [C2252]

Observables: none

This model describes interactions between a tumour and the immune system, with specific emphasis on the role of natural…

Mathematical models of tumor-immune interactions provide an analytic framework in which to address specific questions about tumor-immune dynamics. We present a new mathematical model that describes tumor-immune interactions, focusing on the role of natural killer (NK) and CD8+ T cells in tumor surveillance, with the goal of understanding the dynamics of immune-mediated tumor rejection. The model describes tumor-immune cell interactions using a system of differential equations. The functions describing tumor-immune growth, response, and interaction rates, as well as associated variables, are developed using a least-squares method combined with a numerical differential equations solver. Parameter estimates and model validations use data from published mouse and human studies. Specifically, CD8+ T-tumor and NK-tumor lysis data from chromium release assays as well as in vivo tumor growth data are used. A variable sensitivity analysis is done on the model. The new functional forms developed show that there is a clear distinction between the dynamics of NK and CD8+ T cells. Simulations of tumor growth using different levels of immune stimulating ligands, effector cells, and tumor challenge are able to reproduce data from the published studies. A sensitivity analysis reveals that the variable to which the model is most sensitive is patient specific, and can be measured with a chromium release assay. The variable sensitivity analysis suggests that the model can predict which patients may positively respond to treatment. Computer simulations highlight the importance of CD8+ T-cell activation in cancer therapy. link: http://identifiers.org/pubmed/16140967

Parameters: none

States: none

Observables: none

Abstract We investigate a mathematical model of tumor–immune interactions with chemotherapy, and strategies for optima…

We investigate a mathematical model of tumor-immune interactions with chemotherapy, and strategies for optimally administering treatment. In this paper we analyze the dynamics of this model, characterize the optimal controls related to drug therapy, and discuss numerical results of the optimal strategies. The form of the model allows us to test and compare various optimal control strategies, including a quadratic control, a linear control, and a state-constraint. We establish the existence of the optimal control, and solve for the control in both the quadratic and linear case. In the linear control case, we show that we cannot rule out the possibility of a singular control. An interesting aspect of this paper is that we provide a graphical representation of regions on which the singular control is optimal. link: http://identifiers.org/pubmed/17306310

Parameters:

Name Description
a = 0.002; b = 1.02E-9 Reaction: => T, Rate Law: compartment*a*T*(1-b*T)
K_L = 0.6; q = 3.42E-10; u = 3.0; m = 0.02 Reaction: L => ; T, M, Rate Law: compartment*(m*L+q*L*T+u*L*L+K_L*M*L)
gamma = 0.9 Reaction: M =>, Rate Law: compartment*gamma*M
alpha_1 = 13000.0; h = 600.0; g = 0.025; eta = 1.0 Reaction: => N; T, Rate Law: compartment*(alpha_1+g*T^eta/(h+T^eta)*N)
mu_I = 10.0 Reaction: I =>, Rate Law: compartment*mu_I*I
p = 1.0E-7; f = 0.0412; K_N = 0.6 Reaction: N => ; T, M, Rate Law: compartment*(f*N+p*N*T+K_N*M*N)
V_M=0.0 Reaction: => M, Rate Law: compartment*V_M
alpha_2 = 5.0E8 Reaction: => C, Rate Law: compartment*alpha_2
beta = 0.012; K_C = 0.6 Reaction: C => ; M, Rate Law: compartment*(beta*C+K_C*M*C)
r2 = 3.0E-11; p_I = 0.125; V_L=0.0; g_I = 2.0E7 Reaction: => L; C, T, I, Rate Law: compartment*(r2*C*T+p_I*L*I/(g_I+I)+V_L)
V_I=0.0; w = 2.0E-4; g_T = 100000.0; p_T = 0.6 Reaction: => I; T, L, Rate Law: compartment*(p_T*T/(g_T+T)*L+w*L*I+V_I)
D = 6.6666657777779E-7; K_T = 0.8; c1 = 3.23E-7 Reaction: T => ; N, M, Rate Law: compartment*(c1*N*T+D*T+K_T*M*T)

States:

Name Description
I [Interleukin-2]
T [neoplasm]
M [Combination Chemotherapy]
N [Immune Cell]
C [C120462]
L [cytotoxic T-lymphocyte]

Observables: none

SEEKING BANG-BANG SOLUTIONS OF MIXED IMMUNO-CHEMOTHERAPY OF TUMORS LISETTE G. DE PILLIS, K. RENEE FISTER, WEIQING GU, CR…

It is known that a beneficial cancer treatment approach for a single patient often involves the administration of more than one type of therapy. The question of how best to combine multiple cancer therapies, however, is still open. In this study, we investigate the theoretical interaction of three treatment types (two biological therapies and one chemotherapy) with a growing cancer, and present an analysis of an optimal control strategy for administering all three therapies in combination. In the situations with controls introduced linearly, we find that there are conditions on which the controls exist singularly. Although bang-bang controls (on-off) reflect the drug treatment approach that is often implemented clinically, we have demonstrated, in the context of our mathematical model, that there can exist regions on which this may not be the best strategy for minimizing a tumor burden. We characterize the controls in singular regions by taking time derivatives of the switching functions. We will examine these representations and the conditions necessary for the controls to be minimizing in the singular region. We begin by assuming only one of the controls is singular on a given interval. Then we analyze the conditions on which a pair and then all three controls are singular. link: https://scholarship.claremont.edu/hmcfacpub/439/

Parameters: none

States: none

Observables: none

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

We investigate a mathematical population model of tumor-immune interactions. Thepopulations involved are tumor cells, specific and non-specific immune cells, and con-centrations of therapeutic treatments. We establish the existence of an optimal con-trol for this model and provide necessary conditions for the optimal control triple forsimultaneous application of chemotherapy, tumor infiltrating lymphocyte (TIL) ther-apy, and interleukin-2 (IL-2) treatment. We discuss numerical results for the combina-tion of the chemo-immunotherapy regimens. We find that the qualitative nature of ourresults indicates that chemotherapy is the dominant intervention with TIL interactingin a complementary fashion with the chemotherapy. However, within the optimal con-trol context, the interleukin-2 treatment does not become activated for the estimatedparameter ranges. link: http://identifiers.org/doi/10.1142/S0218339008002435

Parameters:

Name Description
a = 0.002; b = 1.02E-9 Reaction: => T, Rate Law: compartment*a*T*(1-b*T)
K_L = 0.6; q = 3.42E-10; u = 3.0; m = 0.02 Reaction: L => ; T, M, Rate Law: compartment*(m*L+q*L*T+u*L*L+K_L*M*L)
gamma = 0.9 Reaction: M =>, Rate Law: compartment*gamma*M
alpha_1 = 13000.0; h = 600.0; g = 0.025; eta = 1.0 Reaction: => N; T, Rate Law: compartment*(alpha_1+g*T^eta/(h+T^eta)*N)
mu_I = 10.0 Reaction: I =>, Rate Law: compartment*mu_I*I
p = 1.0E-7; f = 0.0412; K_N = 0.6 Reaction: N => ; T, M, Rate Law: compartment*(f*N+p*N*T+K_N*M*N)
V_M=0.0 Reaction: => M, Rate Law: compartment*V_M
alpha_2 = 5.0E8 Reaction: => C, Rate Law: compartment*alpha_2
beta = 0.012; K_C = 0.6 Reaction: C => ; M, Rate Law: compartment*(beta*C+K_C*M*C)
r2 = 3.0E-11; p_I = 0.125; V_L=0.0; g_I = 2.0E7 Reaction: => L; C, T, I, Rate Law: compartment*(r2*C*T+p_I*L*I/(g_I+I)+V_L)
V_I=0.0; w = 2.0E-4; g_T = 100000.0; p_T = 0.6 Reaction: => I; T, L, Rate Law: compartment*(p_T*T/(g_T+T)*L+w*L*I+V_I)
D = 6.6666657777779E-7; K_T = 0.8; c1 = 3.23E-7 Reaction: T => ; N, M, Rate Law: compartment*(c1*N*T+D*T+K_T*M*T)

States:

Name Description
I [Interleukin-2]
T [neoplasm]
M [Combination Chemotherapy]
N [Immune Cell]
C [C120462]
L [cytotoxic T-lymphocyte]

Observables: none

This is an updated version of a previous model that described the dynamics of cancer treatment, with descriptions of tum…

One of the most challenging tasks in constructing a mathematical model of cancer treatment is the calculation of biological parameters from empirical data. This task becomes increasingly difficult if a model involves several cell populations and treatment modalities. A sophisticated model constructed by de Pillis et al., Mixed immunotherapy and chemotherapy of tumours: Modelling, applications and biological interpretations, J. Theor. Biol. 238 (2006), pp. 841-862; involves tumour cells, specific and non-specific immune cells (natural killer (NK) cells, CD8+T cells and other lymphocytes) and employs chemotherapy and two types of immunotherapy (IL-2 supplementation and CD8+T-cell infusion) as treatment modalities. Despite the overall success of the aforementioned model, the problem of illustrating the effects of IL-2 on a growing tumour remains open. In this paper, we update the model of de Pillis et al. and then carefully identify appropriate values for the parameters of the new model according to recent empirical data. We determine new NK and tumour antigen-activated CD8+T-cell count equilibrium values; we complete IL-2 dynamics; and we modify the model in de Pillis et al. to allow for endogenous IL-2 production, IL-2-stimulated NK cell proliferation and IL-2-dependent CD8+T-cell self-regulations. Finally, we show that the potential patient-specific efficacy of immunotherapy may be dependent on experimentally determinable parameters. link: http://identifiers.org/doi/10.1080/17486700802216301

Parameters:

Name Description
phi = 2.38405E-7 Reaction: => I_IL_2; C_Lymphocytes, Rate Law: compartment*phi*C_Lymphocytes
omega = 0.07874; zeta = 2503.6 Reaction: => I_IL_2; L_CD8_T_Cells, Rate Law: compartment*omega*L_CD8_T_Cells*I_IL_2/(zeta+I_IL_2)
alphabeta = 2.25E9; beta = 0.0063 Reaction: => C_Lymphocytes; C_Lymphocytes, Rate Law: compartment*beta*(alphabeta-C_Lymphocytes)
a = 0.431; b = 1.02E-9 Reaction: => T_Tumour_Cells, Rate Law: compartment*a*T_Tumour_Cells*(1-b*T_Tumour_Cells)
j = 0.01245; k = 2.019E7 Reaction: => L_CD8_T_Cells; T_Tumour_Cells, Rate Law: compartment*j*T_Tumour_Cells*L_CD8_T_Cells/(k+T_Tumour_Cells)
delta_C = 1.8328; K_C = 0.034 Reaction: C_Lymphocytes => ; M_Chemotherapy_Drug, Rate Law: compartment*K_C*(1-exp((-1)*delta_C*M_Chemotherapy_Drug))*C_Lymphocytes
gamma = 0.5199 Reaction: M_Chemotherapy_Drug =>, Rate Law: compartment*gamma*M_Chemotherapy_Drug
r_2 = 5.8467E-13 Reaction: => L_CD8_T_Cells; C_Lymphocytes, T_Tumour_Cells, Rate Law: compartment*r_2*C_Lymphocytes*T_Tumour_Cells
v_I = 0.0 Reaction: => I_IL_2, Rate Law: compartment*v_I
p_I = 2.971; g_I = 2503.6 Reaction: => L_CD8_T_Cells; I_IL_2, Rate Law: compartment*p_I*L_CD8_T_Cells*I_IL_2/(g_I+I_IL_2)
g_N = 250360.0; p_N = 0.068 Reaction: => N_Natural_Killer_Cells; I_IL_2, Rate Law: compartment*p_N*N_Natural_Killer_Cells*I_IL_2/(g_N+I_IL_2)
r_1 = 2.9077E-11 Reaction: => L_CD8_T_Cells; N_Natural_Killer_Cells, T_Tumour_Cells, Rate Law: compartment*r_1*N_Natural_Killer_Cells*T_Tumour_Cells
D = 0.0 Reaction: T_Tumour_Cells =>, Rate Law: compartment*D*T_Tumour_Cells
q = 3.422E-10 Reaction: L_CD8_T_Cells => ; T_Tumour_Cells, Rate Law: compartment*q*L_CD8_T_Cells*T_Tumour_Cells
K_L = 0.0486; delta_L = 1.8328 Reaction: L_CD8_T_Cells => ; M_Chemotherapy_Drug, Rate Law: compartment*K_L*(1-exp((-1)*delta_L*M_Chemotherapy_Drug))*L_CD8_T_Cells
phi = 2.38405E-7; m = 0.009 Reaction: L_CD8_T_Cells => ; I_IL_2, Rate Law: compartment*phi*m*L_CD8_T_Cells/(phi+I_IL_2)
v_M = 0.0 Reaction: => M_Chemotherapy_Drug, Rate Law: compartment*v_M
mu_I = 11.7427 Reaction: I_IL_2 =>, Rate Law: compartment*mu_I*I_IL_2
ef = 0.111; f = 0.0125 Reaction: => N_Natural_Killer_Cells; C_Lymphocytes, N_Natural_Killer_Cells, Rate Law: compartment*f*(ef*C_Lymphocytes-N_Natural_Killer_Cells)
c = 2.9077E-13 Reaction: T_Tumour_Cells => ; N_Natural_Killer_Cells, Rate Law: compartment*c*N_Natural_Killer_Cells*T_Tumour_Cells
K_T = 0.9; delta_T = 1.8328 Reaction: T_Tumour_Cells => ; M_Chemotherapy_Drug, Rate Law: compartment*K_T*(1-exp((-1)*delta_T*M_Chemotherapy_Drug))*T_Tumour_Cells
v_L = 0.0 Reaction: => L_CD8_T_Cells, Rate Law: compartment*v_L
K_N = 0.0675; delta_N = 1.8328 Reaction: N_Natural_Killer_Cells => ; M_Chemotherapy_Drug, Rate Law: compartment*K_N*(1-exp((-1)*delta_N*M_Chemotherapy_Drug))*N_Natural_Killer_Cells
u = 4.417E-14; kappa = 2503.6 Reaction: L_CD8_T_Cells => ; C_Lymphocytes, I_IL_2, Rate Law: compartment*u*L_CD8_T_Cells^2*C_Lymphocytes*I_IL_2/(kappa+I_IL_2)
p = 2.794E-13 Reaction: N_Natural_Killer_Cells => ; T_Tumour_Cells, Rate Law: compartment*p*N_Natural_Killer_Cells*T_Tumour_Cells

States:

Name Description
I IL 2 [Interleukin-2]
M Chemotherapy Drug [doxorubicin]
C Lymphocytes [lymphocyte]
N Natural Killer Cells [natural killer cell]
L CD8 T Cells [CD8-Positive T-Lymphocyte]
T Tumour Cells [Neoplastic Cell]

Observables: none

Mathematical modeling of regulatory T cell effects on renal cell carcinoma treatment Lisette dePillis 1, , Trevor Caldw…

Abstract. We present a mathematical model to study the effects of the regu-latory T cells (T reg ) on Renal Cell Carcinoma (RCC) treatment with sunitinib.The drug sunitinib inhibits the natural self-regulation of the immune system,allowing the effector components of the immune system to function for longerperiods of time. This mathematical model builds upon our non-linear ODEmodel by de Pillis et al. (2009) [13] to incorporate sunitinib treatment, regula-tory T cell dynamics, and RCC-specific parameters. The model also elucidatesthe roles of certain RCC-specific parameters in determining key differences be-tween in silico patients whose immune profiles allowed them to respond wellto sunitinib treatment, and those whose profiles did not.Simulations from our model are able to produce results that reflect clinicaloutcomes to sunitinib treatment such as: (1) sunitinib treatments followingstandard protocols led to improved tumor control (over no treatment) in about40% of patients; (2) sunitinib treatments at double the standard dose led to agreater response rate in about 15% the patient population; (3) simulations ofpatient response indicated improved responses to sunitinib treatment when thepatient’s immune strength scaling and the immune system strength coefficientsparameters were low, allowing for a slightly stronger natural immune response link: http://identifiers.org/doi/10.3934/dcdsb.2013.18.915

Parameters:

Name Description
p = 6.682E-14 Reaction: N => ; T, Rate Law: compartment*p*N*T
p_N = 0.0668; f = 0.0125; g_N = 250360.0; e_f = 0.1168 Reaction: => N; C, I, Rate Law: compartment*(f*(e_f*C-N)+p_N*N*I/(g_N+I))
delta_R = 50.02; H_R = 0.227 Reaction: R => ; S, Rate Law: compartment*H_R*(1-exp((-delta_R)*S))*R
j = 0.1245; r_2 = 1.0E-15; g_I = 2503.6; r_1 = 6.682E-12; p_I = 1.111; k = 2.019E7 Reaction: => L; N, C, T, I, Rate Law: compartment*((r_1*N+r_2*C)*T+p_I*L*I/(g_I+I)+j*T/(k+T)*L)
zeta = 2503.6; phi = 3.594E-7; w = 0.05314 Reaction: => I; C, L, Rate Law: compartment*(phi*C+w*L*I/(zeta+I))
n = 0.277 Reaction: S =>, Rate Law: compartment*(-n)*S
mu_I = 11.7427 Reaction: I =>, Rate Law: compartment*mu_I*I
alpha_beta = 2.14E9; beta = 0.0063 Reaction: => C, Rate Law: compartment*beta*(alpha_beta-C)
delta_T = 1.59E-9; c = 8.68E-10; D = 9.47552761007735E-6 Reaction: T => ; R, N, Rate Law: compartment*(c*exp((-delta_T)*R)*N*T+D*T)
g_R = 11.027; w_u = 0.0122; p_R = 0.03598; u = 0.03851 Reaction: => R; C, I, Rate Law: compartment*(u*(w_u*C-R)+p_R*R*I/(g_R+I))
kappa = 2503.6; q = 3.422E-10; z = 2.3085E-13; m = 0.009 Reaction: L => ; T, R, I, Rate Law: compartment*(m*L+q*L*T+z*L*L*R*I/(kappa+I))
vs = 0.0 Reaction: => S, Rate Law: compartment*vs
a = 0.2065; b = 2.145E-10 Reaction: => T, Rate Law: compartment*a*T*(1-b*T)

States:

Name Description
I [Interleukin-2]
S [C71622]
T [Neoplastic Cell]
C [Neoplastic Cell]
N [natural killer cell]
L [C12543]
R [CD4+ CD25+ Regulatory T Cells]

Observables: none

This a model from the article: Channel sharing in pancreatic beta -cells revisited: enhancement of emergent bursting…

Secretion of insulin by electrically coupled populations of pancreatic beta -cells is governed by bursting electrical activity. Isolated beta -cells, however, exhibit atypical bursting or continuous spike activity. We study bursting as an emergent property of the population, focussing on interactions among the subclass of spiking cells. These are modelled by equipping the fast subsystem with a saddle-node-loop bifurcation, which makes it monostable. Such cells can only spike tonically or remain silent when isolated, but can be induced to burst with weak diffusive coupling. With stronger coupling, the cells revert to tonic spiking. We demonstrate that the addition of noise dramatically increases, via a phenomenon like stochastic resonance, the coupling range over which bursting is seen. link: http://identifiers.org/pubmed/11093836

Parameters:

Name Description
tau_potassium_current_n_gate = 20.0; n_infinity = 1.89405943825186E-4; lamda = 0.8 Reaction: n = lamda*(n_infinity-n)/tau_potassium_current_n_gate, Rate Law: lamda*(n_infinity-n)/tau_potassium_current_n_gate
tau_s = 20000.0; s_infinity = 0.00460957217937421 Reaction: s = (s_infinity-s)/tau_s, Rate Law: (s_infinity-s)/tau_s
tau_membrane = 20.0; i_Ca = -7.4446678508483; i_K = 5.0; i_K_ATP = 6.0; i_s = 1.0 Reaction: V_membrane = (-(i_Ca+i_K+i_K_ATP+i_s))/tau_membrane, Rate Law: (-(i_Ca+i_K+i_K_ATP+i_s))/tau_membrane

States:

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

Observables: none

This is a delay differential equation model showing how non-coding RNA, acting as microRNA (miRNA) sponges in a conserve…

Oscillations are crucial to the normal function of living organisms, across a wide variety of biological processes. In eukaryotes, oscillatory dynamics are thought to arise from interactions at the protein and RNA levels; however, the role of non-coding RNA in regulating these dynamics remains understudied. In this work, we show how non-coding RNA acting as microRNA (miRNA) sponges in a conserved miRNA - transcription factor feedback motif, can give rise to oscillatory behaviour, and how to test for this experimentally. Control of these non-coding RNA can dynamically create oscillations or stability, and we show how this behaviour predisposes to oscillations in the stochastic limit. These results, supported by emerging evidence for the role of miRNA sponges in development, point towards key roles of different species of miRNA sponges, such as circular RNA, potentially in the maintenance of yet unexplained oscillatory behaviour. These results help to provide a paradigm for understanding functional differences between the many redundant, but distinct RNA species thought to act as miRNA sponges in nature, such as long non-coding RNA, pseudogenes, competing mRNA, circular RNA, and3' UTRs. link: http://identifiers.org/pubmed/30385313

Parameters:

Name Description
gamma_FM = 100.0; n = 8.0; tau1 = 0.5; beta_FM = 200.0 Reaction: => M; P, Rate Law: compartment*beta_FM/((gamma_FM/delay(P, tau1))^n+1)
k_CM = 10.0 Reaction: M + C =>, Rate Law: compartment*k_CM*M*C
alpha_M = 1.0 Reaction: => M, Rate Law: compartment*alpha_M
delta_F = 0.1 Reaction: F =>, Rate Law: compartment*delta_F*F
delta_M = 1.0 Reaction: M =>, Rate Law: compartment*delta_M*M
alpha_F = 1.0 Reaction: => F, Rate Law: compartment*alpha_F
alpha_C = 1.0 Reaction: => C, Rate Law: compartment*alpha_C
delta_P = 0.1 Reaction: P =>, Rate Law: compartment*delta_P*P
delta_C = 0.01 Reaction: C =>, Rate Law: compartment*delta_C*C
tau2 = 0.5; k_P = 10.0 Reaction: => P; F, Rate Law: compartment*k_P*delay(F, tau2)
k_MF = 0.1 Reaction: M + F =>, Rate Law: compartment*k_MF*M*F

States:

Name Description
M [C25966]
C [C25966]
P [1,4-beta-D-Mannooligosaccharide]
F [Messenger RNA]

Observables: none

An updated representation of S. meliloti metabolism that was manually-curated and encompasses information from 240 liter…

The mutualistic association between leguminous plants and endosymbiotic rhizobial bacteria is a paradigmatic example of a symbiosis driven by metabolic exchanges. Here, we report the reconstruction and modelling of a genome-scale metabolic network of Medicago truncatula (plant) nodulated by Sinorhizobium meliloti (bacterium). The reconstructed nodule tissue contains five spatially distinct developmental zones and encompasses the metabolism of both the plant and the bacterium. Flux balance analysis (FBA) suggests that the metabolic costs associated with symbiotic nitrogen fixation are primarily related to supporting nitrogenase activity, and increasing N2-fixation efficiency is associated with diminishing returns in terms of plant growth. Our analyses support that differentiating bacteroids have access to sugars as major carbon sources, ammonium is the main nitrogen export product of N2-fixing bacteria, and N2 fixation depends on proton transfer from the plant cytoplasm to the bacteria through acidification of the peribacteroid space. We expect that our model, called 'Virtual Nodule Environment' (ViNE), will contribute to a better understanding of the functioning of legume nodules, and may guide experimental studies and engineering of symbiotic nitrogen fixation. link: http://identifiers.org/pubmed/32444627

Parameters: none

States: none

Observables: none

A data-entrained computational model for testing the regulatory logic of the vertebrate unfolded protein responseThis mo…

The vertebrate unfolded protein response (UPR) is characterized by multiple interacting nodes among its three pathways, yet the logic underlying this regulatory complexity is unclear. To begin to address this issue, we created a computational model of the vertebrate UPR that was entrained upon and then validated against experimental data. As part of this validation, the model successfully predicted the phenotypes of cells with lesions in UPR signaling, including a surprising and previously unreported differential role for the eIF2α phosphatase GADD34 in exacerbating severe stress but ameliorating mild stress. We then used the model to test the functional importance of a feedforward circuit within the PERK/CHOP axis and of cross-regulatory control of BiP and CHOP expression. We found that the wiring structure of the UPR appears to balance the ability of the response to remain sensitive to endoplasmic reticulum stress and to be deactivated rapidly by improved protein-folding conditions. This model should serve as a valuable resource for further exploring the regulatory logic of the UPR. link: http://identifiers.org/pubmed/29668363

Parameters:

Name Description
A6_star = 1.0 1; B = 0.444444444444444 1; kcl=4.0; A6tot_norm = 15.0 1; kdA6 = 0.00384 1/(16.6667*s); KBA6 = 1.6E-5 1; U_star = 1.0 1 Reaction: => A6; U, A6, Rate Law: ER*(kdA6*A6_star+kcl*(U-U_star)*(A6tot_norm-A6)/(1+B/KBA6))
KA4g=0.75; C_star = 1.0 1; etaC=0.012; Kth4g=0.1; kdg = 0.003468 1/(16.6667*s); g_star = 1.0 1; KC=5.0; A4_star = 1.0 1 Reaction: => g; A4, C, Rate Law: ER*(kdg*g_star+etaC*((A4-A4_star)+KA4g*(A4-A4_star)*(C-C_star))/((A4-A4_star)+Kth4g*(A4-A4_star)*(C-C_star)+KC))
C_star = 1.0 1; ktC=1.0E-4; kdC = 0.005478 1/(16.6667*s); c_star = 1.0 1; Ep_star = 1.0 1 Reaction: => C; Ep, c, Rate Law: ER*(kdC*C_star/c_star+ktC*(Ep-Ep_star))*c
kdx = 0.006546 1/(16.6667*s) Reaction: x => ; A6, Rate Law: ER*kdx*x
Ip_star = 1.0 1; KII=0.01; B = 0.444444444444444 1; delta=1.5; Ip = 1.0 1 Reaction: U => ; x, Rate Law: ER*delta*U/(1+KII*(Ip-Ip_star))*B
kdb = 0.001284 1/(16.6667*s); alphaI = 0.2 1; Ip_star = 1.0 1; betaI = 0.1 1; Ip = 1.0 1 Reaction: b => ; A4, A6, Rate Law: ER*kdb*(1+alphaI*(Ip-Ip_star))/(1+betaI*(Ip-Ip_star))*b
ksp=0.00785; Kx=3.0; xtot_norm = 16.0 1; Ip = 1.0 1 Reaction: => x, Rate Law: ER*ksp*Ip*(xtot_norm-x)/((Kx+xtot_norm)-x)
gamma=0.001; A4_star = 1.0 1; U_star = 1.0 1; kdA4 = 0.00384 1/(16.6667*s) Reaction: => A4; U, Ep, Rate Law: ER*(kdA4*A4_star+gamma*(U-U_star)*Ep)
kdA4 = 0.00384 1/(16.6667*s) Reaction: A4 =>, Rate Law: ER*kdA4*A4
kdC = 0.005478 1/(16.6667*s) Reaction: C =>, Rate Law: ER*kdC*C
kdg = 0.003468 1/(16.6667*s) Reaction: g => ; C, Rate Law: ER*kdg*g
KA4c=2.0; Kth4c=0.25; c_star = 1.0 1; muA4=0.1; A4_star = 1.0 1; Kc4=0.56; n=2.0; kdc = 0.00393 1/(16.6667*s) Reaction: => c; A6, A4, C, Rate Law: ER*(kdc*c_star+muA4*(1+Kc4*A6)*(A4-A4_star)^n/((A4-A4_star)^n+KA4c^n*(1+Kth4c*A6)^n))
kdG = 0.003852 1/(16.6667*s) Reaction: G =>, Rate Law: ER*kdG*G
kdB = 2.514E-4 1/(16.6667*s); b_star = 1.0 1; Btot_star = 1.0 1 Reaction: => Btot; b, Rate Law: ER*kdB*Btot_star/b_star*b
kdc = 0.00393 1/(16.6667*s) Reaction: c => ; C, Rate Law: ER*kdc*c
A6_star = 1.0 1; nA4=2.0; nA=7.0; KA6=3.0; Kth6=1.0E-5; x_star = 1.0 1; Ip = 1.0 1; Kb4=0.5; alphaX=0.002; alphaA6=0.012; kdb = 0.001284 1/(16.6667*s); Ip_star = 1.0 1; betaI = 0.1 1; KA4=3.0; alphaA4=0.007; Kb6=0.56; alphaI = 0.2 1; nA6=2.0; b_star = 1.0 1; KX=8.0; Kth4=0.167; A4_star = 1.0 1 Reaction: => b; A4, A6, x, Rate Law: ER*(kdb*(1+alphaI*(Ip-Ip_star))/(1+betaI*(Ip-Ip_star))*b_star+alphaA6*(1+Kb6*A4)*(A6-A6_star)^nA6/((A6-A6_star)^nA6+KA6^nA6*(1+Kth6*A4^nA))+alphaA4*(1+Kb4*A6)*(A4-A4_star)^nA4/((A4-A4_star)^nA4+KA4^nA4*(1+Kth4*A6)^nA4)+alphaX*(x-x_star)/((x-x_star)+KX))
Etot_norm = 20.0 1; Kph=14.0; kph=0.00651; Pp = 1.0 1 Reaction: => Ep, Rate Law: ER*kph*(Etot_norm-Ep)*Pp/(Kph+(Etot_norm-Ep))
kdeph1=0.03; kdeph2=0.08; G_star = 1.0 1; Kdeph=7.0 Reaction: Ep => ; G, Rate Law: ER*(kdeph1+kdeph2*(G-G_star))*Ep/(Kdeph+Ep)
kdB = 2.514E-4 1/(16.6667*s) Reaction: Btot =>, Rate Law: ER*kdB*Btot
kdA6 = 0.00384 1/(16.6667*s) Reaction: A6 =>, Rate Law: ER*kdA6*A6
KUI=0.01; Ip_star = 1.0 1; KUI = 2.17848410757946 1; Stress = 2.0 1/(16.6667*s); KE=3.0; ksU=0.89; n=4.0; Ip = 1.0 1; KUU=6.0 Reaction: => U; Ep, U, Rate Law: ER*(ksU/(1+KUI*(Ip-Ip_star))+Stress)/(1+Ep/KE+(U/KUU)^n)
g_star = 1.0 1; G_star = 1.0 1; ktG=0.0015; kdG = 0.003852 1/(16.6667*s); Ep_star = 1.0 1 Reaction: => G; Ep, g, Rate Law: ER*(kdG*G_star/g_star+ktG*(Ep-Ep_star))*g

States:

Name Description
g [Protein phosphatase 1 regulatory subunit 15A; mRNA]
c [DNA damage-inducible transcript 3 protein; mRNA]
C [DNA damage-inducible transcript 3 protein]
b [Immunoglobulin Binding Protein; mRNA]
A4 [Cyclic AMP-dependent transcription factor ATF-4]
x [X-box-binding protein 1]
G [Protein phosphatase 1 regulatory subunit 15A]
Ep [Eukaryotic translation initiation factor 2 subunit 1; phosphorylation]
U [unfolded protein [endoplasmic reticulum lumen]]
Btot [Immunoglobulin Binding Protein]
A6 [Cyclic AMP-dependent transcription factor ATF-6 alpha; Cyclic AMP-dependent transcription factor ATF-6 beta]

Observables: none

This a model from the article: A model of cardiac electrical activity incorporating ionic pumps and concentration chan…

link: http://identifiers.org/pubmed/2578676

Parameters: none

States: none

Observables: none

This SBML representation of the yeast metabolic network is made available under the Creative Commons Attribution-Share A…

Metabolic networks adapt to changes in their environment by modulating the activity of their enzymes and transporters; often by changing their abundance. Understanding such quantitative changes can shed light onto how metabolic adaptation works, or how it can fail and lead to a metabolically dysfunctional state. We propose a strategy to quantify metabolic protein requirements for cofactor-utilising enzymes and transporters through constraint-based modelling. The first eukaryotic genome-scale metabolic model to comprehensively represent iron metabolism was constructed, extending the most recent community model of the Saccharomyces cerevisiae metabolic network. Partial functional impairment of the genes involved in the maturation of iron-sulphur (Fe-S) proteins was investigated employing the model and the in silico analysis revealed extensive rewiring of the fluxes in response to this functional impairment, despite its marginal phenotypic effect. The optimal turnover rate of enzymes bearing ion cofactors can be determined via this novel approach; yeast metabolism, at steady state, was determined to employ a constant turnover of its iron-recruiting enzyme at a rate of 3.02 × 10 -11  mmol·(g biomass) -1 ·h  -1 . link: http://identifiers.org/pubmed/30578666

Parameters: none

States: none

Observables: none

MODEL1101180000 @ v0.0.1

This is an SBML version with MesoRD annotations of the model described in: **Self-organized partitioning of dynamicall…

How cells manage to get equal distribution of their structures and molecules at cell division is a crucial issue in biology. In principle, a feedback mechanism could always ensure equality by measuring and correcting the distribution in the progeny. However, an elegant alternative could be a mechanism relying on self-organization, with the interplay between system properties and cell geometry leading to the emergence of equal partitioning. The problem is exemplified by the bacterial Min system that defines the division site by oscillating from pole to pole. Unequal partitioning of Min proteins at division could negatively impact system performance and cell growth because of loss of Min oscillations and imprecise mid-cell determination. In this study, we combine live cell and computational analyses to show that known properties of the Min system together with the gradual reduction of protein exchange through the constricting septum are sufficient to explain the observed highly precise spontaneous protein partitioning. Our findings reveal a novel and effective mechanism of protein partitioning in dividing cells and emphasize the importance of self-organization in basic cellular processes. link: http://identifiers.org/pubmed/21206490

Parameters: none

States: none

Observables: none

MODEL0912503622 @ v0.0.1

This a model from the article: Ion currents underlying sinoatrial node pacemaker activity: a new single cell mathemati…

The ionic currents underlying autorhythmicity of the mammalian sinoatrial node and their wider contribution to each phase of the action potential have been investigated in this study using a new single cell mathematical model. The new model provides a review and update of existing formulations of sinoatrial node membrane currents, derived from a wide range of electrophysiological data available in the literature. Simulations of spontaneous activity suggest that the dominant mechanism underlying pacemaker depolarisation is the inward background Na+ current, ib,Na. In contrast to previous models, the decay of the delayed rectifying K+ current, iK, was insignificant during this phase. Despite the presence of a pseudo-outward background current throughout the pacemaker range of potentials (Na-K pump+leak currents), the hyperpolarisation-activated current i(f) was not essential to pacemaker activity. A closer inspection of the current-voltage characteristics of the model revealed that the "instantaneous" time-independent current was inward for holding potentials in the pacemaker range, which rapidly became outward within 2 ms due to the inactivation of the L-type Ca2+ current, iCa,L. This suggests that reports in the literature in which the net background current is outward throughout the pacemaker range of potentials may be exaggerated. The magnitudes of the action potential overshoot and the maximum diastolic potential were determined largely by the reversal potentials of iCa,L and iK respectively. The action potential was sustained by the incomplete deactivation of iCa,L and the Na-Ca exchanger, iNaCa. Despite the incorporation of "square-root" activation by [K]o of all K+ currents, the model was unable to correctly simulate the response to elevated [K]o. link: http://identifiers.org/pubmed/8869126

Parameters: none

States: none

Observables: none

This model attempts to provide a mathematical framework for describing the dynamics of receptor-drug complex formation o…

Chimeric drugs with selective potential toward specific cell types constitute one of the most promising forefronts of modern Pharmacology. We present a mathematical model to test and optimize these synthetic constructs, as an alternative to conventional empirical design. We take as a case study a chimeric construct composed of epidermal growth factor (EGF) linked to different mutants of interferon (IFN). Our model quantitatively reproduces all the experimental results, illustrating how chimeras using mutants of IFN with reduced affinity exhibit enhanced selectivity against cell overexpressing EGF receptor. We also investigate how chimeric selectivity can be improved based on the balance between affinity rates, receptor abundance, activity of ligand subunits, and linker length between subunits. The simplicity and generality of the model facilitate a straightforward application to other chimeric constructs, providing a quantitative systematic design and optimization of these selective drugs against certain cell-based diseases, such as Alzheimer's and cancer.CPT: Pharmacometrics & Systems Pharmacology (2013) 2, e26; doi:10.1038/psp.2013.2; advance online publication 13 February 2013. link: http://identifiers.org/pubmed/23887616

Parameters: none

States: none

Observables: none

This model gives a mathematical description of the interactions between tumor cells, cytotoxic T lymphocytes and helper…

Activation of CD8+ cytotoxic T lymphocytes (CTLs) is naturally regarded as a major antitumor mechanism of the immune system. In contrast, CD4+ T cells are commonly classified as helper T cells (HTCs) on the basis of their roles in providing help to the generation and maintenance of effective CD8+ cytotoxic and memory T cells. In order to get a better insight on the role of HTCs in a tumor immune system, we incorporate the third population of HTCs into a previous two dimensional ordinary differential equations (ODEs) model. Further we introduce the adoptive cellular immunotherapy (ACI) as the treatment to boost the immune system to fight against tumors. Compared tumor cells (TCs) and effector cells (ECs), the recruitment of HTCs changes the dynamics of the system substantially, by the effects through particular parameters, i.e., the activation rate of ECs by HTCs, π (scaled as π), and the HTCs stimulation rate by the presence of identified tumor antigens, k2 (scaled as υ2). We describe the stability regions of the interior equilibria É (no treatment case) and E+ (treatment case) in the scaled (π,υ2) parameter space respectively. Both π and υ2 can destabilize É and E+ and cause Hopf bifurcations. Our results show that HTCs might play a crucial role in the long term periodic oscillation behaviors of tumor immune system interactions. They also show that TCs may be eradicated from the patient's body under the ACI treatment. link: http://identifiers.org/doi/10.3934/dcdsb.2014.19.55

Parameters:

Name Description
beta = 0.002; alpha = 1.636 Reaction: => x_Tumor_Cells, Rate Law: compartment*alpha*x_Tumor_Cells*(1-beta*x_Tumor_Cells)
rho = 0.01 Reaction: => y_Effector_Cells; z_Helper_T_Cells, Rate Law: compartment*rho*y_Effector_Cells*z_Helper_T_Cells
delta_2 = 0.055 Reaction: z_Helper_T_Cells =>, Rate Law: compartment*delta_2*z_Helper_T_Cells
delta_1 = 0.3743 Reaction: y_Effector_Cells =>, Rate Law: compartment*delta_1*y_Effector_Cells
omega_2 = 0.002 Reaction: => z_Helper_T_Cells; x_Tumor_Cells, Rate Law: compartment*omega_2*x_Tumor_Cells*z_Helper_T_Cells
omega_1 = 0.04 Reaction: => y_Effector_Cells; x_Tumor_Cells, Rate Law: compartment*omega_1*x_Tumor_Cells*y_Effector_Cells
sigma_2 = 0.38 Reaction: => z_Helper_T_Cells, Rate Law: compartment*sigma_2

States:

Name Description
x Tumor Cells [neoplastic cell]
y Effector Cells [Effector Immune Cell]
z Helper T Cells [helper T cell]

Observables: none

MODEL1811050001 @ v0.0.1

The length of the G1 phase in the cell cycle shows significant variability in different cell types and tissue types. To…

The length of the G1 phase in the cell cycle shows significant variability in different cell types and tissue types. To gain insights into the control of G1 length, we generated an E2F activity reporter that captures free E2F activity after dissociation from Rb sequestration and followed its kinetics of activation at the single-cell level, in real time. Our results demonstrate that its activity is precisely coordinated with S phase progression. Quantitative analysis indicates that there is a pre-S phase delay between E2F transcriptional dynamic and activity dynamics. This delay is variable among different cell types and is strongly modulated by the cyclin D/CDK4/6 complex activity through Rb phosphorylation. Our findings suggest that the main function of this complex is to regulate the appropriate timing of G1 length. link: http://identifiers.org/pubmed/29309421

Parameters: none

States: none

Observables: none

This model is based on: Dynamic modeling of signal transduction by mTOR complexes in cancer Author: Mohammadreza Do…

Signal integration has a crucial role in the cell fate decision and dysregulation of the cellular signaling pathways is a primary characteristic of cancer. As a signal integrator, mTOR shows a complex dynamical behavior which determines the cell fate at different cellular processes levels, including cell cycle progression, cell survival, cell death, metabolic reprogramming, and aging. The dynamics of the complex responses to rapamycin in cancer cells have been attributed to its differential time-dependent inhibitory effects on mTORC1 and mTORC2, the two main complexes of mTOR. Two explanations were previously provided for this phenomenon: 1-Rapamycin does not inhibit mTORC2 directly, whereas it prevents mTORC2 formation by sequestering free mTOR protein (Le Chatelier's principle). 2-Components like Phosphatidic Acid (PA) further stabilize mTORC2 compared with mTORC1. To understand the mechanism by which rapamycin differentially inhibits the mTOR complexes in the cancer cells, we present a mathematical model of rapamycin mode of action based on the first explanation, i.e., Le Chatelier's principle. Translating the interactions among components of mTORC1 and mTORC2 into a mathematical model revealed the dynamics of rapamycin action in different doses and time-intervals of rapamycin treatment. This model shows that rapamycin has stronger effects on mTORC1 compared with mTORC2, simply due to its direct interaction with free mTOR and mTORC1, but not mTORC2, without the need to consider other components that might further stabilize mTORC2. Based on our results, even when mTORC2 is less stable compared with mTORC1, it can be less inhibited by rapamycin. link: http://identifiers.org/pubmed/31493485

Parameters:

Name Description
K_el_Rapam = 0.0718632 1/h Reaction: Cytosolic_Rapamycin =>, Rate Law: compartment*K_el_Rapam*Cytosolic_Rapamycin
K_syn_mTOR = 1.6E-27 mol/s Reaction: => mTOR, Rate Law: compartment*K_syn_mTOR
K_syn_Rictor = 5.9E-28 mol/s Reaction: => Rictor, Rate Law: compartment*K_syn_Rictor
Rapamycin_Dose = 0.0; K_abs_Rapam = 2.77 1/h Reaction: => Cytosolic_Rapamycin, Rate Law: compartment*K_abs_Rapam*Rapamycin_Dose
k_forward_Raptor_release = 0.01 1/s; k_reverse_Raptor_release = 1.0E-5 1/(mol*s) Reaction: mTORC1_Rapamycin => mTOR_Rapamycin + Raptor, Rate Law: compartment*(k_forward_Raptor_release*mTORC1_Rapamycin-k_reverse_Raptor_release*mTOR_Rapamycin*Raptor)
k_form_mTOR_Rapam = 1920000.0 1/(mol*s); k_diss_mTOR_Rapam = 0.022 1/s Reaction: mTOR + Cytosolic_Rapamycin => mTOR_Rapamycin, Rate Law: compartment*(k_form_mTOR_Rapam*mTOR*Cytosolic_Rapamycin-k_diss_mTOR_Rapam*mTOR_Rapamycin)
K_syn_Raptor = 2.15E-27 mol/s Reaction: => Raptor, Rate Law: compartment*K_syn_Raptor
K_deg_Raptor = 1.0E-8 1/s Reaction: Raptor =>, Rate Law: compartment*K_deg_Raptor*Raptor
K_deg_mTOR = 1.0E-8 1/s Reaction: mTOR =>, Rate Law: compartment*K_deg_mTOR*mTOR
k_form_C2 = 1.6666666E7 1/(mol*s); k_diss_C2 = 0.08333 1/s Reaction: mTOR + Rictor => mTORC2, Rate Law: compartment*(k_form_C2*mTOR*Rictor-k_diss_C2*mTORC2)
k_form_C1 = 1.6666666E7 1/(mol*s); k_diss_C1 = 0.08333 1/s Reaction: mTOR + Raptor => mTORC1, Rate Law: compartment*(k_form_C1*mTOR*Raptor-k_diss_C1*mTORC1)
k_diss_C1_Rapam = 0.022 1/s; k_form_C1_Rapam = 1920000.0 1/(mol*s) Reaction: mTORC1 + Cytosolic_Rapamycin => mTORC1_Rapamycin, Rate Law: compartment*(k_form_C1_Rapam*mTORC1*Cytosolic_Rapamycin-k_diss_C1_Rapam*mTORC1_Rapamycin)
K_deg_Rictor = 1.0E-8 1/s Reaction: Rictor =>, Rate Law: compartment*K_deg_Rictor*Rictor

States:

Name Description
Raptor [Regulatory-Associated Protein of mTOR]
mTOR [431220; mTOR Inhibitor]
mTORC2 [mTORC2]
Rictor [Rapamycin-Insensitive Companion of mTOR]
mTOR Rapamycin [sirolimus; mTOR Inhibitor; 431220]
Cytosolic Rapamycin [sirolimus; Cytosol]
mTORC1 [mTORC1]
mTORC1 Rapamycin [sirolimus; mTORC1]

Observables: none

This is a mathematical describing the interaction between the prostate adenocarcinoma tumor environment, the prostate sp…

Adenocarcinoma is the most frequent cancer affecting the prostate walnut-size gland in the male reproductive system. Such cancer may have a very slow progression or may be associated with a "dark prognosis" when tumor cells are spreading very quickly. Prostate cancers have the particular properties to be marked by the level of prostate specific antigen (PSA) in blood which allows to follow its evolution. At least in its first phase, prostate adenocarcinoma is most often hormone-dependent and, consequently, hormone therapy is a possible treatment. Since few years, hormone therapy started to be provided intermittently for improving patient's quality of life. Today, durations of on- and off-treatment periods are still chosen empirically, most likely explaining why there is no clear benefit from the survival point of view. We therefore developed a model for describing the interaction between the tumor environment, the PSA produced by hormone-dependent and hormone-independent tumor cells, respectively, and the level of androgens. Model parameters were identified using a genetic algorithm applied to the PSA time series measured in a few patients who initially received prostatectomy and were then treated by intermittent hormone therapy (LHRH analogs and anti-androgen). The measured PSA time series is quite correctly reproduced by free runs over the whole follow-up. Model parameter values allow for distinguishing different types of patient (age and Gleason score) meaning that the model can be individualized. We thus showed that the long-term evolution of the cancer can be affected by durations of on- and off-treatment periods. link: http://identifiers.org/pubmed/30292801

Parameters: none

States: none

Observables: none

Dreyfuss2013 - Genome-Scale Metabolic Model - N.crassa iJDZ836Genome-scale metabolic model of the filamentous fungus Neu…

The filamentous fungus Neurospora crassa played a central role in the development of twentieth-century genetics, biochemistry and molecular biology, and continues to serve as a model organism for eukaryotic biology. Here, we have reconstructed a genome-scale model of its metabolism. This model consists of 836 metabolic genes, 257 pathways, 6 cellular compartments, and is supported by extensive manual curation of 491 literature citations. To aid our reconstruction, we developed three optimization-based algorithms, which together comprise Fast Automated Reconstruction of Metabolism (FARM). These algorithms are: LInear MEtabolite Dilution Flux Balance Analysis (limed-FBA), which predicts flux while linearly accounting for metabolite dilution; One-step functional Pruning (OnePrune), which removes blocked reactions with a single compact linear program; and Consistent Reproduction Of growth/no-growth Phenotype (CROP), which reconciles differences between in silico and experimental gene essentiality faster than previous approaches. Against an independent test set of more than 300 essential/non-essential genes that were not used to train the model, the model displays 93% sensitivity and specificity. We also used the model to simulate the biochemical genetics experiments originally performed on Neurospora by comprehensively predicting nutrient rescue of essential genes and synthetic lethal interactions, and we provide detailed pathway-based mechanistic explanations of our predictions. Our model provides a reliable computational framework for the integration and interpretation of ongoing experimental efforts in Neurospora, and we anticipate that our methods will substantially reduce the manual effort required to develop high-quality genome-scale metabolic models for other organisms. link: http://identifiers.org/pubmed/23935467

Parameters: none

States: none

Observables: none

This model examines the role of helper and cytotoxic T cells in an anti-tumour response, with implicit inclusions of imm…

We develop a mathematical model to examine the role of helper and cytotoxic T cells in an anti-tumour immune response. The model comprises three ordinary differential equations describing the dynamics of the tumour cells, the helper and the cytotoxic T cells, and implicitly accounts for immunosuppressive effects. The aim is to investigate how the anti-tumour immune response varies with the level of infiltrating helper and cytotoxic T cells. Through a combination of analytical studies and numerical simulations, our model exemplifies the three Es of immunoediting: elimination, equilibrium and escape. Specifically, it reveals that the three Es of immunoediting depend highly on the infiltration rates of the helper and cytotoxic T cells. The model’s results indicate that both the helper and cytotoxic T cells play a key role in tumour elimination. They also show that combination therapies that boost the immune system and block tumour-induced immunosuppression may have a synergistic effect in reducing tumour growth. link: http://identifiers.org/doi/10.1080/23737867.2018.1465863

Parameters:

Name Description
gamma = 10.0 Reaction: => N_Tumour; N_Tumour, Rate Law: compartment*gamma*(1-N_Tumour)*N_Tumour
sigma_H = 0.5 Reaction: => T_H, Rate Law: compartment*sigma_H
Ntilde = 0.04; alpha = 0.19 Reaction: => T_H; T_H, N_Tumour, Rate Law: compartment*alpha*N_Tumour*T_H/(Ntilde^2+N_Tumour^2)
sigma_C = 2.0 Reaction: => T_C, Rate Law: compartment*sigma_C
delta_H = 1.0 Reaction: T_H =>, Rate Law: compartment*delta_H*T_H
p = 0.5; k = 4.15 Reaction: T_C => ; N_Tumour, Rate Law: compartment*(1-p)*k*T_C*N_Tumour

States:

Name Description
T C [cytotoxic T cell]
N Tumour [Neoplastic Cell]
T H [helper T cell]

Observables: none

Duarte2004 - Genome-scale metabolic network of Saccharomyces cerevisiae (iND750)This model is described in the article:…

A fully compartmentalized genome-scale metabolic model of Saccharomyces cerevisiae that accounts for 750 genes and their associated transcripts, proteins, and reactions has been reconstructed and validated. All of the 1149 reactions included in this in silico model are both elementally and charge balanced and have been assigned to one of eight cellular locations (extracellular space, cytosol, mitochondrion, peroxisome, nucleus, endoplasmic reticulum, Golgi apparatus, or vacuole). When in silico predictions of 4154 growth phenotypes were compared to two published large-scale gene deletion studies, an 83% agreement was found between iND750's predictions and the experimental studies. Analysis of the failure modes showed that false predictions were primarily caused by iND750's limited inclusion of cellular processes outside of metabolism. This study systematically identified inconsistencies in our knowledge of yeast metabolism that require specific further experimental investigation. link: http://identifiers.org/pubmed/15197165

Parameters: none

States: none

Observables: none

MODEL6399676120 @ v0.0.1

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2011 T…

Metabolism is a vital cellular process, and its malfunction is a major contributor to human disease. Metabolic networks are complex and highly interconnected, and thus systems-level computational approaches are required to elucidate and understand metabolic genotype-phenotype relationships. We have manually reconstructed the global human metabolic network based on Build 35 of the genome annotation and a comprehensive evaluation of >50 years of legacy data (i.e., bibliomic data). Herein we describe the reconstruction process and demonstrate how the resulting genome-scale (or global) network can be used (i) for the discovery of missing information, (ii) for the formulation of an in silico model, and (iii) as a structured context for analyzing high-throughput biological data sets. Our comprehensive evaluation of the literature revealed many gaps in the current understanding of human metabolism that require future experimental investigation. Mathematical analysis of network structure elucidated the implications of intracellular compartmentalization and the potential use of correlated reaction sets for alternative drug target identification. Integrated analysis of high-throughput data sets within the context of the reconstruction enabled a global assessment of functional metabolic states. These results highlight some of the applications enabled by the reconstructed human metabolic network. The establishment of this network represents an important step toward genome-scale human systems biology. link: http://identifiers.org/pubmed/17267599

Parameters: none

States: none

Observables: none

In this paper, a nonlinear mathematical model is proposed and analyzed to study the effect of environmental toxicant on…

In this paper, a nonlinear mathematical model is proposed and analyzed to study the effect of environmental toxicant on the immune response of the body. Criteria for local stability, instability and global stability are obtained. It is shown that the immune response of the body decreases as the concentration of environmental toxicant increases, and certain criteria are obtained under which it settles down at its equilibrium level. In the absence of toxicant, an oscillatory behavior of immune system and pathogenic growth is observed. However, in the presence of toxicant, oscillatory behavior is not observed. These studies show that the toxicant may have a grave effect on our body's defense mechanism. link: http://identifiers.org/doi/10.1142/S0218339007002301

Parameters:

Name Description
Q0 = 5.0 Reaction: => T, Rate Law: compartment*Q0
beta = 0.5 Reaction: => P, Rate Law: compartment*beta*P
delta_0 = 0.4 Reaction: T =>, Rate Law: compartment*delta_0*T
alpha0 = 0.1 Reaction: M =>, Rate Law: compartment*alpha0*M
a = 0.8; gamma = 0.05; n = 0.1; k1 = 0.6; n1 = 0.5 Reaction: I => ; P, U, Rate Law: compartment*(a*I+n*gamma*I*P+n1*k1*U*I)
theta_0 = 1.2 Reaction: => U; T, Rate Law: compartment*theta_0*T
mu = 0.04; b = 0.3 Reaction: => I; P, Rate Law: compartment*(mu+b*I*P)
beta0 = 0.2; gamma = 0.05 Reaction: P => ; I, Rate Law: compartment*(gamma*I*P+beta0*P*P)
delta_1 = 0.02; k1 = 0.6 Reaction: U => ; I, Rate Law: compartment*(delta_1*U+k1*U*I)
alpha = 2.4 Reaction: => M; P, Rate Law: compartment*alpha*P

States:

Name Description
I [BTO:0005810]
U U
M M
P [Microorganism]
T [C894]

Observables: none

In this paper, a nonlinear mathematical model is proposed and analyzed to study the effect of environmental toxicant on…

In this paper, a nonlinear mathematical model is proposed and analyzed to study the effect of environmental toxicant on the immune response of the body. Criteria for local stability, instability and global stability are obtained. It is shown that the immune response of the body decreases as the concentration of environmental toxicant increases, and certain criteria are obtained under which it settles down at its equilibrium level. In the absence of toxicant, an oscillatory behavior of immune system and pathogenic growth is observed. However, in the presence of toxicant, oscillatory behavior is not observed. These studies show that the toxicant may have a grave effect on our body's defense mechanism. link: http://identifiers.org/doi/10.1142/S0218339007002301

Parameters:

Name Description
beta = 0.9 Reaction: => P, Rate Law: compartment*beta*P
alpha0 = 0.1 Reaction: M =>, Rate Law: compartment*alpha0*M
mu = 0.04; b = 0.3 Reaction: => I; P, Rate Law: compartment*(mu+b*I*P)
beta0 = 0.2; gamma = 0.05 Reaction: P => ; I, Rate Law: compartment*(gamma*I*P+beta0*P*P)
a = 0.8; gamma = 0.05; n = 0.1 Reaction: I => ; P, Rate Law: compartment*(a*I+n*gamma*I*P)
alpha = 2.4 Reaction: => M; P, Rate Law: compartment*alpha*P

States:

Name Description
I [BTO:0005810]
M M
P [Microorganism]

Observables: none

MODELING THE INTERACTION BETWEEN AVASCULAR CANCEROUS CELLS AND ACQUIRED IMMUNE RESPONSE B. DUBEY, UMA S. DUBEY and SAND…

This paper deals with the interaction between dispersed cancer cells and the major populations of the immune system, namely, the T helper cells, T Cytotoxic cells, B cells, and antibodies produced. The system is described by a set of five ordinary differential equations. Both local and global stability of the system has been investigated. It has been observed that under appropriate conditions this interaction is capable of controlling the growth of these cancer cells. The analytical findings are supported by numerical and computational analytical methods. link: http://identifiers.org/doi/10.1142/S0218339008002605

Parameters:

Name Description
mu_10 = 0.2 Reaction: Th =>, Rate Law: compartment*mu_10*Th
alpha = 0.18 Reaction: => T, Rate Law: compartment*alpha*T
gamma_2 = 0.3; mu_3 = 0.45; gamma_1 = 0.4 Reaction: => B; T, Th, Rate Law: compartment*(mu_3*T+gamma_1*T*B+gamma_2*Th*B)
mu_2 = 1.4; beta_1 = 0.3; beta_2 = 0.05 Reaction: => Tc; T, Th, Rate Law: compartment*(mu_2*T+beta_1*T*Tc+beta_2*Th*Tc)
mu_30 = 0.03 Reaction: B =>, Rate Law: compartment*mu_30*B
mu_1 = 1.5; mu_11 = 0.3 Reaction: => Th; T, Rate Law: compartment*(mu_1*T+mu_11*T*Th)
alpha_0 = 4.6; alpha_1 = 0.101; delta_2 = 0.008 Reaction: T => ; Tc, A, Rate Law: compartment*(alpha_0*T*T+alpha_1*T*Tc+delta_2*T*A)
mu_20 = 0.0412 Reaction: Tc =>, Rate Law: compartment*mu_20*Tc
mu_40 = 0.3; delta_1 = 0.5 Reaction: A => ; T, Rate Law: compartment*(mu_40*A+delta_1*T*A)
mu_4 = 0.35 Reaction: => A; B, Rate Law: compartment*mu_4*B

States:

Name Description
B [C12474]
A [Antibody]
Th [helper T-lymphocyte]
T [C12476]
Tc Tc

Observables: none

The main purpose of this article is to formulate a deterministic mathematical model for the transmission of malaria that…

The main purpose of this article is to formulate a deterministic mathematical model for the transmission of malaria that considers two host types in the human population. The first type is called "non-immune" comprising all humans who have never acquired immunity against malaria and the second type is called "semi-immune". Non-immune are divided into susceptible, exposed and infectious and semi-immune are divided into susceptible, exposed, infectious and immune. We obtain an explicit formula for the reproductive number, R(0) which is a function of the weight of the transmission semi-immune-mosquito-semi-immune, R(0a), and the weight of the transmission non-immune-mosquito-non-immune, R(0e). Then, we study the existence of endemic equilibria by using bifurcation analysis. We give a simple criterion when R(0) crosses one for forward and backward bifurcation. We explore the possibility of a control for malaria through a specific sub-group such as non-immune or semi-immune or mosquitoes. link: http://identifiers.org/pubmed/22880962

Parameters: none

States: none

Observables: none

This is a mathematical model of Hsp70 induction. To model heat shock effects, the model incorporates temperature depende…

A proper response to rapid environmental changes is essential for cell survival and requires efficient modifications in the pattern of gene expression. In this respect, a prominent example is Hsp70, a chaperone protein whose synthesis is dynamically regulated in stress conditions. In this paper, we expand a formal model of Hsp70 heat induction originally proposed in previous articles. To accurately capture various modes of heat shock effects, we not only introduce temperature dependencies in transcription to Hsp70 mRNA and in dissociation of transcriptional complexes, but we also derive a new formal expression for the temperature dependence in protein denaturation. We calibrate our model using comprehensive sets of both previously published experimental data and also biologically justified constraints. Interestingly, we obtain a biologically plausible temperature dependence of the transcriptional complex dissociation, despite the lack of biological constraints imposed in the calibration process. Finally, based on a sensitivity analysis of the model carried out in both deterministic and stochastic settings, we suggest that the regulation of the binding of transcriptional complexes plays a key role in Hsp70 induction upon heat shock. In conclusion, we provide a model that is able to capture the essential dynamics of the Hsp70 heat induction whilst being biologically sound in terms of temperature dependencies, description of protein denaturation and imposed calibration constraints. link: http://identifiers.org/pubmed/31181241

Parameters:

Name Description
I_7_T = 0.6628 Reaction: HSE_HSF_3 => HSE + HSF_3, Rate Law: compartment*I_7_T*HSE_HSF_3
k2 = 0.218 Reaction: HSF + HSP => HSP_HSF, Rate Law: compartment*k2*HSF*HSP
k4 = 18.85 Reaction: => HSP; mRNA, Rate Law: compartment*k4*mRNA
I1 = 0.003028 Reaction: HSP_S => HSP + S, Rate Law: compartment*I1*HSP_S
I3 = 2.392 Reaction: HSP + HSF_3 => HSF + HSP_HSF, Rate Law: compartment*I3*HSP*HSF_3
k_8_T = 96.0699331208704 Reaction: => mRNA; HSE_HSF_3, Rate Law: compartment*k_8_T*HSE_HSF_3
k_11_T = 0.00191435208585195 Reaction: P => S, Rate Law: compartment*k_11_T*P
I2 = 1.162 Reaction: HSP_HSF => HSP + HSF, Rate Law: compartment*I2*HSP_HSF
k1 = 12.6 Reaction: HSP + S => HSP_S, Rate Law: compartment*k1*HSP*S
k6 = 0.08899 Reaction: => HSP, Rate Law: compartment*k6
k7 = 5892.0 Reaction: HSE + HSF_3 => HSE_HSF_3, Rate Law: compartment*k7*HSE*HSF_3
k9 = 0.001888 Reaction: HSP =>, Rate Law: compartment*k9*HSP
k3 = 446500.0 Reaction: HSF => HSF_3, Rate Law: compartment*k3*HSF^3
k5 = 8.709E-4 Reaction: mRNA =>, Rate Law: compartment*k5*mRNA
k10 = 0.09813 Reaction: HSP_S => HSP + P, Rate Law: compartment*k10*HSP_S

States:

Name Description
S [MI:0908; Protein]
HSF [CCO:37068]
HSP HSF [CCO:37068; Benzylpenicilloic acid]
P [Protein]
HSE [SO:0001850]
HSE HSF 3 [SO:0001850; CCO:37068]
mRNA [Benzylpenicilloic acid; Messenger RNA]
HSP S [Benzylpenicilloic acid]
HSP [Benzylpenicilloic acid]
HSF 3 [CCO:37068]

Observables: none

BIOMD0000000616 @ v0.0.1

Dunster2014 - WBC Interactions (Model1)This is a sub-model of a three-step inflammatory response modelling study. The mo…

There is growing interest in inflammation due to its involvement in many diverse medical conditions, including Alzheimer's disease, cancer, arthritis and asthma. The traditional view that resolution of inflammation is a passive process is now being superceded by an alternative hypothesis whereby its resolution is an active, anti-inflammatory process that can be manipulated therapeutically. This shift in mindset has stimulated a resurgence of interest in the biological mechanisms by which inflammation resolves. The anti-inflammatory processes central to the resolution of inflammation revolve around macrophages and are closely related to pro-inflammatory processes mediated by neutrophils and their ability to damage healthy tissue. We develop a spatially averaged model of inflammation centring on its resolution, accounting for populations of neutrophils and macrophages and incorporating both pro- and anti-inflammatory processes. Our ordinary differential equation model exhibits two outcomes that we relate to healthy and unhealthy states. We use bifurcation analysis to investigate how variation in the system parameters affects its outcome. We find that therapeutic manipulation of the rate of macrophage phagocytosis can aid in resolving inflammation but success is critically dependent on the rate of neutrophil apoptosis. Indeed our model predicts that an effective treatment protocol would take a dual approach, targeting macrophage phagocytosis alongside neutrophil apoptosis. link: http://identifiers.org/pubmed/25053556

Parameters:

Name Description
Bat = 0.1; Gat = 1.0 Reaction: => c; a, Rate Law: default_compartment*Gat*a^2/(Bat^2+a^2)/default_compartment
vt = 0.1 Reaction: n =>, Rate Law: default_compartment*vt*n/default_compartment
t1 = 10.0; A = 1.0; alt = 0.05 Reaction: => c, Rate Law: default_compartment*alt*piecewise(sin(time)^2, time < (A*pi), 0)*piecewise(1, time < t1, 0)/default_compartment
Tt = 0.001 Reaction: a => ; m, Rate Law: default_compartment*Tt*m*a/default_compartment
Gmt = 0.01 Reaction: m =>, Rate Law: default_compartment*Gmt*m/default_compartment
Gat = 1.0 Reaction: a =>, Rate Law: default_compartment*Gat*a/default_compartment

States:

Name Description
c [Interleukin-8]
m [macrophage]
a [neutrophil]
n [neutrophil]

Observables: none

We undertake a mathematical investigation of a model for the generation of thrombin, an enzyme central to haemostatic bl…

We undertake a mathematical investigation of a model for the generation of thrombin, an enzyme central to haemostatic blood coagulation, as well as to thrombotic disorders, that is the end product of a complicated protein cascade with multiple feedbacks that ensures its production in the right place at the right time. In a laboratory setting, its central role is reflected in thrombin evolution over time being used as a measure of the ability of a patient's blood to clot. Here, we present a model for the generation of thrombin (based on earlier work) and analyse it using the method of matched asymptotic expansions to derive a sequence of simplified models that characterize the roles of distinct interactions over various timescales. In particular, we are able through the asymptotic analysis to provide simplified models that are an excellent substitute for the full model (capturing the explosive growth and decay of thrombin) and approximations for the key experimental measurements used to describe thrombin's characteristic evolution over time. The asymptotic results are validated against numerical simulations. link: http://identifiers.org/doi/10.1093/imamat/hxw007

Parameters:

Name Description
k_tilde_3a = 150.0; q_tilde_3a = 1.0; k_tilde_3b = 0.038; k_tilde_1b = 0.19 Reaction: Xa_ATIII = k_tilde_1b*Xa+k_tilde_3a*k_tilde_3b/q_tilde_3a*Va_Xa, Rate Law: k_tilde_1b*Xa+k_tilde_3a*k_tilde_3b/q_tilde_3a*Va_Xa
q_tilde_3a = 1.0; k_tilde_3b = 0.038; k_tilde_3c = 1.0 Reaction: Va_Xa = (q_tilde_3a*Xa*Va-k_tilde_3b*Va_Xa)-k_tilde_3c*q_tilde_3a*APC*Va_Xa/(Va_Xa+1), Rate Law: (q_tilde_3a*Xa*Va-k_tilde_3b*Va_Xa)-k_tilde_3c*q_tilde_3a*APC*Va_Xa/(Va_Xa+1)
gamma_tilde_1a = 0.77; k_tilde_3a = 150.0; k_tilde_1a = 150.0; k_tilde_3c = 1.0; k_tilde_1b = 0.19 Reaction: Xa = ((k_tilde_1a*gamma_tilde_1a*exp((-gamma_tilde_1a)*time)+k_tilde_3c*k_tilde_3a*APC*Va_Xa/(Va_Xa+1))-k_tilde_1b*Xa)-k_tilde_3a*Xa*Va, Rate Law: ((k_tilde_1a*gamma_tilde_1a*exp((-gamma_tilde_1a)*time)+k_tilde_3c*k_tilde_3a*APC*Va_Xa/(Va_Xa+1))-k_tilde_1b*Xa)-k_tilde_3a*Xa*Va
k_tilde_5a = 0.0011 Reaction: PC = (-k_tilde_5a)*PC, Rate Law: (-k_tilde_5a)*PC
k_tilde_2am = 7.2; k_tilde_2b = 0.013; k_tilde_2a = 2.0 Reaction: V = (-k_tilde_2a)*IIa*V/(V+k_tilde_2am*(1+Fibrinogen))-k_tilde_2a*k_tilde_2b*Xa*V/(V+1+II), Rate Law: (-k_tilde_2a)*IIa*V/(V+k_tilde_2am*(1+Fibrinogen))-k_tilde_2a*k_tilde_2b*Xa*V/(V+1+II)
k_tilde_4b = 530.0; k_tilde_4a = 0.12; q_tilde_4a = 0.004; k_tilde_4bm = 3.6 Reaction: IIa = (k_tilde_4a*Xa_L*II/(V+1+II)+k_tilde_4a*k_tilde_4b*Va_Xa_L*II/(q_tilde_4a*(II+k_tilde_4bm)))-IIa, Rate Law: (k_tilde_4a*Xa_L*II/(V+1+II)+k_tilde_4a*k_tilde_4b*Va_Xa_L*II/(q_tilde_4a*(II+k_tilde_4bm)))-IIa
k_tilde_6 = 1500.0 Reaction: Fibrin = k_tilde_6*Fibrinogen, Rate Law: k_tilde_6*Fibrinogen
k_tilde_4b = 530.0; q_tilde_4a = 0.004; k_tilde_4bm = 3.6 Reaction: II = (-q_tilde_4a)*Xa_L*II/(V+1+II)-k_tilde_4b*Va_Xa_L*II/(II+k_tilde_4bm), Rate Law: (-q_tilde_4a)*Xa_L*II/(V+1+II)-k_tilde_4b*Va_Xa_L*II/(II+k_tilde_4bm)
k_tilde_b = 5.0E-4; l_tilde_b = 0.05 Reaction: Va_Xa_L = 0.5*((k_tilde_b+l_tilde_b+Va_Xa)-((k_tilde_b+l_tilde_b+Va_Xa)^2-4*l_tilde_b*Va_Xa)^(0.5)), Rate Law: missing
k_tilde_x = 385.0; l_tilde_x = 7.69 Reaction: Xa_L = 0.5*((k_tilde_x+l_tilde_x+Xa)-((k_tilde_x+l_tilde_x+Xa)^2-4*l_tilde_x*Xa)^(0.5)), Rate Law: missing
k_tilde_3c = 1.0 Reaction: Va_inactive = APC*Va/(Va+1)+k_tilde_3c*APC*Va_Xa/(Va_Xa+1), Rate Law: APC*Va/(Va+1)+k_tilde_3c*APC*Va_Xa/(Va_Xa+1)
q_tilde_3a = 1.0; k_tilde_2am = 7.2; k_tilde_2b = 0.013; k_tilde_3b = 0.038 Reaction: Va = ((IIa*V/(V+k_tilde_2am*(1+Fibrinogen))+k_tilde_2b*Xa*V/(V+1+II)+k_tilde_3b/q_tilde_3a*Va_Xa)-APC*Va/(Va+1))-Xa*Va, Rate Law: ((IIa*V/(V+k_tilde_2am*(1+Fibrinogen))+k_tilde_2b*Xa*V/(V+1+II)+k_tilde_3b/q_tilde_3a*Va_Xa)-APC*Va/(Va+1))-Xa*Va
k_tilde_5a = 0.0011; k_tilde_5b = 0.27 Reaction: APC = k_tilde_5a*PC-k_tilde_5b*APC, Rate Law: k_tilde_5a*PC-k_tilde_5b*APC
k_tilde_5b = 0.27 Reaction: APC_inactive = k_tilde_5b*APC, Rate Law: k_tilde_5b*APC

States:

Name Description
Va Xa L Va:Xa:L
IIa ATIII IIa:ATIII
Xa ATIII Xa:ATIII
Fibrinogen Fibrinogen
Fibrin Fibrin
APC inactive APC_inactive
V V
Xa Xa
Va Va
IIa IIa
Xa L Xa:L
Va Xa Va:Xa
APC APC
Va inactive Va_inactive
PC PC
II II

Observables: none

MODEL1202030000 @ v0.0.1

This model is from the article: A thermodynamic switch modulates abscisic acid receptor sensitivity. Dupeux F, Santi…

Abscisic acid (ABA) is a key hormone regulating plant growth, development and the response to biotic and abiotic stress. ABA binding to pyrabactin resistance (PYR)/PYR1-like (PYL)/Regulatory Component of Abscisic acid Receptor (RCAR) intracellular receptors promotes the formation of stable complexes with certain protein phosphatases type 2C (PP2Cs), leading to the activation of ABA signalling. The PYR/PYL/RCAR family contains 14 genes in Arabidopsis and is currently the largest plant hormone receptor family known; however, it is unclear what functional differentiation exists among receptors. Here, we identify two distinct classes of receptors, dimeric and monomeric, with different intrinsic affinities for ABA and whose differential properties are determined by the oligomeric state of their apo forms. Moreover, we find a residue in PYR1, H60, that is variable between family members and plays a key role in determining oligomeric state. In silico modelling of the ABA activation pathway reveals that monomeric receptors have a competitive advantage for binding to ABA and PP2Cs. This work illustrates how receptor oligomerization can modulate hormonal responses and more generally, the sensitivity of a ligand-dependent signalling system. link: http://identifiers.org/pubmed/21847091

Parameters: none

States: none

Observables: none

MODEL1202030001 @ v0.0.1

This model is from the article: A thermodynamic switch modulates abscisic acid receptor sensitivit y. Dupeux F, Sant…

Abscisic acid (ABA) is a key hormone regulating plant growth, development and the response to biotic and abiotic stress. ABA binding to pyrabactin resistance (PYR)/PYR1-like (PYL)/Regulatory Component of Abscisic acid Receptor (RCAR) intracellular receptors promotes the formation of stable complexes with certain protein phosphatases type 2C (PP2Cs), leading to the activation of ABA signalling. The PYR/PYL/RCAR family contains 14 genes in Arabidopsis and is currently the largest plant hormone receptor family known; however, it is unclear what functional differentiation exists among receptors. Here, we identify two distinct classes of receptors, dimeric and monomeric, with different intrinsic affinities for ABA and whose differential properties are determined by the oligomeric state of their apo forms. Moreover, we find a residue in PYR1, H60, that is variable between family members and plays a key role in determining oligomeric state. In silico modelling of the ABA activation pathway reveals that monomeric receptors have a competitive advantage for binding to ABA and PP2Cs. This work illustrates how receptor oligomerization can modulate hormonal responses and more generally, the sensitivity of a ligand-dependent signalling system. link: http://identifiers.org/pubmed/21847091

Parameters: none

States: none

Observables: none

BIOMD0000000117 @ v0.0.1

This model is according to the paper *Signal-induced Ca2+ oscillations: Properties of a model based on Ca2+-induced Ca2+…

We consider a simple, minimal model for signal-induced Ca2+ oscillations based on Ca(2+)-induced Ca2+ release. The model takes into account the existence of two pools of intracellular Ca2+, namely, one sensitive to inositol 1,4,5 trisphosphate (InsP3) whose synthesis is elicited by the stimulus, and one insensitive to InsP3. The discharge of the latter pool into the cytosol is activated by cytosolic Ca2+. Oscillations in cytosolic Ca2+ arise in this model either spontaneously or in an appropriate range of external stimulation; these oscillations do not require the concomitant, periodic variation of InsP3. The following properties of the model are reviewed and compared with experimental observations: (a) Control of the frequency of Ca2+ oscillations by the external stimulus or extracellular Ca2+; (b) correlation of latency with period of Ca2+ oscillations obtained at different levels of stimulation; (c) effect of a transient increase in InsP3; (d) phase shift and transient suppression of Ca2+ oscillations by Ca2+ pulses, and (e) propagation of Ca2+ waves. It is shown that on all these counts the model provides a simple, unified explanation for a number of experimental observations in a variety of cell types. The model based on Ca(2+)-induced Ca2+ release can be extended to incorporate variations in the level of InsP3 as well as desensitization of the InsP3 receptor; besides accounting for the phenomena described by the minimal model, the extended model might also account for the occurrence of complex Ca2+ oscillations. link: http://identifiers.org/pubmed/1647878

Parameters:

Name Description
v0 = 1.0 Reaction: => z, Rate Law: v0*Cytosol
K2 = 1.0; n = 2.0; VM2 = 65.0 Reaction: z => y, Rate Law: intracellular_Ca_storepool*VM2*z^n/(K2^n+z^n)
kf = 1.0 Reaction: y => z, Rate Law: kf*y*Cytosol
m = 2.0; KR = 2.0; VM3 = 500.0; p = 4.0; KA = 0.9 Reaction: y => z, Rate Law: Cytosol*VM3*y^m/(KR^m+y^m)*z^p/(KA^p+z^p)
v1 = 7.3; beta = 0.0 Reaction: => z, Rate Law: v1*beta*Cytosol
k = 10.0 Reaction: z =>, Rate Law: k*z*extracellular

States:

Name Description
z [calcium(2+); Calcium cation]
y [calcium(2+); Calcium cation]

Observables: none

BIOMD0000000113 @ v0.0.1

Model reproduces Fig 4 of the paper. For fraction of phosphorylated protein, W_star, the model reproduces panel b in the…

Given the ubiquitous nature of signal-induced Ca2+ oscillations, the question arises as to how cellular responses are affected by repetitive Ca2+ spikes. Among these responses, we focus on those involving protein phosphorylation. We examine, by numerical simulations of a theoretical model, the situation where a protein is phosphorylated by a Ca(2+)-activated kinase and dephosphorylated by a phosphatase. This reversible phosphorylation system is coupled to a mechanism generating cytosolic Ca2+ oscillations; for definiteness, this oscillatory mechanism is based on the process of Ca(2+)-induced Ca2+ release. The analysis shows that the average fraction of phosphorylated protein increases with the frequency of repetitive Ca2+ spikes; the latter frequency generally rises with the extent of external stimulation. Protein phosphorylation therefore provides a mechanism for the encoding of the external stimulation in terms of the frequency of signal-induced Ca2+ oscillations. Such a frequency encoding requires precise kinetic conditions on the Michaelis-Menten constants of the kinase and phosphatase, their maximal rates, and the degree of cooperativity in kinase activation by Ca2+. In particular, the most efficient encoding of Ca2+ oscillations based on protein phosphorylation occurs in conditions of zero-order ultrasensitivity, when the kinase and phosphatase are saturated by their protein substrate. The kinetic analysis uncovers a wide variety of temporal patterns of phosphorylation that could be driven by signal-induced Ca2+ oscillations. link: http://identifiers.org/pubmed/1316185

Parameters:

Name Description
v0 = 1.0 Reaction: => Z, Rate Law: cytosol*v0
K_A = 0.9; m = 2.0; Vm3 = 500.0; p = 4.0; Kr = 2.0 Reaction: Y => Z, Rate Law: store*Vm3*Y^m*Z^p/((Kr^m+Y^m)*(K_A^p+Z^p))
K1 = 0.01; K2 = 0.01; vk = NaN; vp = 2.5 Reaction: => W_star; Wt, Rate Law: cytosol*vp/Wt*(vk/vp*(1-W_star)/((K1+1)-W_star)-W_star/(K2+W_star))
kf = 1.0 Reaction: Y => Z, Rate Law: store*kf*Y
Vm2 = 65.0; n = 2.0; Kp = 1.0 Reaction: Z => Y, Rate Law: cytosol*Vm2*Z^n/(Kp^n+Z^n)
k = 10.0 Reaction: Z =>, Rate Law: cytosol*k*Z
v1_beta = 2.7 Reaction: => Z, Rate Law: cytosol*v1_beta

States:

Name Description
Z [calcium(2+); Calcium cation]
Y [calcium(2+); Calcium cation]
W star Phosphorylated protein

Observables: none

MODEL1949107276 @ v0.0.1

This is the constraint based model from: **Iterative reconstruction of a global metabolic model of Acinetobacter bayly…

BACKGROUND: Genome-scale metabolic models are powerful tools to study global properties of metabolic networks. They provide a way to integrate various types of biological information in a single framework, providing a structured representation of available knowledge on the metabolism of the respective species. RESULTS: We reconstructed a constraint-based metabolic model of Acinetobacter baylyi ADP1, a soil bacterium of interest for environmental and biotechnological applications with large-spectrum biodegradation capabilities. Following initial reconstruction from genome annotation and the literature, we iteratively refined the model by comparing its predictions with the results of large-scale experiments: (1) high-throughput growth phenotypes of the wild-type strain on 190 distinct environments, (2) genome-wide gene essentialities from a knockout mutant library, and (3) large-scale growth phenotypes of all mutant strains on 8 minimal media. Out of 1412 predictions, 1262 were initially consistent with our experimental observations. Inconsistencies were systematically examined, leading in 65 cases to model corrections. The predictions of the final version of the model, which included three rounds of refinements, are consistent with the experimental results for (1) 91% of the wild-type growth phenotypes, (2) 94% of the gene essentiality results, and (3) 94% of the mutant growth phenotypes. To facilitate the exploitation of the metabolic model, we provide a web interface allowing online predictions and visualization of results on metabolic maps. CONCLUSION: The iterative reconstruction procedure led to significant model improvements, showing that genome-wide mutant phenotypes on several media can significantly facilitate the transition from genome annotation to a high-quality model. link: http://identifiers.org/pubmed/18840283

Parameters: none

States: none

Observables: none

Dutta-Roy2015 - Opening of the multiple AMPA receptor conductance statesThis model is described in the article: [Ligand…