SBMLBioModels: T - U

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Thiele2013 - Liver bile duct cells: MODEL1310110041v0.0.1

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

Details

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

Thiele2013 - Liver hepatocytes: MODEL1310110014v0.0.1

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

Details

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

Thiele2013 - Lung macrophages: MODEL1310110051v0.0.1

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

Details

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

Thiele2013 - Lung pneumocytes: MODEL1310110010v0.0.1

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

Details

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

Thiele2013 - Lymph node germinal center cells: MODEL1310110036v0.0.1

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

Details

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

Thiele2013 - Lymph node non germinal center cells: MODEL1310110007v0.0.1

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

Details

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

Thiele2013 - Nasopharynx respiratory epithelial cells: MODEL1310110052v0.0.1

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

Details

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

Thiele2013 - Oral mucosa squamous epithelial cells: MODEL1310110027v0.0.1

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

Details

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

Thiele2013 - Ovary ovarian stroma cells: MODEL1310110029v0.0.1

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

Details

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

Thiele2013 - Pancreas exocrine glandular cells: MODEL1310110024v0.0.1

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

Details

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

Thiele2013 - Pancreas islets of Langerhans: MODEL1310110013v0.0.1

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

Details

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

Thiele2013 - Parathyroid gland glandular cells: MODEL1310110028v0.0.1

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

Details

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

Thiele2013 - Placenta decidual cells: MODEL1310110049v0.0.1

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

Details

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

Thiele2013 - Placenta trophoblastic cells: MODEL1310110055v0.0.1

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

Details

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

Thiele2013 - Prostate glandular cells: MODEL1310110018v0.0.1

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

Details

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

Thiele2013 - Rectum glandular cells: MODEL1310110057v0.0.1

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

Details

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

Thiele2013 - Salivary gland glandular cells: MODEL1310110062v0.0.1

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

Details

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

Thiele2013 - Seminal vesicle glandular cells: MODEL1310110031v0.0.1

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

Details

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

Thiele2013 - Skeletal muscle myocytes: MODEL1310110011v0.0.1

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

Details

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

Thiele2013 - Skin epidermal cells: MODEL1310110003v0.0.1

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

Details

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

Thiele2013 - Small intestine glandular cells: MODEL1310110020v0.0.1

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

Details

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

Thiele2013 - Smooth muscle smooth muscle cells: MODEL1310110025v0.0.1

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

Details

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

Thiele2013 - Spleen cells in red pulp: MODEL1310110060v0.0.1

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

Details

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

Thiele2013 - Spleen cells in white pulp: MODEL1310110002v0.0.1

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

Details

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

Thiele2013 - Stomach lower glandular cells: MODEL1310110046v0.0.1

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

Details

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

Thiele2013 - Stomach upper glandular cells: MODEL1310110059v0.0.1

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

Details

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

Thiele2013 - Testis cells in seminiferus ducts: MODEL1310110039v0.0.1

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

Details

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

Thiele2013 - Testis Leydig cells: MODEL1310110008v0.0.1

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

Details

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

Thiele2013 - Thyroid gland glandular cells: MODEL1310110015v0.0.1

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

Details

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

Thiele2013 - Tonsil germinal center cells: MODEL1310110021v0.0.1

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

Details

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

Thiele2013 - Tonsil non germinal center cells: MODEL1310110045v0.0.1

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

Details

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

Thiele2013 - Tonsil squamous epithelial cells: MODEL1310110058v0.0.1

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

Details

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

Thiele2013 - Urinary bladder urothelial cells: MODEL1310110035v0.0.1

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

Details

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

Thiele2013 - Uterus post menopause cells in endometrial stroma: MODEL1310110017v0.0.1

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

Details

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

Thiele2013 - Uterus post menopause glandular cells: MODEL1310110004v0.0.1

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

Details

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

Thiele2013 - Uterus pre menopause cells in endometrial stroma: MODEL1310110022v0.0.1

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

Details

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

Thiele2013 - Uterus pre menopause glandular cells: MODEL1310110006v0.0.1

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

Details

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

Thiele2013 - Vagina squamous epithelial cells: MODEL1310110019v0.0.1

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

Details

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

Thiele2013 - Vulva anal skin epidermal cells: MODEL1310110061v0.0.1

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

Details

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

Thomas2009_Buchnera_a_FBA: MODEL7434234848v0.0.1

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

Details

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

Thomsen1988_AdenylateCyclase_Inhibition: BIOMD0000000082v0.0.1

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

Details

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

Parameters:

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

States:

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

Thomsen1989_AdenylateCyclase: BIOMD0000000080v0.0.1

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

Details

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

Parameters:

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

States:

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

Thomson2009 – Unlimited multistability in multisite phosphorylation systems: MODEL2002110001v0.0.1

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

Details

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

Tiago2010_FeMetabolism_FeAdequate: BIOMD0000000260v0.0.1

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

Details

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

Parameters:

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

States:

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

Tiago2010_FeMetabolism_FeDeficient: BIOMD0000000259v0.0.1

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

Details

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

Parameters:

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

States:

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

Tiago2010_FeMetabolism_FeLoaded: BIOMD0000000261v0.0.1

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

Details

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

Parameters:

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

States:

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

Tiemann2011_PhenotypeTransitions: MODEL1112150000v0.0.1

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

Details

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

Tikidji-Hamburyan2018 - Rod phototransduction under strong illumination: MODEL1710030000v0.0.1

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

Details

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

Tiveci2005 - Calcium dynamics in brain energy metabolism and Alzheimer's disease: MODEL1409240003v0.0.1

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

Details

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

TLR4-NFkB-phospho-NF-kB signaling in BMDM: MODEL1809230001v0.0.1

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

Details

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

Tolic2000_InsulinGlucoseFeedback: BIOMD0000000372v0.0.1

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

Details

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

Parameters:

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

States:

NameDescription
x1x1
x2x2
Ip[Insulin]
G[glucose]
x3x3
Ii[Insulin]

Tomida2003 - Calcium Oscillatory-induced translocation of nuclear factor of activated T cells: BIOMD0000000678v0.0.1

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

Details

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

Parameters:

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

States:

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

Tomàs-Gamisans2016 - Genome-Scale Metabolic Model of Pichia pastoris (version 2): MODEL1508040001v0.0.1

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

Details

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

Topp200 - Model of b-Cell Mass, Insulin, and Glucose Kinetics:Pathways to Diabetes: MODEL2001080002v0.0.1

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

Details

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

Topp2000_BetaCellMass_Diabetes: BIOMD0000000341v0.0.1

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

Details

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

Parameters:

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

States:

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

Torres2019 - Contextualized P. pastoris GSMM under glucose growth and various oxygenation levels: MODEL1910040001v0.0.1

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

Details

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

Tran2009_CaDynamicsSERCA_Cardiac: MODEL1006230121v0.0.1

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

Details

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

Tran2009_CardiacActiveForceGeneration: MODEL1006230116v0.0.1

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

Details

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

Traynard2016 - Mammalian cell cycle regulation - Logical Model: MODEL1611230001v0.0.1

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

Details

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

Trisilowati2018 - Optimal control of tumor-immune system interaction with treatment: BIOMD0000000880v0.0.1

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

Details

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

Parameters:

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

States:

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

Troein2011_ClockCircuit_OstreococcusTauri: BIOMD0000000350v0.0.1

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

Details

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

Parameters:

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

States:

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

Tsai2014 - Cell cycle duration control by oscillatory Dynamics in Early Xenopus laevis Embryos: BIOMD0000000719v0.0.1

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

Details

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

Parameters:

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

States:

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

Tseng2012 - Circadian clock of N.crassa: BIOMD0000000437v0.0.1

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

Details

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

Parameters:

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

States:

NameDescription
total FRQc[Frequency clock protein]
s61[White collar 1 protein; White collar 2 protein]
degraded L WCCCVVDndegraded_L_WCCCVVDn
T[temperature]
L WCC[White collar 1 protein; White collar 2 protein]
degraded WC2cdegraded_WC2c
degraded VVDndegraded_VVDn
degraded active hypoWCCndegraded_active_hypoWCCn
hypoFRQc[Frequency clock protein]
frq genefrq_gene
c hypoFRQ to hyperFRQc_hypoFRQ_to_hyperFRQ
PeriodPeriod
vvd mRNAvvd_mRNA
total WC1[White collar 1 protein]
degraded frq mRNAdegraded_frq_mRNA
degraded VVDcdegraded_VVDc
VVDcVVDc
wc2 genewc2_gene
degraded hyperFFCndegraded_hyperFFCn
L WCCVVDn[White collar 1 protein; White collar 2 protein]
degraded wc2 mRNAdegraded_wc2_mRNA
wc2 mRNAwc2_mRNA
hypoFRQn[Frequency clock protein]
frq mRNA[messenger RNA; Frequency clock protein]
total hypoWCC[White collar 1 protein; White collar 2 protein]
wc1 mRNAwc1_mRNA
hyperWCCc[White collar 1 protein; White collar 2 protein]
total WCCn[White collar 1 protein; White collar 2 protein]
hyperFRQn[Frequency clock protein]
degraded hyperWCCcdegraded_hyperWCCc
hyperWCCn[White collar 1 protein; White collar 2 protein]
total VVDtotal_VVD
degraded hyperFRQcdegraded_hyperFRQc
timetime
total hypo FRQ[Frequency clock protein]
degraded vvd mRNAdegraded_vvd_mRNA
total WC2[White collar 2 protein]
total hyperWCC[White collar 1 protein; White collar 2 protein]
total hyper FRQ[Frequency clock protein]
hypoWCCn[White collar 1 protein; White collar 2 protein]
n hypoFRQ to hyperFRQn_hypoFRQ_to_hyperFRQ
hypoWCCc[White collar 1 protein; White collar 2 protein]
degraded hyperWCCndegraded_hyperWCCn
VVDnVVDn
active hypoWCCnactive_hypoWCCn
hyperFRQchyperFRQc
WC1c[White collar 1 protein]
degraded wc1 mRNAdegraded_wc1_mRNA
vvd genevvd_gene
degraded WC1cdegraded_WC1c
total FRQ[Frequency clock protein]
wc1 genewc1_gene
WC2c[White collar 2 protein]
total FRQn[Frequency clock protein]

Tsur2019 - Predicting response to pembrolizumab in metastatic melanoma by a new personalization algorithm: MODEL1911150001v0.0.1

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

Details

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

Tsur2019 - Response of patients with melanoma to immune checkpoint blockade: BIOMD0000000838v0.0.1

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

Details

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

Parameters:

NameDescription
mu_a = 0.231Reaction: A =>, Rate Law: compartment*mu_a*A
b = 92330.0; alpha_A = 2986.0Reaction: => A; M, Rate Law: compartment*alpha_A*M/(M+b)
gamma_mel = 0.04496Reaction: => M, Rate Law: compartment*gamma_mel*M
nu_mel = 0.1245; g = 6.01E7Reaction: M => ; T, Rate Law: compartment*nu_mel*T*M/(M+g)
mu_e = 0.1777Reaction: T =>, Rate Law: compartment*mu_e*T
alpha_e = 831.8Reaction: => T; A, Rate Law: compartment*alpha_e*A

States:

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

Turner2015 - Human/Mosquito SEIR/SEI Model: MODEL1805220001v0.0.1

Mathematical model of malaria transmission between humans and mosquitoes.

Details

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

Turner2015-Human/Mosquito ELP Model: BIOMD0000000922v0.0.1

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

Details

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

Parameters:

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

States:

NameDescription
Population of Pupae[MIRO_30000050]
Population of Eggs[MIRO_30000049]
Population of Larvae[MIRO_30000028]

Twycross2010_Auxin_Transport: MODEL1005200000v0.0.1

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

Details

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

Tylutki2017-four-compartment PBPK heart model accounting for cardiac metabolism: MODEL2003190003v0.0.1

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

Details

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

Tymoshenko2015 - Genome scale metabolic model - ToxoNet1: MODEL1504280000v0.0.1

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

Details

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

Tyson1991 - Cell Cycle 2 var: BIOMD0000000006v0.0.1

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

Details

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

Parameters:

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

States:

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

Tyson1991 - Cell Cycle 6 var: BIOMD0000000005v0.0.1

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

Details

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

Parameters:

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

States:

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

Tyson1999_CircClock: BIOMD0000000036v0.0.1

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

Details

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

Parameters:

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

States:

NameDescription
MM
P[Period circadian protein]

Tyson2001_Cell_Cycle_Regulation: BIOMD0000000195v0.0.1

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

Details

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

Parameters:

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

States:

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

Tyson2003_Activator_Inhibitor: BIOMD0000000306v0.0.1

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

Details

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

Parameters:

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

States:

NameDescription
XX
RR
E[protein]
Ep[phosphorylated residue; Phosphoprotein]

Tyson2003_Mutual_Activation: BIOMD0000000311v0.0.1

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

Details

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

Parameters:

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

States:

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

Tyson2003_Mutual_Inhibition: BIOMD0000000310v0.0.1

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

Details

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

Parameters:

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

States:

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

Tyson2003_NegFB_Homeostasis: BIOMD0000000309v0.0.1

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

Details

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

Parameters:

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

States:

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

Tyson2003_NegFB_Oscillator: BIOMD0000000308v0.0.1

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

Details

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

Parameters:

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

States:

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

Tyson2003_Perfect_Adaption: BIOMD0000000312v0.0.1

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

Details

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

Parameters:

NameDescription
k3 = 1.0 per_sReaction: => X; S, Rate Law: env*k3*S
k2 = 2.0 per_M_per_sReaction: R => ; X, Rate Law: env*k2*R*X
k1 = 2.0 per_sReaction: => R; S, Rate Law: env*k1*S
tau = 4.0 sReaction: S = 1*floor(time/tau), Rate Law: missing
k4 = 1.0 per_sReaction: X =>, Rate Law: env*k4*X

States:

NameDescription
SS
X[protein]
R[protein]

Tyson2003_Substrate_Depletion_Osc: BIOMD0000000307v0.0.1

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

Details

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

Parameters:

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

States:

NameDescription
XX
RR
E[protein]
Ep[Phosphoprotein; phosphorylated residue]

U


Ueda2001_CircClock: BIOMD0000000022v0.0.1

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

Details

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

Parameters:

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

States:

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

Ueda2001_typeBtrichothecenes: MODEL1006230044v0.0.1

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

Details

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

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Details

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

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Details

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

Uhlén2015 - Human tissue-based proteome metabolic network - appendix: MODEL1411240004v0.0.1

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Details

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

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

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

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

Uhlén2015 - Human tissue-based proteome metabolic network - duodenum: MODEL1411240027v0.0.1

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

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Details

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

Uhlén2015 - Human tissue-based proteome metabolic network - esophagus: MODEL1411240020v0.0.1

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

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Details

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

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

Details

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

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Details

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

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Details

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

Uhlén2015 - Human tissue-based proteome metabolic network - liver: MODEL1411240012v0.0.1

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Details

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

Uhlén2015 - Human tissue-based proteome metabolic network - lung: MODEL1411240011v0.0.1

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Details

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

Uhlén2015 - Human tissue-based proteome metabolic network - lymph node: MODEL1411240016v0.0.1

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Details

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

Uhlén2015 - Human tissue-based proteome metabolic network - ovary: MODEL1411240021v0.0.1

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Details

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