SBMLBioModels: M - P

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M


Mardinoglu2014 - Genome-scale metabolic model (HMR version 2.0) - human hepatocytes (iHepatocytes2322)This model is desc…

Several liver disorders result from perturbations in the metabolism of hepatocytes, and their underlying mechanisms can be outlined through the use of genome-scale metabolic models (GEMs). Here we reconstruct a consensus GEM for hepatocytes, which we call iHepatocytes2322, that extends previous models by including an extensive description of lipid metabolism. We build iHepatocytes2322 using Human Metabolic Reaction 2.0 database and proteomics data in Human Protein Atlas, which experimentally validates the incorporated reactions. The reconstruction process enables improved annotation of the proteomics data using the network centric view of iHepatocytes2322. We then use iHepatocytes2322 to analyse transcriptomics data obtained from patients with non-alcoholic fatty liver disease. We show that blood concentrations of chondroitin and heparan sulphates are suitable for diagnosing non-alcoholic steatohepatitis and for the staging of non-alcoholic fatty liver disease. Furthermore, we observe serine deficiency in patients with NASH and identify PSPH, SHMT1 and BCAT1 as potential therapeutic targets for the treatment of non-alcoholic steatohepatitis. link: http://identifiers.org/pubmed/24419221

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Mardinoglu2015 - Curated tissue-specific genome-scale metabolic model - Small intestineThis model is described in the ar…

The gut microbiota has been proposed as an environmental factor that promotes the progression of metabolic diseases. Here, we investigated how the gut microbiota modulates the global metabolic differences in duodenum, jejunum, ileum, colon, liver, and two white adipose tissue depots obtained from conventionally raised (CONV-R) and germ-free (GF) mice using gene expression data and tissue-specific genome-scale metabolic models (GEMs). We created a generic mouse metabolic reaction (MMR) GEM, reconstructed 28 tissue-specific GEMs based on proteomics data, and manually curated GEMs for small intestine, colon, liver, and adipose tissues. We used these functional models to determine the global metabolic differences between CONV-R and GF mice. Based on gene expression data, we found that the gut microbiota affects the host amino acid (AA) metabolism, which leads to modifications in glutathione metabolism. To validate our predictions, we measured the level of AAs and N-acetylated AAs in the hepatic portal vein of CONV-R and GF mice. Finally, we simulated the metabolic differences between the small intestine of the CONV-R and GF mice accounting for the content of the diet and relative gene expression differences. Our analyses revealed that the gut microbiota influences host amino acid and glutathione metabolism in mice. link: http://identifiers.org/pubmed/26475342

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Genome-scale metabolic model for mouse colon tissueThis model is described in the article: [The gut microbiota modulate…

The gut microbiota has been proposed as an environmental factor that promotes the progression of metabolic diseases. Here, we investigated how the gut microbiota modulates the global metabolic differences in duodenum, jejunum, ileum, colon, liver, and two white adipose tissue depots obtained from conventionally raised (CONV-R) and germ-free (GF) mice using gene expression data and tissue-specific genome-scale metabolic models (GEMs). We created a generic mouse metabolic reaction (MMR) GEM, reconstructed 28 tissue-specific GEMs based on proteomics data, and manually curated GEMs for small intestine, colon, liver, and adipose tissues. We used these functional models to determine the global metabolic differences between CONV-R and GF mice. Based on gene expression data, we found that the gut microbiota affects the host amino acid (AA) metabolism, which leads to modifications in glutathione metabolism. To validate our predictions, we measured the level of AAs and N-acetylated AAs in the hepatic portal vein of CONV-R and GF mice. Finally, we simulated the metabolic differences between the small intestine of the CONV-R and GF mice accounting for the content of the diet and relative gene expression differences. Our analyses revealed that the gut microbiota influences host amino acid and glutathione metabolism in mice. link: http://identifiers.org/pubmed/26475342

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Mardinoglu2015 - Tissue-specific genome-scale metabolic network - Adipose tissueThis model is described in the article:…

The gut microbiota has been proposed as an environmental factor that promotes the progression of metabolic diseases. Here, we investigated how the gut microbiota modulates the global metabolic differences in duodenum, jejunum, ileum, colon, liver, and two white adipose tissue depots obtained from conventionally raised (CONV-R) and germ-free (GF) mice using gene expression data and tissue-specific genome-scale metabolic models (GEMs). We created a generic mouse metabolic reaction (MMR) GEM, reconstructed 28 tissue-specific GEMs based on proteomics data, and manually curated GEMs for small intestine, colon, liver, and adipose tissues. We used these functional models to determine the global metabolic differences between CONV-R and GF mice. Based on gene expression data, we found that the gut microbiota affects the host amino acid (AA) metabolism, which leads to modifications in glutathione metabolism. To validate our predictions, we measured the level of AAs and N-acetylated AAs in the hepatic portal vein of CONV-R and GF mice. Finally, we simulated the metabolic differences between the small intestine of the CONV-R and GF mice accounting for the content of the diet and relative gene expression differences. Our analyses revealed that the gut microbiota influences host amino acid and glutathione metabolism in mice. link: http://identifiers.org/pubmed/26475342

Parameters: none

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Mardinoglu2015 - Tissue-specific genome-scale metabolic network - LiverThis model is described in the article: [The gut…

The gut microbiota has been proposed as an environmental factor that promotes the progression of metabolic diseases. Here, we investigated how the gut microbiota modulates the global metabolic differences in duodenum, jejunum, ileum, colon, liver, and two white adipose tissue depots obtained from conventionally raised (CONV-R) and germ-free (GF) mice using gene expression data and tissue-specific genome-scale metabolic models (GEMs). We created a generic mouse metabolic reaction (MMR) GEM, reconstructed 28 tissue-specific GEMs based on proteomics data, and manually curated GEMs for small intestine, colon, liver, and adipose tissues. We used these functional models to determine the global metabolic differences between CONV-R and GF mice. Based on gene expression data, we found that the gut microbiota affects the host amino acid (AA) metabolism, which leads to modifications in glutathione metabolism. To validate our predictions, we measured the level of AAs and N-acetylated AAs in the hepatic portal vein of CONV-R and GF mice. Finally, we simulated the metabolic differences between the small intestine of the CONV-R and GF mice accounting for the content of the diet and relative gene expression differences. Our analyses revealed that the gut microbiota influences host amino acid and glutathione metabolism in mice. link: http://identifiers.org/pubmed/26475342

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Mardinoglu2015 - Generic mouse genome-scale metabolic network (MMR)This model is described in the article: [The gut mic…

The gut microbiota has been proposed as an environmental factor that promotes the progression of metabolic diseases. Here, we investigated how the gut microbiota modulates the global metabolic differences in duodenum, jejunum, ileum, colon, liver, and two white adipose tissue depots obtained from conventionally raised (CONV-R) and germ-free (GF) mice using gene expression data and tissue-specific genome-scale metabolic models (GEMs). We created a generic mouse metabolic reaction (MMR) GEM, reconstructed 28 tissue-specific GEMs based on proteomics data, and manually curated GEMs for small intestine, colon, liver, and adipose tissues. We used these functional models to determine the global metabolic differences between CONV-R and GF mice. Based on gene expression data, we found that the gut microbiota affects the host amino acid (AA) metabolism, which leads to modifications in glutathione metabolism. To validate our predictions, we measured the level of AAs and N-acetylated AAs in the hepatic portal vein of CONV-R and GF mice. Finally, we simulated the metabolic differences between the small intestine of the CONV-R and GF mice accounting for the content of the diet and relative gene expression differences. Our analyses revealed that the gut microbiota influences host amino acid and glutathione metabolism in mice. link: http://identifiers.org/pubmed/26475342

Parameters: none

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Mardinoglu2015 - Tissue-specific genome-scale metabolic network - Adrenal glandThis model is described in the article:…

The gut microbiota has been proposed as an environmental factor that promotes the progression of metabolic diseases. Here, we investigated how the gut microbiota modulates the global metabolic differences in duodenum, jejunum, ileum, colon, liver, and two white adipose tissue depots obtained from conventionally raised (CONV-R) and germ-free (GF) mice using gene expression data and tissue-specific genome-scale metabolic models (GEMs). We created a generic mouse metabolic reaction (MMR) GEM, reconstructed 28 tissue-specific GEMs based on proteomics data, and manually curated GEMs for small intestine, colon, liver, and adipose tissues. We used these functional models to determine the global metabolic differences between CONV-R and GF mice. Based on gene expression data, we found that the gut microbiota affects the host amino acid (AA) metabolism, which leads to modifications in glutathione metabolism. To validate our predictions, we measured the level of AAs and N-acetylated AAs in the hepatic portal vein of CONV-R and GF mice. Finally, we simulated the metabolic differences between the small intestine of the CONV-R and GF mice accounting for the content of the diet and relative gene expression differences. Our analyses revealed that the gut microbiota influences host amino acid and glutathione metabolism in mice. link: http://identifiers.org/pubmed/26475342

Parameters: none

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Mardinoglu2015 - Tissue-specific genome-scale metabolic network - Brain cortexThis model is described in the article: […

The gut microbiota has been proposed as an environmental factor that promotes the progression of metabolic diseases. Here, we investigated how the gut microbiota modulates the global metabolic differences in duodenum, jejunum, ileum, colon, liver, and two white adipose tissue depots obtained from conventionally raised (CONV-R) and germ-free (GF) mice using gene expression data and tissue-specific genome-scale metabolic models (GEMs). We created a generic mouse metabolic reaction (MMR) GEM, reconstructed 28 tissue-specific GEMs based on proteomics data, and manually curated GEMs for small intestine, colon, liver, and adipose tissues. We used these functional models to determine the global metabolic differences between CONV-R and GF mice. Based on gene expression data, we found that the gut microbiota affects the host amino acid (AA) metabolism, which leads to modifications in glutathione metabolism. To validate our predictions, we measured the level of AAs and N-acetylated AAs in the hepatic portal vein of CONV-R and GF mice. Finally, we simulated the metabolic differences between the small intestine of the CONV-R and GF mice accounting for the content of the diet and relative gene expression differences. Our analyses revealed that the gut microbiota influences host amino acid and glutathione metabolism in mice. link: http://identifiers.org/pubmed/26475342

Parameters: none

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Mardinoglu2015 - Tissue-specific genome-scale metabolic network - Brain medullaThis model is described in the article:…

The gut microbiota has been proposed as an environmental factor that promotes the progression of metabolic diseases. Here, we investigated how the gut microbiota modulates the global metabolic differences in duodenum, jejunum, ileum, colon, liver, and two white adipose tissue depots obtained from conventionally raised (CONV-R) and germ-free (GF) mice using gene expression data and tissue-specific genome-scale metabolic models (GEMs). We created a generic mouse metabolic reaction (MMR) GEM, reconstructed 28 tissue-specific GEMs based on proteomics data, and manually curated GEMs for small intestine, colon, liver, and adipose tissues. We used these functional models to determine the global metabolic differences between CONV-R and GF mice. Based on gene expression data, we found that the gut microbiota affects the host amino acid (AA) metabolism, which leads to modifications in glutathione metabolism. To validate our predictions, we measured the level of AAs and N-acetylated AAs in the hepatic portal vein of CONV-R and GF mice. Finally, we simulated the metabolic differences between the small intestine of the CONV-R and GF mice accounting for the content of the diet and relative gene expression differences. Our analyses revealed that the gut microbiota influences host amino acid and glutathione metabolism in mice. link: http://identifiers.org/pubmed/26475342

Parameters: none

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Mardinoglu2015 - Tissue-specific genome-scale metabolic network - Brown fatThis model is described in the article: [The…

The gut microbiota has been proposed as an environmental factor that promotes the progression of metabolic diseases. Here, we investigated how the gut microbiota modulates the global metabolic differences in duodenum, jejunum, ileum, colon, liver, and two white adipose tissue depots obtained from conventionally raised (CONV-R) and germ-free (GF) mice using gene expression data and tissue-specific genome-scale metabolic models (GEMs). We created a generic mouse metabolic reaction (MMR) GEM, reconstructed 28 tissue-specific GEMs based on proteomics data, and manually curated GEMs for small intestine, colon, liver, and adipose tissues. We used these functional models to determine the global metabolic differences between CONV-R and GF mice. Based on gene expression data, we found that the gut microbiota affects the host amino acid (AA) metabolism, which leads to modifications in glutathione metabolism. To validate our predictions, we measured the level of AAs and N-acetylated AAs in the hepatic portal vein of CONV-R and GF mice. Finally, we simulated the metabolic differences between the small intestine of the CONV-R and GF mice accounting for the content of the diet and relative gene expression differences. Our analyses revealed that the gut microbiota influences host amino acid and glutathione metabolism in mice. link: http://identifiers.org/pubmed/26475342

Parameters: none

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Mardinoglu2015 - Tissue-specific genome-scale metabolic network - CerebellumThis model is described in the article: [Th…

The gut microbiota has been proposed as an environmental factor that promotes the progression of metabolic diseases. Here, we investigated how the gut microbiota modulates the global metabolic differences in duodenum, jejunum, ileum, colon, liver, and two white adipose tissue depots obtained from conventionally raised (CONV-R) and germ-free (GF) mice using gene expression data and tissue-specific genome-scale metabolic models (GEMs). We created a generic mouse metabolic reaction (MMR) GEM, reconstructed 28 tissue-specific GEMs based on proteomics data, and manually curated GEMs for small intestine, colon, liver, and adipose tissues. We used these functional models to determine the global metabolic differences between CONV-R and GF mice. Based on gene expression data, we found that the gut microbiota affects the host amino acid (AA) metabolism, which leads to modifications in glutathione metabolism. To validate our predictions, we measured the level of AAs and N-acetylated AAs in the hepatic portal vein of CONV-R and GF mice. Finally, we simulated the metabolic differences between the small intestine of the CONV-R and GF mice accounting for the content of the diet and relative gene expression differences. Our analyses revealed that the gut microbiota influences host amino acid and glutathione metabolism in mice. link: http://identifiers.org/pubmed/26475342

Parameters: none

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Observables: none

Mardinoglu2015 - Tissue-specific genome-scale metabolic network - ColonThis model is described in the article: [The gut…

The gut microbiota has been proposed as an environmental factor that promotes the progression of metabolic diseases. Here, we investigated how the gut microbiota modulates the global metabolic differences in duodenum, jejunum, ileum, colon, liver, and two white adipose tissue depots obtained from conventionally raised (CONV-R) and germ-free (GF) mice using gene expression data and tissue-specific genome-scale metabolic models (GEMs). We created a generic mouse metabolic reaction (MMR) GEM, reconstructed 28 tissue-specific GEMs based on proteomics data, and manually curated GEMs for small intestine, colon, liver, and adipose tissues. We used these functional models to determine the global metabolic differences between CONV-R and GF mice. Based on gene expression data, we found that the gut microbiota affects the host amino acid (AA) metabolism, which leads to modifications in glutathione metabolism. To validate our predictions, we measured the level of AAs and N-acetylated AAs in the hepatic portal vein of CONV-R and GF mice. Finally, we simulated the metabolic differences between the small intestine of the CONV-R and GF mice accounting for the content of the diet and relative gene expression differences. Our analyses revealed that the gut microbiota influences host amino acid and glutathione metabolism in mice. link: http://identifiers.org/pubmed/26475342

Parameters: none

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Mardinoglu2015 - Tissue-specific genome-scale metabolic network - DiaphragmThis model is described in the article: [The…

The gut microbiota has been proposed as an environmental factor that promotes the progression of metabolic diseases. Here, we investigated how the gut microbiota modulates the global metabolic differences in duodenum, jejunum, ileum, colon, liver, and two white adipose tissue depots obtained from conventionally raised (CONV-R) and germ-free (GF) mice using gene expression data and tissue-specific genome-scale metabolic models (GEMs). We created a generic mouse metabolic reaction (MMR) GEM, reconstructed 28 tissue-specific GEMs based on proteomics data, and manually curated GEMs for small intestine, colon, liver, and adipose tissues. We used these functional models to determine the global metabolic differences between CONV-R and GF mice. Based on gene expression data, we found that the gut microbiota affects the host amino acid (AA) metabolism, which leads to modifications in glutathione metabolism. To validate our predictions, we measured the level of AAs and N-acetylated AAs in the hepatic portal vein of CONV-R and GF mice. Finally, we simulated the metabolic differences between the small intestine of the CONV-R and GF mice accounting for the content of the diet and relative gene expression differences. Our analyses revealed that the gut microbiota influences host amino acid and glutathione metabolism in mice. link: http://identifiers.org/pubmed/26475342

Parameters: none

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Mardinoglu2015 - Tissue-specific genome-scale metabolic network - DuodenumThis model is described in the article: [The…

The gut microbiota has been proposed as an environmental factor that promotes the progression of metabolic diseases. Here, we investigated how the gut microbiota modulates the global metabolic differences in duodenum, jejunum, ileum, colon, liver, and two white adipose tissue depots obtained from conventionally raised (CONV-R) and germ-free (GF) mice using gene expression data and tissue-specific genome-scale metabolic models (GEMs). We created a generic mouse metabolic reaction (MMR) GEM, reconstructed 28 tissue-specific GEMs based on proteomics data, and manually curated GEMs for small intestine, colon, liver, and adipose tissues. We used these functional models to determine the global metabolic differences between CONV-R and GF mice. Based on gene expression data, we found that the gut microbiota affects the host amino acid (AA) metabolism, which leads to modifications in glutathione metabolism. To validate our predictions, we measured the level of AAs and N-acetylated AAs in the hepatic portal vein of CONV-R and GF mice. Finally, we simulated the metabolic differences between the small intestine of the CONV-R and GF mice accounting for the content of the diet and relative gene expression differences. Our analyses revealed that the gut microbiota influences host amino acid and glutathione metabolism in mice. link: http://identifiers.org/pubmed/26475342

Parameters: none

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Mardinoglu2015 - Tissue-specific genome-scale metabolic network - Embryonic tissueThis model is described in the article…

The gut microbiota has been proposed as an environmental factor that promotes the progression of metabolic diseases. Here, we investigated how the gut microbiota modulates the global metabolic differences in duodenum, jejunum, ileum, colon, liver, and two white adipose tissue depots obtained from conventionally raised (CONV-R) and germ-free (GF) mice using gene expression data and tissue-specific genome-scale metabolic models (GEMs). We created a generic mouse metabolic reaction (MMR) GEM, reconstructed 28 tissue-specific GEMs based on proteomics data, and manually curated GEMs for small intestine, colon, liver, and adipose tissues. We used these functional models to determine the global metabolic differences between CONV-R and GF mice. Based on gene expression data, we found that the gut microbiota affects the host amino acid (AA) metabolism, which leads to modifications in glutathione metabolism. To validate our predictions, we measured the level of AAs and N-acetylated AAs in the hepatic portal vein of CONV-R and GF mice. Finally, we simulated the metabolic differences between the small intestine of the CONV-R and GF mice accounting for the content of the diet and relative gene expression differences. Our analyses revealed that the gut microbiota influences host amino acid and glutathione metabolism in mice. link: http://identifiers.org/pubmed/26475342

Parameters: none

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Observables: none

Mardinoglu2015 - Tissue-specific genome-scale metabolic network - EyeThis model is described in the article: [The gut m…

The gut microbiota has been proposed as an environmental factor that promotes the progression of metabolic diseases. Here, we investigated how the gut microbiota modulates the global metabolic differences in duodenum, jejunum, ileum, colon, liver, and two white adipose tissue depots obtained from conventionally raised (CONV-R) and germ-free (GF) mice using gene expression data and tissue-specific genome-scale metabolic models (GEMs). We created a generic mouse metabolic reaction (MMR) GEM, reconstructed 28 tissue-specific GEMs based on proteomics data, and manually curated GEMs for small intestine, colon, liver, and adipose tissues. We used these functional models to determine the global metabolic differences between CONV-R and GF mice. Based on gene expression data, we found that the gut microbiota affects the host amino acid (AA) metabolism, which leads to modifications in glutathione metabolism. To validate our predictions, we measured the level of AAs and N-acetylated AAs in the hepatic portal vein of CONV-R and GF mice. Finally, we simulated the metabolic differences between the small intestine of the CONV-R and GF mice accounting for the content of the diet and relative gene expression differences. Our analyses revealed that the gut microbiota influences host amino acid and glutathione metabolism in mice. link: http://identifiers.org/pubmed/26475342

Parameters: none

States: none

Observables: none

Mardinoglu2015 - Tissue-specific genome-scale metabolic network - HeartThis model is described in the article: [The gut…

The gut microbiota has been proposed as an environmental factor that promotes the progression of metabolic diseases. Here, we investigated how the gut microbiota modulates the global metabolic differences in duodenum, jejunum, ileum, colon, liver, and two white adipose tissue depots obtained from conventionally raised (CONV-R) and germ-free (GF) mice using gene expression data and tissue-specific genome-scale metabolic models (GEMs). We created a generic mouse metabolic reaction (MMR) GEM, reconstructed 28 tissue-specific GEMs based on proteomics data, and manually curated GEMs for small intestine, colon, liver, and adipose tissues. We used these functional models to determine the global metabolic differences between CONV-R and GF mice. Based on gene expression data, we found that the gut microbiota affects the host amino acid (AA) metabolism, which leads to modifications in glutathione metabolism. To validate our predictions, we measured the level of AAs and N-acetylated AAs in the hepatic portal vein of CONV-R and GF mice. Finally, we simulated the metabolic differences between the small intestine of the CONV-R and GF mice accounting for the content of the diet and relative gene expression differences. Our analyses revealed that the gut microbiota influences host amino acid and glutathione metabolism in mice. link: http://identifiers.org/pubmed/26475342

Parameters: none

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Observables: none

Mardinoglu2015 - Tissue-specific genome-scale metabolic network - IleumThis model is described in the article: [The gut…

The gut microbiota has been proposed as an environmental factor that promotes the progression of metabolic diseases. Here, we investigated how the gut microbiota modulates the global metabolic differences in duodenum, jejunum, ileum, colon, liver, and two white adipose tissue depots obtained from conventionally raised (CONV-R) and germ-free (GF) mice using gene expression data and tissue-specific genome-scale metabolic models (GEMs). We created a generic mouse metabolic reaction (MMR) GEM, reconstructed 28 tissue-specific GEMs based on proteomics data, and manually curated GEMs for small intestine, colon, liver, and adipose tissues. We used these functional models to determine the global metabolic differences between CONV-R and GF mice. Based on gene expression data, we found that the gut microbiota affects the host amino acid (AA) metabolism, which leads to modifications in glutathione metabolism. To validate our predictions, we measured the level of AAs and N-acetylated AAs in the hepatic portal vein of CONV-R and GF mice. Finally, we simulated the metabolic differences between the small intestine of the CONV-R and GF mice accounting for the content of the diet and relative gene expression differences. Our analyses revealed that the gut microbiota influences host amino acid and glutathione metabolism in mice. link: http://identifiers.org/pubmed/26475342

Parameters: none

States: none

Observables: none

Mardinoglu2015 - Tissue-specific genome-scale metabolic network - JejunumThis model is described in the article: [The g…

The gut microbiota has been proposed as an environmental factor that promotes the progression of metabolic diseases. Here, we investigated how the gut microbiota modulates the global metabolic differences in duodenum, jejunum, ileum, colon, liver, and two white adipose tissue depots obtained from conventionally raised (CONV-R) and germ-free (GF) mice using gene expression data and tissue-specific genome-scale metabolic models (GEMs). We created a generic mouse metabolic reaction (MMR) GEM, reconstructed 28 tissue-specific GEMs based on proteomics data, and manually curated GEMs for small intestine, colon, liver, and adipose tissues. We used these functional models to determine the global metabolic differences between CONV-R and GF mice. Based on gene expression data, we found that the gut microbiota affects the host amino acid (AA) metabolism, which leads to modifications in glutathione metabolism. To validate our predictions, we measured the level of AAs and N-acetylated AAs in the hepatic portal vein of CONV-R and GF mice. Finally, we simulated the metabolic differences between the small intestine of the CONV-R and GF mice accounting for the content of the diet and relative gene expression differences. Our analyses revealed that the gut microbiota influences host amino acid and glutathione metabolism in mice. link: http://identifiers.org/pubmed/26475342

Parameters: none

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Observables: none

Mardinoglu2015 - Tissue-specific genome-scale metabolic network - Kidney cortexThis model is described in the article:…

The gut microbiota has been proposed as an environmental factor that promotes the progression of metabolic diseases. Here, we investigated how the gut microbiota modulates the global metabolic differences in duodenum, jejunum, ileum, colon, liver, and two white adipose tissue depots obtained from conventionally raised (CONV-R) and germ-free (GF) mice using gene expression data and tissue-specific genome-scale metabolic models (GEMs). We created a generic mouse metabolic reaction (MMR) GEM, reconstructed 28 tissue-specific GEMs based on proteomics data, and manually curated GEMs for small intestine, colon, liver, and adipose tissues. We used these functional models to determine the global metabolic differences between CONV-R and GF mice. Based on gene expression data, we found that the gut microbiota affects the host amino acid (AA) metabolism, which leads to modifications in glutathione metabolism. To validate our predictions, we measured the level of AAs and N-acetylated AAs in the hepatic portal vein of CONV-R and GF mice. Finally, we simulated the metabolic differences between the small intestine of the CONV-R and GF mice accounting for the content of the diet and relative gene expression differences. Our analyses revealed that the gut microbiota influences host amino acid and glutathione metabolism in mice. link: http://identifiers.org/pubmed/26475342

Parameters: none

States: none

Observables: none

Mardinoglu2015 - Tissue-specific genome-scale metabolic network - Kidney medullaThis model is described in the article:…

The gut microbiota has been proposed as an environmental factor that promotes the progression of metabolic diseases. Here, we investigated how the gut microbiota modulates the global metabolic differences in duodenum, jejunum, ileum, colon, liver, and two white adipose tissue depots obtained from conventionally raised (CONV-R) and germ-free (GF) mice using gene expression data and tissue-specific genome-scale metabolic models (GEMs). We created a generic mouse metabolic reaction (MMR) GEM, reconstructed 28 tissue-specific GEMs based on proteomics data, and manually curated GEMs for small intestine, colon, liver, and adipose tissues. We used these functional models to determine the global metabolic differences between CONV-R and GF mice. Based on gene expression data, we found that the gut microbiota affects the host amino acid (AA) metabolism, which leads to modifications in glutathione metabolism. To validate our predictions, we measured the level of AAs and N-acetylated AAs in the hepatic portal vein of CONV-R and GF mice. Finally, we simulated the metabolic differences between the small intestine of the CONV-R and GF mice accounting for the content of the diet and relative gene expression differences. Our analyses revealed that the gut microbiota influences host amino acid and glutathione metabolism in mice. link: http://identifiers.org/pubmed/26475342

Parameters: none

States: none

Observables: none

Mardinoglu2015 - Tissue-specific genome-scale metabolic network - LiverThis model is described in the article: [The gut…

The gut microbiota has been proposed as an environmental factor that promotes the progression of metabolic diseases. Here, we investigated how the gut microbiota modulates the global metabolic differences in duodenum, jejunum, ileum, colon, liver, and two white adipose tissue depots obtained from conventionally raised (CONV-R) and germ-free (GF) mice using gene expression data and tissue-specific genome-scale metabolic models (GEMs). We created a generic mouse metabolic reaction (MMR) GEM, reconstructed 28 tissue-specific GEMs based on proteomics data, and manually curated GEMs for small intestine, colon, liver, and adipose tissues. We used these functional models to determine the global metabolic differences between CONV-R and GF mice. Based on gene expression data, we found that the gut microbiota affects the host amino acid (AA) metabolism, which leads to modifications in glutathione metabolism. To validate our predictions, we measured the level of AAs and N-acetylated AAs in the hepatic portal vein of CONV-R and GF mice. Finally, we simulated the metabolic differences between the small intestine of the CONV-R and GF mice accounting for the content of the diet and relative gene expression differences. Our analyses revealed that the gut microbiota influences host amino acid and glutathione metabolism in mice. link: http://identifiers.org/pubmed/26475342

Parameters: none

States: none

Observables: none

Mardinoglu2015 - Tissue-specific genome-scale metabolic network - LungThis model is described in the article: [The gut…

The gut microbiota has been proposed as an environmental factor that promotes the progression of metabolic diseases. Here, we investigated how the gut microbiota modulates the global metabolic differences in duodenum, jejunum, ileum, colon, liver, and two white adipose tissue depots obtained from conventionally raised (CONV-R) and germ-free (GF) mice using gene expression data and tissue-specific genome-scale metabolic models (GEMs). We created a generic mouse metabolic reaction (MMR) GEM, reconstructed 28 tissue-specific GEMs based on proteomics data, and manually curated GEMs for small intestine, colon, liver, and adipose tissues. We used these functional models to determine the global metabolic differences between CONV-R and GF mice. Based on gene expression data, we found that the gut microbiota affects the host amino acid (AA) metabolism, which leads to modifications in glutathione metabolism. To validate our predictions, we measured the level of AAs and N-acetylated AAs in the hepatic portal vein of CONV-R and GF mice. Finally, we simulated the metabolic differences between the small intestine of the CONV-R and GF mice accounting for the content of the diet and relative gene expression differences. Our analyses revealed that the gut microbiota influences host amino acid and glutathione metabolism in mice. link: http://identifiers.org/pubmed/26475342

Parameters: none

States: none

Observables: none

Mardinoglu2015 - Tissue-specific genome-scale metabolic network - MidbrainThis model is described in the article: [The…

The gut microbiota has been proposed as an environmental factor that promotes the progression of metabolic diseases. Here, we investigated how the gut microbiota modulates the global metabolic differences in duodenum, jejunum, ileum, colon, liver, and two white adipose tissue depots obtained from conventionally raised (CONV-R) and germ-free (GF) mice using gene expression data and tissue-specific genome-scale metabolic models (GEMs). We created a generic mouse metabolic reaction (MMR) GEM, reconstructed 28 tissue-specific GEMs based on proteomics data, and manually curated GEMs for small intestine, colon, liver, and adipose tissues. We used these functional models to determine the global metabolic differences between CONV-R and GF mice. Based on gene expression data, we found that the gut microbiota affects the host amino acid (AA) metabolism, which leads to modifications in glutathione metabolism. To validate our predictions, we measured the level of AAs and N-acetylated AAs in the hepatic portal vein of CONV-R and GF mice. Finally, we simulated the metabolic differences between the small intestine of the CONV-R and GF mice accounting for the content of the diet and relative gene expression differences. Our analyses revealed that the gut microbiota influences host amino acid and glutathione metabolism in mice. link: http://identifiers.org/pubmed/26475342

Parameters: none

States: none

Observables: none

Mardinoglu2015 - Tissue-specific genome-scale metabolic network - MuscleThis model is described in the article: [The gu…

The gut microbiota has been proposed as an environmental factor that promotes the progression of metabolic diseases. Here, we investigated how the gut microbiota modulates the global metabolic differences in duodenum, jejunum, ileum, colon, liver, and two white adipose tissue depots obtained from conventionally raised (CONV-R) and germ-free (GF) mice using gene expression data and tissue-specific genome-scale metabolic models (GEMs). We created a generic mouse metabolic reaction (MMR) GEM, reconstructed 28 tissue-specific GEMs based on proteomics data, and manually curated GEMs for small intestine, colon, liver, and adipose tissues. We used these functional models to determine the global metabolic differences between CONV-R and GF mice. Based on gene expression data, we found that the gut microbiota affects the host amino acid (AA) metabolism, which leads to modifications in glutathione metabolism. To validate our predictions, we measured the level of AAs and N-acetylated AAs in the hepatic portal vein of CONV-R and GF mice. Finally, we simulated the metabolic differences between the small intestine of the CONV-R and GF mice accounting for the content of the diet and relative gene expression differences. Our analyses revealed that the gut microbiota influences host amino acid and glutathione metabolism in mice. link: http://identifiers.org/pubmed/26475342

Parameters: none

States: none

Observables: none

Mardinoglu2015 - Tissue-specific genome-scale metabolic network - Olfactory bulbThis model is described in the article:…

The gut microbiota has been proposed as an environmental factor that promotes the progression of metabolic diseases. Here, we investigated how the gut microbiota modulates the global metabolic differences in duodenum, jejunum, ileum, colon, liver, and two white adipose tissue depots obtained from conventionally raised (CONV-R) and germ-free (GF) mice using gene expression data and tissue-specific genome-scale metabolic models (GEMs). We created a generic mouse metabolic reaction (MMR) GEM, reconstructed 28 tissue-specific GEMs based on proteomics data, and manually curated GEMs for small intestine, colon, liver, and adipose tissues. We used these functional models to determine the global metabolic differences between CONV-R and GF mice. Based on gene expression data, we found that the gut microbiota affects the host amino acid (AA) metabolism, which leads to modifications in glutathione metabolism. To validate our predictions, we measured the level of AAs and N-acetylated AAs in the hepatic portal vein of CONV-R and GF mice. Finally, we simulated the metabolic differences between the small intestine of the CONV-R and GF mice accounting for the content of the diet and relative gene expression differences. Our analyses revealed that the gut microbiota influences host amino acid and glutathione metabolism in mice. link: http://identifiers.org/pubmed/26475342

Parameters: none

States: none

Observables: none

Mardinoglu2015 - Tissue-specific genome-scale metabolic network - OvaryThis model is described in the article: [The gut…

The gut microbiota has been proposed as an environmental factor that promotes the progression of metabolic diseases. Here, we investigated how the gut microbiota modulates the global metabolic differences in duodenum, jejunum, ileum, colon, liver, and two white adipose tissue depots obtained from conventionally raised (CONV-R) and germ-free (GF) mice using gene expression data and tissue-specific genome-scale metabolic models (GEMs). We created a generic mouse metabolic reaction (MMR) GEM, reconstructed 28 tissue-specific GEMs based on proteomics data, and manually curated GEMs for small intestine, colon, liver, and adipose tissues. We used these functional models to determine the global metabolic differences between CONV-R and GF mice. Based on gene expression data, we found that the gut microbiota affects the host amino acid (AA) metabolism, which leads to modifications in glutathione metabolism. To validate our predictions, we measured the level of AAs and N-acetylated AAs in the hepatic portal vein of CONV-R and GF mice. Finally, we simulated the metabolic differences between the small intestine of the CONV-R and GF mice accounting for the content of the diet and relative gene expression differences. Our analyses revealed that the gut microbiota influences host amino acid and glutathione metabolism in mice. link: http://identifiers.org/pubmed/26475342

Parameters: none

States: none

Observables: none

Mardinoglu2015 - Tissue-specific genome-scale metabolic network - PancreasThis model is described in the article: [The…

The gut microbiota has been proposed as an environmental factor that promotes the progression of metabolic diseases. Here, we investigated how the gut microbiota modulates the global metabolic differences in duodenum, jejunum, ileum, colon, liver, and two white adipose tissue depots obtained from conventionally raised (CONV-R) and germ-free (GF) mice using gene expression data and tissue-specific genome-scale metabolic models (GEMs). We created a generic mouse metabolic reaction (MMR) GEM, reconstructed 28 tissue-specific GEMs based on proteomics data, and manually curated GEMs for small intestine, colon, liver, and adipose tissues. We used these functional models to determine the global metabolic differences between CONV-R and GF mice. Based on gene expression data, we found that the gut microbiota affects the host amino acid (AA) metabolism, which leads to modifications in glutathione metabolism. To validate our predictions, we measured the level of AAs and N-acetylated AAs in the hepatic portal vein of CONV-R and GF mice. Finally, we simulated the metabolic differences between the small intestine of the CONV-R and GF mice accounting for the content of the diet and relative gene expression differences. Our analyses revealed that the gut microbiota influences host amino acid and glutathione metabolism in mice. link: http://identifiers.org/pubmed/26475342

Parameters: none

States: none

Observables: none

Mardinoglu2015 - Tissue-specific genome-scale metabolic network - Salivary glandThis model is described in the article:…

The gut microbiota has been proposed as an environmental factor that promotes the progression of metabolic diseases. Here, we investigated how the gut microbiota modulates the global metabolic differences in duodenum, jejunum, ileum, colon, liver, and two white adipose tissue depots obtained from conventionally raised (CONV-R) and germ-free (GF) mice using gene expression data and tissue-specific genome-scale metabolic models (GEMs). We created a generic mouse metabolic reaction (MMR) GEM, reconstructed 28 tissue-specific GEMs based on proteomics data, and manually curated GEMs for small intestine, colon, liver, and adipose tissues. We used these functional models to determine the global metabolic differences between CONV-R and GF mice. Based on gene expression data, we found that the gut microbiota affects the host amino acid (AA) metabolism, which leads to modifications in glutathione metabolism. To validate our predictions, we measured the level of AAs and N-acetylated AAs in the hepatic portal vein of CONV-R and GF mice. Finally, we simulated the metabolic differences between the small intestine of the CONV-R and GF mice accounting for the content of the diet and relative gene expression differences. Our analyses revealed that the gut microbiota influences host amino acid and glutathione metabolism in mice. link: http://identifiers.org/pubmed/26475342

Parameters: none

States: none

Observables: none

Mardinoglu2015 - Tissue-specific genome-scale metabolic network - SpleeenThis model is described in the article: [The g…

The gut microbiota has been proposed as an environmental factor that promotes the progression of metabolic diseases. Here, we investigated how the gut microbiota modulates the global metabolic differences in duodenum, jejunum, ileum, colon, liver, and two white adipose tissue depots obtained from conventionally raised (CONV-R) and germ-free (GF) mice using gene expression data and tissue-specific genome-scale metabolic models (GEMs). We created a generic mouse metabolic reaction (MMR) GEM, reconstructed 28 tissue-specific GEMs based on proteomics data, and manually curated GEMs for small intestine, colon, liver, and adipose tissues. We used these functional models to determine the global metabolic differences between CONV-R and GF mice. Based on gene expression data, we found that the gut microbiota affects the host amino acid (AA) metabolism, which leads to modifications in glutathione metabolism. To validate our predictions, we measured the level of AAs and N-acetylated AAs in the hepatic portal vein of CONV-R and GF mice. Finally, we simulated the metabolic differences between the small intestine of the CONV-R and GF mice accounting for the content of the diet and relative gene expression differences. Our analyses revealed that the gut microbiota influences host amino acid and glutathione metabolism in mice. link: http://identifiers.org/pubmed/26475342

Parameters: none

States: none

Observables: none

Mardinoglu2015 - Tissue-specific genome-scale metabolic network - StomachThis model is described in the article: [The g…

The gut microbiota has been proposed as an environmental factor that promotes the progression of metabolic diseases. Here, we investigated how the gut microbiota modulates the global metabolic differences in duodenum, jejunum, ileum, colon, liver, and two white adipose tissue depots obtained from conventionally raised (CONV-R) and germ-free (GF) mice using gene expression data and tissue-specific genome-scale metabolic models (GEMs). We created a generic mouse metabolic reaction (MMR) GEM, reconstructed 28 tissue-specific GEMs based on proteomics data, and manually curated GEMs for small intestine, colon, liver, and adipose tissues. We used these functional models to determine the global metabolic differences between CONV-R and GF mice. Based on gene expression data, we found that the gut microbiota affects the host amino acid (AA) metabolism, which leads to modifications in glutathione metabolism. To validate our predictions, we measured the level of AAs and N-acetylated AAs in the hepatic portal vein of CONV-R and GF mice. Finally, we simulated the metabolic differences between the small intestine of the CONV-R and GF mice accounting for the content of the diet and relative gene expression differences. Our analyses revealed that the gut microbiota influences host amino acid and glutathione metabolism in mice. link: http://identifiers.org/pubmed/26475342

Parameters: none

States: none

Observables: none

Mardinoglu2015 - Tissue-specific genome-scale metabolic network - ThymusThis model is described in the article: [The gu…

The gut microbiota has been proposed as an environmental factor that promotes the progression of metabolic diseases. Here, we investigated how the gut microbiota modulates the global metabolic differences in duodenum, jejunum, ileum, colon, liver, and two white adipose tissue depots obtained from conventionally raised (CONV-R) and germ-free (GF) mice using gene expression data and tissue-specific genome-scale metabolic models (GEMs). We created a generic mouse metabolic reaction (MMR) GEM, reconstructed 28 tissue-specific GEMs based on proteomics data, and manually curated GEMs for small intestine, colon, liver, and adipose tissues. We used these functional models to determine the global metabolic differences between CONV-R and GF mice. Based on gene expression data, we found that the gut microbiota affects the host amino acid (AA) metabolism, which leads to modifications in glutathione metabolism. To validate our predictions, we measured the level of AAs and N-acetylated AAs in the hepatic portal vein of CONV-R and GF mice. Finally, we simulated the metabolic differences between the small intestine of the CONV-R and GF mice accounting for the content of the diet and relative gene expression differences. Our analyses revealed that the gut microbiota influences host amino acid and glutathione metabolism in mice. link: http://identifiers.org/pubmed/26475342

Parameters: none

States: none

Observables: none

Mardinoglu2015 - Tissue-specific genome-scale metabolic network - UterusThis model is described in the article: [The gu…

The gut microbiota has been proposed as an environmental factor that promotes the progression of metabolic diseases. Here, we investigated how the gut microbiota modulates the global metabolic differences in duodenum, jejunum, ileum, colon, liver, and two white adipose tissue depots obtained from conventionally raised (CONV-R) and germ-free (GF) mice using gene expression data and tissue-specific genome-scale metabolic models (GEMs). We created a generic mouse metabolic reaction (MMR) GEM, reconstructed 28 tissue-specific GEMs based on proteomics data, and manually curated GEMs for small intestine, colon, liver, and adipose tissues. We used these functional models to determine the global metabolic differences between CONV-R and GF mice. Based on gene expression data, we found that the gut microbiota affects the host amino acid (AA) metabolism, which leads to modifications in glutathione metabolism. To validate our predictions, we measured the level of AAs and N-acetylated AAs in the hepatic portal vein of CONV-R and GF mice. Finally, we simulated the metabolic differences between the small intestine of the CONV-R and GF mice accounting for the content of the diet and relative gene expression differences. Our analyses revealed that the gut microbiota influences host amino acid and glutathione metabolism in mice. link: http://identifiers.org/pubmed/26475342

Parameters: none

States: none

Observables: none

Mardinoglu2015 - Tissue-specific genome-scale metabolic network - White fatThis model is described in the article: [The…

The gut microbiota has been proposed as an environmental factor that promotes the progression of metabolic diseases. Here, we investigated how the gut microbiota modulates the global metabolic differences in duodenum, jejunum, ileum, colon, liver, and two white adipose tissue depots obtained from conventionally raised (CONV-R) and germ-free (GF) mice using gene expression data and tissue-specific genome-scale metabolic models (GEMs). We created a generic mouse metabolic reaction (MMR) GEM, reconstructed 28 tissue-specific GEMs based on proteomics data, and manually curated GEMs for small intestine, colon, liver, and adipose tissues. We used these functional models to determine the global metabolic differences between CONV-R and GF mice. Based on gene expression data, we found that the gut microbiota affects the host amino acid (AA) metabolism, which leads to modifications in glutathione metabolism. To validate our predictions, we measured the level of AAs and N-acetylated AAs in the hepatic portal vein of CONV-R and GF mice. Finally, we simulated the metabolic differences between the small intestine of the CONV-R and GF mice accounting for the content of the diet and relative gene expression differences. Our analyses revealed that the gut microbiota influences host amino acid and glutathione metabolism in mice. link: http://identifiers.org/pubmed/26475342

Parameters: none

States: none

Observables: none

BIOMD0000000381 @ v0.0.1

This a model from the article: Modelling the onset of Type 1 diabetes: can impaired macrophage phagocytosis make the…

A wave of apoptosis (programmed cell death) occurs normally in pancreatic beta-cells of newborn mice. We previously showed that macrophages from non-obese diabetic (NOD) mice become activated more slowly and engulf apoptotic cells at a lower rate than macrophages from control (Balb/c) mice. It has been hypothesized that this low clearance could result in secondary necrosis, escalating inflammation and self-antigen presentation that later triggers autoimmune, Type 1 diabetes (T1D). We here investigate whether this hypothesis could offer a reasonable and parsimonious explanation for onset of T1D in NOD mice. We quantify variants of the Copenhagen model (Freiesleben De Blasio et al. 1999 Diabetes 48, 1677), based on parameters from NOD and Balb/c experimental data. We show that the original Copenhagen model fails to explain observed phenomena within a reasonable range of parameter values, predicting an unrealistic all-or-none disease occurrence for both strains. However, if we take into account that, in general, activated macrophages produce harmful cytokines only when engulfing necrotic (but not apoptotic) cells, then the revised model becomes qualitatively and quantitatively reasonable. Further, we show that known differences between NOD and Balb/c mouse macrophage kinetics are large enough to account for the fact that an apoptotic wave can trigger escalating inflammatory response in NOD, but not Balb/c mice. In Balb/c mice, macrophages clear the apoptotic wave so efficiently, that chronic inflammation is prevented. link: http://identifiers.org/pubmed/16608707

Parameters:

Name Description
Amax = 2.0E7; f2 = 1.0E-5; W = 4936.39216346718; kc = 1.0; f1 = 1.0E-5; d = 0.5 Reaction: Ba = (W+Amax*Cy/(kc+Cy))-(f1*M+f2*Ma+d)*Ba, Rate Law: (W+Amax*Cy/(kc+Cy))-(f1*M+f2*Ma+d)*Ba
f2 = 1.0E-5; f1 = 1.0E-5; d = 0.5 Reaction: Bn = d*Ba-(f1*M+f2*Ma)*Bn, Rate Law: d*Ba-(f1*M+f2*Ma)*Bn
b = 0.09; J = 50000.0; f1 = 1.0E-5; e1 = 1.0E-8; c = 0.1; k = 0.4 Reaction: M = (((J+(k+b)*Ma)-c*M)-f1*M*Ba)-e1*M*(M+Ma), Rate Law: (((J+(k+b)*Ma)-c*M)-f1*M*Ba)-e1*M*(M+Ma)
delta = 25.0; alpha = 5.0E-9 Reaction: Cy = alpha*Bn*Ma-delta*Cy, Rate Law: alpha*Bn*Ma-delta*Cy
f1 = 1.0E-5; k = 0.4; e2 = 1.0E-8 Reaction: Ma = (f1*M*Ba-k*Ma)-e2*Ma*(M+Ma), Rate Law: (f1*M*Ba-k*Ma)-e2*Ma*(M+Ma)

States:

Name Description
M [macrophage]
Ma [macrophage]
Cy Cy
Bn [pancreatic beta cell]
Ba [pancreatic beta cell]

Observables: none

BIOMD0000000039 @ v0.0.1

In order to reproduce the model, the volume of all compartment is set to 1, and the stoichiometry of CaER and CaM has be…

Intracellular calcium oscillations, which are oscillatory changes of cytosolic calcium concentration in response to agonist stimulation, are experimentally well observed in various living cells. Simple calcium oscillations represent the most common pattern and many mathematical models have been published to describe this type of oscillation. On the other hand, relatively few theoretical studies have been proposed to give an explanation of complex intracellular calcium oscillations, such as bursting and chaos. In this paper, we develop a new possible mechanism for complex calcium oscillations based on the interplay between three calcium stores in the cell: the endoplasmic reticulum (ER), mitochondria and cytosolic proteins. The majority ( approximately 80%) of calcium released from the ER is first very quickly sequestered by mitochondria. Afterwards, a much slower release of calcium from the mitochondria serves as the calcium supply for the intermediate calcium exchanges between the ER and the cytosolic proteins causing bursting calcium oscillations. Depending on the permeability of the ER channels and on the kinetic properties of calcium binding to the cytosolic proteins, different patterns of complex calcium oscillations appear. With our model, we are able to explain simple calcium oscillations, bursting and chaos. Chaos is also observed for calcium oscillations in the bursting mode. link: http://identifiers.org/pubmed/11004387

Parameters:

Name Description
Kch=4100.0; K1=5.0 Reaction: CaER => Ca_cyt; Ca_cyt, Rate Law: Cytosol*Kch*Ca_cyt^2*(CaER-Ca_cyt)/(K1^2+Ca_cyt^2)
Kleak=0.05 Reaction: CaER => Ca_cyt, Rate Law: Cytosol*Kleak*(CaER-Ca_cyt)
Kpump=20.0 Reaction: Ca_cyt => CaER, Rate Law: Endoplasmic_Reticulum*Kpump*Ca_cyt
Kplus=0.1 Reaction: Pr + Ca_cyt => CaPr, Rate Law: Cytosol*Kplus*Ca_cyt*Pr
Kin=300.0; K2=0.8 Reaction: Ca_cyt => CaM; Ca_cyt, Rate Law: Mitochondria*Kin*Ca_cyt^8/(K2^8+Ca_cyt^8)
Kminus=0.01 Reaction: CaPr => Pr + Ca_cyt, Rate Law: Cytosol*Kminus*CaPr
Kout=125.0; K3=5.0; Km=0.00625 Reaction: CaM => Ca_cyt; Ca_cyt, Rate Law: Cytosol*CaM*(Kout*Ca_cyt^2/(K3^2+Ca_cyt^2)+Km)

States:

Name Description
CaPr [calcium(2+); Protein]
Pr [Protein; protein polypeptide chain]
CaER [calcium(2+)]
Ca cyt [calcium(2+)]
CaM [calcium(2+)]

Observables: none

Markevich2004 - MAPK double phosphorylation, ordered Michaelis-MentonThe model corresponds to the schemas 1 and 2 of Mar…

Mitogen-activated protein kinase (MAPK) cascades can operate as bistable switches residing in either of two different stable states. MAPK cascades are often embedded in positive feedback loops, which are considered to be a prerequisite for bistable behavior. Here we demonstrate that in the absence of any imposed feedback regulation, bistability and hysteresis can arise solely from a distributive kinetic mechanism of the two-site MAPK phosphorylation and dephosphorylation. Importantly, the reported kinetic properties of the kinase (MEK) and phosphatase (MKP3) of extracellular signal-regulated kinase (ERK) fulfill the essential requirements for generating a bistable switch at a single MAPK cascade level. Likewise, a cycle where multisite phosphorylations are performed by different kinases, but dephosphorylation reactions are catalyzed by the same phosphatase, can also exhibit bistability and hysteresis. Hence, bistability induced by multisite covalent modification may be a widespread mechanism of the control of protein activity. link: http://identifiers.org/pubmed/14744999

Parameters:

Name Description
Km2 = 500.0; k1cat = 0.01; Km1 = 50.0 Reaction: M => Mp; MAPKK, Rate Law: uVol*k1cat*MAPKK*M/Km1/(1+M/Km1+Mp/Km2)
Km2 = 500.0; Km1 = 50.0; k2cat = 15.0 Reaction: Mp => Mpp; MAPKK, M, Rate Law: uVol*k2cat*MAPKK*Mp/Km2/(1+M/Km1+Mp/Km2)
k4cat = 0.06; Km5 = 78.0; Km4 = 18.0; Km3 = 22.0 Reaction: Mp => M; MKP3, Mpp, Rate Law: uVol*k4cat*MKP3*Mp/Km4/(1+Mpp/Km3+Mp/Km4+M/Km5)
Km5 = 78.0; Km4 = 18.0; Km3 = 22.0; k3cat = 0.084 Reaction: Mpp => Mp; MKP3, M, Rate Law: uVol*k3cat*MKP3*Mpp/Km3/(1+Mpp/Km3+Mp/Km4+M/Km5)

States:

Name Description
M [Mitogen-activated protein kinase 1]
Mp [Mitogen-activated protein kinase 1]
Mpp [Mitogen-activated protein kinase 1]

Observables: none

BIOMD0000000030 @ v0.0.1

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

Mitogen-activated protein kinase (MAPK) cascades can operate as bistable switches residing in either of two different stable states. MAPK cascades are often embedded in positive feedback loops, which are considered to be a prerequisite for bistable behavior. Here we demonstrate that in the absence of any imposed feedback regulation, bistability and hysteresis can arise solely from a distributive kinetic mechanism of the two-site MAPK phosphorylation and dephosphorylation. Importantly, the reported kinetic properties of the kinase (MEK) and phosphatase (MKP3) of extracellular signal-regulated kinase (ERK) fulfill the essential requirements for generating a bistable switch at a single MAPK cascade level. Likewise, a cycle where multisite phosphorylations are performed by different kinases, but dephosphorylation reactions are catalyzed by the same phosphatase, can also exhibit bistability and hysteresis. Hence, bistability induced by multisite covalent modification may be a widespread mechanism of the control of protein activity. link: http://identifiers.org/pubmed/14744999

Parameters:

Name Description
k2 = 0.01 Reaction: M_MAPKK_Y => MpY + MAPKK, Rate Law: cell*k2*M_MAPKK_Y
h7 = 0.01; h_7 = 1.0 Reaction: MpY + MKP => MpY_MKP_Y, Rate Law: cell*(h7*MpY*MKP-h_7*MpY_MKP_Y)
h9 = 0.14; h_9 = 0.0018 Reaction: M_MKP_Y => M + MKP, Rate Law: cell*(h9*M_MKP_Y-h_9*M*MKP)
k6 = 0.01 Reaction: M_MAPKK_T => MpT + MAPKK, Rate Law: cell*k6*M_MAPKK_T
k_3 = 1.0; k3 = 0.032 Reaction: MpY + MAPKK => MpY_MAPKK, Rate Law: cell*(k3*MpY*MAPKK-k_3*MpY_MAPKK)
k8 = 15.0 Reaction: MpT_MAPKK => Mpp + MAPKK, Rate Law: cell*k8*MpT_MAPKK
h_1 = 1.0; h1 = 0.045 Reaction: Mpp + MKP => Mpp_MKP_Y, Rate Law: cell*(h1*Mpp*MKP-h_1*Mpp_MKP_Y)
k4 = 15.0 Reaction: MpY_MAPKK => Mpp + MAPKK, Rate Law: cell*k4*MpY_MAPKK
h2 = 0.092 Reaction: Mpp_MKP_Y => MpT_MKP_Y, Rate Law: cell*h2*Mpp_MKP_Y
h3 = 1.0; h_3 = 0.01 Reaction: MpT_MKP_Y => MpT + MKP, Rate Law: cell*(h3*MpT_MKP_Y-h_3*MpT*MKP)
h_12 = 0.01; h12 = 1.0 Reaction: MpY_MKP_T => MpY + MKP, Rate Law: cell*(h12*MpY_MKP_T-h_12*MpY*MKP)
h_10 = 1.0; h10 = 0.045 Reaction: Mpp + MKP => Mpp_MKP_T, Rate Law: cell*(h10*Mpp*MKP-h_10*Mpp_MKP_T)
k_7 = 1.0; k7 = 0.032 Reaction: MpT + MAPKK => MpT_MAPKK, Rate Law: cell*(k7*MpT*MAPKK-k_7*MpT_MAPKK)
h11 = 0.092 Reaction: Mpp_MKP_T => MpY_MKP_T, Rate Law: cell*h11*Mpp_MKP_T
h8 = 0.47 Reaction: MpY_MKP_Y => M_MKP_Y, Rate Law: cell*h8*MpY_MKP_Y
k5 = 0.02; k_5 = 1.0 Reaction: M + MAPKK => M_MAPKK_T, Rate Law: cell*(k5*M*MAPKK-k_5*M_MAPKK_T)
h6 = 0.086; h_6 = 0.0011 Reaction: M_MKP_T => M + MKP, Rate Law: cell*(h6*M_MKP_T-h_6*M*MKP)
h4 = 0.01; h_4 = 1.0 Reaction: MpT + MKP => MpT_MKP_T, Rate Law: cell*(h4*MpT*MKP-h_4*MpT_MKP_T)
k1 = 0.02; k_1 = 1.0 Reaction: M + MAPKK => M_MAPKK_Y, Rate Law: cell*(k1*M*MAPKK-k_1*M_MAPKK_Y)
h5 = 0.5 Reaction: MpT_MKP_T => M_MKP_T, Rate Law: cell*h5*MpT_MKP_T

States:

Name Description
MAPKK [Dual specificity mitogen-activated protein kinase kinase 1]
MKP [Dual specificity protein phosphatase 1-B]
M MKP Y [Dual specificity protein phosphatase 1-B; Mitogen-activated protein kinase 1]
MpT [Mitogen-activated protein kinase 1]
MpT MKP T [Dual specificity protein phosphatase 1-B; Mitogen-activated protein kinase 1]
MpY MKP T [Dual specificity protein phosphatase 1-B; Mitogen-activated protein kinase 1]
Mpp MKP Y [Dual specificity protein phosphatase 1-B; Mitogen-activated protein kinase 1]
M MKP T [Dual specificity protein phosphatase 1-B; Mitogen-activated protein kinase 1]
M [Mitogen-activated protein kinase 1]
Mpp MKP T [Dual specificity protein phosphatase 1-B; Mitogen-activated protein kinase 1]
MpY MAPKK [Mitogen-activated protein kinase 1; Dual specificity mitogen-activated protein kinase kinase 1]
M MAPKK T [Dual specificity mitogen-activated protein kinase kinase 1; Mitogen-activated protein kinase 1]
MpT MKP Y [Dual specificity protein phosphatase 1-B; Mitogen-activated protein kinase 1]
MpY [Mitogen-activated protein kinase 1]
Mpp [Mitogen-activated protein kinase 1]
MpT MAPKK [Dual specificity mitogen-activated protein kinase kinase 1; Mitogen-activated protein kinase 1]
M MAPKK Y [Dual specificity mitogen-activated protein kinase kinase 1; Mitogen-activated protein kinase 1]
MpY MKP Y [Dual specificity protein phosphatase 1-B; Mitogen-activated protein kinase 1]

Observables: none

BIOMD0000000026 @ v0.0.1

The model corresponds to the schemas 1 and 2 of Markevich et al 2004, as described in the figure 1 and the supplementary…

Mitogen-activated protein kinase (MAPK) cascades can operate as bistable switches residing in either of two different stable states. MAPK cascades are often embedded in positive feedback loops, which are considered to be a prerequisite for bistable behavior. Here we demonstrate that in the absence of any imposed feedback regulation, bistability and hysteresis can arise solely from a distributive kinetic mechanism of the two-site MAPK phosphorylation and dephosphorylation. Importantly, the reported kinetic properties of the kinase (MEK) and phosphatase (MKP3) of extracellular signal-regulated kinase (ERK) fulfill the essential requirements for generating a bistable switch at a single MAPK cascade level. Likewise, a cycle where multisite phosphorylations are performed by different kinases, but dephosphorylation reactions are catalyzed by the same phosphatase, can also exhibit bistability and hysteresis. Hence, bistability induced by multisite covalent modification may be a widespread mechanism of the control of protein activity. link: http://identifiers.org/pubmed/14744999

Parameters:

Name Description
k1 = 0.02; k_1 = 1.0 Reaction: M + MAPKK => M_MAPKK, Rate Law: uVol*(k1*M*MAPKK-k_1*M_MAPKK)
k2 = 0.01 Reaction: M_MAPKK => Mp + MAPKK, Rate Law: uVol*k2*M_MAPKK
k_3 = 1.0; k3 = 0.032 Reaction: Mp + MAPKK => Mp_MAPKK, Rate Law: uVol*(k3*Mp*MAPKK-k_3*Mp_MAPKK)
h_1 = 1.0; h1 = 0.045 Reaction: Mpp + MKP3 => Mpp_MKP3, Rate Law: uVol*(h1*Mpp*MKP3-h_1*Mpp_MKP3)
k4 = 15.0 Reaction: Mp_MAPKK => Mpp + MAPKK, Rate Law: uVol*k4*Mp_MAPKK
h2 = 0.092 Reaction: Mpp_MKP3 => Mp_MKP3_dep, Rate Law: uVol*h2*Mpp_MKP3
h6 = 0.086; h_6 = 0.0011 Reaction: M_MKP3 => M + MKP3, Rate Law: uVol*(h6*M_MKP3-h_6*M*MKP3)
h3 = 1.0; h_3 = 0.01 Reaction: Mp_MKP3_dep => Mp + MKP3, Rate Law: h3*Mp_MKP3_dep-h_3*Mp*MKP3
h5 = 0.5 Reaction: Mp_MKP3 => M_MKP3, Rate Law: uVol*h5*Mp_MKP3
h4 = 0.01; h_4 = 1.0 Reaction: Mp + MKP3 => Mp_MKP3, Rate Law: uVol*(h4*Mp*MKP3-h_4*Mp_MKP3)

States:

Name Description
MAPKK [Dual specificity mitogen-activated protein kinase kinase 1]
Mp MKP3 dep [Dual specificity protein phosphatase 1-B; Mitogen-activated protein kinase 1]
Mp MKP3 [Dual specificity protein phosphatase 1-B; Mitogen-activated protein kinase 1]
Mpp MKP3 [Dual specificity protein phosphatase 1-B; Mitogen-activated protein kinase 1]
Mp MAPKK [Dual specificity mitogen-activated protein kinase kinase 1; Mitogen-activated protein kinase 1]
M MAPKK [Mitogen-activated protein kinase 1; Dual specificity mitogen-activated protein kinase kinase 1]
M [Mitogen-activated protein kinase 1]
Mp [Mitogen-activated protein kinase 1]
M MKP3 [Dual specificity protein phosphatase 1-B; Mitogen-activated protein kinase 1]
Mpp [Mitogen-activated protein kinase 1]
MKP3 [Dual specificity protein phosphatase 1-B]

Observables: none

BIOMD0000000031 @ v0.0.1

The model describes the double phosphorylation of MAP kinase by an ordered mechanism using the Michaelis-Menten formalis…

Mitogen-activated protein kinase (MAPK) cascades can operate as bistable switches residing in either of two different stable states. MAPK cascades are often embedded in positive feedback loops, which are considered to be a prerequisite for bistable behavior. Here we demonstrate that in the absence of any imposed feedback regulation, bistability and hysteresis can arise solely from a distributive kinetic mechanism of the two-site MAPK phosphorylation and dephosphorylation. Importantly, the reported kinetic properties of the kinase (MEK) and phosphatase (MKP3) of extracellular signal-regulated kinase (ERK) fulfill the essential requirements for generating a bistable switch at a single MAPK cascade level. Likewise, a cycle where multisite phosphorylations are performed by different kinases, but dephosphorylation reactions are catalyzed by the same phosphatase, can also exhibit bistability and hysteresis. Hence, bistability induced by multisite covalent modification may be a widespread mechanism of the control of protein activity. link: http://identifiers.org/pubmed/14744999

Parameters:

Name Description
Km2 = 500.0; k2cat = 15.0 Reaction: Mp => Mpp; MAPKK2, M, Rate Law: uVol*k2cat*MAPKK2*Mp/Km2/(1+Mp/Km2)
k1cat = 0.01; Km1 = 50.0 Reaction: M => Mp; MAPKK1, Rate Law: uVol*k1cat*MAPKK1*M/Km1/(1+M/Km1)
k4cat = 0.06; Km5 = 78.0; Km3 = 5.0; Km4 = 18.0 Reaction: Mp => M; MKP3, Mpp, Rate Law: uVol*k4cat*MKP3*Mp/Km4/(1+Mpp/Km3+Mp/Km4+M/Km5)
Km5 = 78.0; Km3 = 5.0; Km4 = 18.0; k3cat = 0.084 Reaction: Mpp => Mp; MKP3, M, Rate Law: uVol*k3cat*MKP3*Mpp/Km3/(1+Mpp/Km3+Mp/Km4+M/Km5)

States:

Name Description
M [Mitogen-activated protein kinase 1]
Mp [Mitogen-activated protein kinase 1]
Mpp [Mitogen-activated protein kinase 1]

Observables: none

The model corresponds to the schema 3 of Markevich et al 2004, as described in the figure 2 and the supplementary table…

Mitogen-activated protein kinase (MAPK) cascades can operate as bistable switches residing in either of two different stable states. MAPK cascades are often embedded in positive feedback loops, which are considered to be a prerequisite for bistable behavior. Here we demonstrate that in the absence of any imposed feedback regulation, bistability and hysteresis can arise solely from a distributive kinetic mechanism of the two-site MAPK phosphorylation and dephosphorylation. Importantly, the reported kinetic properties of the kinase (MEK) and phosphatase (MKP3) of extracellular signal-regulated kinase (ERK) fulfill the essential requirements for generating a bistable switch at a single MAPK cascade level. Likewise, a cycle where multisite phosphorylations are performed by different kinases, but dephosphorylation reactions are catalyzed by the same phosphatase, can also exhibit bistability and hysteresis. Hence, bistability induced by multisite covalent modification may be a widespread mechanism of the control of protein activity. link: http://identifiers.org/pubmed/14744999

Parameters:

Name Description
h6 = 0.086; h_6 = 0.0011 Reaction: M_MKP3_T => M + MKP3, Rate Law: cell*(h6*M_MKP3_T-h_6*M*MKP3)
h9 = 0.14; h_9 = 0.0018 Reaction: M_MKP3_Y => M + MKP3, Rate Law: cell*(h9*M_MKP3_Y-h_9*M*MKP3)
k3 = 0.025; k_3 = 1.0 Reaction: MpY + MEK => MpY_MEK, Rate Law: cell*(k3*MpY*MEK-k_3*MpY_MEK)
h2 = 0.092 Reaction: Mpp_MKP3 => MpT_MKP3_Y, Rate Law: cell*h2*Mpp_MKP3
h_1 = 1.0; h1 = 0.045 Reaction: Mpp + MKP3 => Mpp_MKP3, Rate Law: cell*(h1*Mpp*MKP3-h_1*Mpp_MKP3)
h3 = 1.0; h_3 = 0.01 Reaction: MpT_MKP3_Y => MpT + MKP3, Rate Law: cell*(h3*MpT_MKP3_Y-h_3*MpT*MKP3)
k_5 = 1.0; k5 = 0.05 Reaction: M + MEK => M_MEK_T, Rate Law: cell*(k5*M*MEK-k_5*M_MEK_T)
h8 = 0.47 Reaction: MpY_MKP3 => M_MKP3_Y, Rate Law: cell*h8*MpY_MKP3
k8 = 0.45 Reaction: MpT_MEK => Mpp + MEK, Rate Law: cell*k8*MpT_MEK
k1 = 0.005; k_1 = 1.0 Reaction: M + MEK => M_MEK_Y, Rate Law: cell*(k1*M*MEK-k_1*M_MEK_Y)
k_7 = 1.0; k7 = 0.005 Reaction: MpT + MEK => MpT_MEK, Rate Law: cell*(k7*MpT*MEK-k_7*MpT_MEK)
k6 = 0.008 Reaction: M_MEK_T => MpT + MEK, Rate Law: cell*k6*M_MEK_T
k4 = 0.007 Reaction: MpY_MEK => Mpp + MEK, Rate Law: cell*k4*MpY_MEK
h7 = 0.01; h_7 = 1.0 Reaction: MpY + MKP3 => MpY_MKP3, Rate Law: cell*(h7*MpY*MKP3-h_7*MpY_MKP3)
h4 = 0.01; h_4 = 1.0 Reaction: MpT + MKP3 => MpT_MKP3_T, Rate Law: cell*(h4*MpT*MKP3-h_4*MpT_MKP3_T)
h5 = 0.5 Reaction: MpT_MKP3_T => M_MKP3_T, Rate Law: cell*h5*MpT_MKP3_T
k2 = 1.08 Reaction: M_MEK_Y => MpY + MEK, Rate Law: cell*k2*M_MEK_Y

States:

Name Description
MpT MKP3 Y [Mitogen-activated protein kinase 1; Dual specificity protein phosphatase 1-B]
M MEK Y [Mitogen-activated protein kinase 1; Dual specificity mitogen-activated protein kinase kinase 1]
Mpp MKP3 [Mitogen-activated protein kinase 1; Dual specificity protein phosphatase 1-B]
MEK [Dual specificity mitogen-activated protein kinase kinase 1]
MpY MKP3 [Mitogen-activated protein kinase 1; Dual specificity protein phosphatase 1-B]
M MEK T [Mitogen-activated protein kinase 1; Dual specificity mitogen-activated protein kinase kinase 1]
MpY MEK [Dual specificity mitogen-activated protein kinase kinase 1; Mitogen-activated protein kinase 1]
MpT [Mitogen-activated protein kinase 1]
MpT MKP3 T [Mitogen-activated protein kinase 1; Dual specificity protein phosphatase 1-B]
M MKP3 Y [Dual specificity protein phosphatase 1-B; Mitogen-activated protein kinase 1]
M MKP3 T [Mitogen-activated protein kinase 1; Dual specificity protein phosphatase 1-B]
M [Mitogen-activated protein kinase 1]
MpT MEK [Mitogen-activated protein kinase 1; Dual specificity mitogen-activated protein kinase kinase 1]
MpY [Mitogen-activated protein kinase 1]
Mpp [Mitogen-activated protein kinase 1]
MKP3 [Dual specificity protein phosphatase 1-B]

Observables: none

BIOMD0000000029 @ v0.0.1

The model corresponds to the schema 3 of Markevich et al 2004, as described in the figure 2 and the supplementary table…

Mitogen-activated protein kinase (MAPK) cascades can operate as bistable switches residing in either of two different stable states. MAPK cascades are often embedded in positive feedback loops, which are considered to be a prerequisite for bistable behavior. Here we demonstrate that in the absence of any imposed feedback regulation, bistability and hysteresis can arise solely from a distributive kinetic mechanism of the two-site MAPK phosphorylation and dephosphorylation. Importantly, the reported kinetic properties of the kinase (MEK) and phosphatase (MKP3) of extracellular signal-regulated kinase (ERK) fulfill the essential requirements for generating a bistable switch at a single MAPK cascade level. Likewise, a cycle where multisite phosphorylations are performed by different kinases, but dephosphorylation reactions are catalyzed by the same phosphatase, can also exhibit bistability and hysteresis. Hence, bistability induced by multisite covalent modification may be a widespread mechanism of the control of protein activity. link: http://identifiers.org/pubmed/14744999

Parameters:

Name Description
Km7 = 34.0; kcat5 = 0.084; Km6 = 18.0; Km8 = 40.0; Km5 = 22.0 Reaction: Mpp => MpT; MKP3, MpY, M, Rate Law: cell*kcat5*MKP3*Mpp/Km5/(1+Mpp/Km5+MpT/Km6+MpY/Km7+M/Km8)
kcat4 = 0.45; Km4 = 300.0; Km2 = 40.0; Km1 = 410.0; Km3 = 20.0 Reaction: MpT => Mpp; MEK, M, MpY, Rate Law: cell*kcat4*MEK*MpT/Km4/(1+M*(Km1+Km3)/(Km1*Km3)+MpY/Km2+MpT/Km4)
Km7 = 34.0; kcat7 = 0.108; Km6 = 18.0; Km8 = 40.0; Km5 = 22.0 Reaction: MpY => M; MKP3, Mpp, MpT, Rate Law: cell*kcat7*MKP3*MpY/Km7/(1+Mpp/Km5+MpT/Km6+MpY/Km7+M/Km8)
kcat3 = 0.008; Km4 = 300.0; Km2 = 40.0; Km3 = 20.0; Km1 = 410.0 Reaction: M => MpT; MEK, MpY, Rate Law: cell*kcat3*MEK*M/Km3/(1+M*(Km1+Km3)/(Km1*Km3)+MpY/Km2+MpT/Km4)
Km7 = 34.0; kcat6 = 0.06; Km6 = 18.0; Km8 = 40.0; Km5 = 22.0 Reaction: MpT => M; MKP3, Mpp, MpY, Rate Law: cell*kcat6*MKP3*MpT/Km6/(1+Mpp/Km5+MpT/Km6+MpY/Km7+M/Km8)
Km4 = 300.0; kcat1 = 1.08; Km2 = 40.0; Km1 = 410.0; Km3 = 20.0 Reaction: M => MpY; MEK, MpT, Rate Law: cell*kcat1*MEK*M/Km1/(1+M*(Km1+Km3)/(Km1*Km3)+MpY/Km2+MpT/Km4)
kcat2 = 0.007; Km4 = 300.0; Km2 = 40.0; Km1 = 410.0; Km3 = 20.0 Reaction: MpY => Mpp; MEK, M, MpT, Rate Law: cell*kcat2*MEK*MpY/Km2/(1+M*(Km1+Km3)/(Km1*Km3)+MpY/Km2+MpT/Km4)

States:

Name Description
M [Mitogen-activated protein kinase 1]
MpY [Mitogen-activated protein kinase 1]
Mpp [Mitogen-activated protein kinase 1]
MpT [Mitogen-activated protein kinase 1]

Observables: none

Martinez-Guimera2017 - Generic negative feedback circuit (Model 4)This model is described in the article: ['Molecular h…

The ability of reactive oxygen species (ROS) to cause molecular damage has meant that chronic oxidative stress has been mostly studied from the point of view of being a source of toxicity to the cell. However, the known duality of ROS molecules as both damaging agents and cellular redox signals implies another perspective in the study of sustained oxidative stress. This is a perspective of studying oxidative stress as a constitutive signal within the cell. In this work, we adopt a theoretical perspective as an exploratory and explanatory approach to examine how chronic oxidative stress can interfere with signal processing by redox signalling pathways in the cell. We report that constitutive signals can give rise to a 'molecular habituation' effect that can prime for a gradual loss of biological function. This is because a constitutive signal in the environment has the potential to reduce the responsiveness of a signalling pathway through the prolonged activation of negative regulators. Additionally, we demonstrate how this phenomenon is likely to occur in different signalling pathways exposed to persistent signals and furthermore at different levels of biological organisation. link: http://identifiers.org/pubmed/29146308

Parameters: none

States: none

Observables: none

Martinez-Guimera2017 - Generic negative feedforward circuit (Model 5)This model is described in the article: ['Molecula…

The ability of reactive oxygen species (ROS) to cause molecular damage has meant that chronic oxidative stress has been mostly studied from the point of view of being a source of toxicity to the cell. However, the known duality of ROS molecules as both damaging agents and cellular redox signals implies another perspective in the study of sustained oxidative stress. This is a perspective of studying oxidative stress as a constitutive signal within the cell. In this work, we adopt a theoretical perspective as an exploratory and explanatory approach to examine how chronic oxidative stress can interfere with signal processing by redox signalling pathways in the cell. We report that constitutive signals can give rise to a 'molecular habituation' effect that can prime for a gradual loss of biological function. This is because a constitutive signal in the environment has the potential to reduce the responsiveness of a signalling pathway through the prolonged activation of negative regulators. Additionally, we demonstrate how this phenomenon is likely to occur in different signalling pathways exposed to persistent signals and furthermore at different levels of biological organisation. link: http://identifiers.org/pubmed/29146308

Parameters: none

States: none

Observables: none

Martinez-Guimera2017 - Generic redox signalling model with negative feedback regulation (Model 2)This model is described…

The ability of reactive oxygen species (ROS) to cause molecular damage has meant that chronic oxidative stress has been mostly studied from the point of view of being a source of toxicity to the cell. However, the known duality of ROS molecules as both damaging agents and cellular redox signals implies another perspective in the study of sustained oxidative stress. This is a perspective of studying oxidative stress as a constitutive signal within the cell. In this work, we adopt a theoretical perspective as an exploratory and explanatory approach to examine how chronic oxidative stress can interfere with signal processing by redox signalling pathways in the cell. We report that constitutive signals can give rise to a 'molecular habituation' effect that can prime for a gradual loss of biological function. This is because a constitutive signal in the environment has the potential to reduce the responsiveness of a signalling pathway through the prolonged activation of negative regulators. Additionally, we demonstrate how this phenomenon is likely to occur in different signalling pathways exposed to persistent signals and furthermore at different levels of biological organisation. link: http://identifiers.org/pubmed/29146308

Parameters: none

States: none

Observables: none

Martinez-Guimera2017 - Generic redox signalling model with negative feedback regulation (Model 2)This model is described…

The ability of reactive oxygen species (ROS) to cause molecular damage has meant that chronic oxidative stress has been mostly studied from the point of view of being a source of toxicity to the cell. However, the known duality of ROS molecules as both damaging agents and cellular redox signals implies another perspective in the study of sustained oxidative stress. This is a perspective of studying oxidative stress as a constitutive signal within the cell. In this work, we adopt a theoretical perspective as an exploratory and explanatory approach to examine how chronic oxidative stress can interfere with signal processing by redox signalling pathways in the cell. We report that constitutive signals can give rise to a 'molecular habituation' effect that can prime for a gradual loss of biological function. This is because a constitutive signal in the environment has the potential to reduce the responsiveness of a signalling pathway through the prolonged activation of negative regulators. Additionally, we demonstrate how this phenomenon is likely to occur in different signalling pathways exposed to persistent signals and furthermore at different levels of biological organisation. link: http://identifiers.org/pubmed/29146308

Parameters: none

States: none

Observables: none

Martinez-Guimera2017 - Generic redox signalling model with negative feedforward regulation (Model 3)This model is descri…

The ability of reactive oxygen species (ROS) to cause molecular damage has meant that chronic oxidative stress has been mostly studied from the point of view of being a source of toxicity to the cell. However, the known duality of ROS molecules as both damaging agents and cellular redox signals implies another perspective in the study of sustained oxidative stress. This is a perspective of studying oxidative stress as a constitutive signal within the cell. In this work, we adopt a theoretical perspective as an exploratory and explanatory approach to examine how chronic oxidative stress can interfere with signal processing by redox signalling pathways in the cell. We report that constitutive signals can give rise to a 'molecular habituation' effect that can prime for a gradual loss of biological function. This is because a constitutive signal in the environment has the potential to reduce the responsiveness of a signalling pathway through the prolonged activation of negative regulators. Additionally, we demonstrate how this phenomenon is likely to occur in different signalling pathways exposed to persistent signals and furthermore at different levels of biological organisation. link: http://identifiers.org/pubmed/29146308

Parameters: none

States: none

Observables: none

Martinez-Guimera2017 - Generic redox signalling model without negative regulation (Model 1)This model is described in th…

The ability of reactive oxygen species (ROS) to cause molecular damage has meant that chronic oxidative stress has been mostly studied from the point of view of being a source of toxicity to the cell. However, the known duality of ROS molecules as both damaging agents and cellular redox signals implies another perspective in the study of sustained oxidative stress. This is a perspective of studying oxidative stress as a constitutive signal within the cell. In this work, we adopt a theoretical perspective as an exploratory and explanatory approach to examine how chronic oxidative stress can interfere with signal processing by redox signalling pathways in the cell. We report that constitutive signals can give rise to a 'molecular habituation' effect that can prime for a gradual loss of biological function. This is because a constitutive signal in the environment has the potential to reduce the responsiveness of a signalling pathway through the prolonged activation of negative regulators. Additionally, we demonstrate how this phenomenon is likely to occur in different signalling pathways exposed to persistent signals and furthermore at different levels of biological organisation. link: http://identifiers.org/pubmed/29146308

Parameters: none

States: none

Observables: none

Martinez-Sanchez2015 - T CD4+ lymphocyte transcriptional-signaling regulatory networkThis model is described in the arti…

CD4+ T cells orchestrate the adaptive immune response in vertebrates. While both experimental and modeling work has been conducted to understand the molecular genetic mechanisms involved in CD4+ T cell responses and fate attainment, the dynamic role of intrinsic (produced by CD4+ T lymphocytes) versus extrinsic (produced by other cells) components remains unclear, and the mechanistic and dynamic understanding of the plastic responses of these cells remains incomplete. In this work, we studied a regulatory network for the core transcription factors involved in CD4+ T cell-fate attainment. We first show that this core is not sufficient to recover common CD4+ T phenotypes. We thus postulate a minimal Boolean regulatory network model derived from a larger and more comprehensive network that is based on experimental data. The minimal network integrates transcriptional regulation, signaling pathways and the micro-environment. This network model recovers reported configurations of most of the characterized cell types (Th0, Th1, Th2, Th17, Tfh, Th9, iTreg, and Foxp3-independent T regulatory cells). This transcriptional-signaling regulatory network is robust and recovers mutant configurations that have been reported experimentally. Additionally, this model recovers many of the plasticity patterns documented for different T CD4+ cell types, as summarized in a cell-fate map. We tested the effects of various micro-environments and transient perturbations on such transitions among CD4+ T cell types. Interestingly, most cell-fate transitions were induced by transient activations, with the opposite behavior associated with transient inhibitions. Finally, we used a novel methodology was used to establish that T-bet, TGF-β and suppressors of cytokine signaling proteins are keys to recovering observed CD4+ T cell plastic responses. In conclusion, the observed CD4+ T cell-types and transition patterns emerge from the feedback between the intrinsic or intracellular regulatory core and the micro-environment. We discuss the broader use of this approach for other plastic systems and possible therapeutic interventions. link: http://identifiers.org/pubmed/26090929

Parameters: none

States: none

Observables: none

MODEL6624199343 @ v0.0.1

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

The kinetics of glyoxalase I [(R)-S-lactoylglutathione methylglyoxal-lyase; EC 4.4.1.5] and glyoxalase II (S-2-hydroxyacylglutathione hydrolase; EC 3.1.2.6) from Saccharomyces cerevisiae was studied in situ, in digitonin permeabilized cells, using two different approaches: initial rate analysis and progress curves analysis. Initial rate analysis was performed by hyperbolic regression of initial rates using the program HYPERFIT. Glyoxalase I exhibited saturation kinetics on 0.05-2.5 mM hemithioacetal concentration range, with kinetic parameters Km 0.53 +/- 0.07 mM and V (3.18 +/- 0.16) x 10(-2) mM.min(-1). Glyoxalase II also showed saturation kinetics in the SD-lactoylglutathione concentration range of 0.15-3 mM and Km 0.32 +/- 0.13 mM and V (1.03 +/- 0.10) x 10(-3) mM.min(-1) were obtained. The kinetic parameters of both enzymes were also estimated by nonlinear regression of progress curves using the raw absorbance data and integrated differential rate equations with the program GEPASI. Several optimization methods were used to minimize the sum of squares of residuals. The best parameter fit for the glyoxalase I reaction was obtained with a single curve analysis, using the irreversible Michaelis-Menten model. The kinetic parameters obtained, Km 0.62 +/- 0.18 mM and V (2.86 +/- 0.01) x 10(-2) mM.min(-1), were in agreement with those obtained by initial rate analysis. The results obtained for glyoxalase II, using either the irreversible Michaelis-Menten model or a phenomenological reversible hyperbolic model, showed a high correlation of residuals with time and/or high values of standard deviation associated with Km. The possible causes for the discrepancy between data obtained from initial rate analysis and progress curve analysis, for glyoxalase II, are discussed. link: http://identifiers.org/pubmed/11453985

Parameters: none

States: none

Observables: none

BIOMD0000000050 @ v0.0.1

This a model from the article: Kinetic modelling of Amadori N-(1-deoxy-D-fructos-1-yl)-glycine degradation pathways.…

A kinetic model for N-(1-deoxy-D-fructos-1-yl)-glycine (DFG) thermal decomposition was proposed. Two temperatures (100 and 120 degrees C) and two pHs (5.5 and 6.8) were studied. The measured responses were DFG, 3-deoxyosone, 1-deoxyosone, methylglyoxal, acetic acid, formic acid, glucose, fructose, mannose and melanoidins. For each system the model parameters, the rate constants, were estimated by non-linear regression, via multiresponse modelling. The determinant criterion was used as the statistical fit criterion. Model discrimination was performed by both chemical insight and statistical tests (Posterior Probability and Akaike criterion). Kinetic analysis showed that at lower pH DFG 1,2-enolization is favoured whereas with increasing pH 2,3-enolization becomes a more relevant degradation pathway. The lower amount observed of 1-DG is related with its high reactivity. It was shown that acetic acid, a main degradation product from DFG, was mainly formed through 1-DG degradation. Also from the estimated parameters 3-DG was found to be the main precursor in carbohydrate fragments formation, responsible for colour formation. Some indication was given that as the reaction proceeded other compounds besides DFG become reactants themselves with the formation among others of methylglyoxal. The multiresponse kinetic analysis was shown to be both helpful in deriving relevant kinetic parameters as well as in obtaining insight into the reaction mechanism. link: http://identifiers.org/pubmed/12873422

Parameters:

Name Description
k3=0.0155 Reaction: DFG => Gly + Cn, Rate Law: k3*DFG
k6=0.0274 Reaction: _3DG => FA, Rate Law: k6*_3DG
k13=0.0022 Reaction: Glu => _3DG, Rate Law: k13*Glu
k1=0.0057 Reaction: DFG => E1, Rate Law: k1*DFG
k10=0.0707 Reaction: E1 => Gly + Man, Rate Law: k10*E1
k9=1.9085 Reaction: _1DG => AA, Rate Law: k9*_1DG
k15=0.0159 Reaction: Cn => AA + FA + MG, Rate Law: k15*Cn
k16=0.0134 Reaction: E2 => Gly + Fru, Rate Law: k16*E2
k14=0.0034 Reaction: Gly + Cn => Mel, Rate Law: k14*Cn*Gly
k4=0.0794 Reaction: E1 => Gly + _3DG, Rate Law: k4*E1
k5=0.0907 Reaction: _3DG => Cn, Rate Law: k5*_3DG
k12=8.0E-4 Reaction: Man => Glu, Rate Law: k12*Man
k11=0.1131 Reaction: E1 => Gly + Glu, Rate Law: k11*E1
k2=0.0156 Reaction: DFG => E2, Rate Law: k2*DFG
k7=0.2125 Reaction: E2 => Gly + _1DG, Rate Law: k7*E2
k8=0.0 Reaction: _1DG => Cn, Rate Law: k8*_1DG

States:

Name Description
Gly [glycine; Glycine]
MG [methylglyoxal; Methylglyoxal]
E2 E2
Man [CHEBI_14575; D-Mannose]
FA [formic acid; Formate]
DFG DFG
Mel Mel
1DG _1DG
Cn [CHEBI_23008]
Glu [glucose; C00293]
AA [acetic acid; Acetate]
E1 E1
Fru [fructose; Fructose]
3DG _3DG

Observables: none

**A systems biology study of two distinct growth phases of *Saccharomyces cerevisiae* cultures ** AM Martins, D Camac…

Saccharomyces cerevisiae cultures growing exponentially and after starvation are distinctly different, as shown by several studies at the physiological, biochemical, and morphological levels. One group of studies attempted to be mechanistic, characterizing a few molecules and interactions, while another focused on global observations but remained descriptive or at best phenomenological. Recent advances in large-scale molecular profiling technologies, theoretical, and computational biology, are making possible integrative studies of biological systems, where global observations are explained through computational models with solid theoretical bases. A case study of the systems biology approach applied to the characterization of baker's yeast cultures in exponential growth and post-diauxic phases is presented.

Twenty cell cultures of S. cerevisiae were grown under similar environmental conditions. Samples from ten of these cultures were collected 11 hours after inoculation, while samples from the other ten were collected 4 days after inoculation. These samples were analyzed for their RNA and metabolite composition using Affymetrix chips and gas chromatography-mass spectrometry (GC-MS). The data were interpreted with statistical analyses and through the use of computer simulations of a kinetic model that was built by merging two independent models of glycolysis and glycerol biosynthesis. The simulation results agree with the exponential growth phase data, while no model is available for the post-diauxic phase. We discuss the need for expanding the number of kinetic models of S. cerevisiae, combining metabolism and genetic regulation, and covering all of its biochemistry. link: http://identifiers.org/doi/10.2174/1389202043348643

Parameters: none

States: none

Observables: none

Martins2013 - True and apparent inhibition of amyloid fribril formationThis model is described in the article: [True an…

A possible therapeutic strategy for amyloid diseases involves the use of small molecule compounds to inhibit protein assembly into insoluble aggregates. According to the recently proposed Crystallization-Like Model, the kinetics of amyloid fibrillization can be retarded by decreasing the frequency of new fibril formation or by decreasing the elongation rate of existing fibrils. To the compounds that affect the nucleation and/or the growth steps we call true inhibitors. An apparent inhibition mechanism may however result from the alteration of thermodynamic properties such as the solubility of the amyloidogenic protein. Apparent inhibitors markedly influence protein aggregation kinetics measured in vitro, yet they are likely to lead to disappointing results when tested in vivo. This is because cells and tissues media are in general much more buffered against small variations in composition than the solutions prepared in lab. Here we show how to discriminate between true and apparent inhibition mechanisms from experimental data on protein aggregation kinetics. The goal is to be able to identify false positives much earlier during the drug development process. link: http://identifiers.org/pubmed/23232498

Parameters:

Name Description
deltamt = 1.0; ka = 0.5; kb = 0.001 Reaction: Amyloid = (1-1/(kb*(exp(ka*time)-1)+1))*deltamt, Rate Law: missing

States:

Name Description
Amyloid [amyloid fibril]

Observables: none

MartínJiménez2017 - Genome-scale reconstruction of the human astrocyte metabolic networkThis model is described in the a…

Astrocytes are the most abundant cells of the central nervous system; they have a predominant role in maintaining brain metabolism. In this sense, abnormal metabolic states have been found in different neuropathological diseases. Determination of metabolic states of astrocytes is difficult to model using current experimental approaches given the high number of reactions and metabolites present. Thus, genome-scale metabolic networks derived from transcriptomic data can be used as a framework to elucidate how astrocytes modulate human brain metabolic states during normal conditions and in neurodegenerative diseases. We performed a Genome-Scale Reconstruction of the Human Astrocyte Metabolic Network with the purpose of elucidating a significant portion of the metabolic map of the astrocyte. This is the first global high-quality, manually curated metabolic reconstruction network of a human astrocyte. It includes 5,007 metabolites and 5,659 reactions distributed among 8 cell compartments, (extracellular, cytoplasm, mitochondria, endoplasmic reticle, Golgi apparatus, lysosome, peroxisome and nucleus). Using the reconstructed network, the metabolic capabilities of human astrocytes were calculated and compared both in normal and ischemic conditions. We identified reactions activated in these two states, which can be useful for understanding the astrocytic pathways that are affected during brain disease. Additionally, we also showed that the obtained flux distributions in the model, are in accordance with literature-based findings. Up to date, this is the most complete representation of the human astrocyte in terms of inclusion of genes, proteins, reactions and metabolic pathways, being a useful guide for in-silico analysis of several metabolic behaviors of the astrocyte during normal and pathologic states. link: http://identifiers.org/pubmed/28243200

Parameters: none

States: none

Observables: none

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

Mutants of Physarum polycephalum can be complemented by fusion of plasmodial cells followed by cytoplasmic mixing. Complementation between strains carrying different mutational defects in the sporulation control network may depend on the signaling state of the network components. We have previously suggested that time-resolved somatic complementation (TRSC) analysis with such mutants may be used to probe network architecture and dynamics. By computer simulation it is now shown how and under which conditions the regulatory hierarchy of genes can be determined experimentally. A kinetic model of the sporulation control network is developed, which is then used to demonstrate how the mechanisms of TRSC can be understood and simulated at the kinetic level. On the basis of theoretical considerations, experimental parameters that determine whether functional complementation of two mutations will occur are identified. It is also shown how gene dosage-effect relationships can be employed for network analysis. The theoretical framework provided may be used to systematically analyze network structure and dynamics through time-resolved somatic complementation studies. The conclusions drawn are of general relevance in that they do not depend on the validity of the model from which they were derived. link: http://identifiers.org/pubmed/12750324

Parameters:

Name Description
kG=0.1 Reaction: Ya + Gluc => Yi, Rate Law: kG*Ya*Gluc*compartment
alpha2=50.0 Reaction: => V; S, Rate Law: compartment*alpha2/(1+S^3)
kd=0.1 Reaction: Pr => Pi, Rate Law: compartment*kd*Pr
IfrSfrPfr=0.1 Reaction: Pfr => Pr, Rate Law: compartment*Pfr*IfrSfrPfr
IrSrPr=0.0 Reaction: Pr => Pfr, Rate Law: IrSrPr*Pr*compartment
kai=0.8 Reaction: Xa => Xi, Rate Law: kai*Xa*compartment
ky=1.0 Reaction: preS => S; Ya, Rate Law: preS*ky*Ya*compartment
alpha1=30.0 Reaction: => S; V, Rate Law: compartment*alpha1/(1+V^3)
kd_v=1.0 Reaction: V =>, Rate Law: compartment*V*kd_v
kia=0.1 Reaction: Xi => Xa; Pr, Rate Law: Xi*kia*Pr*compartment
kd_s=1.0 Reaction: S =>, Rate Law: kd_s*S*compartment
kx=0.2 Reaction: prepreS => preS; Xa, Rate Law: prepreS*kx*Xa*compartment

States:

Name Description
prepreS prepreS
Xi Xi
Ya Ya
Yi Yi
V V
Pfr [bilin; IPR001294]
Xa Xa
preS preS
Pr [bilin; IPR001294]
S S
Pi Pi
Gluc [glucose; C00293]

Observables: none

Masel2000 - Drugs to stop prion aggregates and other amyloidsEncoded non-curated model. Issues:  - Missing initial conc…

Amyloid protein aggregates are implicated in many neurodegenerative diseases, including Alzheimer's disease and the prion diseases. Therapeutics to block amyloid formation are often tested in vitro, but it is not clear how to extrapolate from these experiments to a clinical setting, where the effective drug dose may be much lower. Here we address this question using a theoretical kinetic model to calculate the growth rate of protein aggregates as a function of the dose of each of three categories of drug. We find that therapeutics which block the growing ends of amyloids are the most promising, as alternative strategies may be ineffective or even accelerate amyloid formation at low drug concentrations. Our mathematical model can be used to identify and optimise an end-blocking drug in vitro. Our model also suggests an alternative explanation for data previously thought to prove the existence of an entity known as protein X. link: http://identifiers.org/pubmed/11152275

Parameters: none

States: none

Observables: none

This a model from the article: Role of individual ionic current systems in ventricular cells hypothesized by a model s…

Individual ion channels or exchangers are described with a common set of equations for both the sinoatrial node pacemaker and ventricular cells. New experimental data are included, such as the new kinetics of the inward rectifier K+ channel, delayed rectifier K+ channel, and sustained inward current. The gating model of Shirokov et al. (J Gen Physiol 102: 1005-1030, 1993) is used for both the fast Na+ and L-type Ca2+ channels. When combined with a contraction model (Negroni and Lascano: J Mol Cell Cardiol 28: 915-929, 1996), the experimental staircase phenomenon of contraction is reconstructed. The modulation of the action potential by varying the external Ca2+ and K+ concentrations is well simulated. The conductance of I(CaL) dominates membrane conductance during the action potential so that an artificial increase of I(to), I(Kr), I(Ks), or I(KATP) magnifies I(CaL) amplitude. Repolarizing current is provided sequentially by I(Ks), I(Kr), and I(K1). Depression of ATP production results in the shortening of action potential through the activation of I(KATP). The ratio of Ca2+ released from SR over Ca2+ entering via I(CaL) (Ca2+ gain = approximately 15) in excitation-contraction coupling well agrees with the experimental data. The model serves as a predictive tool in generating testable hypotheses. link: http://identifiers.org/pubmed/12877767

Parameters: none

States: none

Observables: none

BIOMD0000000085 @ v0.0.1

This model is according to the paper Reduced-order modeling of biochemical networks: application to the GTPase-cycle sig…

Biochemical systems embed complex networks and hence development and analysis of their detailed models pose a challenge for computation. Coarse-grained biochemical models, called reduced-order models (ROMs), consisting of essential biochemical mechanisms are more useful for computational analysis and for studying important features of a biochemical network. The authors present a novel method to model-reduction by identifying potentially important parameters using multidimensional sensitivity analysis. A ROM is generated for the GTPase-cycle module of m1 muscarinic acetylcholine receptor, Gq, and regulator of G-protein signalling 4 (a GTPase-activating protein or GAP) starting from a detailed model of 48 reactions. The resulting ROM has only 17 reactions. The ROM suggested that complexes of G-protein coupled receptor (GPCR) and GAP–which were proposed in the detailed model as a hypothesis–are required to fit the experimental data. Models previously published in the literature are also simulated and compared with the ROM. Through this comparison, a minimal ROM, that also requires complexes of GPCR and GAP, with just 15 parameters is generated. The proposed reduced-order modelling methodology is scalable to larger networks and provides a general framework for the reduction of models of biochemical systems. link: http://identifiers.org/pubmed/16986265

Parameters:

Name Description
k2=0.00297; k1=25.0 Reaction: species_15 => species_16 + species_7, Rate Law: compartment_0*(k1*species_15-k2*species_16*species_7)
k2=1.28; k1=1.32E8 Reaction: species_5 + species_4 => species_10, Rate Law: compartment_0*(k1*species_5*species_4-k2*species_10)
k1=3.96E9; k2=5.43E-5 Reaction: species_2 + species_4 => species_14, Rate Law: compartment_0*(k1*species_2*species_4-k2*species_14)
k1=853000.0; k2=0.00468 Reaction: species_9 + species_3 => species_10, Rate Law: compartment_0*(k1*species_9*species_3-k2*species_10)
k2=2.22E-9; k1=0.013 Reaction: species_10 => species_13 + species_7, Rate Law: compartment_0*(k1*species_10-k2*species_13*species_7)
k2=9.03E-7; k1=0.013 Reaction: species_5 => species_6 + species_7, Rate Law: compartment_0*(k1*species_5-k2*species_6*species_7)
k1=386000.0; k2=0.0408 Reaction: species_5 + species_0 => species_11, Rate Law: compartment_0*(k1*species_5*species_0-k2*species_11)
k2=0.478; k1=6300000.0 Reaction: species_10 + species_0 => species_15, Rate Law: compartment_0*(k1*species_10*species_0-k2*species_15)
k1=2.0; k2=1470000.0 Reaction: species_13 => species_9 + species_8, Rate Law: compartment_0*(k1*species_13-k2*species_9*species_8)
k2=2940.0; k1=2.75 Reaction: species_16 => species_14 + species_8, Rate Law: compartment_0*(k1*species_16-k2*species_14*species_8)
k2=8.38E-6; k1=529000.0 Reaction: species_1 + species_3 => species_5, Rate Law: compartment_0*(k1*species_1*species_3-k2*species_5)
k1=1.0E-4; k2=3.83 Reaction: species_12 => species_2 + species_8, Rate Law: compartment_0*(k1*species_12-k2*species_2*species_8)
k1=64100.0; k2=0.95 Reaction: species_6 + species_0 => species_12, Rate Law: compartment_0*(k1*species_6*species_0-k2*species_12)
k1=1620000.0; k2=0.00875 Reaction: species_14 + species_3 => species_15, Rate Law: compartment_0*(k1*species_14*species_3-k2*species_15)
k1=9.47E7; k2=0.00227 Reaction: species_6 + species_4 => species_13, Rate Law: compartment_0*(k1*species_6*species_4-k2*species_13)
k2=62.3; k1=1.0E-4 Reaction: species_6 => species_1 + species_8, Rate Law: compartment_0*(k1*species_6-k2*species_1*species_8)
k1=25.0; k2=0.244 Reaction: species_11 => species_12 + species_7, Rate Law: compartment_0*(k1*species_11-k2*species_12*species_7)

States:

Name Description
species 9 [heterotrimeric G-protein complex; receptor complex]
species 2 [IPR000342; heterotrimeric G-protein complex]
species 6 [GDP; heterotrimeric G-protein complex]
species 10 [GTP; heterotrimeric G-protein complex; receptor complex]
species 11 [GTP; IPR000342; heterotrimeric G-protein complex]
species 1 [heterotrimeric G-protein complex]
species 4 [receptor complex; IPR000337]
species 16 [GDP; IPR000342; heterotrimeric G-protein complex; receptor complex]
species 14 [IPR000342; heterotrimeric G-protein complex; receptor complex]
species 3 [GTP; GTP]
species 0 [IPR000342]
species 8 [GDP; GDP]
species 12 [GDP; IPR000342; heterotrimeric G-protein complex]
species 7 [phosphate(3-)]
species 5 [GTP; heterotrimeric G-protein complex]
species 15 [GTP; IPR000342; heterotrimeric G-protein complex; receptor complex]
species 13 [GDP; heterotrimeric G-protein complex; receptor complex]

Observables: none

BIOMD0000000167 @ v0.0.1

The model reproduces Fig 2B of the paper. Model successfully reproduced using MathSBML. To the extent possible under la…

Signal transducer and actuator of transcription (STATs) are a family of transcription factors activated by various cytokines, growth factors and hormones. They are important mediators of immune responses and growth and differentiation of various cell types. The STAT signalling system represents a defined functional module with a pattern of signalling that is conserved from flies to mammals. In order to probe and gain insights into the signalling properties of the STAT module by computational means, we developed a simple non-linear ordinary differential equations model within the 'Virtual Cell' framework. Our results demonstrate that the STAT module can operate as a 'biphasic amplitude filter' with an ability to amplify input signals within a specific intermediate range. We show that dimerisation of phosphorylated STAT is crucial for signal amplification and the amplitude filtering function. We also demonstrate that maximal amplification at intermediate levels of STAT activation is a moderately robust property of STAT module. We propose that these observations can be extrapolated to the analogous SMAD signalling module. link: http://identifiers.org/pubmed/17091582

Parameters:

Name Description
stat_expMax=-0.06 μmol*l^(-1)*μm^(-2)*s^(-1); Ks_exp=0.6 μmol*l^(-1) Reaction: stat_sol => stat_nuc, Rate Law: nuc*stat_expMax*stat_nuc*1/(Ks_exp+stat_nuc)*nm
stat_impMax=0.003 μmol*l^(-1)*μm^(-2)*s^(-1); Ks_imp=3.0 μmol*l^(-1) Reaction: stat_sol => stat_nuc, Rate Law: nuc*stat_impMax*stat_sol*1/(Ks_imp+stat_sol)*nm
Kf_PstatDimerisation=0.6 μmol^(-1)*l*s^(-1); Kr_PstatDimerisation=0.03 s^(-1) Reaction: Pstat_sol => PstatDimer_sol, Rate Law: (Kf_PstatDimerisation*Pstat_sol^2+(-Kr_PstatDimerisation*PstatDimer_sol))*sol
PstatDimer_impMax=0.045 μmol*l^(-1)*μm^(-2)*s^(-1); Kpsd_imp=0.3 μmol*l^(-1) Reaction: PstatDimer_sol => PstatDimer_nuc, Rate Law: PstatDimer_impMax*PstatDimer_sol*1/(Kpsd_imp+PstatDimer_sol)*nm
Kcat_phos=1.0 s^(-1); Km_phos=4.0 μmol*l^(-1) Reaction: stat_sol => Pstat_sol + species_test; statKinase_sol, Rate Law: Kcat_phos*statKinase_sol*stat_sol*1/(Km_phos+stat_sol)*sol
Km_dephos=2.0 μmol*l^(-1); Kcat_dephos=1.0 s^(-1) Reaction: Pstat_nuc => stat_nuc; statPhosphatase_nuc, Rate Law: Kcat_dephos*statPhosphatase_nuc*Pstat_nuc*1/(Km_dephos+Pstat_nuc)*nuc

States:

Name Description
Pstat sol [Signal transducer and activator of transcription 1-alpha/beta]
PstatDimer nuc [Signal transducer and activator of transcription 1-alpha/beta]
statKinase sol statKinase_sol
species test species_test
PstatDimer sol [Signal transducer and activator of transcription 1-alpha/beta]
Pstat nuc [Signal transducer and activator of transcription 1-alpha/beta]
stat sol [Signal transducer and activator of transcription 1-alpha/beta]
stat nuc [Signal transducer and activator of transcription 1-alpha/beta]

Observables: none

MODEL2006300001 @ v0.0.1

<notes xmlns="http://www.sbml.org/sbml/level2/version4"> <body xmlns="http://www.w3.org/1…

The phosphatidylinositol (PI) cycle is central to eukaryotic cell signaling. Its complexity, due to the number of reactions and lipid and inositol phosphate intermediates involved makes it difficult to analyze experimentally. Computational modelling approaches are seen as a way forward to elucidate complex biological regulatory mechanisms when this cannot be achieved solely through experimental approaches. Whilst mathematical modelling is well established in informing biological systems, many models are often informed by data sourced from different cell types (mosaic data), to inform model parameters. For instance, kinetic rate constants are often determined from purified enzyme data in vitro or use experimental concentrations obtained from multiple unrelated cell types. Thus they do not represent any specific cell type nor fully capture cell specific behaviours. In this work, we develop a model of the PI cycle informed by in-vivo omics data taken from a single cell type, namely platelets. Our model recapitulates the known experimental dynamics before and after stimulation with different agonists and demonstrates the importance of lipid- and protein-binding proteins in regulating second messenger outputs. Furthermore, we were able to make a number of predictions regarding the regulation of PI cycle enzymes and the importance of the number of receptors required for successful GPCR signaling. We then consider how pathway behavior differs, when fully informed by data for HeLa cells and show that model predictions remain relatively consistent. However, when informed by mosaic experimental data model predictions greatly vary. Our work illustrates the risks of using mosaic datasets from unrelated cell types which leads to over 75% of outputs not fitting with expected behaviors. link:

Parameters: none

States: none

Observables: none

Mazumdar2008 - Genome-scale metabolic network of Porphyromonas gingivalis (iVM679)This model is described in the article…

The microbial community present in the human mouth is engaged in a complex network of diverse metabolic activities. In addition to serving as energy and building-block sources, metabolites are key players in interspecies and host-pathogen interactions. Metabolites are also implicated in triggering the local inflammatory response, which can affect systemic conditions such as atherosclerosis, obesity, and diabetes. While the genome of several oral pathogens has been sequenced, quantitative understanding of the metabolic functions of any oral pathogen at the system level has not been explored yet. Here we pursue the computational construction and analysis of the genome-scale metabolic network of Porphyromonas gingivalis, a gram-negative anaerobe that is endemic in the human population and largely responsible for adult periodontitis. Integrating information from the genome, online databases, and literature screening, we built a stoichiometric model that encompasses 679 metabolic reactions. By using flux balance approaches and automated network visualization, we analyze the growth capacity under amino-acid-rich medium and provide evidence that amino acid preference and cytotoxic by-product secretion rates are suitably reproduced by the model. To provide further insight into the basic metabolic functions of P. gingivalis and suggest potential drug targets, we study systematically how the network responds to any reaction knockout. We focus specifically on the lipopolysaccharide biosynthesis pathway and identify eight putative targets, one of which has been recently verified experimentally. The current model, which is amenable to further experimental testing and refinements, could prove useful in evaluating the oral microbiome dynamics and in the development of novel biomedical applications. link: http://identifiers.org/pubmed/18931137

Parameters: none

States: none

Observables: none

Mbodj2016 - Mesoderm specification during Drosophila developmentThis model is described in the article: [Qualitative Dy…

Given the complexity of developmental networks, it is often difficult to predict the effect of genetic perturbations, even within coding genes. Regulatory factors generally have pleiotropic effects, exhibit partially redundant roles, and regulate highly interconnected pathways with ample cross-talk. Here, we delineate a logical model encompassing 48 components and 82 regulatory interactions involved in mesoderm specification during Drosophila development, thereby providing a formal integration of all available genetic information from the literature. The four main tissues derived from mesoderm correspond to alternative stable states. We demonstrate that the model can predict known mutant phenotypes and use it to systematically predict the effects of over 300 new, often non-intuitive, loss- and gain-of-function mutations, and combinations thereof. We further validated several novel predictions experimentally, thereby demonstrating the robustness of model. Logical modelling can thus contribute to formally explain and predict regulatory outcomes underlying cell fate decisions. link: http://identifiers.org/pubmed/27599298

Parameters: none

States: none

Observables: none

MODEL9808533471 @ v0.0.1

This a model from the article: Reconstruction of the electrical activity of cardiac Purkinje fibres. McAllister RE,…

  1. The electrical activity of Cardiac Purkinje fibres was reconstructed using a mathematical model of the membrane current. The individual components of ionic curent were described by equations which wee based as closely as possible on previous experiments using the voltage clamp technique. 2. Membrane action potentials and pace-maker activity were calculated and compared with time course of underlying changes in two functionally distinct outeard currents, iX1 and iK2. 3. The repolarization of the theoretical action potential is triggered by the onset of iX1, which becomes activated over the plateau range of potentials. iK2 also activates during the plateau but does not play a controlling role in the repolarization. Hwever, iK2 does govern the slow pace-maker depolarization through its subsequent deactivation at negative potentials. 4. The individual phases of the calculated action potential and their 'experimental' modifications were compared with published records. The upstroke is generated by a Hodgkin-Huxley type sodium conductance (gNa), and rises with a maximum rate of 478 V/sec, somewhat less than experimentally observed values ( up to 800 V/sec). The discrepancy is discussed in relation to experimental attempts at measuring gNa. 5. The ole of the transient outward chloride current (called igr) was studied in calculations of the rapid phase of repolarization and 'notch' configuration...

link: http://identifiers.org/pubmed/1185607

Parameters: none

States: none

Observables: none

McAuley2012 - Whole-body Cholesterol MetabolismLipid metabolism has a key role to play in human longevity and healthy ag…

BACKGROUND: Global demographic changes have stimulated marked interest in the process of aging. There has been, and will continue to be, an unrelenting rise in the number of the oldest old ( >85 years of age). Together with an ageing population there comes an increase in the prevalence of age related disease. Of the diseases of ageing, cardiovascular disease (CVD) has by far the highest prevalence. It is regarded that a finely tuned lipid profile may help to prevent CVD as there is a long established relationship between alterations to lipid metabolism and CVD risk. In fact elevated plasma cholesterol, particularly Low Density Lipoprotein Cholesterol (LDL-C) has consistently stood out as a risk factor for having a cardiovascular event. Moreover it is widely acknowledged that LDL-C may rise with age in both sexes in a wide variety of groups. The aim of this work was to use a whole-body mathematical model to investigate why LDL-C rises with age, and to test the hypothesis that mechanistic changes to cholesterol absorption and LDL-C removal from the plasma are responsible for the rise. The whole-body mechanistic nature of the model differs from previous models of cholesterol metabolism which have either focused on intracellular cholesterol homeostasis or have concentrated on an isolated area of lipoprotein dynamics. The model integrates both current and previously published data relating to molecular biology, physiology, ageing and nutrition in an integrated fashion. RESULTS: The model was used to test the hypothesis that alterations to the rate of cholesterol absorption and changes to the rate of removal of LDL-C from the plasma are integral to understanding why LDL-C rises with age. The model demonstrates that increasing the rate of intestinal cholesterol absorption from 50% to 80% by age 65 years can result in an increase of LDL-C by as much as 34 mg/dL in a hypothetical male subject. The model also shows that decreasing the rate of hepatic clearance of LDL-C gradually to 50% by age 65 years can result in an increase of LDL-C by as much as 116 mg/dL. CONCLUSIONS: Our model clearly demonstrates that of the two putative mechanisms that have been implicated in the dysregulation of cholesterol metabolism with age, alterations to the removal rate of plasma LDL-C has the most significant impact on cholesterol metabolism and small changes to the number of hepatic LDL receptors can result in a significant rise in LDL-C. This first whole-body systems based model of cholesterol balance could potentially be used as a tool to further improve our understanding of whole-body cholesterol metabolism and its dysregulation with age. Furthermore, given further fine tuning the model may help to investigate potential dietary and lifestyle regimes that have the potential to mitigate the effects aging has on cholesterol metabolism. link: http://identifiers.org/pubmed/23046614

Parameters:

Name Description
k18=0.068 Reaction: species_23 => species_7; species_18, species_23, species_18, Rate Law: k18*species_23*species_18
khrs=100.0 Reaction: species_19 => species_18; species_19, species_7, species_19, species_7, Rate Law: khrs*species_19/species_7
k17=0.38 Reaction: species_21 => species_23; species_24, species_21, species_24, Rate Law: k17*species_21*species_24
k5=2.66 Reaction: species_7 => species_4; species_4, species_7, species_4, Rate Law: k5*species_7/species_4
kprs=100.0 Reaction: species_26 => species_25; species_11, species_26, species_11, Rate Law: kprs*species_26/species_11
k1=5.0E-6 Reaction: species_23 => species_11; species_23, Rate Law: k1*species_23
k1=6.0 Reaction: species_4 => species_5; species_4, Rate Law: k1*species_4
k8=5.0E-4 Reaction: species_9 => species_10; species_11, species_11, Rate Law: k8*species_11
k1=0.016 Reaction: species_7 => species_17; species_7, Rate Law: k1*species_7
k1=0.01 Reaction: species_18 => species_20; species_18, Rate Law: k1*species_18
k6=5.286E-4 Reaction: species_2 => species_7; species_5, species_2, species_5, Rate Law: k6*species_2*species_5
k9=1.0 Reaction: species_7 => species_13; species_14, species_7, species_14, species_7, Rate Law: k9*species_14*species_7
k15=0.43 Reaction: species_17 => species_21; species_17, species_22, species_17, species_22, Rate Law: k15*species_17*species_22
k26=1.5E-5 Reaction: species_11 + species_10 => species_30; species_31, species_11, species_10, species_31, Rate Law: k26*species_11*species_10*species_31
k27=0.01 Reaction: species_30 => species_17; species_33, species_30, species_33, Rate Law: k27*species_30*species_33
BS=5.0; BCRt=55326.0; BCRmax=2000.0 Reaction: species_7 => species_2; species_7, species_7, Rate Law: BCRmax/(1+(BCRt/species_7)^BS)
k29=0.05 Reaction: species_30 => species_7; species_34, species_30, species_34, Rate Law: k29*species_30*species_34
HCSt=93925.0; HCSmax=500.0; HS=5.0 Reaction: species_12 => species_7; species_7, species_7, Rate Law: HCSmax/(1+(species_7/HCSt)^HS)
k1=1.0 Reaction: species_1 => species_2; species_1, Rate Law: k1*species_1
k1=5.0E-4 Reaction: species_11 => species_29; species_11, Rate Law: k1*species_11
k1=0.054 Reaction: species_21 => species_7; species_21, Rate Law: k1*species_21
k28=0.001 Reaction: species_30 => species_23; species_33, species_30, species_33, Rate Law: k28*species_30*species_33
k1=4.29 Reaction: species_5 => species_4; species_5, Rate Law: k1*species_5
k10=5.998 Reaction: species_13 => species_7; species_15, species_13, species_15, species_13, Rate Law: k10*species_15*species_13
k1=0.0496 Reaction: species_17 => species_7; species_17, Rate Law: k1*species_17
k1=0.005 Reaction: species_23 => species_7; species_23, Rate Law: k1*species_23
k24=0.1068 Reaction: species_28 => species_11; species_15, species_15, species_28, Rate Law: k24*species_15*species_28
ICSmax=100.0; IS=5.0; ICt=3120.0 Reaction: species_3 => species_2; species_2, species_2, Rate Law: ICSmax/(1+(species_2/ICt)^IS)
k1=0.856 Reaction: species_5 => species_6; species_5, Rate Law: k1*species_5
k11=0.005 Reaction: species_16 => species_10; species_11, species_11, Rate Law: k11*species_11
k20=0.00675 Reaction: species_23 => species_11; species_25, species_25, species_23, Rate Law: k20*species_25*species_23
k7=5.286E-4 Reaction: species_2 => species_8; species_5, species_2, species_5, Rate Law: k7*species_2*species_5
k23=0.017386 Reaction: species_11 => species_28; species_14, species_14, species_11, Rate Law: k23*species_14*species_11
PCSS=5.0; PPCt=80342.0; PCSmax=500.0 Reaction: species_32 => species_11; species_11, Rate Law: PCSmax/(1+(species_11/PPCt)^PCSS)

States:

Name Description
species 9 INHDLS
species 27 PLDLRD
species 1 [cholesterol]
species 18 [Low-density lipoprotein receptor]
species 4 [bile salt]
species 16 HNHDLS
species 20 HLDLRD
species 28 [cholesteryl ester]
species 25 [Low-density lipoprotein receptor]
species 21 [Low-density lipoprotein receptor]
species 17 [Very low-density lipoprotein receptor]
species 29 PSS
species 30 [Vigilin]
species 5 [bile salt]
species 8 [cholesterol]
species 32 PCS
species 12 HCS
species 2 [cholesterol]
species 6 [bile salt]
species 19 HLDLRsS
species 10 [Vigilin]
species 11 [cholesterol]
species 3 ICS
species 23 [Low-density lipoprotein receptor]
species 7 [cholesterol]
species 26 PLDLRsS
species 13 [cholesteryl ester]

Observables: none

BIOMD0000000116 @ v0.0.1

This model encoded according to the paper *Cross-talk and decision making in MAP kinase pathways.* Supplementary Figure…

Cells must respond specifically to different environmental stimuli in order to survive. The signal transduction pathways involved in sensing these stimuli often share the same or homologous proteins. Despite potential cross-wiring, cells show specificity of response. We show, through modeling, that the physiological response of such pathways exposed to simultaneous and temporally ordered inputs can demonstrate system-level mechanisms by which pathways achieve specificity. We apply these results to the hyperosmolar and pheromone mitogen-activated protein (MAP) kinase pathways in the yeast Saccharomyces cerevisiae. These two pathways specifically sense osmolar and pheromone signals, despite sharing a MAPKKK, Ste11, and having homologous MAPKs (Fus3 and Hog1). We show that in a single cell, the pathways are bistable over a range of inputs, and the cell responds to only one stimulus even when exposed to both. Our results imply that these pathways achieve specificity by filtering out spurious cross-talk through mutual inhibition. The variability between cells allows for heterogeneity of the decisions. link: http://identifiers.org/pubmed/17259986

Parameters:

Name Description
parameter_0 = 10.0; parameter_7 = 8.5; parameter_8 = 1.0; parameter_6 = 1.0 Reaction: => species_0, Rate Law: compartment_0*parameter_6*parameter_7/(1+parameter_7/parameter_8)*(parameter_0-species_0)
parameter_10 = 1.0; parameter_2 = 10.0 Reaction: species_1 => species_2, Rate Law: compartment_0*parameter_10*species_1*(parameter_2-species_2)
parameter_12 = 0.0; parameter_4 = 10.0 Reaction: species_0 => species_4, Rate Law: compartment_0*parameter_12*species_0*(parameter_4-species_4)
parameter_9 = 1.0; parameter_1 = 10.0 Reaction: species_0 => species_1, Rate Law: compartment_0*parameter_9*species_0*(parameter_1-species_1)
parameter_16 = 1.0; parameter_14 = 5.0; parameter_3 = 10.0; parameter_15 = 1.0 Reaction: => species_3, Rate Law: compartment_0*parameter_15*parameter_14/(1+parameter_14/parameter_16)*(parameter_3-species_3)
parameter_4 = 10.0; parameter_17 = 1.0 Reaction: species_3 => species_4, Rate Law: compartment_0*parameter_17*species_3*(parameter_4-species_4)
parameter_18 = 1.0; parameter_5 = 10.0 Reaction: species_4 => species_5, Rate Law: compartment_0*parameter_18*species_4*(parameter_5-species_5)
parameter_19 = 1.0; parameter_11 = 1.0 Reaction: species_5 => ; species_2, Rate Law: compartment_0*parameter_11*species_5*species_2/(1+species_5/parameter_19)
parameter_12 = 0.0; parameter_1 = 10.0 Reaction: species_3 => species_1, Rate Law: compartment_0*parameter_12*species_3*(parameter_1-species_1)
parameter_13 = 1.0; parameter_11 = 1.0 Reaction: species_2 => ; species_5, Rate Law: compartment_0*parameter_11*species_5*species_2/(1+species_2/parameter_13)

States:

Name Description
species 2 [Mitogen-activated protein kinase FUS3]
species 3 [Serine/threonine-protein kinase STE11]
species 0 [Serine/threonine-protein kinase STE11]
species 1 [Serine/threonine-protein kinase STE7]
species 4 [MAP kinase kinase PBS2]
species 5 [Mitogen-activated protein kinase HOG1]

Observables: none

Heart disease remains the leading cause of death globally. Although reperfusion following myocardial ischemia can preven…

Heart disease remains the leading cause of death globally. Although reperfusion following myocardial ischemia can prevent death by restoring nutrient flow, ischemia/reperfusion injury can cause significant heart damage. The mechanisms that drive ischemia/reperfusion injury are not well understood; currently, few methods can predict the state of the cardiac muscle cell and its metabolic conditions during ischemia. Here, we explored the energetic sustainability of cardiomyocytes, using a model for cellular metabolism to predict the levels of ATP following hypoxia. We modeled glycolytic metabolism with a system of coupled ordinary differential equations describing the individual metabolic reactions within the cardiomyocyte over time. Reduced oxygen levels and ATP consumption rates were simulated to characterize metabolite responses to ischemia. By tracking biochemical species within the cell, our model enables prediction of the cell's condition up to the moment of reperfusion. The simulations revealed a distinct transition between energetically sustainable and unsustainable ATP concentrations for various energetic demands. Our model illustrates how even low oxygen concentrations allow the cell to perform essential functions. We found that the oxygen level required for a sustainable level of ATP increases roughly linearly with the ATP consumption rate. An extracellular O2 concentration of ∼0.007 mm could supply basic energy needs in non-beating cardiomyocytes, suggesting that increased collateral circulation may provide an important source of oxygen to sustain the cardiomyocyte during extended ischemia. Our model provides a time-dependent framework for studying various intervention strategies to change the outcome of reperfusion. link: http://identifiers.org/pubmed/28487363

Parameters: none

States: none

Observables: none

A new mechanism is proposed for the apparent breakthrough of HIV that occurs approximately 6 months after the commenceme…

A new mechanism is proposed for the apparent breakthrough of HIV that occurs approximately 6 months after the commencement of therapy with zidovudine (AZT). Using a simple mathematical model of the interacting population dynamics of HIV and its major host cell in the circulation (the CD4+ lymphocyte), predicted patterns of HIV plasma viraemia in the weeks following treatment with zidovudine are generated. These are in close agreement with observed patterns despite the fact that the model contains no mechanisms for the development of drug-resistant strains of virus. It is suggested that the patterns of viral abundance observed during the first 6 months after treatment may be the result of non-linearities in the interactions between HIV and CD4+ cells, and that it is only after the first post-treatment burst of viral production that drug resistance plays an important role. link: http://identifiers.org/pubmed/1677807

Parameters: none

States: none

Observables: none

BIOMD0000000375 @ v0.0.1

This a model from the article: Evidence that calcium release-activated current mediates the biphasic electrical acti…

The electrical response of pancreatic beta-cells to step increases in glucose concentration is biphasic, consisting of a prolonged depolarization with action potentials (Phase 1) followed by membrane potential oscillations known as bursts. We have proposed that the Phase 1 response results from the combined depolarizing influences of potassium channel closure and an inward, nonselective cation current (ICRAN) that activates as intracellular calcium stores empty during exposure to basal glucose (Bertram et al., 1995). The stores refill during Phase 1, deactivating ICRAN and allowing steady-state bursting to commence. We support this hypothesis with additional simulations and experimental results indicating that Phase 1 duration is sensitive to the filling state of intracellular calcium stores. First, the duration of the Phase 1 transient increases with duration of prior exposure to basal (2.8 mM) glucose, reflecting the increased time required to fill calcium stores that have been emptying for longer periods. Second, Phase 1 duration is reduced when islets are exposed to elevated K+ to refill calcium stores in the presence of basal glucose. Third, when extracellular calcium is removed during the basal glucose exposure to reduce calcium influx into the stores, Phase 1 duration increases. Finally, no Phase 1 is observed following hyperpolarization of the beta-cell membrane with diazoxide in the continued presence of 11 mm glucose, a condition in which intracellular calcium stores remain full. Application of carbachol to empty calcium stores during basal glucose exposure did not increase Phase 1 duration as the model predicts. Despite this discrepancy, the good agreement between most of the experimental results and the model predictions provides evidence that a calcium release-activated current mediates the Phase 1 electrical response of the pancreatic beta-cell. link: http://identifiers.org/pubmed/9002424

Parameters:

Name Description
J_mem_tot = -2.34898089778648E-5; lambda_er = 250.0; J_er_tot = 0.0359076237623762 Reaction: Ca_i = J_er_tot/lambda_er+J_mem_tot, Rate Law: J_er_tot/lambda_er+J_mem_tot
sigma_er = 1.0; lambda_er = 250.0; J_er_tot = 0.0359076237623762 Reaction: Ca_er_Ca_equations = (-J_er_tot)/(lambda_er*sigma_er), Rate Law: (-J_er_tot)/(lambda_er*sigma_er)
tau_n = 9.085746273364; lambda_n = 1.85; n_infinity = 4.67956725632935E-4 Reaction: n = lambda_n*(n_infinity-n)/tau_n, Rate Law: lambda_n*(n_infinity-n)/tau_n
i_K_Ca = 3.45489443378119; i_leak = 0.0; i_CRAC = -5.81489940359721; Cm = 6158.0; i_Ca = -1342.58335216182; i_K = 17.55; i_K_ATP = 1350.0 Reaction: V_membrane = (-(i_Ca+i_K+i_K_ATP+i_K_Ca+i_CRAC+i_leak))/Cm, Rate Law: (-(i_Ca+i_K+i_K_ATP+i_K_Ca+i_CRAC+i_leak))/Cm
jm_infinity = 0.0179862099620915; tau_j = 8145.05572085199 Reaction: jm = (jm_infinity-jm)/tau_j, Rate Law: (jm_infinity-jm)/tau_j

States:

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

Observables: none

BIOMD0000000415 @ v0.0.1

This model is from the article: Reduction of off-flavor generation in soybean homogenates: a mathematical model. M…

The generation of off-flavors in soybean homogenates such as n-hexanal via the lipoxygenase (LOX) pathway can be a problem in the processed food industry. Previous studies have examined the effect of using soybean varieties missing one or more of the 3 LOX isozymes on n-hexanal generation. A dynamic mathematical model of the soybean LOX pathway using ordinary differential equations was constructed using parameters estimated from existing data with the aim of predicting how n-hexanal generation could be reduced. Time-course simulations of LOX-null beans were run and compared with experimental results. Model L(2), L(3), and L(12) beans were within the range relative to the wild type found experimentally, with L(13) and L(23) beans close to the experimental range. Model L(1) beans produced much more n-hexanal relative to the wild type than those in experiments. Sensitivity analysis indicates that reducing the estimated K(m) parameter for LOX isozyme 3 (L-3) would improve the fit between model predictions and experimental results found in the literature. The model also predicts that increasing L-3 or reducing L-2 levels within beans may reduce n-hexanal generation.This work describes the use of mathematics to attempt to quantify the enzyme-catalyzed conversions of compounds in soybean homogenates into undesirable flavors, primarily from the compound n-hexanal. The effect of different soybean genotypes and enzyme kinetic constants was also studied, leading to recommendations on which combinations might minimize off-flavor levels and what further work might be carried out to substantiate these conclusions. link: http://identifiers.org/pubmed/21535565

Parameters:

Name Description
parameter_7 = 0.05; parameter_9 = 0.038475 Reaction: species_8 => species_15, Rate Law: compartment_1*parameter_9*species_8/(parameter_7+species_8)
parameter_5 = 0.49; parameter_6 = 0.00255 Reaction: species_1 => species_7 + species_8 + species_9 + species_10 + species_11 + species_12 + species_13 + species_14, Rate Law: compartment_1*parameter_6*species_1/(parameter_5+species_1)
parameter_4 = 0.039; parameter_3 = 0.49 Reaction: species_1 => species_7 + species_8 + species_9 + species_10 + species_11 + species_12 + species_13 + species_14, Rate Law: compartment_1*parameter_4*species_1/(parameter_3+species_1)
parameter_1 = 0.49; parameter_2 = 0.00825 Reaction: species_1 => species_7 + species_8 + species_9 + species_10 + species_11 + species_12 + species_13 + species_14, Rate Law: compartment_1*parameter_2*species_1/(parameter_1+species_1)
parameter_7 = 0.05; parameter_8 = 0.285 Reaction: species_7 => species_15, Rate Law: compartment_1*parameter_8*species_7/(parameter_7+species_7)

States:

Name Description
species 14 [hydroperoxide]
species 9 [hydroperoxide]
species 10 [hydroperoxide]
species 11 [hydroperoxide]
species 1 [linoleic acid]
species 8 [hydroperoxide]
species 12 [hydroperoxide]
species 7 [hydroperoxide]
species 15 [6184]
species 13 [hydroperoxide]

Observables: none

An insight into tumor dormancy equilibrium via the analysis of its domain of attraction A. Merola, C. Cosentino *, F. Am…

A B S T R A C T The trajectories of the dynamic system which regulates the competition between the populations of malignant cells and immune cells may tend to an asymptotically stable equilibrium in which the sizes of these populations do not vary, which is called tumor dormancy. Especially for lower steady-state sizes of the population of malignant cells, this equilibrium represents a desirable clinical condition since the tumor growth is blocked. In this context, it is of mandatory importance to analyze the robustness of this clinical favorable state of health in the face of perturbations. To this end, the paper presents an optimization technique to determine whether an assigned rectangular region, which surrounds an asymptotically stable equilibrium point of a quadratic systems, is included into the domain of attraction of the equilibrium itself. The biological relevance of the application of this technique to the analysis of tumor growth dynamics is shown on the basis of a recent quadraticmodel of the tumor–immune system competition dynamics. Indeed the application of the proposedmethodology allows to ensure that a given safety region, determined on the basis of clinical considerations, belongs to the domain of attraction of the tumor blocked equilibrium; therefore for the set of perturbed initial conditions which belong to such region, the convergence to the healthy steady state is guaranteed. The proposed methodology can also provide an optimal strategy for cancer treatment. link: http://identifiers.org/doi/10.1016/j.bspc.2008.02.001

Parameters:

Name Description
r = 0.9; k1 = 0.8; q = 10.0 Reaction: => M, Rate Law: compartment*(q+r*M*(1-M/k1))
alpha = 0.3 Reaction: M => ; N, Rate Law: compartment*alpha*M*N
beta = 0.1; d2 = 0.03 Reaction: Z => ; N, Rate Law: compartment*(beta*N*Z+d2*Z)
beta = 0.1 Reaction: => N; Z, Rate Law: compartment*beta*N*Z
k2 = 0.7; s = 0.8 Reaction: => Z, Rate Law: compartment*s*Z*(1-Z/k2)
d1 = 0.02 Reaction: N =>, Rate Law: compartment*d1*N

States:

Name Description
Z Z
M [Neoplastic Cell]
N N

Observables: none

Messiha2013 - combined glycolysis and pentose phosphate pathway model[BIOMD0000000502](http://identifiers.org/biomodels.…

We present the quantification and kinetic characterisation of the enzymes of the pentose phosphate pathway in Saccharomyces cerevisiae. The data are combined into a mathematical model that describes the dynamics of this system and allows for the predicting changes in metabolite concentrations and fluxes in response to perturbations. We use the model to study the response of yeast to a glucose pulse. We then combine the model with an existing glycolysis one to study the effect of oxidative stress on carbohydrate metabolism. The combination of these two models was made possible by the standardized enzyme kinetic experiments carried out in both studies. This work demonstrates the feasibility of constructing larger network models by merging smaller pathway models. link: http://identifiers.org/doi/10.7287/peerj.preprints.146v2

Parameters:

Name Description
sum_NAD = 1.59 mM Reaction: NADH = sum_NAD-NAD, Rate Law: missing
sum_UxP = 1.39784619487425 mM Reaction: UDG = (sum_UxP-UTP)-UDP, Rate Law: missing
Kudg=0.886 mM; Vmax=0.49356 mM per s; Kg6p=3.8 mM Reaction: G6P + UDG => T6P + UDP; TPS1, TPS2, G6P, UDG, Rate Law: cell*Vmax*G6P*UDG/(Kg6p*Kudg)/((1+G6P/Kg6p)*(1+UDG/Kudg))
k=1.0 per s Reaction: E4P => ; E4P, Rate Law: cell*k*E4P
Vmax=0.12762 mM per s; Kg1p=0.023 mM; Kg6p=0.05 mM; Keq=0.1667 dimensionless Reaction: G6P => G1P; PGM1, PGM2, G6P, G1P, Rate Law: cell*Vmax*(G6P/Kg6p-G1P/(Kg6p*Keq))/(1+G6P/Kg6p+G1P/Kg1p)
Kg6p_GLK1=30.0 mM; Kg6p_HXK1=30.0 mM; kcat_HXK1=10.2 per s; Kg6p_HXK2=30.0 mM; Kit6p_HXK1=0.2 mM; Katp_GLK1=0.865 mM; Katp_HXK1=0.293 mM; kcat_GLK1=0.0721 per s; Keq=2000.0 dimensionless; Katp_HXK2=0.195 mM; kcat_HXK2=63.1 per s; Kglc_HXK2=0.2 mM; Kit6p_HXK2=0.04 mM; Kglc_GLK1=0.0106 mM; Kglc_HXK1=0.15 mM; Kadp_HXK1=0.23 mM; Kadp_GLK1=0.23 mM; Kadp_HXK2=0.23 mM Reaction: GLC + ATP => G6P + ADP; HXK1, T6P, HXK2, GLK1, HXK1, GLC, ATP, G6P, ADP, T6P, HXK2, GLK1, Rate Law: cell*HXK1*kcat_HXK1*(GLC*ATP/(Kglc_HXK1*Katp_HXK1)-G6P*ADP/(Kglc_HXK1*Katp_HXK1*Keq))/((1+GLC/Kglc_HXK1+G6P/Kg6p_HXK1+T6P/Kit6p_HXK1)*(1+ATP/Katp_HXK1+ADP/Kadp_HXK1))+cell*HXK2*kcat_HXK2*(GLC*ATP/(Kglc_HXK2*Katp_HXK2)-G6P*ADP/(Kglc_HXK2*Katp_HXK2*Keq))/((1+GLC/Kglc_HXK2+G6P/Kg6p_HXK2+T6P/Kit6p_HXK2)*(1+ATP/Katp_HXK2+ADP/Kadp_HXK2))+cell*GLK1*kcat_GLK1*(GLC*ATP/(Kglc_GLK1*Katp_GLK1)-G6P*ADP/(Kglc_GLK1*Katp_GLK1*Keq))/((1+GLC/Kglc_GLK1+G6P/Kg6p_GLK1)*(1+ATP/Katp_GLK1+ADP/Kadp_GLK1))
Vmax=6.16 mM per s; Katp=3.0 mM Reaction: ATP => ADP; ATP, Rate Law: cell*Vmax*ATP/Katp/(1+ATP/Katp)
Kadp=0.2 mM; Kbpg=0.003 mM; kcat=58.6 per s; Katp=1.99 mM; Kp3g=4.58 mM; nHadp=2.0 dimensionless; Keq=3200.0 dimensionless Reaction: ADP + BPG => ATP + P3G; PGK1, PGK1, ADP, BPG, P3G, ATP, Rate Law: cell*PGK1*kcat*(ADP/Kadp)^(nHadp-1)*(BPG*ADP/(Kbpg*Kadp)-P3G*ATP/(Kbpg*Kadp*Keq))/((1+BPG/Kbpg+P3G/Kp3g)*(1+(ADP/Kadp)^nHadp+ATP/Katp))
Kx5p=7.7 mM; Keq=1.4 dimensionless; kcat=4020.0 per s; Kru5p=5.97 mM Reaction: Ru5P => X5P; RPE1, RPE1, Ru5P, X5P, Rate Law: cell*RPE1*kcat*(Ru5P-X5P/Keq)/Kru5p/(1+Ru5P/Kru5p+X5P/Kx5p)
Knadh=0.023 mM; Katp=0.73 mM; Kg3p=1.2 mM; Kfbp=4.8 mM; Keq=10000.0 dimensionless; Kadp=2.0 mM; Vmax=0.783333333333333 mM per s; Kdhap=0.54 mM; Knad=0.93 mM Reaction: DHAP + NADH => G3P + NAD; ADP, ATP, F16bP, GPD1, GPD2, DHAP, NADH, G3P, NAD, F16bP, ATP, ADP, Rate Law: cell*Vmax/(Kdhap*Knadh)*(DHAP*NADH-G3P*NAD/Keq)/((1+F16bP/Kfbp+ATP/Katp+ADP/Kadp)*(1+DHAP/Kdhap+G3P/Kg3p)*(1+NADH/Knadh+NAD/Knad))
k=0.00554339592436782 per_mM_per_s Reaction: AcAld + NAD => ACE + NADH; AcAld, NAD, Rate Law: cell*k*AcAld*NAD
Keq=0.19 dimensionless; Kp2g=1.41 mM; kcat=400.0 per s; Kp3g=1.2 mM Reaction: P3G => P2G; GPM1, GPM1, P3G, P2G, Rate Law: cell*GPM1*kcat*(P3G/Kp3g-P2G/(Kp3g*Keq))/(1+P3G/Kp3g+P2G/Kp2g)
sum_AxP = 6.02 mM Reaction: AMP = (sum_AxP-ATP)-ADP, Rate Law: missing
k=0.0745258294103764 per_mM_per_s Reaction: UDP + ATP => UTP + ADP; UDP, ATP, Rate Law: cell*k*UDP*ATP
Knadp=0.045 mM; kcat=189.0 per s; Kg6p=0.042 mM; Knadph=0.017 mM; Kg6l=0.01 mM Reaction: G6P + NADP => G6L + NADPH; ZWF1, ZWF1, G6P, NADP, G6L, NADPH, Rate Law: cell*ZWF1*kcat*G6P*NADP/(Kg6p*Knadp)/((1+G6P/Kg6p+G6L/Kg6l)*(1+NADP/Knadp+NADPH/Knadph))
Kru5p=2.47 mM; kcat=335.0 per s; Kiru5p=9.88 mM; Keq=4.0 dimensionless; Kr5p=5.7 mM Reaction: Ru5P => R5P; RKI1, RKI1, Ru5P, R5P, Rate Law: cell*RKI1*kcat*(Ru5P-R5P/Keq)/Kru5p/(1+Ru5P/Kru5p+R5P/Kr5p)
Kg6l=0.8 mM; kcat=4.3 per s; Kp6g=0.5 mM Reaction: G6L => P6G; SOL3, SOL3, G6L, P6G, Rate Law: cell*SOL3*kcat*G6L/Kg6l/(1+G6L/Kg6l+P6G/Kp6g)
sum_NADP = 0.33 mM Reaction: NADP = sum_NADP-NADPH, Rate Law: missing
Kgap_TAL1=0.272 mM; Ke4p_NQM1=0.305 mM; kcat_NQM1=0.694 per s; Kf6p_TAL1=1.44 mM; kcat_TAL1=0.694 per s; Kgap_NQM1=0.272 mM; Ks7p_NQM1=0.786 mM; Kf6p_NQM1=1.04 mM; Ke4p_TAL1=0.362 mM; Keq=1.05 dimensionless; Ks7p_TAL1=0.786 mM Reaction: GAP + S7P => F6P + E4P; TAL1, NQM1, TAL1, GAP, S7P, F6P, E4P, NQM1, Rate Law: cell*(TAL1*kcat_TAL1*(GAP*S7P-F6P*E4P/Keq)/(Kgap_TAL1*Ks7p_TAL1)/((1+GAP/Kgap_TAL1+F6P/Kf6p_TAL1)*(1+S7P/Ks7p_TAL1+E4P/Ke4p_TAL1))+NQM1*kcat_NQM1*(GAP*S7P-F6P*E4P/Keq)/(Kgap_NQM1*Ks7p_NQM1)/((1+GAP/Kgap_NQM1+F6P/Kf6p_NQM1)*(1+S7P/Ks7p_NQM1+E4P/Ke4p_NQM1)))
Kigap=10.0 mM; Keq=0.069 mM; Kf16bp=0.4507 mM; Kgap=2.4 mM; kcat=4.139 per s; Kdhap=2.0 mM Reaction: F16bP => DHAP + GAP; FBA1, FBA1, F16bP, DHAP, GAP, Rate Law: cell*FBA1*kcat*(F16bP/Kf16bp-DHAP*GAP/(Kf16bp*Keq))/(1+F16bP/Kf16bp+DHAP/Kdhap+GAP/Kgap+F16bP*GAP/(Kf16bp*Kigap)+DHAP*GAP/(Kdhap*Kgap))
Vmax=13.2552 mM per s; Kiutp=0.11 mM; Kg1p=0.32 mM; Kutp=0.11 mM; Kiudg=0.0035 mM Reaction: G1P + UTP => UDG; UGP1, UTP, G1P, UDG, Rate Law: cell*Vmax*UTP*G1P/(Kutp*Kg1p)/(Kiutp/Kutp+UTP/Kutp+G1P/Kg1p+UTP*G1P/(Kutp*Kg1p)+Kiutp/Kutp*UDG/Kiudg+G1P*UDG/(Kg1p*Kiudg))
Knadp_GND2=0.094 mM; Kru5p_GND1=0.1 mM; kcat_GND2=27.3 per s; Knadph_GND2=0.055 mM; kcat_GND1=28.0 per s; Knadp_GND1=0.094 mM; Kru5p_GND2=0.1 mM; Knadph_GND1=0.055 mM; Kp6g_GND2=0.115 mM; Kp6g_GND1=0.062 mM Reaction: P6G + NADP => Ru5P + NADPH; GND1, GND2, GND1, P6G, NADP, Ru5P, NADPH, GND2, Rate Law: cell*(GND1*kcat_GND1*P6G*NADP/(Kp6g_GND1*Knadp_GND1)/((1+P6G/Kp6g_GND1+Ru5P/Kru5p_GND1)*(1+NADP/Knadp_GND1+NADPH/Knadph_GND1))+GND2*kcat_GND2*P6G*NADP/((1+P6G/Kp6g_GND2+Ru5P/Kru5p_GND2)*(1+NADP/Knadp_GND2+NADPH/Knadph_GND2)))
Kglc=0.9 mM; Vmax=3.35 mM per s; Ki=0.91 dimensionless Reaction: GLCx => GLC; GLCx, GLC, Rate Law: cell*Vmax*(GLCx-GLC)/Kglc/(1+GLCx/Kglc+GLC/Kglc+Ki*GLCx/Kglc*GLC/Kglc)
Vmax=0.883333333333333 mM per s; Kg3p=3.5 mM Reaction: G3P => GLY; HOR2, RHR2, G3P, Rate Law: cell*Vmax*G3P/Kg3p/(1+G3P/Kg3p)
Keq=0.45 dimensionless; k=0.75 per_mM_per_s Reaction: ADP => ATP + AMP; ADP, AMP, ATP, Rate Law: cell*k*(ADP*ADP-AMP*ATP/Keq)
Kg6p=1.0257 mM; kcat=487.36 per s; Keq=0.29 dimensionless; Kf6p=0.307 mM Reaction: G6P => F6P; PGI1, PGI1, G6P, F6P, Rate Law: cell*PGI1*kcat*(G6P/Kg6p-F6P/(Kg6p*Keq))/(1+G6P/Kg6p+F6P/Kf6p)
Kpep_ENO1=0.5 mM; kcat_ENO2=19.87 per s; Kpep_ENO2=0.5 mM; Kp2g_ENO2=0.104 mM; Kp2g_ENO1=0.043 mM; Keq=6.7 dimensionless; kcat_ENO1=7.6 per s Reaction: P2G => PEP; ENO1, ENO2, ENO1, P2G, PEP, ENO2, Rate Law: cell*ENO1*kcat_ENO1*(P2G/Kp2g_ENO1-PEP/(Kp2g_ENO1*Keq))/(1+P2G/Kp2g_ENO1+PEP/Kpep_ENO1)+cell*ENO2*kcat_ENO2*(P2G/Kp2g_ENO2-PEP/(Kp2g_ENO2*Keq))/(1+P2G/Kp2g_ENO2+PEP/Kpep_ENO2)
kcat=20.146 per s; Kiatp=9.3 mM; Kpyr=21.0 mM; Katp=1.5 mM; Kf16p=0.2 mM; Kpep=0.281 mM; L0=100.0 dimensionless; Keq=6500.0 dimensionless; Kadp=0.243 mM Reaction: ADP + PEP => ATP + PYR; CDC19, F16bP, CDC19, PEP, ADP, PYR, ATP, F16bP, Rate Law: cell*CDC19*kcat*(PEP*ADP-PYR*ATP/Keq)/(Kpep*Kadp)/((1+PEP/Kpep+PYR/Kpyr+L0*(ATP/Kiatp+1)/(F16bP/Kf16p+1))*(1+ADP/Kadp+ATP/Katp))
Ke4p_TAL = 0.946 mM; Keq=1.2 dimensionless; kcat=40.5 per s; Kgap_TAL = 0.1 mM; Ks7p_TAL = 0.15 mM; Kr5p_TAL = 0.235 mM; Kx5p_TAL = 0.67 mM; Kf6p_TAL = 1.1 mM Reaction: X5P + R5P => GAP + S7P; TKL1, E4P, F6P, TKL1, X5P, R5P, GAP, S7P, E4P, F6P, Rate Law: cell*TKL1*kcat*(X5P*R5P-GAP*S7P/Keq)/(Kx5p_TAL*Kr5p_TAL)/((1+X5P/Kx5p_TAL+GAP/Kgap_TAL)*(1+E4P/Ke4p_TAL+F6P/Kf6p_TAL+R5P/Kr5p_TAL+S7P/Ks7p_TAL))
Kpyr_PDC1=8.5 mM; Kpyr_PDC6=2.92 mM; Kpyr_PDC5=7.08 mM; kcat_PDC6=9.21 per s; kcat_PDC5=10.32 per s; kcat_PDC1=12.14 per s Reaction: PYR => AcAld; PDC1, PDC5, PDC6, PDC1, PYR, PDC5, PDC6, Rate Law: cell*PDC1*kcat_PDC1*PYR/Kpyr_PDC1/(1+PYR/Kpyr_PDC1)+cell*PDC5*kcat_PDC5*PYR/Kpyr_PDC5/(1+PYR/Kpyr_PDC5)+cell*PDC6*kcat_PDC6*PYR/Kpyr_PDC6/(1+PYR/Kpyr_PDC6)
Kinad=0.92 mM; Knadh=0.11 mM; kcat=176.0 per s; Kacald=0.4622 mM; Kinadh=0.031 mM; Keq=14492.7536231884 dimensionless; Kietoh=90.0 mM; Knad=0.17 mM; Kiacald=1.1 mM; Ketoh=17.0 mM Reaction: AcAld + NADH => EtOH + NAD; ADH1, ADH1, AcAld, NADH, EtOH, NAD, Rate Law: cell*ADH1*kcat*(AcAld*NADH/(Kacald*Kinadh)-EtOH*NAD/(Kacald*Kinadh*Keq))/(1+NADH/Kinadh+AcAld*Knadh/(Kinadh*Kacald)+EtOH*Knad/(Kinad*Ketoh)+NAD/Kinad+AcAld*NADH/(Kinadh*Kacald)+NADH*EtOH*Knad/(Kinadh*Kinad*Ketoh)+AcAld*NAD*Knadh/(Kinadh*Kinad*Kacald)+EtOH*NAD/(Ketoh*Kinad)+AcAld*NADH*EtOH/(Kinadh*Kietoh*Kacald)+AcAld*EtOH*NAD/(Kiacald*Kinad*Ketoh))
Cf16=0.397 dimensionless; Kadp=1.0 mM; Katp=0.71 mM; Kiatp=0.65 mM; gR=5.12 dimensionless; Keq=800.0 dimensionless; Cf26=0.0174 dimensionless; Kf26=6.82E-4 mM; Kf6p=0.1 mM; Kf16=0.111 mM; kcat=209.6 per s; Camp=0.0845 dimensionless; L0=0.66 dimensionless; Ciatp=100.0 dimensionless; Kamp=0.0995 mM; Catp=3.0 dimensionless Reaction: ATP + F6P => ADP + F16bP; AMP, F26bP, PFK1, PFK2, PFK2, F6P, ATP, F16bP, ADP, AMP, F26bP, Rate Law: cell*PFK2*kcat*gR*F6P/Kf6p*ATP/Katp*(1-F16bP*ADP/(F6P*ATP)/Keq)*(1+F6P/Kf6p+ATP/Katp+gR*F6P/Kf6p*ATP/Katp+F16bP/Kf16+ADP/Kadp+gR*F16bP/Kf16*ADP/Kadp)/((1+F6P/Kf6p+ATP/Katp+gR*F6P/Kf6p*ATP/Katp+F16bP/Kf16+ADP/Kadp+gR*F16bP/Kf16*ADP/Kadp)^2+L0*((1+Ciatp*ATP/Kiatp)/(1+ATP/Kiatp))^2*((1+Camp*AMP/Kamp)/(1+AMP/Kamp))^2*((1+Cf26*F26bP/Kf26+Cf16*F16bP/Kf16)/(1+F26bP/Kf26+F16bP/Kf16))^2*(1+Catp*ATP/Katp)^2)
Keq=0.00533412710224736 dimensionless; Kgap_TDH3=0.423 mM; Knadh_TDH3=0.06 mM; Kbpg_TDH3=0.909 mM; kcat_TDH3=18.162 per s; Knad_TDH3=0.09 mM; Knad_TDH1=0.09 mM; Kgap_TDH1=0.495 mM; Kbpg_TDH1=0.0098 mM; Knadh_TDH1=0.06 mM; kcat_TDH1=19.12 per s Reaction: GAP + NAD => BPG + NADH; TDH1, TDH3, TDH1, GAP, NAD, BPG, NADH, TDH3, Rate Law: cell*TDH1*kcat_TDH1*(GAP*NAD/(Kgap_TDH1*Knad_TDH1)-BPG*NADH/(Kgap_TDH1*Knad_TDH1*Keq))/((1+GAP/Kgap_TDH1+BPG/Kbpg_TDH1)*(1+NAD/Knad_TDH1+NADH/Knadh_TDH1))+cell*TDH3*kcat_TDH3*(GAP*NAD/(Kgap_TDH3*Knad_TDH3)-BPG*NADH/(Kgap_TDH3*Knad_TDH3*Keq))/((1+GAP/Kgap_TDH3+BPG/Kbpg_TDH3)*(1+NAD/Knad_TDH3+NADH/Knadh_TDH3))
kcat=47.1 per s; Keq=10.0 dimensionless; Ke4p_TAL = 0.946 mM; Kgap_TAL = 0.1 mM; Ks7p_TAL = 0.15 mM; Kr5p_TAL = 0.235 mM; Kx5p_TAL = 0.67 mM; Kf6p_TAL = 1.1 mM Reaction: X5P + E4P => GAP + F6P; TKL1, R5P, S7P, TKL1, X5P, E4P, GAP, F6P, R5P, S7P, Rate Law: cell*TKL1*kcat*(X5P*E4P-GAP*F6P/Keq)/(Kx5p_TAL*Ke4p_TAL)/((1+X5P/Kx5p_TAL+GAP/Kgap_TAL)*(1+E4P/Ke4p_TAL+F6P/Kf6p_TAL+R5P/Kr5p_TAL+S7P/Ks7p_TAL))
Kt6p=0.5 mM; Vmax=2.33999999999999 mM per s Reaction: T6P => TRH; TPS1, TPS2, T6P, Rate Law: cell*Vmax*T6P/Kt6p/(1+T6P/Kt6p)
Keq=0.045 dimensionless; Kigap=35.1 mM; Kdhap=6.454 mM; kcat=564.38 per s; Kgap=5.25 mM Reaction: DHAP => GAP; TPI1, TPI1, DHAP, GAP, Rate Law: cell*TPI1*kcat/Kdhap*(DHAP-GAP/Keq)/(1+DHAP/Kdhap+GAP/Kgap*(1+(GAP/Kigap)^4))

States:

Name Description
ACE [acetate]
ATP [ATP; ATP]
G1P [D-glucopyranose 1-phosphate]
F16bP [beta-D-fructofuranose 1,6-bisphosphate; beta-D-Fructose 1,6-bisphosphate]
GLC [D-glucopyranose; D-Glucose]
GLY [glycerol; Glycerol]
AMP [AMP; AMP]
DHAP [dihydroxyacetone phosphate; Glycerone phosphate]
NADPH [NADPH(4-); NADPH]
P2G [2-phospho-D-glyceric acid; 2-Phospho-D-glycerate]
T6P [alpha,alpha-trehalose 6-phosphate]
P3G [3-phospho-D-glyceric acid; 3-Phospho-D-glycerate]
P6G [6-phosphonatooxy-D-gluconate; 6-Phospho-D-gluconate]
UTP [UTP(4-)]
UDG [UDP-D-glucose]
GLCx [D-glucopyranose; D-Glucose]
NADH [NADH; NADH]
PYR [pyruvate; Pyruvate]
AcAld [acetaldehyde; Acetaldehyde]
R5P [alpha-D-ribofuranose 5-phosphate; alpha-D-Ribose 5-phosphate]
NADP [NADP(3-); NADP+]
EtOH [ethanol; Ethanol]
BPG [3-phospho-D-glyceroyl dihydrogen phosphate; 3-Phospho-D-glyceroyl phosphate]
X5P [D-xylulose 5-phosphate(2-); D-Xylulose 5-phosphate]
F6P [beta-D-fructofuranose 6-phosphate; beta-D-Fructose 6-phosphate]
S7P [sedoheptulose 7-phosphate(2-); Sedoheptulose 7-phosphate]
GAP [D-glyceraldehyde 3-phosphate; D-Glyceraldehyde 3-phosphate]
TRH [alpha,alpha-trehalose; alpha,alpha-Trehalose]
UDP [UDP]
E4P [D-erythrose 4-phosphate(2-); D-Erythrose 4-phosphate]
Ru5P [D-ribulose 5-phosphate(2-); D-Ribulose 5-phosphate]
G6P [alpha-D-glucose 6-phosphate; alpha-D-Glucose 6-phosphate]
PEP [phosphoenolpyruvate; Phosphoenolpyruvate]
NAD [NAD(+); NAD+]
ADP [ADP; ADP]
G6L [6-O-phosphonato-D-glucono-1,5-lactone(2-); D-Glucono-1,5-lactone 6-phosphate]
G3P [sn-glycerol 3-phosphate; sn-Glycerol 3-phosphate]

Observables: none

Messiha2013 - Pentose phosphate pathway modelThis model describes the dynamic behaviour of the pentose phosphate pathway…

We present the quantification and kinetic characterisation of the enzymes of the pentose phosphate pathway in Saccharomyces cerevisiae. The data are combined into a mathematical model that describes the dynamics of this system and allows for the predicting changes in metabolite concentrations and fluxes in response to perturbations. We use the model to study the response of yeast to a glucose pulse. We then combine the model with an existing glycolysis one to study the effect of oxidative stress on carbohydrate metabolism. The combination of these two models was made possible by the standardized enzyme kinetic experiments carried out in both studies. This work demonstrates the feasibility of constructing larger network models by merging smaller pathway models. link: http://identifiers.org/doi/10.7287/peerj.preprints.146v2

Parameters:

Name Description
k=1.0 per s Reaction: R5P => ; R5P, Rate Law: cell*k*R5P
Knadp=0.045 mM; kcat=189.0 per s; Kg6p=0.042 mM; Knadph=0.017 mM; Kg6l=0.01 mM Reaction: G6P + NADP => G6L + NADPH; ZWF1, ZWF1, G6P, NADP, G6L, NADPH, Rate Law: cell*ZWF1*kcat*G6P*NADP/(Kg6p*Knadp)/((1+G6P/Kg6p+G6L/Kg6l)*(1+NADP/Knadp+NADPH/Knadph))
Kru5p=2.47 mM; kcat=335.0 per s; Kiru5p=9.88 mM; Keq=4.0 dimensionless; Kr5p=5.7 mM Reaction: Ru5P => R5P; RKI1, RKI1, Ru5P, R5P, Rate Law: cell*RKI1*kcat*(Ru5P-R5P/Keq)/Kru5p/(1+Ru5P/Kru5p+R5P/Kr5p)
kcat=47.1 per s; Keq=10.0 dimensionless; Ke4p_TAL = 0.946 mM; Kgap_TAL = 0.1 mM; Ks7p_TAL = 0.15 mM; Kr5p_TAL = 0.235 mM; Kx5p_TAL = 0.67 mM; Kf6p_TAL = 1.1 mM Reaction: X5P + E4P => GAP + F6P; TKL1, R5P, S7P, TKL1, X5P, E4P, GAP, F6P, R5P, S7P, Rate Law: cell*TKL1*kcat*(X5P*E4P-GAP*F6P/Keq)/(Kx5p_TAL*Ke4p_TAL)/((1+X5P/Kx5p_TAL+GAP/Kgap_TAL)*(1+E4P/Ke4p_TAL+F6P/Kf6p_TAL+R5P/Kr5p_TAL+S7P/Ks7p_TAL))
Kg6l=0.8 mM; kcat=4.3 per s; Kp6g=0.5 mM Reaction: G6L => P6G; SOL3, SOL3, G6L, P6G, Rate Law: cell*SOL3*kcat*G6L/Kg6l/(1+G6L/Kg6l+P6G/Kp6g)
sum_NADP = 0.33 mM Reaction: NADP = sum_NADP-NADPH, Rate Law: missing
Kgap_TAL1=0.272 mM; Ke4p_NQM1=0.305 mM; kcat_NQM1=0.694 per s; Kf6p_TAL1=1.44 mM; kcat_TAL1=0.694 per s; Kgap_NQM1=0.272 mM; Ks7p_NQM1=0.786 mM; Kf6p_NQM1=1.04 mM; Ke4p_TAL1=0.362 mM; Keq=1.05 dimensionless; Ks7p_TAL1=0.786 mM Reaction: GAP + S7P => F6P + E4P; TAL1, NQM1, TAL1, GAP, S7P, F6P, E4P, NQM1, Rate Law: cell*(TAL1*kcat_TAL1*(GAP*S7P-F6P*E4P/Keq)/(Kgap_TAL1*Ks7p_TAL1)/((1+GAP/Kgap_TAL1+F6P/Kf6p_TAL1)*(1+S7P/Ks7p_TAL1+E4P/Ke4p_TAL1))+NQM1*kcat_NQM1*(GAP*S7P-F6P*E4P/Keq)/(Kgap_NQM1*Ks7p_NQM1)/((1+GAP/Kgap_NQM1+F6P/Kf6p_NQM1)*(1+S7P/Ks7p_NQM1+E4P/Ke4p_NQM1)))
Ke4p_TAL = 0.946 mM; Keq=1.2 dimensionless; kcat=40.5 per s; Kgap_TAL = 0.1 mM; Ks7p_TAL = 0.15 mM; Kr5p_TAL = 0.235 mM; Kx5p_TAL = 0.67 mM; Kf6p_TAL = 1.1 mM Reaction: X5P + R5P => GAP + S7P; TKL1, E4P, F6P, TKL1, X5P, R5P, GAP, S7P, E4P, F6P, Rate Law: cell*TKL1*kcat*(X5P*R5P-GAP*S7P/Keq)/(Kx5p_TAL*Kr5p_TAL)/((1+X5P/Kx5p_TAL+GAP/Kgap_TAL)*(1+E4P/Ke4p_TAL+F6P/Kf6p_TAL+R5P/Kr5p_TAL+S7P/Ks7p_TAL))
Kx5p=7.7 mM; Keq=1.4 dimensionless; kcat=4020.0 per s; Kru5p=5.97 mM Reaction: Ru5P => X5P; RPE1, RPE1, Ru5P, X5P, Rate Law: cell*RPE1*kcat*(Ru5P-X5P/Keq)/Kru5p/(1+Ru5P/Kru5p+X5P/Kx5p)
Knadp_GND2=0.094 mM; Kru5p_GND1=0.1 mM; kcat_GND2=27.3 per s; Knadph_GND2=0.055 mM; kcat_GND1=28.0 per s; Knadp_GND1=0.094 mM; Kru5p_GND2=0.1 mM; Knadph_GND1=0.055 mM; Kp6g_GND2=0.115 mM; Kp6g_GND1=0.062 mM Reaction: P6G + NADP => Ru5P + NADPH; GND1, GND2, GND1, P6G, NADP, Ru5P, NADPH, GND2, Rate Law: cell*(GND1*kcat_GND1*P6G*NADP/(Kp6g_GND1*Knadp_GND1)/((1+P6G/Kp6g_GND1+Ru5P/Kru5p_GND1)*(1+NADP/Knadp_GND1+NADPH/Knadph_GND1))+GND2*kcat_GND2*P6G*NADP/((1+P6G/Kp6g_GND2+Ru5P/Kru5p_GND2)*(1+NADP/Knadp_GND2+NADPH/Knadph_GND2)))

States:

Name Description
NADP [NADP(3-); NADP+]
R5P [alpha-D-ribofuranose 5-phosphate; alpha-D-Ribose 5-phosphate]
X5P [D-xylulose 5-phosphate(2-); D-Xylulose 5-phosphate]
F6P [D-fructose 6-phosphate(2-); D-Fructose 6-phosphate]
S7P [sedoheptulose 7-phosphate(2-); Sedoheptulose 7-phosphate]
E4P [D-erythrose 4-phosphate(2-); D-Erythrose 4-phosphate]
GAP [glyceraldehyde 3-phosphate(2-); Glyceraldehyde 3-phosphate]
G6P [D-glucose 6-phosphate; D-Glucose 6-phosphate]
NADPH [NADPH(4-); NADPH]
Ru5P [D-ribulose 5-phosphate(2-); D-Ribulose 5-phosphate]
P6G [6-phosphonatooxy-D-gluconate; 6-Phospho-D-gluconate]
G6L [6-O-phosphonato-D-glucono-1,5-lactone(2-); D-Glucono-1,5-lactone 6-phosphate]

Observables: none

BIOMD0000000224 @ v0.0.1

This a model from the article: Calcium spiking. Meyer T, Stryer L Annu Rev Biophys Biophys Chem1991:20:153-74…

link: http://identifiers.org/pubmed/1867714

Parameters:

Name Description
E = 1.0 microM4 Reaction: => g; CaI, Rate Law: E*(CaI*0.01)^4*(1-g)
A = 20.0 psec; L = 0.01 psec; k1 = 0.5 microM Reaction: CaS => CaI; IP3, g, Rate Law: (1-g)*(A*(IP3*0.5)^4/(IP3*0.5+k1)^4+L)*CaS
F = 0.02 psec Reaction: g => ; CaI, Rate Law: F
k2 = 0.15 microM; B = 40.0 microMpsec Reaction: CaI => CaS, Rate Law: B*(CaI*0.01)^2/((CaI*0.01)^2+k2^2)
D = 2.0 psec Reaction: IP3 =>, Rate Law: D*IP3*0.5
k3 = 1.0 microM; R = 0.09; C = 1.1 microMpsec Reaction: => IP3; CaI, Rate Law: C*(1-k3/(CaI*0.01+k3)*1/(1+R))

States:

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

Observables: none

Miao2010 - Innate and adaptive immune responses to primary Influenza A Virus infectionThis model is described in the art…

Seasonal and pandemic influenza A virus (IAV) continues to be a public health threat. However, we lack a detailed and quantitative understanding of the immune response kinetics to IAV infection and which biological parameters most strongly influence infection outcomes. To address these issues, we use modeling approaches combined with experimental data to quantitatively investigate the innate and adaptive immune responses to primary IAV infection. Mathematical models were developed to describe the dynamic interactions between target (epithelial) cells, influenza virus, cytotoxic T lymphocytes (CTLs), and virus-specific IgG and IgM. IAV and immune kinetic parameters were estimated by fitting models to a large data set obtained from primary H3N2 IAV infection of 340 mice. Prior to a detectable virus-specific immune response (before day 5), the estimated half-life of infected epithelial cells is approximately 1.2 days, and the half-life of free infectious IAV is approximately 4 h. During the adaptive immune response (after day 5), the average half-life of infected epithelial cells is approximately 0.5 days, and the average half-life of free infectious virus is approximately 1.8 min. During the adaptive phase, model fitting confirms that CD8(+) CTLs are crucial for limiting infected cells, while virus-specific IgM regulates free IAV levels. This may imply that CD4 T cells and class-switched IgG antibodies are more relevant for generating IAV-specific memory and preventing future infection via a more rapid secondary immune response. Also, simulation studies were performed to understand the relative contributions of biological parameters to IAV clearance. This study provides a basis to better understand and predict influenza virus immunity. link: http://identifiers.org/pubmed/20410284

Parameters:

Name Description
rho_E = 6.2E-8 substance Reaction: s4 => s1; s1, s1, Rate Law: rho_E*s1
pi_a = 100.0 substance Reaction: s7 => s3; s2, s2, s2, Rate Law: pi_a*s2
beta_a = 2.4E-6 substance Reaction: s1 => s2; s3, s1, s3, s1, s3, Rate Law: beta_a*s1*s3
c_V = 4.2 substance Reaction: s3 => s6; s3, s3, Rate Law: c_V*s3
delta_Es = 0.6 substance Reaction: s2 => s5; s2, s2, Rate Law: delta_Es*s2

States:

Name Description
s1 [Epithelial cell]
s5 s5
s6 s6
s7 s7
s2 [Epithelial cell]
s4 s4
s3 [Influenza A virus (strain A/X-31 H3N2)]

Observables: none

</head> <body>This model is from the paper available at http://www.ncbi.nlm.nih.gov/pubmed/25171166. The original mode…

The B cell response to influenza infection of the respiratory tract contributes to viral clearance and establishes profound resistance to reinfection by related viruses. Numerous studies have measured virus-specific antibody-secreting cell (ASC) frequencies in different anatomical compartments after influenza infection and provided a general picture of the kinetics of ASC formation and dispersion. However, the dynamics of ASC populations are difficult to determine experimentally and have received little attention. Here, we applied mathematical modeling to investigate the dynamics of ASC growth, death, and migration over the 2-week period following primary influenza infection in mice. Experimental data for model fitting came from high frequency measurements of virus-specific IgM, IgG, and IgA ASCs in the mediastinal lymph node (MLN), spleen, and lung. Model construction was based on a set of assumptions about ASC gain and loss from the sampled sites, and also on the directionality of ASC trafficking pathways. Most notably, modeling results suggest that differences in ASC fate and trafficking patterns reflect the site of formation and the expressed antibody class. Essentially all early IgA ASCs in the MLN migrated to spleen or lung, whereas cell death was likely the major reason for IgM and IgG ASC loss from the MLN. In contrast, the spleen contributed most of the IgM and IgG ASCs that migrated to the lung, but essentially none of the IgA ASCs. This finding points to a critical role for regional lymph nodes such as the MLN in the rapid generation of IgA ASCs that seed the lung. Results for the MLN also suggest that ASC death is a significant early feature of the B cell response. Overall, our analysis is consistent with accepted concepts in many regards, but it also indicates novel features of the B cell response to influenza that warrant further investigation. link: http://identifiers.org/pubmed/25171166

Parameters: none

States: none

Observables: none

BIOMD0000000422 @ v0.0.1

This model is from the article: Mathematical modeling elucidates the role of transcriptional feedback in gibberellin s…

The hormone gibberellin (GA) is a key regulator of plant growth. Many of the components of the gibberellin signal transduction [e.g., GIBBERELLIN INSENSITIVE DWARF 1 (GID1) and DELLA], biosynthesis [e.g., GA 20-oxidase (GA20ox) and GA3ox], and deactivation pathways have been identified. Gibberellin binds its receptor, GID1, to form a complex that mediates the degradation of DELLA proteins. In this way, gibberellin relieves DELLA-dependent growth repression. However, gibberellin regulates expression of GID1, GA20ox, and GA3ox, and there is also evidence that it regulates DELLA expression. In this paper, we use integrated mathematical modeling and experiments to understand how these feedback loops interact to control gibberellin signaling. Model simulations are in good agreement with in vitro data on the signal transduction and biosynthesis pathways and in vivo data on the expression levels of gibberellin-responsive genes. We find that GA-GID1 interactions are characterized by two timescales (because of a lid on GID1 that can open and close slowly relative to GA-GID1 binding and dissociation). Furthermore, the model accurately predicts the response to exogenous gibberellin after a number of chemical and genetic perturbations. Finally, we investigate the role of the various feedback loops in gibberellin signaling. We find that regulation of GA20ox transcription plays a significant role in both modulating the level of endogenous gibberellin and generating overshoots after the removal of exogenous gibberellin. Moreover, although the contribution of other individual feedback loops seems relatively small, GID1 and DELLA transcriptional regulation acts synergistically with GA20ox feedback. link: http://identifiers.org/pubmed/22523240

Parameters:

Name Description
gammaGA20ox=3.514 substance Reaction: s27 => s6; s27, Rate Law: gammaGA20ox*s27
ua1=10.0 substance Reaction: s62 + s16 => s45; s62, s16, Rate Law: ua1*s62*s16
kd15=0.008827838388125 substance Reaction: s31 => s24 + s27; s31, Rate Law: kd15*s31
thetaGA20ox=0.6383 substance; muGA20ox = 0.047770755070625 substance Reaction: s11 => s39; s16, s16, Rate Law: muGA20ox*s16/(s16+thetaGA20ox)
deltaGA20ox=0.192990314378105 substance Reaction: s6 => s27; s39, s39, Rate Law: deltaGA20ox*s39
um=6.92208879449283 substance Reaction: s45 => s62 + s22; s45, Rate Law: um*s45
muGID = 0.045708818961487 substance Reaction: s42 => s15; s42, Rate Law: muGID*s42
muGA3ox = 0.102600014140148 substance Reaction: s41 => s35; s41, Rate Law: muGA3ox*s41
ud2=2.8184 substance Reaction: s36 => s62 + s16; s36, Rate Law: ud2*s36
muDELLA = 0.070794578438414 substance Reaction: s40 => s34; s40, Rate Law: muDELLA*s40
muGA = 0.290804218727464 substance Reaction: s24 => s68; s24, Rate Law: muGA*s24
thetaGA3ox=0.0082 substance; muGA3ox = 0.102600014140148 substance Reaction: s35 => s41; s16, s16, Rate Law: muGA3ox*s16/(s16+thetaGA3ox)
kd9=0.008827838388125 substance Reaction: s29 => s26 + s28; s29, Rate Law: kd9*s29
ka24=3099.18915892587 substance Reaction: s25 + s27 => s30; s25, s27, Rate Law: ka24*s25*s27
la=1.35 substance Reaction: s1 + s2 => s65; s1, s2, Rate Law: la*s1*s2
ua2=316.2278 substance Reaction: s62 + s16 => s36; s62, s16, Rate Law: ua2*s62*s16
ka15=2073.22402517968 substance Reaction: s24 + s27 => s31; s24, s27, Rate Law: ka15*s24*s27
ud1=0.133045441797809 substance Reaction: s45 => s62 + s16; s45, Rate Law: ud1*s45
kd12=2.67298621993027 substance Reaction: s32 => s23 + s27; s32, Rate Law: kd12*s32
km9=763.777072066507 substance Reaction: s29 => s28 + s1; s29, Rate Law: km9*s29
deltaDELLA=5.27749140286577E-4 substance Reaction: s7 => s16; s40, s40, Rate Law: deltaDELLA*s40
q=0.025118864315096 substance Reaction: s65 => s62; s65, Rate Law: q*s65
deltaGA3ox=0.019299031437811 substance Reaction: s5 => s28; s41, s41, Rate Law: deltaGA3ox*s41
km24=2.58846077319221 substance Reaction: s30 => s27 + s26; s30, Rate Law: km24*s30
thetaGID=5.5995E-4 substance; muGID = 0.045708818961487 substance Reaction: s15 => s42; s16, s16, Rate Law: muGID*s16/(s16+thetaGID)
km12=198.80427707769 substance Reaction: s32 => s27 + s24; s32, Rate Law: km12*s32
p=0.077624711662869 substance Reaction: s62 => s65; s62, Rate Law: p*s62
deltaGID=19.2990314378105 substance Reaction: s33 => s2; s42, s42, Rate Law: deltaGID*s42
omegaGA4 = 0.0 substance; A1=0.0307 substance; Pmem = 2.66664 substance Reaction: s66 => s1, Rate Law: Pmem*A1*omegaGA4
gammaGID=3.514 substance Reaction: s2 => s33; s2, Rate Law: gammaGID*s2
thetaDELLA=0.01 substance; muDELLA = 0.070794578438414 substance Reaction: s34 => s40; s16, s16, Rate Law: muDELLA*thetaDELLA/(s16+thetaDELLA)
ka12=2904.11853677638 substance Reaction: s23 + s27 => s32; s23, s27, Rate Law: ka12*s23*s27
muGA20ox = 0.047770755070625 substance Reaction: s39 => s11; s39, Rate Law: muGA20ox*s39
kd24=0.01588492846351 substance Reaction: s30 => s25 + s27; s30, Rate Law: kd24*s30
omegaGA12 = 0.006602803853512 substance Reaction: s3 => s23, Rate Law: omegaGA12
km15=763.777072066507 substance Reaction: s31 => s27 + s25; s31, Rate Law: km15*s31
Pmem = 2.66664 substance; B1=3.9795E-4 substance; muGA = 0.290804218727464 substance Reaction: s1 => s71; s1, Rate Law: (muGA+Pmem*B1)*s1
ld=2.84315148627376 substance Reaction: s65 => s1 + s2; s65, Rate Law: ld*s65
ka9=2073.22402517968 substance Reaction: s26 + s28 => s29; s26, s28, Rate Law: ka9*s26*s28
gammaGA3ox=3.514 substance Reaction: s28 => s5; s28, Rate Law: gammaGA3ox*s28

States:

Name Description
s23 [gibberellin A12]
s5 GA3ox_source
s24 [gibberellin A15 (diacid form)]
s40 [DELLA protein GAI]
s35 ga3ox_source
s7 DELLA_source
s71 sa1_degraded
s31 [gibberellin A15 (diacid form); GA20OX1]
s34 della_source
s36 [gibberellin A4; Gibberellin receptor GID1A; DELLA protein GAI]
s6 GA20ox_source
s32 [gibberellin A12; GA20OX1]
s22 [DELLA protein GAI]
s70 sa8_degraded
s11 ga20ox_source
s15 gid_source
s45 [gibberellin A4; DELLA protein GAI; Gibberellin receptor GID1A]
s69 sa7_degraded
s3 GA12_source
s1 [gibberellin A4]
s67 sa5_degraded
s41 [GA3OX3]
s68 sa6_degraded
s25 [gibberellin A24]
s2 [Gibberellin receptor GID1A]
s33 GID_source
s16 [DELLA protein GAI]
s30 [gibberellin A24; GA20OX1]
s26 [gibberellin A9]
s62 [gibberellin A4; Gibberellin receptor GID1A]
s42 [Gibberellin receptor GID1A]
s28 [GA3OX3]
s39 [GA20OX1]
s65 [gibberellin A4; Gibberellin receptor GID1A]
s66 GA4_source
s29 [gibberellin A9; GA3OX3]
s27 [GA20OX1]

Observables: none

Millard2016 - E. coli central carbon and energy metabolismThis model is described in the article: [Metabolic regulation…

The metabolism of microorganisms is regulated through two main mechanisms: changes of enzyme capacities as a consequence of gene expression modulation (“hierarchical control”) and changes of enzyme activities through metabolite-enzyme interactions. An increasing body of evidence indicates that hierarchical control is insufficient to explain metabolic behaviors, but the system-wide impact of metabolic regulation remains largely uncharacterized. To clarify its role, we developed and validated a detailed kinetic model of Escherichia coli central metabolism that links growth to environment. Metabolic control analyses confirm that the control is widely distributed across the network and highlight strong interconnections between all the pathways. Exploration of the model solution space reveals that several robust properties emerge from metabolic regulation, from the molecular level (e.g. homeostasis of total metabolite pool) to the overall cellular physiology (e.g. coordination of carbon uptake, catabolism, energy and redox production, and growth), while allowing a large degree of flexibility at most individual metabolic steps. These properties have important physiological implications for E. coli and significantly expand the self-regulating capacities of its metabolism. link: http://identifiers.org/doi/10.1371/journal.pcbi.1005396

Parameters: none

States: none

Observables: none

Calibrated kinetic model of glucose and acetate metabolisms of Escherichia coli, as detailed in Millard et al., 2020 (DO…

Overflow metabolism refers to the production of seemingly wasteful by-products by cells during growth on glucose even when oxygen is abundant. Two theories have been proposed to explain acetate overflow in Escherichia coli - global control of the central metabolism and local control of the acetate pathway - but neither accounts for all observations. Here, we develop a kinetic model of E. coli metabolism that quantitatively accounts for observed behaviors and successfully predicts the response of E. coli to new perturbations. We reconcile these theories and clarify the origin, control and regulation of the acetate flux. We also find that, in turns, acetate regulates glucose metabolism by coordinating the expression of glycolytic and TCA genes. Acetate should not be considered a wasteful end-product since it is also a co-substrate and a global regulator of glucose metabolism in E. coli. This has broad implications for our understanding of overflow metabolism. link: http://identifiers.org/doi/10.1101/2020.08.18.255356

Parameters: none

States: none

Observables: none

Milne2011 - Genome-scale metabolic network of Clostridium beijerinckii (iCB925)This model is described in the article:…

BACKGROUND: Solventogenic clostridia offer a sustainable alternative to petroleum-based production of butanol–an important chemical feedstock and potential fuel additive or replacement. C. beijerinckii is an attractive microorganism for strain design to improve butanol production because it (i) naturally produces the highest recorded butanol concentrations as a byproduct of fermentation; and (ii) can co-ferment pentose and hexose sugars (the primary products from lignocellulosic hydrolysis). Interrogating C. beijerinckii metabolism from a systems viewpoint using constraint-based modeling allows for simulation of the global effect of genetic modifications. RESULTS: We present the first genome-scale metabolic model (iCM925) for C. beijerinckii, containing 925 genes, 938 reactions, and 881 metabolites. To build the model we employed a semi-automated procedure that integrated genome annotation information from KEGG, BioCyc, and The SEED, and utilized computational algorithms with manual curation to improve model completeness. Interestingly, we found only a 34% overlap in reactions collected from the three databases–highlighting the importance of evaluating the predictive accuracy of the resulting genome-scale model. To validate iCM925, we conducted fermentation experiments using the NCIMB 8052 strain, and evaluated the ability of the model to simulate measured substrate uptake and product production rates. Experimentally observed fermentation profiles were found to lie within the solution space of the model; however, under an optimal growth objective, additional constraints were needed to reproduce the observed profiles–suggesting the existence of selective pressures other than optimal growth. Notably, a significantly enriched fraction of actively utilized reactions in simulations–constrained to reflect experimental rates–originated from the set of reactions that overlapped between all three databases (P = 3.52 × 10-9, Fisher's exact test). Inhibition of the hydrogenase reaction was found to have a strong effect on butanol formation–as experimentally observed. CONCLUSIONS: Microbial production of butanol by C. beijerinckii offers a promising, sustainable, method for generation of this important chemical and potential biofuel. iCM925 is a predictive model that can accurately reproduce physiological behavior and provide insight into the underlying mechanisms of microbial butanol production. As such, the model will be instrumental in efforts to better understand, and metabolically engineer, this microorganism for improved butanol production. link: http://identifiers.org/pubmed/21846360

Parameters: none

States: none

Observables: none

BIOMD0000000498 @ v0.0.1

Mitchell2013 - Liver Iron MetabolismThe model includes the core regulatory components of human liver iron metabolism. T…

Iron is essential for all known life due to its redox properties; however, these same properties can also lead to its toxicity in overload through the production of reactive oxygen species. Robust systemic and cellular control are required to maintain safe levels of iron, and the liver seems to be where this regulation is mainly located. Iron misregulation is implicated in many diseases, and as our understanding of iron metabolism improves, the list of iron-related disorders grows. Recent developments have resulted in greater knowledge of the fate of iron in the body and have led to a detailed map of its metabolism; however, a quantitative understanding at the systems level of how its components interact to produce tight regulation remains elusive. A mechanistic computational model of human liver iron metabolism, which includes the core regulatory components, is presented here. It was constructed based on known mechanisms of regulation and on their kinetic properties, obtained from several publications. The model was then quantitatively validated by comparing its results with previously published physiological data, and it is able to reproduce multiple experimental findings. A time course simulation following an oral dose of iron was compared to a clinical time course study and the simulation was found to recreate the dynamics and time scale of the systems response to iron challenge. A disease state simulation of haemochromatosis was created by altering a single reaction parameter that mimics a human haemochromatosis gene (HFE) mutation. The simulation provides a quantitative understanding of the liver iron overload that arises in this disease. This model supports and supplements understanding of the role of the liver as an iron sensor and provides a framework for further modelling, including simulations to identify valuable drug targets and design of experiments to improve further our knowledge of this system. link: http://identifiers.org/pubmed/24244122

Parameters:

Name Description
k1=3.209E-5 Reaction: species_1 => ; species_1, species_1, Rate Law: compartment_1*k1*species_1
k1=22922.0 Reaction: species_24 => species_2 + species_25; species_24, species_24, Rate Law: compartment_1*k1*species_24
K=3.0E-6; a=2.0; n=1.0 Reaction: species_2 => species_43; species_4, species_4, species_2, species_4, species_2, Rate Law: a*species_4^n/(K^n+species_4^n)*species_2
k1=3.9438E11 Reaction: species_8 + species_10 => species_18; species_8, species_10, species_8, species_10, Rate Law: compartment_3*k1*species_8^2*species_10
k1=108000.0 Reaction: species_24 => species_26 + species_25; species_24, species_24, Rate Law: compartment_1*k1*species_24
k1=1.597E-5 Reaction: species_6 => ; species_6, species_6, Rate Law: compartment_1*k1*species_6
k1=6.418E-5 Reaction: species_8 => ; species_8, species_8, Rate Law: compartment_3*k1*species_8
n=5.0; a=2.315E-4; K=5.0E-9 Reaction: species_4 => ; species_7, species_7, species_4, species_7, species_4, Rate Law: compartment_1*a*species_7^n/(K^n+species_7^n)*species_4
k1=4.0E-4 Reaction: species_2 => ; species_2, species_2, Rate Law: compartment_1*k1*species_2
k1=0.024 Reaction: species_19 => species_15 + species_43; species_19, species_19, Rate Law: compartment_3*k1*species_19
k1=4.71E10 Reaction: species_2 + species_25 => species_24; species_2, species_25, species_2, species_25, Rate Law: compartment_1*k1*species_2*species_25
k1=69600.0 Reaction: species_15 + species_43 => species_19; species_15, species_43, species_15, species_43, Rate Law: compartment_3*k1*species_15*species_43
k1=0.003535 Reaction: species_16 => species_12 + species_43; species_16, species_16, Rate Law: compartment_3*k1*species_16
K=2.5E-6; a=3.2E-5; n=1.0 Reaction: species_10 => ; species_43, species_43, species_10, species_43, species_10, Rate Law: compartment_3*a*(1-species_43^n/(K^n+species_43^n))*species_10
k1=5.6E-4 Reaction: species_7 => ; species_7, species_7, Rate Law: compartment_1*k1*species_7
k1=1102000.0 Reaction: species_9 + species_8 => species_17; species_9, species_8, species_9, species_8, Rate Law: compartment_3*k1*species_9*species_8
k1=8.37E-5 Reaction: species_18 => ; species_18, species_18, Rate Law: compartment_3*k1*species_18
Km=1.78E-5; V=2.18E-5 Reaction: species_5 => species_11; species_5, species_5, Rate Law: V*species_5/(Km+species_5)
k1=8.37E-7 Reaction: species_17 => ; species_17, species_17, Rate Law: compartment_3*k1*species_17
k1=0.0018 Reaction: species_18 => species_8 + species_10; species_18, species_18, Rate Law: compartment_3*k1*species_18
K=2.0E-6; C=17777.7 Reaction: species_5 => species_2; species_1, species_1, species_5, species_1, species_5, Rate Law: compartment_1*species_1*C*species_5/(K+species_5)
a=1.0E-9; K=5.0E-6; n=1.0 Reaction: => species_4; species_6, species_6, species_6, Rate Law: compartment_1*a*(1-species_6^n/(K^n+species_6^n))
k1=0.8333 Reaction: species_16 => species_2 + species_3; species_16, species_16, Rate Law: k1*species_16
k1=837400.0 Reaction: species_43 + species_3 => species_12; species_43, species_3, species_43, species_3, Rate Law: compartment_3*k1*species_43*species_3
k1=1.203E-5 Reaction: species_25 => ; species_25, species_25, Rate Law: compartment_1*k1*species_25
K=1.0E-9; a=2.1432E-15 Reaction: => species_1; species_5, species_5, species_5, Rate Law: compartment_1*a*species_5/(K+species_5)
k1=0.0061 Reaction: species_15 => species_43 + species_10; species_15, species_15, Rate Law: compartment_3*k1*species_15
v=3.0E-11 Reaction: => species_10, Rate Law: compartment_3*v
a=6.0E-12; K=1.0E-6; n=1.0 Reaction: => species_3; species_6, species_6, species_6, Rate Law: compartment_3*a*species_6^n/(K^n+species_6^n)
v=2.3469E-11 Reaction: => species_8, Rate Law: compartment_3*v
K=1.203E-5 Reaction: species_26 => species_2; species_26, species_25, species_26, species_25, species_26, species_25, Rate Law: compartment_1*K*species_26/species_25*species_25
basal=0.0; a1=5.0E-12; n=5.0; K=1.35E-7; a=5.0E-12; K1=6.0E-7 Reaction: => species_7; species_18, species_19, species_18, species_19, species_18, species_19, Rate Law: compartment_1*(basal+a*species_18^n/(K^n+species_18^n)+a1*species_19/(K1+species_19))
kloss=13.112 Reaction: species_26 => species_2; species_26, species_25, species_26, species_25, species_26, species_25, Rate Law: compartment_1*species_26*kloss*(1+0.048*species_26/species_25/(1+species_26/species_25))
k1=0.08 Reaction: species_17 => species_9 + species_8; species_17, species_17, Rate Law: compartment_3*k1*species_17
K=1.0E-6; n=1.0; a=4.0E-11 Reaction: => species_6; species_2, species_2, species_2, Rate Law: compartment_1*a*(1-species_2^n/(K^n+species_2^n))
V=1.034E-5; Km=1.25E-4 Reaction: species_11 => species_5; species_11, species_11, Rate Law: V*species_11/(Km+species_11)
a=2.312E-13; K=1.0E-6; n=1.0 Reaction: => species_25; species_6, species_6, species_6, Rate Law: compartment_1*a*(1-species_6^n/(K^n+species_6^n))
k1=222390.0 Reaction: species_43 + species_10 => species_15; species_43, species_10, species_43, species_10, Rate Law: compartment_3*k1*species_43*species_10
k1=121400.0 Reaction: species_12 + species_43 => species_16; species_12, species_43, species_12, species_43, Rate Law: compartment_3*k1*species_12*species_43
k1=8.37E-6 Reaction: species_3 => ; species_3, species_3, Rate Law: compartment_3*k1*species_3
k1=9.142E-4 Reaction: species_12 => species_43 + species_3; species_12, species_12, Rate Law: compartment_3*k1*species_12

States:

Name Description
species 9 [Transferrin receptor protein 1; Hereditary hemochromatosis protein]
species 2 [Iron; iron atom]
species 6 [Cytoplasmic aconitate hydratase]
species 19 [Transferrin receptor protein 2; Serotransferrin; Iron; iron atom]
species 10 [Transferrin receptor protein 2]
species 11 [Heme; ferroheme b]
species 1 [Heme oxygenase 1]
species 18 [Hereditary hemochromatosis protein; Transferrin receptor protein 2]
species 4 [Solute carrier family 40 member 1]
species 16 [Serotransferrin; Transferrin receptor protein 1; Iron; iron atom]
species 24 [Ferritin light chain; Iron; iron atom]
species 43 [Serotransferrin; Iron; iron atom]
species 3 [Transferrin receptor protein 1]
species 25 [Ferritin light chain]
species 8 [Hereditary hemochromatosis protein]
species 17 [Transferrin receptor protein 1; Hereditary hemochromatosis protein]
species 12 [Serotransferrin; Transferrin receptor protein 2; Iron; iron atom]
species 7 [Hepcidin]
species 5 [Heme; ferroheme b]
species 15 [Serotransferrin; Transferrin receptor protein 2; Iron; iron atom]
species 26 [Iron; iron atom]

Observables: none

Mathematical model of blood coagulation investigating effects of varied factor VIIa on thrombin generation. Model derive…

INTRODUCTION: The therapeutic potential of a hemostatic agent can be assessed by investigating its effects on the quantitative parameters of thrombin generation. For recombinant activated factor VII (rFVIIa)–a promising hemostasis-inducing biologic–experimental studies addressing its effects on thrombin generation yielded disparate results. To elucidate the inherent ability of rFVIIa to modulate thrombin production, it is necessary to identify rFVIIa-induced effects that are compatible with the available biochemical knowledge about thrombin generation mechanisms. MATERIALS AND METHODS: The existing body of knowledge about coagulation biochemistry can be rigorously represented by a computational model that incorporates the known reactions and parameter values constituting the biochemical network. We used a thoroughly validated numerical model to generate activated factor VII (FVIIa) titration curves in the cases of normal blood composition, hemophilia A and B blood, blood lacking factor VII, blood lacking tissue factor pathway inhibitor, and diluted blood. We utilized the generated curves to perform systematic fold-change analyses for five quantitative parameters characterizing thrombin accumulation. RESULTS: The largest fold changes induced by increasing FVIIa concentration were observed for clotting time, thrombin peak time, and maximum slope of the thrombin curve. By contrast, thrombin peak height was much less affected by FVIIa titrations, and the area under the thrombin curve stayed practically unchanged. Comparisons with experimental data demonstrated that the computationally derived patterns can be observed in vitro. CONCLUSIONS: rFVIIa modulates thrombin generation primarily by accelerating the process, without significantly affecting the total amount of generated thrombin. link: http://identifiers.org/pubmed/21641634

Parameters: none

States: none

Observables: none

Blood coagulation model using an updated Hockin2002 model. New reactions for factor X and V activation by IXa and mIIa r…

BACKGROUND: Hypothermia, which can result from tissue hypoperfusion, body exposure, and transfusion of cold resuscitation fluids, is a major factor contributing to coagulopathy of trauma and surgery. Despite considerable efforts, the mechanisms of hypothermia-induced blood coagulation impairment have not been fully understood. We introduce a kinetic modeling approach to investigate the effects of hypothermia on thrombin generation. METHODS: We extended a validated computational model to predict and analyze the impact of low temperatures (with or without concomitant blood dilution) on thrombin generation and its quantitative parameters. The computational model reflects the existing knowledge about the mechanistic details of thrombin generation biochemistry. We performed the analysis for an "average" subject, as well as for 472 subjects in the control group of the Leiden Thrombophilia Study. RESULTS: We computed and analyzed thousands of kinetic curves characterizing the generation of thrombin and the formation of the thrombin-antithrombin complex (TAT). In all simulations, hypothermia in the temperature interval 31°C to 36°C progressively slowed down thrombin generation, as reflected by clotting time, thrombin peak time, and prothrombin time, which increased in all subjects (P < 10(-5)). Maximum slope of the thrombin curve was progressively decreased, and the area under the thrombin curve was increased in hypothermia (P < 10(-5)); thrombin peak height remained practically unaffected. TAT formation was noticeably delayed (P < 10(-5)), but the final TAT levels were not significantly affected. Hypothermia-induced fold changes in the affected thrombin generation parameters were larger for lower temperatures, but were practically independent of the parameter itself and of the subjects' clotting factor composition, despite substantial variability in the subject group. Hypothermia and blood dilution acted additively on the thrombin generation parameters. CONCLUSIONS: We developed a general computational strategy that can be used to simulate the effects of changing temperature on the kinetics of biochemical systems and applied this strategy to analyze the effects of hypothermia on thrombin generation. We found that thrombin generation can be noticeably impaired in subjects with different blood plasma composition even in moderate hypothermia. Our work provides mechanistic support to the notion that thrombin generation impairment may be a key factor in coagulopathy induced by hypothermia and complicated by blood plasma dilution. link: http://identifiers.org/pubmed/23868891

Parameters: none

States: none

Observables: none

Mathematical model of the blood coagulation cascade. Extended Hockin model with contributions from Kim2007, Naski1991, S…

Current mechanistic knowledge of protein interactions driving blood coagulation has come largely from experiments with simple synthetic systems, which only partially represent the molecular composition of human blood plasma. Here, we investigate the ability of the suggested molecular mechanisms to account for fibrin generation and degradation kinetics in diverse, physiologically relevant in vitro systems. We represented the protein interaction network responsible for thrombin generation, fibrin formation, and fibrinolysis as a computational kinetic model and benchmarked it against published and newly generated data reflecting diverse experimental conditions. We then applied the model to investigate the ability of fibrinogen and a recently proposed prothrombin complex concentrate composition, PCC-AT (a combination of the clotting factors II, IX, X, and antithrombin), to restore normal thrombin and fibrin generation in diluted plasma. The kinetic model captured essential features of empirically detected effects of prothrombin, fibrinogen, and thrombin-activatable fibrinolysis inhibitor titrations on fibrin formation and degradation kinetics. Moreover, the model qualitatively predicted the impact of tissue factor and tPA/tenecteplase level variations on the fibrin output. In the majority of considered cases, PCC-AT combined with fibrinogen accurately approximated both normal thrombin and fibrin generation in diluted plasma, which could not be accomplished by fibrinogen or PCC-AT acting alone. We conclude that a common network of protein interactions can account for key kinetic features characterizing fibrin accumulation and degradation in human blood plasma under diverse experimental conditions. Combined PCC-AT/fibrinogen supplementation is a promising strategy to reverse the deleterious effects of dilution-induced coagulopathy associated with traumatic bleeding. link: http://identifiers.org/pubmed/24958246

Parameters: none

States: none

Observables: none

Mathematical model of the blood coagulation cascade with new kinetic rates to simulate acidosis. Extended Hockin2002 mod…

Acidosis, a frequent complication of trauma and complex surgery, results from tissue hypoperfusion and IV resuscitation with acidic fluids. While acidosis is known to inhibit the function of distinct enzymatic reactions, its cumulative effect on the blood coagulation system is not fully understood. Here, we use computational modeling to test the hypothesis that acidosis delays and reduces the amount of thrombin generation in human blood plasma. Moreover, we investigate the sensitivity of different thrombin generation parameters to acidosis, both at the individual and population level.We used a kinetic model to simulate and analyze the generation of thrombin and thrombin-antithrombin complexes (TAT), which were the end points of this study. Large groups of temporal thrombin and TAT trajectories were simulated and used to calculate quantitative parameters, such as clotting time (CT), thrombin peak time, maximum slope of the thrombin curve, thrombin peak height, area under the thrombin trajectory (AUC), and prothrombin time. The resulting samples of parameter values at different pH levels were compared to assess the acidosis-induced effects. To investigate intersubject variability, we parameterized the computational model using the data on clotting factor composition for 472 subjects from the Leiden Thrombophilia Study. To compare acidosis-induced relative parameter changes in individual ("virtual") subjects, we estimated the probabilities of relative change patterns by counting the pattern occurrences in our virtual subjects. Distribution overlaps for thrombin generation parameters at distinct pH levels were quantified using the Bhattacharyya coefficient.Acidosis in the range of pH 6.9 to 7.3 progressively increased CT, thrombin peak time, AUC, and prothrombin time, while decreasing maximum slope of the thrombin curve and thrombin peak height (P < 10). Acidosis delayed the onset and decreased the amount of TAT generation (P < 10). As a measure of intrasubject variability, maximum slope of the thrombin curve and CT displayed the largest and second-largest acidosis-induced relative changes, and AUC displayed the smallest relative changes among all thrombin generation parameters in our virtual subject group (1-sided 95% lower confidence limit on the fraction of subjects displaying the patterns, 0.99). As a measure of intersubject variability, the overlaps between the maximum slope of the thrombin curve distributions at acidotic pH levels with the maximum slope of the thrombin curve distribution at physiological pH level systematically exceeded analogous distribution overlaps for CT, thrombin peak time, and prothrombin time.Acidosis affected all quantitative parameters of thrombin and TAT generation. While maximum slope of the thrombin curve showed the highest sensitivity to acidosis at the individual-subject level, it may be outperformed by CT, thrombin peak time, and prothrombin time as an indicator of acidosis at the subject-group level. link: http://identifiers.org/pubmed/25839182

Parameters:

Name Description
k07 = 23000.0 Reaction: IIa + VII => IIa + VIIa, Rate Law: compartment_1*k07*IIa*VII
k29 = 103.0; k30 = 1.0E8 Reaction: Xa_Va + II => Xa_Va_II, Rate Law: compartment_1*(k30*Xa_Va*II-k29*Xa_Va_II)
k27 = 0.2; k28 = 4.0E8 Reaction: Xa + Va => Xa_Va, Rate Law: compartment_1*(k28*Xa*Va-k27*Xa_Va)
k21 = 1.0E8; k20 = 0.001 Reaction: IXa_VIIIa + X => IXa_VIIIa_X, Rate Law: compartment_1*(k21*IXa_VIIIa*X-k20*IXa_VIIIa_X)
k26_0 = 2.0E7 Reaction: IIa + V => IIa + Va, Rate Law: compartment_1*k26_0*IIa*V
k40 = 490.0 Reaction: IXa + ATIII => IXa_ATIII, Rate Law: compartment_1*k40*IXa*ATIII
k37 = 5.0E7 Reaction: TF_VIIa + Xa_TFPI => TF_VIIa_Xa_TFPI, Rate Law: compartment_1*k37*TF_VIIa*Xa_TFPI
k41 = 7100.0 Reaction: IIa + ATIII => IIa_ATIII, Rate Law: compartment_1*k41*IIa*ATIII
k9 = 2.5E7; k8 = 1.05 Reaction: TF_VIIa + X => TF_VIIa_X, Rate Law: compartment_1*(k9*TF_VIIa*X-k8*TF_VIIa_X)
k43_0 = 5700.0 Reaction: IXa + X => IXa + Xa, Rate Law: compartment_1*k43_0*IXa*X
k25 = 0.001 Reaction: IXa_VIIIa => VIIIa1_L + VIIIa2 + IXa, Rate Law: compartment_1*k25*IXa_VIIIa
k38 = 4200.0 Reaction: Xa + ATIII => Xa_ATIII, Rate Law: compartment_1*k38*Xa*ATIII
k16_0 = 7500.0 Reaction: Xa + II => Xa + IIa, Rate Law: compartment_1*k16_0*Xa*II
k23 = 22000.0; k24 = 0.006 Reaction: VIIIa => VIIIa1_L + VIIIa2, Rate Law: compartment_1*(k24*VIIIa-k23*VIIIa1_L*VIIIa2)
k35 = 1.1E-4; k36 = 3.2E8 Reaction: TF_VIIa_Xa + TFPI => TF_VIIa_Xa_TFPI, Rate Law: compartment_1*(k36*TF_VIIa_Xa*TFPI-k35*TF_VIIa_Xa_TFPI)
k22_0 = 8.2 Reaction: IXa_VIIIa_X => IXa_VIIIa + Xa, Rate Law: compartment_1*k22_0*IXa_VIIIa_X
k44_0 = 3000000.0 Reaction: mIIa + V => mIIa + Va, Rate Law: compartment_1*k44_0*mIIa*V
k32_0 = 2.3E8 Reaction: mIIa + Xa_Va => IIa + Xa_Va, Rate Law: compartment_1*k32_0*mIIa*Xa_Va
k15_0 = 1.8 Reaction: TF_VIIa_IX => TF_VIIa + IXa, Rate Law: compartment_1*k15_0*TF_VIIa_IX
k4 = 2.3E7; k3 = 0.0031 Reaction: TF + VIIa => TF_VIIa, Rate Law: compartment_1*(k4*TF*VIIa-k3*TF_VIIa)
k39 = 7100.0 Reaction: mIIa + ATIII => mIIa_ATIII, Rate Law: compartment_1*k39*mIIa*ATIII
k34 = 900000.0; k33 = 3.6E-4 Reaction: Xa + TFPI => Xa_TFPI, Rate Law: compartment_1*(k34*Xa*TFPI-k33*Xa_TFPI)
k2 = 3200000.0; k1 = 0.0031 Reaction: TF + VII => TF_VII, Rate Law: compartment_1*(k2*TF*VII-k1*TF_VII)
k10_0 = 6.0 Reaction: TF_VIIa_X => TF_VIIa_Xa, Rate Law: compartment_1*k10_0*TF_VIIa_X
k12 = 2.2E7; k11 = 19.0 Reaction: TF_VIIa + Xa => TF_VIIa_Xa, Rate Law: compartment_1*(k12*TF_VIIa*Xa-k11*TF_VIIa_Xa)
k42 = 230.0 Reaction: TF_VIIa + ATIII => TF_VIIa_ATIII, Rate Law: compartment_1*k42*TF_VIIa*ATIII
k31_0 = 63.5 Reaction: Xa_Va_II => Xa_Va + mIIa, Rate Law: compartment_1*k31_0*Xa_Va_II
k06 = 1.3E7 Reaction: Xa + VII => Xa + VIIa, Rate Law: compartment_1*k06*Xa*VII
k05 = 440000.0 Reaction: TF_VIIa + VII => TF_VIIa + VIIa, Rate Law: compartment_1*k05*TF_VIIa*VII
k19 = 1.0E7; k18 = 0.005 Reaction: IXa + VIIIa => IXa_VIIIa, Rate Law: compartment_1*(k19*IXa*VIIIa-k18*IXa_VIIIa)
k14 = 1.0E7; k13 = 2.4 Reaction: TF_VIIa + IX => TF_VIIa_IX, Rate Law: compartment_1*(k14*TF_VIIa*IX-k13*TF_VIIa_IX)
k17_0 = 2.0E7 Reaction: IIa + VIII => IIa + VIIIa, Rate Law: compartment_1*k17_0*IIa*VIII

States:

Name Description
IIa ATIII [Prothrombin; Antithrombin-III]
TFPI [Tissue factor pathway inhibitor]
Xa ATIII [Antithrombin-III; Coagulation factor X]
VIII [Coagulation factor VIII]
ATIII [Antithrombin-III]
Xa Va II [Coagulation factor V; Coagulation factor X; Prothrombin]
V [Coagulation factor V]
Xa [Coagulation factor X]
VIIIa1 L [Coagulation factor VIII]
TF VIIa ATIII [Coagulation factor VII; Antithrombin-III; Tissue factor]
IXa ATIII [Coagulation factor IX; Antithrombin-III]
TF VIIa X [Coagulation factor X; Coagulation factor VII; Tissue factor]
TF [Tissue factor]
TF VIIa Xa [Coagulation factor X; Coagulation factor VII; Tissue factor]
TF VIIa Xa TFPI [Tissue factor; Coagulation factor X; Coagulation factor VII; Tissue factor pathway inhibitor]
mIIa ATIII [Prothrombin; Antithrombin-III]
X [Coagulation factor X]
Xa Va [Coagulation factor X; Coagulation factor V]
TF VII [Coagulation factor VII; Tissue factor]
VIIIa2 [Coagulation factor VIII]
TF VIIa [Tissue factor; Coagulation factor VII]
VIIIa [Coagulation factor VIII]
Va [Coagulation factor V]
mIIa [Prothrombin]
IIa [Prothrombin]
Xa TFPI [Coagulation factor X; Tissue factor pathway inhibitor]
VIIa [Coagulation factor VII]
IXa VIIIa X [Coagulation factor IX; Coagulation factor X; Coagulation factor VIII]
TF VIIa IX [Coagulation factor IX; Tissue factor; Coagulation factor VII]
IXa [Coagulation factor IX]
VII [Coagulation factor VII]
II [Prothrombin]
IX [Coagulation factor IX]
IXa VIIIa [Coagulation factor VIII; Coagulation factor IX]

Observables: none

Mathematical model of blood coagulation. Extended model of Mitrophanov2011 (which is an extension of Hockin2002). Additi…

The use of prothrombin complex concentrates in trauma- and surgery-induced coagulopathy is complicated by the possibility of thromboembolic events. To explore the effects of these agents on thrombin generation (TG), we investigated combinations of coagulation factors equivalent to 3- and 4-factor prothrombin complex concentrates with and without added antithrombin (AT), as well as recombinant factor VIIa (rFVIIa), in a dilutional model. These data were then used to develop a computational model to test whether such a model could predict the TG profiles of these agents used to treat dilutional coagulopathy.We measured TG in plasma collected from 10 healthy volunteers using Calibrated Automated Thrombogram. TG measurements were performed in undiluted plasma, 3-fold saline-diluted plasma, and diluted plasma supplemented with the following factors: rFVIIa (group rFVIIa); factors (F)II, FIX, FX, and AT (group "combination of coagulation factors" [CCF]-AT); or FII, FVII, FIX, and FX (group CCF-FVII). We extended an existing computational model of TG to include additional reactions that impact the Calibrated Automated Thrombogram readout. We developed and applied a computational strategy to train the model using only a subset of the obtained TG data and used the remaining data for model validation.rFVIIa decreased lag time and the time to thrombin peak generation beyond their predilution levels (P < 0.001) but did not restore normal thrombin peak height (P < 0.001). CCF-FVII supplementation decreased lag time (P = 0.034) and thrombin peak time (P < 0.001) and increased both peak height (P < 0.001) and endogenous thrombin potential (P = 0.055) beyond their predilution levels. CCF-AT supplementation in diluted plasma resulted in an improvement in TG without causing the exaggerated effects of rFVIIa and CCF-FVII supplementation. The differences between the effects of CCF-AT and supplementation with rFVIIa and CCF-FVII were significant for lag time (P < 0.001 and P = 0.005, respectively), time to thrombin peak (P < 0.001 and P = 0.004, respectively), velocity index (P < 0.001 and P = 0.019, respectively), thrombin peak height (P < 0.001 for both comparisons), and endogenous thrombin potential (P = 0.034 and P = 0.019, respectively). The computational model generated subject-specific predictions and identified typical patterns of TG improvement.In this study of the effects of hemodilution, CCF-AT supplementation improved the dilution-impaired plasma TG potential in a more balanced way than either rFVIIa alone or CCF-FVII supplementation. Predictive computational modeling can guide plasma dilution/supplementation experiments. link: http://identifiers.org/pubmed/27541717

Parameters: none

States: none

Observables: none

MODEL1006230055 @ v0.0.1

This a model from the article: Influence of delayed viral production on viral dynamics in HIV-1 infected patients. M…

We present and analyze a model for the interaction of human immunodeficiency virus type 1 (HIV-1) with target cells that includes a time delay between initial infection and the formation of productively infected cells. Assuming that the variation among cells with respect to this 'intracellular' delay can be approximated by a gamma distribution, a high flexible distribution that can mimic a variety of biologically plausible delays, we provide analytical solutions for the expected decline in plasma virus concentration after the initiation of antiretroviral therapy with one or more protease inhibitors. We then use the model to investigate whether the parameters that characterize viral dynamics can be identified from biological data. Using non-linear least-squares regression to fit the model to simulated data in which the delays conform to a gamma distribution, we show that good estimates for free viral clearance rates, infected cell death rates, and parameters characterizing the gamma distribution can be obtained. For simulated data sets in which the delays were generated using other biologically plausible distributions, reasonably good estimates for viral clearance rates, infected cell death rates, and mean delay times can be obtained using the gamma-delay model. For simulated data sets that include added simulated noise, viral clearance rate estimates are not as reliable. If the mean intracellular delay is known, however, we show that reasonable estimates for the viral clearance rate can be obtained by taking the harmonic mean of viral clearance rate estimates from a group of patients. These results demonstrate that it is possible to incorporate distributed intracellular delays into existing models for HIV dynamics and to use these refined models to estimate the half-life of free virus from data on the decline in HIV-1 RNA following treatment. link: http://identifiers.org/pubmed/9780612

Parameters: none

States: none

Observables: none

Mizuno2012 - AlzPathway: a comprehensive map of Alzheimer's diseaseNon-kinetic molecular map. Pure SBML file of AlzPath…

BACKGROUND: Alzheimer's disease (AD) is the most common cause of dementia among the elderly. To clarify pathogenesis of AD, thousands of reports have been accumulating. However, knowledge of signaling pathways in the field of AD has not been compiled as a database before. DESCRIPTION: Here, we have constructed a publicly available pathway map called "AlzPathway" that comprehensively catalogs signaling pathways in the field of AD. We have collected and manually curated over 100 review articles related to AD, and have built an AD pathway map using CellDesigner. AlzPathway is currently composed of 1347 molecules and 1070 reactions in neuron, brain blood barrier, presynaptic, postsynaptic, astrocyte, and microglial cells and their cellular localizations. AlzPathway is available as both the SBML (Systems Biology Markup Language) map for CellDesigner and the high resolution image map. AlzPathway is also available as a web service (online map) based on Payao system, a community-based, collaborative web service platform for pathway model curation, enabling continuous updates by AD researchers. CONCLUSIONS: AlzPathway is the first comprehensive map of intra, inter and extra cellular AD signaling pathways which can enable mechanistic deciphering of AD pathogenesis. The AlzPathway map is accessible at http://alzpathway.org/. link: http://identifiers.org/pubmed/22647208

Parameters: none

States: none

Observables: none

Dynamics of Breast Cancer under Different Rates of Chemoradiotherapy. Mkango SB1, Shaban N1, Mureithi E1, Ngoma T2. Auth…

A type of cancer which originates from the breast tissue is referred to as breast cancer. Globally, it is the most common cause of death in women. Treatments such as radiotherapy, chemotherapy, hormone therapy, immunotherapy, and gene therapy are the main strategies in the fight against breast cancer. The present study aims at investigating the effects of the combined radiotherapy and chemotherapy as a way to treat breast cancer, and different treatment approaches are incorporated into the model. Also, the model is fitted to data on patients with breast cancer in Tanzania. We determine new treatment strategies, and finally, we show that when sufficient amount of chemotherapy and radiotherapy with a low decay rate is used, the drug will be significantly more effective in combating the disease while health cells remain above the threshold. link: http://identifiers.org/pubmed/31611927

Parameters: none

States: none

Observables: none

Model published in the paper Chaperone availability subordinates cell cycle entry to growth and stress by David F. Mo…

The precise coordination of growth and proliferation has a universal prevalence in cell homeostasis. As a prominent property, cell size is modulated by the coordination between these processes in bacterial, yeast, and mammalian cells, but the underlying molecular mechanisms are largely unknown. Here, we show that multifunctional chaperone systems play a concerted and limiting role in cell-cycle entry, specifically driving nuclear accumulation of the G1 Cdk-cyclin complex. Based on these findings, we establish and test a molecular competition model that recapitulates cell-cycle-entry dependence on growth rate. As key predictions at a single-cell level, we show that availability of the Ydj1 chaperone and nuclear accumulation of the G1 cyclin Cln3 are inversely dependent on growth rate and readily respond to changes in protein synthesis and stress conditions that alter protein folding requirements. Thus, chaperone workload would subordinate Start to the biosynthetic machinery and dynamically adjust proliferation to the growth potential of the cell. link: http://identifiers.org/pubmed/30988162

Parameters: none

States: none

Observables: none

MODEL1207300000 @ v0.0.1

This model is from the article: A model of flux regulation in the cholesterol biosynthesis pathway: Immune mediated gr…

The cholesterol biosynthesis pathway has recently been shown to play an important role in the innate immune response to viral infection with host protection occurring through a coordinate down regulation of the enzymes catalysing each metabolic step. In contrast, statin based drugs, which form the principle pharmaceutical agents for decreasing the activity of this pathway, target a single enzyme. Here, we build an ordinary differential equation model of the cholesterol biosynthesis pathway in order to investigate how the two regulatory strategies impact upon the behaviour of the pathway. We employ a modest set of assumptions: that the pathway operates away from saturation, that each metabolite is involved in multiple cellular interactions and that mRNA levels reflect enzyme concentrations. Using data taken from primary bone marrow derived macrophage cells infected with murine cytomegalovirus or treated with IFNγ, we show that, under these assumptions, coordinate down-regulation of enzyme activity imparts a graduated reduction in flux along the pathway. In contrast, modelling a statin-like treatment that achieves the same degree of down-regulation in cholesterol production, we show that this delivers a step change in flux along the pathway. The graduated reduction mediated by physiological coordinate regulation of multiple enzymes supports a mechanism that allows a greater level of specificity, altering cholesterol levels with less impact upon interactions branching from the pathway, than pharmacological step reductions. We argue that coordinate regulation is likely to show a long-term evolutionary advantage over single enzyme regulation. Finally, the results from our models have implications for future pharmaceutical therapies intended to target cholesterol production with greater specificity and fewer off target effects, suggesting that this can be achieved by mimicking the coordinated down-regulation observed in immunological responses. link: http://identifiers.org/pubmed/22664637

Parameters: none

States: none

Observables: none

MODEL1703310000 @ v0.0.1

Ramirez2017 - Human global metabolism in brown and white adipocytesRecon 2.1A, an update to Recon 2.1x, is suitable for…

White adipocytes are specialized for energy storage, whereas brown adipocytes are specialized for energy expenditure. Explicating this difference can help identify therapeutic targets for obesity. A common tool to assess metabolic differences between such cells is the Seahorse Extracellular Flux (XF) Analyzer, which measures oxygen consumption and media acidification in the presence of different substrates and perturbagens. Here, we integrate the Analyzer's metabolic profile from human white and brown adipocytes with a genome-scale metabolic model to predict flux differences across the metabolic map. Predictions matched experimental data for the metabolite 4-aminobutyrate, the protein ABAT, and the fluxes for glucose, glutamine, and palmitate. We also uncovered a difference in how adipocytes dispose of nitrogenous waste, with brown adipocytes secreting less ammonia and more urea than white adipocytes. Thus, the method and software we developed allow for broader metabolic phenotyping and provide a distinct approach to uncovering metabolic differences. link: http://identifiers.org/pubmed/29241534

Parameters: none

States: none

Observables: none

MODEL2021729243 @ v0.0.1

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

A better understanding of human metabolism and its relationship with diseases is an important task in human systems biology studies. In this paper, we present a high-quality human metabolic network manually reconstructed by integrating genome annotation information from different databases and metabolic reaction information from literature. The network contains nearly 3000 metabolic reactions, which were reorganized into about 70 human-specific metabolic pathways according to their functional relationships. By analysis of the functional connectivity of the metabolites in the network, the bow-tie structure, which was found previously by structure analysis, is reconfirmed. Furthermore, the distribution of the disease related genes in the network suggests that the IN (substrates) subset of the bow-tie structure has more flexibility than other parts. link: http://identifiers.org/pubmed/17882155

Parameters: none

States: none

Observables: none

MODEL2021747594 @ v0.0.1

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

A better understanding of human metabolism and its relationship with diseases is an important task in human systems biology studies. In this paper, we present a high-quality human metabolic network manually reconstructed by integrating genome annotation information from different databases and metabolic reaction information from literature. The network contains nearly 3000 metabolic reactions, which were reorganized into about 70 human-specific metabolic pathways according to their functional relationships. By analysis of the functional connectivity of the metabolites in the network, the bow-tie structure, which was found previously by structure analysis, is reconfirmed. Furthermore, the distribution of the disease related genes in the network suggests that the IN (substrates) subset of the bow-tie structure has more flexibility than other parts. link: http://identifiers.org/pubmed/17882155

Parameters: none

States: none

Observables: none

MODEL2426780967 @ v0.0.1

**Increased glycolytic flux as an outcome of whole-genome duplication in yeast.** GC Conant and KH Wolfe, Mol Syst Biol…

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

Parameters: none

States: none

Observables: none

MODEL2427021978 @ v0.0.1

**Increased glycolytic flux as an outcome of whole-genome duplication in yeast.** GC Conant and KH Wolfe, Mol Syst Biol…

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

Parameters: none

States: none

Observables: none

MODEL3631586579 @ v0.0.1

This is a kinetic model of the monomeric photosystem II (PSII). The model is partially based on the earlier model use…

MOTIVATION: It is a question of whether the supramolecular organization of the protein complex has an impact on its function, or not. In the case of the photosystem II (PSII), water splitting might be influenced by cooperation of the PSIIs. Since PSII is the source of the atmospheric oxygen and because better understanding of the water splitting may contribute to the effective use of water as an alternative energy source, possible cooperation should be analyzed and discussed. RESULTS: We suggest that the dimeric organization of the PSII induces cooperation in the water splitting. We show that the model of monomeric PSII is unable to produce the oxygen after the second short flash (associated with the double turnover of the PSII), in contrast to the experimental data and model of dimeric PSII with considered cooperation. On the basis of this fact and partially from the support from other studies, we concluded that the double turnover of the PSII induced by short flashes might be caused by the cooperation in the water splitting. We further discuss a possibility that the known pathway of the electron transport through the PSII might be incomplete and besides D1-Y161, other cofactor which is able to oxidize the special chlorophyll pair (P680) must be considered in the monomeric PSII to explain the oxygen production after the second short flash. AVAILABILITY: Commented SBML codes (.XML files) of the monomeric and dimeric PSII models will be available (at the time of publication) in the BioModels database (www.ebi.ac.uk/biomodels). link: http://identifiers.org/pubmed/18845578

Parameters: none

States: none

Observables: none

MODEL3632127506 @ v0.0.1

This is a kinetic model of the dimeric photosystem II (PSII). The model has partially based on the earlier model used…

MOTIVATION: It is a question of whether the supramolecular organization of the protein complex has an impact on its function, or not. In the case of the photosystem II (PSII), water splitting might be influenced by cooperation of the PSIIs. Since PSII is the source of the atmospheric oxygen and because better understanding of the water splitting may contribute to the effective use of water as an alternative energy source, possible cooperation should be analyzed and discussed. RESULTS: We suggest that the dimeric organization of the PSII induces cooperation in the water splitting. We show that the model of monomeric PSII is unable to produce the oxygen after the second short flash (associated with the double turnover of the PSII), in contrast to the experimental data and model of dimeric PSII with considered cooperation. On the basis of this fact and partially from the support from other studies, we concluded that the double turnover of the PSII induced by short flashes might be caused by the cooperation in the water splitting. We further discuss a possibility that the known pathway of the electron transport through the PSII might be incomplete and besides D1-Y161, other cofactor which is able to oxidize the special chlorophyll pair (P680) must be considered in the monomeric PSII to explain the oxygen production after the second short flash. AVAILABILITY: Commented SBML codes (.XML files) of the monomeric and dimeric PSII models will be available (at the time of publication) in the BioModels database (www.ebi.ac.uk/biomodels). link: http://identifiers.org/pubmed/18845578

Parameters: none

States: none

Observables: none

MODEL6624091635 @ v0.0.1

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

Glucose addition and subsequent run-out experiments were compared to simulations with a detailed glycolytic model of Lactococcus lactis. The model was constructed largely on bases of enzyme kinetic data taken from literature and not adjusted for the specific simulations shown here. Upon glucose depletion a rapid increase in PEP, inorganic phosphate and a gradual decrease in fructose 1,6-bisphosphate (FBP) were measured and predicted by simulation. The dynamic changes in these and other intermediate concentrations as measured in the experiments were well predicted by the kinetic model. link: http://identifiers.org/pubmed/12241048

Parameters: none

States: none

Observables: none

MODEL8262229752 @ v0.0.1

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

Quorum sensing (QS) is an important determinant of bacterial phenotype. Many cell functions are regulated by intricate and multimodal QS signal transduction processes. The LuxS/AI-2 QS system is highly conserved among Eubacteria and AI-2 is reported as a 'universal' signal molecule. To understand the hierarchical organization of AI-2 circuitry, a comprehensive approach incorporating stochastic simulations was developed. We investigated the synthesis, uptake, and regulation of AI-2, developed testable hypotheses, and made several discoveries: (1) the mRNA transcript and protein levels of AI-2 synthases, Pfs and LuxS, do not contribute to the dramatically increased level of AI-2 found when cells are grown in the presence of glucose; (2) a concomitant increase in metabolic flux through this synthesis pathway in the presence of glucose only partially accounts for this difference. We predict that 'high-flux' alternative pathways or additional biological steps are involved in AI-2 synthesis; and (3) experimental results validate this hypothesis. This work demonstrates the utility of linking cell physiology with systems-based stochastic models that can be assembled de novo with partial knowledge of biochemical pathways. link: http://identifiers.org/pubmed/17170762

Parameters: none

States: none

Observables: none

Created by The MathWorks, Inc. SimBiology tool, Version 3.3

Modulated immune signal (CD14-TLR and TNF) in leishmaniasis can be linked to EGFR pathway involved in wound healing, through crosstalk points. This signaling network can be further linked to a synthetic gene circuit acting as a positive feedback loop to elicit a synchronized intercellular communication among the immune cells which may contribute to a better understanding of signaling dynamics in leishmaniasis.Network reconstruction with positive feedback loop, simulation (ODE 15s solver) and sensitivity analysis of CD14-TLR, TNF and EGFR was done in SimBiology (MATLAB 7.11.1). Cytoscape and adjacency matrix were used to calculate network topology. PCA was extracted by using sensitivity coefficient in MATLAB. Model reduction was done using time, flux and sensitivity score.Network has five crosstalk points: NIK, IκB-NFκB and MKK (4/7, 3/6, 1/2) which show high flux and sensitivity. PI3K in EGFR pathway shows high flux and sensitivity. PCA score was high for cytoplasmic ERK1/2, PI3K, Atk, STAT1/3 and nuclear JNK. Of the 125 parameters, 20% are crucial as deduced by model reduction.EGFR can be linked to CD14-TLR and TNF through the MAPK crosstalk points. These pathways may be controlled through Ras and Raf that lie upstream of signaling components ERK ½ (c) and JNK (n) that have a high PCA score via a synthetic gene circuit for activating cell-cell communication to elicit an inflammatory response. Also a disease resolving effect may be achieved through PI3K in the EGFR pathway.The reconstructed signaling network can be linked to a gene circuit with a positive feedback loop, for cell-cell communication resulting in synchronized response in the immune cell population, for disease resolving effect in leishmaniasis. link: http://identifiers.org/pubmed/23994140

Parameters:

Name Description
mw286a7792_09c4_443e_98f4_a68f66a1f380=1.0 1/second Reaction: mwb71eb539_dca6_47ab_8df5_430d84af0bfb => mw97345a67_a8e8_42aa_8e62_69e9d2b6cf45; mwb71eb539_dca6_47ab_8df5_430d84af0bfb, Rate Law: mw286a7792_09c4_443e_98f4_a68f66a1f380*mwb71eb539_dca6_47ab_8df5_430d84af0bfb
mwf89fc9a4_ad1e_4e59_8a06_4b8dc2cc84a7=0.28 nanomole/second; mwaad66a38_26d2_41fc_9261_79c57500a6d4=1.5 nanomole Reaction: mw6ee00a71_ab68_454b_b1cd_60c1ebd19cfa => mwf8cfed1b_6fcf_4cba_bc30_b44490814a7a; mw6ee00a71_ab68_454b_b1cd_60c1ebd19cfa, Rate Law: mwf89fc9a4_ad1e_4e59_8a06_4b8dc2cc84a7*mw6ee00a71_ab68_454b_b1cd_60c1ebd19cfa/(mwaad66a38_26d2_41fc_9261_79c57500a6d4+mw6ee00a71_ab68_454b_b1cd_60c1ebd19cfa)
mwb38e4258_82d9_4b48_8059_eccf9fd6f8e3=0.2 nanomole; mw99befd62_975f_49e1_bfaf_22a482ce44ea=1.2 nanomole/second Reaction: mwb4633da9_f9d6_4ad8_a7e5_da075c830e17 => mw7204ab72_2ee5_4b92_b420_2583dacc4343; mwb4633da9_f9d6_4ad8_a7e5_da075c830e17, Rate Law: mw99befd62_975f_49e1_bfaf_22a482ce44ea*mwb4633da9_f9d6_4ad8_a7e5_da075c830e17/(mwb38e4258_82d9_4b48_8059_eccf9fd6f8e3+mwb4633da9_f9d6_4ad8_a7e5_da075c830e17)
mw9d622ba3_b43b_4101_bef8_c964c2f158a0=0.99 nanomole/second; mw2b1ea101_d4a1_42e9_a70f_cb8026911ed5=0.08 nanomole Reaction: mw6ee00a71_ab68_454b_b1cd_60c1ebd19cfa => mw805b55df_cc91_4227_bb52_930e961b682c; mw6ee00a71_ab68_454b_b1cd_60c1ebd19cfa, Rate Law: mw9d622ba3_b43b_4101_bef8_c964c2f158a0*mw6ee00a71_ab68_454b_b1cd_60c1ebd19cfa/(mw2b1ea101_d4a1_42e9_a70f_cb8026911ed5+mw6ee00a71_ab68_454b_b1cd_60c1ebd19cfa)
mweaee0b65_7c40_4c9e_bd70_c5454eeb41fa=0.9 nanomole/second; mw84020ddc_e419_4aa4_ab12_e84989ad461d=0.3 nanomole Reaction: mw05469f51_73f7_4ba1_9f1a_bce5fea143c2 => mwf20834c8_a115_460b_859c_4e3ca1ffd953; mw05469f51_73f7_4ba1_9f1a_bce5fea143c2, Rate Law: mweaee0b65_7c40_4c9e_bd70_c5454eeb41fa*mw05469f51_73f7_4ba1_9f1a_bce5fea143c2/(mw84020ddc_e419_4aa4_ab12_e84989ad461d+mw05469f51_73f7_4ba1_9f1a_bce5fea143c2)
mw1c3fcb1f_0b90_46dd_b13a_2950fb9e18ae=0.2 nanomole/second; mw8a65d230_2abb_478d_ab8a_6719d972483d=0.01 nanomole Reaction: mw308b75ec_28b7_4d97_92e2_51a8ce04116a => mw75377e12_e23d_44b3_9823_5fac9b23edc8; mw308b75ec_28b7_4d97_92e2_51a8ce04116a, Rate Law: mw1c3fcb1f_0b90_46dd_b13a_2950fb9e18ae*mw308b75ec_28b7_4d97_92e2_51a8ce04116a/(mw8a65d230_2abb_478d_ab8a_6719d972483d+mw308b75ec_28b7_4d97_92e2_51a8ce04116a)
mw1670fb0f_e301_4b7a_93d4_35fe7f504e92=0.03 nanomole; mwcad6928f_259d_4125_987e_977e0c40ef7d=1.5 nanomole/second Reaction: mw46ee629a_dd6b_4163_9da1_2614bb1d74bc => mwb71eb539_dca6_47ab_8df5_430d84af0bfb; mw46ee629a_dd6b_4163_9da1_2614bb1d74bc, Rate Law: mwcad6928f_259d_4125_987e_977e0c40ef7d*mw46ee629a_dd6b_4163_9da1_2614bb1d74bc/(mw1670fb0f_e301_4b7a_93d4_35fe7f504e92+mw46ee629a_dd6b_4163_9da1_2614bb1d74bc)
mw107b07de_5145_436d_9fd7_e4e2103106d7=1.2 nanomole/second; mwd51a525a_5fea_42c6_a8fd_40429ee627cf=0.02 nanomole Reaction: mw805b55df_cc91_4227_bb52_930e961b682c => mw46ee629a_dd6b_4163_9da1_2614bb1d74bc; mw805b55df_cc91_4227_bb52_930e961b682c, Rate Law: mw107b07de_5145_436d_9fd7_e4e2103106d7*mw805b55df_cc91_4227_bb52_930e961b682c/(mwd51a525a_5fea_42c6_a8fd_40429ee627cf+mw805b55df_cc91_4227_bb52_930e961b682c)
mwd4bfd4cc_6fd4_4b3b_980d_547ce2740b7e=2.2 nanomole/second; mw9ac53fed_0388_4261_b457_030cd631fa0e=0.1 nanomole Reaction: mw7204ab72_2ee5_4b92_b420_2583dacc4343 => mw6939cefe_e7ff_4a3f_b45b_a9234d1b5573; mw7204ab72_2ee5_4b92_b420_2583dacc4343, Rate Law: mwd4bfd4cc_6fd4_4b3b_980d_547ce2740b7e*mw7204ab72_2ee5_4b92_b420_2583dacc4343/(mw9ac53fed_0388_4261_b457_030cd631fa0e+mw7204ab72_2ee5_4b92_b420_2583dacc4343)
mw0e1c63a9_8b8a_4ec7_9608_0059208d992f=0.01 nanomole; mw6ac279a2_23fe_4e48_a910_2a94ef61244c=0.1 nanomole/second Reaction: mw308b75ec_28b7_4d97_92e2_51a8ce04116a => mw46ee629a_dd6b_4163_9da1_2614bb1d74bc; mw308b75ec_28b7_4d97_92e2_51a8ce04116a, Rate Law: mw6ac279a2_23fe_4e48_a910_2a94ef61244c*mw308b75ec_28b7_4d97_92e2_51a8ce04116a/(mw0e1c63a9_8b8a_4ec7_9608_0059208d992f+mw308b75ec_28b7_4d97_92e2_51a8ce04116a)
mw4c0ee457_fb1c_48fa_a0b7_ff10d632d1e0=2.0 1/second Reaction: mw4079e13c_446e_4aa2_9ec4_233583833d02 => mwe5fade7d_1715_4bb1_843f_923da8ecddf1; mw4079e13c_446e_4aa2_9ec4_233583833d02, Rate Law: mw4c0ee457_fb1c_48fa_a0b7_ff10d632d1e0*mw4079e13c_446e_4aa2_9ec4_233583833d02
mw9d566811_669e_4b95_8452_c4853f54a2de=0.35 1/second Reaction: mwf20834c8_a115_460b_859c_4e3ca1ffd953 => mwb4633da9_f9d6_4ad8_a7e5_da075c830e17; mwf20834c8_a115_460b_859c_4e3ca1ffd953, Rate Law: mw9d566811_669e_4b95_8452_c4853f54a2de*mwf20834c8_a115_460b_859c_4e3ca1ffd953
mwc29ba5b1_b0e7_4fa1_9e46_a4c0bdbdacc4=0.2 nanomole; mw5990b7f9_7d15_4306_9047_6237ecf066ca=0.9 nanomole/second Reaction: mw75377e12_e23d_44b3_9823_5fac9b23edc8 => mw67d0cf04_d6a7_4725_a869_098a96a3350d; mw75377e12_e23d_44b3_9823_5fac9b23edc8, Rate Law: mw5990b7f9_7d15_4306_9047_6237ecf066ca*mw75377e12_e23d_44b3_9823_5fac9b23edc8/(mwc29ba5b1_b0e7_4fa1_9e46_a4c0bdbdacc4+mw75377e12_e23d_44b3_9823_5fac9b23edc8)
mwa68f7af3_30af_4fa0_9290_9e005c875763=1.4 1/second Reaction: mw6939cefe_e7ff_4a3f_b45b_a9234d1b5573 => mw8a358487_b18b_42df_a646_cd75eb5bfcc2; mw6939cefe_e7ff_4a3f_b45b_a9234d1b5573, Rate Law: mwa68f7af3_30af_4fa0_9290_9e005c875763*mw6939cefe_e7ff_4a3f_b45b_a9234d1b5573
mw8adff9cb_4657_413f_a2bd_100d4aa53076=0.98 nanomole/second; mwc9cf88fa_c525_4372_80e1_c72b1cc758f1=0.15 nanomole Reaction: mw702be69a_eb4f_425e_87c7_ef7d85254536 => mwbee11634_55df_4a3f_998a_634dfaf46fd7; mw702be69a_eb4f_425e_87c7_ef7d85254536, Rate Law: mw8adff9cb_4657_413f_a2bd_100d4aa53076*mw702be69a_eb4f_425e_87c7_ef7d85254536/(mwc9cf88fa_c525_4372_80e1_c72b1cc758f1+mw702be69a_eb4f_425e_87c7_ef7d85254536)
mw1a4dcdaf_ff4b_41a9_ac1d_79fd2d942260=0.6 1/second Reaction: mw0be0d193_fd6b_4824_8928_dbade8b5c99c => mw280197c8_98de_43f0_bf01_0f332a1ab689; mw0be0d193_fd6b_4824_8928_dbade8b5c99c, Rate Law: mw1a4dcdaf_ff4b_41a9_ac1d_79fd2d942260*mw0be0d193_fd6b_4824_8928_dbade8b5c99c
mw85a8c1da_f29f_4dcf_a515_bf9f9921240b=0.2 nanomole; mwf5a1613b_fb22_43b0_b95a_2c18ecbcedd8=0.5 nanomole/second Reaction: mw308b75ec_28b7_4d97_92e2_51a8ce04116a => mw136c8391_14f4_4a28_83a3_35cc74a2e040; mw308b75ec_28b7_4d97_92e2_51a8ce04116a, Rate Law: mwf5a1613b_fb22_43b0_b95a_2c18ecbcedd8*mw308b75ec_28b7_4d97_92e2_51a8ce04116a/(mw85a8c1da_f29f_4dcf_a515_bf9f9921240b+mw308b75ec_28b7_4d97_92e2_51a8ce04116a)
mwa4c28075_8524_4874_aee5_c38231bfbaae=1.5 nanomole; mw66285193_607e_42b6_b726_c2409a2ce563=1.0 nanomole/second Reaction: mw3832f277_aef2_4f1d_87af_abc2a3c1a7d5 => mw13651143_feb5_49a5_adab_9105c2647446; mw3832f277_aef2_4f1d_87af_abc2a3c1a7d5, Rate Law: mw66285193_607e_42b6_b726_c2409a2ce563*mw3832f277_aef2_4f1d_87af_abc2a3c1a7d5/(mwa4c28075_8524_4874_aee5_c38231bfbaae+mw3832f277_aef2_4f1d_87af_abc2a3c1a7d5)
mw6a74caa7_9d44_449b_854b_c1678b36ac1d=0.8 nanomole; mw78df1f4c_2a96_4d8f_a009_c19ba0ec406a=0.2 nanomole/second Reaction: mw280197c8_98de_43f0_bf01_0f332a1ab689 => mw9bb804c9_3e4e_4684_9f6b_4e6f6706a58e; mw280197c8_98de_43f0_bf01_0f332a1ab689, Rate Law: mw78df1f4c_2a96_4d8f_a009_c19ba0ec406a*mw280197c8_98de_43f0_bf01_0f332a1ab689/(mw6a74caa7_9d44_449b_854b_c1678b36ac1d+mw280197c8_98de_43f0_bf01_0f332a1ab689)
mw9a480703_d4bb_4de8_8975_13a18205ce53=0.19 1/second Reaction: mw13651143_feb5_49a5_adab_9105c2647446 => mw17ae9adc_54ab_407b_a34d_8413a3a10cc6; mw13651143_feb5_49a5_adab_9105c2647446, Rate Law: mw9a480703_d4bb_4de8_8975_13a18205ce53*mw13651143_feb5_49a5_adab_9105c2647446
mw661c7759_2bd3_4c93_bb0a_823bb37b9820=0.299 1/second Reaction: mw280197c8_98de_43f0_bf01_0f332a1ab689 => mw323a57b4_8e59_4116_9ad1_fe547b89c858; mw280197c8_98de_43f0_bf01_0f332a1ab689, Rate Law: mw661c7759_2bd3_4c93_bb0a_823bb37b9820*mw280197c8_98de_43f0_bf01_0f332a1ab689
mwa7160f91_3c68_402a_b3bd_acd8490c5d2d=0.56 nanomole; mw2b6193d2_d588_46b7_8463_ce7bc30e1575=1.3 nanomole/second Reaction: mw2dc73059_a841_48d5_b4bd_3ac24d94c42e => mw7204ab72_2ee5_4b92_b420_2583dacc4343; mw2dc73059_a841_48d5_b4bd_3ac24d94c42e, Rate Law: mw2b6193d2_d588_46b7_8463_ce7bc30e1575*mw2dc73059_a841_48d5_b4bd_3ac24d94c42e/(mwa7160f91_3c68_402a_b3bd_acd8490c5d2d+mw2dc73059_a841_48d5_b4bd_3ac24d94c42e)
mw5aa11378_86b4_45f6_aea1_27208e47e559=0.72 1/second Reaction: mwe5fade7d_1715_4bb1_843f_923da8ecddf1 => mw262497ec_3d54_4367_bfe3_76a9c57497cb; mwe5fade7d_1715_4bb1_843f_923da8ecddf1, Rate Law: mw5aa11378_86b4_45f6_aea1_27208e47e559*mwe5fade7d_1715_4bb1_843f_923da8ecddf1
mw251bb80a_5527_4b9c_9834_99556d4e824a=0.01 nanomole; mw75017b10_387d_43e4_9fb1_fed7ce6bd490=0.3 nanomole/second Reaction: mw308b75ec_28b7_4d97_92e2_51a8ce04116a => mw702be69a_eb4f_425e_87c7_ef7d85254536; mw308b75ec_28b7_4d97_92e2_51a8ce04116a, Rate Law: mw75017b10_387d_43e4_9fb1_fed7ce6bd490*mw308b75ec_28b7_4d97_92e2_51a8ce04116a/(mw251bb80a_5527_4b9c_9834_99556d4e824a+mw308b75ec_28b7_4d97_92e2_51a8ce04116a)
mw2b132eeb_ce2a_4a53_8c22_c102ebd2edb9=1.1 1/second Reaction: mwc844b7c0_98f5_4d0d_8f0c_00dfe8b54e6d => mw4d2e70a7_f499_461d_ae18_bc53b365b091; mwc844b7c0_98f5_4d0d_8f0c_00dfe8b54e6d, Rate Law: mw2b132eeb_ce2a_4a53_8c22_c102ebd2edb9*mwc844b7c0_98f5_4d0d_8f0c_00dfe8b54e6d
mw0733f43b_b430_40c4_8b93_1555a4bdbaa1=0.6 nanomole/second; mw56211dd8_6a88_465e_bed2_f603bf8c5b52=0.6 nanomole Reaction: mw6ee00a71_ab68_454b_b1cd_60c1ebd19cfa => mw136c8391_14f4_4a28_83a3_35cc74a2e040; mw6ee00a71_ab68_454b_b1cd_60c1ebd19cfa, Rate Law: mw0733f43b_b430_40c4_8b93_1555a4bdbaa1*mw6ee00a71_ab68_454b_b1cd_60c1ebd19cfa/(mw56211dd8_6a88_465e_bed2_f603bf8c5b52+mw6ee00a71_ab68_454b_b1cd_60c1ebd19cfa)
mw6834a7ac_63c4_4741_b0fc_069c665f1de2=1.18 1/second Reaction: mw8cc67de0_64e6_428f_ab09_4c2825cc172c => mw6ee00a71_ab68_454b_b1cd_60c1ebd19cfa; mw8cc67de0_64e6_428f_ab09_4c2825cc172c, Rate Law: mw6834a7ac_63c4_4741_b0fc_069c665f1de2*mw8cc67de0_64e6_428f_ab09_4c2825cc172c
mwbad3f510_fbca_4aa7_a4c2_5c1b47297802=2.35 nanomole/second; mw2fa0d3fe_4e99_49d2_a339_089198589a1e=0.43 nanomole; mw4d5fd70d_8603_4056_adfa_5af26d657455=1.0 Reaction: mw8a358487_b18b_42df_a646_cd75eb5bfcc2 => mwc844b7c0_98f5_4d0d_8f0c_00dfe8b54e6d; mw8a358487_b18b_42df_a646_cd75eb5bfcc2, Rate Law: mwbad3f510_fbca_4aa7_a4c2_5c1b47297802*mw8a358487_b18b_42df_a646_cd75eb5bfcc2^mw4d5fd70d_8603_4056_adfa_5af26d657455/(mw2fa0d3fe_4e99_49d2_a339_089198589a1e+mw8a358487_b18b_42df_a646_cd75eb5bfcc2^mw4d5fd70d_8603_4056_adfa_5af26d657455)
mwbf5d43e3_e386_4b05_997d_4e70cbff9498=0.15 nanomole; mw5fdc2487_13a9_449a_b90c_95446ddf7f37=1.3 nanomole/second Reaction: mw136c8391_14f4_4a28_83a3_35cc74a2e040 => mw2dc73059_a841_48d5_b4bd_3ac24d94c42e; mw136c8391_14f4_4a28_83a3_35cc74a2e040, Rate Law: mw5fdc2487_13a9_449a_b90c_95446ddf7f37*mw136c8391_14f4_4a28_83a3_35cc74a2e040/(mwbf5d43e3_e386_4b05_997d_4e70cbff9498+mw136c8391_14f4_4a28_83a3_35cc74a2e040)
mw883852ed_c433_4dec_baa0_386309fc085c=0.24 nanomole/second; mw6069097b_159a_4bcf_a591_e496d06cf0a9=1.2 nanomole Reaction: mw323a57b4_8e59_4116_9ad1_fe547b89c858 => mw173d8585_5817_4b4c_932a_cf7d673680ac; mw323a57b4_8e59_4116_9ad1_fe547b89c858, Rate Law: mw883852ed_c433_4dec_baa0_386309fc085c*mw323a57b4_8e59_4116_9ad1_fe547b89c858/(mw6069097b_159a_4bcf_a591_e496d06cf0a9+mw323a57b4_8e59_4116_9ad1_fe547b89c858)
mwd2f6a3b7_5a74_4d77_b40c_1a6713b98554=0.42 1/second Reaction: mw67d0cf04_d6a7_4725_a869_098a96a3350d => mw1f12e5bc_ebbc_4347_b6b7_5cd1740ac69a; mw67d0cf04_d6a7_4725_a869_098a96a3350d, Rate Law: mwd2f6a3b7_5a74_4d77_b40c_1a6713b98554*mw67d0cf04_d6a7_4725_a869_098a96a3350d
mw26de6022_cc14_484b_a172_db4173a1ccaa=0.7 nanomole/second; mw7e75e47c_6d88_49fb_a9c4_9154f12cc4d5=1.5 nanomole Reaction: mw32c21c39_237b_4d4c_bb5d_117cb30ce68a => mw75377e12_e23d_44b3_9823_5fac9b23edc8; mw32c21c39_237b_4d4c_bb5d_117cb30ce68a, Rate Law: mw26de6022_cc14_484b_a172_db4173a1ccaa*mw32c21c39_237b_4d4c_bb5d_117cb30ce68a/(mw7e75e47c_6d88_49fb_a9c4_9154f12cc4d5+mw32c21c39_237b_4d4c_bb5d_117cb30ce68a)
mwafa60fbe_9272_468d_94e7_b82b985f938c=0.61 1/second Reaction: mwbee11634_55df_4a3f_998a_634dfaf46fd7 => mwd9e7a9b9_6f1b_4bbc_afa5_6cb192b62ce8; mwbee11634_55df_4a3f_998a_634dfaf46fd7, Rate Law: mwafa60fbe_9272_468d_94e7_b82b985f938c*mwbee11634_55df_4a3f_998a_634dfaf46fd7
mw244e346b_4442_45db_864e_0442ceca94d1=0.5 nanomole; mwb3751ef8_2226_4ec3_9ac9_f92f5771a1a4=2.0E-4 nanomole/second Reaction: mw280197c8_98de_43f0_bf01_0f332a1ab689 => mw9a5baf6d_0285_4ad3_9499_059c553d9cf6; mw280197c8_98de_43f0_bf01_0f332a1ab689, Rate Law: mwb3751ef8_2226_4ec3_9ac9_f92f5771a1a4*mw280197c8_98de_43f0_bf01_0f332a1ab689/(mw244e346b_4442_45db_864e_0442ceca94d1+mw280197c8_98de_43f0_bf01_0f332a1ab689)
mw6e048357_d06d_4522_bb79_a91c4f53bda7=0.6 1/second Reaction: mwa5d6f7e4_dc4d_4931_91ce_1e78e7b2f195 => mw4079e13c_446e_4aa2_9ec4_233583833d02; mwa5d6f7e4_dc4d_4931_91ce_1e78e7b2f195, Rate Law: mw6e048357_d06d_4522_bb79_a91c4f53bda7*mwa5d6f7e4_dc4d_4931_91ce_1e78e7b2f195
mwf88d190e_a505_4f7e_ac8d_e43997c74b9c=0.62 nanomole; mw1a1570ff_e786_473f_860b_2e7694acfcc2=1.14 nanomole/second Reaction: mwd9e7a9b9_6f1b_4bbc_afa5_6cb192b62ce8 => mwfed5a135_c91b_4d20_91b2_3a61723544dd; mwd9e7a9b9_6f1b_4bbc_afa5_6cb192b62ce8, Rate Law: mw1a1570ff_e786_473f_860b_2e7694acfcc2*mwd9e7a9b9_6f1b_4bbc_afa5_6cb192b62ce8/(mwf88d190e_a505_4f7e_ac8d_e43997c74b9c+mwd9e7a9b9_6f1b_4bbc_afa5_6cb192b62ce8)
mw36ee8f87_d06f_4d16_ac13_f4075b56c6f4=0.55 1/second Reaction: mw262497ec_3d54_4367_bfe3_76a9c57497cb => mw8bffd47e_34de_4738_81bf_7a39a40b3ae8; mw262497ec_3d54_4367_bfe3_76a9c57497cb, Rate Law: mw36ee8f87_d06f_4d16_ac13_f4075b56c6f4*mw262497ec_3d54_4367_bfe3_76a9c57497cb
mw37ac6d2c_1be9_4998_a9c5_8761d3e0ba0f=0.2 nanomole; mw31c3bf7d_10cd_412a_9a76_0fb66845c18d=0.6 nanomole/second Reaction: mwf8cfed1b_6fcf_4cba_bc30_b44490814a7a => mw702be69a_eb4f_425e_87c7_ef7d85254536; mwf8cfed1b_6fcf_4cba_bc30_b44490814a7a, Rate Law: mw31c3bf7d_10cd_412a_9a76_0fb66845c18d*mwf8cfed1b_6fcf_4cba_bc30_b44490814a7a/(mw37ac6d2c_1be9_4998_a9c5_8761d3e0ba0f+mwf8cfed1b_6fcf_4cba_bc30_b44490814a7a)
mw4945db3d_e20c_4870_b96b_6fb98c4b12f6=1.0 nanomole; mwdeab2870_570e_4b2c_b73d_84c1ad8c2262=1.0 nanomole/second Reaction: mwb4633da9_f9d6_4ad8_a7e5_da075c830e17 => mw173d8585_5817_4b4c_932a_cf7d673680ac; mwb4633da9_f9d6_4ad8_a7e5_da075c830e17, Rate Law: mwdeab2870_570e_4b2c_b73d_84c1ad8c2262*mwb4633da9_f9d6_4ad8_a7e5_da075c830e17/(mw4945db3d_e20c_4870_b96b_6fb98c4b12f6+mwb4633da9_f9d6_4ad8_a7e5_da075c830e17)
mw78a1e67e_883c_497f_86a6_f85da783010e=0.2 nanomole/second; mw5d6cf9c6_4dc0_4fe6_9afc_da397fe896b2=1.5 nanomole Reaction: mw173d8585_5817_4b4c_932a_cf7d673680ac => mw32c21c39_237b_4d4c_bb5d_117cb30ce68a; mw173d8585_5817_4b4c_932a_cf7d673680ac, Rate Law: mw78a1e67e_883c_497f_86a6_f85da783010e*mw173d8585_5817_4b4c_932a_cf7d673680ac/(mw5d6cf9c6_4dc0_4fe6_9afc_da397fe896b2+mw173d8585_5817_4b4c_932a_cf7d673680ac)
mw0b0869f4_26bb_4d13_9124_b2c1b28e3ae1=1.5 nanomole; mw2f1f65d1_5633_4625_b2b7_0eb267eac293=0.4 nanomole/second Reaction: mw173d8585_5817_4b4c_932a_cf7d673680ac => mw702be69a_eb4f_425e_87c7_ef7d85254536; mw173d8585_5817_4b4c_932a_cf7d673680ac, Rate Law: mw2f1f65d1_5633_4625_b2b7_0eb267eac293*mw173d8585_5817_4b4c_932a_cf7d673680ac/(mw0b0869f4_26bb_4d13_9124_b2c1b28e3ae1+mw173d8585_5817_4b4c_932a_cf7d673680ac)
mw826aae9f_9728_4bbb_a11b_60578912218b=1.2 1/second Reaction: mw4d2e70a7_f499_461d_ae18_bc53b365b091 => mw8cc67de0_64e6_428f_ab09_4c2825cc172c; mw4d2e70a7_f499_461d_ae18_bc53b365b091, Rate Law: mw826aae9f_9728_4bbb_a11b_60578912218b*mw4d2e70a7_f499_461d_ae18_bc53b365b091
mw13b39522_0751_4041_a78e_871cd5d81592=0.2 nanomole; mw2a0659f9_eab8_4ada_8f82_23068b9986eb=1.5 nanomole/second Reaction: mw97345a67_a8e8_42aa_8e62_69e9d2b6cf45 => mw5c67812a_17f5_43cf_8acb_9bde272c1911; mw97345a67_a8e8_42aa_8e62_69e9d2b6cf45, Rate Law: mw2a0659f9_eab8_4ada_8f82_23068b9986eb*mw97345a67_a8e8_42aa_8e62_69e9d2b6cf45/(mw13b39522_0751_4041_a78e_871cd5d81592+mw97345a67_a8e8_42aa_8e62_69e9d2b6cf45)
mwb69d510c_dcde_4bfb_9e4a_89954f6a7bf5=1.5 nanomole; mw3690266b_c916_4ba1_a98a_b589dc75c1cd=0.8 nanomole/second Reaction: mw9bb804c9_3e4e_4684_9f6b_4e6f6706a58e => mw64453fc5_a275_4bba_84f0_2af249b31514; mw9bb804c9_3e4e_4684_9f6b_4e6f6706a58e, Rate Law: mw3690266b_c916_4ba1_a98a_b589dc75c1cd*mw9bb804c9_3e4e_4684_9f6b_4e6f6706a58e/(mwb69d510c_dcde_4bfb_9e4a_89954f6a7bf5+mw9bb804c9_3e4e_4684_9f6b_4e6f6706a58e)
mw933afd80_4eff_4c6c_967b_d15b2244e55d=1.0 nanomole; mw0f1ee85e_95a3_42c7_94ae_71f36061aaf0=1.0 nanomole/second Reaction: mwb4633da9_f9d6_4ad8_a7e5_da075c830e17 => mw32c21c39_237b_4d4c_bb5d_117cb30ce68a; mwb4633da9_f9d6_4ad8_a7e5_da075c830e17, Rate Law: mw0f1ee85e_95a3_42c7_94ae_71f36061aaf0*mwb4633da9_f9d6_4ad8_a7e5_da075c830e17/(mw933afd80_4eff_4c6c_967b_d15b2244e55d+mwb4633da9_f9d6_4ad8_a7e5_da075c830e17)
mw6d4dc2a5_6fe8_4d80_93f4_b9f438b6eb0e=1.0 nanomole; mw18e075a4_dde4_42be_9315_e0e90d461b99=0.6 nanomole/second Reaction: mw9a5baf6d_0285_4ad3_9499_059c553d9cf6 => mw05469f51_73f7_4ba1_9f1a_bce5fea143c2; mw9a5baf6d_0285_4ad3_9499_059c553d9cf6, Rate Law: mw18e075a4_dde4_42be_9315_e0e90d461b99*mw9a5baf6d_0285_4ad3_9499_059c553d9cf6/(mw6d4dc2a5_6fe8_4d80_93f4_b9f438b6eb0e+mw9a5baf6d_0285_4ad3_9499_059c553d9cf6)
mwf0b9efb6_f0e9_4704_b5b1_dec2a68c3321=0.48 1/second Reaction: mw8bffd47e_34de_4738_81bf_7a39a40b3ae8 => mw308b75ec_28b7_4d97_92e2_51a8ce04116a; mw8bffd47e_34de_4738_81bf_7a39a40b3ae8, Rate Law: mwf0b9efb6_f0e9_4704_b5b1_dec2a68c3321*mw8bffd47e_34de_4738_81bf_7a39a40b3ae8
mw18baeb4d_ad18_4c22_95c4_2ada0f618c65=1.8 nanomole; mw234b354b_eb7b_4af6_a678_9339f6b5eb8d=0.01 nanomole/second Reaction: mw280197c8_98de_43f0_bf01_0f332a1ab689 => mw3832f277_aef2_4f1d_87af_abc2a3c1a7d5; mw280197c8_98de_43f0_bf01_0f332a1ab689, Rate Law: mw234b354b_eb7b_4af6_a678_9339f6b5eb8d*mw280197c8_98de_43f0_bf01_0f332a1ab689/(mw18baeb4d_ad18_4c22_95c4_2ada0f618c65+mw280197c8_98de_43f0_bf01_0f332a1ab689)
mwb336e12c_0e62_4fff_94c0_2771b1a19065=0.2 1/second Reaction: mw64453fc5_a275_4bba_84f0_2af249b31514 => mwda4716f1_ae00_4149_aec3_12531380425a; mw64453fc5_a275_4bba_84f0_2af249b31514, Rate Law: mwb336e12c_0e62_4fff_94c0_2771b1a19065*mw64453fc5_a275_4bba_84f0_2af249b31514

States:

Name Description
mw3832f277 aef2 4f1d 87af abc2a3c1a7d5 [Tyrosine-protein kinase JAK1]
mwbee11634 55df 4a3f 998a 634dfaf46fd7 [Mitogen-activated protein kinase 8]
mw13651143 feb5 49a5 adab 9105c2647446 [Signal transducer and activator of transcription 1-alpha/beta]
mw97345a67 a8e8 42aa 8e62 69e9d2b6cf45 [mitogen-activated protein kinase p38 binding]
mwf20834c8 a115 460b 859c 4e3ca1ffd953 [diglyceride]
mw46ee629a dd6b 4163 9da1 2614bb1d74bc [Dual specificity mitogen-activated protein kinase kinase 3; Protein kinase byr1]
mw6ee00a71 ab68 454b b1cd 60c1ebd19cfa [tumor necrosis factor receptor superfamily complex; Receptor-interacting serine/threonine-protein kinase 1; Tumor necrosis factor receptor type 1-associated DEATH domain protein; TNF receptor-associated factor 2]
mw5c67812a 17f5 43cf 8acb 9bde272c1911 [Proto-oncogene c-Fos]
mw702be69a eb4f 425e 87c7 ef7d85254536 [Mitogen-activated protein kinase kinase kinase 7]
mw05469f51 73f7 4ba1 9f1a bce5fea143c2 [1-phosphatidyl-1D-myo-inositol 4,5-bisphosphate]
mw2dc73059 a841 48d5 b4bd 3ac24d94c42e [IkappaB kinase complex]
mw9bb804c9 3e4e 4684 9f6b 4e6f6706a58e [Phosphatidylinositol 4-phosphate 3-kinase C2 domain-containing subunit alpha]
mwe5fade7d 1715 4bb1 843f 923da8ecddf1 [Myeloid differentiation primary response protein MyD88]
mw8cc67de0 64e6 428f ab09 4c2825cc172c [Tumor necrosis factor receptor superfamily member 1A]
mw262497ec 3d54 4367 bfe3 76a9c57497cb [Interleukin-1 receptor-associated kinase 1]
mw4079e13c 446e 4aa2 9ec4 233583833d02 [Toll-Like Receptors Cascades; Monocyte differentiation antigen CD14; Toll-like receptor 2]
mw9a5baf6d 0285 4ad3 9499 059c553d9cf6 [1-phosphatidylinositol 4,5-bisphosphate phosphodiesterase gamma-1; cytokine-mediated signaling pathway]
mw64453fc5 a275 4bba 84f0 2af249b31514 [Tyrosine-protein kinase BTK]
mw7204ab72 2ee5 4b92 b420 2583dacc4343 [I-kappaB phosphorylation]
mwda4716f1 ae00 4149 aec3 12531380425a [Tyrosine-protein kinase BTK]
mw4d2e70a7 f499 461d ae18 bc53b365b091 [Tumor necrosis factor]
mwd9e7a9b9 6f1b 4bbc afa5 6cb192b62ce8 [Mitogen-activated protein kinase 8]
mw6939cefe e7ff 4a3f b45b a9234d1b5573 [Nuclear factor NF-kappa-B p105 subunit]
mw280197c8 98de 43f0 bf01 0f332a1ab689 [Epidermal growth factor receptor]
mwf8cfed1b 6fcf 4cba bc30 b44490814a7a [Mitogen-activated protein kinase kinase kinase 1]
mwa5d6f7e4 dc4d 4931 91ce 1e78e7b2f195 [lipophosphoglycan]
mw308b75ec 28b7 4d97 92e2 51a8ce04116a [Mitogen-activated protein kinase kinase kinase 7; TGF-beta-activated kinase 1 and MAP3K7-binding protein 3]
mw8bffd47e 34de 4738 81bf 7a39a40b3ae8 [TNF receptor-associated factor 6]
mw32c21c39 237b 4d4c bb5d 117cb30ce68a [RAF proto-oncogene serine/threonine-protein kinase]
mw136c8391 14f4 4a28 83a3 35cc74a2e040 [NF-kappaB-inducing kinase activity]
mw17ae9adc 54ab 407b a34d 8413a3a10cc6 [Signal transducer and activator of transcription 1-alpha/beta]
mw67d0cf04 d6a7 4725 a869 098a96a3350d [Mitogen-activated protein kinase 3; Mitogen-activated protein kinase CPK1]
mw173d8585 5817 4b4c 932a cf7d673680ac [IPR020849]
mw1f12e5bc ebbc 4347 b6b7 5cd1740ac69a [Mitogen-activated protein kinase 3; Mitogen-activated protein kinase CPK1]
mw8a358487 b18b 42df a646 cd75eb5bfcc2 [Nuclear factor NF-kappa-B p105 subunit]
mwb4633da9 f9d6 4ad8 a7e5 da075c830e17 [Protein kinase C alpha type; Putative serine/threonine-protein kinase-like protein CCR3]
mw323a57b4 8e59 4116 9ad1 fe547b89c858 [Growth factor receptor-bound protein 2; Son of sevenless homolog 2; IPR000980]
mw75377e12 e23d 44b3 9823 5fac9b23edc8 [Dual specificity mitogen-activated protein kinase kinase 2; Protein kinase byr1]
mw805b55df cc91 4227 bb52 930e961b682c [Mitogen-activated protein kinase kinase kinase 5]
mwfed5a135 c91b 4d20 91b2 3a61723544dd [Transcription factor AP-1]
mwb71eb539 dca6 47ab 8df5 430d84af0bfb [mitogen-activated protein kinase p38 binding]
mw0be0d193 fd6b 4824 8928 dbade8b5c99c [Pro-epidermal growth factor]
mwc844b7c0 98f5 4d0d 8f0c 00dfe8b54e6d [Tumor necrosis factor]

Observables: none

Created by The MathWorks, Inc. SimBiology tool, Version 3.3

Network of signaling proteins and functional interaction between the infected cell and the leishmanial parasite, though are not well understood, may be deciphered computationally by reconstructing the immune signaling network. As we all know signaling pathways are well-known abstractions that explain the mechanisms whereby cells respond to signals, collections of pathways form networks, and interactions between pathways in a network, known as cross-talk, enables further complex signaling behaviours. In silico perturbations can help identify sensitive crosstalk points in the network which can be pharmacologically tested. In this study, we have developed a model for immune signaling cascade in leishmaniasis and based upon the interaction analysis obtained through simulation, we have developed a model network, between four signaling pathways i.e., CD14, epidermal growth factor (EGF), tumor necrotic factor (TNF) and PI3 K mediated signaling. Principal component analysis of the signaling network showed that EGF and TNF pathways can be potent pharmacological targets to curb leishmaniasis. The approach is illustrated with a proposed workable model of epidermal growth factor receptor (EGFR) that modulates the immune response. EGFR signaling represents a critical junction between inflammation related signal and potent cell regulation machinery that modulates the expression of cytokines. link: http://identifiers.org/pubmed/24432155

Parameters: none

States: none

Observables: none

BIOMD0000000776 @ v0.0.1

The paper describes a model of resistance of cancer to chemotherapy. Created by COPASI 4.25 (Build 207) This model…

The goal of palliative cancer chemotherapy treatment is to prolong survival and improve quality of life when tumour eradication is not feasible. Chemotherapy protocol design is considered in this context using a simple, robust, model of advanced tumour growth with Gompertzian dynamics, taking into account the effects of drug resistance. It is predicted that reduced chemotherapy protocols can readily lead to improved survival times due to the effects of competition between resistant and sensitive tumour cells. Very early palliation is also predicted to quickly yield near total tumour resistance and thus decrease survival duration. Finally, our simulations indicate that failed curative attempts using dose densification, a common protocol escalation strategy, can reduce survival times. link: http://identifiers.org/pubmed/19135065

Parameters:

Name Description
N = 1.00002E10 1; Ninf = 2.0E12 1; b = 0.005928 1/d; C0 = 2.0 1 Reaction: S =>, Rate Law: tme*(-b)*ln(N/Ninf)*C0*S
N = 1.00002E10 1; Ninf = 2.0E12 1; b = 0.005928 1/d Reaction: => R, Rate Law: tme*(-b)*ln(N/Ninf)*R
N = 1.00002E10 1; Ninf = 2.0E12 1; t1 = 1.0E-6 1; t2 = 1.0E-6 1; b = 0.005928 1/d Reaction: S => R, Rate Law: tme*(-b)*ln(N/Ninf)*(t1*S-t2*R)

States:

Name Description
S [malignant cell]
R [malignant cell]

Observables: none

BIOMD0000000315 @ v0.0.1

This is the model of the in vitro DNA oscillator called oligator with the optmized set of parameters described in the ar…

Living organisms perform and control complex behaviours by using webs of chemical reactions organized in precise networks. This powerful system concept, which is at the very core of biology, has recently become a new foundation for bioengineering. Remarkably, however, it is still extremely difficult to rationally create such network architectures in artificial, non-living and well-controlled settings. We introduce here a method for such a purpose, on the basis of standard DNA biochemistry. This approach is demonstrated by assembling de novo an efficient chemical oscillator: we encode the wiring of the corresponding network in the sequence of small DNA templates and obtain the predicted dynamics. Our results show that the rational cascading of standard elements opens the possibility to implement complex behaviours in vitro. Because of the simple and well-controlled environment, the corresponding chemical network is easily amenable to quantitative mathematical analysis. These synthetic systems may thus accelerate our understanding of the underlying principles of biological dynamic modules. link: http://identifiers.org/pubmed/21283142

Parameters:

Name Description
k0d = 0.0294 nM_per_min; k0r = 3.43457943925 per_min Reaction: T1 + alpha => alpha_T1, Rate Law: sample*(k0d*T1*alpha-k0r*alpha_T1)
k26d = 1.7262 per_min Reaction: Inh => empty, Rate Law: sample*k26d*Inh
k17d = 0.0171 nM_per_min; k17r = 0.610714285714 per_min Reaction: beta + T3_Inh => beta_T3_Inh, Rate Law: sample*(k17d*beta*T3_Inh-k17r*beta_T3_Inh)
k9r = 0.0171 nM_per_min; k9d = 0.610714285714 per_min Reaction: T2_beta => T2 + beta, Rate Law: sample*(k9d*T2_beta-k9r*T2*beta)
k6d = 3.34 per_min Reaction: alpha_alpha_T1 => alpha_T1_alpha, Rate Law: sample*k6d*alpha_alpha_T1
k19d = 5.566848 per_min Reaction: beta_T3_Inh => Inh + beta_Inh_T3, Rate Law: sample*k19d*beta_T3_Inh
k20d = 3.2064 per_min Reaction: beta_Inh_T3 => beta_T3_Inh, Rate Law: sample*k20d*beta_Inh_T3
k10r = 0.0294 nM_per_min; k10d = 3.43457943925 per_min Reaction: alpha_T2_beta => alpha + T2_beta, Rate Law: sample*(k10d*alpha_T2_beta-k10r*alpha*T2_beta)
k3r = 0.0294 nM_per_min; k3d = 3.43457943925 per_min Reaction: alpha_T1_alpha => alpha + T1_alpha, Rate Law: sample*(k3d*alpha_T1_alpha-k3r*alpha*T1_alpha)
k5d = 11.8408 per_min Reaction: alpha_T1_alpha => alpha + alpha_alpha_T1, Rate Law: sample*k5d*alpha_T1_alpha
k18d = 17.024 per_min Reaction: beta_T3 => beta_Inh_T3, Rate Law: sample*k18d*beta_T3
k2r = 0.0294 nM_per_min; k2d = 3.43457943925 per_min Reaction: T1_alpha => T1 + alpha, Rate Law: sample*(k2d*T1_alpha-k2r*T1*alpha)
k11d = 11.8408 per_min Reaction: alpha_T2 => alpha_beta_T2, Rate Law: sample*k11d*alpha_T2
k23r = 0.021546 nM_per_min; k23d = 4.15391351351E-5 nM_per_min Reaction: alpha + Inh_T1 => alpha_T1 + Inh, Rate Law: sample*(k23d*alpha*Inh_T1-k23r*alpha_T1*Inh)
k21d = 0.027 nM_per_min; k21r = 0.00608108108108 per_min Reaction: T1 + Inh => Inh_T1, Rate Law: sample*(k21d*T1*Inh-k21r*Inh_T1)
k1r = 0.0294 nM_per_min; k1d = 3.43457943925 per_min Reaction: alpha_T1_alpha => alpha + alpha_T1, Rate Law: sample*(k1d*alpha_T1_alpha-k1r*alpha*alpha_T1)
k12d = 9.2239832 per_min Reaction: alpha_T2_beta => beta + alpha_beta_T2, Rate Law: sample*k12d*alpha_T2_beta
k14r = 0.610714285714 per_min; k14d = 0.0171 nM_per_min Reaction: beta + T3 => beta_T3, Rate Law: sample*(k14d*beta*T3-k14r*beta_T3)
k13d = 2.60186 per_min Reaction: alpha_beta_T2 => alpha_T2_beta, Rate Law: sample*k13d*alpha_beta_T2
k25d = 0.485802 per_min Reaction: beta => empty, Rate Law: sample*k25d*beta
k16d = 0.027 nM_per_min; k16r = 0.00186296832954 per_min Reaction: T3 + Inh => T3_Inh, Rate Law: sample*(k16d*T3*Inh-k16r*T3_Inh)
k8r = 0.0171 nM_per_min; k8d = 0.610714285714 per_min Reaction: alpha_T2_beta => alpha_T2 + beta, Rate Law: sample*(k8d*alpha_T2_beta-k8r*alpha_T2*beta)
k4d = 15.2 per_min Reaction: alpha_T1 => alpha_alpha_T1, Rate Law: sample*k4d*alpha_T1
k24d = 0.411 per_min Reaction: alpha => empty, Rate Law: sample*k24d*alpha
k7d = 0.0294 nM_per_min; k7r = 3.43457943925 per_min Reaction: alpha + T2 => alpha_T2, Rate Law: sample*(k7d*alpha*T2-k7r*alpha_T2)
k15r = 0.027 nM_per_min; k15d = 0.00186296832954 per_min Reaction: beta_T3_Inh => beta_T3 + Inh, Rate Law: sample*(k15d*beta_T3_Inh-k15r*beta_T3*Inh)
k22r = 4.15391351351E-5 nM_per_min; k22d = 0.021546 nM_per_min Reaction: T1_alpha + Inh => alpha + Inh_T1, Rate Law: sample*(k22d*T1_alpha*Inh-k22r*alpha*Inh_T1)

States:

Name Description
T3 Inh [deoxyribonucleic acid; DNA]
Inh [deoxyribonucleic acid; DNA]
alpha T2 beta [deoxyribonucleic acid; DNA]
T1 alpha [deoxyribonucleic acid; DNA]
alpha [deoxyribonucleic acid; DNA]
alpha T2 [deoxyribonucleic acid; DNA]
Inh T1 [deoxyribonucleic acid; DNA]
alpha alpha T1 [deoxyribonucleic acid; DNA]
empty Inh_T1
beta [deoxyribonucleic acid; DNA]
T3 [deoxyribonucleic acid; DNA]
alpha beta T2 [deoxyribonucleic acid; DNA]
beta T3 Inh [deoxyribonucleic acid; DNA]
beta T3 [deoxyribonucleic acid; DNA]
T1 [deoxyribonucleic acid; DNA]
beta Inh T3 [deoxyribonucleic acid; DNA]
T2 [deoxyribonucleic acid; DNA]
alpha T1 [deoxyribonucleic acid; DNA]
T2 beta [deoxyribonucleic acid; DNA]
alpha T1 alpha [deoxyribonucleic acid; DNA]

Observables: none

Montagud2010 - Genome-scale metabolic network of Synechocystis sp. PCC6803 (iSyn669)This model is described in the artic…

BACKGROUND: Synechocystis sp. PCC6803 is a cyanobacterium considered as a candidate photo-biological production platform–an attractive cell factory capable of using CO2 and light as carbon and energy source, respectively. In order to enable efficient use of metabolic potential of Synechocystis sp. PCC6803, it is of importance to develop tools for uncovering stoichiometric and regulatory principles in the Synechocystis metabolic network. RESULTS: We report the most comprehensive metabolic model of Synechocystis sp. PCC6803 available, iSyn669, which includes 882 reactions, associated with 669 genes, and 790 metabolites. The model includes a detailed biomass equation which encompasses elementary building blocks that are needed for cell growth, as well as a detailed stoichiometric representation of photosynthesis. We demonstrate applicability of iSyn669 for stoichiometric analysis by simulating three physiologically relevant growth conditions of Synechocystis sp. PCC6803, and through in silico metabolic engineering simulations that allowed identification of a set of gene knock-out candidates towards enhanced succinate production. Gene essentiality and hydrogen production potential have also been assessed. Furthermore, iSyn669 was used as a transcriptomic data integration scaffold and thereby we found metabolic hot-spots around which gene regulation is dominant during light-shifting growth regimes. CONCLUSIONS: iSyn669 provides a platform for facilitating the development of cyanobacteria as microbial cell factories. link: http://identifiers.org/pubmed/21083885

Parameters: none

States: none

Observables: none

BIOMD0000000191 @ v0.0.1

SBML creators: Armando Reyes-Palomares * , Raul Montañez *, Carlos Rodriguez-Caso +, Francisca Sanchez-Jimenez * , Migue…

We use a modeling and simulation approach to carry out an in silico analysis of the metabolic pathways involving arginine as a precursor of nitric oxide or polyamines in aorta endothelial cells. Our model predicts conditions of physiological steady state, as well as the response of the system to changes in the control parameter, external arginine concentration. Metabolic flux control analysis allowed us to predict the values of flux control coefficients for all the transporters and enzymes included in the model. This analysis fulfills the flux control coefficient summation theorem and shows that both the low affinity transporter and arginase share the control of the fluxes through these metabolic pathways. link: http://identifiers.org/pubmed/17520329

Parameters:

Name Description
Kmeffllat=847.0 microM; Vmaxefflhat=160.5 microMpermin; Kiornhat=360.0 microM; Kmhat=70.0 microM; Kmlat=847.0 microM; Vmaxeffllat=420.0 microMpermin Reaction: ORN => ; ARGex, ARGin, Rate Law: cytosol*(Vmaxefflhat/(1+ARGex/Kmhat)*ORN/(Kiornhat*(1+ARGin/Kmhat)+ORN)+Vmaxeffllat/(1+ARGex/Kmlat)*ORN/(Kmeffllat*(1+ARGin/Kmlat)+ORN))
Vmaxodc=0.013 microMpermin; Kmodc=90.0 microM Reaction: ORN =>, Rate Law: cytosol*Vmaxodc*ORN/(Kmodc+ORN)
Kmnos1=16.0 microM; Vmaxnos1=1.33 microMpermin Reaction: ARGin =>, Rate Law: cytosol*Vmaxnos1*ARGin/(Kmnos1+ARGin)
Kmarg=1500.0 microM; Vmaxarg=110.0 microMpermin; Kioarg=1000.0 microM Reaction: ARGin => ORN; ORN, Rate Law: cytosol*Vmaxarg*ARGin/(Kmarg*(1+ORN/Kioarg)+ARGin)
Kiornhat=360.0 microM; Kmhat=70.0 microM; Kmlat=847.0 microM; Vmaxlat=420.0 microMpermin; Vmaxhat=160.5 microM Reaction: ARGex => ARGin; ORN, Rate Law: extracellular*(ARGex/(Kmhat+ARGex)*Vmaxhat/(1+ORN/Kiornhat+ARGin/Kmhat)+ARGex/(Kmlat+ARGex)*Vmaxlat/(1+ORN/Kiornhat+ARGin/Kmlat))

States:

Name Description
ORN [L-ornithine; L-Ornithine]
ARGin [L-arginine; L-Arginine]
ARGex [L-arginine; L-Arginine]

Observables: none

Moore2004 - Chronic Myeloid Leukemic cells and T-lymphocytes interactionA mathematical model for the interaction of betw…

In this paper, we propose and analyse a mathematical model for chronic myelogenous leukemia (CML), a cancer of the blood. We model the interaction between naive T cells, effector T cells, and CML cancer cells in the body, using a system of ordinary differential equations which gives rates of change of the three cell populations. One of the difficulties in modeling CML is the scarcity of experimental data which can be used to estimate parameters values. To compensate for the resulting uncertainties, we use Latin hypercube sampling (LHS) on large ranges of possible parameter values in our analysis. A major goal of this work is the determination of parameters which play a critical role in remission or clearance of the cancer in the model. Our analysis examines 12 parameters, and identifies two of these, the growth and death rates of CML, as critical to the outcome of the system. Our results indicate that the most promising research avenues for treatments of CML should be those that affect these two significant parameters (CML growth and death rates), while altering the other parameters should have little effect on the outcome. link: http://identifiers.org/pubmed/15038986

Parameters:

Name Description
gamma_e = 0.0077 0.0864*l/s Reaction: T_cell_effector => Sink; CML, Rate Law: COMpartment*gamma_e*CML*T_cell_effector
eta = 43.0 1/Ml; kn = 0.063 1/(0.0115741*ms) Reaction: T_cell_naive => Sink; CML, Rate Law: COMpartment*kn*T_cell_naive*CML/(CML+eta)
gamma_c = 0.047 0.0864*l/s Reaction: CML => Sink; T_cell_effector, Rate Law: COMpartment*gamma_c*T_cell_effector*CML
dc = 0.68 1/(0.0115741*ms) Reaction: CML => Sink, Rate Law: COMpartment*dc*CML
de = 0.12 1/(0.0115741*ms) Reaction: T_cell_effector => Sink, Rate Law: COMpartment*de*T_cell_effector
eta = 43.0 1/Ml; alpha_e = 0.53 1/(0.0115741*ms) Reaction: Source => T_cell_effector; CML, Rate Law: COMpartment*alpha_e*T_cell_effector*CML/(CML+eta)
rc = 0.23 1/(0.0115741*ms); Cmax = 190000.0 1/Ml Reaction: Source => CML, Rate Law: COMpartment*rc*CML*ln(Cmax/CML)
sn = 0.071 1/(11.5741*l*s) Reaction: Source => T_cell_naive, Rate Law: COMpartment*sn*Source
eta = 43.0 1/Ml; alpha_n = 0.56 1; kn = 0.063 1/(0.0115741*ms) Reaction: Source => T_cell_effector; T_cell_naive, CML, Rate Law: COMpartment*alpha_n*kn*T_cell_naive*CML/(CML+eta)
dn = 0.05 1/(0.0115741*ms) Reaction: T_cell_naive => Sink, Rate Law: COMpartment*dn*T_cell_naive

States:

Name Description
T cell naive [Naive T-Lymphocyte]
Source Source
CML [leukemia cell]
T cell effector [Effector T-Lymphocyte]
Sink Sink

Observables: none

Its a mathematical model depicting CML (chronic myelogenous leukemia) interaction with T cells and impact of T cell acti…

In this paper, we propose and analyse a mathematical model for chronic myelogenous leukemia (CML), a cancer of the blood. We model the interaction between naive T cells, effector T cells, and CML cancer cells in the body, using a system of ordinary differential equations which gives rates of change of the three cell populations. One of the difficulties in modeling CML is the scarcity of experimental data which can be used to estimate parameters values. To compensate for the resulting uncertainties, we use Latin hypercube sampling (LHS) on large ranges of possible parameter values in our analysis. A major goal of this work is the determination of parameters which play a critical role in remission or clearance of the cancer in the model. Our analysis examines 12 parameters, and identifies two of these, the growth and death rates of CML, as critical to the outcome of the system. Our results indicate that the most promising research avenues for treatments of CML should be those that affect these two significant parameters (CML growth and death rates), while altering the other parameters should have little effect on the outcome. link: http://identifiers.org/pubmed/15038986

Parameters:

Name Description
Kn = 0.062 1/d; n = 720.0 mmol/l; An = 0.14 dimensionless; Ae = 0.98 1/d Reaction: => eff_Tcells; naive_Tcells, tumor_cells, Rate Law: TumorMicroenvr*(An*Kn*naive_Tcells*tumor_cells/(tumor_cells+n)+Ae*eff_Tcells*tumor_cells/(tumor_cells+n))
gamma_E = 0.057 l/(mmol*d); De = 0.3 1/d Reaction: eff_Tcells => ; tumor_cells, Rate Law: TumorMicroenvr*(De*eff_Tcells+gamma_E*tumor_cells*eff_Tcells)
Sn = 0.37 mmol/(l*d) Reaction: => naive_Tcells, Rate Law: TumorMicroenvr*Sn
Dn = 0.23 1/d; Kn = 0.062 1/d; n = 720.0 mmol/l Reaction: naive_Tcells => ; tumor_cells, Rate Law: TumorMicroenvr*(Dn*naive_Tcells+Kn*naive_Tcells*tumor_cells/(tumor_cells+n))
gamma_C = 0.0034 l/(mmol*d); Dc = 0.024 1/d Reaction: tumor_cells => ; eff_Tcells, Rate Law: TumorMicroenvr*(Dc*tumor_cells-gamma_C*tumor_cells*eff_Tcells)
Cmax = 230000.0 mmol/l; Rc = 0.0057 1/d Reaction: => tumor_cells, Rate Law: TumorMicroenvr*Rc*tumor_cells*ln(Cmax/tumor_cells)

States:

Name Description
naive Tcells [Naive T-Lymphocyte]
tumor cells [neoplasm]
eff Tcells [Effector T-Lymphocyte]

Observables: none

This model is described within the paper: A G1 arrest due to proteostasis decline delimits replicative lifespan in yeast…

Loss of proteostasis and cellular senescence are key hallmarks of aging, but direct cause-effect relationships are not well understood. We show that most yeast cells arrest in G1 before death with low nuclear levels of Cln3, a key G1 cyclin extremely sensitive to chaperone status. Chaperone availability is seriously compromised in aged cells, and the G1 arrest coincides with massive aggregation of a metastable chaperone-activity reporter. Moreover, G1-cyclin overexpression increases lifespan in a chaperone-dependent manner. As a key prediction of a model integrating autocatalytic protein aggregation and a minimal Start network, enforced protein aggregation causes a severe reduction in lifespan, an effect that is greatly alleviated by increased expression of specific chaperones or cyclin Cln3. Overall, our data show that proteostasis breakdown, by compromising chaperone activity and G1-cyclin function, causes an irreversible arrest in G1, configuring a molecular pathway postulating proteostasis decay as a key contributing effector of cell senescence. link: http://identifiers.org/pubmed/31518229

Parameters: none

States: none

Observables: none

Morgan2016 - Dynamics of cholesterol metabolism and ageingThis model is described in the article: [Mathematically model…

Cardiovascular disease (CVD) is the leading cause of morbidity and mortality in the UK. This condition becomes increasingly prevalent during ageing; 34.1% and 29.8% of males and females respectively, over 75 years of age have an underlying cardiovascular problem. The dysregulation of cholesterol metabolism is inextricably correlated with cardiovascular health and for this reason low density lipoprotein cholesterol (LDL-C) and high density lipoprotein cholesterol (HDL-C) are routinely used as biomarkers of CVD risk. The aim of this work was to use mathematical modelling to explore how cholesterol metabolism is affected by the ageing process. To do this we updated a previously published whole-body mathematical model of cholesterol metabolism to include an additional 96 mechanisms that are fundamental to this biological system. Additional mechanisms were added to cholesterol absorption, cholesterol synthesis, reverse cholesterol transport (RCT), bile acid synthesis, and their enterohepatic circulation. The sensitivity of the model was explored by the use of both local and global parameter scans. In addition, acute cholesterol feeding was used to explore the effectiveness of the regulatory mechanisms which are responsible for maintaining whole-body cholesterol balance. It was found that our model behaves as a hypo-responder to cholesterol feeding, while both the hepatic and intestinal pools of cholesterol increased significantly. The model was also used to explore the effects of ageing in tandem with three different cholesterol ester transfer protein (CETP) genotypes. Ageing in the presence of an atheroprotective CETP genotype, conferring low CETP activity, resulted in a 0.6% increase in LDL-C. In comparison, ageing with a genotype reflective of high CETP activity, resulted in a 1.6% increase in LDL-C. Thus, the model has illustrated the importance of CETP genotypes such as I405V, and their potential role in healthy ageing. link: http://identifiers.org/pubmed/27157786

Parameters: none

States: none

Observables: none

BIOMD0000000406 @ v0.0.1

This model is from the article: Overexpression limits of fission yeast cell-cycle regulators in vivo and in silico.…

Cellular systems are generally robust against fluctuations of intracellular parameters such as gene expression level. However, little is known about expression limits of genes required to halt cellular systems. In this study, using the fission yeast Schizosaccharomyces pombe, we developed a genetic 'tug-of-war' (gTOW) method to assess the overexpression limit of certain genes. Using gTOW, we determined copy number limits for 31 cell-cycle regulators; the limits varied from 1 to >100. Comparison with orthologs of the budding yeast Saccharomyces cerevisiae suggested the presence of a conserved fragile core in the eukaryotic cell cycle. Robustness profiles of networks regulating cytokinesis in both yeasts (septation-initiation network (SIN) and mitotic exit network (MEN)) were quite different, probably reflecting differences in their physiologic functions. Fragility in the regulation of GTPase spg1 was due to dosage imbalance against GTPase-activating protein (GAP) byr4. Using the gTOW data, we modified a mathematical model and successfully reproduced the robustness of the S. pombe cell cycle with the model. link: http://identifiers.org/pubmed/22146300

Parameters:

Name Description
kini_dash2 = 10.0; kini_dash3 = 0.0; preRC = 0.0; kini_dash = 10.0 Reaction: s89 => s90; s67, s56, s63, Rate Law: (kini_dash*s56+kini_dash2*s67+kini_dash3*s63)*preRC
kscig = 0.002; kscig_dash = 0.04; Cdc10T = 1.0 Reaction: s55 => s67; s71, Rate Law: kscig*Cdc10T+kscig_dash*s71
kpyp2 = 0.01; k25 = 0.0 Reaction: s60 => s56; s83, s64, Rate Law: (kpyp2+k25)*s60
kpyp = 0.6; beta = 10.0; UDNA = 0.0; k25 = 0.0; k255 = 0.1 Reaction: s153 => s149; s64, s83, Rate Law: k25*k255*s153+kpyp*s153/(1+beta*UDNA)
kisrw_dash = 40.0; Puc1 = 1.0; kisrw_dash2 = 1.0; kisrw_dash4 = 4.0; kisrw = 1.5; Jisrw = 0.01; kisrw_dash3 = 4.0 Reaction: s47 => s65; s56, s49, s75, s67, Rate Law: (kisrw+kisrw_dash*s67+kisrw_dash2*s56+kisrw_dash3*Puc1+kisrw_dash4*s75)*s47/(Jisrw+s47)
kipre = 1.0; n = 4.0; kipre_dash = 1.0; kori = 125.0; Jipre = 0.01 Reaction: s91 => s92; s67, s56, s63, Rate Law: kori/(1+((kipre*s56+kipre_dash*s67)/Jipre)^n)*s91
ksrum = 1.0 Reaction: s52 => s166, Rate Law: ksrum
Vdc18 = 0.0 Reaction: s84 => s88; s130, Rate Law: Vdc18*s84
Vdrum = 0.0 Reaction: s161 => s56 + s61; s4, Rate Law: Vdrum*s161
kdci1_dash = 5.0; kdci1 = 0.1; kdci1_dash2 = 0.2 Reaction: s75 => s77; s48, s47, Rate Law: (kdci1+kdci1_dash*s48+kdci1_dash2*s47)*s75
kasrw = 1.25; kasrw_dash = 30.0; Jasrw = 0.01; Srw1T = 1.0 Reaction: s65 => s47; s48, Rate Law: (kasrw+kasrw_dash*s48)*(Srw1T-s47)/(Jasrw+(Srw1T-s47))
kic10 = 0.01; Jic10 = 0.01; kic10_dash = 3.0 Reaction: s71 => s70; s67, Rate Law: (kic10+kic10_dash*s67)*s71/(Jic10+s71)
ksflp = 0.0015; ksflp_dash = 0.015 Reaction: s78 => s81; s48, Rate Law: ksflp+ksflp_dash*s48
kac10 = 0.125; Jac10 = 0.01; Cdc10T = 1.0 Reaction: s70 => s71, Rate Law: kac10*(Cdc10T-s71)/(Jac10+(Cdc10T-s71))
Vi25 = 0.3; UDNA = 0.0; Vi25_dash2 = 1.0; Ji25 = 0.03; Vi25_dash = 0.24 Reaction: s83 => s82; s81, s157, Rate Law: (Vi25_dash+Vi25_dash2*s81+Vi25*UDNA)*s83/(Ji25+s83)
lcm = 1.0; lcp = 3.0 Reaction: s166 + s67 => s149, Rate Law: lcp*s67*s166-lcm*s149
kaie = 0.0975; kaie_dash = 0.05; Jaie = 0.01 Reaction: s51 => s50; s75, s56, Rate Law: (kaie*s56+kaie_dash*s75)*(1-s50)/(Jaie+(1-s50))
Jawee = 0.04; Vawee_dash = 0.24; Wee1T = 1.0; Vawee_dash2 = 1.0 Reaction: s79 => s80; s81, Rate Law: (Vawee_dash+Vawee_dash2*s81)*(Wee1T-s80)/(Jawee+(Wee1T-s80))
ksc18 = 0.005; ksc18_dash = 0.075; Cdc10T = 1.0 Reaction: s85 => s84; s71, Rate Law: ksc18*((Cdc10T-s71)+s71)+ksc18_dash*s71
lp = 500.0; lm = 100.0 Reaction: s56 + s166 => s161, Rate Law: lp*s56*s166-lm*s161
Cdc25T = 1.0; Ja25 = 0.03; Va25_dash2 = 1.0 Reaction: s82 => s83; s56, Rate Law: Va25_dash2*s56*(Cdc25T-s83)/(Ja25+(Cdc25T-s83))
Vamik_dash = 0.75; Vamik = 0.25; Cdc10T = 1.0 Reaction: s73 => s72; s71, Rate Law: Vamik*Cdc10T+Vamik_dash*s71
kdcig_dash = 1.0; kdcig = 0.02 Reaction: s149 => s166 + s94; s48, Rate Law: (kdcig+kdcig_dash*s48)*s149
kscyc = 0.03 Reaction: s57 => s56, Rate Law: kscyc
kdflp = 0.1 Reaction: s81 => s93, Rate Law: kdflp*s81
ksci1 = 0.0015 Reaction: s76 => s75, Rate Law: ksci1
Vimik_dash3 = 0.25; Vimik_dash2 = 10.0; Vimik = 0.75; Vimik_dash = 10.0 Reaction: s72 => s74; s67, s56, s60, Rate Law: (Vimik+Vimik_dash*s67+Vimik_dash2*s56+Vimik_dash3*s60)*s72
Jislp = 0.01; kislp = 0.2 Reaction: s48 => s66, Rate Law: kislp*s48/(Jislp+s48)
Jiie = 0.01; kiie = 0.04 Reaction: s50 => s51, Rate Law: kiie*s50/(Jiie+s50)
kmik_dash2 = 4.0 Reaction: s149 => s153; s72, Rate Law: kmik_dash2*s72*s149
Vdcyc = 0.0 Reaction: s161 => s166 + s46; s9, Rate Law: Vdcyc*s161
krepl = 2.0 Reaction: s90 => s91, Rate Law: krepl*s90
kaslp = 1.0; Slp1T = 1.0; Jaslp = 0.01 Reaction: s66 => s48; s50, Rate Law: kaslp*s50*(Slp1T-s48)/(Jaslp+(Slp1T-s48))
Viwee_dash2 = 1.0; Jiwee = 0.03; Viwee_dash = 0.0 Reaction: s80 => s79; s56, Rate Law: (Viwee_dash+Viwee_dash2*s56)*s80/(Jiwee+s80)
kmik_dash = 0.01; kwee = 0.0 Reaction: s161 => s137; s80, s72, Rate Law: (kmik_dash*s72+kwee)*s161

States:

Name Description
s78 [mRNA cleavage and polyadenylation factor clp1]
s76 [G2/mitotic-specific cyclin cig1]
s83 [M-phase inducer phosphatase]
s92 [deoxyribonucleic acid]
s57 [G2/mitotic-specific cyclin cdc13]
s153 [Cyclin-dependent kinase inhibitor rum1; G2/mitotic-specific cyclin cig2]
s50 IE
s93 sa370_degraded
s71 [Start control protein cdc10]
s47 [WD repeat-containing protein srw1]
s81 [mRNA cleavage and polyadenylation factor clp1]
s52 [Cyclin-dependent kinase inhibitor rum1]
s72 [Mitosis inhibitor protein kinase mik1]
s46 sa4_degraded
s77 sa353_degraded
s70 [Start control protein cdc10]
s89 [nuclear pre-replicative complex]
s51 iIE
s166 [Cyclin-dependent kinase inhibitor rum1]
s48 [WD repeat-containing protein slp1]
s67 [G2/mitotic-specific cyclin cig2]
s55 [G2/mitotic-specific cyclin cig2]
s84 [Cell division control protein 18]
s149 [G2/mitotic-specific cyclin cig2; Cyclin-dependent kinase inhibitor rum1]
s91 [deoxyribonucleic acid]
s80 [Mitosis inhibitor protein kinase wee1]
s75 [G2/mitotic-specific cyclin cig1]
s94 sa44_degraded
s73 [Mitosis inhibitor protein kinase mik1]
s161 [Cyclin-dependent kinase inhibitor rum1; G2/mitotic-specific cyclin cdc13]
s56 [G2/mitotic-specific cyclin cdc13]
s79 [Mitosis inhibitor protein kinase wee1]
s82 [M-phase inducer phosphatase]
s137 [G2/mitotic-specific cyclin cdc13; Cyclin-dependent kinase inhibitor rum1]
s74 sa347_degraded
s90 [origin recognition complex]
s88 sa386_degraded
s66 [WD repeat-containing protein slp1]
s85 [Cell division control protein 18]
s60 [G2/mitotic-specific cyclin cdc13; Phosphoprotein]
s65 [WD repeat-containing protein srw1]

Observables: none

BIOMD0000000324 @ v0.0.1

This is the full model (eq. 1 and 2) of the voltage oscillations in barnacle muscle fibers described in the article: Vo…

Barnacle muscle fibers subjected to constant current stimulation produce a variety of types of oscillatory behavior when the internal medium contains the Ca++ chelator EGTA. Oscillations are abolished if Ca++ is removed from the external medium, or if the K+ conductance is blocked. Available voltage-clamp data indicate that the cell's active conductance systems are exceptionally simple. Given the complexity of barnacle fiber voltage behavior, this seems paradoxical. This paper presents an analysis of the possible modes of behavior available to a system of two noninactivating conductance mechanisms, and indicates a good correspondence to the types of behavior exhibited by barnacle fiber. The differential equations of a simple equivalent circuit for the fiber are dealt with by means of some of the mathematical techniques of nonlinear mechanics. General features of the system are (a) a propensity to produce damped or sustained oscillations over a rather broad parameter range, and (b) considerable latitude in the shape of the oscillatory potentials. It is concluded that for cells subject to changeable parameters (either from cell to cell or with time during cellular activity), a system dominated by two noninactivating conductances can exhibit varied oscillatory and bistable behavior. link: http://identifiers.org/pubmed/7260316

Parameters: none

States: none

Observables: none

BIOMD0000000280 @ v0.0.1

This is the reduced model of the voltage oscillations in barnacle muscle fibers, generally known as the Morris-Lecar mod…

Barnacle muscle fibers subjected to constant current stimulation produce a variety of types of oscillatory behavior when the internal medium contains the Ca++ chelator EGTA. Oscillations are abolished if Ca++ is removed from the external medium, or if the K+ conductance is blocked. Available voltage-clamp data indicate that the cell's active conductance systems are exceptionally simple. Given the complexity of barnacle fiber voltage behavior, this seems paradoxical. This paper presents an analysis of the possible modes of behavior available to a system of two noninactivating conductance mechanisms, and indicates a good correspondence to the types of behavior exhibited by barnacle fiber. The differential equations of a simple equivalent circuit for the fiber are dealt with by means of some of the mathematical techniques of nonlinear mechanics. General features of the system are (a) a propensity to produce damped or sustained oscillations over a rather broad parameter range, and (b) considerable latitude in the shape of the oscillatory potentials. It is concluded that for cells subject to changeable parameters (either from cell to cell or with time during cellular activity), a system dominated by two noninactivating conductances can exhibit varied oscillatory and bistable behavior. link: http://identifiers.org/pubmed/7260316

Parameters: none

States: none

Observables: none

BIOMD0000000150 @ v0.0.1

Notes from the original DOCQS curator: In this version of the CDK2/Cyclin A complex activation there is discrepancy i…

Eukaryotic cell cycle progression is controlled by the ordered action of cyclin-dependent kinases, activation of which occurs through the binding of the cyclin to the Cdk followed by phosphorylation of a conserved threonine in the T-loop of the Cdk by Cdk-activating kinase (CAK). Despite our understanding of the structural changes, which occur upon Cdk/cyclin formation and activation, little is known about the dynamics of the molecular events involved. We have characterized the mechanism of Cdk2/cyclin A complex formation and activation at the molecular and dynamic level by rapid kinetics and demonstrate here that it is a two-step process. The first step involves the rapid association between the PSTAIRE helix of Cdk2 and helices 3 and 5 of the cyclin to yield an intermediate complex in which the threonine in the T-loop is not accessible for phosphorylation. Additional contacts between the C-lobe of the Cdk and the N-terminal helix of the cyclin then induce the isomerization of the Cdk into a fully mature form by promoting the exposure of the T-loop for phosphorylation by CAK and the formation of the substrate binding site. This conformational change is selective for the cyclin partner. link: http://identifiers.org/pubmed/11959850

Parameters:

Name Description
kf=0.813; kb=0.557 Reaction: CDK2cycA => CDK2cycA_star_, Rate Law: kf*CDK2cycA*geometry-kb*CDK2cycA_star_*geometry
kb=25.0; kf=1.9E7 Reaction: Cdk2 + CyclinA => CDK2cycA, Rate Law: kf*Cdk2*CyclinA*geometry-kb*CDK2cycA*geometry

States:

Name Description
CyclinA [IPR015453]
CDK2cycA star [Cyclin-dependent kinase 1; IPR015453]
Cdk2 [Cyclin-dependent kinase 1]
CDK2cycA [Cyclin-dependent kinase 1; IPR015453]

Observables: none

Morris2008 - Fitting protein aggregation data via F-W 2-step mechanismThis model is described in the article: [Fitting…

The aggregation of proteins has been hypothesized to be an underlying cause of many neurological disorders including Alzheimer's, Parkinson's, and Huntington's diseases; protein aggregation is also important to normal life function in cases such as G to F-actin, glutamate dehydrogenase, and tubulin and flagella formation. For this reason, the underlying mechanism of protein aggregation, and accompanying kinetic models for protein nucleation and growth (growth also being called elongation, polymerization, or fibrillation in the literature), have been investigated for more than 50 years. As a way to concisely present the key prior literature in the protein aggregation area, Table 1 in the main text summarizes 23 papers by 10 groups of authors that provide 5 basic classes of mechanisms for protein aggregation over the period from 1959 to 2007. However, and despite this major prior effort, still lacking are both (i) anything approaching a consensus mechanism (or mechanisms), and (ii) a generally useful, and thus widely used, simplest/"Ockham's razor" kinetic model and associated equations that can be routinely employed to analyze a broader range of protein aggregation kinetic data. Herein we demonstrate that the 1997 Finke-Watzky (F-W) 2-step mechanism of slow continuous nucleation, A –> B (rate constant k1), followed by typically fast, autocatalytic surface growth, A + B –> 2B (rate constant k2), is able to quantitatively account for the kinetic curves from all 14 representative data sets of neurological protein aggregation found by a literature search (the prion literature was largely excluded for the purposes of this study in order provide some limit to the resultant literature that was covered). The F-W model is able to deconvolute the desired nucleation, k1, and growth, k2, rate constants from those 14 data sets obtained by four different physical methods, for three different proteins, and in nine different labs. The fits are generally good, and in many cases excellent, with R2 values >or=0.98 in all cases. As such, this contribution is the current record of the widest set of protein aggregation data best fit by what is also the simplest model offered to date. Also provided is the mathematical connection between the 1997 F-W 2-step mechanism and the 2000 3-step mechanism proposed by Saitô and co-workers. In particular, the kinetic equation for Saitô's 3-step mechanism is shown to be mathematically identical to the earlier, 1997 2-step F-W mechanism under the 3 simplifying assumptions Saitô and co-workers used to derive their kinetic equation. A list of the 3 main caveats/limitations of the F-W kinetic model is provided, followed by the main conclusions from this study as well as some needed future experiments. link: http://identifiers.org/pubmed/18247636

Parameters:

Name Description
k1 = 4.0E-5 Reaction: A => B; A, Rate Law: Brain*k1*A
k2 = 1.57E-6; k1 = 4.0E-5; A0 = 184713.375796178 Reaction: B = A0-(k1/k2+A0)/(1+k1/(k2*A0)*exp((k1+k2*A0)*time)), Rate Law: missing
k2 = 1.57E-6 Reaction: A + B => B; A, B, Rate Law: Brain*k2*A*B

States:

Name Description
B [PR:P04156]
A [PR:P04156]

Observables: none

Morris2009 - α-Synuclein aggregation variable temperature and pHThis model is described in the article: [Alpha-synuclei…

The aggregation of proteins is believed to be intimately connected to many neurodegenerative disorders. We recently reported an "Ockham's razor"/minimalistic approach to analyze the kinetic data of protein aggregation using the Finke-Watzky (F-W) 2-step model of nucleation (A–>B, rate constant k(1)) and autocatalytic growth (A+B–>2B, rate constant k(2)). With that kinetic model we have analyzed 41 representative protein aggregation data sets in two recent publications, including amyloid beta, alpha-synuclein, polyglutamine, and prion proteins (Morris, A. M., et al. (2008) Biochemistry 47, 2413-2427; Watzky, M. A., et al. (2008) Biochemistry 47, 10790-10800). Herein we use the F-W model to reanalyze protein aggregation kinetic data obtained under the experimental conditions of variable temperature or pH 2.0 to 8.5. We provide the average nucleation (k(1)) and growth (k(2)) rate constants and correlations with variable temperature or varying pH for the protein alpha-synuclein. From the variable temperature data, activation parameters DeltaG(double dagger), DeltaH(double dagger), and DeltaS(double dagger) are provided for nucleation and growth, and those values are compared to the available parameters reported in the previous literature determined using an empirical method. Our activation parameters suggest that nucleation and growth are energetically similar for alpha-synuclein aggregation (DeltaG(double dagger)(nucleation)=23(3) kcal/mol; DeltaG(double dagger)(growth)=22(1) kcal/mol at 37 degrees C). From the variable pH data, the F-W analyses show a maximal k(1) value at pH approximately 3, as well as minimal k(1) near the isoelectric point (pI) of alpha-synuclein. Since solubility and net charge are minimized at the pI, either or both of these factors may be important in determining the kinetics of the nucleation step. On the other hand, the k(2) values increase with decreasing pH (i.e., do not appear to have a minimum or maximum near the pI) which, when combined with the k(1) vs. pH (and pI) data, suggest that solubility and charge are less important factors for growth, and that charge is important in the k(1), nucleation step of alpha-synuclein. The chemically well-defined nucleation (k(1)) rate constants obtained from the F-W analysis are, as expected, different than the 1/lag-time empirical constants previously obtained. However, k(2)xA (where k(2) is the rate constant for autocatalytic growth and A is the initial protein concentration) is related to the empirical constant, k(app) obtained previously. Overall, the average nucleation and average growth rate constants for alpha-synuclein aggregation as a function of pH and variable temperature have been quantitated. Those values support the previously suggested formation of a partially folded intermediate that promotes aggregation under high temperature or acidic conditions. link: http://identifiers.org/pubmed/19101068

Parameters:

Name Description
k1 = 8.0E-6; k2 = 0.034; A0 = 3.55 Reaction: B = A0-(k1/k2+A0)/(1+k1/(k2*A0)*exp((k1+k2*A0)*time)), Rate Law: missing
k1 = 8.0E-6 Reaction: A => B; A, Rate Law: Brain*k1*A
k2 = 0.034 Reaction: A + B => B; A, B, Rate Law: Brain*k2*A*B

States:

Name Description
B [Alpha-synuclein]
A [Alpha-synuclein]

Observables: none

BIOMD0000000018 @ v0.0.1

Morrison1989 - Folate CycleThe model describes the folate cycle kinetics in breast cancer cells.This model is described…

A mathematical description of polyglutamated folate kinetics for human breast carcinoma cells (MCF-7) has been formulated based upon experimental folate, methotrexate (MTX), purine, and pyrimidine pool sizes as well as reaction rate parameters obtained from intact MCF-7 cells and their enzyme isolates. The schema accounts for the interconversion of highly polyglutamated tetrahydrofolate, 5-methyl-FH4, 5-10-CH2FH4, dihydrofolate (FH2), 10-formyl-FH4 (FFH4), and 10-formyl-FH2 (FFH2), as well as formation and transport of the MTX polyglutamates. Inhibition mechanisms have been chosen to reproduce all observed non-, un-, and pure competition inhibition patterns. Steady state folate concentrations and thymidylate and purine synthesis rates in drug-free intact cells were used to determine normal folate Vmax values. The resulting average-cell folate model, examined for its ability to predict folate pool behavior following exposure to 1 microM MTX over 21 h, agreed well with the experiment, including a relative preservation of the FFH4 and CH2FH4 pools. The results depend strongly on thymidylate synthase (TS) reaction mechanism, especially the assumption that MTX di- and triglutamates inhibit TS synthesis as greatly in the intact cell as they do with purified enzyme. The effects of cell cycle dependence of TS and dihydrofolate reductase activities were also examined by introducing G- to S-phase activity ratios of these enzymes into the model. For activity ratios down to at least 5%, cell population averaged folate pools were only slightly affected, while CH2FH4 pools in S-phase cells were reduced to as little as 10% of control values. Significantly, these folate pool dynamics were indicated to arise from both direct inhibition by MTX polyglutamates as well as inhibition by elevated levels of polyglutamated FH2 and FFH2. link: http://identifiers.org/pubmed/2732237

Parameters:

Name Description
hp=23.2 Reaction: FH4 + HCHO => CH2FH4, Rate Law: cell*hp*FH4*HCHO
Vm=4.65 Reaction: MTX1 =>, Rate Law: cell*Vm*MTX1
Vm=0.42 Reaction: MTX3b => MTX3 + DHFRf, Rate Law: cell*Vm*MTX3b
Km2=100.0; Km1=100.0; Vm=4656.0 Reaction: FGAR => AICAR; glutamine, Rate Law: cell*Vm*glutamine/Km1/(1+glutamine/Km1)*FGAR/Km2/(1+FGAR/Km2)
Vm=0.118 Reaction: MTX3 => MTX4, Rate Law: cell*Vm*MTX3
Vm=163000.0 Reaction: MTX4 + DHFRf => MTX4b, Rate Law: cell*Vm*DHFRf*MTX4
Ki1=5.0; Ki24=31.0; Km1=4.9; Ki1f=1.0; Ki23=43.0; Vm=4126.0; Km2=52.0; Ki21=84.0; Ki22=60.0; Ki25=22.0 Reaction: CHOFH4 + GAR => FGAR + FH4; FH2f, FFH2, MTX1, MTX2, MTX3, MTX4, MTX5, Rate Law: cell*Vm*CHOFH4*GAR/(GAR*CHOFH4+CHOFH4*Km2+(GAR+Km2)*Km1*(1+MTX1/Ki21+MTX2/Ki22+MTX3/Ki23+MTX4/Ki24+MTX5/Ki25+FH2f/Ki1+FFH2/Ki1f))
Vm=23100.0 Reaction: MTX1 + DHFRf => MTX1b, Rate Law: cell*Vm*DHFRf*MTX1
Vm=44300.0 Reaction: MTX2 + DHFRf => MTX2b, Rate Law: cell*Vm*DHFRf*MTX2
Km2=210.0; Vm=18330.0; Km1=1.7 Reaction: FH4 + serine => CH2FH4, Rate Law: cell*Vm*serine/Km2/(1+serine/Km2)*FH4/Km1/(1+FH4/Km1)
Vm=314000.0 Reaction: MTX5 + DHFRf => MTX5b, Rate Law: cell*Vm*DHFRf*MTX5
Ki1=0.4; Vm=224.8; Ki21=59.0; Ki22=21.3; Ki24=2.77; Ki25=1.0; Km1=50.0; Km2=50.0; Ki23=7.68 Reaction: CH2FH4 + NADPH => CH3FH4; FH2f, MTX1, MTX2, MTX3, MTX4, MTX5, Rate Law: cell*Vm*CH2FH4*NADPH/(NADPH*CH2FH4+CH2FH4*Km2+(NADPH+Km2)*Km1*(1+MTX1/Ki21+MTX2/Ki22+MTX3/Ki23+MTX4/Ki24+MTX5/Ki25+FH2f/Ki1))
Vm=0.129 Reaction: MTX1 => MTX2, Rate Law: cell*Vm*MTX1
Vm=0.0 Reaction: MTX2 =>, Rate Law: cell*Vm*MTX2
Vm=0.03 Reaction: DHFRf => ; FH2b, Rate Law: Vm*cell*(DHFRf+FH2b)
Vm=1.22E7; Km1=3200.0; Km2=10000.0 Reaction: CH2FH4 => FH4; glycine, Rate Law: cell*Vm*glycine/Km2/(1+glycine/Km2)*CH2FH4/Km1/(1+CH2FH4/Km1)
kter=2109.4 Reaction: FH2f => FH4; FH2b, Rate Law: cell*kter*FH2b
Vm=0.195 Reaction: MTX2 => MTX1, Rate Law: cell*Vm*MTX2
Ki1=2.89; Ki22=31.5; Ki25=5.89; Ki23=2.33; Km2=24.0; Vm=31675.0; Km1=5.5; Ki1f=5.3; Ki24=3.61; Ki21=40.0 Reaction: CHOFH4 + AICAR => FH4; FH2f, FFH2, MTX1, MTX2, MTX3, MTX4, MTX5, Rate Law: cell*Vm*CHOFH4*AICAR/(AICAR*CHOFH4+CHOFH4*Km2+(AICAR+Km2)*Km1*(1+MTX1/Ki21+MTX2/Ki22+MTX3/Ki23+MTX4/Ki24+MTX5/Ki25+FH2f/Ki1+FFH2/Ki1f))
Vm=0.369 Reaction: MTX2 => MTX3, Rate Law: cell*Vm*MTX2
Ki1=2.89; Ki22=31.5; Ki25=5.89; Ki23=2.33; Km2=24.0; Vm=9503.0; Ki24=3.61; Km1=5.3; Ki21=40.0; Ki1f=5.5 Reaction: FFH2 + AICAR => FH2f; FH2f, MTX1, MTX2, MTX3, MTX4, MTX5, Rate Law: cell*Vm*FFH2*AICAR/(AICAR*FFH2+FFH2*Km2+(AICAR+Km2)*Km1*(1+MTX1/Ki21+MTX2/Ki22+MTX3/Ki23+MTX4/Ki24+MTX5/Ki25+FH2f/Ki1+FFH2/Ki1f))
Vm=85100.0 Reaction: MTX3 + DHFRf => MTX3b, Rate Law: cell*Vm*DHFRf*MTX3
Vm=0.031 Reaction: MTX4 => MTX3, Rate Law: cell*Vm*MTX4
Vm=65.0 Reaction: FH2f => FFH2, Rate Law: cell*Vm*FH2f
Vm=22600.0; Km1=125.0; Km2=2900.0 Reaction: CH3FH4 + homocysteine => FH4, Rate Law: cell*Vm*homocysteine/Km2/(1+homocysteine/Km2)*CH3FH4/Km1/(1+CH3FH4/Km1)
Vm=0.185 Reaction: MTX4 => MTX5, Rate Law: cell*Vm*MTX4
Vm=0.191 Reaction: MTX5 => MTX4, Rate Law: cell*Vm*MTX5
Km2=21.8; Km1=3.0; Vm=68500.0 Reaction: CH2FH4 + NADP => CHOFH4, Rate Law: cell*Vm*CH2FH4/Km1/(1+CH2FH4/Km1)*NADP/Km2/(1+NADP/Km2)
Ki1f=1.6; Ki24=0.065; Ki1=3.0; Ki25=0.047; Ki22=0.08; Vm=58.0; Km1=2.5; Ki21=13.0; Ki23=0.07; Km2=1.8 Reaction: CH2FH4 + dUMP => FH2f; FH2f, FFH2, MTX1, MTX2, MTX3, MTX4, MTX5, Rate Law: cell*Vm*CH2FH4*dUMP/(dUMP*CH2FH4*(1+MTX1/Ki21+MTX2/Ki22+MTX3/Ki23+MTX4/Ki24+MTX5/Ki25+FH2f/Ki1)+Km1*dUMP*(FFH2/Ki1f*MTX1/Ki21+(1+FFH2/Ki1f)*(1+MTX2/Ki22+MTX3/Ki23+MTX4/Ki24+MTX5/Ki25+FH2f/Ki1))+Km1*Km2*(1+MTX2/Ki22+MTX3/Ki23+MTX4/Ki24+MTX5/Ki25+FH2f/Ki1))
Km2=56.0; Vm=3600.0; Km1=230.0; Km3=1600.0 Reaction: FH4 + formate + ATP => CHOFH4, Rate Law: cell*Vm/((1+Km1/FH4)*(1+Km2/ATP)*(1+Km3/formate))
Vm=82.2; Km=8.2 Reaction: EMTX => MTX1, Rate Law: ext*Vm*EMTX/(Km+EMTX)
hl=0.3 Reaction: CH2FH4 => FH4 + HCHO, Rate Law: cell*hl*CH2FH4
Vm=0.025 Reaction: MTX3 => MTX2, Rate Law: cell*Vm*MTX3
k0=0.0192; k1=0.04416 Reaction: => DHFRf; EMTX, Rate Law: cell*(k0+k1*EMTX)
Vm=0.063 Reaction: MTX3 =>, Rate Law: cell*Vm*MTX3

States:

Name Description
CH3FH4 [5-methyltetrahydrofolic acid; 5-Methyltetrahydrofolate]
FH4 [5,6,7,8-tetrahydrofolic acid; Tetrahydrofolate]
MTX4b [Methotrexate]
MTX3 [Methotrexate]
DHFRf [Dihydrofolate reductase]
AICAR [AICA ribonucleotide; 1-(5'-Phosphoribosyl)-5-amino-4-imidazolecarboxamide]
NADPH [NADPH; NADPH]
MTX5 [Methotrexate]
MTX2b [Methotrexate]
MTX1 [Methotrexate]
MTX4 [Methotrexate]
homocysteine [homocysteine; Homocysteine]
DHFRtot [Dihydrofolate reductase]
FGAR [5'-Phosphoribosyl-N-formylglycinamide]
GAR [5'-Phosphoribosylglycinamide]
CHOFH4 [10-formyltetrahydrofolic acid; 10-Formyltetrahydrofolate]
MTX2 [Methotrexate]
FFH2 [10-formyldihydrofolic acid; 10-Formyldihydrofolate]
MTX3b [Methotrexate]
FH2f [dihydrofolic acid; Dihydrofolate]
CH2FH4 [(6R)-5,10-methylenetetrahydrofolate(2-); 5,10-Methylenetetrahydrofolate]
MTX5b [Methotrexate]
MTX1b [Methotrexate]
HCHO [formaldehyde; Formaldehyde]
dUMP [dUMP; dUMP]

Observables: none

Mosca2012 - Central Carbon Metabolism Regulated by AKTThe role of the PI3K/Akt/PKB signalling pathway in oncogenesis has…

Signal transduction and gene regulation determine a major reorganization of metabolic activities in order to support cell proliferation. Protein Kinase B (PKB), also known as Akt, participates in the PI3K/Akt/mTOR pathway, a master regulator of aerobic glycolysis and cellular biosynthesis, two activities shown by both normal and cancer proliferating cells. Not surprisingly considering its relevance for cellular metabolism, Akt/PKB is often found hyperactive in cancer cells. In the last decade, many efforts have been made to improve the understanding of the control of glucose metabolism and the identification of a therapeutic window between proliferating cancer cells and proliferating normal cells. In this context, we have modeled the link between the PI3K/Akt/mTOR pathway, glycolysis, lactic acid production, and nucleotide biosynthesis. We used a computational model to compare two metabolic states generated by two different levels of signaling through the PI3K/Akt/mTOR pathway: one of the two states represents the metabolism of a growing cancer cell characterized by aerobic glycolysis and cellular biosynthesis, while the other state represents the same metabolic network with a reduced glycolytic rate and a higher mitochondrial pyruvate metabolism. Biochemical reactions that link glycolysis and pentose phosphate pathway revealed their importance for controlling the dynamics of cancer glucose metabolism. link: http://identifiers.org/pubmed/23181020

Parameters:

Name Description
Kapp=195172.0; Kadp=0.4; Katp=0.86; parameter_44 = 27.81; Kiatp=2.5; L=1.0; parameter_17 = 1000.0; Kfbp=4.0E-4; Kpyr=10.0; Kpep=0.014 Reaction: species_30 + species_3 => species_31 + species_4; species_6, species_3, species_30, species_4, species_6, species_31, Rate Law: compartment_1*parameter_44*(parameter_17*species_3/Kadp/(1+parameter_17*species_3/Kadp)*parameter_17*species_30/Kpep*(1+parameter_17*species_30/Kpep)^3/(L*(1+parameter_17*species_4/Kiatp)^4/(1+parameter_17*species_6/Kfbp)^4+(1+parameter_17*species_30/Kpep)^4)-parameter_17*species_4*parameter_17*species_31/(Katp*Kpyr*Kapp)/(parameter_17*species_4/Katp+parameter_17*species_31/Kpyr+parameter_17*species_4*parameter_17*species_31/(Katp*Kpyr)+1))
Kq=0.0035; parameter_31 = 86.85; Kapp=651.0; Ka=1.0E-4; Kb=0.0011; Kp=2.0E-5 Reaction: species_1 + species_4 => species_2 + species_3; species_1, species_4, species_2, species_3, Rate Law: compartment_1*parameter_31/(Ka*Kb)*(species_1*species_4-species_2*species_3/Kapp)/(1+species_1/Ka+species_4/Kb+species_1*species_4/(Ka*Kb)+species_2/Kp+species_3/Kq+species_2*species_3/(Kp*Kq)+species_1*species_3/(Ka*Kq)+species_2*species_4/(Kp*Kb))
alfa=1.0; beta=1.0; parameter_45 = 340.3; parameter_83 = 0.0047; parameter_85 = 2.0E-6; parameter_86 = 3.0E-4; parameter_84 = 7.0E-5; parameter_26 = 54.0471638909003 Reaction: species_31 + species_18 => species_32 + species_19; species_18, species_31, species_32, species_19, Rate Law: compartment_1*(parameter_45*species_18*species_31/(alfa*parameter_85*parameter_86)-parameter_26*species_32*species_19/(beta*parameter_83*parameter_84))/(1+species_18/parameter_85+species_31/parameter_86+species_18*species_31/(alfa*parameter_85*parameter_86)+species_32*species_19/(beta*parameter_83*parameter_84)+species_32/parameter_83+species_19/parameter_84)
Kery4p=1.0E-6; parameter_81 = 5.0E-5; Kfbp=6.0E-5; parameter_32 = 7778.0; parameter_82 = 4.0E-4; Kpg=1.5E-5; parameter_13 = 17486.5107913669 Reaction: species_2 => species_5; species_7, species_6, species_8, species_2, species_5, species_7, species_6, species_8, Rate Law: compartment_1*(parameter_32*species_2/parameter_82-parameter_13*species_5/parameter_81)/(1+species_2/parameter_82+species_5/parameter_81+species_7/Kery4p+species_6/Kfbp+species_8/Kpg)
Keq=2.26; Vf=141.2 Reaction: species_3 => species_4 + species_20; species_3, species_4, species_20, Rate Law: compartment_1*Vf*species_3^2*(1-species_4*species_20/Keq)/(((1+species_3)^2+(1+species_4)*(1+species_20))-1)
K3=1.733E-7; K6=0.4653; K2=4.765E-8; K7=2.524; Vmax=58.27; K5=0.8683; K1=8.23E-9; Keq_TAL=2.703; K4=6.095E-9 Reaction: species_17 + species_16 => species_5 + species_7; species_17, species_16, species_7, species_5, Rate Law: compartment_1*Vmax*(species_17*species_16-species_7*species_5/Keq_TAL)/((K1+species_16)*species_17+(K2+K6*species_5)*species_16+(K3+K5*species_5)*species_7+K4*species_5+K7*species_17*species_7)
parameter_30 = 23.03; keq=1.0; KGlc=0.0093; KGlc_e=0.01 Reaction: species_9 => species_1; species_9, species_1, Rate Law: compartment_1*parameter_30*(species_9-species_1/keq)/(KGlc_e*(1+species_1/KGlc)+species_9)
KNADP=3.67E-9; KG6P=6.67E-8; KATP=7.49E-7; Kapp=2000.0; KNADPH=3.12E-9; KPGA23=2.289E-6; parameter_33 = 1.008 Reaction: species_2 + species_10 => species_8 + species_11; species_4, species_12, species_2, species_10, species_8, species_11, species_4, species_12, Rate Law: compartment_1*parameter_33/KG6P/KNADP*(species_2*species_10-species_8*species_11/Kapp)/(1+species_10*(1+species_2/KG6P)/KNADP+species_4/KATP+species_11/KNADPH+species_12/KPGA23)
KRu5P=1.9E-7; KX5P=5.0E-7; Keq_RUPE=2.7; Vmax=1.471 Reaction: species_13 => species_14; species_13, species_14, Rate Law: compartment_1*Vmax*(species_13-species_14/Keq_RUPE)/(species_13+KRu5P*(1+species_14/KX5P))
parameter_57 = 6.3E-5; parameter_15 = 0.203875968992248; parameter_56 = 3.0E-5; parameter_55 = 7.364 Reaction: species_22 => species_2; species_22, species_2, Rate Law: compartment_1*(parameter_55*species_22/parameter_57-parameter_15*species_2/parameter_56)/(1+species_22/parameter_57+species_2/parameter_56)
Vmax=0.7646; Keq_R5PI=3.0; KRu5P=7.8E-7; KR5P=2.2E-6 Reaction: species_13 => species_15; species_13, species_15, Rate Law: compartment_1*Vmax*(species_13-species_15/Keq_R5PI)/(species_13+KRu5P*(1+species_15/KR5P))
KiPi=0.0047; KGLYb=1.5E-4; parameter_4 = 0.0177545693277311; parameter_60 = 0.0101; parameter_61 = 0.0017; parameter_58 = 0.03347; parameter_59 = 1.5E-4; parameter_62 = 0.004 Reaction: species_24 + species_23 => species_24 + species_22; species_24, species_23, species_22, Rate Law: compartment_1*(parameter_58*species_24*species_23/(parameter_61*parameter_62)-parameter_4*species_24*species_22/(KGLYb*parameter_60))/(1+species_24/parameter_61+species_23/KiPi+species_24/parameter_59+species_22/parameter_60+species_24*species_23/(parameter_61*KiPi)+species_24*species_22/(parameter_59*parameter_60))
Kapp=100000.0; KR5P=5.7E-7; Vmax=0.5104; KATP=3.0E-8 Reaction: species_15 + species_4 => species_20 + species_21; species_15, species_4, species_21, species_20, Rate Law: compartment_1*Vmax*(species_15*species_4-species_21*species_20/Kapp)/((KATP+species_4)*(KR5P+species_15))
Kf=17400.0; Kr=158.0; Keq=267100.0; parameter_41 = 32040.0; parameter_17 = 1000.0 Reaction: species_22 + species_4 => species_24 + species_3 + species_23; species_22, species_4, species_24, species_23, species_3, Rate Law: compartment_1*parameter_41/Kf*parameter_17*species_22*parameter_17*species_4*parameter_17*species_24*(1-(parameter_17*species_23)^2*parameter_17*species_3/(parameter_17*species_22*parameter_17*species_4*Keq))/(1+parameter_17*species_22*parameter_17*species_4*parameter_17*species_24/Kf+parameter_17*species_24*(parameter_17*species_23)^2*parameter_17*species_3/Kr)
Keq_TKL2=29.7; K3=5.48E-8; parameter_36 = 0.1761; K1=1.84E-9; K7=0.215; K6=0.122; K4=3.0E-10; K5=0.0287; K2=3.055E-7 Reaction: species_14 + species_7 => species_16 + species_5; species_7, species_14, species_16, species_5, Rate Law: compartment_1*parameter_36*(species_7*species_14-species_16*species_5/Keq_TKL2)/((K1+species_7)*species_14+(K2+K6*species_5)*species_7+(K3+K5*species_5)*species_16+K4*species_5+K7*species_14*species_16)
Katp=2.1E-5; parameter_17 = 1000.0; alfa=0.32; Kfbp=5.0; Kf26bp=8.4E-7; parameter_42 = 107.6; Kf6p=1.0; Kcit=6.8; L=4.1; Kapp=247.0; Kiatp=20.0; Kadp=5.0; beta=0.98 Reaction: species_5 + species_4 => species_6 + species_3; species_26, species_25, species_4, species_26, species_5, species_25, species_3, species_6, Rate Law: compartment_1*parameter_42*parameter_17*species_4/Katp/(1+parameter_17*species_4/Katp)*(1+beta*parameter_17*species_26/(alfa*Kf26bp))/(1+parameter_17*species_26/(alfa*Kf26bp))*(parameter_17*species_5*(1+parameter_17*species_26/(alfa*Kf26bp))/(Kf6p*(1+parameter_17*species_26/Kf26bp))*(1+parameter_17*species_5*(1+parameter_17*species_26/(alfa*Kf26bp))/(Kf6p*(1+parameter_17*species_26/Kf26bp)))^3/(L*(1+parameter_17*species_25/Kcit)^4*(1+parameter_17*species_4/Kiatp)^4/(1+parameter_17*species_26/Kf26bp)^4+(1+parameter_17*species_5*(1+parameter_17*species_26/(alfa*Kf26bp))/(Kf6p*(1+parameter_17*species_26/Kf26bp)))^4)-parameter_17*species_3*parameter_17*species_6/(Kadp*Kfbp*Kapp)/(parameter_17*species_3/Kadp+parameter_17*species_6/Kfbp+parameter_17*species_3*parameter_17*species_6/(Kadp*Kfbp)+1))
k1=6210.0 Reaction: species_4 => species_3 + species_23; species_4, Rate Law: compartment_1*k1*species_4
parameter_2 = 1.932E-5 Reaction: species_10 = parameter_2-species_11, Rate Law: missing
K6=0.00774; K4=4.96E-9; K1=4.177E-7; Keq_TKL=2.08; parameter_35 = 1056.0; K5=0.41139; K2=3.055E-7; K3=1.2432E-5; K7=48.8 Reaction: species_15 + species_14 => species_16 + species_17; species_15, species_14, species_16, species_17, Rate Law: compartment_1*parameter_35*(species_15*species_14-species_16*species_17/Keq_TKL)/((K1+species_15)*species_14+(K2+K6*species_17)*species_15+(K3+K5*species_17)*species_16+K4*species_17+K7*species_14*species_16)
parameter_8 = 11.5595061728395; parameter_68 = 8.0E-5; parameter_69 = 1.6E-4; parameter_70 = 9.0E-6; parameter_37 = 14.63 Reaction: species_6 => species_16 + species_27; species_6, species_27, species_16, Rate Law: compartment_1*(parameter_37*species_6/parameter_70-parameter_8*species_27*species_16/(parameter_68*parameter_69))/(1+species_6/parameter_70+species_27/parameter_68+species_16/parameter_69+species_27*species_16/(parameter_68*parameter_69))
parameter_9 = 49.2079666512274; parameter_38 = 5.976; parameter_72 = 5.1E-4; parameter_71 = 0.0016 Reaction: species_16 => species_27; species_16, species_27, Rate Law: compartment_1*(parameter_38*species_16/parameter_72-parameter_9*species_27/parameter_71)/(1+species_16/parameter_72+species_27/parameter_71)
parameter_46 = 4982000.0; Keq=300.0 Reaction: species_18 => species_19; species_18, species_19, Rate Law: compartment_1*parameter_46*species_18*(1-species_19/(species_18*Keq))/((1+species_18+1+species_19)-1)
parameter_49 = 1.3E-4; parameter_51 = 7.9E-5; parameter_50 = 2.7E-4; alfa=1.0; beta=1.0; parameter_11 = 71.7220990679741; parameter_52 = 4.0E-5; parameter_40 = 73.41 Reaction: species_12 + species_3 => species_28 + species_4; species_12, species_3, species_28, species_4, Rate Law: compartment_1*(parameter_40*species_12*species_3/(alfa*parameter_51*parameter_52)-parameter_11*species_28*species_4/(beta*parameter_49*parameter_50))/(1+species_12/parameter_51+species_3/parameter_52+species_12*species_3/(alfa*parameter_51*parameter_52)+species_28*species_4/(beta*parameter_49*parameter_50)+species_28/parameter_49+species_4/parameter_50)
parameter_1 = 0.0114 Reaction: species_3 = parameter_1-species_4, Rate Law: missing
K6PG1=1.0E-8; Kapp=141.7; KNADPH=4.5E-9; parameter_34 = 31.02; KPGA23=1.2E-7; KATP=1.54E-7; KNADP=1.8E-8; K6PG2=5.8E-8 Reaction: species_8 + species_10 => species_13 + species_11; species_12, species_4, species_8, species_10, species_13, species_11, species_12, species_4, Rate Law: compartment_1*parameter_34/K6PG1/KNADP*(species_8*species_10-species_13*species_11/Kapp)/((1+species_10/KNADP)*(1+species_8/K6PG1+species_12/KPGA23)+species_4/KATP+species_11*(1+species_8/K6PG2)/KNADPH)
parameter_78 = 154.0; parameter_80 = 1.9E-4; parameter_79 = 1.2E-4; parameter_22 = 58.9795390787319 Reaction: species_28 => species_29; species_28, species_29, Rate Law: compartment_1*(parameter_78*species_28/parameter_80-parameter_22*species_29/parameter_79)/(1+species_28/parameter_80+species_29/parameter_79)
parameter_47 = 127800.0; Keq=0.2 Reaction: species_11 => species_10; species_11, species_10, Rate Law: compartment_1*parameter_47*species_11*(1-species_10/(species_11*Keq))/((1+species_11+1+species_10)-1)
y=12.5; Keq=1000000.0; parameter_48 = 9801000.0 Reaction: species_31 + species_34 + species_23 + species_3 => species_33 + species_4; species_31, species_23, species_3, species_34, species_4, species_33, Rate Law: compartment_1*parameter_48*species_31^(1/y)*species_23*species_3*species_34^(5/(2*y))*(1-species_4*species_33^(3/y)/(species_31^(1/y)*species_34^(5/(2*y))*species_23*species_3*Keq))/(((1+species_31)^(1/y)*(1+species_34)^(5/(2*y))*(1+species_23)*(1+species_3)+(1+species_4)*(1+species_33)^(3/y))-1)
parameter_73 = 2.2E-5; parameter_10 = 135.42497838741; parameter_75 = 1.9E-4; parameter_77 = 0.029; parameter_76 = 9.0E-5; parameter_74 = 1.0E-5; parameter_39 = 109.1 Reaction: species_16 + species_19 + species_23 => species_12 + species_18; species_19, species_16, species_23, species_12, species_18, Rate Law: compartment_1*(parameter_39*species_19*species_16*species_23/(parameter_76*parameter_75*parameter_77)-parameter_10*species_12*species_18/(parameter_73*parameter_74))/(1+species_19/parameter_76+species_19*species_16/(parameter_76*parameter_75)+species_19*species_16*species_23/(parameter_76*parameter_75*parameter_77)+species_12*species_18/(parameter_73*parameter_74)+species_18/parameter_74)
parameter_3 = 0.001345 Reaction: species_18 = parameter_3-species_19, Rate Law: missing
parameter_24 = 179.83480680891; parameter_43 = 160.9; parameter_53 = 6.0E-5; parameter_54 = 3.8E-5 Reaction: species_29 => species_30; species_29, species_30, Rate Law: compartment_1*(parameter_43*species_29/parameter_54-parameter_24*species_30/parameter_53)/(1+species_29/parameter_54+species_30/parameter_53)
parameter_7 = 6.03725213205671E-5; KiG1P=0.0074; nH=1.75; Kamp=1.9E-12; parameter_66 = 0.015; parameter_27 = 0.00311; parameter_64 = 0.0044; parameter_63 = 0.01049; parameter_67 = 0.0046; parameter_65 = 0.0015; KPi=2.0E-4 Reaction: species_24 + species_23 => species_24 + species_22; species_24, species_23, species_22, Rate Law: compartment_1*(parameter_63*species_24*species_23/(parameter_66*KPi)-parameter_7*species_24*species_22/(parameter_64*parameter_65))/(1+species_24/parameter_66+species_23/parameter_67+species_24/parameter_64+species_22/KiG1P+species_24*species_23/(parameter_66*KPi)+species_24*species_22/(parameter_64*parameter_65))*parameter_27^nH/Kamp/(1+parameter_27^nH/Kamp)

States:

Name Description
species 9 [endoplasmic reticulum; glucose]
species 27 [glycerone phosphate(2-)]
species 31 [pyruvate]
species 1 [glucose]
species 18 [NADH]
species 4 [ATP]
species 16 [glyceraldehyde 3-phosphate]
species 20 [AMP]
species 28 [3-phosphoglyceric acid]
species 34 [singlet dioxygen]
species 32 [lactate]
species 8 [6-O-phosphono-D-glucono-1,5-lactone]
species 30 [phosphoenolpyruvate]
species 12 [683]
species 17 [sedoheptulose 7-phosphate]
species 5 [keto-D-fructose 6-phosphate]
species 15 [aldehydo-D-ribose 5-phosphate(2-)]
species 21 [7339]
species 2 [alpha-D-glucose 6-phosphate]
species 29 [3-ADP-2-phosphoglyceric acid]
species 6 [alpha-D-fructofuranose 1,6-bisphosphate]
species 19 [NAD]
species 10 [NADP]
species 33 [carbon dioxide]
species 11 [salicyl alcohol]
species 24 [glycogen]
species 14 [D-xylulose 5-phosphate(2-)]
species 22 [D-glucopyranose 1-phosphate]
species 3 [ADP]
species 23 [phosphate(3-)]
species 7 [D-erythrose 4-phosphate]
species 13 [D-ribulose 5-phosphate(2-)]

Observables: none

# Mouse Iron Distribution Dynamics Dynamic model of iron distribution in mice. This model includes only normal iron with…

Iron is an essential element of most living organisms but is a dangerous substance when poorly liganded in solution. The hormone hepcidin regulates the export of iron from tissues to the plasma contributing to iron homeostasis and also restricting its availability to infectious agents. Disruption of iron regulation in mammals leads to disorders such as anemia and hemochromatosis, and contributes to the etiology of several other diseases such as cancer and neurodegenerative diseases. Here we test the hypothesis that hepcidin alone is able to regulate iron distribution in different dietary regimes in the mouse using a computational model of iron distribution calibrated with radioiron tracer data.A model was developed and calibrated to the data from adequate iron diet, which was able to simulate the iron distribution under a low iron diet. However simulation of high iron diet shows considerable deviations from the experimental data. Namely the model predicts more iron in red blood cells and less iron in the liver than what was observed in experiments.These results suggest that hepcidin alone is not sufficient to regulate iron homeostasis in high iron conditions and that other factors are important. The model was able to simulate anemia when hepcidin was increased but was unable to simulate hemochromatosis when hepcidin was suppressed, suggesting that in high iron conditions additional regulatory interactions are important. link: http://identifiers.org/pubmed/28521769

Parameters:

Name Description
kInBM = 15.7690636138556 Reaction: Fe2Tf => FeBM + Tf, Rate Law: kInBM*Fe2Tf*Plasma
kInLiver = 2.97790545667672 Reaction: Fe1Tf => FeLiver + Tf, Rate Law: kInLiver*Fe1Tf*Plasma
VLiverNTBI = 0.0261147638001175; Km = 0.0159421218669513; Ki = 1.0E-9 Reaction: FeLiver => NTBI; Hepcidin, Rate Law: VLiverNTBI*Liver*FeLiver/((Km+FeLiver)*(1+Hepcidin/Ki))
kDuoLoss = 0.0270113302698216 Reaction: FeDuo => FeOutside, Rate Law: kDuoLoss*FeDuo*Duodenum
kFe1Tf_Fe2Tf = 1.084322005E9 Reaction: Fe1Tf + NTBI => Fe2Tf, Rate Law: Plasma*kFe1Tf_Fe2Tf*Fe1Tf*NTBI
kNTBI_Fe1Tf = 1.084322005E9 Reaction: NTBI + Tf => Fe1Tf, Rate Law: Plasma*kNTBI_Fe1Tf*NTBI*Tf
VRestNTBI = 0.0109451335200198; Km = 0.0159421218669513; Ki = 1.0E-9 Reaction: FeRest => NTBI; Hepcidin, Rate Law: VRestNTBI*RestOfBody*FeRest/((Km+FeRest)*(1+Hepcidin/Ki))
kBMSpleen = 0.061902954378781 Reaction: FeBM => FeSpleen, Rate Law: kBMSpleen*FeBM*BoneMarrow
vRBCSpleen = 0.0235286 Reaction: FeRBC => FeSpleen, Rate Law: vRBCSpleen*FeRBC*RBC
vDiet = 0.00377422331938439 Reaction: => FeDuo, Rate Law: Duodenum*vDiet
Km = 0.0159421218669513; VDuoNTBI = 0.200241893786814; Ki = 1.0E-9 Reaction: FeDuo => NTBI; Hepcidin, Rate Law: VDuoNTBI*Duodenum*FeDuo/((Km+FeDuo)*(1+Hepcidin/Ki))
v=1.7393E-8 Reaction: => Hepcidin, Rate Law: Plasma*v
kInRest = 6.16356235352873 Reaction: Fe1Tf => FeRest + Tf, Rate Law: kInRest*Fe1Tf*Plasma
k1=0.75616 Reaction: Hepcidin =>, Rate Law: Plasma*k1*Hepcidin
kInDuo = 0.0689984226081531 Reaction: Fe1Tf => FeDuo + Tf, Rate Law: kInDuo*Fe1Tf*Plasma
VSpleenNTBI = 1.342204923; Km = 0.0159421218669513; Ki = 1.0E-9 Reaction: FeSpleen => NTBI; Hepcidin, Rate Law: VSpleenNTBI*Spleen*FeSpleen/((Km+FeSpleen)*(1+Hepcidin/Ki))
kInRBC = 1.08447580176706 Reaction: FeBM => FeRBC, Rate Law: kInRBC*FeBM*BoneMarrow
kRestLoss = 0.023533240736163 Reaction: FeRest => FeOutside, Rate Law: RestOfBody*kRestLoss*FeRest

States:

Name Description
FeRest [iron cation]
Fe2Tf [iron(3+); Serotransferrin]
NTBI [iron cation]
FeSpleen [iron cation]
FeBM [iron cation]
FeRBC [iron cation]
Fe1Tf [Serotransferrin; iron(3+)]
FeLiver [iron cation]
FeDuo [iron cation]
Tf [Serotransferrin]
Hepcidin [Hepcidin]
FeOutside [iron cation]

Observables: none

# Mouse Iron Distribution Dynamics Dynamic model of iron distribution in mice. This model includes normal iron and radio…

Iron is an essential element of most living organisms but is a dangerous substance when poorly liganded in solution. The hormone hepcidin regulates the export of iron from tissues to the plasma contributing to iron homeostasis and also restricting its availability to infectious agents. Disruption of iron regulation in mammals leads to disorders such as anemia and hemochromatosis, and contributes to the etiology of several other diseases such as cancer and neurodegenerative diseases. Here we test the hypothesis that hepcidin alone is able to regulate iron distribution in different dietary regimes in the mouse using a computational model of iron distribution calibrated with radioiron tracer data.A model was developed and calibrated to the data from adequate iron diet, which was able to simulate the iron distribution under a low iron diet. However simulation of high iron diet shows considerable deviations from the experimental data. Namely the model predicts more iron in red blood cells and less iron in the liver than what was observed in experiments.These results suggest that hepcidin alone is not sufficient to regulate iron homeostasis in high iron conditions and that other factors are important. The model was able to simulate anemia when hepcidin was increased but was unable to simulate hemochromatosis when hepcidin was suppressed, suggesting that in high iron conditions additional regulatory interactions are important. link: http://identifiers.org/pubmed/28521769

Parameters:

Name Description
kInBM = 15.7690636138556 Reaction: Fe2Tf_0 => FeBM + Tf, Rate Law: kInBM*Fe2Tf_0*Plasma
kInLiver = 2.97790545667672 Reaction: Fe1Tf => FeLiver + Tf, Rate Law: kInLiver*Fe1Tf*Plasma
VLiverNTBI = 0.0261147638001175; Km = 0.0159421218669513; Ki = 1.0E-9 Reaction: FeLiver_0 => NTBI_0; FeLiver, Hepcidin, Rate Law: VLiverNTBI*Liver*FeLiver_0/((Km+FeLiver_0+FeLiver)*(1+Hepcidin/Ki))
kDuoLoss = 0.0270113302698216 Reaction: FeDuo => FeOutside, Rate Law: kDuoLoss*FeDuo*Duodenum
kFe1Tf_Fe2Tf = 1.084322005E9 Reaction: Fe1Tf + NTBI => Fe2Tf_, Rate Law: Plasma*kFe1Tf_Fe2Tf*Fe1Tf*NTBI
kNTBI_Fe1Tf = 1.084322005E9 Reaction: NTBI + Tf => Fe1Tf, Rate Law: Plasma*kNTBI_Fe1Tf*NTBI*Tf
VRestNTBI = 0.0109451335200198; Km = 0.0159421218669513; Ki = 1.0E-9 Reaction: FeRest => NTBI; FeRest_0, Hepcidin, Rate Law: VRestNTBI*RestOfBody*FeRest/((Km+FeRest+FeRest_0)*(1+Hepcidin/Ki))
kBMSpleen = 0.061902954378781 Reaction: FeBM_0 => FeSpleen, Rate Law: kBMSpleen*FeBM_0*BoneMarrow
vRBCSpleen = 0.0235286 Reaction: FeRBC_0 => FeSpleen_0, Rate Law: vRBCSpleen*FeRBC_0*RBC
vDiet = 0.00377422331938439 Reaction: => FeDuo_0, Rate Law: Duodenum*vDiet
Km = 0.0159421218669513; VDuoNTBI = 0.200241893786814; Ki = 1.0E-9 Reaction: FeDuo => NTBI; FeDuo_0, Hepcidin, Rate Law: VDuoNTBI*Duodenum*FeDuo/((Km+FeDuo+FeDuo_0)*(1+Hepcidin/Ki))
v=1.7393E-8 Reaction: => Hepcidin, Rate Law: Plasma*v
kInRest = 6.16356235352873 Reaction: Fe2Tf => FeRest + FeRest_0 + Tf, Rate Law: kInRest*Fe2Tf*Plasma
k1=0.75616 Reaction: Hepcidin =>, Rate Law: Plasma*k1*Hepcidin
kInDuo = 0.0689984226081531 Reaction: Fe1Tf => FeDuo + Tf, Rate Law: kInDuo*Fe1Tf*Plasma
VSpleenNTBI = 1.342204923; Km = 0.0159421218669513; Ki = 1.0E-9 Reaction: FeSpleen => NTBI; FeSpleen_0, Hepcidin, Rate Law: VSpleenNTBI*Spleen*FeSpleen/((Km+FeSpleen+FeSpleen_0)*(1+Hepcidin/Ki))
kInRBC = 1.08447580176706 Reaction: FeBM_0 => FeRBC, Rate Law: kInRBC*FeBM_0*BoneMarrow
kRestLoss = 0.023533240736163 Reaction: FeRest => FeOutside, Rate Law: RestOfBody*kRestLoss*FeRest

States:

Name Description
FeRest [iron cation]
FeOutside 0 [iron cation]
NTBI 0 [iron cation]
Fe2Tf [iron(3+); Serotransferrin]
NTBI [iron cation]
Fe1Tf 0 [iron(3+); Serotransferrin]
Fe2Tf 0 [Serotransferrin; iron(3+)]
FeSpleen [iron cation]
FeRBC 0 [iron cation]
FeLiver 0 [iron cation]
FeBM [iron cation]
FeRBC [iron cation]
FeSpleen 0 [iron cation]
Fe1Tf [Serotransferrin; iron(3+)]
FeLiver [iron cation]
FeDuo [iron cation]
Tf [Serotransferrin]
Hepcidin [Hepcidin]
FeBM 0 [iron cation]
FeDuo 0 [iron cation]

Observables: none

# Mouse Iron Distribution Dynamics Dynamic model of iron distribution in mice. This model includes only normal iron with…

Iron is an essential element of most living organisms but is a dangerous substance when poorly liganded in solution. The hormone hepcidin regulates the export of iron from tissues to the plasma contributing to iron homeostasis and also restricting its availability to infectious agents. Disruption of iron regulation in mammals leads to disorders such as anemia and hemochromatosis, and contributes to the etiology of several other diseases such as cancer and neurodegenerative diseases. Here we test the hypothesis that hepcidin alone is able to regulate iron distribution in different dietary regimes in the mouse using a computational model of iron distribution calibrated with radioiron tracer data.A model was developed and calibrated to the data from adequate iron diet, which was able to simulate the iron distribution under a low iron diet. However simulation of high iron diet shows considerable deviations from the experimental data. Namely the model predicts more iron in red blood cells and less iron in the liver than what was observed in experiments.These results suggest that hepcidin alone is not sufficient to regulate iron homeostasis in high iron conditions and that other factors are important. The model was able to simulate anemia when hepcidin was increased but was unable to simulate hemochromatosis when hepcidin was suppressed, suggesting that in high iron conditions additional regulatory interactions are important. link: http://identifiers.org/pubmed/28521769

Parameters:

Name Description
kInBM = 15.7690636138556 Reaction: Fe2Tf => FeBM + Tf, Rate Law: kInBM*Fe2Tf*Plasma
kInLiver = 2.97790545667672 Reaction: Fe2Tf => FeLiver + Tf, Rate Law: kInLiver*Fe2Tf*Plasma
VLiverNTBI = 0.0261147638001175; Km = 0.0159421218669513; Ki = 1.0E-9 Reaction: FeLiver => NTBI; Hepcidin, Rate Law: VLiverNTBI*Liver*FeLiver/((Km+FeLiver)*(1+Hepcidin/Ki))
kDuoLoss = 0.0270113302698216 Reaction: FeDuo => FeOutside, Rate Law: kDuoLoss*FeDuo*Duodenum
kFe1Tf_Fe2Tf = 1.084322005E9 Reaction: Fe1Tf + NTBI => Fe2Tf, Rate Law: Plasma*kFe1Tf_Fe2Tf*Fe1Tf*NTBI
kNTBI_Fe1Tf = 1.084322005E9 Reaction: NTBI + Tf => Fe1Tf, Rate Law: Plasma*kNTBI_Fe1Tf*NTBI*Tf
VRestNTBI = 0.0109451335200198; Km = 0.0159421218669513; Ki = 1.0E-9 Reaction: FeRest => NTBI; Hepcidin, Rate Law: VRestNTBI*RestOfBody*FeRest/((Km+FeRest)*(1+Hepcidin/Ki))
kBMSpleen = 0.061902954378781 Reaction: FeBM => FeSpleen, Rate Law: kBMSpleen*FeBM*BoneMarrow
vRBCSpleen = 0.0235286 Reaction: FeRBC => FeSpleen, Rate Law: vRBCSpleen*FeRBC*RBC
Km = 0.0159421218669513; VDuoNTBI = 0.200241893786814; Ki = 1.0E-9 Reaction: FeDuo => NTBI; Hepcidin, Rate Law: VDuoNTBI*Duodenum*FeDuo/((Km+FeDuo)*(1+Hepcidin/Ki))
v=8.54927E-9 Reaction: => Hepcidin, Rate Law: Plasma*v
kInRest = 6.16356235352873 Reaction: Fe2Tf => FeRest + Tf, Rate Law: kInRest*Fe2Tf*Plasma
k1=0.75616 Reaction: Hepcidin =>, Rate Law: Plasma*k1*Hepcidin
kInDuo = 0.0689984226081531 Reaction: Fe1Tf => FeDuo + Tf, Rate Law: kInDuo*Fe1Tf*Plasma
VSpleenNTBI = 1.342204923; Km = 0.0159421218669513; Ki = 1.0E-9 Reaction: FeSpleen => NTBI; Hepcidin, Rate Law: VSpleenNTBI*Spleen*FeSpleen/((Km+FeSpleen)*(1+Hepcidin/Ki))
vDiet = 0.0 Reaction: => FeDuo, Rate Law: Duodenum*vDiet
kInRBC = 1.08447580176706 Reaction: FeBM => FeRBC, Rate Law: kInRBC*FeBM*BoneMarrow
kRestLoss = 0.023533240736163 Reaction: FeRest => FeOutside, Rate Law: RestOfBody*kRestLoss*FeRest

States:

Name Description
FeRest [iron cation]
Fe2Tf [Serotransferrin; iron(3+)]
NTBI [iron cation]
FeSpleen [iron cation]
FeBM [iron cation]
FeRBC [iron cation]
Fe1Tf [iron(3+); Serotransferrin]
FeLiver [iron cation]
FeDuo [iron cation]
Tf [Serotransferrin]
Hepcidin [Hepcidin]
FeOutside [iron cation]

Observables: none

# Mouse Iron Distribution Dynamics Dynamic model of iron distribution in mice. This model attempts to fit the radioiron…

Iron is an essential element of most living organisms but is a dangerous substance when poorly liganded in solution. The hormone hepcidin regulates the export of iron from tissues to the plasma contributing to iron homeostasis and also restricting its availability to infectious agents. Disruption of iron regulation in mammals leads to disorders such as anemia and hemochromatosis, and contributes to the etiology of several other diseases such as cancer and neurodegenerative diseases. Here we test the hypothesis that hepcidin alone is able to regulate iron distribution in different dietary regimes in the mouse using a computational model of iron distribution calibrated with radioiron tracer data.A model was developed and calibrated to the data from adequate iron diet, which was able to simulate the iron distribution under a low iron diet. However simulation of high iron diet shows considerable deviations from the experimental data. Namely the model predicts more iron in red blood cells and less iron in the liver than what was observed in experiments.These results suggest that hepcidin alone is not sufficient to regulate iron homeostasis in high iron conditions and that other factors are important. The model was able to simulate anemia when hepcidin was increased but was unable to simulate hemochromatosis when hepcidin was suppressed, suggesting that in high iron conditions additional regulatory interactions are important. link: http://identifiers.org/pubmed/28521769

Parameters:

Name Description
kInBM = 15.7690636138556 Reaction: Fe2Tf => FeBM_0 + FeBM + Tf, Rate Law: kInBM*Fe2Tf*Plasma
kInLiver = 2.97790545667672 Reaction: Fe2Tf_ => FeLiver + Tf, Rate Law: kInLiver*Fe2Tf_*Plasma
VLiverNTBI = 0.0261147638001175; Km = 0.0159421218669513; Ki = 1.0E-9 Reaction: FeLiver => NTBI; FeLiver_0, Hepcidin, Rate Law: VLiverNTBI*Liver*FeLiver/((Km+FeLiver+FeLiver_0)*(1+Hepcidin/Ki))
kDuoLoss = 0.0270113302698216 Reaction: FeDuo => FeOutside, Rate Law: kDuoLoss*FeDuo*Duodenum
kFe1Tf_Fe2Tf = 1.084322005E9 Reaction: Fe1Tf + NTBI => Fe2Tf_, Rate Law: Plasma*kFe1Tf_Fe2Tf*Fe1Tf*NTBI
kNTBI_Fe1Tf = 1.084322005E9 Reaction: NTBI_0 + Tf => Fe1Tf_0, Rate Law: Plasma*kNTBI_Fe1Tf*NTBI_0*Tf
VRestNTBI = 0.0109451335200198; Km = 0.0159421218669513; Ki = 1.0E-9 Reaction: FeRest => NTBI; FeRest_0, Hepcidin, Rate Law: VRestNTBI*RestOfBody*FeRest/((Km+FeRest+FeRest_0)*(1+Hepcidin/Ki))
kBMSpleen = 0.061902954378781 Reaction: FeBM_0 => FeSpleen, Rate Law: kBMSpleen*FeBM_0*BoneMarrow
vRBCSpleen = 0.0235286 Reaction: FeRBC => FeSpleen, Rate Law: vRBCSpleen*FeRBC*RBC
vDiet = 0.00377422331938439 Reaction: => FeDuo_0, Rate Law: Duodenum*vDiet
Km = 0.0159421218669513; VDuoNTBI = 0.200241893786814; Ki = 1.0E-9 Reaction: FeDuo => NTBI; FeDuo_0, Hepcidin, Rate Law: VDuoNTBI*Duodenum*FeDuo/((Km+FeDuo+FeDuo_0)*(1+Hepcidin/Ki))
v=1.7393E-8 Reaction: => Hepcidin, Rate Law: Plasma*v
kInRest = 6.16356235352873 Reaction: Fe2Tf => FeRest + FeRest_0 + Tf, Rate Law: kInRest*Fe2Tf*Plasma
k1=0.75616 Reaction: Hepcidin =>, Rate Law: Plasma*k1*Hepcidin
kInDuo = 0.0689984226081531 Reaction: Fe2Tf_0 => FeDuo_0 + Tf, Rate Law: kInDuo*Fe2Tf_0*Plasma
kInRBC = 1.08447580176706 Reaction: FeBM => FeRBC_0, Rate Law: kInRBC*FeBM*BoneMarrow
VSpleenNTBI = 1.342204923; Km = 0.0159421218669513; Ki = 1.0E-9 Reaction: FeSpleen => NTBI; FeSpleen_0, Hepcidin, Rate Law: VSpleenNTBI*Spleen*FeSpleen/((Km+FeSpleen+FeSpleen_0)*(1+Hepcidin/Ki))
kRestLoss = 0.023533240736163 Reaction: FeRest => FeOutside, Rate Law: RestOfBody*kRestLoss*FeRest

States:

Name Description
FeRest [iron cation]
FeRBC 0 [iron cation]
FeRBC [iron cation]
FeSpleen 0 [iron cation]
Fe1Tf [iron(3+); Serotransferrin]
FeLiver [iron cation]
FeBM 0 [iron cation]
FeOutside 0 [iron cation]
NTBI 0 [iron cation]
Fe2Tf [Serotransferrin; iron(3+)]
NTBI [iron cation]
Fe1Tf 0 [Serotransferrin; iron(3+)]
Fe2Tf 0 [iron(3+); Serotransferrin]
FeSpleen [iron cation]
FeRest 0 [iron cation]
FeLiver 0 [iron cation]
FeBM [iron cation]
FeDuo [iron cation]
Tf [Serotransferrin]
Hepcidin [Hepcidin]
FeOutside [iron cation]
FeDuo 0 [iron cation]

Observables: none

# Mouse Iron Distribution Dynamics Dynamic model of iron distribution in mice. This model includes only normal iron with…

Iron is an essential element of most living organisms but is a dangerous substance when poorly liganded in solution. The hormone hepcidin regulates the export of iron from tissues to the plasma contributing to iron homeostasis and also restricting its availability to infectious agents. Disruption of iron regulation in mammals leads to disorders such as anemia and hemochromatosis, and contributes to the etiology of several other diseases such as cancer and neurodegenerative diseases. Here we test the hypothesis that hepcidin alone is able to regulate iron distribution in different dietary regimes in the mouse using a computational model of iron distribution calibrated with radioiron tracer data.A model was developed and calibrated to the data from adequate iron diet, which was able to simulate the iron distribution under a low iron diet. However simulation of high iron diet shows considerable deviations from the experimental data. Namely the model predicts more iron in red blood cells and less iron in the liver than what was observed in experiments.These results suggest that hepcidin alone is not sufficient to regulate iron homeostasis in high iron conditions and that other factors are important. The model was able to simulate anemia when hepcidin was increased but was unable to simulate hemochromatosis when hepcidin was suppressed, suggesting that in high iron conditions additional regulatory interactions are important. link: http://identifiers.org/pubmed/28521769

Parameters:

Name Description
kInBM = 15.7690636138556 Reaction: Fe2Tf => FeBM + Tf, Rate Law: kInBM*Fe2Tf*Plasma
kInLiver = 2.97790545667672 Reaction: Fe2Tf => FeLiver + Tf, Rate Law: kInLiver*Fe2Tf*Plasma
VLiverNTBI = 0.0261147638001175; Km = 0.0159421218669513; Ki = 1.0E-9 Reaction: FeLiver => NTBI; Hepcidin, Rate Law: VLiverNTBI*Liver*FeLiver/((Km+FeLiver)*(1+Hepcidin/Ki))
kDuoLoss = 0.0270113302698216 Reaction: FeDuo => FeOutside, Rate Law: kDuoLoss*FeDuo*Duodenum
kFe1Tf_Fe2Tf = 1.084322005E9 Reaction: Fe1Tf + NTBI => Fe2Tf, Rate Law: Plasma*kFe1Tf_Fe2Tf*Fe1Tf*NTBI
kNTBI_Fe1Tf = 1.084322005E9 Reaction: NTBI + Tf => Fe1Tf, Rate Law: Plasma*kNTBI_Fe1Tf*NTBI*Tf
VRestNTBI = 0.0109451335200198; Km = 0.0159421218669513; Ki = 1.0E-9 Reaction: FeRest => NTBI; Hepcidin, Rate Law: VRestNTBI*RestOfBody*FeRest/((Km+FeRest)*(1+Hepcidin/Ki))
kBMSpleen = 0.061902954378781 Reaction: FeBM => FeSpleen, Rate Law: kBMSpleen*FeBM*BoneMarrow
vRBCSpleen = 0.0235286 Reaction: FeRBC => FeSpleen, Rate Law: vRBCSpleen*FeRBC*RBC
Km = 0.0159421218669513; VDuoNTBI = 0.200241893786814; Ki = 1.0E-9 Reaction: FeDuo => NTBI; Hepcidin, Rate Law: VDuoNTBI*Duodenum*FeDuo/((Km+FeDuo)*(1+Hepcidin/Ki))
kInRest = 6.16356235352873 Reaction: Fe1Tf => FeRest + Tf, Rate Law: kInRest*Fe1Tf*Plasma
k1=0.75616 Reaction: Hepcidin =>, Rate Law: Plasma*k1*Hepcidin
kInDuo = 0.0689984226081531 Reaction: Fe1Tf => FeDuo + Tf, Rate Law: kInDuo*Fe1Tf*Plasma
VSpleenNTBI = 1.342204923; Km = 0.0159421218669513; Ki = 1.0E-9 Reaction: FeSpleen => NTBI; Hepcidin, Rate Law: VSpleenNTBI*Spleen*FeSpleen/((Km+FeSpleen)*(1+Hepcidin/Ki))
kInRBC = 1.08447580176706 Reaction: FeBM => FeRBC, Rate Law: kInRBC*FeBM*BoneMarrow
vDiet = 0.00415624 Reaction: => FeDuo, Rate Law: Duodenum*vDiet
v=2.30942E-8 Reaction: => Hepcidin, Rate Law: Plasma*v
kRestLoss = 0.023533240736163 Reaction: FeRest => FeOutside, Rate Law: RestOfBody*kRestLoss*FeRest

States:

Name Description
FeRest [iron cation]
Fe2Tf [Serotransferrin; iron(3+)]
NTBI [iron cation]
FeSpleen [iron cation]
FeBM [iron cation]
FeRBC [iron cation]
Fe1Tf [Serotransferrin; iron(3+)]
FeLiver [iron cation]
FeDuo [iron cation]
Tf [Serotransferrin]
Hepcidin [Hepcidin]
FeOutside [iron cation]

Observables: none

Role of vascular normalization in benefit from metronomic chemotherapy. Mpekris F1, Baish JW2, Stylianopoulos T3, Jain R…

Metronomic dosing of chemotherapy-defined as frequent administration at lower doses-has been shown to be more efficacious than maximum tolerated dose treatment in preclinical studies, and is currently being tested in the clinic. Although multiple mechanisms of benefit from metronomic chemotherapy have been proposed, how these mechanisms are related to one another and which one is dominant for a given tumor-drug combination is not known. To this end, we have developed a mathematical model that incorporates various proposed mechanisms, and report here that improved function of tumor vessels is a key determinant of benefit from metronomic chemotherapy. In our analysis, we used multiple dosage schedules and incorporated interactions among cancer cells, stem-like cancer cells, immune cells, and the tumor vasculature. We found that metronomic chemotherapy induces functional normalization of tumor blood vessels, resulting in improved tumor perfusion. Improved perfusion alleviates hypoxia, which reprograms the immunosuppressive tumor microenvironment toward immunostimulation and improves drug delivery and therapeutic outcomes. Indeed, in our model, improved vessel function enhanced the delivery of oxygen and drugs, increased the number of effector immune cells, and decreased the number of regulatory T cells, which in turn killed a larger number of cancer cells, including cancer stem-like cells. Vessel function was further improved owing to decompression of intratumoral vessels as a result of increased killing of cancer cells, setting up a positive feedback loop. Our model enables evaluation of the relative importance of these mechanisms, and suggests guidelines for the optimal use of metronomic therapy. link: http://identifiers.org/pubmed/28174262

Parameters: none

States: none

Observables: none

BIOMD0000000367 @ v0.0.1

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

This paper focus on the quest for mechanisms that are able to create tolerance and an activation threshold in the extrinsic coagulation cascade. We propose that the interplay of coagulation inhibitor and blood flow creates threshold behavior. First we test this hypothesis in a minimal, four dimensional model. This model can be analysed by means of time scale analysis. We find indeed that only the interplay of blood flow and inhibition together are able to produce threshold behavior. The mechanism relays on a combination of raw substance supply and wash-out effect by the blood flow and a stabilization of the resting state by the inhibition. We use the insight into this minimal model to interpret the simulation results of a large model. Here, we find that the initiating steps (TF that produces together with fVII(a) factor Xa) does not exhibit threshold behavior, but the overall system does. Hence, the threshold behavior appears via the feedback loop (in that fIIa produces indirectly fXa that in turn produces fIIa again) inhibited by ATIII and blood flow. link: http://identifiers.org/pubmed/17936855

Parameters:

Name Description
zeta = 0.5; b = 1.5; mu_z = 0.4 Reaction: z = ((-b)*y*z+zeta*mu_z)-zeta*z, Rate Law: ((-b)*y*z+zeta*mu_z)-zeta*z
zeta = 0.5; mu_x = 4.0; r = 0.2 Reaction: x = ((-r)*x*y+zeta*mu_x)-zeta*x, Rate Law: ((-r)*x*y+zeta*mu_x)-zeta*x
zeta = 0.5; r = 0.2; b = 1.5 Reaction: y = (r*x*y-b*y*z)-zeta*y, Rate Law: (r*x*y-b*y*z)-zeta*y

States:

Name Description
x x
z z
y y

Observables: none

Mueller2015 - Hepatocyte proliferation, T160 phosphorylation of CDK2This model is described in the article: [T160-phosp…

Liver regeneration is a tightly controlled process mainly achieved by proliferation of usually quiescent hepatocytes. The specific molecular mechanisms ensuring cell division only in response to proliferative signals such as hepatocyte growth factor (HGF) are not fully understood. Here, we combined quantitative time-resolved analysis of primary mouse hepatocyte proliferation at the single cell and at the population level with mathematical modeling. We showed that numerous G1/S transition components are activated upon hepatocyte isolation whereas DNA replication only occurs upon additional HGF stimulation. In response to HGF, Cyclin:CDK complex formation was increased, p21 rather than p27 was regulated, and Rb expression was enhanced. Quantification of protein levels at the restriction point showed an excess of CDK2 over CDK4 and limiting amounts of the transcription factor E2F-1. Analysis with our mathematical model revealed that T160 phosphorylation of CDK2 correlated best with growth factor-dependent proliferation, which we validated experimentally on both the population and the single cell level. In conclusion, we identified CDK2 phosphorylation as a gate-keeping mechanism to maintain hepatocyte quiescence in the absence of HGF. link: http://identifiers.org/pubmed/25771250

Parameters:

Name Description
kp_c2cak = 101.599112819407 Reaction: S13 => S18; S13, Rate Law: Nucleus*kp_c2cak*S13/cell
ks_e2fe2f = 0.459601740303536; ks_e2fmyc = 2.49174531457788E-6; tf = 0.635098964160441 Reaction: => S14; S14, Rate Law: Nucleus*(ks_e2fe2f*S14+ks_e2fmyc)*tf/cell
kd_p21c4 = 1430.78413614709 Reaction: S19 => S10 + S12; S19, Rate Law: cell*kd_p21c4*S19/cell
kb_p21c2 = 997.938141166465 Reaction: S4 + S12 => S20; S4, S12, Rate Law: cell*kb_p21c2*S4*S12/cell
ks_c2myc = 0.157511710670132; ks_c2e2f = 2.19944932286058; tf = 0.635098964160441 Reaction: => S4; S14, S16, Rate Law: cell*(ks_c2myc*tf+ks_c2e2f*(S14+S16))/cell
kdeg_rbbound = 0.0889964132806627 Reaction: S16 => S14; S16, Rate Law: Nucleus*kdeg_rbbound*S16/cell
kb_p21c4 = 14.3083360067931 Reaction: S10 + S12 => S19; S10, S12, Rate Law: cell*kb_p21c4*S10*S12/cell
kdeg_p21erkskp2 = 2.82976267377082E-4; erk = 0.16; kdeg_p21skp2 = 0.750574831653576; kdeg_p21c2skp2 = 0.040108041739907 Reaction: S23 => S18; S18, S14, S23, Rate Law: Nucleus*(kdeg_p21erkskp2*erk+kdeg_p21c2skp2*S18+kdeg_p21skp2)*S14*S23/cell
kcatdp_rbc4 = 2892.0219338341; nrb = 3.0; Km_dprb = 0.118988383643671; kinh_pp1 = 16634.9400020267 Reaction: S15 => S1; S18, S15, Rate Law: Nucleus*kcatdp_rbc4*S15^nrb/(Km_dprb^nrb+S15^nrb)*1/(1+kinh_pp1*S18)/cell
kdp_c2cak = 101.282119534273 Reaction: S18 => S13; S18, Rate Law: Nucleus*kdp_c2cak*S18/cell
kdeg_e2ffree = 0.100037217670528 Reaction: S14 => ; S14, Rate Law: Nucleus*kdeg_e2ffree*S14/cell
kb_rbe2f = 229.976400323907 Reaction: S1 + S14 => S2; S1, S14, Rate Law: Nucleus*kb_rbe2f*S1*S14/cell
kd_rbpe2f = 87735.365961809 Reaction: S16 => S14 + S15; S16, Rate Law: Nucleus*kd_rbpe2f*S16/cell
nrb = 3.0; Km_prb = 2.03458881189349; kcatp_rbc2 = 7142308.07232621 Reaction: S16 => S14 + S21; S18, S16, Rate Law: Nucleus*kcatp_rbc2*S18*S16^nrb/(Km_prb^nrb+S16^nrb)/cell
kdeg_rbfree = 0.346759895758394 Reaction: S1 => ; S1, Rate Law: Nucleus*kdeg_rbfree*S1/cell
gsk3b = 0.47; kdeg_c4 = 1.01433121526038; kdeg_c4gsk3b = 0.107637073030656 Reaction: S19 => S12; S19, Rate Law: cell*(kdeg_c4+kdeg_c4gsk3b*gsk3b)*S19/cell
kd_rbe2f = 11499.4014796088 Reaction: S2 => S1 + S14; S2, Rate Law: Nucleus*kd_rbe2f*S2/cell
kdeg_e2fbound = 0.0999954023364359 Reaction: S2 => S1; S2, Rate Law: Nucleus*kdeg_e2fbound*S2/cell
ks_rb = 72.5245257602228; ks_rbe2f = 20.0129834334888 Reaction: => S1; S14, Rate Law: Nucleus*(ks_rb+ks_rbe2f*S14)/cell
scale_TotCDK2T160 = 2.728395741944; Vnuc = 0.25; Vcyto = 12.67 Reaction: ObsTotCDK2T160_obs = scale_TotCDK2T160*Vnuc*(S18+S23)/(Vnuc+Vcyto), Rate Law: missing
erk = 0.16; gsk3b = 0.47; kdeg_p21erk = 0.736488746268804; kdeg_p21gsk3b = 0.00464010657330714 Reaction: S12 => ; S12, Rate Law: cell*(kdeg_p21gsk3b*gsk3b+kdeg_p21erk*erk)*S12/cell
scale_PhosRbS800 = 0.82377467648995; Vnuc = 0.25; Vcyto = 12.67 Reaction: ObsPhosRbS800_obs = scale_PhosRbS800*Vnuc*S21/(Vnuc+Vcyto), Rate Law: missing
ks_c4 = 14298.6715905912; tf = 0.635098964160441 Reaction: => S10, Rate Law: cell*ks_c4*tf/cell
scale_TotE2F = 28.7418; Vnuc = 0.25; Vcyto = 12.67; scale_TotRb = 0.2605 Reaction: ObsTotE2F_obs = (scale_TotE2F+scale_TotRb)*Vnuc*(S2+S14+S16)/(Vnuc+Vcyto), Rate Law: missing
kd_p21c2 = 9.98179979713068 Reaction: S20 => S4 + S12; S20, Rate Law: cell*kd_p21c2*S20/cell
Vratio = 0.0197316495659037; kimport = 0.0744777523096695 Reaction: S12 => S11; S12, Rate Law: kimport/Vratio*S12/cell
Vnuc = 0.25; Vcyto = 12.67; scale_TotRb = 0.2605 Reaction: ObsTotRb_obs = scale_TotRb*Vnuc*(S1+S2+S15+S16+S21)/(Vnuc+Vcyto), Rate Law: missing
Vnuc = 0.25; scale_Totp21CDK2 = 0.339790715037712; Vcyto = 12.67 Reaction: ObsCDK2P21_obs = scale_Totp21CDK2*(Vnuc*(S3+S23)+Vcyto*S20)/(Vnuc+Vcyto), Rate Law: missing
nrb = 3.0; kcatdp_rbc2 = 0.00313841707547858; Km_dprb = 0.118988383643671; kinh_pp1 = 16634.9400020267 Reaction: S21 => S15; S18, S21, Rate Law: Nucleus*kcatdp_rbc2*S21^nrb/(Km_dprb^nrb+S21^nrb)*1/(1+kinh_pp1*S18)/cell
ks_p21p53 = 3.84136205729286E-6; tfp21 = 0.635098964160441; ks_p21e2f = 0.811617200647839 Reaction: => S12; S14, Rate Law: cell*(ks_p21p53+ks_p21e2f*S14)*tfp21/cell
kcatp_rbc4 = 2797.82326282727; nrb = 3.0; Km_prb = 2.03458881189349 Reaction: S1 => S15; S24, S1, Rate Law: Nucleus*kcatp_rbc4*S24*S1^nrb/(Km_prb^nrb+S1^nrb)/cell
kb_rbpe2f = 182.218452288549 Reaction: S14 + S15 => S16; S14, S15, Rate Law: Nucleus*kb_rbpe2f*S14*S15/cell
k_dna = 0.00949790539669408 Reaction: S5 => S17; S18, S14, S5, Rate Law: Nucleus*k_dna*S18*S14*S5/cell
gsk3b = 0.47; kdeg_c2 = 0.225746618767114; kdeg_c2gsk3b = 1.55090179808215E-5 Reaction: S3 => S11; S3, Rate Law: Nucleus*(kdeg_c2+kdeg_c2gsk3b*gsk3b)*S3/cell
k_delay = 23.6658781343201 Reaction: S27 => S28; S27, Rate Law: Nucleus*k_delay*S27/cell
Vnuc = 0.25; scale_Totp21 = 0.1728; Vcyto = 12.67 Reaction: ObsTotP21_obs = scale_Totp21*(Vnuc*(S3+S11+S23+S24)+Vcyto*(S12+S19+S20))/(Vnuc+Vcyto), Rate Law: missing
kdeg_rbp21 = 0.863570809432207 Reaction: S16 => S14; S11, S16, Rate Law: Nucleus*kdeg_rbp21*S11*S16/cell
kdeg_c4 = 1.01433121526038 Reaction: S24 => ; S24, Rate Law: Nucleus*kdeg_c4*S24/cell

States:

Name Description
S11 [Cyclin-dependent kinase inhibitor 1B]
S14 [Transcription factor E2F1]
S16 [Transcription factor E2F1; Retinoblastoma-associated protein; phosphorylated]
ObsDNAContent obs [deoxyribonucleic acid]
inhp53 [Cellular tumor antigen p53]
S13 [phosphorylated; cyclin E1-CDK2 complex]
S23 [cyclin E1-CDK2 complex; Cyclin-dependent kinase inhibitor 1B; phosphorylated]
inhakt [RAC-alpha serine/threonine-protein kinase]
inhc4d1 [cyclin D1-CDK4 complex]
S17 [pre-replicative complex]
S28 [pre-replicative complex]
S4 [cyclin E1-CDK2 complex; phosphorylated]
S1 [Retinoblastoma-associated protein; phosphorylated]
S25 [pre-replicative complex]
ObsTotE2F obs [Transcription factor E2F1]
inherk [Mitogen-activated protein kinase 3]
S19 [cyclin D1-CDK4 complex; Cyclin-dependent kinase inhibitor 1B]
ObsPhosRbS800 obs [Retinoblastoma-associated protein; phosphorylated]
S5 [pre-replicative complex]
S10 [cyclin D1-CDK4 complex]
S3 [cyclin E1-CDK2 complex; Cyclin-dependent kinase inhibitor 1B; phosphorylated]
S27 [pre-replicative complex]
ObsTotCDK2T160 obs [Cyclin-dependent kinase 2; phosphorylated]
S2 [Transcription factor E2F1; Retinoblastoma-associated protein; phosphorylated]
S26 [cyclin D1-CDK4 complex]
S21 [Retinoblastoma-associated protein; phosphorylated]
ObsTotP21 obs [Cyclin-dependent kinase inhibitor 1B]
ObsCDK2P21 obs [Cyclin-dependent kinase inhibitor 1B; Cyclin-dependent kinase 2]
S20 [cyclin E1-CDK2 complex; Cyclin-dependent kinase inhibitor 1B; phosphorylated]
hgf [Hepatocyte growth factor]
ObsTotRb obs [Retinoblastoma-associated protein]
S15 [Retinoblastoma-associated protein; phosphorylated]
S12 [Cyclin-dependent kinase inhibitor 1B]
S24 [cyclin D1-CDK4 complex; Cyclin-dependent kinase inhibitor 1B]
S22 [pre-replicative complex]

Observables: none

A mechanistically detailed model of the cell cycle control network of Saccharomyces cerevisiae.

Understanding how cellular functions emerge from the underlying molecular mechanisms is a key challenge in biology. This will require computational models, whose predictive power is expected to increase with coverage and precision of formulation. Genome-scale models revolutionised the metabolic field and made the first whole-cell model possible. However, the lack of genome-scale models of signalling networks blocks the development of eukaryotic whole-cell models. Here, we present a comprehensive mechanistic model of the molecular network that controls the cell division cycle in Saccharomyces cerevisiae. We use rxncon, the reaction-contingency language, to neutralise the scalability issues preventing formulation, visualisation and simulation of signalling networks at the genome-scale. We use parameter-free modelling to validate the network and to predict genotype-to-phenotype relationships down to residue resolution. This mechanistic genome-scale model offers a new perspective on eukaryotic cell cycle control, and opens up for similar models—and eventually whole-cell models—of human cells. link: https://doi.org/10.1038/s41467-019-08903-w

Parameters: none

States: none

Observables: none

Mufudza2012 - Estrogen effect on the dynamics of breast cancerThis deterministic model shows the dynamics of breast canc…

Worldwide, breast cancer has become the second most common cancer in women. The disease has currently been named the most deadly cancer in women but little is known on what causes the disease. We present the effects of estrogen as a risk factor on the dynamics of breast cancer. We develop a deterministic mathematical model showing general dynamics of breast cancer with immune response. This is a four-population model that includes tumor cells, host cells, immune cells, and estrogen. The effects of estrogen are then incorporated in the model. The results show that the presence of extra estrogen increases the risk of developing breast cancer. link: http://identifiers.org/pubmed/23365616

Parameters:

Name Description
sigma3 = 0.3; s = 0.4; mu = 0.29; rho = 0.2; gamma3 = 0.085; omega = 0.3; v = 0.4 Reaction: I = (((s+rho*I*T/(omega+T))-gamma3*I*T)-mu*I)-sigma3*I*E/(v+E), Rate Law: (((s+rho*I*T/(omega+T))-gamma3*I*T)-mu*I)-sigma3*I*E/(v+E)
beta1 = 0.3; alpha1 = 0.7; sigma1 = 1.2; delta1 = 1.0 Reaction: H = H*((alpha1-beta1*H)-delta1*T)-sigma1*H*E, Rate Law: H*((alpha1-beta1*H)-delta1*T)-sigma1*H*E
beta2 = 0.4; alpha3 = 1.0; gamma2 = 0.9; sigma2 = 0.94 Reaction: T = (T*(alpha3-beta2*T)-gamma2*I*T)+sigma2*H*E, Rate Law: (T*(alpha3-beta2*T)-gamma2*I*T)+sigma2*H*E

States:

Name Description
I [immune response; cell]
T [neoplastic cell]
H [cell]

Observables: none

Mathematical model for HIV, malaria and HIV-malaria co-infection.

A deterministic model for the co-interaction of HIV and malaria in a community is presented and rigorously analyzed. Two sub-models, namely the HIV-only and malaria-only sub-models, are considered first of all. Unlike the HIV-only sub-model, which has a globally-asymptotically stable disease-free equilibrium whenever the associated reproduction number is less than unity, the malaria-only sub-model undergoes the phenomenon of backward bifurcation, where a stable disease-free equilibrium co-exists with a stable endemic equilibrium, for a certain range of the associated reproduction number less than unity. Thus, for malaria, the classical requirement of having the associated reproduction number to be less than unity, although necessary, is not sufficient for its elimination. It is also shown, using centre manifold theory, that the full HIV-malaria co-infection model undergoes backward bifurcation. Simulations of the full HIV-malaria model show that the two diseases co-exist whenever their reproduction numbers exceed unity (with no competitive exclusion occurring). Further, the reduction in sexual activity of individuals with malaria symptoms decreases the number of new cases of HIV and the mixed HIV-malaria infection while increasing the number of malaria cases. Finally, these simulations show that the HIV-induced increase in susceptibility to malaria infection has marginal effect on the new cases of HIV and malaria but increases the number of new cases of the dual HIV-malaria infection. link: http://identifiers.org/pubmed/19364156

Parameters: none

States: none

Observables: none

The emergence and fast global spread of COVID-19 has presented one of the greatest public health challenges in modern ti…

The emergence and fast global spread of COVID-19 has presented one of the greatest public health challenges in modern times with no proven cure or vaccine. Africa is still early in this epidemic, therefore the extent of disease severity is not yet clear. We used a mathematical model to fit to the observed cases of COVID-19 in South Africa to estimate the basic reproductive number and critical vaccination coverage to control the disease for different hypothetical vaccine efficacy scenarios. We also estimated the percentage reduction in effective contacts due to the social distancing measures implemented. Early model estimates show that COVID-19 outbreak in South Africa had a basic reproductive number of 2.95 (95% credible interval [CrI] 2.83-3.33). A vaccine with 70% efficacy had the capacity to contain COVID-19 outbreak but at very higher vaccination coverage 94.44% (95% Crl 92.44-99.92%) with a vaccine of 100% efficacy requiring 66.10% (95% Crl 64.72-69.95%) coverage. Social distancing measures put in place have so far reduced the number of social contacts by 80.31% (95% Crl 79.76-80.85%). These findings suggest that a highly efficacious vaccine would have been required to contain COVID-19 in South Africa. Therefore, the current social distancing measures to reduce contacts will remain key in controlling the infection in the absence of vaccines and other therapeutics. link: http://identifiers.org/pubmed/32706790

Parameters: none

States: none

Observables: none

Mukhopadhyay2013 - T cell receptor proximal signaling reveals emergent ultrasensitivityThis model is described in the ar…

Receptor phosphorylation is thought to be tightly regulated because phosphorylated receptors initiate signaling cascades leading to cellular activation. The T cell antigen receptor (TCR) on the surface of T cells is phosphorylated by the kinase Lck and dephosphorylated by the phosphatase CD45 on multiple immunoreceptor tyrosine-based activation motifs (ITAMs). Intriguingly, Lck sequentially phosphorylates ITAMs and ZAP-70, a cytosolic kinase, binds to phosphorylated ITAMs with differential affinities. The purpose of multiple ITAMs, their sequential phosphorylation, and the differential ZAP-70 affinities are unknown. Here, we use a systems model to show that this signaling architecture produces emergent ultrasensitivity resulting in switch-like responses at the scale of individual TCRs. Importantly, this switch-like response is an emergent property, so that removal of multiple ITAMs, sequential phosphorylation, or differential affinities abolishes the switch. We propose that highly regulated TCR phosphorylation is achieved by an emergent switch-like response and use the systems model to design novel chimeric antigen receptors for therapy. link: http://identifiers.org/pubmed/23555234

Parameters: none

States: none

Observables: none

A campaign for malaria control, using Long Lasting Insecticide Nets (LLINs) was launched in South Sudan in 2009. The suc…

A campaign for malaria control, using Long Lasting Insecticide Nets (LLINs) was launched in South Sudan in 2009. The success of such a campaign often depends upon adequate available resources and reliable surveillance data which help officials understand existing infections. An optimal allocation of resources for malaria control at a sub-national scale is therefore paramount to the success of efforts to reduce malaria prevalence. In this paper, we extend an existing SIR mathematical model to capture the effect of LLINs on malaria transmission. Available data on malaria is utilized to determine realistic parameter values of this model using a Bayesian approach via Markov Chain Monte Carlo (MCMC) methods. Then, we explore the parasite prevalence on a continued rollout of LLINs in three different settings in order to create a sub-national projection of malaria. Further, we calculate the model's basic reproductive number and study its sensitivity to LLINs' coverage and its efficacy. From the numerical simulation results, we notice a basic reproduction number, [Formula: see text], confirming a substantial increase of incidence cases if no form of intervention takes place in the community. This work indicates that an effective use of LLINs may reduce [Formula: see text] and hence malaria transmission. We hope that this study will provide a basis for recommending a scaling-up of the entry point of LLINs' distribution that targets households in areas at risk of malaria. link: http://identifiers.org/pubmed/29879166

Parameters: none

States: none

Observables: none

Muller2008 - Simplified MAPK activation Dynamics (Model B)Simplified mathematical model (model B) for predicting MAPK si…

Activation of the fibroblast growth factor (FGFR) and melanocyte stimulating hormone (MC1R) receptors stimulates B-Raf and C-Raf isoforms that regulate the dynamics of MAPK1,2 signaling. Network topology motifs in mammalian cells include feed-forward and feedback loops and bifans where signals from two upstream molecules integrate to modulate the activity of two downstream molecules. We computationally modeled and experimentally tested signal processing in the FGFR/MC1R/B-Raf/C-Raf/MAPK1,2 network in human melanoma cells; identifying 7 regulatory loops and a bifan motif. Signaling from FGFR leads to sustained activation of MAPK1,2, whereas signaling from MC1R results in transient activation of MAPK1,2. The dynamics of MAPK activation depends critically on the expression level and connectivity to C-Raf, which is critical for a sustained MAPK1,2 response. A partially incoherent bifan motif with a feedback loop acts as a logic gate to integrate signals and regulate duration of activation of the MAPK signaling cascade. Further reducing a 106-node ordinary differential equations network encompassing the complete network to a 6-node network encompassing rate-limiting processes sustains the feedback loops and the bifan, providing sufficient information to predict biological responses. link: http://identifiers.org/pubmed/18171696

Parameters:

Name Description
E = 10.0; f13 = 0.6 0.06*ml/(mol*s) Reaction: => C_Raf; C_Raf_inactive, FGFR, Rate Law: Compartment*f13*((E-C_Raf)-C_Raf_inactive)*FGFR
E = 10.0; f53 = 1.5 0.06*ml/(mol*s) Reaction: => C_Raf; C_Raf_inactive, MAPK, Rate Law: Compartment*f53*((E-C_Raf)-C_Raf_inactive)*MAPK
f14 = 0.1 1/(59.9999*s) Reaction: => B_Raf; FGFR, Rate Law: Compartment*f14*FGFR
g1 = 0.0 Reaction: g1_0 = g1, Rate Law: missing
f24 = 0.8 1/(59.9999*s) Reaction: => B_Raf; MSH, Rate Law: Compartment*f24*MSH
b2 = 10.0; a2 = 10.0 0.06*mmol/(l*s); g2 = 1.0 Reaction: => MSH; g2_0, Rate Law: Compartment*a2*g2/(b2+g2)
f45 = 0.1 1/(59.9999*s) Reaction: => MAPK; B_Raf, Rate Law: Compartment*f45*B_Raf
d3 = 1.0 1/(59.9999*s) Reaction: C_Raf =>, Rate Law: Compartment*d3*C_Raf
d6 = 0.001 1/(59.9999*s) Reaction: C_Raf_inactive =>, Rate Law: Compartment*d6*C_Raf_inactive
f35 = 0.3 1/(59.9999*s) Reaction: => MAPK; C_Raf, Rate Law: Compartment*f35*C_Raf
d5 = 1.0 1/(59.9999*s) Reaction: MAPK =>, Rate Law: Compartment*d5*MAPK
h36_y3 = 0.1 0.06*ml/(mol*s) Reaction: C_Raf => C_Raf_inactive; MSH, Rate Law: Compartment*h36_y3*MSH*C_Raf
d1 = 0.2 1/(59.9999*s) Reaction: FGFR =>, Rate Law: Compartment*d1*FGFR
b1 = 10.0; a1 = 10.0 0.06*mmol/(l*s); g1 = 0.0 Reaction: => FGFR; g1_0, Rate Law: Compartment*a1*g1/(b1+g1)
d2 = 0.1 1/(59.9999*s) Reaction: MSH =>, Rate Law: Compartment*d2*MSH
g2 = 1.0 Reaction: g2_0 = g2, Rate Law: missing
d4 = 1.1 1/(59.9999*s) Reaction: B_Raf =>, Rate Law: Compartment*d4*B_Raf

States:

Name Description
FGFR [Fibroblast growth factor receptor 1]
C Raf [RAF proto-oncogene serine/threonine-protein kinase]
C Raf inactive [RAF proto-oncogene serine/threonine-protein kinase]
B Raf [Serine/threonine-protein kinase B-raf]
MAPK [Mitogen-activated protein kinase 1]
g2 0 [Melanocyte-stimulating hormone; Stimulus]
MSH [melanocyte-stimulating hormone receptor]
g1 0 [Fibroblast growth factor 1; Stimulus]

Observables: none

MODEL5950552398 @ v0.0.1

This model is described and analysed in a series of three articles: **Model of 2,3-bisphosphoglycerate metabolism in t…

This is the third of three papers [see also Mulquiney, Bubb and Kuchel (1999) Biochem. J. 342, 565-578; Mulquiney and Kuchel (1999) Biochem. J. 342, 579-594] for which the general goal was to explain the regulation and control of 2,3-bisphosphoglycerate (2,3-BPG) metabolism in human erythrocytes. 2,3-BPG is a major modulator of haemoglobin oxygen affinity and hence is vital in blood oxygen transport. A detailed mathematical model of erythrocyte metabolism was presented in the first two papers. The model was refined through an iterative loop of experiment and simulation and it was used to predict outcomes that are consistent with the metabolic behaviour of the erythrocyte under a wide variety of experimental and physiological conditions. For the present paper, the model was examined using computer simulation and Metabolic Control Analysis. The analysis yielded several new insights into the regulation and control of 2,3-BPG metabolism. Specifically it was found that: (1) the feedback inhibition of hexokinase and phosphofructokinase by 2, 3-BPG are equally as important as the product inhibition of 2,3-BPG synthase in controlling the normal in vivo steady-state concentration of 2,3-BPG; (2) H(+) and oxygen are effective regulators of 2,3-BPG concentration and that increases in 2,3-BPG concentrations are achieved with only small changes in glycolytic rate; (3) these two effectors exert most of their influence through hexokinase and phosphofructokinase; (4) flux through the 2,3-BPG shunt changes in absolute terms in response to different energy demands placed on the cell. This response of the 2,3-BPG shunt contributes an [ATP]-stabilizing effect. A 'cost' of this is that 2, 3-BPG concentrations are very sensitive to the energy demand of the cell and; (5) the flux through the 2,3-BPG shunt does not change in response to different non-glycolytic demands for NADH. link: http://identifiers.org/pubmed/10477270

Parameters: none

States: none

Observables: none

MODEL1008060000 @ v0.0.1

Munz2009 - Zombie Impulsive KillingThis is the basic SZR model with impulsive killing described in the article. This mo…

Zombies are a popular figure in pop culture/entertainment and they are usually portrayed as being brought about through an outbreak or epidemic. Consequently, we model a zombie attack, using biological assumptions based on popular zombie movies. We introduce a basic model for zombie infection, determine equilibria and their stability, and illustrate the outcome with numerical solutions. We then refine the model to introduce a latent period of zombification, whereby humans are infected, but not infectious, before becoming undead. We then modify the model to include the effects of possible quarantine or a cure. Finally, we examine the impact of regular, impulsive reductions in the number of zombies and derive conditions under which eradication can occur. We show that only quick, aggressive attacks can stave off the doomsday scenario: the collapse of society as zombies overtake us all. link: http://www.mathworks.co.uk/matlabcentral/linkexchange/links/1749-when-zombies-attack-mathematical-modelling-of-an-outbreak-of-zombie-infection

Parameters: none

States: none

Observables: none

MODEL1009230000 @ v0.0.1

Munz2009 - Zombie basic SZRThis is the basic SZR model for zombie infection. It is based on a classic mathematical mode…

Zombies are a popular figure in pop culture/entertainment and they are usually portrayed as being brought about through an outbreak or epidemic. Consequently, we model a zombie attack, using biological assumptions based on popular zombie movies. We introduce a basic model for zombie infection, determine equilibria and their stability, and illustrate the outcome with numerical solutions. We then refine the model to introduce a latent period of zombification, whereby humans are infected, but not infectious, before becoming undead. We then modify the model to include the effects of possible quarantine or a cure. Finally, we examine the impact of regular, impulsive reductions in the number of zombies and derive conditions under which eradication can occur. We show that only quick, aggressive attacks can stave off the doomsday scenario: the collapse of society as zombies overtake us all. link: http://www.mathworks.co.uk/matlabcentral/linkexchange/links/1749-when-zombies-attack-mathematical-modelling-of-an-outbreak-of-zombie-infection

Parameters: none

States: none

Observables: none

BIOMD0000000882 @ v0.0.1

Munz2009 - Zombie SIZRC This is the model with an latent infection and cure for zombies described in the article. This…

Zombies are a popular figure in pop culture/entertainment and they are usually portrayed as being brought about through an outbreak or epidemic. Consequently, we model a zombie attack, using biological assumptions based on popular zombie movies. We introduce a basic model for zombie infection, determine equilibria and their stability, and illustrate the outcome with numerical solutions. We then refine the model to introduce a latent period of zombification, whereby humans are infected, but not infectious, before becoming undead. We then modify the model to include the effects of possible quarantine or a cure. Finally, we examine the impact of regular, impulsive reductions in the number of zombies and derive conditions under which eradication can occur. We show that only quick, aggressive attacks can stave off the doomsday scenario: the collapse of society as zombies overtake us all. link: http://www.mathworks.co.uk/matlabcentral/linkexchange/links/1749-when-zombies-attack-mathematical-modelling-of-an-outbreak-of-zombie-infection

Parameters:

Name Description
delta = 1.0E-4; alpha = 0.005 Reaction: => Removal; Susceptible, Zombie, Rate Law: compartment*(alpha*Susceptible*Zombie+delta*Susceptible)
zeta = 1.0E-4; beta = 0.0095 Reaction: => Zombie; Susceptible, Removal, Rate Law: compartment*(beta*Susceptible*Zombie+zeta*Removal)
p = 0.05 Reaction: => Susceptible, Rate Law: compartment*p
delta = 1.0E-4; beta = 0.0095 Reaction: Susceptible => ; Zombie, Rate Law: compartment*(beta*Susceptible*Zombie+delta*Susceptible)
alpha = 0.005 Reaction: Zombie => ; Susceptible, Rate Law: compartment*alpha*Susceptible*Zombie
zeta = 1.0E-4 Reaction: Removal => ; Susceptible, Zombie, Rate Law: compartment*zeta*Removal

States:

Name Description
Removal [C64914]
Zombie Zombie
Susceptible [Susceptibility]

Observables: none

MODEL1008060002 @ v0.0.1

Munz2009 - Zombie SIZRQThis is the model with latent infection and quarantine described in the article. This model was…

Zombies are a popular figure in pop culture/entertainment and they are usually portrayed as being brought about through an outbreak or epidemic. Consequently, we model a zombie attack, using biological assumptions based on popular zombie movies. We introduce a basic model for zombie infection, determine equilibria and their stability, and illustrate the outcome with numerical solutions. We then refine the model to introduce a latent period of zombification, whereby humans are infected, but not infectious, before becoming undead. We then modify the model to include the effects of possible quarantine or a cure. Finally, we examine the impact of regular, impulsive reductions in the number of zombies and derive conditions under which eradication can occur. We show that only quick, aggressive attacks can stave off the doomsday scenario: the collapse of society as zombies overtake us all. link: http://www.mathworks.co.uk/matlabcentral/linkexchange/links/1749-when-zombies-attack-mathematical-modelling-of-an-outbreak-of-zombie-infection

Parameters: none

States: none

Observables: none

This model is from the article: The influence of cytokinin-auxin cross-regulation on cell-fate determination in Arab…

Root growth and development in Arabidopsis thaliana are sustained by a specialised zone termed the meristem, which contains a population of dividing and differentiating cells that are functionally analogous to a stem cell niche in animals. The hormones auxin and cytokinin control meristem size antagonistically. Local accumulation of auxin promotes cell division and the initiation of a lateral root primordium. By contrast, high cytokinin concentrations disrupt the regular pattern of divisions that characterises lateral root development, and promote differentiation. The way in which the hormones interact is controlled by a genetic regulatory network. In this paper, we propose a deterministic mathematical model to describe this network and present model simulations that reproduce the experimentally observed effects of cytokinin on the expression of auxin regulated genes. We show how auxin response genes and auxin efflux transporters may be affected by the presence of cytokinin. We also analyse and compare the responses of the hormones auxin and cytokinin to changes in their supply with the responses obtained by genetic mutations of SHY2, which encodes a protein that plays a key role in balancing cytokinin and auxin regulation of meristem size. We show that although shy2 mutations can qualitatively reproduce the effect of varying auxin and cytokinin supply on their response genes, some elements of the network respond differently to changes in hormonal supply and to genetic mutations, implying a different, general response of the network. We conclude that an analysis based on the ratio between these two hormones may be misleading and that a mathematical model can serve as a useful tool for stimulate further experimental work by predicting the response of the network to changes in hormone levels and to other genetic mutations. link: http://identifiers.org/pubmed/21640126

Parameters:

Name Description
psiARF = 0.1; psiARFIAA = 0.1; thetaARF = 0.1; thetaARF2 = 0.01; thARFIAA = 0.1; thARRBph = 0.1 Reaction: F1 = ARF/thetaARF/(1+ARF/thetaARF+ARF2/thetaARF2+ARFIAA/thARFIAA+ARF*IAAp/psiARFIAA+ARF^2/psiARF+ARRBph/thARRBph), Rate Law: missing
muAux = 0.1; ka = 100.0; eps = 0.01; alphaAux = 1.0; kd = 1.0; etaAuxTIR1 = 10.0 Reaction: => Aux; TIR1, AuxTIR1, Rate Law: muAux*(alphaAux-Aux)-1/eps*etaAuxTIR1*(ka*Aux*TIR1-kd*AuxTIR1)
thARRAph = 0.1; thARRBph = 0.1 Reaction: F4 = ARRBph/thARRBph/(1+ARRAph/thARRAph+ARRBph/thARRBph), Rate Law: missing
qa = 1.0; qd = 1.0 Reaction: => ARF2; ARF, Rate Law: qa*ARF^2-qd*ARF2
eps = 0.01; deltaPINp = 1.0 Reaction: => PINp; PINm, Rate Law: 1/eps*(deltaPINp*PINm-PINp)
alphaAHK = 1.0; etaAHKph = 1.0 Reaction: CkAHK = alphaAHK-etaAHKph*(AHKph+CkAHKph), Rate Law: missing
lambda1 = 0.1; phiARp = 2.0 Reaction: => ARm; F5a, F5b, Rate Law: phiARp*(lambda1*F5a+F5b)-ARm
ud = 1.0; eps = 0.01; ua = 1.0 Reaction: => ARRBph; CkAHKph, CkAHK, ARRBp, Rate Law: 1/eps*(ua*CkAHKph*ARRBp-ud*CkAHK*ARRBph)
phiCRp = 2.0 Reaction: => CRm; F4, Rate Law: phiCRp*F4-CRm
alphaPH = 1.0 Reaction: CkAHKph = ((alphaPH-AHKph)-ARRAph)-ARRBph, Rate Law: missing
deltaARRAp = 1.0; eps = 0.01; sa = 1.0; etaAHKph = 1.0; sd = 1.0 Reaction: => ARRAp; ARRAm, CkAHK, ARRAph, CkAHKph, Rate Law: 1/eps*((deltaARRAp*ARRAm-ARRAp)+etaAHKph*(sd*CkAHK*ARRAph-sa*CkAHKph*ARRAp))
alphaTIR1 = 1.0 Reaction: TIR1 = (alphaTIR1-AuxTIR1)-AuxTIAA, Rate Law: missing
ka = 100.0; eps = 0.01; la = 0.5; kd = 1.0; ld = 0.1 Reaction: => AuxTIR1; Aux, TIR1, AuxTIAA, IAAp, Rate Law: 1/eps*(((ka*Aux*TIR1-kd*AuxTIR1)+(ld+1)*AuxTIAA)-la*AuxTIR1*IAAp)
lambda1 = 0.1; phiIAAp = 100.0; lambda3 = 0.02 Reaction: => IAAm; F1, F2, F3, Rate Law: phiIAAp*(lambda1*F1+F2+lambda3*F3)-IAAm
eps = 0.01; sa = 1.0; sd = 1.0 Reaction: => ARRAph; CkAHKph, ARRAp, CkAHK, ARRAph, Rate Law: 1/eps*(sa*CkAHKph*ARRAp-sd*CkAHK*ARRAph)
eps = 0.01; la = 0.5; ld = 0.1 Reaction: => AuxTIAA; AuxTIAA, IAAp, AuxTIR1, Rate Law: 1/eps*(la*IAAp*AuxTIR1-(ld+1)*AuxTIAA)
phiARRAp = 100.0 Reaction: => ARRAm; F6, Rate Law: phiARRAp*F6-ARRAm
eps = 0.01; etaCkPh = 1.0; ra = 1.0; rd = 1.0; muCk = 0.1; alphaCk = 1.0 Reaction: => Ck; AHKph, CkAHKph, Rate Law: muCk*(alphaCk-Ck)-etaCkPh/eps*(ra*AHKph*Ck-rd*CkAHKph)
eps = 0.01; ra = 1.0; rd = 1.0 Reaction: => AHKph; CkAHKph, Ck, Rate Law: 1/eps*(rd*CkAHKph-ra*AHKph*Ck)
alphaARF = 1.0 Reaction: ARF = (alphaARF-2*ARF2)-ARFIAA, Rate Law: missing
psiARF = 0.1; psiARFIAA = 0.1; thetaARF = 0.1; thetaARF2 = 0.01; thARFIAA = 0.1 Reaction: F5a = ARF/thetaARF/(1+ARF/thetaARF+ARF2/thetaARF2+ARFIAA/thARFIAA+ARF*IAAp/psiARFIAA+ARF^2/psiARF), Rate Law: missing
thetaARp = 0.1 Reaction: F6 = ARp/thetaARp/(1+ARp/thetaARp), Rate Law: missing
eps = 0.01; etaARFIAA = 1.0; la = 0.5; pa = 10.0; ld = 0.1; deltaIAAp = 1.0; pd = 10.0 Reaction: => IAAp; IAAm, AuxTIR1, AuxTIAA, ARFIAA, ARF, Rate Law: 1/eps*((deltaIAAp*IAAm-la*IAAp*AuxTIR1)+ld*AuxTIAA)+etaARFIAA*(pd*ARFIAA-pa*IAAp*ARF)
eps = 0.01; deltaCRp = 1.0 Reaction: => CRp; CRm, Rate Law: 1/eps*(deltaCRp*CRm-CRp)
eps = 0.01; muIAAs = 1.0 Reaction: => IAAs; AuxTIAA, Rate Law: 1/eps*(AuxTIAA-muIAAs*IAAs)
pa = 10.0; pd = 10.0 Reaction: => ARFIAA; ARF, IAAp, Rate Law: pa*ARF*IAAp-pd*ARFIAA
eps = 0.01; deltaARp = 1.0 Reaction: => ARp; ARm, Rate Law: 1/eps*(deltaARp*ARm-ARp)
lambda1 = 0.1; phiPINp = 100.0 Reaction: => PINm; F5a, F5b, Rate Law: phiPINp*(lambda1*F5a+F5b)-PINm
alphaARRB = 2.0; etaAHKph = 1.0 Reaction: ARRBp = alphaARRB-etaAHKph*ARRBph, Rate Law: missing

States:

Name Description
AHKph [Histidine kinase 4; Phosphoprotein]
F3 F3
F5b F5b
CkAHK [cytokinin; Histidine kinase 4]
IAAs [Auxin-responsive protein IAA1]
ARF2 [Auxin response factor 2]
AuxTIAA [auxin; Protein TRANSPORT INHIBITOR RESPONSE 1; Auxin-responsive protein IAA1]
ARRBph [Two-component response regulator ARR1; Phosphoprotein]
ARFIAA [Auxin response factor 2; Auxin-responsive protein IAA1]
F6 F6
AuxTIR1 [auxin; Protein TRANSPORT INHIBITOR RESPONSE 1]
F1 F1
ARRAph [Two-component response regulator ARR2; Phosphoprotein]
ARRAp [Two-component response regulator ARR1]
ARm [Protein AUXIN RESPONSE 4]
PINm [Peptidyl-prolyl cis-trans isomerase Pin1]
ARRAm [Two-component response regulator ARR1]
Aux [auxin]
CRm [Ethylene-responsive transcription factor CRF1]
CkAHKph [cytokinin; Histidine kinase 4]
PINp [Peptidyl-prolyl cis-trans isomerase Pin1]
F4 F4
ARF [Auxin response factor 2]
TIR1 [Protein TRANSPORT INHIBITOR RESPONSE 1]
ARp [Protein AUXIN RESPONSE 4]
CRp [Ethylene-responsive transcription factor CRF1]
IAAm [Auxin-responsive protein IAA1; messenger RNA]
ARRBp [Two-component response regulator ARR1]
F2 F2
F5a F5a
IAAp [Auxin-responsive protein IAA1]
Ck [cytokinin]

Observables: none

Muraro2014 - Vascular patterning in Arabidopsis rootsUsing a multicellular model, maintanence of vascular patterning in…

As multicellular organisms grow, positional information is continually needed to regulate the pattern in which cells are arranged. In the Arabidopsis root, most cell types are organized in a radially symmetric pattern; however, a symmetry-breaking event generates bisymmetric auxin and cytokinin signaling domains in the stele. Bidirectional cross-talk between the stele and the surrounding tissues involving a mobile transcription factor, SHORT ROOT (SHR), and mobile microRNA species also determines vascular pattern, but it is currently unclear how these signals integrate. We use a multicellular model to determine a minimal set of components necessary for maintaining a stable vascular pattern. Simulations perturbing the signaling network show that, in addition to the mutually inhibitory interaction between auxin and cytokinin, signaling through SHR, microRNA165/6, and PHABULOSA is required to maintain a stable bisymmetric pattern. We have verified this prediction by observing loss of bisymmetry in shr mutants. The model reveals the importance of several features of the network, namely the mutual degradation of microRNA165/6 and PHABULOSA and the existence of an additional negative regulator of cytokinin signaling. These components form a plausible mechanism capable of patterning vascular tissues in the absence of positional inputs provided by the transport of hormones from the shoot. link: http://identifiers.org/pubmed/24381155

Parameters:

Name Description
lambda_IAA2 = 10.0; mu_m_IAA2 = 10.0; F_IAA2 = 0.0 Reaction: IAA2m = lambda_IAA2*F_IAA2-mu_m_IAA2*IAA2m, Rate Law: lambda_IAA2*F_IAA2-mu_m_IAA2*IAA2m
delta_AHP6 = 1.0; mu_p_AHP6 = 1.0 Reaction: AHP6p = delta_AHP6*AHP6m-mu_p_AHP6*AHP6p, Rate Law: delta_AHP6*AHP6m-mu_p_AHP6*AHP6p
mu_p_PHB = 1.0; delta_PHB = 1.0 Reaction: PHBp = delta_PHB*PHBm-mu_p_PHB*PHBp, Rate Law: delta_PHB*PHBm-mu_p_PHB*PHBp
F_CK = 0.0; p_ck = 2.0; d_ck = 10.0; phloem_rate_ck = 1.0 Reaction: Cytokinin = phloem_rate_ck*p_ck*F_CK-d_ck*Cytokinin, Rate Law: phloem_rate_ck*p_ck*F_CK-d_ck*Cytokinin
lambda_ARR5 = 20.0; mu_m_ARR5 = 10.0; F_ARR5 = 0.0 Reaction: ARR5m = lambda_ARR5*F_ARR5-mu_m_ARR5*ARR5m, Rate Law: lambda_ARR5*F_ARR5-mu_m_ARR5*ARR5m
mu_m_PIN1 = 0.0; lambda_PIN1 = 0.0; F_PIN1 = 0.0 Reaction: PIN1m = lambda_PIN1*F_PIN1-mu_m_PIN1*PIN1m, Rate Law: lambda_PIN1*F_PIN1-mu_m_PIN1*PIN1m
mu_m_AHP6 = 1.0; lambda_AHP6 = 2.0; F_AHP6 = 0.0 Reaction: AHP6m = lambda_AHP6*F_AHP6-mu_m_AHP6*AHP6m, Rate Law: lambda_AHP6*F_AHP6-mu_m_AHP6*AHP6m
phloem_rate_ax = 1.0; p_ax = 0.06; d_ax = 1.0 Reaction: Auxin = phloem_rate_ax*p_ax-d_ax*Auxin, Rate Law: phloem_rate_ax*p_ax-d_ax*Auxin
lambda_PIN3 = 0.0; F_PIN3 = 0.0; mu_m_PIN3 = 0.0 Reaction: PIN3m = lambda_PIN3*F_PIN3-mu_m_PIN3*PIN3m, Rate Law: lambda_PIN3*F_PIN3-mu_m_PIN3*PIN3m
delta_CKX3 = 1.0; mu_p_CKX3 = 1.0 Reaction: CKX3p = delta_CKX3*CKX3m-mu_p_CKX3*CKX3p, Rate Law: delta_CKX3*CKX3m-mu_p_CKX3*CKX3p
mu_p_ARR5 = 10.0; delta_ARR5 = 10.0 Reaction: ARR5p = delta_ARR5*ARR5m-mu_p_ARR5*ARR5p, Rate Law: delta_ARR5*ARR5m-mu_p_ARR5*ARR5p
d_phb = 1.0; d_mirna_mrna = 10.0; p_phb = 2.0 Reaction: PHBm = (p_phb-d_phb*PHBm)-d_mirna_mrna*PHBm*miRNA, Rate Law: (p_phb-d_phb*PHBm)-d_mirna_mrna*PHBm*miRNA
mu_p_IAA2 = 10.0; delta_IAA2 = 10.0 Reaction: IAA2p = delta_IAA2*IAA2m-mu_p_IAA2*IAA2p, Rate Law: delta_IAA2*IAA2m-mu_p_IAA2*IAA2p
mu_m_PIN7 = 1.0; lambda_PIN7 = 1.0; F_PIN7 = 0.0 Reaction: PIN7m = lambda_PIN7*F_PIN7-mu_m_PIN7*PIN7m, Rate Law: lambda_PIN7*F_PIN7-mu_m_PIN7*PIN7m

States:

Name Description
AHP6m [Pseudo histidine-containing phosphotransfer protein 6]
Cytokinin [cytokinin]
IAA2p [Auxin-responsive protein IAA2]
PIN3m [Auxin efflux carrier component 3]
ARR5p [Two-component response regulator ARR5]
PHBp [Homeobox-leucine zipper protein ATHB-14]
ARR5m [Two-component response regulator ARR5]
Auxin [Auxin transporter protein 1]
miRNA [SBO:0000316]
PHBm [Homeobox-leucine zipper protein ATHB-14]
IAA2m [Auxin-responsive protein IAA2]
PIN1m [Peptidyl-prolyl cis-trans isomerase Pin1]
CKX3p [Cytokinin dehydrogenase 3]
AHP6p [Pseudo histidine-containing phosphotransfer protein 6]
PIN7m [Auxin efflux carrier component 7]

Observables: none

Murphy2016 - Differences in predictions of ODE models of tumor growthComparison of 7 ODE models for tumour size. This mo…

While mathematical models are often used to predict progression of cancer and treatment outcomes, there is still uncertainty over how to best model tumor growth. Seven ordinary differential equation (ODE) models of tumor growth (exponential, Mendelsohn, logistic, linear, surface, Gompertz, and Bertalanffy) have been proposed, but there is no clear guidance on how to choose the most appropriate model for a particular cancer.We examined all seven of the previously proposed ODE models in the presence and absence of chemotherapy. We derived equations for the maximum tumor size, doubling time, and the minimum amount of chemotherapy needed to suppress the tumor and used a sample data set to compare how these quantities differ based on choice of growth model.We find that there is a 12-fold difference in predicting doubling times and a 6-fold difference in the predicted amount of chemotherapy needed for suppression depending on which growth model was used.Our results highlight the need for careful consideration of model assumptions when developing mathematical models for use in cancer treatment planning. link: http://identifiers.org/pubmed/26921070

Parameters:

Name Description
a_exp = 0.0246 Reaction: V_exp = a_exp*V_exp, Rate Law: a_exp*V_exp
a_surf = 0.291; b_surf = 708.0 Reaction: V_surf = a_surf*V_surf/(V_surf+b_surf)^(1/3), Rate Law: a_surf*V_surf/(V_surf+b_surf)^(1/3)
a_mend = 0.105; b_mend = 0.785 Reaction: V_mend = a_mend*V_mend^b_mend, Rate Law: a_mend*V_mend^b_mend
a_bert = 0.2344; b_bert = 3.46E-19 Reaction: V_bert = a_bert*V_bert^(2/3)-b_bert*V_bert, Rate Law: a_bert*V_bert^(2/3)-b_bert*V_bert
c_gomp = 10700.0; a_gomp = 0.0919; b_gomp = 15500.0 Reaction: V_gomp = a_gomp*V_gomp*ln(b_gomp/(V_gomp+c_gomp)), Rate Law: a_gomp*V_gomp*ln(b_gomp/(V_gomp+c_gomp))
b_lin = 4300.0; a_lin = 132.0 Reaction: V_lin = a_lin*V_lin/(V_lin+b_lin), Rate Law: a_lin*V_lin/(V_lin+b_lin)
b_log = 6920.0; a_log = 0.0295 Reaction: V_log = a_log*V_log*(1-V_log/b_log), Rate Law: a_log*V_log*(1-V_log/b_log)

States:

Name Description
V gomp V_gomp
V surf V_surf
V lin V_lin
V mend V_mend
V log V_log
V bert V_bert
V exp [Exponential Function]

Observables: none

Musante2017 - Switching behaviour of PP2A inhibition by ARPP-16 - mutual inhibitionsThis model is described in the artic…

ARPP-16, ARPP-19, and ENSA are inhibitors of protein phosphatase PP2A. ARPP-19 and ENSA phosphorylated by Greatwall kinase inhibit PP2A during mitosis. ARPP-16 is expressed in striatal neurons where basal phosphorylation by MAST3 kinase inhibits PP2A and regulates key components of striatal signaling. The ARPP-16/19 proteins were discovered as substrates for PKA, but the function of PKA phosphorylation is unknown. We find that phosphorylation by PKA or MAST3 mutually suppresses the ability of the other kinase to act on ARPP-16. Phosphorylation by PKA also acts to prevent inhibition of PP2A by ARPP-16 phosphorylated by MAST3. Moreover, PKA phosphorylates MAST3 at multiple sites resulting in its inhibition. Mathematical modeling highlights the role of these three regulatory interactions to create a switch-like response to cAMP. Together the results suggest a complex antagonistic interplay between the control of ARPP-16 by MAST3 and PKA that creates a mechanism whereby cAMP mediates PP2A disinhibition. link: http://identifiers.org/doi/10.7554/eLife.24998

Parameters:

Name Description
ModelValue_1 = 2.0; kmpp2a = 0.0161515151515152 Reaction: BB = A46+ModelValue_1+kmpp2a, Rate Law: missing
ModelValue_12 = 0.5; ModelValue_14 = 1.0; ModelValue_23 = 2.36; ModelValue_10 = 0.935; ModelValue_11 = 1.6; ModelValue_0 = 10.0; ModelValue_13 = 5.0 Reaction: A88 = ModelValue_10*PKA*(ModelValue_0-A88)/((ModelValue_11+ModelValue_23*A46/ModelValue_0+ModelValue_0)-A88)-ModelValue_12*ModelValue_13*A88/(ModelValue_14+A88), Rate Law: ModelValue_10*PKA*(ModelValue_0-A88)/((ModelValue_11+ModelValue_23*A46/ModelValue_0+ModelValue_0)-A88)-ModelValue_12*ModelValue_13*A88/(ModelValue_14+A88)
ModelValue_1 = 2.0 Reaction: Complex = (BB-(BB^2-4*A46*ModelValue_1)^(0.5))/2, Rate Law: missing
ModelValue_2 = 2.7; ModelValue_15 = 0.01865; kppx = 0.05 Reaction: M = kppx*(ModelValue_2-M)-ModelValue_15*A88*M, Rate Law: kppx*(ModelValue_2-M)-ModelValue_15*A88*M
ModelValue_22 = 0.37526; ModelValue_9 = 0.09; ModelValue_6 = 0.05; ModelValue_0 = 10.0; ModelValue_8 = 0.0988 Reaction: A46 = ModelValue_8*M*(ModelValue_0-A46)/(ModelValue_9+ModelValue_22*A88/ModelValue_0+(ModelValue_0-A46))-ModelValue_6*Complex, Rate Law: ModelValue_8*M*(ModelValue_0-A46)/(ModelValue_9+ModelValue_22*A88/ModelValue_0+(ModelValue_0-A46))-ModelValue_6*Complex
ModelValue_3 = 12.0; ModelValue_17 = 2.0; ModelValue_16 = 0.7; ModelValue_20 = 0.0; ModelValue_19 = 0.02335; ModelValue_18 = 10.0 Reaction: PKA = ModelValue_16*(ModelValue_3-PKA)*ModelValue_20^ModelValue_17/(ModelValue_18^ModelValue_17+ModelValue_20^ModelValue_17)-ModelValue_19*A46*PKA, Rate Law: ModelValue_16*(ModelValue_3-PKA)*ModelValue_20^ModelValue_17/(ModelValue_18^ModelValue_17+ModelValue_20^ModelValue_17)-ModelValue_19*A46*PKA

States:

Name Description
BB [urn:miriam:pr:PR%3AP56212-2]
A88 [urn:miriam:pr:PR_P56212-2]
M [Microtubule-associated serine/threonine-protein kinase 3]
Complex [protein phosphatase type 2A complex]
PKA [cAMP-dependent protein kinase catalytic subunit alpha]
A46 [urn:miriam:pr:PR_P56212-2]

Observables: none

Musante2017 - Switching behaviour of PP2A inhibition by ARPP-16 - mutual inhibitions and PKA inhibits MAST3This model is…

ARPP-16, ARPP-19, and ENSA are inhibitors of protein phosphatase PP2A. ARPP-19 and ENSA phosphorylated by Greatwall kinase inhibit PP2A during mitosis. ARPP-16 is expressed in striatal neurons where basal phosphorylation by MAST3 kinase inhibits PP2A and regulates key components of striatal signaling. The ARPP-16/19 proteins were discovered as substrates for PKA, but the function of PKA phosphorylation is unknown. We find that phosphorylation by PKA or MAST3 mutually suppresses the ability of the other kinase to act on ARPP-16. Phosphorylation by PKA also acts to prevent inhibition of PP2A by ARPP-16 phosphorylated by MAST3. Moreover, PKA phosphorylates MAST3 at multiple sites resulting in its inhibition. Mathematical modeling highlights the role of these three regulatory interactions to create a switch-like response to cAMP. Together the results suggest a complex antagonistic interplay between the control of ARPP-16 by MAST3 and PKA that creates a mechanism whereby cAMP mediates PP2A disinhibition. link: http://identifiers.org/doi/10.7554/eLife.24998

Parameters:

Name Description
ModelValue_3 = 12.0; ModelValue_18 = 0.7; ModelValue_20 = 10.0; ModelValue_22 = 0.0; ModelValue_19 = 2.0; ModelValue_21 = 0.02335 Reaction: PKA = ModelValue_18*(ModelValue_3-PKA)*ModelValue_22^ModelValue_19/(ModelValue_20^ModelValue_19+ModelValue_22^ModelValue_19)-ModelValue_21*A46*PKA, Rate Law: ModelValue_18*(ModelValue_3-PKA)*ModelValue_22^ModelValue_19/(ModelValue_20^ModelValue_19+ModelValue_22^ModelValue_19)-ModelValue_21*A46*PKA
ModelValue_1 = 2.0; kmpp2a = 0.0161515151515152 Reaction: BB = A46+ModelValue_1+kmpp2a, Rate Law: missing
ModelValue_1 = 2.0 Reaction: Complex = (BB-(BB^2-4*A46*ModelValue_1)^(0.5))/2, Rate Law: missing
ModelValue_12 = 0.5; ModelValue_14 = 1.0; ARPPtot = 10.0; ModelValue_28 = 2.36; ModelValue_10 = 0.935; ModelValue_11 = 1.6; ModelValue_0 = 10.0; ModelValue_13 = 5.0 Reaction: A88 = ModelValue_10*PKA*(ARPPtot-A88)/((ModelValue_11+ModelValue_28*A46/ModelValue_0+ARPPtot)-A88)-ModelValue_12*ModelValue_13*A88/(ModelValue_14+A88), Rate Law: ModelValue_10*PKA*(ARPPtot-A88)/((ModelValue_11+ModelValue_28*A46/ModelValue_0+ARPPtot)-A88)-ModelValue_12*ModelValue_13*A88/(ModelValue_14+A88)
ModelValue_2 = 2.7; kpka = 0.097; ModelValue_17 = 0.01865; ModelValue_30 = 0.05 Reaction: M = (ModelValue_30*(ModelValue_2-M)-ModelValue_17*A88*M)-kpka*PKA*M, Rate Law: (ModelValue_30*(ModelValue_2-M)-ModelValue_17*A88*M)-kpka*PKA*M
ModelValue_2 = 2.7; ARPPtot = 10.0; ModelValue_9 = 0.09; ModelValue_29 = 1.2; ModelValue_6 = 0.05; ModelValue_0 = 10.0; ModelValue_8 = 0.0988; ModelValue_25 = 0.37526 Reaction: A46 = ModelValue_8*M*(ARPPtot-A46)/(ModelValue_9+ModelValue_25*A88/ModelValue_0+ModelValue_29*(ModelValue_2-M)/ModelValue_2+(ARPPtot-A46))-ModelValue_6*Complex, Rate Law: ModelValue_8*M*(ARPPtot-A46)/(ModelValue_9+ModelValue_25*A88/ModelValue_0+ModelValue_29*(ModelValue_2-M)/ModelValue_2+(ARPPtot-A46))-ModelValue_6*Complex

States:

Name Description
BB [urn:miriam:pr:PR%3AP56212-2]
A88 [urn:miriam:pr:PR_P56212-2]
M [Microtubule-associated serine/threonine-protein kinase 3]
Complex [protein phosphatase type 2A complex]
PKA [cAMP-dependent protein kinase catalytic subunit alpha]
A46 [urn:miriam:pr:PR_P56212-2]

Observables: none

Musante2017 - Switching behaviour of PP2A inhibition by ARPP-16 - mutual inhibitions and PKA inhibits MAST3 and dominant…

ARPP-16, ARPP-19, and ENSA are inhibitors of protein phosphatase PP2A. ARPP-19 and ENSA phosphorylated by Greatwall kinase inhibit PP2A during mitosis. ARPP-16 is expressed in striatal neurons where basal phosphorylation by MAST3 kinase inhibits PP2A and regulates key components of striatal signaling. The ARPP-16/19 proteins were discovered as substrates for PKA, but the function of PKA phosphorylation is unknown. We find that phosphorylation by PKA or MAST3 mutually suppresses the ability of the other kinase to act on ARPP-16. Phosphorylation by PKA also acts to prevent inhibition of PP2A by ARPP-16 phosphorylated by MAST3. Moreover, PKA phosphorylates MAST3 at multiple sites resulting in its inhibition. Mathematical modeling highlights the role of these three regulatory interactions to create a switch-like response to cAMP. Together the results suggest a complex antagonistic interplay between the control of ARPP-16 by MAST3 and PKA that creates a mechanism whereby cAMP mediates PP2A disinhibition. link: http://identifiers.org/doi/10.7554/eLife.24998

Parameters:

Name Description
ModelValue_3 = 12.0; ModelValue_18 = 0.7; ModelValue_20 = 10.0; ModelValue_22 = 0.0; ModelValue_19 = 2.0; ModelValue_21 = 0.02335 Reaction: PKA = ModelValue_18*(ModelValue_3-PKA)*ModelValue_22^ModelValue_19/(ModelValue_20^ModelValue_19+ModelValue_22^ModelValue_19)-ModelValue_21*A46*PKA, Rate Law: ModelValue_18*(ModelValue_3-PKA)*ModelValue_22^ModelValue_19/(ModelValue_20^ModelValue_19+ModelValue_22^ModelValue_19)-ModelValue_21*A46*PKA
ModelValue_1 = 2.0 Reaction: Complex = (BB-(BB^2-4*A46*ModelValue_1)^(0.5))/2, Rate Law: missing
ModelValue_12 = 0.5; ModelValue_14 = 1.0; ARPPtot = 10.0; ModelValue_28 = 2.36; ModelValue_10 = 0.935; ModelValue_11 = 1.6; ModelValue_0 = 10.0; ModelValue_13 = 5.0 Reaction: A88 = ModelValue_10*PKA*(ARPPtot-A88)/((ModelValue_11+ModelValue_28*A46/ModelValue_0+ARPPtot)-A88)-ModelValue_12*ModelValue_13*A88/(ModelValue_14+A88), Rate Law: ModelValue_10*PKA*(ARPPtot-A88)/((ModelValue_11+ModelValue_28*A46/ModelValue_0+ARPPtot)-A88)-ModelValue_12*ModelValue_13*A88/(ModelValue_14+A88)
ModelValue_2 = 2.7; kpka = 0.097; ModelValue_17 = 0.01865; ModelValue_33 = 0.05 Reaction: M = (ModelValue_33*(ModelValue_2-M)-ModelValue_17*A88*M)-kpka*PKA*M, Rate Law: (ModelValue_33*(ModelValue_2-M)-ModelValue_17*A88*M)-kpka*PKA*M
ModelValue_1 = 2.0; kmpp2a = 0.0484545454545436 Reaction: BB = A46+ModelValue_1+kmpp2a, Rate Law: missing
ModelValue_2 = 2.7; ARPPtot = 10.0; ModelValue_9 = 0.09; ModelValue_29 = 1.2; ModelValue_6 = 0.05; ModelValue_0 = 10.0; ModelValue_8 = 0.0988; ModelValue_25 = 0.37526 Reaction: A46 = ModelValue_8*M*(ARPPtot-A46)/(ModelValue_9+ModelValue_25*A88/ModelValue_0+ModelValue_29*(ModelValue_2-M)/ModelValue_2+(ARPPtot-A46))-ModelValue_6*Complex, Rate Law: ModelValue_8*M*(ARPPtot-A46)/(ModelValue_9+ModelValue_25*A88/ModelValue_0+ModelValue_29*(ModelValue_2-M)/ModelValue_2+(ARPPtot-A46))-ModelValue_6*Complex

States:

Name Description
BB [urn:miriam:pr:PR_P56212-2]
A88 [urn:miriam:pr:PR_P56212-2]
M [Microtubule-associated serine/threonine-protein kinase 3]
Complex [protein phosphatase type 2A complex]
PKA [cAMP-dependent protein kinase catalytic subunit alpha]
A46 [urn:miriam:pr:PR_P56212-2]

Observables: none

A mathematical model was designed to explore the co-interaction of gonorrhea and HIV in the presence of antiretroviral t…

A mathematical model was designed to explore the co-interaction of gonorrhea and HIV in the presence of antiretroviral therapy and gonorrhea treatment. Qualitative and comprehensive mathematical techniques have been used to analyse the model. The gonorrhea-only and HIV-only sub-models are first considered. Analytic expressions for the threshold parameter in each sub-model and the co-interaction model are derived. Global dynamics of this co-interaction shows that whenever the threshold parameter for the respective sub-models and co-interaction model is less than unity, the epidemics dies out, while the reverse results in persistence of the epidemics in the community. The impact of gonorrhea and its treatment on HIV dynamics is also investigated. Numerical simulations using a set of reasonable parameter values show that the two epidemics co-exists whenever their reproduction numbers exceed unity (with no competitive exclusion). Further, simulations of the full HIV-gonorrhea model also suggests that an increase in the number of individuals infected with gonorrhea (either singly or dually with HIV) in the presence of treatment results in a decrease in gonorrhea-only cases, dual-infection cases but increases the number of HIV-only cases. link: http://identifiers.org/pubmed/20869424

Parameters: none

States: none

Observables: none

Objective: Coronavirus disease 2019 (COVID-19) is a pandemic respiratory illness spreading from person-to-person caused…

OBJECTIVE:Coronavirus disease 2019 (COVID-19) is a pandemic respiratory illness spreading from person-to-person caused by a novel coronavirus and poses a serious public health risk. The goal of this study was to apply a modified susceptible-exposed-infectious-recovered (SEIR) compartmental mathematical model for prediction of COVID-19 epidemic dynamics incorporating pathogen in the environment and interventions. The next generation matrix approach was used to determine the basic reproduction number [Formula: see text]. The model equations are solved numerically using fourth and fifth order Runge-Kutta methods. RESULTS:We found an [Formula: see text] of 2.03, implying that the pandemic will persist in the human population in the absence of strong control measures. Results after simulating various scenarios indicate that disregarding social distancing and hygiene measures can have devastating effects on the human population. The model shows that quarantine of contacts and isolation of cases can help halt the spread on novel coronavirus. link: http://identifiers.org/pubmed/32703315

Parameters: none

States: none

Observables: none

Mathematical analysis of a cholera model with public health interventions

Cholera, an acute gastro-intestinal infection and a waterborne disease continues to emerge in developing countries and remains an important global health challenge. We formulate a mathematical model that captures some essential dynamics of cholera transmission to study the impact of public health educational campaigns, vaccination and treatment as control strategies in curtailing the disease. The education-induced, vaccination-induced and treatment-induced reproductive numbers R(E), R(V), R(T) respectively and the combined reproductive number R(C) are compared with the basic reproduction number R(0) to assess the possible community benefits of these control measures. A Lyapunov functional approach is also used to analyse the stability of the equilibrium points. We perform sensitivity analysis on the key parameters that drive the disease dynamics in order to determine their relative importance to disease transmission and prevalence. Graphical representations are provided to qualitatively support the analytical results. link: http://identifiers.org/pubmed/null

Parameters: none

States: none

Observables: none

N


BIOMD0000000353 @ v0.0.1

This model is from the article: Kinetic modeling and exploratory numerical simulation of chloroplastic starch degrad…

BACKGROUND: Higher plants and algae are able to fix atmospheric carbon dioxide through photosynthesis and store this fixed carbon in large quantities as starch, which can be hydrolyzed into sugars serving as feedstock for fermentation to biofuels and precursors. Rational engineering of carbon flow in plant cells requires a greater understanding of how starch breakdown fluxes respond to variations in enzyme concentrations, kinetic parameters, and metabolite concentrations. We have therefore developed and simulated a detailed kinetic ordinary differential equation model of the degradation pathways for starch synthesized in plants and green algae, which to our knowledge is the most complete such model reported to date. RESULTS: Simulation with 9 internal metabolites and 8 external metabolites, the concentrations of the latter fixed at reasonable biochemical values, leads to a single reference solution showing β-amylase activity to be the rate-limiting step in carbon flow from starch degradation. Additionally, the response coefficients for stromal glucose to the glucose transporter k(cat) and KM are substantial, whereas those for cytosolic glucose are not, consistent with a kinetic bottleneck due to transport. Response coefficient norms show stromal maltopentaose and cytosolic glucosylated arabinogalactan to be the most and least globally sensitive metabolites, respectively, and β-amylase k(cat) and KM for starch to be the kinetic parameters with the largest aggregate effect on metabolite concentrations as a whole. The latter kinetic parameters, together with those for glucose transport, have the greatest effect on stromal glucose, which is a precursor for biofuel synthetic pathways. Exploration of the steady-state solution space with respect to concentrations of 6 external metabolites and 8 dynamic metabolite concentrations show that stromal metabolism is strongly coupled to starch levels, and that transport between compartments serves to lower coupling between metabolic subsystems in different compartments. CONCLUSIONS: We find that in the reference steady state, starch cleavage is the most significant determinant of carbon flux, with turnover of oligosaccharides playing a secondary role. Independence of stationary point with respect to initial dynamic variable values confirms a unique stationary point in the phase space of dynamically varying concentrations of the model network. Stromal maltooligosaccharide metabolism was highly coupled to the available starch concentration. From the most highly converged trajectories, distances between unique fixed points of phase spaces show that cytosolic maltose levels depend on the total concentrations of arabinogalactan and glucose present in the cytosol. In addition, cellular compartmentalization serves to dampen much, but not all, of the effects of one subnetwork on another, such that kinetic modeling of single compartments would likely capture most dynamics that are fast on the timescale of the transport reactions. link: http://identifiers.org/pubmed/21682905

Parameters:

Name Description
R06050CY_GlcAG_KM = 2100.0 µmol/l; R06050CY_G1P_KM = 2000.0 µmol/l; R06050CY_GlcAG_Ki = 3800.0 µmol/l; R06050CY_AG_KM = 3800.0 µmol/l; R06050CY_Pi_KM = 5900.0 µmol/l; R06050CY_kcat = 50.0 1/s; R06050CY_Keq = 6.15E-4 1; R06050CY_G1P_Ki = 3100.0 µmol/l Reaction: cpd_C00569Glc_CY + cpd_C00009tot_CY => cpd_C00103tot_CY + cpd_C00569_CY; ec_2_4_1_1_CY, Rate Law: Cytosol*R06050CY_kcat*ec_2_4_1_1_CY/Cytosol*(cpd_C00569Glc_CY/Cytosol*cpd_C00009tot_CY/Cytosol-cpd_C00103tot_CY/Cytosol*cpd_C00569_CY/Cytosol/R06050CY_Keq)/(R06050CY_GlcAG_Ki*R06050CY_Pi_KM+R06050CY_Pi_KM*cpd_C00569Glc_CY/Cytosol+R06050CY_GlcAG_KM*cpd_C00009tot_CY/Cytosol+cpd_C00569Glc_CY/Cytosol*cpd_C00009tot_CY/Cytosol+R06050CY_GlcAG_Ki*R06050CY_Pi_KM/(R06050CY_G1P_Ki*R06050CY_AG_KM)*(R06050CY_AG_KM*cpd_C00103tot_CY/Cytosol+R06050CY_G1P_KM*cpd_C00569_CY/Cytosol+cpd_C00103tot_CY/Cytosol*cpd_C00569_CY/Cytosol))
f_bamylase = 0.582 1; conv_gm_umole = 1.0 µg/mol; R02112CS_Gn_KM = 0.5 g/l; f_G3 = 0.13 1; R02112CS_Gn_kcat = 0.073 1/s Reaction: cpd_C00369Glc_CS => cpd_C01835_CS; ec_3_2_1_2_CS, cpd_C00369_CS, cpd_C00369db_CS, Rate Law: ChloroplastStroma*R02112CS_Gn_kcat*ec_3_2_1_2_CS/ChloroplastStroma*f_G3*(f_bamylase*cpd_C00369_CS/ChloroplastStroma+cpd_C00369db_CS/ChloroplastStroma)/(conv_gm_umole*(f_G3*(f_bamylase*cpd_C00369_CS/ChloroplastStroma+cpd_C00369db_CS/ChloroplastStroma)+R02112CS_Gn_KM))
R02112CS_G2C_KM = 4.19 g²/l²; f_bamylase = 0.582 1; f_G2 = 0.87 1; conv_gm_umole = 1.0 µg/mol; R02112CS_Keq = 18800.0 g/l; R02112CS_Gn_KM = 0.5 g/l; C00208_MW = 3.42E-4 µg/mol; R02112CS_Gn_kcat = 0.073 1/s Reaction: cpd_C00369Glc_CS => cpd_C00208_CS; ec_3_2_1_2_CS, cpd_C00369_CS, cpd_C00369db_CS, Rate Law: ChloroplastStroma*R02112CS_Gn_kcat*ec_3_2_1_2_CS/ChloroplastStroma*(f_G2*(f_bamylase*cpd_C00369_CS/ChloroplastStroma+cpd_C00369db_CS/ChloroplastStroma)-(cpd_C00208_CS/ChloroplastStroma*C00208_MW)^2/R02112CS_Keq)/(conv_gm_umole*(f_G2*(f_bamylase*cpd_C00369_CS/ChloroplastStroma+cpd_C00369db_CS/ChloroplastStroma)+R02112CS_Gn_KM*(1+(cpd_C00208_CS/ChloroplastStroma*C00208_MW)^2/R02112CS_G2C_KM)))
f_bamylase = 0.582 1; ec_3_2_1_68_CS_kcat = 0.0198 1/s Reaction: cpd_C00369db_CS = ec_3_2_1_68_CS/ChloroplastStroma*ec_3_2_1_68_CS_kcat*((1-1/(1+exp((-100)*(cpd_C00369db_CS/ChloroplastStroma/(cpd_C00369_CS/ChloroplastStroma*(1-f_bamylase))-0.3))))+1/(1+exp((-100)*(cpd_C00369db_CS/ChloroplastStroma/(cpd_C00369_CS/ChloroplastStroma*(1-f_bamylase))-0.3)))*(1-1.429*(cpd_C00369db_CS/ChloroplastStroma/(cpd_C00369_CS/ChloroplastStroma*(1-f_bamylase))-0.3)))*ChloroplastStroma, Rate Law: ec_3_2_1_68_CS/ChloroplastStroma*ec_3_2_1_68_CS_kcat*((1-1/(1+exp((-100)*(cpd_C00369db_CS/ChloroplastStroma/(cpd_C00369_CS/ChloroplastStroma*(1-f_bamylase))-0.3))))+1/(1+exp((-100)*(cpd_C00369db_CS/ChloroplastStroma/(cpd_C00369_CS/ChloroplastStroma*(1-f_bamylase))-0.3)))*(1-1.429*(cpd_C00369db_CS/ChloroplastStroma/(cpd_C00369_CS/ChloroplastStroma*(1-f_bamylase))-0.3)))*ChloroplastStroma
TC_2_A_1_1_17_KM = 19300.0 µmol/l; TC_2_A_1_1_17_kcat = 240.278 1/s Reaction: cpd_C00031_CS => cpd_C00031_CY; tc_2_A_1_1_17_CIMS, Rate Law: ChloroplastStroma*TC_2_A_1_1_17_kcat*tc_2_A_1_1_17_CIMS/ChloroplastIntermembraneSpace*cpd_C00031_CS/ChloroplastStroma/(TC_2_A_1_1_17_KM+cpd_C00031_CS/ChloroplastStroma)
R05196CS_G3_Ki = 746.42 µmol/l; R05196CS_G5_Ki = 100.0 µmol/l; R05196CS_Glc_KM = 11700.0 µmol/l; R05196CS_G3_KM = 3300.0 µmol/l; R05196CS_Keq = 1.0 1; R05196CS_G5_KM = 210.0 µmol/l; R05196CS_kcat = 50.0 1/s Reaction: cpd_C01835_CS => cpd_C00031_CS + cpd_G00343_CS; ec_2_4_1_25_CS, Rate Law: ChloroplastStroma*R05196CS_kcat*ec_2_4_1_25_CS/ChloroplastStroma*((cpd_C01835_CS/ChloroplastStroma)^2-cpd_C00031_CS/ChloroplastStroma*cpd_G00343_CS/ChloroplastStroma/R05196CS_Keq)/(R05196CS_G3_KM*cpd_C01835_CS/ChloroplastStroma+(cpd_C01835_CS/ChloroplastStroma)^2+R05196CS_G3_KM*R05196CS_G3_Ki/(R05196CS_Glc_KM*R05196CS_G5_Ki)*(R05196CS_G5_KM*cpd_C00031_CS/ChloroplastStroma*(1+cpd_C01835_CS/ChloroplastStroma/R05196CS_G3_Ki)+R05196CS_Glc_KM*cpd_G00343_CS/ChloroplastStroma*(1+cpd_C01835_CS/ChloroplastStroma/R05196CS_G3_Ki)+cpd_C00031_CS/ChloroplastStroma*cpd_G00343_CS/ChloroplastStroma))
TC_2_A_84_1_2_kcat = 5.963 1/s; TC_2_A_84_1_2_KM = 4000.0 µmol/l Reaction: cpd_C00208_CS => cpd_C00208_CY; tc_2_A_84_1_2_CIMS, Rate Law: ChloroplastStroma*TC_2_A_84_1_2_kcat*tc_2_A_84_1_2_CIMS/ChloroplastIntermembraneSpace*cpd_C00208_CS/ChloroplastStroma/(TC_2_A_84_1_2_KM+cpd_C00208_CS/ChloroplastStroma)
AT2G40840CY_Keq = 1.0 1; AT2G40840CY_G2_KM = 4600.0 µmol/l; AT2G40840CY_Glc_KM = 11700.0 µmol/l; AT2G40840CY_G2_Ki = 2190.476 µmol/l; AT2G40840CY_AG_Ki = 1000.0 µmol/l; AT2G40840CY_GlcAG_KM = 1100.0 µmol/l; AT2G40840CY_AG_KM = 1100.0 µmol/l; AT2G40840CY_kcat = 50.0 1/s; AT2G40840CY_GlcAG_Ki = 1000.0 µmol/l Reaction: cpd_C00208_CY + cpd_C00569_CY => cpd_C00031_CY + cpd_C00569Glc_CY; ec_2_4_1_25_CY, Rate Law: Cytosol*AT2G40840CY_kcat*ec_2_4_1_25_CY/Cytosol*(cpd_C00208_CY/Cytosol*cpd_C00569_CY/Cytosol-cpd_C00031_CY/Cytosol*cpd_C00569Glc_CY/Cytosol/AT2G40840CY_Keq)/(AT2G40840CY_AG_KM*cpd_C00208_CY/Cytosol+AT2G40840CY_G2_KM*cpd_C00569_CY/Cytosol+cpd_C00208_CY/Cytosol*cpd_C00569_CY/Cytosol+AT2G40840CY_G2_KM*AT2G40840CY_AG_Ki/(AT2G40840CY_Glc_KM*AT2G40840CY_GlcAG_Ki)*(AT2G40840CY_GlcAG_KM*cpd_C00031_CY/Cytosol*(1+cpd_C00208_CY/Cytosol/AT2G40840CY_G2_Ki)+AT2G40840CY_Glc_KM*cpd_C00569Glc_CY/Cytosol*(1+cpd_C00569_CY/Cytosol/AT2G40840CY_AG_Ki)+cpd_C00031_CY/Cytosol*cpd_C00569Glc_CY/Cytosol))
R02112CS_G5_kcat = 0.0913 1/s; G00343_MW = 8.28E-4 µg/mol; conv_gm_umole = 1.0 µg/mol; R02112CS_G5_KM = 1.46 g/l Reaction: cpd_G00343_CS => cpd_C00208_CS + cpd_C01835_CS; ec_3_2_1_2_CS, Rate Law: ChloroplastStroma*R02112CS_G5_kcat*ec_3_2_1_2_CS/ChloroplastStroma*cpd_G00343_CS/ChloroplastStroma*G00343_MW/(conv_gm_umole*(cpd_G00343_CS/ChloroplastStroma*G00343_MW+R02112CS_G5_KM))
C00369_MW = 0.27 µg/mol; N_Glc_Starch = 1667.0 1 Reaction: cpd_C00369_CS = cpd_C00369Glc_CS/ChloroplastStroma*C00369_MW/N_Glc_Starch*ChloroplastStroma, Rate Law: missing
R00299CY_G6P_KM = 47.0 µmol/l; R00299CY_kfor = 180.0 1/s; R00299CY_G16P_Kip = 30.0 µmol/l; R00299CY_Glc_Ki = 47.0 µmol/l; R00299CY_MgADP_Ki = 1000.0 µmol/l; R00299CY_MgATP_Ki = 1000.0 µmol/l; R00299CY_G6P_Kip = 10.0 µmol/l; R00299CY_krev = 1.16129032258065 1/s; R00299CY_BPG_Kip = 4000.0 µmol/l; R00299CY_GSH_Kip = 3000.0 µmol/l; R00299CY_MgATP_KM = 1000.0 µmol/l; R00299CY_G6P_Ki = 47.0 µmol/l Reaction: cpd_C00002tot_CY + cpd_C00031_CY => cpd_C00092tot_CY + cpd_C00008tot_CY + cpd_C00080_CY; ec_2_7_1_1_CY, cpd_C00051_CY, cpd_C00660tot_CY, cpd_C03339tot_CY, Rate Law: Cytosol*ec_2_7_1_1_CY/Cytosol*(R00299CY_kfor*cpd_C00002tot_CY/Cytosol*cpd_C00031_CY/Cytosol/(R00299CY_Glc_Ki*R00299CY_MgATP_KM)-R00299CY_krev*cpd_C00092tot_CY/Cytosol*cpd_C00008tot_CY/Cytosol/(R00299CY_MgADP_Ki*R00299CY_G6P_KM))/(1+cpd_C00002tot_CY/Cytosol/R00299CY_MgATP_Ki+cpd_C00031_CY/Cytosol/R00299CY_Glc_Ki*(1+cpd_C00092tot_CY/Cytosol/R00299CY_G6P_Kip+cpd_C00660tot_CY/Cytosol/R00299CY_G16P_Kip+cpd_C03339tot_CY/Cytosol/R00299CY_BPG_Kip+cpd_C00051_CY/Cytosol/R00299CY_GSH_Kip)+cpd_C00002tot_CY/Cytosol*cpd_C00031_CY/Cytosol/(R00299CY_Glc_Ki*R00299CY_MgATP_KM)+cpd_C00092tot_CY/Cytosol/R00299CY_G6P_Ki+cpd_C00008tot_CY/Cytosol/R00299CY_MgADP_Ki+cpd_C00092tot_CY/Cytosol*cpd_C00008tot_CY/Cytosol/(R00299CY_MgADP_Ki*R00299CY_G6P_KM))

States:

Name Description
cpd C00569 CY [simple chemical; arabinogalactan; Arabinogalactan]
cpd C00002tot CY [simple chemical; ATP; ATP]
cpd C00080 CY [non-macromolecular ion; H+; proton]
cpd C00103tot CY [simple chemical; C11450; alpha-D-glucose 1-phosphate]
cpd C00208 CY [simple chemical; maltose; Maltose]
cpd C00369Glc CS [simple chemical; MOD:00726; Starch; starch]
cpd C00031 CS [simple chemical; D-glucose]
cpd C00031 CY [simple chemical; D-Glucose; D-glucose]
cpd C00569Glc CY [simple chemical; arabinogalactan; Arabinogalactan; MOD:00726]
cpd C00369db CS [simple chemical; Starch; starch; MOD:00726]
cpd C00009tot CY [simple chemical; Orthophosphate; phosphate(3-)]
cpd C00208 CS [simple chemical; maltose; Maltose]
cpd C00369 CS [simple chemical; Starch; starch]
cpd G00343 CS [simple chemical; maltopentaose]
cpd C00092tot CY [simple chemical; alpha-D-Glucose 6-phosphate; alpha-D-glucose 6-phosphate]
cpd C01835 CS [simple chemical; maltotriose; Maltotriose]
cpd C00008tot CY [simple chemical; ADP; ADP]

Observables: none

Mathematical model of blood coagulation and the effects of inhibitors of Xa, Va:Xa and IIa.

The present study began with mathematical modeling of how inhibitors of both factor Xa (fXa) and thrombin affect extrinsic pathway-triggered blood coagulation. Numerical simulation demonstrated a stronger inhibition of thrombin generation by a thrombin inhibitor than a fXa inhibitor, but both prolonged clot time to a similar extent when they were given an equal dissociation constant (30 nm) for interaction with their respective target enzymes. These differences were then tested by comparison with the real inhibitors DX-9065a and argatroban, specific competitive inhibitors of fXa and thrombin, respectively, with similar K(i) values. Comparisons were made in extrinsically triggered human citrated plasma, for which endogenous thrombin potential and clot formation were simultaneously measured with a Wallac multilabel counter equipped with both fluorometric and photometric detectors and a fluorogenic reporter substrate. The results demonstrated stronger inhibition of endogenous thrombin potential by argatroban than by DX-9065a, especially when coagulation was initiated at higher tissue factor concentrations, while argatroban appeared to be slightly less potent in its ability to prolong clot time. This study demonstrates differential inhibition of thrombin generation by fXa and thrombin inhibitors and has implications for the pharmacological regulation of blood coagulation by the anticoagulant protease inhibitors. link: http://identifiers.org/pubmed/12496240

Parameters:

Name Description
k02 = 2.2; k01 = 0.1 Reaction: TF_VIIa + IX => TF_VIIa_IX, Rate Law: compartment*(k01*TF_VIIa*IX-k02*TF_VIIa_IX)
k17 = 29.0 Reaction: VIIIa_IXa_X => VIIIa_IXa + Xa, Rate Law: compartment*k17*VIIIa_IXa_X
k24 = 0.1; k25 = 0.1 Reaction: Xa + Va => Va_Xa, Rate Law: compartment*(k24*Xa*Va-k25*Va_Xa)
k31 = 84.0 Reaction: Fibrinogen_IIa => Fibrin + IIa, Rate Law: compartment*k31*Fibrinogen_IIa
k41 = 3.0; k40 = 0.1 Reaction: Va_Xa + Xa_Inhibitor => Va_Xa_Xa_Inhibitor, Rate Law: compartment*(k40*Va_Xa*Xa_Inhibitor-k41*Va_Xa_Xa_Inhibitor)
k06 = 1.4 Reaction: TF_VIIa_X => TF_VIIa + Xa, Rate Law: compartment*k06*TF_VIIa_X
k36 = 0.1; k37 = 3.0 Reaction: Xa + Xa_Inhibitor => Xa_Xa_Inhibitor, Rate Law: compartment*(k36*Xa*Xa_Inhibitor-k37*Xa_Xa_Inhibitor)
k08 = 2.1; k07 = 0.1 Reaction: Xa + VIII => Xa_VIII, Rate Law: compartment*(k07*Xa*VIII-k08*Xa_VIII)
k43 = 3.0; k42 = 0.1 Reaction: IIa + IIa_Inhibitor => IIa_IIa_Inhibitor, Rate Law: compartment*(k42*IIa*IIa_Inhibitor-k43*IIa_IIa_Inhibitor)
k16 = 19.0; k15 = 0.1 Reaction: VIIIa_IXa + X => VIIIa_IXa_X, Rate Law: compartment*(k15*VIIIa_IXa*X-k16*VIIIa_IXa_X)
k30 = 720.0; k29 = 0.1 Reaction: Fibrinogen + IIa => Fibrinogen_IIa, Rate Law: compartment*(k29*Fibrinogen*IIa-k30*Fibrinogen_IIa)
k28 = 35.0 Reaction: Va_Xa_II => Va_Xa + IIa, Rate Law: compartment*k28*Va_Xa_II
k14 = 0.17; k13 = 0.1 Reaction: VIIIa + IXa => VIIIa_IXa, Rate Law: compartment*(k13*VIIIa*IXa-k14*VIIIa_IXa)
k04 = 0.1; k05 = 5.5 Reaction: TF_VIIa + X => TF_VIIa_X, Rate Law: compartment*(k04*TF_VIIa*X-k05*TF_VIIa_X)
k12 = 0.9 Reaction: IIa_VIII => IIa + VIIIa, Rate Law: compartment*k12*IIa_VIII
k34 = 0.011 Reaction: Xa => Xa_inact, Rate Law: compartment*k34*Xa
k20 = 0.043 Reaction: Xa_V => Xa + Va, Rate Law: compartment*k20*Xa_V
k18 = 0.1; k19 = 1.0 Reaction: Xa + V => Xa_V, Rate Law: compartment*(k18*Xa*V-k19*Xa_V)
k32 = 0.0011 Reaction: VIIIa => VIIIa_inact, Rate Law: compartment*k32*VIIIa
k35 = 0.024 Reaction: IIa => IIa_inact, Rate Law: compartment*k35*IIa
k11 = 15.0; k10 = 0.1 Reaction: IIa + VIII => IIa_VIII, Rate Law: compartment*(k10*IIa*VIII-k11*IIa_VIII)
k33 = 0.0017 Reaction: IXa => IXa_inact, Rate Law: compartment*k33*IXa
k23 = 0.26 Reaction: IIa_V => IIa + Va, Rate Law: compartment*k23*IIa_V
k03 = 0.47 Reaction: TF_VIIa_IX => TF_VIIa + IXa, Rate Law: compartment*k03*TF_VIIa_IX
k38 = 0.1; k39 = 0.1 Reaction: Va + Xa_Xa_Inhibitor => Va_Xa_Xa_Inhibitor, Rate Law: compartment*(k38*Va*Xa_Xa_Inhibitor-k39*Va_Xa_Xa_Inhibitor)
k22 = 7.2; k21 = 0.1 Reaction: IIa + V => IIa_V, Rate Law: compartment*(k21*IIa*V-k22*IIa_V)
k26 = 0.1; k27 = 100.0 Reaction: Va_Xa + II => Va_Xa_II, Rate Law: compartment*(k26*Va_Xa*II-k27*Va_Xa_II)
k09 = 0.023 Reaction: Xa_VIII => Xa + VIIIa, Rate Law: compartment*k09*Xa_VIII

States:

Name Description
IIa inact [Thrombin]
VIIIa IXa X [Coagulation Factor X Human; Coagulation Factor IX Human; Coagulation Factor VIII]
VIII [Coagulation Factor VIII]
Fibrin [Fibrin]
IIa IIa Inhibitor [EC 3.4.21.5 (thrombin) inhibitor; Thrombin]
V [Coagulation Factor V]
Xa VIII [Coagulation Factor VIII; Coagulation Factor X Human]
Xa [Coagulation Factor X Human]
IIa Inhibitor [EC 3.4.21.5 (thrombin) inhibitor]
VIIIa inact [Coagulation Factor VIII]
Va Xa [Coagulation Factor X Human; Coagulation Factor V]
IIa VIII [Coagulation Factor VIII; Thrombin]
TF VIIa X [Coagulation Factor X Human; Coagulation Factor VII Human; Tissue Factor]
Fibrinogen IIa [Thrombin; Fibrinogen]
Xa V [Coagulation Factor V; Coagulation Factor X Human]
Xa Xa Inhibitor [EC 3.4.21.5 (thrombin) inhibitor; Coagulation Factor X Human]
Fibrinogen [Fibrinogen]
X [Coagulation Factor X Human]
Va Xa II [Coagulation Factor X Human; Prothrombin; Coagulation Factor V]
Xa inact [Coagulation Factor X Human]
TF VIIa [Coagulation Factor VII Human; Tissue Factor]
VIIIa [Coagulation Factor VIII]
Xa Inhibitor [EC 3.4.21.6 (coagulation factor Xa) inhibitor]
IIa V [Coagulation Factor V; Thrombin]
Va [Coagulation Factor V]
IIa [Thrombin]
TF VIIa IX [Coagulation Factor IX Human; Tissue Factor; Coagulation Factor VII Human]
Va Xa Xa Inhibitor [Coagulation Factor X Human; Coagulation Factor V; EC 3.4.21.6 (coagulation factor Xa) inhibitor]
IXa [Coagulation Factor IX Human]
VIIIa IXa [Coagulation Factor IX Human; Coagulation Factor VIII]
II [Prothrombin]
IX [Coagulation Factor IX Human]
IXa inact [Coagulation Factor IX Human]

Observables: none

Nair2015 - Interaction between neuromodulators via GPCRs - Effect on cAMP/PKA signaling (D1 Neuron)This model is describ…

Transient changes in striatal dopamine (DA) concentration are considered to encode a reward prediction error (RPE) in reinforcement learning tasks. Often, a phasic DA change occurs concomitantly with a dip in striatal acetylcholine (ACh), whereas other neuromodulators, such as adenosine (Adn), change slowly. There are abundant adenylyl cyclase (AC) coupled GPCRs for these neuromodulators in striatal medium spiny neurons (MSNs), which play important roles in plasticity. However, little is known about the interaction between these neuromodulators via GPCRs. The interaction between these transient neuromodulator changes and the effect on cAMP/PKA signaling via Golf- and Gi/o-coupled GPCR are studied here using quantitative kinetic modeling. The simulations suggest that, under basal conditions, cAMP/PKA signaling could be significantly inhibited in D1R+ MSNs via ACh/M4R/Gi/o and an ACh dip is required to gate a subset of D1R/Golf-dependent PKA activation. Furthermore, the interaction between ACh dip and DA peak, via D1R and M4R, is synergistic. In a similar fashion, PKA signaling in D2+ MSNs is under basal inhibition via D2R/Gi/o and a DA dip leads to a PKA increase by disinhibiting A2aR/Golf, but D2+ MSNs could also respond to the DA peak via other intracellular pathways. This study highlights the similarity between the two types of MSNs in terms of high basal AC inhibition by Gi/o and the importance of interactions between Gi/o and Golf signaling, but at the same time predicts differences between them with regard to the sign of RPE responsible for PKA activation.Dopamine transients are considered to carry reward-related signal in reinforcement learning. An increase in dopamine concentration is associated with an unexpected reward or salient stimuli, whereas a decrease is produced by omission of an expected reward. Often dopamine transients are accompanied by other neuromodulatory signals, such as acetylcholine and adenosine. We highlight the importance of interaction between acetylcholine, dopamine, and adenosine signals via adenylyl-cyclase coupled GPCRs in shaping the dopamine-dependent cAMP/PKA signaling in striatal neurons. Specifically, a dopamine peak and an acetylcholine dip must interact, via D1 and M4 receptor, and a dopamine dip must interact with adenosine tone, via D2 and A2a receptor, in direct and indirect pathway neurons, respectively, to have any significant downstream PKA activation. link: http://identifiers.org/pubmed/26468202

Parameters:

Name Description
mw05f4bef4_5e8d_4a92_bb74_cc0bb4c0260e = 1.0; mw77fab49b_2ba6_4efe_9342_285f4fd3b7fa = 0.01 Reaction: mw1c97b02d_169a_4eb8_bc84_1be57c51a255 + mw219e8fae_a38b_4620_8726_e6bd1829a351 => mwf46d3666_f0f3_4f05_9603_d7e6bb69005e, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw77fab49b_2ba6_4efe_9342_285f4fd3b7fa*mw1c97b02d_169a_4eb8_bc84_1be57c51a255*mw219e8fae_a38b_4620_8726_e6bd1829a351-mw05f4bef4_5e8d_4a92_bb74_cc0bb4c0260e*mwf46d3666_f0f3_4f05_9603_d7e6bb69005e)
ModelValue_145 = 100.0; AChdip = 1.0; ModelValue_143 = 100.0; ModelValue_138 = 0.0; ModelValue_144 = 0.001 Reaction: mw3e1a2fbf_37b1_490c_9528_6cb6bbf11b21 = (1-ModelValue_138)*ModelValue_143+ModelValue_138*(ModelValue_144+(ModelValue_145-ModelValue_144)*AChdip), Rate Law: missing
mw7419e1e3_b601_44a8_93ff_e5b31995791e = 0.08; mw4a930624_fcc1_4d08_8e24_9a0082418629 = 0.04 Reaction: mw2f3e9c55_e57f_416e_b4b1_cc49a26192c0 + mw06380287_79c9_4f85_aed6_fa34e7bcdff1 => mwc57c3c2e_69d5_4336_aff5_d1f429420df2, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw4a930624_fcc1_4d08_8e24_9a0082418629*mw2f3e9c55_e57f_416e_b4b1_cc49a26192c0*mw06380287_79c9_4f85_aed6_fa34e7bcdff1-mw7419e1e3_b601_44a8_93ff_e5b31995791e*mwc57c3c2e_69d5_4336_aff5_d1f429420df2)
mwf633f298_303f_46d1_b644_ae07ae366f45 = 3.0 Reaction: mw6e845d87_603e_4463_874d_866f554303df => mw3d9e6efb_8e12_49c9_a87f_e067914b951d + mw9710c658_a2a1_4f49_b494_af109853f251, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mwf633f298_303f_46d1_b644_ae07ae366f45*mw6e845d87_603e_4463_874d_866f554303df
mw515fcf69_b724_40d9_84ba_5f92d75ae5a7 = 1.5E-4 Reaction: mw1c97b02d_169a_4eb8_bc84_1be57c51a255 + mw7df45520_98cc_4c0b_91a7_c6e7297de98a => mw619502c3_e319_4e29_a677_b2b5f74fc2cf, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mw515fcf69_b724_40d9_84ba_5f92d75ae5a7*mw1c97b02d_169a_4eb8_bc84_1be57c51a255*mw7df45520_98cc_4c0b_91a7_c6e7297de98a
mw269c014a_6379_44c3_813b_52d8145506e7 = 1.0E-4; mwc4c3d33d_b2b7_4ab2_a171_1864ea638ec0 = 0.1 Reaction: mw522cacf1_5e61_4b95_8742_cf61cb824893 + mwccd3a17c_e207_4663_9b16_327b78882497 => mw3fcd1ec2_a459_49d4_89f7_361e276096d6, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw269c014a_6379_44c3_813b_52d8145506e7*mw522cacf1_5e61_4b95_8742_cf61cb824893*mwccd3a17c_e207_4663_9b16_327b78882497-mwc4c3d33d_b2b7_4ab2_a171_1864ea638ec0*mw3fcd1ec2_a459_49d4_89f7_361e276096d6)
mwdb2a670f_13fb_4bda_8c72_d706c6bc37e9 = 2.8125E-5 Reaction: mw1c97b02d_169a_4eb8_bc84_1be57c51a255 + mwed1b3928_8d78_44d1_aee7_9d11d6437cfc => mw56dff932_134c_4d88_a611_daad00623fd0, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mwdb2a670f_13fb_4bda_8c72_d706c6bc37e9*mw1c97b02d_169a_4eb8_bc84_1be57c51a255*mwed1b3928_8d78_44d1_aee7_9d11d6437cfc
mw448bd49f_40ad_46c9_81f6_3494057dc37d = 0.003; mwa466eec8_9bc0_44d5_8027_d5925b378429 = 5.0 Reaction: mwe2fc02e6_2684_4071_932a_f7a8bd13b2fe + mw351f6cee_3e64_4b8e_8e60_24b1aca99a92 => mw0b46978f_b522_4cde_97f0_574cd7dbbae7, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw448bd49f_40ad_46c9_81f6_3494057dc37d*mwe2fc02e6_2684_4071_932a_f7a8bd13b2fe*mw351f6cee_3e64_4b8e_8e60_24b1aca99a92-mwa466eec8_9bc0_44d5_8027_d5925b378429*mw0b46978f_b522_4cde_97f0_574cd7dbbae7)
mw0b1ccae3_37fa_4a23_a817_cd8fc458dc79 = 0.1; mw6f753a0e_a7ec_4b4b_bcfc_edb95a3f1296 = 2.0 Reaction: mw1c97b02d_169a_4eb8_bc84_1be57c51a255 + mw3d9e6efb_8e12_49c9_a87f_e067914b951d => mw6e845d87_603e_4463_874d_866f554303df, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw0b1ccae3_37fa_4a23_a817_cd8fc458dc79*mw1c97b02d_169a_4eb8_bc84_1be57c51a255*mw3d9e6efb_8e12_49c9_a87f_e067914b951d-mw6f753a0e_a7ec_4b4b_bcfc_edb95a3f1296*mw6e845d87_603e_4463_874d_866f554303df)
mw9510e553_a7fd_4c9a_b284_19b3cc01ae7d = 7.5E-5; mwb494aae2_da19_4ac0_96e2_0dcd9440edc2 = 1.0 Reaction: mw7df45520_98cc_4c0b_91a7_c6e7297de98a + mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a => mw619502c3_e319_4e29_a677_b2b5f74fc2cf, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw9510e553_a7fd_4c9a_b284_19b3cc01ae7d*mw7df45520_98cc_4c0b_91a7_c6e7297de98a*mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a-mwb494aae2_da19_4ac0_96e2_0dcd9440edc2*mw619502c3_e319_4e29_a677_b2b5f74fc2cf)
mw009f9583_4e96_4672_ab71_0ef4b697aa6f = 6.4; mw2226fa14_2b95_45a6_8705_4b38073fc5f7 = 8.0E-4 Reaction: mw522cacf1_5e61_4b95_8742_cf61cb824893 + mw1184c368_03fc_435a_9086_dc6ed3067935 => mw0459271f_3b39_40a4_948f_aed773482cfc, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw2226fa14_2b95_45a6_8705_4b38073fc5f7*mw522cacf1_5e61_4b95_8742_cf61cb824893*mw1184c368_03fc_435a_9086_dc6ed3067935-mw009f9583_4e96_4672_ab71_0ef4b697aa6f*mw0459271f_3b39_40a4_948f_aed773482cfc)
mw1db20a7e_3972_4c3a_83c0_c6fcd7c9cb45 = 3.0E-4; mw4e2575eb_3641_422c_b836_d854958d4d1e = 8.0 Reaction: mw68d3f409_9462_4515_8c07_bc105fa0eaf1 + mw24435476_9c30_4878_b26f_4b3c5a0685c6 => mw4179e1ff_9035_4c67_a67c_099e25beb9b0, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw1db20a7e_3972_4c3a_83c0_c6fcd7c9cb45*mw68d3f409_9462_4515_8c07_bc105fa0eaf1*mw24435476_9c30_4878_b26f_4b3c5a0685c6-mw4e2575eb_3641_422c_b836_d854958d4d1e*mw4179e1ff_9035_4c67_a67c_099e25beb9b0)
mwb494aae2_da19_4ac0_96e2_0dcd9440edc2 = 1.0; mw00f3118f_5d5a_48d0_bcc4_749d5f9dc73a = 1.75E-4 Reaction: mw2badefa3_32e8_4b66_9e69_245d9ec74e33 + mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a => mw07c7392b_8d89_4b94_97c5_59f7e256b6f2, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw00f3118f_5d5a_48d0_bcc4_749d5f9dc73a*mw2badefa3_32e8_4b66_9e69_245d9ec74e33*mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a-mwb494aae2_da19_4ac0_96e2_0dcd9440edc2*mw07c7392b_8d89_4b94_97c5_59f7e256b6f2)
mwfcfb91ff_a495_41f9_bdff_fcef779112fd = 30.0 Reaction: mwa2c44a01_28c9_4dbd_b034_364f9b5b6cc3 => mw9bcba6bc_9788_4f7f_afb5_1c8f3b33c3d1, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mwfcfb91ff_a495_41f9_bdff_fcef779112fd*mwa2c44a01_28c9_4dbd_b034_364f9b5b6cc3
mwa390f769_ebf1_4023_8af0_1c00e2a9bf82 = 0.002; mw0beb6cc4_36bd_4022_8993_29f981652ebe = 1.0 Reaction: mw2f3e9c55_e57f_416e_b4b1_cc49a26192c0 + mw4855b1cd_d7bc_4072_9736_dca30bbe448d => mwcf1bb70c_9d0b_4e82_b58a_6f8e73208af9, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mwa390f769_ebf1_4023_8af0_1c00e2a9bf82*mw2f3e9c55_e57f_416e_b4b1_cc49a26192c0*mw4855b1cd_d7bc_4072_9736_dca30bbe448d-mw0beb6cc4_36bd_4022_8993_29f981652ebe*mwcf1bb70c_9d0b_4e82_b58a_6f8e73208af9)
mw5301f7f5_60df_4eb9_ba3b_81e6519d1cbb = 5.0 Reaction: mw0a10f9cb_3f4b_4bfa_ace9_0ecd2bd74b5e => mw1c97b02d_169a_4eb8_bc84_1be57c51a255 + mwd794c746_c826_4ba1_9e09_a9d1e122d925, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mw5301f7f5_60df_4eb9_ba3b_81e6519d1cbb*mw0a10f9cb_3f4b_4bfa_ace9_0ecd2bd74b5e
mwefa9bb47_f13f_4a21_a62d_a4debcf7b52b = 90.0; mw066c69e2_66da_4621_9180_bce71b7077c3 = 1.0 Reaction: mw3e1a2fbf_37b1_490c_9528_6cb6bbf11b21 + mwd86ce0dc_7329_4b27_9de0_ee6bffee3083 => mwe4e36b8e_18b8_4c76_bd46_13614b71da5c, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw066c69e2_66da_4621_9180_bce71b7077c3*mw3e1a2fbf_37b1_490c_9528_6cb6bbf11b21*mwd86ce0dc_7329_4b27_9de0_ee6bffee3083-mwefa9bb47_f13f_4a21_a62d_a4debcf7b52b*mwe4e36b8e_18b8_4c76_bd46_13614b71da5c)
mwdcc4ce84_732d_4f5b_84e2_e5b93617200b = 0.3 Reaction: mw8825a609_0983_4fb4_a264_e2f7e43d17b3 => mw3fcd1ec2_a459_49d4_89f7_361e276096d6 + mw24435476_9c30_4878_b26f_4b3c5a0685c6, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mwdcc4ce84_732d_4f5b_84e2_e5b93617200b*mw8825a609_0983_4fb4_a264_e2f7e43d17b3
mw2561b5ab_39c9_4453_99d8_f0f37779b63a = 10.0 Reaction: mw4179e1ff_9035_4c67_a67c_099e25beb9b0 => mw2f3e9c55_e57f_416e_b4b1_cc49a26192c0 + mw68d3f409_9462_4515_8c07_bc105fa0eaf1, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mw2561b5ab_39c9_4453_99d8_f0f37779b63a*mw4179e1ff_9035_4c67_a67c_099e25beb9b0
mwca52f04a_bb5f_4d3f_ba6d_939bbb3895b9 = 2.0; mw326e0065_b4f6_41ae_b1d0_66092dc5ebb2 = 0.13 Reaction: mw1c97b02d_169a_4eb8_bc84_1be57c51a255 + mw1041345b_f015_436c_9eff_98211008aa1c => mw1f3b8982_3b8c_42b6_8b0f_49b037cbda43, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw326e0065_b4f6_41ae_b1d0_66092dc5ebb2*mw1c97b02d_169a_4eb8_bc84_1be57c51a255*mw1041345b_f015_436c_9eff_98211008aa1c-mwca52f04a_bb5f_4d3f_ba6d_939bbb3895b9*mw1f3b8982_3b8c_42b6_8b0f_49b037cbda43)
mwb494aae2_da19_4ac0_96e2_0dcd9440edc2 = 1.0; mw72ceb3da_d538_4f25_8e69_f322eb0b5e57 = 0.00105 Reaction: mwfe9ed415_d5af_469c_a549_d8981f1eb01f + mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a => mw166e3335_56c3_41ef_af0f_b583860991c1, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw72ceb3da_d538_4f25_8e69_f322eb0b5e57*mwfe9ed415_d5af_469c_a549_d8981f1eb01f*mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a-mwb494aae2_da19_4ac0_96e2_0dcd9440edc2*mw166e3335_56c3_41ef_af0f_b583860991c1)
mw88c9326a_fbe9_4dd8_aded_b5be3f012691 = 2.3 Reaction: mwde741b91_d5bf_44a9_ad45_404d7259d051 => mw081c9f7b_011e_440f_971d_d0316d2a1e6c + mw24435476_9c30_4878_b26f_4b3c5a0685c6, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mw88c9326a_fbe9_4dd8_aded_b5be3f012691*mwde741b91_d5bf_44a9_ad45_404d7259d051
mw5175a06e_3927_4993_9242_8f76b0aaf42f = 100.0 Reaction: mwb80e4fa1_4849_4ed5_b3b0_3e3025c61ad8 + mw9bcba6bc_9788_4f7f_afb5_1c8f3b33c3d1 => mwd8ea533a_c66e_4de4_8c5c_0d4201d8c8a2, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mw5175a06e_3927_4993_9242_8f76b0aaf42f*mwb80e4fa1_4849_4ed5_b3b0_3e3025c61ad8*mw9bcba6bc_9788_4f7f_afb5_1c8f3b33c3d1
mw65cae8fe_0eac_4792_88bf_2dfb441030e5 = 0.5 Reaction: mw619502c3_e319_4e29_a677_b2b5f74fc2cf => mw1c97b02d_169a_4eb8_bc84_1be57c51a255 + mw7df45520_98cc_4c0b_91a7_c6e7297de98a, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mw65cae8fe_0eac_4792_88bf_2dfb441030e5*mw619502c3_e319_4e29_a677_b2b5f74fc2cf
mwac1bc66c_2623_47e6_a76d_c1629d962be5 = 10.0 Reaction: mw1f3b8982_3b8c_42b6_8b0f_49b037cbda43 => mw1041345b_f015_436c_9eff_98211008aa1c + mw9710c658_a2a1_4f49_b494_af109853f251, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mwac1bc66c_2623_47e6_a76d_c1629d962be5*mw1f3b8982_3b8c_42b6_8b0f_49b037cbda43
mw6af7af00_75ac_4f58_8383_7047a5fb5181 = 1.0 Reaction: mw0459271f_3b39_40a4_948f_aed773482cfc => mw522cacf1_5e61_4b95_8742_cf61cb824893 + mw24435476_9c30_4878_b26f_4b3c5a0685c6, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mw6af7af00_75ac_4f58_8383_7047a5fb5181*mw0459271f_3b39_40a4_948f_aed773482cfc
mwe737a297_e5be_46ed_af75_ccc7428c3977 = 0.001; mwddcb8d81_9f5a_457e_a54c_a0c1b1f29f0b = 0.9 Reaction: mwccd3a17c_e207_4663_9b16_327b78882497 + mw7086a13a_619e_4069_b163_d8a05fc55f42 => mw619502c3_e319_4e29_a677_b2b5f74fc2cf, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mwe737a297_e5be_46ed_af75_ccc7428c3977*mwccd3a17c_e207_4663_9b16_327b78882497*mw7086a13a_619e_4069_b163_d8a05fc55f42-mwddcb8d81_9f5a_457e_a54c_a0c1b1f29f0b*mw619502c3_e319_4e29_a677_b2b5f74fc2cf)
mw8e4e88b6_60b3_43bd_8f5c_923712ee64ea = 5.0; mwb17941e5_1ad5_42b9_98c6_e62b1a697dbb = 0.003 Reaction: mwdb9dc389_2bf0_4039_9f09_282f5511958b + mw351f6cee_3e64_4b8e_8e60_24b1aca99a92 => mw6b2f1c44_e0be_4406_bcef_ad5061d519e4, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mwb17941e5_1ad5_42b9_98c6_e62b1a697dbb*mwdb9dc389_2bf0_4039_9f09_282f5511958b*mw351f6cee_3e64_4b8e_8e60_24b1aca99a92-mw8e4e88b6_60b3_43bd_8f5c_923712ee64ea*mw6b2f1c44_e0be_4406_bcef_ad5061d519e4)
mwcd307ee9_33da_4303_9c28_644ad2d1630c = 0.1; mw0a255671_d9ca_4384_a153_ce17e1111453 = 0.2 Reaction: mw724f1afe_8032_40ae_96ca_808ab7b8b943 + mw8e34c23f_1891_4dc9_8f97_dc2f12a1706c => mwfe9ed415_d5af_469c_a549_d8981f1eb01f, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw0a255671_d9ca_4384_a153_ce17e1111453*mw724f1afe_8032_40ae_96ca_808ab7b8b943*mw8e34c23f_1891_4dc9_8f97_dc2f12a1706c-mwcd307ee9_33da_4303_9c28_644ad2d1630c*mwfe9ed415_d5af_469c_a549_d8981f1eb01f)
mw0dd72d64_80e1_4f76_a444_fd175dbfab0c = 15.0 Reaction: mw6b2f1c44_e0be_4406_bcef_ad5061d519e4 => mwaf471bc1_f98a_4115_b0ee_45c189ea20b5 + mwdb9dc389_2bf0_4039_9f09_282f5511958b + mw8e34c23f_1891_4dc9_8f97_dc2f12a1706c, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mw0dd72d64_80e1_4f76_a444_fd175dbfab0c*mw6b2f1c44_e0be_4406_bcef_ad5061d519e4
mwaa3af366_350e_4f18_936b_6372dc484f82 = 4.0E-4 Reaction: mw1c97b02d_169a_4eb8_bc84_1be57c51a255 + mw724f1afe_8032_40ae_96ca_808ab7b8b943 => mw7086a13a_619e_4069_b163_d8a05fc55f42, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mwaa3af366_350e_4f18_936b_6372dc484f82*mw1c97b02d_169a_4eb8_bc84_1be57c51a255*mw724f1afe_8032_40ae_96ca_808ab7b8b943
mwd1b16e73_4fcb_4e4c_9804_3137259ba749 = 1.0E-6; mw36cb62c6_0b3c_4d1b_9001_3b37aa7477e2 = 9.0 Reaction: mw3d9e6efb_8e12_49c9_a87f_e067914b951d + mw1c97b02d_169a_4eb8_bc84_1be57c51a255 => mw1041345b_f015_436c_9eff_98211008aa1c, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mwd1b16e73_4fcb_4e4c_9804_3137259ba749*mw3d9e6efb_8e12_49c9_a87f_e067914b951d*mw1c97b02d_169a_4eb8_bc84_1be57c51a255^2-mw36cb62c6_0b3c_4d1b_9001_3b37aa7477e2*mw1041345b_f015_436c_9eff_98211008aa1c)
mwb494aae2_da19_4ac0_96e2_0dcd9440edc2 = 1.0; mwb56b5ab7_47cc_4fbc_b68b_dfdc6be6d7a4 = 5.5E-4 Reaction: mw42919ead_5972_4151_85ac_fcc88ca105a6 + mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a => mwbae3bd11_0ab4_4587_a931_9c5dc5e777ba, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mwb56b5ab7_47cc_4fbc_b68b_dfdc6be6d7a4*mw42919ead_5972_4151_85ac_fcc88ca105a6*mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a-mwb494aae2_da19_4ac0_96e2_0dcd9440edc2*mwbae3bd11_0ab4_4587_a931_9c5dc5e777ba)
mwb0a6bd5e_87a0_425c_a5c7_ea69903e0bf3 = 10.0 Reaction: mwbae3bd11_0ab4_4587_a931_9c5dc5e777ba => mw1c97b02d_169a_4eb8_bc84_1be57c51a255 + mw42919ead_5972_4151_85ac_fcc88ca105a6, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mwb0a6bd5e_87a0_425c_a5c7_ea69903e0bf3*mwbae3bd11_0ab4_4587_a931_9c5dc5e777ba
mw649b47b3_4c3a_4ac9_ae94_5c38ccf81e39 = 0.002; mwc911f28c_b62f_4269_84ed_d852f6da24f9 = 0.01 Reaction: mw8e34c23f_1891_4dc9_8f97_dc2f12a1706c + mw56dff932_134c_4d88_a611_daad00623fd0 => mw07c7392b_8d89_4b94_97c5_59f7e256b6f2, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw649b47b3_4c3a_4ac9_ae94_5c38ccf81e39*mw8e34c23f_1891_4dc9_8f97_dc2f12a1706c*mw56dff932_134c_4d88_a611_daad00623fd0-mwc911f28c_b62f_4269_84ed_d852f6da24f9*mw07c7392b_8d89_4b94_97c5_59f7e256b6f2)
mw5623544e_e7e1_439f_88d3_3b0cbea8ccf5 = 30.0 Reaction: mw8e34c23f_1891_4dc9_8f97_dc2f12a1706c => mwfed0682b_39f1_4b09_94e8_c45a51744092, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mw5623544e_e7e1_439f_88d3_3b0cbea8ccf5*mw8e34c23f_1891_4dc9_8f97_dc2f12a1706c
mwe79f507b_73c9_4056_ae91_6244dcbc49bb = 0.5; mw26206710_ba98_4010_9e5b_c3aae2ce29ec = 1.0 Reaction: mw2f3e9c55_e57f_416e_b4b1_cc49a26192c0 + mw3fcd1ec2_a459_49d4_89f7_361e276096d6 => mw8825a609_0983_4fb4_a264_e2f7e43d17b3, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mwe79f507b_73c9_4056_ae91_6244dcbc49bb*mw2f3e9c55_e57f_416e_b4b1_cc49a26192c0*mw3fcd1ec2_a459_49d4_89f7_361e276096d6-mw26206710_ba98_4010_9e5b_c3aae2ce29ec*mw8825a609_0983_4fb4_a264_e2f7e43d17b3)
mw62c51fcf_c107_4d3c_849e_9b168df54490 = 10.0 Reaction: mwcf1bb70c_9d0b_4e82_b58a_6f8e73208af9 => mw24435476_9c30_4878_b26f_4b3c5a0685c6 + mw4855b1cd_d7bc_4072_9736_dca30bbe448d, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mw62c51fcf_c107_4d3c_849e_9b168df54490*mwcf1bb70c_9d0b_4e82_b58a_6f8e73208af9
mwce0df80f_1563_453d_b33d_a88f6b2c93b7 = 90.0; mw80292f32_fd53_4b5d_872a_e21c2d90c52a = 0.01 Reaction: mw3e1a2fbf_37b1_490c_9528_6cb6bbf11b21 + mwf82770b9_766a_4c4e_851a_d76da19e8517 => mw9d5c5c9d_301d_4e43_ba7b_7d21ccbdc2c2, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw80292f32_fd53_4b5d_872a_e21c2d90c52a*mw3e1a2fbf_37b1_490c_9528_6cb6bbf11b21*mwf82770b9_766a_4c4e_851a_d76da19e8517-mwce0df80f_1563_453d_b33d_a88f6b2c93b7*mw9d5c5c9d_301d_4e43_ba7b_7d21ccbdc2c2)
mwa4148cd1_a298_447c_aea8_226688c3f526 = 2.0 Reaction: mwf46d3666_f0f3_4f05_9603_d7e6bb69005e => mw219e8fae_a38b_4620_8726_e6bd1829a351 + mw9710c658_a2a1_4f49_b494_af109853f251, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mwa4148cd1_a298_447c_aea8_226688c3f526*mwf46d3666_f0f3_4f05_9603_d7e6bb69005e
mwb494aae2_da19_4ac0_96e2_0dcd9440edc2 = 1.0; mwb93138ce_a80b_4b26_b927_6b4a00651b64 = 3.0E-4 Reaction: mwd794c746_c826_4ba1_9e09_a9d1e122d925 + mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a => mw0a10f9cb_3f4b_4bfa_ace9_0ecd2bd74b5e, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mwb93138ce_a80b_4b26_b927_6b4a00651b64*mwd794c746_c826_4ba1_9e09_a9d1e122d925*mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a-mwb494aae2_da19_4ac0_96e2_0dcd9440edc2*mw0a10f9cb_3f4b_4bfa_ace9_0ecd2bd74b5e)
mw858f28f3_086a_436b_ba23_4fc7372c8884 = 5.0; mw3fc2c1ed_0097_4f7f_bcd5_904dc6ad5a56 = 0.005 Reaction: mw0b46978f_b522_4cde_97f0_574cd7dbbae7 + mwbe974953_e869_4622_b4a8_745555c8d7fd => mw6b2f1c44_e0be_4406_bcef_ad5061d519e4, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw3fc2c1ed_0097_4f7f_bcd5_904dc6ad5a56*mw0b46978f_b522_4cde_97f0_574cd7dbbae7*mwbe974953_e869_4622_b4a8_745555c8d7fd-mw858f28f3_086a_436b_ba23_4fc7372c8884*mw6b2f1c44_e0be_4406_bcef_ad5061d519e4)
mwcabc0868_2435_4850_964b_e3ddee39f5ad = 30.0 Reaction: mw07c7392b_8d89_4b94_97c5_59f7e256b6f2 => mwbae3bd11_0ab4_4587_a931_9c5dc5e777ba + mw9bcba6bc_9788_4f7f_afb5_1c8f3b33c3d1, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mwcabc0868_2435_4850_964b_e3ddee39f5ad*mw07c7392b_8d89_4b94_97c5_59f7e256b6f2
mw6ae3f7a6_bf58_475e_930e_6bf7a79f3761 = 5.0; mw1ef56a9a_9d9b_4490_8fcd_53b7e50bf5d6 = 50.0 Reaction: mw7086a13a_619e_4069_b163_d8a05fc55f42 + mwa2c44a01_28c9_4dbd_b034_364f9b5b6cc3 => mw2075d2cf_955e_4150_98b8_847103c53845, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw1ef56a9a_9d9b_4490_8fcd_53b7e50bf5d6*mw7086a13a_619e_4069_b163_d8a05fc55f42*mwa2c44a01_28c9_4dbd_b034_364f9b5b6cc3-mw6ae3f7a6_bf58_475e_930e_6bf7a79f3761*mw2075d2cf_955e_4150_98b8_847103c53845)
mwd05b4199_53ad_4807_9a8c_d93ce35be857 = 60.0 Reaction: mwe4e36b8e_18b8_4c76_bd46_13614b71da5c => mwa2c44a01_28c9_4dbd_b034_364f9b5b6cc3 + mw9d5c5c9d_301d_4e43_ba7b_7d21ccbdc2c2 + mwb80e4fa1_4849_4ed5_b3b0_3e3025c61ad8, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mwd05b4199_53ad_4807_9a8c_d93ce35be857*mwe4e36b8e_18b8_4c76_bd46_13614b71da5c
mw9c2302f8_3d47_4247_a338_a02c53fc5331 = 1.0E-4; mwb494aae2_da19_4ac0_96e2_0dcd9440edc2 = 1.0 Reaction: mw724f1afe_8032_40ae_96ca_808ab7b8b943 + mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a => mw7086a13a_619e_4069_b163_d8a05fc55f42, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw9c2302f8_3d47_4247_a338_a02c53fc5331*mw724f1afe_8032_40ae_96ca_808ab7b8b943*mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a-mwb494aae2_da19_4ac0_96e2_0dcd9440edc2*mw7086a13a_619e_4069_b163_d8a05fc55f42)
mwc728d91d_7616_43db_bd1d_55e49e9c026a = 0.125 Reaction: mw56dff932_134c_4d88_a611_daad00623fd0 => mw1c97b02d_169a_4eb8_bc84_1be57c51a255 + mwed1b3928_8d78_44d1_aee7_9d11d6437cfc, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mwc728d91d_7616_43db_bd1d_55e49e9c026a*mw56dff932_134c_4d88_a611_daad00623fd0
mwb494aae2_da19_4ac0_96e2_0dcd9440edc2 = 1.0; mwa1bc2233_5bb9_4135_88ed_bb51640faec8 = 5.625E-5 Reaction: mwed1b3928_8d78_44d1_aee7_9d11d6437cfc + mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a => mw56dff932_134c_4d88_a611_daad00623fd0, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mwa1bc2233_5bb9_4135_88ed_bb51640faec8*mwed1b3928_8d78_44d1_aee7_9d11d6437cfc*mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a-mwb494aae2_da19_4ac0_96e2_0dcd9440edc2*mw56dff932_134c_4d88_a611_daad00623fd0)
mw8db06baf_d8bb_4a1a_b415_2d51fa1e53ba = 0.2 Reaction: mw166e3335_56c3_41ef_af0f_b583860991c1 => mw7086a13a_619e_4069_b163_d8a05fc55f42 + mwfed0682b_39f1_4b09_94e8_c45a51744092, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mw8db06baf_d8bb_4a1a_b415_2d51fa1e53ba*mw166e3335_56c3_41ef_af0f_b583860991c1
mwc52aebc2_571c_4f96_84ee_0613ae73db89 = 0.01; mw9330e49a_b214_4807_b614_4241a4a12c43 = 0.01 Reaction: mwa2c44a01_28c9_4dbd_b034_364f9b5b6cc3 + mwfe9ed415_d5af_469c_a549_d8981f1eb01f => mwd794c746_c826_4ba1_9e09_a9d1e122d925, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mwc52aebc2_571c_4f96_84ee_0613ae73db89*mwa2c44a01_28c9_4dbd_b034_364f9b5b6cc3*mwfe9ed415_d5af_469c_a549_d8981f1eb01f-mw9330e49a_b214_4807_b614_4241a4a12c43*mwd794c746_c826_4ba1_9e09_a9d1e122d925)
mw1a6a8649_d7cb_4379_983a_cca2acac3112 = 2.5 Reaction: mw07c7392b_8d89_4b94_97c5_59f7e256b6f2 => mw1c97b02d_169a_4eb8_bc84_1be57c51a255 + mw2badefa3_32e8_4b66_9e69_245d9ec74e33, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mw1a6a8649_d7cb_4379_983a_cca2acac3112*mw07c7392b_8d89_4b94_97c5_59f7e256b6f2
mwb494aae2_da19_4ac0_96e2_0dcd9440edc2 = 1.0; mw541807fb_7d9f_4788_9f21_cc62846b5826 = 6.25E-5 Reaction: mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a + mw29ba9e7c_6865_4817_8775_be2dbc29651e => mw2075d2cf_955e_4150_98b8_847103c53845, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw541807fb_7d9f_4788_9f21_cc62846b5826*mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a*mw29ba9e7c_6865_4817_8775_be2dbc29651e-mwb494aae2_da19_4ac0_96e2_0dcd9440edc2*mw2075d2cf_955e_4150_98b8_847103c53845)

States:

Name Description
mw8825a609 0983 4fb4 a264 e2f7e43d17b3 [calcium(2+); Serine/threonine-protein phosphatase 2A 65 kDa regulatory subunit A alpha isoform; Serine/threonine-protein phosphatase 2A 55 kDa regulatory subunit B alpha isoform; Serine/threonine-protein phosphatase 2A catalytic subunit alpha isoform; Protein phosphatase 1 regulatory subunit 1B]
mwf46d3666 f0f3 4f05 9603 d7e6bb69005e [3',5'-cyclic AMP; cAMP-specific 3',5'-cyclic phosphodiesterase 4D]
mw8e34c23f 1891 4dc9 8f97 dc2f12a1706c [GTP; Guanine nucleotide-binding protein G(olf) subunit alpha]
mw1041345b f015 436c 9eff 98211008aa1c [cAMP and cAMP-inhibited cGMP 3',5'-cyclic phosphodiesterase 10A]
mw2f3e9c55 e57f 416e b4b1 cc49a26192c0 [Protein phosphatase 1 regulatory subunit 1B]
mw46dccec6 6f0f 40f6 a10c 2f34ae7a005a [ATP]
mw0b46978f b522 4cde 97f0 574cd7dbbae7 [D(1A) dopamine receptor; Guanine nucleotide-binding protein G(I)/G(S)/G(T) subunit beta-1; Guanine nucleotide-binding protein G(olf) subunit alpha; Guanine nucleotide-binding protein G(I)/G(S)/G(O) subunit gamma-2]
mwb80e4fa1 4849 4ed5 b3b0 3e3025c61ad8 [Guanine nucleotide-binding protein G(I)/G(S)/G(T) subunit beta-1; Guanine nucleotide-binding protein G(I)/G(S)/G(O) subunit gamma-2]
mw3e1a2fbf 37b1 490c 9528 6cb6bbf11b21 [acetylcholine]
mw24435476 9c30 4878 b26f 4b3c5a0685c6 [Protein phosphatase 1 regulatory subunit 1B]
mwfed0682b 39f1 4b09 94e8 c45a51744092 [GDP; Guanine nucleotide-binding protein G(olf) subunit alpha]
mw3d9e6efb 8e12 49c9 a87f e067914b951d [cAMP and cAMP-inhibited cGMP 3',5'-cyclic phosphodiesterase 10A]
mw07c7392b 8d89 4b94 97c5 59f7e256b6f2 [calcium(2+); GTP; ATP; Adenylate cyclase type 5; Guanine nucleotide-binding protein G(olf) subunit alpha; Guanine nucleotide-binding protein G(i) subunit alpha-1]
mw7086a13a 619e 4069 b163 d8a05fc55f42 [ATP; Adenylate cyclase type 5]
mwaf471bc1 f98a 4115 b0ee 45c189ea20b5 [Guanine nucleotide-binding protein G(I)/G(S)/G(T) subunit beta-1]
mw619502c3 e319 4e29 a677 b2b5f74fc2cf [calcium(2+); ATP; Adenylate cyclase type 5]
mw56dff932 134c 4d88 a611 daad00623fd0 [calcium(2+); GTP; ATP; Adenylate cyclase type 5; Guanine nucleotide-binding protein G(i) subunit alpha-1]
mw351f6cee 3e64 4b8e 8e60 24b1aca99a92 [Guanine nucleotide-binding protein G(I)/G(S)/G(T) subunit beta-1; Guanine nucleotide-binding protein G(olf) subunit alpha; Guanine nucleotide-binding protein G(I)/G(S)/G(O) subunit gamma-2]
mwd794c746 c826 4ba1 9e09 a9d1e122d925 [GTP; Adenylate cyclase type 5; Guanine nucleotide-binding protein G(olf) subunit alpha; Guanine nucleotide-binding protein G(i) subunit alpha-1]
mw0a10f9cb 3f4b 4bfa ace9 0ecd2bd74b5e [GTP; ATP; Adenylate cyclase type 5; Guanine nucleotide-binding protein G(olf) subunit alpha; Guanine nucleotide-binding protein G(i) subunit alpha-1]
mwa2c44a01 28c9 4dbd b034 364f9b5b6cc3 [GTP; Guanine nucleotide-binding protein G(i) subunit alpha-1]
mw9bcba6bc 9788 4f7f afb5 1c8f3b33c3d1 [GDP; Guanine nucleotide-binding protein G(i) subunit alpha-1]
mw6e845d87 603e 4463 874d 866f554303df [3',5'-cyclic AMP; cAMP and cAMP-inhibited cGMP 3',5'-cyclic phosphodiesterase 10A]
mw081c9f7b 011e 440f 971d d0316d2a1e6c [Serine/threonine-protein phosphatase 2A 65 kDa regulatory subunit A alpha isoform; Serine/threonine-protein phosphatase 2A 55 kDa regulatory subunit B alpha isoform; Serine/threonine-protein phosphatase 2A catalytic subunit alpha isoform]
mwbae3bd11 0ab4 4587 a931 9c5dc5e777ba [calcium(2+); GTP; ATP; Adenylate cyclase type 5; Guanine nucleotide-binding protein G(olf) subunit alpha]
mwde741b91 d5bf 44a9 ad45 404d7259d051 [Serine/threonine-protein phosphatase 2A 65 kDa regulatory subunit A alpha isoform; Serine/threonine-protein phosphatase 2A 55 kDa regulatory subunit B alpha isoform; Serine/threonine-protein phosphatase 2A catalytic subunit alpha isoform; Protein phosphatase 1 regulatory subunit 1B]
mwe4e36b8e 18b8 4c76 bd46 13614b71da5c [acetylcholine; Muscarinic acetylcholine receptor M4; Guanine nucleotide-binding protein G(i) subunit alpha-1; Guanine nucleotide-binding protein G(I)/G(S)/G(T) subunit beta-1; Guanine nucleotide-binding protein G(I)/G(S)/G(O) subunit gamma-2]
mw6b2f1c44 e0be 4406 bcef ad5061d519e4 [dopamine; D(1A) dopamine receptor; Guanine nucleotide-binding protein G(I)/G(S)/G(T) subunit beta-1; Guanine nucleotide-binding protein G(olf) subunit alpha; Guanine nucleotide-binding protein G(I)/G(S)/G(O) subunit gamma-2]
totalActivePKA [3',5'-cyclic AMP; cAMP-dependent protein kinase catalytic subunit alpha; cAMP-dependent protein kinase catalytic subunit beta; cAMP-dependent protein kinase type I-alpha regulatory subunit; cAMP-dependent protein kinase type I-beta regulatory subunit]
mw1f3b8982 3b8c 42b6 8b0f 49b037cbda43 [3',5'-cyclic AMP; cAMP and cAMP-inhibited cGMP 3',5'-cyclic phosphodiesterase 10A]
mw522cacf1 5e61 4b95 8742 cf61cb824893 [IPR006186; Serine/threonine-protein phosphatase 2A 65 kDa regulatory subunit A alpha isoform; Serine/threonine-protein phosphatase 2A 55 kDa regulatory subunit B alpha isoform; Serine/threonine-protein phosphatase 2A catalytic subunit alpha isoform]
mw4179e1ff 9035 4c67 a67c 099e25beb9b0 [cAMP-dependent protein kinase catalytic subunit alpha; cAMP-dependent protein kinase catalytic subunit beta; Protein phosphatase 1 regulatory subunit 1B]

Observables: none

Nair2015 - Interaction between neuromodulators via GPCRs - Effect on cAMP/PKA signaling (D2 Neuron)This model is describ…

Transient changes in striatal dopamine (DA) concentration are considered to encode a reward prediction error (RPE) in reinforcement learning tasks. Often, a phasic DA change occurs concomitantly with a dip in striatal acetylcholine (ACh), whereas other neuromodulators, such as adenosine (Adn), change slowly. There are abundant adenylyl cyclase (AC) coupled GPCRs for these neuromodulators in striatal medium spiny neurons (MSNs), which play important roles in plasticity. However, little is known about the interaction between these neuromodulators via GPCRs. The interaction between these transient neuromodulator changes and the effect on cAMP/PKA signaling via Golf- and Gi/o-coupled GPCR are studied here using quantitative kinetic modeling. The simulations suggest that, under basal conditions, cAMP/PKA signaling could be significantly inhibited in D1R+ MSNs via ACh/M4R/Gi/o and an ACh dip is required to gate a subset of D1R/Golf-dependent PKA activation. Furthermore, the interaction between ACh dip and DA peak, via D1R and M4R, is synergistic. In a similar fashion, PKA signaling in D2+ MSNs is under basal inhibition via D2R/Gi/o and a DA dip leads to a PKA increase by disinhibiting A2aR/Golf, but D2+ MSNs could also respond to the DA peak via other intracellular pathways. This study highlights the similarity between the two types of MSNs in terms of high basal AC inhibition by Gi/o and the importance of interactions between Gi/o and Golf signaling, but at the same time predicts differences between them with regard to the sign of RPE responsible for PKA activation.Dopamine transients are considered to carry reward-related signal in reinforcement learning. An increase in dopamine concentration is associated with an unexpected reward or salient stimuli, whereas a decrease is produced by omission of an expected reward. Often dopamine transients are accompanied by other neuromodulatory signals, such as acetylcholine and adenosine. We highlight the importance of interaction between acetylcholine, dopamine, and adenosine signals via adenylyl-cyclase coupled GPCRs in shaping the dopamine-dependent cAMP/PKA signaling in striatal neurons. Specifically, a dopamine peak and an acetylcholine dip must interact, via D1 and M4 receptor, and a dopamine dip must interact with adenosine tone, via D2 and A2a receptor, in direct and indirect pathway neurons, respectively, to have any significant downstream PKA activation. link: http://identifiers.org/pubmed/26468202

Parameters:

Name Description
mwf633f298_303f_46d1_b644_ae07ae366f45 = 3.0 Reaction: mw6e845d87_603e_4463_874d_866f554303df => mw3d9e6efb_8e12_49c9_a87f_e067914b951d + mw9710c658_a2a1_4f49_b494_af109853f251, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mwf633f298_303f_46d1_b644_ae07ae366f45*mw6e845d87_603e_4463_874d_866f554303df
mwdad9965c_2334_481f_8544_f1a81385a28e = 0.005; mwc23d8bf6_2a60_4760_8bf5_c1bab432ab52 = 1.0 Reaction: A2AR + mwbe974953_e869_4622_b4a8_745555c8d7fd => A2ARAdn, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mwdad9965c_2334_481f_8544_f1a81385a28e*A2AR*mwbe974953_e869_4622_b4a8_745555c8d7fd-mwc23d8bf6_2a60_4760_8bf5_c1bab432ab52*A2ARAdn)
mw515fcf69_b724_40d9_84ba_5f92d75ae5a7 = 1.5E-4 Reaction: mw1c97b02d_169a_4eb8_bc84_1be57c51a255 + mw7df45520_98cc_4c0b_91a7_c6e7297de98a => mw619502c3_e319_4e29_a677_b2b5f74fc2cf, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mw515fcf69_b724_40d9_84ba_5f92d75ae5a7*mw1c97b02d_169a_4eb8_bc84_1be57c51a255*mw7df45520_98cc_4c0b_91a7_c6e7297de98a
mw80292f32_fd53_4b5d_872a_e21c2d90c52a = 0.1; mwce0df80f_1563_453d_b33d_a88f6b2c93b7 = 200.0 Reaction: mw3e1a2fbf_37b1_490c_9528_6cb6bbf11b21 + mwf82770b9_766a_4c4e_851a_d76da19e8517 => mw9d5c5c9d_301d_4e43_ba7b_7d21ccbdc2c2, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw80292f32_fd53_4b5d_872a_e21c2d90c52a*mw3e1a2fbf_37b1_490c_9528_6cb6bbf11b21*mwf82770b9_766a_4c4e_851a_d76da19e8517-mwce0df80f_1563_453d_b33d_a88f6b2c93b7*mw9d5c5c9d_301d_4e43_ba7b_7d21ccbdc2c2)
mwfe873584_629a_46c8_aae9_fdacdb9ad266 = 0.1; mwf3c85708_890c_45d1_bcbc_fe90e9ca792f = 10.0 Reaction: mwd1171b65_ed6c_4413_bf47_5ed80038a7bd + mwccd3a17c_e207_4663_9b16_327b78882497 => mw4855b1cd_d7bc_4072_9736_dca30bbe448d, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mwfe873584_629a_46c8_aae9_fdacdb9ad266*mwd1171b65_ed6c_4413_bf47_5ed80038a7bd*mwccd3a17c_e207_4663_9b16_327b78882497-mwf3c85708_890c_45d1_bcbc_fe90e9ca792f*mw4855b1cd_d7bc_4072_9736_dca30bbe448d)
mwdb2a670f_13fb_4bda_8c72_d706c6bc37e9 = 2.8125E-5 Reaction: mw1c97b02d_169a_4eb8_bc84_1be57c51a255 + mwed1b3928_8d78_44d1_aee7_9d11d6437cfc => mw56dff932_134c_4d88_a611_daad00623fd0, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mwdb2a670f_13fb_4bda_8c72_d706c6bc37e9*mw1c97b02d_169a_4eb8_bc84_1be57c51a255*mwed1b3928_8d78_44d1_aee7_9d11d6437cfc
mw0b1ccae3_37fa_4a23_a817_cd8fc458dc79 = 0.1; mw6f753a0e_a7ec_4b4b_bcfc_edb95a3f1296 = 2.0 Reaction: mw1c97b02d_169a_4eb8_bc84_1be57c51a255 + mw3d9e6efb_8e12_49c9_a87f_e067914b951d => mw6e845d87_603e_4463_874d_866f554303df, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw0b1ccae3_37fa_4a23_a817_cd8fc458dc79*mw1c97b02d_169a_4eb8_bc84_1be57c51a255*mw3d9e6efb_8e12_49c9_a87f_e067914b951d-mw6f753a0e_a7ec_4b4b_bcfc_edb95a3f1296*mw6e845d87_603e_4463_874d_866f554303df)
mw9510e553_a7fd_4c9a_b284_19b3cc01ae7d = 7.5E-5; mwb494aae2_da19_4ac0_96e2_0dcd9440edc2 = 1.0 Reaction: mw7df45520_98cc_4c0b_91a7_c6e7297de98a + mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a => mw619502c3_e319_4e29_a677_b2b5f74fc2cf, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw9510e553_a7fd_4c9a_b284_19b3cc01ae7d*mw7df45520_98cc_4c0b_91a7_c6e7297de98a*mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a-mwb494aae2_da19_4ac0_96e2_0dcd9440edc2*mw619502c3_e319_4e29_a677_b2b5f74fc2cf)
mw009f9583_4e96_4672_ab71_0ef4b697aa6f = 6.4; mw2226fa14_2b95_45a6_8705_4b38073fc5f7 = 8.0E-4 Reaction: mw522cacf1_5e61_4b95_8742_cf61cb824893 + mw1184c368_03fc_435a_9086_dc6ed3067935 => mw0459271f_3b39_40a4_948f_aed773482cfc, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw2226fa14_2b95_45a6_8705_4b38073fc5f7*mw522cacf1_5e61_4b95_8742_cf61cb824893*mw1184c368_03fc_435a_9086_dc6ed3067935-mw009f9583_4e96_4672_ab71_0ef4b697aa6f*mw0459271f_3b39_40a4_948f_aed773482cfc)
mw066c69e2_66da_4621_9180_bce71b7077c3 = 12.0; mwefa9bb47_f13f_4a21_a62d_a4debcf7b52b = 200.0 Reaction: mw3e1a2fbf_37b1_490c_9528_6cb6bbf11b21 + mwd86ce0dc_7329_4b27_9de0_ee6bffee3083 => mwe4e36b8e_18b8_4c76_bd46_13614b71da5c, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw066c69e2_66da_4621_9180_bce71b7077c3*mw3e1a2fbf_37b1_490c_9528_6cb6bbf11b21*mwd86ce0dc_7329_4b27_9de0_ee6bffee3083-mwefa9bb47_f13f_4a21_a62d_a4debcf7b52b*mwe4e36b8e_18b8_4c76_bd46_13614b71da5c)
mw1b9e5266_efac_4696_a213_80f9f83d948a = 9.0E-4; mwa27c20d8_b6ed_4617_a6f6_9af2752d3a33 = 2.0 Reaction: mw32351ce4_eaaf_4827_8efa_342224548d8a + mw24435476_9c30_4878_b26f_4b3c5a0685c6 => mw0130a500_18e9_470f_9fac_70af44dc4a9e, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw1b9e5266_efac_4696_a213_80f9f83d948a*mw32351ce4_eaaf_4827_8efa_342224548d8a*mw24435476_9c30_4878_b26f_4b3c5a0685c6-mwa27c20d8_b6ed_4617_a6f6_9af2752d3a33*mw0130a500_18e9_470f_9fac_70af44dc4a9e)
ModelValue_131 = 0.0; ModelValue_124 = 0.0; DAdip = 10.0; DApeak = 10.0; ModelValue_138 = 10.0 Reaction: mw3e1a2fbf_37b1_490c_9528_6cb6bbf11b21 = ((1-ModelValue_124)-ModelValue_131)*ModelValue_138+ModelValue_124*DAdip+ModelValue_131*DApeak, Rate Law: missing
mwb494aae2_da19_4ac0_96e2_0dcd9440edc2 = 1.0; mw00f3118f_5d5a_48d0_bcc4_749d5f9dc73a = 1.75E-4 Reaction: mw2badefa3_32e8_4b66_9e69_245d9ec74e33 + mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a => mw07c7392b_8d89_4b94_97c5_59f7e256b6f2, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw00f3118f_5d5a_48d0_bcc4_749d5f9dc73a*mw2badefa3_32e8_4b66_9e69_245d9ec74e33*mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a-mwb494aae2_da19_4ac0_96e2_0dcd9440edc2*mw07c7392b_8d89_4b94_97c5_59f7e256b6f2)
mw0dd72d64_80e1_4f76_a444_fd175dbfab0c = 30.0 Reaction: A2ARAdnGolf => mwaf471bc1_f98a_4115_b0ee_45c189ea20b5 + A2ARAdn + mw8e34c23f_1891_4dc9_8f97_dc2f12a1706c, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mw0dd72d64_80e1_4f76_a444_fd175dbfab0c*A2ARAdnGolf
mwfcfb91ff_a495_41f9_bdff_fcef779112fd = 30.0 Reaction: mwa2c44a01_28c9_4dbd_b034_364f9b5b6cc3 => mw9bcba6bc_9788_4f7f_afb5_1c8f3b33c3d1, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mwfcfb91ff_a495_41f9_bdff_fcef779112fd*mwa2c44a01_28c9_4dbd_b034_364f9b5b6cc3
mw5301f7f5_60df_4eb9_ba3b_81e6519d1cbb = 5.0 Reaction: mw0a10f9cb_3f4b_4bfa_ace9_0ecd2bd74b5e => mw1c97b02d_169a_4eb8_bc84_1be57c51a255 + mwd794c746_c826_4ba1_9e09_a9d1e122d925, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mw5301f7f5_60df_4eb9_ba3b_81e6519d1cbb*mw0a10f9cb_3f4b_4bfa_ace9_0ecd2bd74b5e
mw034d8151_fae1_4738_b675_39c38a58118d = 0.022 Reaction: mw1c97b02d_169a_4eb8_bc84_1be57c51a255 + mw42919ead_5972_4151_85ac_fcc88ca105a6 => mwbae3bd11_0ab4_4587_a931_9c5dc5e777ba, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mw034d8151_fae1_4738_b675_39c38a58118d*mw1c97b02d_169a_4eb8_bc84_1be57c51a255*mw42919ead_5972_4151_85ac_fcc88ca105a6
mwca52f04a_bb5f_4d3f_ba6d_939bbb3895b9 = 2.0; mw326e0065_b4f6_41ae_b1d0_66092dc5ebb2 = 0.13 Reaction: mw1c97b02d_169a_4eb8_bc84_1be57c51a255 + mw1041345b_f015_436c_9eff_98211008aa1c => mw1f3b8982_3b8c_42b6_8b0f_49b037cbda43, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw326e0065_b4f6_41ae_b1d0_66092dc5ebb2*mw1c97b02d_169a_4eb8_bc84_1be57c51a255*mw1041345b_f015_436c_9eff_98211008aa1c-mwca52f04a_bb5f_4d3f_ba6d_939bbb3895b9*mw1f3b8982_3b8c_42b6_8b0f_49b037cbda43)
mwb494aae2_da19_4ac0_96e2_0dcd9440edc2 = 1.0; mw72ceb3da_d538_4f25_8e69_f322eb0b5e57 = 0.00105 Reaction: mwfe9ed415_d5af_469c_a549_d8981f1eb01f + mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a => mw166e3335_56c3_41ef_af0f_b583860991c1, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw72ceb3da_d538_4f25_8e69_f322eb0b5e57*mwfe9ed415_d5af_469c_a549_d8981f1eb01f*mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a-mwb494aae2_da19_4ac0_96e2_0dcd9440edc2*mw166e3335_56c3_41ef_af0f_b583860991c1)
mw88c9326a_fbe9_4dd8_aded_b5be3f012691 = 2.3 Reaction: mwde741b91_d5bf_44a9_ad45_404d7259d051 => mw081c9f7b_011e_440f_971d_d0316d2a1e6c + mw24435476_9c30_4878_b26f_4b3c5a0685c6, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mw88c9326a_fbe9_4dd8_aded_b5be3f012691*mwde741b91_d5bf_44a9_ad45_404d7259d051
mw65cae8fe_0eac_4792_88bf_2dfb441030e5 = 0.5 Reaction: mw619502c3_e319_4e29_a677_b2b5f74fc2cf => mw1c97b02d_169a_4eb8_bc84_1be57c51a255 + mw7df45520_98cc_4c0b_91a7_c6e7297de98a, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mw65cae8fe_0eac_4792_88bf_2dfb441030e5*mw619502c3_e319_4e29_a677_b2b5f74fc2cf
mwac1bc66c_2623_47e6_a76d_c1629d962be5 = 10.0 Reaction: mw1f3b8982_3b8c_42b6_8b0f_49b037cbda43 => mw1041345b_f015_436c_9eff_98211008aa1c + mw9710c658_a2a1_4f49_b494_af109853f251, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mwac1bc66c_2623_47e6_a76d_c1629d962be5*mw1f3b8982_3b8c_42b6_8b0f_49b037cbda43
mwe737a297_e5be_46ed_af75_ccc7428c3977 = 0.001; mwddcb8d81_9f5a_457e_a54c_a0c1b1f29f0b = 0.9 Reaction: mw724f1afe_8032_40ae_96ca_808ab7b8b943 + mwccd3a17c_e207_4663_9b16_327b78882497 => mw7df45520_98cc_4c0b_91a7_c6e7297de98a, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mwe737a297_e5be_46ed_af75_ccc7428c3977*mw724f1afe_8032_40ae_96ca_808ab7b8b943*mwccd3a17c_e207_4663_9b16_327b78882497-mwddcb8d81_9f5a_457e_a54c_a0c1b1f29f0b*mw7df45520_98cc_4c0b_91a7_c6e7297de98a)
mw2f090a45_946b_4587_a3e3_b29f3bb5c6ae = 100.0 Reaction: mwfed0682b_39f1_4b09_94e8_c45a51744092 + mwaf471bc1_f98a_4115_b0ee_45c189ea20b5 => mw351f6cee_3e64_4b8e_8e60_24b1aca99a92, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mw2f090a45_946b_4587_a3e3_b29f3bb5c6ae*mwfed0682b_39f1_4b09_94e8_c45a51744092*mwaf471bc1_f98a_4115_b0ee_45c189ea20b5
mwcd307ee9_33da_4303_9c28_644ad2d1630c = 0.1; mw0a255671_d9ca_4384_a153_ce17e1111453 = 0.2 Reaction: mw724f1afe_8032_40ae_96ca_808ab7b8b943 + mw8e34c23f_1891_4dc9_8f97_dc2f12a1706c => mwfe9ed415_d5af_469c_a549_d8981f1eb01f, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw0a255671_d9ca_4384_a153_ce17e1111453*mw724f1afe_8032_40ae_96ca_808ab7b8b943*mw8e34c23f_1891_4dc9_8f97_dc2f12a1706c-mwcd307ee9_33da_4303_9c28_644ad2d1630c*mwfe9ed415_d5af_469c_a549_d8981f1eb01f)
mw68039b16_b516_4fba_bedd_d4bbc1a23a02 = 9.1; mw894b221b_266d_4277_ac01_83579ed103e6 = 0.006 Reaction: mw4b358131_010c_4545_ac4a_13a6c8bc34c4 + mwccd3a17c_e207_4663_9b16_327b78882497 => mw65a14789_ffcf_4bfd_9d53_d2eb2f4d0896, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw894b221b_266d_4277_ac01_83579ed103e6*mw4b358131_010c_4545_ac4a_13a6c8bc34c4*mwccd3a17c_e207_4663_9b16_327b78882497-mw68039b16_b516_4fba_bedd_d4bbc1a23a02*mw65a14789_ffcf_4bfd_9d53_d2eb2f4d0896)
mwd1b16e73_4fcb_4e4c_9804_3137259ba749 = 1.0E-6; mw36cb62c6_0b3c_4d1b_9001_3b37aa7477e2 = 9.0 Reaction: mw3d9e6efb_8e12_49c9_a87f_e067914b951d + mw1c97b02d_169a_4eb8_bc84_1be57c51a255 => mw1041345b_f015_436c_9eff_98211008aa1c, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mwd1b16e73_4fcb_4e4c_9804_3137259ba749*mw3d9e6efb_8e12_49c9_a87f_e067914b951d*mw1c97b02d_169a_4eb8_bc84_1be57c51a255^2-mw36cb62c6_0b3c_4d1b_9001_3b37aa7477e2*mw1041345b_f015_436c_9eff_98211008aa1c)
mw8186cb1d_66c4_4855_bcbb_82d75173ae8a = 20.0 Reaction: mw166e3335_56c3_41ef_af0f_b583860991c1 => mw1c97b02d_169a_4eb8_bc84_1be57c51a255 + mwfe9ed415_d5af_469c_a549_d8981f1eb01f, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mw8186cb1d_66c4_4855_bcbb_82d75173ae8a*mw166e3335_56c3_41ef_af0f_b583860991c1
mwb494aae2_da19_4ac0_96e2_0dcd9440edc2 = 1.0; mwb56b5ab7_47cc_4fbc_b68b_dfdc6be6d7a4 = 5.5E-4 Reaction: mw42919ead_5972_4151_85ac_fcc88ca105a6 + mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a => mwbae3bd11_0ab4_4587_a931_9c5dc5e777ba, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mwb56b5ab7_47cc_4fbc_b68b_dfdc6be6d7a4*mw42919ead_5972_4151_85ac_fcc88ca105a6*mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a-mwb494aae2_da19_4ac0_96e2_0dcd9440edc2*mwbae3bd11_0ab4_4587_a931_9c5dc5e777ba)
mwb0a6bd5e_87a0_425c_a5c7_ea69903e0bf3 = 10.0 Reaction: mwbae3bd11_0ab4_4587_a931_9c5dc5e777ba => mw1c97b02d_169a_4eb8_bc84_1be57c51a255 + mw42919ead_5972_4151_85ac_fcc88ca105a6, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mwb0a6bd5e_87a0_425c_a5c7_ea69903e0bf3*mwbae3bd11_0ab4_4587_a931_9c5dc5e777ba
mw649b47b3_4c3a_4ac9_ae94_5c38ccf81e39 = 0.002; mwc911f28c_b62f_4269_84ed_d852f6da24f9 = 0.01 Reaction: mw8e34c23f_1891_4dc9_8f97_dc2f12a1706c + mw2075d2cf_955e_4150_98b8_847103c53845 => mw0a10f9cb_3f4b_4bfa_ace9_0ecd2bd74b5e, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw649b47b3_4c3a_4ac9_ae94_5c38ccf81e39*mw8e34c23f_1891_4dc9_8f97_dc2f12a1706c*mw2075d2cf_955e_4150_98b8_847103c53845-mwc911f28c_b62f_4269_84ed_d852f6da24f9*mw0a10f9cb_3f4b_4bfa_ace9_0ecd2bd74b5e)
mw6bf18344_b899_4a62_ac8d_5f8bdd4bbe2f = 8.0 Reaction: mw3a3e53fb_bbbf_4433_9f75_a12610dbc312 => mw9417144e_14b1_40d9_bd4b_ccd9f4714305 + mw24435476_9c30_4878_b26f_4b3c5a0685c6, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mw6bf18344_b899_4a62_ac8d_5f8bdd4bbe2f*mw3a3e53fb_bbbf_4433_9f75_a12610dbc312
mw5623544e_e7e1_439f_88d3_3b0cbea8ccf5 = 30.0 Reaction: mw8e34c23f_1891_4dc9_8f97_dc2f12a1706c => mwfed0682b_39f1_4b09_94e8_c45a51744092, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mw5623544e_e7e1_439f_88d3_3b0cbea8ccf5*mw8e34c23f_1891_4dc9_8f97_dc2f12a1706c
mwa466eec8_9bc0_44d5_8027_d5925b378429 = 1.0; mw448bd49f_40ad_46c9_81f6_3494057dc37d = 0.005 Reaction: A2AR + mw351f6cee_3e64_4b8e_8e60_24b1aca99a92 => mw0b46978f_b522_4cde_97f0_574cd7dbbae7, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw448bd49f_40ad_46c9_81f6_3494057dc37d*A2AR*mw351f6cee_3e64_4b8e_8e60_24b1aca99a92-mwa466eec8_9bc0_44d5_8027_d5925b378429*mw0b46978f_b522_4cde_97f0_574cd7dbbae7)
mwb494aae2_da19_4ac0_96e2_0dcd9440edc2 = 1.0; mwb93138ce_a80b_4b26_b927_6b4a00651b64 = 3.0E-4 Reaction: mwd794c746_c826_4ba1_9e09_a9d1e122d925 + mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a => mw0a10f9cb_3f4b_4bfa_ace9_0ecd2bd74b5e, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mwb93138ce_a80b_4b26_b927_6b4a00651b64*mwd794c746_c826_4ba1_9e09_a9d1e122d925*mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a-mwb494aae2_da19_4ac0_96e2_0dcd9440edc2*mw0a10f9cb_3f4b_4bfa_ace9_0ecd2bd74b5e)
mwed967767_31e4_4e9e_8117_5372f9f4f79a = 3.0 Reaction: mw0130a500_18e9_470f_9fac_70af44dc4a9e => mw32351ce4_eaaf_4827_8efa_342224548d8a + mw1184c368_03fc_435a_9086_dc6ed3067935, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mwed967767_31e4_4e9e_8117_5372f9f4f79a*mw0130a500_18e9_470f_9fac_70af44dc4a9e
mw8db06baf_d8bb_4a1a_b415_2d51fa1e53ba = 0.25 Reaction: mwfe9ed415_d5af_469c_a549_d8981f1eb01f => mw724f1afe_8032_40ae_96ca_808ab7b8b943 + mwfed0682b_39f1_4b09_94e8_c45a51744092, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mw8db06baf_d8bb_4a1a_b415_2d51fa1e53ba*mwfe9ed415_d5af_469c_a549_d8981f1eb01f
mwcabc0868_2435_4850_964b_e3ddee39f5ad = 30.0 Reaction: mw56dff932_134c_4d88_a611_daad00623fd0 => mw619502c3_e319_4e29_a677_b2b5f74fc2cf + mw9bcba6bc_9788_4f7f_afb5_1c8f3b33c3d1, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mwcabc0868_2435_4850_964b_e3ddee39f5ad*mw56dff932_134c_4d88_a611_daad00623fd0
mwe4474191_0c92_406c_a6f5_4a167f541d36 = 0.25 Reaction: mw2075d2cf_955e_4150_98b8_847103c53845 => mw1c97b02d_169a_4eb8_bc84_1be57c51a255 + mw29ba9e7c_6865_4817_8775_be2dbc29651e, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mwe4474191_0c92_406c_a6f5_4a167f541d36*mw2075d2cf_955e_4150_98b8_847103c53845
mw6ae3f7a6_bf58_475e_930e_6bf7a79f3761 = 5.0; mw1ef56a9a_9d9b_4490_8fcd_53b7e50bf5d6 = 50.0 Reaction: mw619502c3_e319_4e29_a677_b2b5f74fc2cf + mwa2c44a01_28c9_4dbd_b034_364f9b5b6cc3 => mw56dff932_134c_4d88_a611_daad00623fd0, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw1ef56a9a_9d9b_4490_8fcd_53b7e50bf5d6*mw619502c3_e319_4e29_a677_b2b5f74fc2cf*mwa2c44a01_28c9_4dbd_b034_364f9b5b6cc3-mw6ae3f7a6_bf58_475e_930e_6bf7a79f3761*mw56dff932_134c_4d88_a611_daad00623fd0)
mwd05b4199_53ad_4807_9a8c_d93ce35be857 = 60.0 Reaction: mwe4e36b8e_18b8_4c76_bd46_13614b71da5c => mwa2c44a01_28c9_4dbd_b034_364f9b5b6cc3 + mw9d5c5c9d_301d_4e43_ba7b_7d21ccbdc2c2 + mwb80e4fa1_4849_4ed5_b3b0_3e3025c61ad8, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mwd05b4199_53ad_4807_9a8c_d93ce35be857*mwe4e36b8e_18b8_4c76_bd46_13614b71da5c
mw17d612a2_c9d5_4251_8122_5f037fc630e3 = 0.00105 Reaction: mw1c97b02d_169a_4eb8_bc84_1be57c51a255 + mw29ba9e7c_6865_4817_8775_be2dbc29651e => mw2075d2cf_955e_4150_98b8_847103c53845, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mw17d612a2_c9d5_4251_8122_5f037fc630e3*mw1c97b02d_169a_4eb8_bc84_1be57c51a255*mw29ba9e7c_6865_4817_8775_be2dbc29651e
mw9c2302f8_3d47_4247_a338_a02c53fc5331 = 1.0E-4; mwb494aae2_da19_4ac0_96e2_0dcd9440edc2 = 1.0 Reaction: mw724f1afe_8032_40ae_96ca_808ab7b8b943 + mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a => mw7086a13a_619e_4069_b163_d8a05fc55f42, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw9c2302f8_3d47_4247_a338_a02c53fc5331*mw724f1afe_8032_40ae_96ca_808ab7b8b943*mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a-mwb494aae2_da19_4ac0_96e2_0dcd9440edc2*mw7086a13a_619e_4069_b163_d8a05fc55f42)
mwffa5af7e_9155_4942_9424_cd94ac5018ed = 0.055; mw0060906c_a035_468c_aa1c_130959bcf15a = 200.0 Reaction: mwf82770b9_766a_4c4e_851a_d76da19e8517 + mwd8ea533a_c66e_4de4_8c5c_0d4201d8c8a2 => mwd86ce0dc_7329_4b27_9de0_ee6bffee3083, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mwffa5af7e_9155_4942_9424_cd94ac5018ed*mwf82770b9_766a_4c4e_851a_d76da19e8517*mwd8ea533a_c66e_4de4_8c5c_0d4201d8c8a2-mw0060906c_a035_468c_aa1c_130959bcf15a*mwd86ce0dc_7329_4b27_9de0_ee6bffee3083)
mw2037ae7c_b1dc_4517_a61c_88a1f2bdcd12 = 1.0; mw49e66b9c_64bd_428a_9090_15e4132e9781 = 3.7E-4 Reaction: mw1184c368_03fc_435a_9086_dc6ed3067935 + mw68d3f409_9462_4515_8c07_bc105fa0eaf1 => mwb320746f_6a8c_4c8b_ae55_23db454339d8, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw49e66b9c_64bd_428a_9090_15e4132e9781*mw1184c368_03fc_435a_9086_dc6ed3067935*mw68d3f409_9462_4515_8c07_bc105fa0eaf1-mw2037ae7c_b1dc_4517_a61c_88a1f2bdcd12*mwb320746f_6a8c_4c8b_ae55_23db454339d8)
mwc728d91d_7616_43db_bd1d_55e49e9c026a = 0.125 Reaction: mw56dff932_134c_4d88_a611_daad00623fd0 => mw1c97b02d_169a_4eb8_bc84_1be57c51a255 + mwed1b3928_8d78_44d1_aee7_9d11d6437cfc, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mwc728d91d_7616_43db_bd1d_55e49e9c026a*mw56dff932_134c_4d88_a611_daad00623fd0
mwb494aae2_da19_4ac0_96e2_0dcd9440edc2 = 1.0; mwa1bc2233_5bb9_4135_88ed_bb51640faec8 = 5.625E-5 Reaction: mwed1b3928_8d78_44d1_aee7_9d11d6437cfc + mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a => mw56dff932_134c_4d88_a611_daad00623fd0, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mwa1bc2233_5bb9_4135_88ed_bb51640faec8*mwed1b3928_8d78_44d1_aee7_9d11d6437cfc*mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a-mwb494aae2_da19_4ac0_96e2_0dcd9440edc2*mw56dff932_134c_4d88_a611_daad00623fd0)
mwb494aae2_da19_4ac0_96e2_0dcd9440edc2 = 1.0; mw541807fb_7d9f_4788_9f21_cc62846b5826 = 6.25E-5 Reaction: mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a + mw29ba9e7c_6865_4817_8775_be2dbc29651e => mw2075d2cf_955e_4150_98b8_847103c53845, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mw541807fb_7d9f_4788_9f21_cc62846b5826*mw46dccec6_6f0f_40f6_a10c_2f34ae7a005a*mw29ba9e7c_6865_4817_8775_be2dbc29651e-mwb494aae2_da19_4ac0_96e2_0dcd9440edc2*mw2075d2cf_955e_4150_98b8_847103c53845)
mwc52aebc2_571c_4f96_84ee_0613ae73db89 = 0.01; mw9330e49a_b214_4807_b614_4241a4a12c43 = 0.01 Reaction: mwa2c44a01_28c9_4dbd_b034_364f9b5b6cc3 + mwfe9ed415_d5af_469c_a549_d8981f1eb01f => mwd794c746_c826_4ba1_9e09_a9d1e122d925, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*(mwc52aebc2_571c_4f96_84ee_0613ae73db89*mwa2c44a01_28c9_4dbd_b034_364f9b5b6cc3*mwfe9ed415_d5af_469c_a549_d8981f1eb01f-mw9330e49a_b214_4807_b614_4241a4a12c43*mwd794c746_c826_4ba1_9e09_a9d1e122d925)
mw1a6a8649_d7cb_4379_983a_cca2acac3112 = 2.5 Reaction: mw07c7392b_8d89_4b94_97c5_59f7e256b6f2 => mw1c97b02d_169a_4eb8_bc84_1be57c51a255 + mw2badefa3_32e8_4b66_9e69_245d9ec74e33, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mw1a6a8649_d7cb_4379_983a_cca2acac3112*mw07c7392b_8d89_4b94_97c5_59f7e256b6f2
mw3a56b314_299f_48d2_a179_97bf6a30f38f = 0.006 Reaction: mw9417144e_14b1_40d9_bd4b_ccd9f4714305 => mw081c9f7b_011e_440f_971d_d0316d2a1e6c, Rate Law: mw26af457f_7462_4410_a392_e0bbb6071ea5*mw3a56b314_299f_48d2_a179_97bf6a30f38f*mw9417144e_14b1_40d9_bd4b_ccd9f4714305

States:

Name Description
mw1041345b f015 436c 9eff 98211008aa1c [cAMP and cAMP-inhibited cGMP 3',5'-cyclic phosphodiesterase 10A]
mw1c97b02d 169a 4eb8 bc84 1be57c51a255 [3',5'-cyclic AMP]
mwed1b3928 8d78 44d1 aee7 9d11d6437cfc [calcium(2+); GTP; Adenylate cyclase type 5; Guanine nucleotide-binding protein G(i) subunit alpha-1]
mw46dccec6 6f0f 40f6 a10c 2f34ae7a005a [ATP]
mw3e1a2fbf 37b1 490c 9528 6cb6bbf11b21 [dopamine]
mw2badefa3 32e8 4b66 9e69 245d9ec74e33 [calcium(2+); GTP; Adenylate cyclase type 5; Guanine nucleotide-binding protein G(olf) subunit alpha; Guanine nucleotide-binding protein G(i) subunit alpha-1]
mwd86ce0dc 7329 4b27 9de0 ee6bffee3083 [D(2) dopamine receptor; Guanine nucleotide-binding protein G(i) subunit alpha-1; Guanine nucleotide-binding protein G(I)/G(S)/G(T) subunit beta-1; Guanine nucleotide-binding protein G(I)/G(S)/G(O) subunit gamma-2]
A2AR [Adenosine receptor A2a]
mw3d9e6efb 8e12 49c9 a87f e067914b951d [cAMP and cAMP-inhibited cGMP 3',5'-cyclic phosphodiesterase 10A]
mwfed0682b 39f1 4b09 94e8 c45a51744092 [GDP; Guanine nucleotide-binding protein G(olf) subunit alpha]
mw07c7392b 8d89 4b94 97c5 59f7e256b6f2 [calcium(2+); GTP; ATP; Adenylate cyclase type 5; Guanine nucleotide-binding protein G(olf) subunit alpha; Guanine nucleotide-binding protein G(i) subunit alpha-1]
mwccd3a17c e207 4663 9b16 327b78882497 [calcium(2+)]
mw32351ce4 eaaf 4827 8efa 342224548d8a [Cyclin-dependent-like kinase 5]
mw619502c3 e319 4e29 a677 b2b5f74fc2cf [calcium(2+); ATP; Adenylate cyclase type 5]
mw56dff932 134c 4d88 a611 daad00623fd0 [calcium(2+); GTP; ATP; Adenylate cyclase type 5; Guanine nucleotide-binding protein G(i) subunit alpha-1]
mw9417144e 14b1 40d9 bd4b ccd9f4714305 [Serine/threonine-protein phosphatase 2A 65 kDa regulatory subunit A alpha isoform; Serine/threonine-protein phosphatase 2A 55 kDa regulatory subunit B alpha isoform; Serine/threonine-protein phosphatase 2A catalytic subunit alpha isoform]
mw0a10f9cb 3f4b 4bfa ace9 0ecd2bd74b5e [GTP; ATP; Adenylate cyclase type 5; Guanine nucleotide-binding protein G(olf) subunit alpha; Guanine nucleotide-binding protein G(i) subunit alpha-1]
mw724f1afe 8032 40ae 96ca 808ab7b8b943 [Adenylate cyclase type 5]
A2ARAdnGolf [adenosine; Adenosine receptor A2a; Guanine nucleotide-binding protein G(I)/G(S)/G(T) subunit beta-1; Guanine nucleotide-binding protein G(olf) subunit alpha; Guanine nucleotide-binding protein G(I)/G(S)/G(O) subunit gamma-2]
mwa2c44a01 28c9 4dbd b034 364f9b5b6cc3 [GTP; Guanine nucleotide-binding protein G(i) subunit alpha-1]
mw6e845d87 603e 4463 874d 866f554303df [3',5'-cyclic AMP; cAMP and cAMP-inhibited cGMP 3',5'-cyclic phosphodiesterase 10A]
mw9bcba6bc 9788 4f7f afb5 1c8f3b33c3d1 [GDP; Guanine nucleotide-binding protein G(i) subunit alpha-1]
A2ARAdn [adenosine; Adenosine receptor A2a]
mw29ba9e7c 6865 4817 8775 be2dbc29651e [GTP; Adenylate cyclase type 5; Guanine nucleotide-binding protein G(i) subunit alpha-1]
mw081c9f7b 011e 440f 971d d0316d2a1e6c [Serine/threonine-protein phosphatase 2A 65 kDa regulatory subunit A alpha isoform; Serine/threonine-protein phosphatase 2A 55 kDa regulatory subunit B alpha isoform; Serine/threonine-protein phosphatase 2A catalytic subunit alpha isoform]
mwd794c746 c826 4ba1 9e09 a9d1e122d925 [GTP; Adenylate cyclase type 5; Guanine nucleotide-binding protein G(olf) subunit alpha; Guanine nucleotide-binding protein G(i) subunit alpha-1]
mw7df45520 98cc 4c0b 91a7 c6e7297de98a [calcium(2+); Adenylate cyclase type 5]
mw0130a500 18e9 470f 9fac 70af44dc4a9e [Cyclin-dependent-like kinase 5; Protein phosphatase 1 regulatory subunit 1B]
mwfe9ed415 d5af 469c a549 d8981f1eb01f [GTP; Adenylate cyclase type 5; Guanine nucleotide-binding protein G(olf) subunit alpha]
mwf82770b9 766a 4c4e 851a d76da19e8517 [D(2) dopamine receptor]
mw42919ead 5972 4151 85ac fcc88ca105a6 [calcium(2+); GTP; Adenylate cyclase type 5; Guanine nucleotide-binding protein G(olf) subunit alpha]
mw1184c368 03fc 435a 9086 dc6ed3067935 [Protein phosphatase 1 regulatory subunit 1B]
mw1f3b8982 3b8c 42b6 8b0f 49b037cbda43 [3',5'-cyclic AMP; cAMP and cAMP-inhibited cGMP 3',5'-cyclic phosphodiesterase 10A]

Observables: none

BIOMD0000000251 @ v0.0.1

This model describes the activation of immediate early genes such as cFos after EGF or heregulin (HRG) stimulation of th…

Activation of ErbB receptors by epidermal growth factor (EGF) or heregulin (HRG) determines distinct cell-fate decisions, although signals propagate through shared pathways. Using mathematical modeling and experimental approaches, we unravel how HRG and EGF generate distinct, all-or-none responses of the phosphorylated transcription factor c-Fos. In the cytosol, EGF induces transient and HRG induces sustained ERK activation. In the nucleus, however, ERK activity and c-fos mRNA expression are transient for both ligands. Knockdown of dual-specificity phosphatases extends HRG-stimulated nuclear ERK activation, but not c-fos mRNA expression, implying the existence of a HRG-induced repressor of c-fos transcription. Further experiments confirmed that this repressor is mainly induced by HRG, but not EGF, and requires new protein synthesis. We show how a spatially distributed, signaling-transcription cascade robustly discriminates between transient and sustained ERK activities at the c-Fos system level. The proposed control mechanisms are general and operate in different cell types, stimulated by various ligands. link: http://identifiers.org/pubmed/20493519

Parameters:

Name Description
tau1 = 3.07; L = 1.0; K1 = 1.09 Reaction: => x1, Rate Law: compartment*((-x1)/tau1+K1*L/tau1)
k6=0.13; k7 = 0.5; n=1.1 Reaction: => cFOSp; ppERKn, pRSKn, Rate Law: compartment*((ppERKn*pRSKn)^n/(k6^n+(ppERKn*pRSKn)^n)-k7*cFOSp)
k8=0.08; k7 = 0.5 Reaction: => cFOSm; cFOSp, Rate Law: compartment*(k7*cFOSp-k8*cFOSm)
k2=50.0; k1=15.0; k3=14.0 Reaction: => ppERKn; ppERKc, DUSP, Rate Law: compartment*((k1*ppERKc-k2*ppERKn)-k3*DUSP*ppERKn)
tau2 = 472.0; L = 1.0; K2 = 2.89 Reaction: => x2, Rate Law: compartment*((-x2)/tau2+K2*L/tau2)
k=1.0 Reaction: => DUSP; ppERKn, Rate Law: compartment*k*ppERKn
k4=0.1; k5=0.15 Reaction: => pRSKn; ppERKn, Rate Law: compartment*(k4*ppERKn-k5*pRSKn)
k13 = 0.06; k11 = 0.11; k12=0.001 Reaction: => pcFOS; cFOS, ppERKc, Rate Law: compartment*((k11*cFOS*ppERKc-k12*pcFOS)-k13*pcFOS)
k9=0.3; k13 = 0.06; k10=0.3; k11 = 0.11 Reaction: => cFOS; cFOSm, ppERKc, pcFOS, Rate Law: compartment*(((k9*cFOSm-k10*cFOS)-k11*cFOS*ppERKc)+k13*pcFOS)

States:

Name Description
ppERKn [Phosphoprotein; Mitogen-activated protein kinase 1; Mitogen-activated protein kinase 3; p-ERK1/2/5 [nucleoplasm]]
cFOSp [FOS [nucleoplasm]; FOS, AP-1, C-FOS, p55]
x1 x1
cFOS [Proto-oncogene c-Fos; FOS [nucleoplasm]]
pcFOS [Proto-oncogene c-Fos; p-T325,T331,S362,S374-FOS [nucleoplasm]]
cFOSm [FOS [nucleoplasm]; Proto-oncogene c-Fos]
x2 x2
DUSP [DUSP, MKP; IPR014393; ERK-specific DUSP [nucleoplasm]; Dual specificity protein phosphatase 8; Dual specificity protein phosphatase 10; Dual specificity protein phosphatase 5; Dual specificity protein phosphatase 4; Dual specificity protein phosphatase 2; Dual specificity protein phosphatase 1]
pRSKn [Ribosomal protein S6 kinase [nucleoplasm]]
ppERKc [Phosphoprotein; p-T185,Y187-MAPK1 [cytosol]; p-T202,Y204-MAPK3 [cytosol]]

Observables: none

This mechanistic model describes the activation of immediate early genes such as cFos after EGF or heregulin (HRG) stimu…

Activation of ErbB receptors by epidermal growth factor (EGF) or heregulin (HRG) determines distinct cell-fate decisions, although signals propagate through shared pathways. Using mathematical modeling and experimental approaches, we unravel how HRG and EGF generate distinct, all-or-none responses of the phosphorylated transcription factor c-Fos. In the cytosol, EGF induces transient and HRG induces sustained ERK activation. In the nucleus, however, ERK activity and c-fos mRNA expression are transient for both ligands. Knockdown of dual-specificity phosphatases extends HRG-stimulated nuclear ERK activation, but not c-fos mRNA expression, implying the existence of a HRG-induced repressor of c-fos transcription. Further experiments confirmed that this repressor is mainly induced by HRG, but not EGF, and requires new protein synthesis. We show how a spatially distributed, signaling-transcription cascade robustly discriminates between transient and sustained ERK activities at the c-Fos system level. The proposed control mechanisms are general and operate in different cell types, stimulated by various ligands. link: http://identifiers.org/pubmed/20493519

Parameters:

Name Description
V14 = 5.636949216; K14 = 34180.48 Reaction: DUSP_c => pDUSP_c; ppERK_c, Rate Law: cytoplasm*V14*ppERK_c*DUSP_c/(K14+DUSP_c)
K4 = 60.0; K3 = 160.0; V4 = 0.648 Reaction: ppERK_c => pERK_c; pERK_c, Rate Law: cytoplasm*V4*ppERK_c/(K4*(1+pERK_c/K3)+ppERK_c)
p50 = 26.59483436 Reaction: DUSP_n_pERK_n => DUSP_n + ERK_n, Rate Law: nucleus*p50*DUSP_n_pERK_n
p38 = 2.57E-4 Reaction: c_FOS_c =>, Rate Law: cytoplasm*p38*c_FOS_c
m51 = 9.544308421; p51 = 0.01646825 Reaction: DUSP_n + ERK_n => DUSP_n_ERK_n, Rate Law: nucleus*(p51*DUSP_n*ERK_n-m51*DUSP_n_ERK_n)
p49 = 0.314470502; m49 = 2.335459127 Reaction: DUSP_n + pERK_n => DUSP_n_pERK_n, Rate Law: nucleus*(p49*DUSP_n*pERK_n-m49*DUSP_n_pERK_n)
KexERKP = 0.018; Vc = 940.0; KimERKP = 0.012; Vn = 220.0 Reaction: pERK_c => pERK_n, Rate Law: KimERKP*Vc*pERK_c-KexERKP*Vn*pERK_n
KimRSKP = 0.025925065; KexRSKP = 0.129803956; Vc = 940.0; Vn = 220.0 Reaction: pRSK_c => pRSK_n, Rate Law: KimRSKP*Vc*pRSK_c-KexRSKP*Vn*pRSK_n
V115 = 13.74244; K115 = 2122.045 Reaction: pMEK => MEK, Rate Law: cytoplasm*V115*pMEK/(K115+pMEK)
V106 = 0.109304; K106 = 606.871 Reaction: RsD => RsT; HRG, Rate Law: cytoplasm*V106*HRG*RsD/(K106+RsD)
p47 = 0.001670815; m47 = 15.80783969 Reaction: DUSP_n + ppERK_n => DUSP_n_ppERK_n, Rate Law: nucleus*(p47*DUSP_n*ppERK_n-m47*DUSP_n_ppERK_n)
p58 = 2.70488E-4; Vn = 220.0 Reaction: PreFmRNA => FmRNA, Rate Law: p58*Vn*PreFmRNA
K102 = 237.2001; V102 = 0.09858154 Reaction: A1_2 => A1, Rate Law: cytoplasm*V102*A1_2/(K102+A1_2)
V108 = 0.03436149; K108 = 11.5048 Reaction: RsT => RsD; A2_2, Rate Law: cytoplasm*V108*A2_2*RsT/(K108+RsT)
p11 = 1.26129E-4; Vn = 220.0 Reaction: PreDUSPmRNA => DUSPmRNA, Rate Law: p11*Vn*PreDUSPmRNA
K31 = 185.9760682; V31 = 0.655214248; KF31 = 0.013844393; nF31 = 2.800340453; n31 = 1.988003164 Reaction: => PreFOSmRNA; pCREB_n, pElk1_n, Fn, Rate Law: nucleus*V31*(pCREB_n*pElk1_n)^n31/(K31^n31+(pCREB_n*pElk1_n)^n31+(Fn/KF31)^nF31)
p32 = 0.003284434; Vn = 220.0 Reaction: PreFOSmRNA => c_FOSmRNA, Rate Law: p32*Vn*PreFOSmRNA
p45 = 2.57E-4 Reaction: FOSn =>, Rate Law: nucleus*p45*FOSn
KimERK = 0.012; KexERK = 0.018; Vc = 940.0; Vn = 220.0 Reaction: ERK_c => ERK_n, Rate Law: KimERK*Vc*ERK_c-KexERK*Vn*ERK_n
K105 = 1.027895; V105 = 0.05393704 Reaction: RsD => RsT; EGF, Rate Law: cytoplasm*V105*EGF*RsD/(K105+RsD)
K107 = 424.6884; V107 = 5.291093 Reaction: RsT => RsD; A1_2, Rate Law: cytoplasm*V107*A1_2*RsT/(K107+RsT)
K57 = 0.637490056; V57 = 1.026834758; n57 = 3.584464176 Reaction: => PreFmRNA; FOSn_2, Rate Law: nucleus*V57*FOSn_2^n57/(K57^n57+FOSn_2^n57)
p33 = 6.01234209304622E-4 Reaction: c_FOSmRNA =>, Rate Law: cytoplasm*p33*c_FOSmRNA
p55 = 26.59483436 Reaction: pDUSP_n_pERK_n => pDUSP_n + ERK_n, Rate Law: nucleus*p55*pDUSP_n_pERK_n
KexDUSP = 0.070467899; Vc = 940.0; KimDUSP = 0.024269764; Vn = 220.0 Reaction: DUSP_c => DUSP_n, Rate Law: KimDUSP*Vc*DUSP_c-KexDUSP*Vn*DUSP_n
KimFOS = 0.54528521; Vc = 940.0; KexFOS = 0.133249762; Vn = 220.0 Reaction: c_FOS_c => FOSn, Rate Law: KimFOS*Vc*c_FOS_c-KexFOS*Vn*FOSn
V29 = 0.518529841; K29 = 21312.69109 Reaction: Elk1_n => pElk1_n; ppERK_n, Rate Law: nucleus*V29*ppERK_n*Elk1_n/(K29+Elk1_n)
KimFOSP = 0.54528521; KexFOSP = 0.133249762; Vc = 940.0; Vn = 220.0 Reaction: pc_FOS_c => FOSn_2, Rate Law: KimFOSP*Vc*pc_FOS_c-KexFOSP*Vn*FOSn_2
KimERKPP = 0.011; KexERKPP = 0.013; Vc = 940.0; Vn = 220.0 Reaction: ppERK_c => ppERK_n, Rate Law: KimERKPP*Vc*ppERK_c-KexERKPP*Vn*ppERK_n
p59 = 0.001443889 Reaction: FmRNA =>, Rate Law: cytoplasm*p59*FmRNA
V112 = 0.8850982; K112 = 4665.217 Reaction: Kin_2 => Kin; A3_2, Rate Law: cytoplasm*V112*A3_2*Kin_2/(K112+Kin_2)
m54 = 2.335459127; p54 = 0.314470502 Reaction: pDUSP_n + pERK_n => pDUSP_n_pERK_n, Rate Law: nucleus*(p54*pDUSP_n*pERK_n-m54*pDUSP_n_pERK_n)
p23 = 4.81E-5 Reaction: pDUSP_n =>, Rate Law: nucleus*p23*pDUSP_n
V27 = 19.23118154; K27 = 441.5834425 Reaction: CREB_n => pCREB_n; pRSK_n, Rate Law: nucleus*V27*pRSK_n*CREB_n/(K27+CREB_n)
K111 = 858.3423; V111 = 0.02487469 Reaction: Kin => Kin_2; HRG, Rate Law: cytoplasm*V111*HRG*Kin/(K111+Kin)
K20 = 735598.6967; V20 = 0.157678678 Reaction: DUSP_n => pDUSP_n; ppERK_n, Rate Law: nucleus*V20*ppERK_n*DUSP_n/(K20+DUSP_n)
p63 = 4.13466150826031E-5 Reaction: Fn =>, Rate Law: nucleus*cytoplasm*p63*Fn/nucleus
m56 = 9.544308421; p56 = 0.01646825 Reaction: pDUSP_n + ERK_n => pDUSP_n_ERK_n, Rate Law: nucleus*(p56*pDUSP_n*ERK_n-m56*pDUSP_n_ERK_n)
K104 = 4046.71; V104 = 4.635749 Reaction: A2_2 => A2, Rate Law: cytoplasm*V104*A2_2/(K104+A2_2)
V6 = 19.4987234631759; K6 = 29.9407371620698; K5 = 29.94073716 Reaction: ppERK_n => pERK_n; pERK_n, Rate Law: nucleus*V6*ppERK_n/(K6*(1+pERK_n/K5)+ppERK_n)
K44 = 0.051168202; V44 = 0.078344305 Reaction: FOSn_2 => FOSn, Rate Law: nucleus*V44*FOSn_2/(K44+FOSn_2)
p53 = 0.686020478 Reaction: pDUSP_n_ppERK_n => pDUSP_n + pERK_n, Rate Law: nucleus*p53*pDUSP_n_ppERK_n
K103 = 1334.132; V103 = 0.3573399 Reaction: A2 => A2_2; HRG, Rate Law: cytoplasm*V103*HRG*A2/(K103+A2)
p60 = 0.002448164 Reaction: => F; FmRNA, Rate Law: cytoplasm*p60*FmRNA
p61 = 3.49860901414122E-5 Reaction: F =>, Rate Law: cytoplasm*p61*F
p46 = 4.81E-5 Reaction: FOSn_2 =>, Rate Law: nucleus*p46*FOSn_2
p12 = 0.007875765 Reaction: DUSPmRNA =>, Rate Law: cytoplasm*p12*DUSPmRNA
V43 = 0.076717457; K43 = 1157.116021 Reaction: FOSn => FOSn_2; pRSK_n, Rate Law: nucleus*V43*pRSK_n*FOSn/(K43+FOSn)
K114 = 7.774197; V114 = 0.03957055 Reaction: MEK => pMEK; Kin_2, Rate Law: cytoplasm*V114*Kin_2*MEK/(K114+MEK)
V113 = 0.05377297; K113 = 20.50809 Reaction: MEK => pMEK; RsT, Rate Law: cytoplasm*V113*RsT*MEK/(K113+MEK)
V110 = 0.08258693; K110 = 425.5268 Reaction: A3_2 => A3, Rate Law: cytoplasm*V110*A3_2/(K110+A3_2)
V42 = 0.909968714; K42 = 3992.061328 Reaction: FOSn => FOSn_2; ppERK_n, Rate Law: nucleus*V42*ppERK_n*FOSn/(K42+FOSn)
p48 = 0.686020478 Reaction: DUSP_n_ppERK_n => DUSP_n + pERK_n, Rate Law: nucleus*p48*DUSP_n_ppERK_n
p52 = 0.001670815; m52 = 15.80783969 Reaction: pDUSP_n + ppERK_n => pDUSP_n_ppERK_n, Rate Law: nucleus*(p52*pDUSP_n*ppERK_n-m52*pDUSP_n_ppERK_n)
K30 = 15.04396629; V30 = 13.79479021 Reaction: pElk1_n => Elk1_n, Rate Law: nucleus*V30*pElk1_n/(K30+pElk1_n)
V109 = 0.1374307; K109 = 7424.816 Reaction: A3 => A3_2; HRG, Rate Law: cytoplasm*V109*HRG*A3/(K109+A3)
V101 = 0.01807448; K101 = 3475.168 Reaction: A1 => A1_2; EGF, Rate Law: cytoplasm*V101*EGF*A1/(K101+A1)
K2 = 350.0; V2 = 0.22; Fct = 0.7485; K1 = 307.041525298866 Reaction: pERK_c => ppERK_c; pMEK, ERK_c, Rate Law: cytoplasm*V2*Fct*pMEK*pERK_c/(K2*(1+ERK_c/K1)+pERK_c)
p34 = 7.64816282169636E-5 Reaction: => c_FOS_c; c_FOSmRNA, Rate Law: cytoplasm*p34*c_FOSmRNA
K2 = 350.0; V1 = 0.342848369838443; Fct = 0.7485; K1 = 307.041525298866 Reaction: ERK_c => pERK_c; pMEK, pERK_c, Rate Law: cytoplasm*V1*Fct*pMEK*ERK_c/(K1*(1+pERK_c/K2)+ERK_c)
V21 = 0.005648117; K21 = 387.8377182 Reaction: pDUSP_n => DUSP_n, Rate Law: nucleus*V21*pDUSP_n/(K21+pDUSP_n)
p13 = 0.001245747 Reaction: => DUSP_c; DUSPmRNA, Rate Law: cytoplasm*p13*DUSPmRNA
KimF = 0.019898797; KexF = 0.396950616; Vc = 940.0; Vn = 220.0 Reaction: F => Fn, Rate Law: KimF*Vc*F-KexF*Vn*Fn
KimDUSPP = 0.024269764; KexDUSPP = 0.070467899; Vc = 940.0; Vn = 220.0 Reaction: pDUSP_c => pDUSP_n, Rate Law: KimDUSPP*Vc*pDUSP_c-KexDUSPP*Vn*pDUSP_n
V10 = 29.24109258; n10 = 3.970849295; K10 = 169.0473748 Reaction: => PreDUSPmRNA; ppERK_n, Rate Law: nucleus*V10*ppERK_n^n10/(K10^n10+ppERK_n^n10)
K4 = 60.0; K3 = 160.0; V3 = 0.72 Reaction: pERK_c => ERK_c; ppERK_c, Rate Law: cytoplasm*V3*pERK_c/(K3*(1+ppERK_c/K4)+pERK_c)

States:

Name Description
PreFmRNA PreFmRNA
A1 2 A1_2
RsT RsT
A2 2 A2_2
RsD RsD
Elk1 n Elk1_n
DUSP n DUSP_n
pDUSP n pDUSP_n
pDUSP n ERK n pDUSP_n_ERK_n
pERK c pERK_c
Fn Fn
DUSP c DUSP_c
pElk1 n pElk1_n
MEK MEK
pRSK c pRSK_c
pRSK n pRSK_n
pERK n pERK_n
A3 A3
A1 A1
FOSn FOSn
ppERK n ppERK_n
A3 2 A3_2
CREB n CREB_n
Kin Kin
FmRNA FmRNA
ERK c ERK_c
DUSP n pERK n DUSP_n_pERK_n
A2 A2
c FOS c c_FOS_c
DUSPmRNA DUSPmRNA
pMEK pMEK
pDUSP n pERK n pDUSP_n_pERK_n
c FOSmRNA c_FOSmRNA
FOSn 2 FOSn_2
DUSP n ppERK n DUSP_n_ppERK_n
ppERK c ppERK_c
PreFOSmRNA PreFOSmRNA
pDUSP n ppERK n pDUSP_n_ppERK_n
PreDUSPmRNA PreDUSPmRNA
ERK n ERK_n
pCREB n pCREB_n
F F
Kin 2 Kin_2

Observables: none

MODEL1101170000 @ v0.0.1

This is an SBML version of the model described in: **A kinetic model of dopamine- and calcium-dependent striatal synapti…

Corticostriatal synapse plasticity of medium spiny neurons is regulated by glutamate input from the cortex and dopamine input from the substantia nigra. While cortical stimulation alone results in long-term depression (LTD), the combination with dopamine switches LTD to long-term potentiation (LTP), which is known as dopamine-dependent plasticity. LTP is also induced by cortical stimulation in magnesium-free solution, which leads to massive calcium influx through NMDA-type receptors and is regarded as calcium-dependent plasticity. Signaling cascades in the corticostriatal spines are currently under investigation. However, because of the existence of multiple excitatory and inhibitory pathways with loops, the mechanisms regulating the two types of plasticity remain poorly understood. A signaling pathway model of spines that express D1-type dopamine receptors was constructed to analyze the dynamic mechanisms of dopamine- and calcium-dependent plasticity. The model incorporated all major signaling molecules, including dopamine- and cyclic AMP-regulated phosphoprotein with a molecular weight of 32 kDa (DARPP32), as well as AMPA receptor trafficking in the post-synaptic membrane. Simulations with dopamine and calcium inputs reproduced dopamine- and calcium-dependent plasticity. Further in silico experiments revealed that the positive feedback loop consisted of protein kinase A (PKA), protein phosphatase 2A (PP2A), and the phosphorylation site at threonine 75 of DARPP-32 (Thr75) served as the major switch for inducing LTD and LTP. Calcium input modulated this loop through the PP2B (phosphatase 2B)-CK1 (casein kinase 1)-Cdk5 (cyclin-dependent kinase 5)-Thr75 pathway and PP2A, whereas calcium and dopamine input activated the loop via PKA activation by cyclic AMP (cAMP). The positive feedback loop displayed robust bi-stable responses following changes in the reaction parameters. Increased basal dopamine levels disrupted this dopamine-dependent plasticity. The present model elucidated the mechanisms involved in bidirectional regulation of corticostriatal synapses and will allow for further exploration into causes and therapies for dysfunctions such as drug addiction. link: http://identifiers.org/pubmed/20169176

Parameters: none

States: none

Observables: none

the model depicts a unique endemic equilibrium with a transcritical bifurcation when the basic reproductive number is un…

HIV-infected individuals are increasingly becoming susceptible to liver disease and, hence, liver-related mortality is on a rise. The presence of CD4+ in the liver and the presence of C-X-C chemokine receptor type 4 (CXCR4) on human hepatocytes provide a conducive environment for HIV invasion. In this study, a mathematical model is used to analyse the dynamics of HIV in the liver with the aim of investigating the existence of liver enzyme elevation in HIV mono-infected individuals. In the presence of HIV-specific cytotoxic T-lymphocytes, the model depicts a unique endemic equilibrium with a transcritical bifurcation when the basic reproductive number is unity. Results of the study show that the level of liver enzyme alanine aminotransferase (ALT) increases with increase in the rate of hepatocytes production. Numerical simulations reveal significant elevation of alanine aminotransferase with increase in viral load. The findings presuppose that while liver damage in HIV infection has mostly been associated with HIV/HBV coinfection and use of antiretroviral therapy (ART), it is possible to have liver damage solely with HIV infection. link: http://identifiers.org/pubmed/23291466

Parameters: none

States: none

Observables: none

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

B cell chronic lymphocytic leukemia (B-CLL) is known to havesubstantial clinical heterogeneity. There is no cure, but treatments allow fordisease management. However, the wide range of clinical courses experiencedby B-CLL patients makes prognosis and hence treatment a significant chal-lenge. In an attempt to study disease progression across different patients viaa unified yet flexible approach, we present a mathematical model of B-CLLwith immune response, that can capture both rapid and slow disease progres-sion. This model includes four different cell populations in the peripheral bloodof humans: B-CLL cells, NK cells, cytotoxic T cells and helper T cells. Weanalyze existing data in the medical literature, determine ranges of values forparameters of the model, and compare our model outcomes to clinical patientdata. The goal of this work is to provide a tool that may shed light on factorsaffecting the course of disease progression in patients. This modeling tool canserve as a foundation upon which future treatments can be based. link: http://identifiers.org/doi/10.3934/dcdsb.2013.18.1053

Parameters: none

States: none

Observables: none

Mathematical model of blood coagulation factor alpha-thrombin and conversion of fibrinogen to fibrin with ATIII inhibiti…

In this study we report a kinetic model for the alpha-thrombin-catalyzed production of fibrin I and fibrin II at pH 7.4, 37 degrees C, gamma/2 0.17. The fibrin is produced by the action of human alpha-thrombin on plasma levels of human fibrinogen in the presence of the major inhibitor of alpha-thrombin in plasma, antithrombin III (AT). This model quantitatively accounts for the time dependence of alpha-thrombin-catalyzed release of fibrinopeptides A and B concurrent with the inactivation of alpha-thrombin by AT and delineates the concerted interactions of alpha-thrombin, fibrin(ogen), and AT during the production of a fibrin clot. The model also provides a method for estimating the concentration of alpha-thrombin required to produce a clot of known composition and predicts a direct relationship between the plasma concentration of fibrinogen and the amount of fibrin produced by a bolus of alpha-thrombin. The predicted relationship between the concentration of fibrinogen and the amount of fibrin produced in plasma provides a plausible explanation for the observed linkage between plasma concentrations of fibrinogen and the risk for ischemic heart disease. link: http://identifiers.org/pubmed/2071587

Parameters: none

States: none

Observables: none

MODEL1910030001 @ v0.0.1

This model is based on paper: Combination of singularly perturbed vector field method and method of directly defining t…

We propose a new method to solve a system of complex ordinary differential equations (ODEs) with hidden hierarchy. Given a complex system of the ODE, the hierarchy of the system is generally hidden. Once we reveal the hierarchy of the system, the system can be reduced into subsystems called slow and fast subsystems. This division of slow and fast subsystems reduces the analysis and hence reduces the computation time, which can be expensive. In our new method, we first apply the singularly perturbed vector field method that is the global quasi-linearization method. This method exposes the hierarchy of a given complex system. Subsequently, we apply a version of the homotopy analysis method called the method of directly defining the inverse mapping. We applied our new method to the immunotherapy of advanced prostate cancer. link: http://identifiers.org/pubmed/30384811

Parameters: none

States: none

Observables: none

Nayak2015 - Blood Coagulation Network - Predicting the Effects of Various Therapies on BiomarkersNote:The SBML model is…

A number of therapeutics have been developed or are under development aiming to modulate the coagulation network to treat various diseases. We used a systems model to better understand the effect of modulating various components on blood coagulation. A computational model of the coagulation network was built to match in-house in vitro thrombin generation and activated Partial Thromboplastin Time (aPTT) data with various concentrations of recombinant factor VIIa (FVIIa) or factor Xa added to normal human plasma or factor VIII-deficient plasma. Sensitivity analysis applied to the model revealed that lag time, peak thrombin concentration, area under the curve (AUC) of the thrombin generation profile, and aPTT show different sensitivity to changes in coagulation factors' concentrations and type of plasma used (normal or factor VIII-deficient). We also used the model to explore how variability in concentrations of the proteins in coagulation network can impact the response to FVIIa treatment. link: http://identifiers.org/pubmed/26312163

Parameters:

Name Description
k6 = 0.009975373 Reaction: Xa + VII => Xa + VIIa; Xa, VII, Xa, VII, Rate Law: k6*Xa*VII
k30 = 0.10001522; k29 = 149.91541 Reaction: Xa_Va + II => Xa_Va_II; Xa_Va, II, Xa_Va_II, Xa_Va, II, Xa_Va_II, Rate Law: k30*Xa_Va*II-k29*Xa_Va_II
k32 = 0.21872155 Reaction: mIIa + Xa_Va => IIa + Xa_Va; mIIa, Xa_Va, mIIa, Xa_Va, Rate Law: k32*mIIa*Xa_Va
k10 = 8.9987819 Reaction: TF_VIIa_X => TF_VIIa_Xa; TF_VIIa_X, TF_VIIa_X, Rate Law: k10*TF_VIIa_X
k25 = 0.0013357963 Reaction: IXa_VIIIa_X => VIIIa1_L + VIIIa2 + X + IXa; IXa_VIIIa_X, IXa_VIIIa_X, Rate Law: k25*IXa_VIIIa_X
k22 = 42.71401 Reaction: IXa_VIIIa_X => IXa_VIIIa + Xa; IXa_VIIIa_X, IXa_VIIIa_X, Rate Law: k22*IXa_VIIIa_X
k26 = 0.0013946425 Reaction: IXa_VIIIa => VIIIa1_L + VIIIa2 + IXa; IXa_VIIIa, IXa_VIIIa, Rate Law: k26*IXa_VIIIa
mwea0d7c35_f4d2_4205_8c59_11ac05134dde = 1.0958881E-4 Reaction: mwbdb849d8_2b25_4551_8de8_adc8bead2303 => mw931f65a6_3967_4ac2_9904_ba791b216fc2; mwbdb849d8_2b25_4551_8de8_adc8bead2303, mwbdb849d8_2b25_4551_8de8_adc8bead2303, Rate Law: mwea0d7c35_f4d2_4205_8c59_11ac05134dde*mwbdb849d8_2b25_4551_8de8_adc8bead2303
mw4fc81076_be53_4fc3_9ade_3587e8d60355 = 0.1857857 Reaction: mw3cec90c2_500e_4f30_b6be_325ef5194755 => mwa6be116e_72f1_439e_bca6_eb61f79cc68e + mwedf22864_05a0_40c3_a0d5_ede45a3e7e8f; mw3cec90c2_500e_4f30_b6be_325ef5194755, mw3cec90c2_500e_4f30_b6be_325ef5194755, Rate Law: mw4fc81076_be53_4fc3_9ade_3587e8d60355*mw3cec90c2_500e_4f30_b6be_325ef5194755
k21 = 0.048795021; k20 = 0.0013766033 Reaction: IXa_VIIIa + X => IXa_VIIIa_X; IXa_VIIIa, X, IXa_VIIIa_X, IXa_VIIIa, X, IXa_VIIIa_X, Rate Law: k21*IXa_VIIIa*X-k20*IXa_VIIIa_X
mw8482ca53_fca1_4841_ac2f_2469a76a758e = 0.12914436; mw1511789f_5e7b_43bf_b162_d930b027a867 = 0.006 Reaction: Xa + Va => Xa_Va; Xa, Va, Xa_Va, Xa, Va, Xa_Va, Rate Law: mw8482ca53_fca1_4841_ac2f_2469a76a758e*Xa*Va-mw1511789f_5e7b_43bf_b162_d930b027a867*Xa_Va
mwa2636601_825e_4846_aa2d_c35bd242ec99 = 0.032359973 Reaction: mw8bdbd17d_f542_4b8c_88c6_a82eaf997a43 => mwedf22864_05a0_40c3_a0d5_ede45a3e7e8f + mwf5c3f9df_7ccf_4ca7_b241_471a66720da8; mw8bdbd17d_f542_4b8c_88c6_a82eaf997a43, mw8bdbd17d_f542_4b8c_88c6_a82eaf997a43, Rate Law: mwa2636601_825e_4846_aa2d_c35bd242ec99*mw8bdbd17d_f542_4b8c_88c6_a82eaf997a43
k16 = 3.764127E-5 Reaction: Xa + II => Xa + IIa + mwbdb849d8_2b25_4551_8de8_adc8bead2303; Xa, II, Xa, II, Rate Law: k16*Xa*II
mw7aeacec0_be36_49bf_8548_7a3e2b5fe3cb = 0.029887563 Reaction: mwa4fcfa0c_6944_42fc_8c74_7865f13953c8 => mwedf22864_05a0_40c3_a0d5_ede45a3e7e8f + IXa + mwf5c3f9df_7ccf_4ca7_b241_471a66720da8; mwa4fcfa0c_6944_42fc_8c74_7865f13953c8, mwa4fcfa0c_6944_42fc_8c74_7865f13953c8, Rate Law: mw7aeacec0_be36_49bf_8548_7a3e2b5fe3cb*mwa4fcfa0c_6944_42fc_8c74_7865f13953c8
k15 = 2.3887492 Reaction: TF_VIIa_IX => TF_VIIa + IXa; TF_VIIa_IX, TF_VIIa_IX, Rate Law: k15*TF_VIIa_IX
mw61fdd721_9193_442c_bc9e_f1058c4720e7 = 1.2943783E-5 Reaction: mw6d041b25_87db_4394_9b8b_7ac61e01f359 => VIIa + Xa; mw6d041b25_87db_4394_9b8b_7ac61e01f359, mw6d041b25_87db_4394_9b8b_7ac61e01f359, Rate Law: mw61fdd721_9193_442c_bc9e_f1058c4720e7*mw6d041b25_87db_4394_9b8b_7ac61e01f359
mw7300dcac_9389_4201_88c7_7effa7fdb0f3 = 10.565569 Reaction: mwe70b2c96_44b9_48eb_967a_7eb850a916a6 => mw6591152c_8b5a_4c9b_b095_956988a01ba0 + IXa; mwe70b2c96_44b9_48eb_967a_7eb850a916a6, mwe70b2c96_44b9_48eb_967a_7eb850a916a6, Rate Law: mw7300dcac_9389_4201_88c7_7effa7fdb0f3*mwe70b2c96_44b9_48eb_967a_7eb850a916a6
mw6843129b_7601_452f_be5d_977f7203bfb5 = 0.0345 Reaction: mw7a1594c9_f04f_478c_9f5f_ccbe0b95a820 => Xa + VIIIa; mw7a1594c9_f04f_478c_9f5f_ccbe0b95a820, mw7a1594c9_f04f_478c_9f5f_ccbe0b95a820, Rate Law: mw6843129b_7601_452f_be5d_977f7203bfb5*mw7a1594c9_f04f_478c_9f5f_ccbe0b95a820
k3 = 0.0019496187; k4 = 0.075680013 Reaction: TF + VIIa => TF_VIIa; TF, VIIa, TF_VIIa, TF, VIIa, TF_VIIa, Rate Law: k4*TF*VIIa-k3*TF_VIIa
k17 = 1.44895 Reaction: IIa + VIII => IIa + VIIIa; IIa, VIII, IIa, VIII, Rate Law: k17*IIa*VIII
mw234b484f_d2d5_4ae8_a077_217c600588d8 = 0.24027638 Reaction: mw2e632a32_3823_4933_95cb_19567cbcc66a => mwedf22864_05a0_40c3_a0d5_ede45a3e7e8f + mw18e5caa7_26eb_4521_b217_da75bb3193ad; mw2e632a32_3823_4933_95cb_19567cbcc66a, mw2e632a32_3823_4933_95cb_19567cbcc66a, Rate Law: mw234b484f_d2d5_4ae8_a077_217c600588d8*mw2e632a32_3823_4933_95cb_19567cbcc66a
mwc85f8d37_7f39_41b2_8ea4_00b5adad2eac = 0.07934338; mw807b9a99_fb16_421f_b724_69f29f3fcfb2 = 1.9895374 Reaction: mwedf22864_05a0_40c3_a0d5_ede45a3e7e8f + VIIIa => mw8bdbd17d_f542_4b8c_88c6_a82eaf997a43; mwedf22864_05a0_40c3_a0d5_ede45a3e7e8f, VIIIa, mw8bdbd17d_f542_4b8c_88c6_a82eaf997a43, mwedf22864_05a0_40c3_a0d5_ede45a3e7e8f, VIIIa, mw8bdbd17d_f542_4b8c_88c6_a82eaf997a43, Rate Law: mwc85f8d37_7f39_41b2_8ea4_00b5adad2eac*mwedf22864_05a0_40c3_a0d5_ede45a3e7e8f*VIIIa-mw807b9a99_fb16_421f_b724_69f29f3fcfb2*mw8bdbd17d_f542_4b8c_88c6_a82eaf997a43
k38 = 1.0556718E-6 Reaction: Xa + ATIII => Xa_ATIII; Xa, ATIII, Xa, ATIII, Rate Law: k38*Xa*ATIII
k11 = 9.5; k12 = 0.032999929 Reaction: TF_VIIa + Xa => TF_VIIa_Xa; TF_VIIa, Xa, TF_VIIa_Xa, TF_VIIa, Xa, TF_VIIa_Xa, Rate Law: k12*TF_VIIa*Xa-k11*TF_VIIa_Xa
mw0e80d629_98c1_44a6_bd57_3a4027c87b4c = 2.0869571; mw70d2f292_be41_4999_99cb_9c146808db85 = 0.077518002 Reaction: mwedf22864_05a0_40c3_a0d5_ede45a3e7e8f + IXa_VIIIa => mwa4fcfa0c_6944_42fc_8c74_7865f13953c8; mwedf22864_05a0_40c3_a0d5_ede45a3e7e8f, IXa_VIIIa, mwa4fcfa0c_6944_42fc_8c74_7865f13953c8, mwedf22864_05a0_40c3_a0d5_ede45a3e7e8f, IXa_VIIIa, mwa4fcfa0c_6944_42fc_8c74_7865f13953c8, Rate Law: mw70d2f292_be41_4999_99cb_9c146808db85*mwedf22864_05a0_40c3_a0d5_ede45a3e7e8f*IXa_VIIIa-mw0e80d629_98c1_44a6_bd57_3a4027c87b4c*mwa4fcfa0c_6944_42fc_8c74_7865f13953c8
mwaec203ce_06d5_4003_bfdb_7244d3d77255 = 0.0011427258 Reaction: mw64e9cef3_5dd3_43f3_ad04_58e8fc07a91b => IXa + Xa; mw64e9cef3_5dd3_43f3_ad04_58e8fc07a91b, mw64e9cef3_5dd3_43f3_ad04_58e8fc07a91b, Rate Law: mwaec203ce_06d5_4003_bfdb_7244d3d77255*mw64e9cef3_5dd3_43f3_ad04_58e8fc07a91b
k19 = 0.11749508; k18 = 0.0050724996 Reaction: IXa + VIIIa => IXa_VIIIa; IXa, VIIIa, IXa_VIIIa, IXa, VIIIa, IXa_VIIIa, Rate Law: k19*IXa*VIIIa-k18*IXa_VIIIa
k5 = 3.3894832E-4 Reaction: TF_VIIa + VII => TF_VIIa + VIIa; TF_VIIa, VII, TF_VIIa, VII, Rate Law: k5*TF_VIIa*VII
k42 = 3.2905257E-7 Reaction: TF_VIIa + ATIII => TF_VIIa_ATIII; TF_VIIa, ATIII, TF_VIIa, ATIII, Rate Law: k42*TF_VIIa*ATIII
k37 = 0.025386917 Reaction: TF_VIIa + Xa_TFPI => TF_VIIa_Xa_TFPI; TF_VIIa, Xa_TFPI, TF_VIIa, Xa_TFPI, Rate Law: k37*TF_VIIa*Xa_TFPI
k27 = 4.0233556E-4 Reaction: IIa + V => IIa + Va; IIa, V, IIa, V, Rate Law: k27*IIa*V
k13 = 20.6708; k14 = 0.010569458 Reaction: TF_VIIa + IX => TF_VIIa_IX; TF_VIIa, IX, TF_VIIa_IX, TF_VIIa, IX, TF_VIIa_IX, Rate Law: k14*TF_VIIa*IX-k13*TF_VIIa_IX
mwb01ef86f_18d8_45e7_a452_31878dcb3d49 = 30.668349; mwc0cb654e_d95f_4d4b_8dc2_3a21afd35a19 = 0.13081564 Reaction: mw6591152c_8b5a_4c9b_b095_956988a01ba0 + IX => mwe70b2c96_44b9_48eb_967a_7eb850a916a6; mw6591152c_8b5a_4c9b_b095_956988a01ba0, IX, mwe70b2c96_44b9_48eb_967a_7eb850a916a6, mw6591152c_8b5a_4c9b_b095_956988a01ba0, IX, mwe70b2c96_44b9_48eb_967a_7eb850a916a6, Rate Law: mwc0cb654e_d95f_4d4b_8dc2_3a21afd35a19*mw6591152c_8b5a_4c9b_b095_956988a01ba0*IX-mwb01ef86f_18d8_45e7_a452_31878dcb3d49*mwe70b2c96_44b9_48eb_967a_7eb850a916a6
k7 = 1.1527134E-5 Reaction: IIa + VII => IIa + VIIa; IIa, VII, IIa, VII, Rate Law: k7*IIa*VII
mw7b89687a_3110_4d5f_a9ec_7ca8761f0d41 = 84.659935; mw05b4111c_4463_4be0_aa1e_5a8f50c7bf67 = 0.059664002 Reaction: VIIa + X => mw6d041b25_87db_4394_9b8b_7ac61e01f359; VIIa, X, mw6d041b25_87db_4394_9b8b_7ac61e01f359, VIIa, X, mw6d041b25_87db_4394_9b8b_7ac61e01f359, Rate Law: mw05b4111c_4463_4be0_aa1e_5a8f50c7bf67*VIIa*X-mw7b89687a_3110_4d5f_a9ec_7ca8761f0d41*mw6d041b25_87db_4394_9b8b_7ac61e01f359
k39 = 3.55E-6 Reaction: mIIa + ATIII => mIIa_ATIII; mIIa, ATIII, mIIa, ATIII, Rate Law: k39*mIIa*ATIII
mw4d2fe532_2ccd_42c4_9b4b_759022a87484 = 1.4001578; mwb63aa5ed_b6d8_4241_9987_54828945aea3 = 0.1289308 Reaction: mwedf22864_05a0_40c3_a0d5_ede45a3e7e8f + IXa_VIIIa_X => mwe0bb059d_deaa_45fa_b7dc_ec1c4409c4ca; mwedf22864_05a0_40c3_a0d5_ede45a3e7e8f, IXa_VIIIa_X, mwe0bb059d_deaa_45fa_b7dc_ec1c4409c4ca, mwedf22864_05a0_40c3_a0d5_ede45a3e7e8f, IXa_VIIIa_X, mwe0bb059d_deaa_45fa_b7dc_ec1c4409c4ca, Rate Law: mwb63aa5ed_b6d8_4241_9987_54828945aea3*mwedf22864_05a0_40c3_a0d5_ede45a3e7e8f*IXa_VIIIa_X-mw4d2fe532_2ccd_42c4_9b4b_759022a87484*mwe0bb059d_deaa_45fa_b7dc_ec1c4409c4ca
mw44adf04a_f1e2_4ca9_9615_5a9f4d3bbea8 = 0.13304333; mwc189e7ea_7518_4a4f_be0f_03f2d073b29e = 83.206626 Reaction: IXa + X => mw64e9cef3_5dd3_43f3_ad04_58e8fc07a91b; IXa, X, mw64e9cef3_5dd3_43f3_ad04_58e8fc07a91b, IXa, X, mw64e9cef3_5dd3_43f3_ad04_58e8fc07a91b, Rate Law: mw44adf04a_f1e2_4ca9_9615_5a9f4d3bbea8*IXa*X-mwc189e7ea_7518_4a4f_be0f_03f2d073b29e*mw64e9cef3_5dd3_43f3_ad04_58e8fc07a91b
mw7be1d52f_926f_47e0_964b_d3303c8453b1 = 0.05 Reaction: mw6d041b25_87db_4394_9b8b_7ac61e01f359 + mw6591152c_8b5a_4c9b_b095_956988a01ba0 => VIIa + Xa + mw6591152c_8b5a_4c9b_b095_956988a01ba0; mw6d041b25_87db_4394_9b8b_7ac61e01f359, mw6591152c_8b5a_4c9b_b095_956988a01ba0, mw6d041b25_87db_4394_9b8b_7ac61e01f359, mw6591152c_8b5a_4c9b_b095_956988a01ba0, Rate Law: mw7be1d52f_926f_47e0_964b_d3303c8453b1*mw6d041b25_87db_4394_9b8b_7ac61e01f359*mw6591152c_8b5a_4c9b_b095_956988a01ba0
mwaf2c7981_908c_4f4c_898e_2491a9f04e17 = 0.10523968; mw1ddc2a05_bc78_4434_a2d9_d06701483346 = 19.338228 Reaction: mwa6be116e_72f1_439e_bca6_eb61f79cc68e + mw6a8501d2_9479_41ae_8616_1e8d0e1bbfa9 => mw3cec90c2_500e_4f30_b6be_325ef5194755; mwa6be116e_72f1_439e_bca6_eb61f79cc68e, mw6a8501d2_9479_41ae_8616_1e8d0e1bbfa9, mw3cec90c2_500e_4f30_b6be_325ef5194755, mwa6be116e_72f1_439e_bca6_eb61f79cc68e, mw6a8501d2_9479_41ae_8616_1e8d0e1bbfa9, mw3cec90c2_500e_4f30_b6be_325ef5194755, Rate Law: mwaf2c7981_908c_4f4c_898e_2491a9f04e17*mwa6be116e_72f1_439e_bca6_eb61f79cc68e*mw6a8501d2_9479_41ae_8616_1e8d0e1bbfa9-mw1ddc2a05_bc78_4434_a2d9_d06701483346*mw3cec90c2_500e_4f30_b6be_325ef5194755
k41 = 3.917682E-6 Reaction: IIa + ATIII => IIa_ATIII; IIa, ATIII, IIa, ATIII, Rate Law: k41*IIa*ATIII
mwd6b996b1_d7fe_42de_b17e_b2482109c54d = 0.1043597; mwc5dc3645_536d_4bb4_88c7_4aeac4f5a241 = 2.0649128 Reaction: mwedf22864_05a0_40c3_a0d5_ede45a3e7e8f + Va => mw2e632a32_3823_4933_95cb_19567cbcc66a; mwedf22864_05a0_40c3_a0d5_ede45a3e7e8f, Va, mw2e632a32_3823_4933_95cb_19567cbcc66a, mwedf22864_05a0_40c3_a0d5_ede45a3e7e8f, Va, mw2e632a32_3823_4933_95cb_19567cbcc66a, Rate Law: mwd6b996b1_d7fe_42de_b17e_b2482109c54d*mwedf22864_05a0_40c3_a0d5_ede45a3e7e8f*Va-mwc5dc3645_536d_4bb4_88c7_4aeac4f5a241*mw2e632a32_3823_4933_95cb_19567cbcc66a
k33 = 1.801577E-4; k34 = 4.5E-4 Reaction: Xa + TFPI => Xa_TFPI; Xa, TFPI, Xa_TFPI, Xa, TFPI, Xa_TFPI, Rate Law: k34*Xa*TFPI-k33*Xa_TFPI
mw95e328a0_be5b_4260_b6e4_d85c4c4aae9e = 0.050084768; mw9bcd5c0b_3384_4d5e_92ce_70b13d64e8b8 = 0.11573051 Reaction: IIa + mwd68cbf38_9266_4dfb_aa00_f817c3421aec => mwa6be116e_72f1_439e_bca6_eb61f79cc68e; IIa, mwd68cbf38_9266_4dfb_aa00_f817c3421aec, mwa6be116e_72f1_439e_bca6_eb61f79cc68e, IIa, mwd68cbf38_9266_4dfb_aa00_f817c3421aec, mwa6be116e_72f1_439e_bca6_eb61f79cc68e, Rate Law: mw9bcd5c0b_3384_4d5e_92ce_70b13d64e8b8*IIa*mwd68cbf38_9266_4dfb_aa00_f817c3421aec-mw95e328a0_be5b_4260_b6e4_d85c4c4aae9e*mwa6be116e_72f1_439e_bca6_eb61f79cc68e
k9 = 0.036245656; k8 = 1.3800407 Reaction: TF_VIIa + X => TF_VIIa_X; TF_VIIa, X, TF_VIIa_X, TF_VIIa, X, TF_VIIa_X, Rate Law: k9*TF_VIIa*X-k8*TF_VIIa_X
k31 = 29.479266 Reaction: Xa_Va_II => Xa_Va + mIIa + mwbdb849d8_2b25_4551_8de8_adc8bead2303; Xa_Va_II, Xa_Va_II, Rate Law: k31*Xa_Va_II
mwa4cc6bbe_c310_445f_bba7_a94868342831 = 10740.276; mw3b48c5e7_774a_4dc4_917f_8f8cff8d9c4b = 90.211653 Reaction: IIa + mwd3e1ba39_ab10_4702_addd_fb6a7e184a4b => IIa + mwfa9d903a_b5e5_4a38_a649_dfe4719577aa; IIa, mwd3e1ba39_ab10_4702_addd_fb6a7e184a4b, IIa, mwd3e1ba39_ab10_4702_addd_fb6a7e184a4b, Rate Law: mw3b48c5e7_774a_4dc4_917f_8f8cff8d9c4b*IIa*mwd3e1ba39_ab10_4702_addd_fb6a7e184a4b/(mwa4cc6bbe_c310_445f_bba7_a94868342831+mwd3e1ba39_ab10_4702_addd_fb6a7e184a4b)
mw6b555ed1_194e_4fa4_9688_8105aa7c60c0 = 0.013215482 Reaction: mwe0bb059d_deaa_45fa_b7dc_ec1c4409c4ca => mwedf22864_05a0_40c3_a0d5_ede45a3e7e8f + IXa + X + mwf5c3f9df_7ccf_4ca7_b241_471a66720da8; mwe0bb059d_deaa_45fa_b7dc_ec1c4409c4ca, mwe0bb059d_deaa_45fa_b7dc_ec1c4409c4ca, Rate Law: mw6b555ed1_194e_4fa4_9688_8105aa7c60c0*mwe0bb059d_deaa_45fa_b7dc_ec1c4409c4ca
mwec1b7289_5544_4c2b_b9f6_bf6524cabda5 = 3.15; mwaa306898_0d0f_4748_b48a_fcd56bdc0b16 = 0.15 Reaction: Xa + VIII => mw7a1594c9_f04f_478c_9f5f_ccbe0b95a820; Xa, VIII, mw7a1594c9_f04f_478c_9f5f_ccbe0b95a820, Xa, VIII, mw7a1594c9_f04f_478c_9f5f_ccbe0b95a820, Rate Law: mwaa306898_0d0f_4748_b48a_fcd56bdc0b16*Xa*VIII-mwec1b7289_5544_4c2b_b9f6_bf6524cabda5*mw7a1594c9_f04f_478c_9f5f_ccbe0b95a820

States:

Name Description
IX IX
mwa4fcfa0c 6944 42fc 8c74 7865f13953c8 APC_IXa_VIIIa
mwedf22864 05a0 40c3 a0d5 ede45a3e7e8f APC
mw6a8501d2 9479 41ae 8616 1e8d0e1bbfa9 PC
ATIII ATIII
Xa Va II Xa_Va_II
mw931f65a6 3967 4ac2 9904 ba791b216fc2 F12_deg
Xa Xa
mwf5c3f9df 7ccf 4ca7 b241 471a66720da8 VIIIa_deg
TF VIIa X TF_VIIa_X
TF VIIa Xa TF_VIIa_Xa
X X
Xa Va Xa_Va
mwe0bb059d deaa 45fa b7dc ec1c4409c4ca APC_IXa_VIIIa_X
mw18e5caa7 26eb 4521 b217 da75bb3193ad Va_deg
mw7a1594c9 f04f 478c 9f5f ccbe0b95a820 Xa_VIII
TF VIIa TF_VIIa
VIIIa VIIIa
Va Va
IIa IIa
Xa TFPI Xa_TFPI
VIIa VIIa
IXa VIIIa X IXa_VIIIa_X
TF VIIa IX TF_VIIa_IX
mwd68cbf38 9266 4dfb aa00 f817c3421aec Tmod
mw3cec90c2 500e 4f30 b6be 325ef5194755 IIa_Tmod_PC
IXa IXa
mwbdb849d8 2b25 4551 8de8 adc8bead2303 F12
IXa VIIIa IXa_VIIIa
II II
mwa6be116e 72f1 439e bca6 eb61f79cc68e IIa_Tmod
mw2e632a32 3823 4933 95cb 19567cbcc66a APC_Va

Observables: none

This model is from the article: An old paper revisited: ‘‘A mathematical model of carbohydrate energy metabolism. Int…

We revisit an old Russian paper by V.V. Dynnik, R. Heinrich and E.E. Sel'kov (1980a,b) describing: "A mathematical model of carbohydrate energy metabolism. Interaction between glycolysis, the Krebs cycle and the H-transporting shuttles at varying ATPases load". We analyse the model mathematically and calculate the control coefficients as a function of ATPase loads. We also evaluate the structure of the metabolic network in terms of elementary flux modes. We show how this model can respond to an ATPase load as well as to the glucose supply. We also show how this simple model can help in understanding the articulation between the major blocks of energetic metabolism, i.e. glycolysis, the Krebs cycle and the H-transporting shuttles. link: http://identifiers.org/pubmed/18304584

Parameters: none

States: none

Observables: none

BIOMD0000000232 @ v0.0.1

This a model from the article: Mitochondrial energetic metabolism: a simplified model of TCA cycle with ATP production…

Mitochondria play a central role in cellular energetic metabolism. The essential parts of this metabolism are the tricarboxylic acid (TCA) cycle, the respiratory chain and the adenosine triphosphate (ATP) synthesis machinery. Here a simplified model of these three metabolic components with a limited set of differential equations is presented. The existence of a steady state is demonstrated and results of numerical simulations are presented. The relevance of a simple model to represent actual in vivo behavior is discussed. link: http://identifiers.org/pubmed/19007794

Parameters:

Name Description
At = 4.16 millimolar Reaction: ADP = At-ATP, Rate Law: missing
k2=0.152 per millimolar per second Reaction: Pyr + NAD => AcCoA + NADH, Rate Law: mitochondrion*k2*Pyr*NAD
JANT = NaN millimolar per second Reaction: ATP => ADP, Rate Law: mitochondrion*JANT
k8=3.6 per second Reaction: OAA =>, Rate Law: mitochondrion*k8*OAA
k7=0.04 per millimolar per second Reaction: Pyr + ATP => OAA + ADP, Rate Law: mitochondrion*k7*Pyr*ATP
Keq=0.3975 dimensionless; k6=0.0032 per second Reaction: OAA => KG, Rate Law: mitochondrion*k6*(OAA-KG/Keq)
k3=57.142 per millimolar per second Reaction: OAA + AcCoA => Cit, Rate Law: mitochondrion*k3*OAA*AcCoA
Jresp = NaN millimolar per second Reaction: NADH + O2 + H => NAD + H2O + He, Rate Law: mitochondrion*Jresp
Nt = 1.07 millimolar Reaction: NADH = Nt-NAD, Rate Law: missing
k1=0.038 millimolar per second Reaction: => Pyr, Rate Law: mitochondrion*k1
k5=0.082361 per millimolar squared per second; At = 4.16 millimolar Reaction: KG + ADP + NAD => OAA + ATP + NADH, Rate Law: mitochondrion*k5*KG*NAD*(At-ATP)
k4=0.053 per millimolar per second Reaction: Cit + NAD => KG + NADH, Rate Law: mitochondrion*k4*Cit*NAD
JATP = NaN millimolar per second Reaction: ADP + iP + He => ATP + H2O + H, Rate Law: mitochondrion*JATP
Jleak = NaN millimolar per second Reaction: He => H, Rate Law: mitochondrion*Jleak

States:

Name Description
O2 [dioxygen; Oxygen]
iP [phosphate(3-); Orthophosphate]
ATP [ATP; ATP]
NADH [NADH; NADH]
Cit [citrate(3-); Citrate]
Pyr [pyruvate; Pyruvate]
AcCoA [acetyl-CoA; Acetyl-CoA]
H2O [water; H2O]
OAA [oxaloacetate(2-); Oxaloacetate]
ADP [ADP; ADP]
He [proton; H+]
NAD [NAD(+); NAD+]
H [proton; H+]
KG [2-oxoglutarate(2-); 2-Oxoglutarate]

Observables: none

This a model from the article: A mathematical model for IL-6-mediated, stem cell driven tumor growth and targeted trea…

Targeting key regulators of the cancer stem cell phenotype to overcome their critical influence on tumor growth is a promising new strategy for cancer treatment. Here we present a modeling framework that operates at both the cellular and molecular levels, for investigating IL-6 mediated, cancer stem cell driven tumor growth and targeted treatment with anti-IL6 antibodies. Our immediate goal is to quantify the influence of IL-6 on cancer stem cell self-renewal and survival, and to characterize the subsequent impact on tumor growth dynamics. By including the molecular details of IL-6 binding, we are able to quantify the temporal changes in fractional occupancies of bound receptors and their influence on tumor volume. There is a strong correlation between the model output and experimental data for primary tumor xenografts. We also used the model to predict tumor response to administration of the humanized IL-6R monoclonal antibody, tocilizumab (TCZ), and we found that as little as 1mg/kg of TCZ administered weekly for 7 weeks is sufficient to result in tumor reduction and a sustained deceleration of tumor growth. link: http://identifiers.org/pubmed/29351275

Parameters:

Name Description
R_Td = 2.075E-7; P_DD = 0.0133297534723066 Reaction: => IL_6R_on_D, Rate Law: compartment*R_Td*P_DD
K_f = 2.35 1/(fmol*d) Reaction: => IL_6__Cell_bound_IL_6R_complex_on_E; IL_6__L, IL_6R_on_E, Rate Law: compartment*K_f*IL_6__L*IL_6R_on_E
P_S = 0.899999967997301; alpha_S = 0.6 1/d Reaction: => Cancer_Stem_Cell_S, Rate Law: compartment*alpha_S*P_S*Cancer_Stem_Cell_S
R_Ts = 1.66E-6 fmol; P_phiS = 539.99998079838 Reaction: => IL_6R_on_S, Rate Law: compartment*R_Ts*P_phiS
A_out = 2.0; alpha_E = 0.666487673615332 1/d Reaction: => Differentiated_tumor_cell_D; Progenitor_tumor_cell_E, Rate Law: compartment*A_out*alpha_E*Progenitor_tumor_cell_E
delta_D = 0.0612 1/d; phi_D = 0.0; gamma_D = 2.38 Reaction: Differentiated_tumor_cell_D =>, Rate Law: compartment*delta_D*Differentiated_tumor_cell_D/(1+gamma_D*phi_D)
K_r = 2.24 1/d Reaction: IL_6__Cell_bound_IL_6R_complex_on_D => IL_6__L + IL_6R_on_D; IL_6__Cell_bound_IL_6R_complex_on_D, Rate Law: compartment*K_r*IL_6__Cell_bound_IL_6R_complex_on_D
lambda = 0.4152 1/d Reaction: IL_6__L =>, Rate Law: compartment*lambda*IL_6__L
R_Te = 2.075E-7; P_etaE = 119.993373526503 Reaction: => IL_6R_on_E, Rate Law: compartment*R_Te*P_etaE
R_Te = 2.075E-7; D_etaE = 6.12E-4 Reaction: IL_6__Cell_bound_IL_6R_complex_on_E => ; IL_6R_on_E, Rate Law: compartment*IL_6__Cell_bound_IL_6R_complex_on_E*R_Te*D_etaE/(IL_6R_on_E+IL_6__Cell_bound_IL_6R_complex_on_E)
R_Td = 2.075E-7; D_DD = 6.12E-4 Reaction: IL_6R_on_D => ; IL_6__Cell_bound_IL_6R_complex_on_D, Rate Law: compartment*IL_6R_on_D*R_Td*D_DD/(IL_6R_on_D+IL_6__Cell_bound_IL_6R_complex_on_D)
R_Ts = 1.66E-6 fmol; D_phiS = 12.6 Reaction: IL_6__Cell_bound_IL_6R_complex_on_S => ; IL_6R_on_S, Rate Law: compartment*IL_6__Cell_bound_IL_6R_complex_on_S*R_Ts*D_phiS/(IL_6R_on_S+IL_6__Cell_bound_IL_6R_complex_on_S)
gamma_S = 2.38; phi_S = 0.0; delta_S = 0.0126 1/d Reaction: Cancer_Stem_Cell_S =>, Rate Law: compartment*delta_S*Cancer_Stem_Cell_S/(1+gamma_S*phi_S)
alpha_E = 0.666487673615332 1/d Reaction: Progenitor_tumor_cell_E =>, Rate Law: compartment*alpha_E*Progenitor_tumor_cell_E
P_S = 0.899999967997301; alpha_S = 0.6 1/d; A_in = 2.0 Reaction: => Progenitor_tumor_cell_E; Cancer_Stem_Cell_S, Rate Law: compartment*A_in*alpha_S*(1-P_S)*Cancer_Stem_Cell_S
K_p = 24.95 1/d Reaction: IL_6__Cell_bound_IL_6R_complex_on_D => IL_6R_on_D; IL_6__Cell_bound_IL_6R_complex_on_D, Rate Law: compartment*K_p*IL_6__Cell_bound_IL_6R_complex_on_D
delta_E = 0.0612 1/d; gamma_E = 2.38; phi_E = 0.0 Reaction: Progenitor_tumor_cell_E =>, Rate Law: compartment*delta_E*Progenitor_tumor_cell_E/(1+gamma_E*phi_E)
rho = 7.0E-7 fmol/d Reaction: => IL_6__L; Cancer_Stem_Cell_S, Progenitor_tumor_cell_E, Differentiated_tumor_cell_D, Rate Law: compartment*rho*(Cancer_Stem_Cell_S+Progenitor_tumor_cell_E+Differentiated_tumor_cell_D)

States:

Name Description
IL 6 Cell bound IL 6R complex on S [Receptor; Interleukin-6; Interleukin-6; Cancer Stem Cell]
IL 6 Cell bound IL 6R complex on E [Interleukin-6; Receptor; Interleukin-6; Ancestor]
tumor tumor
Cancer Stem Cell S [Head and Neck Squamous Cell Carcinoma; head and neck squamous cell carcinoma; Cancer Stem Cell]
Progenitor tumor cell E [head and neck squamous cell carcinoma; Head and Neck Squamous Cell Carcinoma; Ancestor]
IL 6R on S [Receptor; Interleukin-6 receptor subunit alpha; Interleukin-6; Cancer Stem Cell]
Differentiated tumor cell D [head and neck squamous cell carcinoma; Head and Neck Squamous Cell Carcinoma; Interleukin-6; differentiated]
IL 6 Cell bound IL 6R complex on D [Interleukin-6; Interleukin-6; Receptor; differentiated]
IL 6R on E [Interleukin-6; Receptor; Interleukin-6 receptor subunit alpha; Ancestor]
IL 6R on D [Interleukin-6; Receptor; Interleukin-6 receptor subunit alpha; differentiated]
IL 6 L [Interleukin-6]

Observables: none

We propose a compartmental mathematical model for the spread of the COVID-19 disease with special focus on the transmiss…

We propose a compartmental mathematical model for the spread of the COVID-19 disease with special focus on the transmissibility of super-spreaders individuals. We compute the basic reproduction number threshold, we study the local stability of the disease free equilibrium in terms of the basic reproduction number, and we investigate the sensitivity of the model with respect to the variation of each one of its parameters. Numerical simulations show the suitability of the proposed COVID-19 model for the outbreak that occurred in Wuhan, China. link: http://identifiers.org/pubmed/32341628

Parameters: none

States: none

Observables: none

Use of the bacterium Wolbachia is an innovative new strategy designed to break the cycle of dengue transmission. There a…

Use of the bacterium Wolbachia is an innovative new strategy designed to break the cycle of dengue transmission. There are two main mechanisms by which Wolbachia could achieve this: by reducing the level of dengue virus in the mosquito and/or by shortening the host mosquito's lifespan. However, although Wolbachia shortens the lifespan, it also gives a breeding advantage which results in complex population dynamics. This study focuses on the development of a mathematical model to quantify the effect on human dengue cases of introducing Wolbachia into the mosquito population. The model consists of a compartment-based system of first-order differential equations; seasonal forcing in the mosquito population is introduced through the adult mosquito death rate. The analysis focuses on a single dengue outbreak typical of a region with a strong seasonally-varying mosquito population. We found that a significant reduction in human dengue cases can be obtained provided that Wolbachia-carrying mosquitoes persist when competing with mosquitoes without Wolbachia. Furthermore, using the Wolbachia strain WMel reduces the mosquito lifespan by at most 10% and allows them to persist in competition with non-Wolbachia-carrying mosquitoes. Mosquitoes carrying the WMelPop strain, however, are not likely to persist as it reduces the mosquito lifespan by up to 50%. When all other effects of Wolbachia on the mosquito physiology are ignored, cytoplasmic incompatibility alone results in a reduction in the number of human dengue cases. A sensitivity analysis of the parameters in the model shows that the transmission probability, the biting rate and the average adult mosquito death rate are the most important parameters for the outcome of the cumulative proportion of human individuals infected with dengue. link: http://identifiers.org/pubmed/25645184

Parameters: none

States: none

Observables: none

This a model from the article: Modeling defective interfering virus therapy for AIDS: conditions for DIV survival. N…

The administration of a genetically engineered defective interfering virus (DIV) that interferes with HIV-1 replication has been proposed as a therapy for HIV-1 infection and AIDS. The proposed interfering virus, which is designed to superinfect HIV-1 infected cells, carries ribozymes that cleave conserved regions in HIV-1 RNA that code for the viral envelope protein. Thus DIV infection of HIV-1 infected cells should reduce or eliminate viral production by these cells. The success of this therapeutic strategy will depend both on the intercellular interaction of DIV and HIV-1, and on the overall dynamics of virus and T cells in the body. To study these dynamical issues, we have constructed a mathematical model of the interaction of HIV-1, DIV, and CD4+ cells in vivo. The results of both mathematical analysis and numerical simulation indicate that survival of the engineered DIV purely on a peripheral blood HIV-1 infection is unlikely. However, analytical results indicate that DIV might well survive on HIV-1 infected CD4+ cells in lymphoid organs such as lymph nodes and spleen, or on other HIV-1 infected cells in these organs. link: http://identifiers.org/pubmed/7881191

Parameters: none

States: none

Observables: none

This a model from the article: Modeling defective interfering virus therapy for AIDS: conditions for DIV survival. N…

The administration of a genetically engineered defective interfering virus (DIV) that interferes with HIV-1 replication has been proposed as a therapy for HIV-1 infection and AIDS. The proposed interfering virus, which is designed to superinfect HIV-1 infected cells, carries ribozymes that cleave conserved regions in HIV-1 RNA that code for the viral envelope protein. Thus DIV infection of HIV-1 infected cells should reduce or eliminate viral production by these cells. The success of this therapeutic strategy will depend both on the intercellular interaction of DIV and HIV-1, and on the overall dynamics of virus and T cells in the body. To study these dynamical issues, we have constructed a mathematical model of the interaction of HIV-1, DIV, and CD4+ cells in vivo. The results of both mathematical analysis and numerical simulation indicate that survival of the engineered DIV purely on a peripheral blood HIV-1 infection is unlikely. However, analytical results indicate that DIV might well survive on HIV-1 infected CD4+ cells in lymphoid organs such as lymph nodes and spleen, or on other HIV-1 infected cells in these organs. link: http://identifiers.org/pubmed/7881191

Parameters: none

States: none

Observables: none

BIOMD0000000875 @ v0.0.1

This is the general model without delay described by the equation system (1) in: **A model of HIV-1 pathogenesis that in…

Mathematical modeling combined with experimental measurements have yielded important insights into HIV-1 pathogenesis. For example, data from experiments in which HIV-infected patients are given potent antiretroviral drugs that perturb the infection process have been used to estimate kinetic parameters underlying HIV infection. Many of the models used to analyze data have assumed drug treatments to be completely efficacious and that upon infection a cell instantly begins producing virus. We consider a model that allows for less then perfect drug effects and which includes a delay in the initiation of virus production. We present detailed analysis of this delay differential equation model and compare the results to a model without delay. Our analysis shows that when drug efficacy is less than 100%, as may be the case in vivo, the predicted rate of decline in plasma virus concentration depends on three factors: the death rate of virus producing cells, the efficacy of therapy, and the length of the delay. Thus, previous estimates of infected cell loss rates can be improved upon by considering more realistic models of viral infection. link: http://identifiers.org/pubmed/10701304

Parameters:

Name Description
k = 3.43E-8 l/(s*#) Reaction: T => T_i; V_I, Rate Law: plasma*k*V_I*T
delta = 0.5 1/ms Reaction: T_i =>, Rate Law: plasma*delta*T_i
np = 0.5 1; N = 480.0 1; delta = 0.5 1/ms Reaction: => V_I; T_i, Rate Law: plasma*(1-np)*N*delta*T_i
delta1 = 0.03 1/ms Reaction: T =>, Rate Law: plasma*delta1*T
c = 3.0 1/ms Reaction: V_I =>, Rate Law: plasma*c*V_I
lambda = 10.0 #/(l*s) Reaction: => T, Rate Law: plasma*lambda

States:

Name Description
V NI V_NI
T [uninfected]
T i [infected cell]
V I [C14283]

Observables: none

MODEL8102792069 @ v0.0.1

described in: **A model of HIV-1 pathogenesis that includes an intracellular delay.** Nelson PW, Murray JD, Perelson A…

Mathematical modeling combined with experimental measurements have yielded important insights into HIV-1 pathogenesis. For example, data from experiments in which HIV-infected patients are given potent antiretroviral drugs that perturb the infection process have been used to estimate kinetic parameters underlying HIV infection. Many of the models used to analyze data have assumed drug treatments to be completely efficacious and that upon infection a cell instantly begins producing virus. We consider a model that allows for less then perfect drug effects and which includes a delay in the initiation of virus production. We present detailed analysis of this delay differential equation model and compare the results to a model without delay. Our analysis shows that when drug efficacy is less than 100%, as may be the case in vivo, the predicted rate of decline in plasma virus concentration depends on three factors: the death rate of virus producing cells, the efficacy of therapy, and the length of the delay. Thus, previous estimates of infected cell loss rates can be improved upon by considering more realistic models of viral infection. link: http://identifiers.org/pubmed/10701304

Parameters: none

States: none

Observables: none

This is the reduced model (model 8) described in: **Dynamics within the CD95 death-inducing signaling complex decide lif…

This study explores the dilemma in cellular signaling that triggering of CD95 (Fas/APO-1) in some situations results in cell death and in others leads to the activation of NF-kappaB. We established an integrated kinetic mathematical model for CD95-mediated apoptotic and NF-kappaB signaling. Systematic model reduction resulted in a surprisingly simple model well approximating experimentally observed dynamics. The model postulates a new link between c-FLIP(L) cleavage in the death-inducing signaling complex (DISC) and the NF-kappaB pathway. We validated experimentally that CD95 stimulation resulted in an interaction of p43-FLIP with the IKK complex followed by its activation. Furthermore, we showed that the apoptotic and NF-kappaB pathways diverge already at the DISC. Model and experimental analysis of DISC formation showed that a subtle balance of c-FLIP(L) and procaspase-8 determines life/death decisions in a nonlinear manner. We present an integrated model describing the complex dynamics of CD95-mediated apoptosis and NF-kappaB signaling. link: http://identifiers.org/pubmed/20212524

Parameters:

Name Description
k3 = 0.6693316 Reaction: L_RF + FL => L_RF_FL, Rate Law: default*k3*L_RF*FL
k2 = 1.277248E-4 Reaction: L_RF + C8 => L_RF_C8, Rate Law: default*k2*L_RF*C8
k10 = 0.1205258 Reaction: C8 + C3_star => p43_p41 + C3_star, Rate Law: default*k10*C8*C3_star
k5 = 5.946569E-4 Reaction: L_RF_FS + C8 => L_RF_C8_FS, Rate Law: default*k5*L_RF_FS*C8
k4 = 1.0E-5 Reaction: L_RF + FS => L_RF_FS, Rate Law: default*k4*L_RF*FS
k9 = 0.002249759 Reaction: C3 + C8_star => C3_star + C8_star, Rate Law: default*k9*C3*C8_star
k16 = 0.02229912 Reaction: p43_FLIP_IKK_star =>, Rate Law: default*k16*p43_FLIP_IKK_star
k14 = 0.3588224 Reaction: NF_kB_IkB + p43_FLIP_IKK_star => NF_kB_IkB_P + p43_FLIP_IKK_star, Rate Law: default*k14*NF_kB_IkB*p43_FLIP_IKK_star
k7 = 0.8875063 Reaction: L_RF_FL + FS => L_RF_FL_FS, Rate Law: default*k7*L_RF_FL*FS
k13 = 7.204261E-4 Reaction: p43_FLIP + IKK => p43_FLIP_IKK_star, Rate Law: default*k13*p43_FLIP*IKK
k11 = 0.02891451 Reaction: C8_star =>, Rate Law: default*k11*C8_star
k17 = 0.0064182 Reaction: NF_kB_star =>, Rate Law: default*k17*NF_kB_star
k12 = 0.1502914 Reaction: C3_star =>, Rate Law: default*k12*C3_star
k1 = 1.0 Reaction: L + RF => L_RF, Rate Law: default*k1*L*RF
k6 = 0.9999999 Reaction: L_RF_FS + FL => L_RF_FL_FS, Rate Law: default*k6*L_RF_FS*FL
k15 = 3.684162 Reaction: NF_kB_IkB_P => NF_kB_star, Rate Law: default*k15*NF_kB_IkB_P
k8 = 8.044378E-4 Reaction: p43_p41 + p43_p41 => C8_star, Rate Law: default*k8*p43_p41*p43_p41

States:

Name Description
C3 star [Caspase-3]
L RF FL [CD95 ligand; CASP8 and FADD-like apoptosis regulator; FAS-associated death domain protein; Tumor necrosis factor receptor superfamily member 6]
C3 [Caspase-3]
p43 FLIP [CASP8 and FADD-like apoptosis regulator]
L [CD95 ligand]
IKK [NF-kappa-B essential modulator; Inhibitor of nuclear factor kappa-B kinase subunit beta; Inhibitor of nuclear factor kappa-B kinase subunit alpha]
p43 p41 [Caspase-8]
C8 [Caspase-8]
L RF C8 [CD95 ligand; Caspase-8; Tumor necrosis factor receptor superfamily member 6; FAS-associated death domain protein]
FL [CASP8 and FADD-like apoptosis regulator]
NF kB star [NF-kappaB complex; Nuclear factor NF-kappa-B p105 subunit]
p43 FLIP IKK star p43-FLIP:IKK*
L RF FS [CD95 ligand; CASP8 and FADD-like apoptosis regulator; FAS-associated death domain protein; Tumor necrosis factor receptor superfamily member 6]
NF kB IkB [IkBs:NFkB [cytosol]; NF-kappa-B inhibitor alpha; Nuclear factor NF-kappa-B p105 subunit]
C8 star [Caspase-8]
L RF [FAS-associated death domain protein; Tumor necrosis factor receptor superfamily member 6; CD95 ligand]
L RF FL FS [CD95 ligand; CASP8 and FADD-like apoptosis regulator; FAS-associated death domain protein; Tumor necrosis factor receptor superfamily member 6]
L RF FL FL [CD95 ligand; CASP8 and FADD-like apoptosis regulator; FAS-associated death domain protein; Tumor necrosis factor receptor superfamily member 6]
FS [CASP8 and FADD-like apoptosis regulator]
NF kB IkB P [NFkB Complex [cytosol]; Phospho-NF-kappaB Inhibitor [cytosol]; NF-kappa-B inhibitor alpha; Nuclear factor NF-kappa-B p105 subunit]
L RF C8 FS [CD95 ligand; CASP8 and FADD-like apoptosis regulator; Caspase-8; FAS-associated death domain protein; Tumor necrosis factor receptor superfamily member 6]
RF [FAS-associated death domain protein; Tumor necrosis factor receptor superfamily member 6]
L RF FS FS [CD95 ligand; CASP8 and FADD-like apoptosis regulator; FAS-associated death domain protein; Tumor necrosis factor receptor superfamily member 6]

Observables: none

Neves2008 - Role of cell shape and size in controlling intracellular signallingThe role of cell shape and size in the fl…

The role of cell size and shape in controlling local intracellular signaling reactions, and how this spatial information originates and is propagated, is not well understood. We have used partial differential equations to model the flow of spatial information from the beta-adrenergic receptor to MAPK1,2 through the cAMP/PKA/B-Raf/MAPK1,2 network in neurons using real geometries. The numerical simulations indicated that cell shape controls the dynamics of local biochemical activity of signal-modulated negative regulators, such as phosphodiesterases and protein phosphatases within regulatory loops to determine the size of microdomains of activated signaling components. The model prediction that negative regulators control the flow of spatial information to downstream components was verified experimentally in rat hippocampal slices. These results suggest a mechanism by which cellular geometry, the presence of regulatory loops with negative regulators, and key reaction rates all together control spatial information transfer and microdomain characteristics within cells. link: http://identifiers.org/pubmed/18485874

Parameters:

Name Description
KMOLE = 0.00166112956810631 item^(-1)*μmol*l^(-1)*μm^(-3); Vmax_PPase_mek = NaN 0.001*dimensionless*m^(-3)*mol*s^(-1); Km=15.7 0.001*dimensionless*m^(-3)*mol Reaction: MEK_active_cyto => MEK_cyto; PP2A_cyto, Rate Law: Vmax_PPase_mek*0.00166112956810631*MEK_active_cyto*1/(Km+0.00166112956810631*MEK_active_cyto)*cyto*1*1/KMOLE
I=0.0 dimensionless*A*m^(-2); Kf_AC_activation=500.0 1000*dimensionless*m^3*mol^(-1)*s^(-1); Kr_AC_activation=1.0 s^(-1) Reaction: G_a_s_cyto + AC_cyto_mem => AC_active_cyto_mem, Rate Law: (Kf_AC_activation*0.00166112956810631*G_a_s_cyto*AC_cyto_mem+(-Kr_AC_activation*AC_active_cyto_mem))*cyto_mem
KMOLE = 0.00166112956810631 item^(-1)*μmol*l^(-1)*μm^(-3); Km=0.77 0.001*dimensionless*m^(-3)*mol; Vmax_PPase_MAPK = NaN 0.001*dimensionless*m^(-3)*mol*s^(-1) Reaction: MAPK_active_cyto => MAPK_cyto; PP2A_cyto, Rate Law: Vmax_PPase_MAPK*0.00166112956810631*MAPK_active_cyto*1/(Km+0.00166112956810631*MAPK_active_cyto)*cyto*1*1/KMOLE
I=0.0 dimensionless*A*m^(-2); Kr=0.2 s^(-1); Kf=1.0 1000*dimensionless*m^3*mol^(-1)*s^(-1) Reaction: BAR_cyto_mem + iso_extra => iso_BAR_cyto_mem, Rate Law: (Kf*BAR_cyto_mem*0.00166112956810631*iso_extra+(-Kr*iso_BAR_cyto_mem))*cyto_mem
Vmax_PPase_Raf = NaN 0.001*dimensionless*m^(-3)*mol*s^(-1); KMOLE = 0.00166112956810631 item^(-1)*μmol*l^(-1)*μm^(-3); Km=15.7 0.001*dimensionless*m^(-3)*mol Reaction: B_Raf_active_cyto => B_Raf_cyto; PP2A_cyto, Rate Law: Vmax_PPase_Raf*0.00166112956810631*B_Raf_active_cyto*1/(Km+0.00166112956810631*B_Raf_active_cyto)*cyto*1*1/KMOLE
KMOLE = 0.00166112956810631 item^(-1)*μmol*l^(-1)*μm^(-3); Vmax_pde4_p_pde4_p = NaN 0.001*dimensionless*m^(-3)*mol*s^(-1); Km_pde4_p=1.3 0.001*dimensionless*m^(-3)*mol Reaction: cAMP_cyto => AMP_cyto; PDE4_P_cyto, Rate Law: Vmax_pde4_p_pde4_p*0.00166112956810631*cAMP_cyto*1/(Km_pde4_p+0.00166112956810631*cAMP_cyto)*cyto*1*1/KMOLE
Km_PDE4=1.3 0.001*dimensionless*m^(-3)*mol; KMOLE = 0.00166112956810631 item^(-1)*μmol*l^(-1)*μm^(-3); Vmax_PDE4_PDE4 = NaN 0.001*dimensionless*m^(-3)*mol*s^(-1) Reaction: cAMP_cyto => AMP_cyto; PDE4_cyto, Rate Law: Vmax_PDE4_PDE4*0.00166112956810631*cAMP_cyto*1/(Km_PDE4+0.00166112956810631*cAMP_cyto)*cyto*1*1/KMOLE
I=0.0 dimensionless*A*m^(-2); Km_AC_active=32.0 0.001*dimensionless*m^(-3)*mol; Vmax_AC_active_AC_active = NaN item*μm^(-2)*s^(-1) Reaction: ATP_cyto => cAMP_cyto; AC_active_cyto_mem, Rate Law: Vmax_AC_active_AC_active*0.00166112956810631*ATP_cyto*1/(Km_AC_active+0.00166112956810631*ATP_cyto)*cyto_mem
Kf_G_binds_BAR=0.3 1000*dimensionless*m^3*mol^(-1)*s^(-1); I=0.0 dimensionless*A*m^(-2); Kr_G_binds_BAR=0.1 s^(-1) Reaction: BAR_cyto_mem + G_protein_cyto => BAR_G_cyto_mem, Rate Law: (Kf_G_binds_BAR*BAR_cyto_mem*0.00166112956810631*G_protein_cyto+(-Kr_G_binds_BAR*BAR_G_cyto_mem))*cyto_mem
Km=0.046296 0.001*dimensionless*m^(-3)*mol; KMOLE = 0.00166112956810631 item^(-1)*μmol*l^(-1)*μm^(-3); Vmax_MEK_activates_MAPK = NaN 0.001*dimensionless*m^(-3)*mol*s^(-1) Reaction: MAPK_cyto => MAPK_active_cyto; MEK_active_cyto, Rate Law: Vmax_MEK_activates_MAPK*0.00166112956810631*MAPK_cyto*1/(Km+0.00166112956810631*MAPK_cyto)*cyto*1*1/KMOLE
Km=6.0 0.001*dimensionless*m^(-3)*mol; KMOLE = 0.00166112956810631 item^(-1)*μmol*l^(-1)*μm^(-3); Vmax_pp_ptp = NaN 0.001*dimensionless*m^(-3)*mol*s^(-1) Reaction: PTP_PKA_cyto => PTP_cyto; PTP_PP_cyto, Rate Law: Vmax_pp_ptp*0.00166112956810631*PTP_PKA_cyto*1/(Km+0.00166112956810631*PTP_PKA_cyto)*cyto*1*1/KMOLE
Vmax_highKM_PDE = NaN 0.001*dimensionless*m^(-3)*mol*s^(-1); Km=15.0 0.001*dimensionless*m^(-3)*mol; KMOLE = 0.00166112956810631 item^(-1)*μmol*l^(-1)*μm^(-3) Reaction: cAMP_cyto => AMP_cyto; PDE_high_km_cyto, Rate Law: Vmax_highKM_PDE*0.00166112956810631*cAMP_cyto*1/(Km+0.00166112956810631*cAMP_cyto)*cyto*1*1/KMOLE
I=0.0 dimensionless*A*m^(-2); Kr_activate_Gs=0.0 1000000*dimensionless*m^6*mol^(-2)*s^(-1); Kf_activate_Gs=0.025 s^(-1) Reaction: iso_BAR_G_cyto_mem => iso_BAR_cyto_mem + bg_cyto + G_a_s_cyto, Rate Law: (Kf_activate_Gs*iso_BAR_G_cyto_mem-Kr_activate_Gs*iso_BAR_cyto_mem*0.00166112956810631*bg_cyto*0.00166112956810631*G_a_s_cyto)*cyto_mem
KMOLE = 0.00166112956810631 item^(-1)*μmol*l^(-1)*μm^(-3); Km=0.15909 0.001*dimensionless*m^(-3)*mol; Vmax_Raf_activates_MEK = NaN 0.001*dimensionless*m^(-3)*mol*s^(-1) Reaction: MEK_cyto => MEK_active_cyto; B_Raf_active_cyto, Rate Law: Vmax_Raf_activates_MEK*0.00166112956810631*MEK_cyto*1/(Km+0.00166112956810631*MEK_cyto)*cyto*1*1/KMOLE
Vmax_PKA_P_PDE = NaN 0.001*dimensionless*m^(-3)*mol*s^(-1); Km=0.5 0.001*dimensionless*m^(-3)*mol; KMOLE = 0.00166112956810631 item^(-1)*μmol*l^(-1)*μm^(-3) Reaction: PDE4_cyto => PDE4_P_cyto; PKA_cyto, Rate Law: Vmax_PKA_P_PDE*0.00166112956810631*PDE4_cyto*1/(Km+0.00166112956810631*PDE4_cyto)*cyto*1*1/KMOLE
I=0.0 dimensionless*A*m^(-2); Km_grk=15.0 item*μm^(-2); Vmax_grk_GRK = NaN item*μm^(-2)*s^(-1) Reaction: iso_BAR_cyto_mem => iso_BAR_p_cyto_mem; GRK_cyto, Rate Law: Vmax_grk_GRK*iso_BAR_cyto_mem*1/(Km_grk+iso_BAR_cyto_mem)*cyto_mem
Km=0.5 0.001*dimensionless*m^(-3)*mol; KMOLE = 0.00166112956810631 item^(-1)*μmol*l^(-1)*μm^(-3); Vmax_PKA_activates_Raf = NaN 0.001*dimensionless*m^(-3)*mol*s^(-1) Reaction: B_Raf_cyto => B_Raf_active_cyto; PKA_cyto, Rate Law: Vmax_PKA_activates_Raf*0.00166112956810631*B_Raf_cyto*1/(Km+0.00166112956810631*B_Raf_cyto)*cyto*1*1/KMOLE
KMOLE = 0.00166112956810631 item^(-1)*μmol*l^(-1)*μm^(-3); Kr_GTPase=0.0 s^(-1); Kf_GTPase=0.06667 s^(-1) Reaction: G_a_s_cyto => G_GDP_cyto, Rate Law: (Kf_GTPase*0.00166112956810631*G_a_s_cyto+(-Kr_GTPase*0.00166112956810631*G_GDP_cyto))*cyto*1*1/KMOLE
Kr_G_binds_iso_BAR=0.1 s^(-1); I=0.0 dimensionless*A*m^(-2); Kf_G_binds_iso_BAR=10.0 1000*dimensionless*m^3*mol^(-1)*s^(-1) Reaction: iso_BAR_cyto_mem + G_protein_cyto => iso_BAR_G_cyto_mem, Rate Law: (Kf_G_binds_iso_BAR*iso_BAR_cyto_mem*0.00166112956810631*G_protein_cyto+(-Kr_G_binds_iso_BAR*iso_BAR_G_cyto_mem))*cyto_mem
KMOLE = 0.00166112956810631 item^(-1)*μmol*l^(-1)*μm^(-3); Km=0.46 0.001*dimensionless*m^(-3)*mol; Vmax_PTP = NaN 0.001*dimensionless*m^(-3)*mol*s^(-1) Reaction: MAPK_active_cyto => MAPK_cyto; PTP_cyto, Rate Law: Vmax_PTP*0.00166112956810631*MAPK_active_cyto*1/(Km+0.00166112956810631*MAPK_active_cyto)*cyto*1*1/KMOLE
I=0.0 dimensionless*A*m^(-2); Km_GRK_bg=4.0 item*μm^(-2); Vmax_GRK_bg_GRK_bg = NaN item*μm^(-2)*s^(-1) Reaction: iso_BAR_cyto_mem => iso_BAR_p_cyto_mem; GRK_bg_cyto, Rate Law: Vmax_GRK_bg_GRK_bg*iso_BAR_cyto_mem*1/(Km_GRK_bg+iso_BAR_cyto_mem)*cyto_mem
KMOLE = 0.00166112956810631 item^(-1)*μmol*l^(-1)*μm^(-3); Kr=2.8E-4 s^(-1); Kf=0.0059 1000*dimensionless*m^3*mol^(-1)*s^(-1) Reaction: R2C2_cyto + cAMP_cyto => c_R2C2_cyto, Rate Law: (Kf*0.00166112956810631*R2C2_cyto*0.00166112956810631*cAMP_cyto+(-Kr*0.00166112956810631*c_R2C2_cyto))*cyto*1*1/KMOLE
Km_pp2a_4=8.0 0.001*dimensionless*m^(-3)*mol; KMOLE = 0.00166112956810631 item^(-1)*μmol*l^(-1)*μm^(-3); Vmax_pp2a_4_pp2a_4 = NaN 0.001*dimensionless*m^(-3)*mol*s^(-1) Reaction: PDE4_P_cyto => PDE4_cyto; PP_PDE_cyto, Rate Law: Vmax_pp2a_4_pp2a_4*0.00166112956810631*PDE4_P_cyto*1/(Km_pp2a_4+0.00166112956810631*PDE4_P_cyto)*cyto*1*1/KMOLE
I=0.0 dimensionless*A*m^(-2); Kf=1.0 1000*dimensionless*m^3*mol^(-1)*s^(-1); Kr=0.062 s^(-1) Reaction: iso_extra + BAR_G_cyto_mem => iso_BAR_G_cyto_mem, Rate Law: (Kf*0.00166112956810631*iso_extra*BAR_G_cyto_mem+(-Kr*iso_BAR_G_cyto_mem))*cyto_mem
Vmax_PTP_PKA = NaN 0.001*dimensionless*m^(-3)*mol*s^(-1); KMOLE = 0.00166112956810631 item^(-1)*μmol*l^(-1)*μm^(-3); Km=9.0 0.001*dimensionless*m^(-3)*mol Reaction: MAPK_active_cyto => MAPK_cyto; PTP_PKA_cyto, Rate Law: Vmax_PTP_PKA*0.00166112956810631*MAPK_active_cyto*1/(Km+0.00166112956810631*MAPK_active_cyto)*cyto*1*1/KMOLE
Vmax_AC_basal_AC_basal = NaN item*μm^(-2)*s^(-1); I=0.0 dimensionless*A*m^(-2); Km_AC_basal=1030.0 0.001*dimensionless*m^(-3)*mol Reaction: ATP_cyto => cAMP_cyto; AC_cyto_mem, Rate Law: Vmax_AC_basal_AC_basal*0.00166112956810631*ATP_cyto*1/(Km_AC_basal+0.00166112956810631*ATP_cyto)*cyto_mem
Kf=8.35 1000*dimensionless*m^3*mol^(-1)*s^(-1); KMOLE = 0.00166112956810631 item^(-1)*μmol*l^(-1)*μm^(-3); Kr=0.0167 s^(-1) Reaction: c3_R2C2_cyto + cAMP_cyto => PKA_cyto, Rate Law: (Kf*0.00166112956810631*c3_R2C2_cyto*0.00166112956810631*cAMP_cyto+(-Kr*0.00166112956810631*PKA_cyto))*cyto*1*1/KMOLE
KMOLE = 0.00166112956810631 item^(-1)*μmol*l^(-1)*μm^(-3); Kr_bg_binds_GRK=0.25 s^(-1); Kf_bg_binds_GRK=1.0 1000*dimensionless*m^3*mol^(-1)*s^(-1) Reaction: GRK_cyto + bg_cyto => GRK_bg_cyto, Rate Law: (Kf_bg_binds_GRK*0.00166112956810631*GRK_cyto*0.00166112956810631*bg_cyto+(-Kr_bg_binds_GRK*0.00166112956810631*GRK_bg_cyto))*cyto*1*1/KMOLE
Kf_trimer=6.0 1000*dimensionless*m^3*mol^(-1)*s^(-1); KMOLE = 0.00166112956810631 item^(-1)*μmol*l^(-1)*μm^(-3); Kr_trimer=0.0 s^(-1) Reaction: bg_cyto + G_GDP_cyto => G_protein_cyto, Rate Law: (Kf_trimer*0.00166112956810631*bg_cyto*0.00166112956810631*G_GDP_cyto+(-Kr_trimer*0.00166112956810631*G_protein_cyto))*cyto*1*1/KMOLE
Km=0.1 0.001*dimensionless*m^(-3)*mol; KMOLE = 0.00166112956810631 item^(-1)*μmol*l^(-1)*μm^(-3); Vmax_PKA_P_PTP = NaN 0.001*dimensionless*m^(-3)*mol*s^(-1) Reaction: PTP_cyto => PTP_PKA_cyto; PKA_cyto, Rate Law: Vmax_PKA_P_PTP*0.00166112956810631*PTP_cyto*1/(Km+0.00166112956810631*PTP_cyto)*cyto*1*1/KMOLE

States:

Name Description
iso extra iso_extra
PDE4 P cyto [cAMP-specific 3',5'-cyclic phosphodiesterase 4A; IPR003607]
GRK cyto GRK_cyto
bg cyto bg_cyto
PKA cyto [Protein kinase, cAMP-dependent, catalytic, alphacAMP-dependent protein kinase catalytic subunit alpha]
BAR cyto mem [Beta-1 adrenergic receptor]
B Raf cyto [V-raf murine sarcoma viral oncogene B1-like protein]
MEK active cyto [Dual specificity mitogen-activated protein kinase kinase 1]
c3 R2C2 cyto [cAMP-dependent protein kinase complex]
R2C2 cyto [cAMP-dependent protein kinase complex]
G a s cyto [Guanine nucleotide-binding protein G(olf) subunit alpha]
AMP cyto [AMP; AMP]
B Raf active cyto [V-raf murine sarcoma viral oncogene B1-like protein]
iso BAR cyto mem [Beta-1 adrenergic receptor]
c R2C2 cyto [cAMP-dependent protein kinase complex]
cAMP cyto [3',5'-cyclic AMP; 3',5'-Cyclic AMP]
GRK bg cyto GRK_bg_cyto
G GDP cyto [GDP; IPR001019; GDP]
PTP PKA cyto [Tyrosine-protein phosphatase non-receptor type 7; Protein kinase, cAMP-dependent, catalytic, alphacAMP-dependent protein kinase catalytic subunit alpha]
MEK cyto [Dual specificity mitogen-activated protein kinase kinase 1]
iso BAR p cyto mem [Beta-1 adrenergic receptor]
G protein cyto [heterotrimeric G-protein complex]
AC cyto mem [Adenylate cyclase type 2; IPR001054]
PDE4 cyto [cAMP-specific 3',5'-cyclic phosphodiesterase 4A; IPR003607]
PTP cyto [Tyrosine-protein phosphatase non-receptor type 7]
AC active cyto mem [Adenylate cyclase type 2; IPR001054]
MAPK cyto [Mitogen-activated protein kinase 1]
BAR G cyto mem [Beta-1 adrenergic receptor]
iso BAR G cyto mem [Beta-1 adrenergic receptor; heterotrimeric G-protein complex]
ATP cyto [ATP; ATP]
c2 R2C2 cyto [cAMP-dependent protein kinase complex]
MAPK active cyto [Mitogen-activated protein kinase 1]

Observables: none

Its a mathematcial model explaining regulation of HIF via FIH and oxygen. Model is further validated by Experimental dat…

Activation of the hypoxia-inducible factor (HIF) pathway is a critical step in the transcriptional response to hypoxia. Although many of the key proteins involved have been characterised, the dynamics of their interactions in generating this response remain unclear. In the present study, we have generated a comprehensive mathematical model of the HIF-1α pathway based on core validated components and dynamic experimental data, and confirm the previously described connections within the predicted network topology. Our model confirms previous work demonstrating that the steps leading to optimal HIF-1α transcriptional activity require sequential inhibition of both prolyl- and asparaginyl-hydroxylases. We predict from our model (and confirm experimentally) that there is residual activity of the asparaginyl-hydroxylase FIH (factor inhibiting HIF) at low oxygen tension. Furthermore, silencing FIH under conditions where prolyl-hydroxylases are inhibited results in increased HIF-1α transcriptional activity, but paradoxically decreases HIF-1α stability. Using a core module of the HIF network and mathematical proof supported by experimental data, we propose that asparaginyl hydroxylation confers a degree of resistance upon HIF-1α to proteosomal degradation. Thus, through in vitro experimental data and in silico predictions, we provide a comprehensive model of the dynamic regulation of HIF-1α transcriptional activity by hydroxylases and use its predictive and adaptive properties to explain counter-intuitive biological observations. link: http://identifiers.org/pubmed/23390316

Parameters: none

States: none

Observables: none

Its a mathematcial model explaining regulation of HIF via FIH and oxygen. Model is further validated by Experimental dat…

Activation of the hypoxia-inducible factor (HIF) pathway is a critical step in the transcriptional response to hypoxia. Although many of the key proteins involved have been characterised, the dynamics of their interactions in generating this response remain unclear. In the present study, we have generated a comprehensive mathematical model of the HIF-1α pathway based on core validated components and dynamic experimental data, and confirm the previously described connections within the predicted network topology. Our model confirms previous work demonstrating that the steps leading to optimal HIF-1α transcriptional activity require sequential inhibition of both prolyl- and asparaginyl-hydroxylases. We predict from our model (and confirm experimentally) that there is residual activity of the asparaginyl-hydroxylase FIH (factor inhibiting HIF) at low oxygen tension. Furthermore, silencing FIH under conditions where prolyl-hydroxylases are inhibited results in increased HIF-1α transcriptional activity, but paradoxically decreases HIF-1α stability. Using a core module of the HIF network and mathematical proof supported by experimental data, we propose that asparaginyl hydroxylation confers a degree of resistance upon HIF-1α to proteosomal degradation. Thus, through in vitro experimental data and in silico predictions, we provide a comprehensive model of the dynamic regulation of HIF-1α transcriptional activity by hydroxylases and use its predictive and adaptive properties to explain counter-intuitive biological observations. link: http://identifiers.org/pubmed/23390316

Parameters: none

States: none

Observables: none

Feedback regulation in cell signalling: Lessons for cancer therapeuticsThis model is described in the article: [Feedba…

The notion of feedback is fundamental for understanding signal transduction networks. Feedback loops attenuate or amplify signals, change the network dynamics and modify the input-output relationships between the signal and the target. Negative feedback provides robustness to noise and adaptation to perturbations, but as a double-edged sword can prevent effective pathway inhibition by a drug. Positive feedback brings about switch-like network responses and can convert analog input signals into digital outputs, triggering cell fate decisions and phenotypic changes. We show how a multitude of protein-protein interactions creates hidden feedback loops in signal transduction cascades. Drug treatments that interfere with feedback regulation can cause unexpected adverse effects. Combinatorial molecular interactions generated by pathway crosstalk and feedback loops often bypass the block caused by targeted therapies against oncogenic mutated kinases. We discuss mechanisms of drug resistance caused by network adaptations and suggest that development of effective drug combinations requires understanding of how feedback loops modulate drug responses. link: http://identifiers.org/pubmed/26481970

Parameters:

Name Description
k8r = 0.01; k8f = 0.001 Reaction: RasGDP => RasGTP; aRTK, Rate Law: compartment*(k8f*RasGDP*aRTK-k8r*RasGTP)
k4f = 0.001; k4r = 0.01 Reaction: mTORC1 => amTORC1; aAkt, Rate Law: compartment*(k4f*mTORC1*aAkt-k4r*amTORC1)
k9f = 0.001; k9r = 0.01 Reaction: Raf => aRaf; RasGTP, Rate Law: compartment*(k9f*Raf*RasGTP-k9r*aRaf)
k6f = 0.1; k6r = 0.001 Reaction: IRS => iIRS; aS6K, Rate Law: compartment*(k6f*IRS*aS6K-k6r*iIRS)
k13f = 0.1; k13r = 0.001 Reaction: RTK => iRTK; aERK, Rate Law: compartment*(k13f*RTK*aERK-k13r*iRTK)
k2fa = 0.001; k2f = 0.001; k2r = 0.01 Reaction: PI3K => aPI3K; aIRS, aRTK, Rate Law: compartment*((k2f*aIRS+k2fa*aRTK)*PI3K-k2r*aPI3K)
k10r = 0.01; k10f = 0.001 Reaction: MEK => aMEK; aRaf, Rate Law: compartment*(k10f*MEK*aRaf-k10r*aMEK)
k5r = 0.01; k5f = 0.001 Reaction: S6K => aS6K; amTORC1, Rate Law: compartment*(k5f*S6K*amTORC1-k5r*aS6K)
k7fa = 0.01; k7r = 0.01; k7f = 0.01 Reaction: RTK => aRTK; FOXO, Rate Law: compartment*((k7f+k7fa*FOXO)*RTK-k7r*aRTK)
k3r = 0.01; k3f = 0.001 Reaction: Akt => aAkt; aPI3K, Rate Law: compartment*(k3f*Akt*aPI3K-k3r*aAkt)
k16r = 0.001; k16f = 0.01 Reaction: MEK + MEKI => iMEK, Rate Law: compartment*(k16f*MEK*MEKI-k16r*iMEK)
k11r = 0.01; k11f = 0.001 Reaction: ERK => aERK; aMEK, Rate Law: compartment*(k11f*ERK*aMEK-k11r*aERK)
k14f = 0.1; k14r = 0.001 Reaction: FOXO => iFOXO; aAkt, Rate Law: compartment*(k14f*FOXO*aAkt-k14r*iFOXO)
k12r = 0.001; k12f = 0.01 Reaction: Raf => iRaf; aERK, Rate Law: compartment*(k12f*Raf*aERK-k12r*iRaf)
k15r = 0.001; k15f = 0.01 Reaction: Akt + AktI => iAkt, Rate Law: compartment*(k15f*Akt*AktI-k15r*iAkt)
k1f = 0.01; k1r = 0.01 Reaction: IRS => aIRS, Rate Law: compartment*(k1f*IRS-k1r*aIRS)

States:

Name Description
iAkt [RAC-alpha serine/threonine-protein kinase]
Akt [RAC-alpha serine/threonine-protein kinase]
iIRS [urn:miriam:sbo:SBO%3A0000015]
RasGDP [GTPase HRas]
aERK [Mitogen-activated protein kinase 3]
iRaf [RAF proto-oncogene serine/threonine-protein kinase]
iMEK [Dual specificity mitogen-activated protein kinase kinase 1]
aRaf [RAF proto-oncogene serine/threonine-protein kinase]
amTORC1 [Serine/threonine-protein kinase mTOR]
S6K [Ribosomal protein S6 kinase beta-1]
RTK [Epithelial discoidin domain-containing receptor 1]
MEKI [inhibitor]
PI3K [urn:miriam:uniprot:C17270]
MEK [Dual specificity mitogen-activated protein kinase kinase 1]
AktI [inhibitor]
aAkt [RAC-alpha serine/threonine-protein kinase]
IRS [Insulin receptor substrate 1]
iRTK [Epithelial discoidin domain-containing receptor 1]
aS6K [Ribosomal protein S6 kinase beta-1]
aIRS [Insulin receptor substrate 1]
aMEK [Dual specificity mitogen-activated protein kinase kinase 1]
mTORC1 [Serine/threonine-protein kinase mTOR]
Raf [RAF proto-oncogene serine/threonine-protein kinase]
FOXO [Forkhead box protein O1]
RasGTP [GTPase HRas]
aRTK [Epithelial discoidin domain-containing receptor 1]
ERK [Mitogen-activated protein kinase 3]
iFOXO [Forkhead box protein O1]
aPI3K [Phosphatidylinositol-4,5-Bisphosphate 3-Kinase]

Observables: none

NguyenLK2011 - Ubiquitination dynamics in Ring1B-Bmi1 systemThis theoretical model investigates the dynamics of Ring1B/B…

In an active, self-ubiquitinated state, the Ring1B ligase monoubiquitinates histone H2A playing a critical role in Polycomb-mediated gene silencing. Following ubiquitination by external ligases, Ring1B is targeted for proteosomal degradation. Using biochemical data and computational modeling, we show that the Ring1B ligase can exhibit abrupt switches, overshoot transitions and self-perpetuating oscillations between its distinct ubiquitination and activity states. These different Ring1B states display canonical or multiply branched, atypical polyubiquitin chains and involve association with the Polycomb-group protein Bmi1. Bistable switches and oscillations may lead to all-or-none histone H2A monoubiquitination rates and result in discrete periods of gene (in)activity. Switches, overshoots and oscillations in Ring1B catalytic activity and proteosomal degradation are controlled by the abundances of Bmi1 and Ring1B, and the activities and abundances of external ligases and deubiquitinases, such as E6-AP and USP7. link: http://identifiers.org/pubmed/22194680

Parameters:

Name Description
k1=2.0; k2=0.2 Reaction: Bmi1 + R1B => Z, Rate Law: compartment*(k1*Bmi1*R1B-k2*Z)
v=7.5E-6 Reaction: => R1B, Rate Law: compartment*v
k1=0.02; k2=0.2 Reaction: Z => Zub, Rate Law: compartment*Z*(k1*Z+k2*Zub)
k=0.001 Reaction: R1Bubd => R1B; USP7tot, Rate Law: compartment*k*USP7tot*R1Bubd
k1=0.2; k2=0.2 Reaction: R1B => R1Bub, Rate Law: compartment*R1B*(k1*R1B+k2*R1Bub)
k1=3.0E-5 Reaction: R1Bubd =>, Rate Law: compartment*k1*R1Bubd
k=0.005 Reaction: R1Buba => R1B; USP7tot, Rate Law: compartment*k*USP7tot*R1Buba
k1=0.002; k2=2.0; k3=0.2 Reaction: H2A => H2Auba; R1Bub, Zub, R1Buba, Rate Law: compartment*H2A*(k1*R1Bub+k2*Zub+k3*R1Buba)
k1=0.01 Reaction: R1B => R1Bubd, Rate Law: compartment*k1*R1B
kc=0.005; Km=0.0025 Reaction: Zub => Z; USP7tot, Rate Law: compartment*kc*USP7tot*Zub/(Km+Zub)
k1=0.002 Reaction: Bmi1 => Bmi1ubd, Rate Law: compartment*k1*Bmi1
k=0.0075 Reaction: R1Bub => R1B; USP7tot, Rate Law: compartment*k*USP7tot*R1Bub
k1=0.012; k2=2.0E-5 Reaction: Zub => R1Buba + Bmi1, Rate Law: compartment*(k1*Zub-k2*R1Buba*Bmi1)

States:

Name Description
Bmi1ubd [Polycomb complex protein BMI-1]
R1Bubd [E3 ubiquitin-protein ligase RING1]
Z [Polycomb complex protein BMI-1; E3 ubiquitin-protein ligase RING1]
Bmi1 [Polycomb complex protein BMI-1]
R1Buba [E3 ubiquitin-protein ligase RING1]
R1Bub [E3 ubiquitin-protein ligase RING1]
H2A [Histone H2AX]
Zub [E3 ubiquitin-protein ligase RING1; Polycomb complex protein BMI-1]
H2Auba [Histone H2AX]
R1B [E3 ubiquitin-protein ligase RING1]

Observables: none

MODEL8687196544 @ v0.0.1

This a model from the article: A quantitative analysis of cardiac myocyte relaxation: a simulation study. Niederer S…

The determinants of relaxation in cardiac muscle are poorly understood, yet compromised relaxation accompanies various pathologies and impaired pump function. In this study, we develop a model of active contraction to elucidate the relative importance of the [Ca2+]i transient magnitude, the unbinding of Ca2+ from troponin C (TnC), and the length-dependence of tension and Ca2+ sensitivity on relaxation. Using the framework proposed by one of our researchers, we extensively reviewed experimental literature, to quantitatively characterize the binding of Ca2+ to TnC, the kinetics of tropomyosin, the availability of binding sites, and the kinetics of crossbridge binding after perturbations in sarcomere length. Model parameters were determined from multiple experimental results and modalities (skinned and intact preparations) and model results were validated against data from length step, caged Ca2+, isometric twitches, and the half-time to relaxation with increasing sarcomere length experiments. A factorial analysis found that the [Ca2+]i transient and the unbinding of Ca2+ from TnC were the primary determinants of relaxation, with a fivefold greater effect than that of length-dependent maximum tension and twice the effect of tension-dependent binding of Ca2+ to TnC and length-dependent Ca2+ sensitivity. The affects of the [Ca2+]i transient and the unbinding rate of Ca2+ from TnC were tightly coupled with the effect of increasing either factor, depending on the reference [Ca2+]i transient and unbinding rate. link: http://identifiers.org/pubmed/16339881

Parameters: none

States: none

Observables: none

BIOMD0000000042 @ v0.0.1

This model was automatically converted from model BIOMD0000000042 by using [libSBML](http://sbml.org/Software/libSBML)…

We report sustained oscillations in glycolysis conducted in an open system (a continuous-flow, stirred tank reactor; CSTR) with inflow of yeast extract as well as glucose. Depending on the operating conditions, we observe simple or complex periodic oscillations or chaos. We report the response of the system to instantaneous additions of small amounts of several substrates as functions of the amount added and the phase of the addition. We simulate oscillations and perturbations by a kinetic model based on the mechanism of glycolysis in a CSTR. We find that the response to particular perturbations forms an efficient tool for elucidating the mechanism of biochemical oscillations. link: http://identifiers.org/pubmed/17029704

Parameters:

Name Description
V2 = 1.5; K2 = 0.0016; k2 = 0.017; K2ATP = 0.01 Reaction: F6P + ATP => FBP + ADP; AMP, Rate Law: compartment*V2*ATP*F6P^2/((K2*(1+k2*(ATP/AMP)^2)+F6P^2)*(K2ATP+ATP))
k9f = 10.0; k9b = 10.0 Reaction: AMP + ATP => ADP, Rate Law: compartment*(k9f*AMP*ATP-k9b*ADP^2)
k3b = 50.0; k3f = 1.0 Reaction: FBP => GAP, Rate Law: compartment*(k3f*FBP-k3b*GAP^2)
V4 = 10.0; K4GAP = 1.0; K4NAD = 1.0 Reaction: GAP + NAD => DPG + NADH, Rate Law: compartment*V4*NAD*GAP/((K4GAP+GAP)*(K4NAD+NAD))
K1GLC = 0.1; V1 = 0.5; K1ATP = 0.063 Reaction: GLC + ATP => F6P + ADP, Rate Law: compartment*V1*ATP*GLC/((K1GLC+GLC)*(K1ATP+ATP))
flow = 0.011 Reaction: => ATP, Rate Law: compartment*(3.5-ATP)*flow
V6 = 10.0; K6ADP = 0.3; K6PEP = 0.2 Reaction: PEP + ADP => PYR + ATP, Rate Law: compartment*V6*ADP*PEP/((K6PEP+PEP)*(K6ADP+ADP))
k5f = 1.0; k5b = 0.5 Reaction: DPG + ADP => PEP + ATP, Rate Law: compartment*(k5f*DPG*ADP-k5b*PEP*ATP)
k8b = 1.43E-4; k8f = 1.0 Reaction: ACA + NADH => EtOH + NAD, Rate Law: compartment*(k8f*NADH*ACA-k8b*NAD*EtOH)
V7 = 2.0; K7PYR = 0.3 Reaction: PYR => ACA, Rate Law: compartment*V7*PYR/(K7PYR+PYR)
k10 = 0.05 Reaction: F6P => P, Rate Law: compartment*k10*F6P

States:

Name Description
ATP [ATP; ATP]
DPG [3-phospho-D-glyceroyl dihydrogen phosphate; 3-Phospho-D-glyceroyl phosphate]
NADH [NADH; NADH]
P P
PYR [pyruvate; Pyruvate]
EtOH [ethanol; Ethanol]
FBP [keto-D-fructose 1,6-bisphosphate; beta-D-Fructose 1,6-bisphosphate]
GLC [glucose; C00293]
F6P [CHEBI_20935; beta-D-Fructose 6-phosphate]
AMP [AMP; AMP]
ACA [acetaldehyde; Acetaldehyde]
GAP [D-glyceraldehyde 3-phosphate; D-Glyceraldehyde 3-phosphate]
PEP [phosphoenolpyruvate; Phosphoenolpyruvate]
ADP [ADP; ADP]
NAD [NAD(+); NAD+]

Observables: none

BIOMD0000000213 @ v0.0.1

This is an SBML version of the folate cycle model model from: **A mathematical model of the folate cycle: new insights…

A mathematical model is developed for the folate cycle based on standard biochemical kinetics. We use the model to provide new insights into several different mechanisms of folate homeostasis. The model reproduces the known pool sizes of folate substrates and the fluxes through each of the loops of the folate cycle and has the qualitative behavior observed in a variety of experimental studies. Vitamin B(12) deficiency, modeled as a reduction in the V(max) of the methionine synthase reaction, results in a secondary folate deficiency via the accumulation of folate as 5-methyltetrahydrofolate (the "methyl trap"). One form of homeostasis is revealed by the fact that a 100-fold up-regulation of thymidylate synthase and dihydrofolate reductase (known to occur at the G(1)/S transition) dramatically increases pyrimidine production without affecting the other reactions of the folate cycle. The model also predicts that an almost total inhibition of dihydrofolate reductase is required to significantly inhibit the thymidylate synthase reaction, consistent with experimental and clinical studies on the effects of methotrexate. Sensitivity to variation in enzymatic parameters tends to be local in the cycle and inversely proportional to the number of reactions that interconvert two folate substrates. Another form of homeostasis is a consequence of the nonenzymatic binding of folate substrates to folate enzymes. Without folate binding, the velocities of the reactions decrease approximately linearly as total folate is decreased. In the presence of folate binding and allosteric inhibition, the velocities show a remarkable constancy as total folate is decreased. link: http://identifiers.org/pubmed/15496403

Parameters:

Name Description
MTCH_VmaxF = 800000.0; MTCH_VmaxR = 20000.0; MTCH_Km_5_10_CHTHF = 250.0; MTCH_Km_10fTHF = 100.0 Reaction: _5_10_CHTHF => _10fTHF, Rate Law: MTCH_VmaxF*_5_10_CHTHF/(MTCH_Km_5_10_CHTHF+_5_10_CHTHF)-MTCH_VmaxR*_10fTHF/(MTCH_Km_10fTHF+_10fTHF)
FTD_Vmax = 14000.0; FTD_Km_10fTHF = 20.0 Reaction: _10fTHF => THF, Rate Law: FTD_Vmax*_10fTHF/(FTD_Km_10fTHF+_10fTHF)
DHFR_Km_NADPH = 4.0; DHFR_Vmax = 50.0; DHFR_Km_DHF = 0.5 Reaction: DHF => THF; NADPH, Rate Law: DHFR_Vmax*NADPH/(DHFR_Km_NADPH+NADPH)*DHF/(DHFR_Km_DHF+DHF)
AICART_Vmax = 45000.0; AICART_Km_10fTHF = 5.9; AICART_Km_AICAR = 100.0 Reaction: _10fTHF => THF; AICAR, Rate Law: AICART_Vmax*AICAR/(AICART_Km_AICAR+AICAR)*_10fTHF/(AICART_Km_10fTHF+_10fTHF)
MTD_VmaxR = 594000.0; MTD_Km_5_10_CHTHF = 10.0; MTD_VmaxF = 200000.0; MTD_Km_5_10_CH2THF = 2.0 Reaction: _5_10_CH2THF => _5_10_CHTHF, Rate Law: MTD_VmaxF*_5_10_CH2THF/(MTD_Km_5_10_CH2THF+_5_10_CH2THF)-MTD_VmaxR*_5_10_CHTHF/(MTD_Km_5_10_CHTHF+_5_10_CHTHF)
MS_Vmax = 500.0; MS_Km_Hcy = 0.1; MS_Km_5mTHF = 25.0; MS_Kd = 1.0 Reaction: _5mTHF => THF; Hcy, Rate Law: MS_Vmax*_5mTHF/MS_Km_5mTHF*Hcy/MS_Km_Hcy/(MS_Kd/MS_Km_5mTHF+_5mTHF/MS_Km_5mTHF+Hcy/MS_Km_Hcy+_5mTHF*Hcy/(MS_Km_5mTHF*MS_Km_Hcy))
PGT_Km_10fTHF = 4.9; PGT_Km_GAR = 520.0; PGT_Vmax = 16200.0 Reaction: _10fTHF => THF; GAR, Rate Law: PGT_Vmax*GAR/(PGT_Km_GAR+GAR)*_10fTHF/(PGT_Km_10fTHF+_10fTHF)
TS_Km_dUMP = 6.3; TS_Vmax = 50.0; TS_Km_5_10_CH2THF = 14.0 Reaction: _5_10_CH2THF => DHF; dUMP, Rate Law: TS_Vmax*dUMP/(TS_Km_dUMP+dUMP)*_5_10_CH2THF/(TS_Km_5_10_CH2THF+_5_10_CH2THF)
NE_k2 = 12.0; NE_k1 = 0.15 Reaction: THF => _5_10_CH2THF; HCOOH, Rate Law: HCOOH*NE_k1*THF-NE_k2*_5_10_CH2THF
FTS_Km_HCOOH = 43.0; FTS_Km_THF = 3.0; FTS_Vmax = 2000.0 Reaction: THF => _10fTHF; HCOOH, Rate Law: FTS_Vmax*HCOOH/(FTS_Km_HCOOH+HCOOH)*THF/(FTS_Km_THF+THF)
SHMT_Km_Ser = 600.0; SHMT_Km_THF = 50.0; SHMT_VmaxR = 25000.0; SHMT_VmaxF = 40000.0 Reaction: THF => _5_10_CH2THF; Ser, Gly, Rate Law: SHMT_VmaxF*Ser/(SHMT_Km_Ser+Ser)*THF/(SHMT_Km_THF+THF)-SHMT_VmaxR*Gly/(SHMT_Km_Ser+Gly)*_5_10_CH2THF/(SHMT_Km_THF+_5_10_CH2THF)
MTHFR_Km_5_10_CH2THF = 50.0; MTHFR_Vmax = 6000.0; MTHFR_Km_NADPH = 16.0 Reaction: _5_10_CH2THF => _5mTHF; NADPH, Rate Law: MTHFR_Vmax*NADPH/(MTHFR_Km_NADPH+NADPH)*_5_10_CH2THF/(MTHFR_Km_5_10_CH2THF+_5_10_CH2THF)

States:

Name Description
10fTHF [10-formyltetrahydrofolic acid; 10-Formyltetrahydrofolate]
5mTHF [5-methyltetrahydrofolic acid; 5-Methyltetrahydrofolate]
5 10 CHTHF [(6R)-5,10-methenyltetrahydrofolic acid; 5,10-Methenyltetrahydrofolate]
5 10 CH2THF [(6R)-5,10-methylenetetrahydrofolic acid; 5,10-Methylenetetrahydrofolate]
THF [(6S)-5,6,7,8-tetrahydrofolic acid; Tetrahydrofolate]
DHF [dihydrofolic acid; Dihydrofolate]

Observables: none

MODEL6655501972 @ v0.0.1

This is an SBML version of the folate cycle model model from: **A mathematical model of the folate cycle: new insights…

A mathematical model is developed for the folate cycle based on standard biochemical kinetics. We use the model to provide new insights into several different mechanisms of folate homeostasis. The model reproduces the known pool sizes of folate substrates and the fluxes through each of the loops of the folate cycle and has the qualitative behavior observed in a variety of experimental studies. Vitamin B(12) deficiency, modeled as a reduction in the V(max) of the methionine synthase reaction, results in a secondary folate deficiency via the accumulation of folate as 5-methyltetrahydrofolate (the "methyl trap"). One form of homeostasis is revealed by the fact that a 100-fold up-regulation of thymidylate synthase and dihydrofolate reductase (known to occur at the G(1)/S transition) dramatically increases pyrimidine production without affecting the other reactions of the folate cycle. The model also predicts that an almost total inhibition of dihydrofolate reductase is required to significantly inhibit the thymidylate synthase reaction, consistent with experimental and clinical studies on the effects of methotrexate. Sensitivity to variation in enzymatic parameters tends to be local in the cycle and inversely proportional to the number of reactions that interconvert two folate substrates. Another form of homeostasis is a consequence of the nonenzymatic binding of folate substrates to folate enzymes. Without folate binding, the velocities of the reactions decrease approximately linearly as total folate is decreased. In the presence of folate binding and allosteric inhibition, the velocities show a remarkable constancy as total folate is decreased. link: http://identifiers.org/pubmed/15496403

Parameters: none

States: none

Observables: none

MODEL1007200000 @ v0.0.1

This is the model described in the article: In silico experimentation with a model of hepatic mitochondrial folate met…

In eukaryotes, folate metabolism is compartmentalized and occurs in both the cytosol and the mitochondria. The function of this compartmentalization and the great changes that occur in the mitochondrial compartment during embryonic development and in rapidly growing cancer cells are gradually becoming understood, though many aspects remain puzzling and controversial.We explore the properties of cytosolic and mitochondrial folate metabolism by experimenting with a mathematical model of hepatic one-carbon metabolism. The model is based on known biochemical properties of mitochondrial and cytosolic enzymes. We use the model to study questions about the relative roles of the cytosolic and mitochondrial folate cycles posed in the experimental literature. We investigate: the control of the direction of the mitochondrial and cytosolic serine hydroxymethyltransferase (SHMT) reactions, the role of the mitochondrial bifunctional enzyme, the role of the glycine cleavage system, the effects of variations in serine and glycine inputs, and the effects of methionine and protein loading.The model reproduces many experimental findings and gives new insights into the underlying properties of mitochondrial folate metabolism. Particularly interesting is the remarkable stability of formate production in the mitochondria in the face of large changes in serine and glycine input. The model shows that in the presence of the bifunctional enzyme (as in embryonic tissues and cancer cells), the mitochondria primarily support cytosolic purine and pyrimidine synthesis via the export of formate, while in adult tissues the mitochondria produce serine for gluconeogenesis. link: http://identifiers.org/pubmed/17150100

Parameters: none

States: none

Observables: none

This model represents NIK-dependent p100 processing into p52 followed by binding to RelB and NIK-dependent IkBd degradat…

Signaling pathways often share molecular components, tying the activity of one pathway to the functioning of another. In the NFκB signaling system, distinct kinases mediate inflammatory and developmental signaling via RelA and RelB, respectively. Although the substrates of the developmental, so-called noncanonical, pathway are induced by inflammatory/canonical signaling, crosstalk is limited. Through dynamical systems modeling, we identified the underlying regulatory mechanism. We found that as the substrate of the noncanonical kinase NIK, the nfkb2 gene product p100, transitions from a monomer to a multimeric complex, it may compete with and inhibit p100 processing to the active p52. Although multimeric complexes of p100 (IκBδ) are known to inhibit preexisting RelA:p50 through sequestration, here we report that p100 complexes can inhibit the enzymatic formation of RelB:p52. We show that the dose–response systems properties of this complex substrate competition motif are poorly accounted for by standard Michaelis–Menten kinetics, but require more detailed mass action formulations. In sum, although tonic inflammatory signaling is required for adequate expression of the noncanonical pathway precursors, the substrate complex competition motif identified here can prevent amplification of the active RelB:p52 dimer in elevated inflammatory conditions to ensure reliable RelB-dependent developmental signaling independent of inflammatory context. link: http://identifiers.org/doi/10.1073/pnas.1816000116

Parameters:

Name Description
k1=0.0228 Reaction: RelB =>, Rate Law: compartment*k1*RelB
k=1000.0; Kd=50.0; canon = 1.0 Reaction: => p100, Rate Law: compartment*k*canon/(Kd+canon)
k1=0.05 Reaction: p100_NIK => p52 + NIK, Rate Law: compartment*k1*p100_NIK
k2=0.00144; k1=9.6E-4 Reaction: RelB + p52 => RelB_p52, Rate Law: compartment*(k1*RelB*p52-k2*RelB_p52)
k1=1.6E-5; k2=2.4E-4 Reaction: p100 => IkBd, Rate Law: compartment*(k1*p100^2-k2*IkBd)
k1=3.8E-4 Reaction: IkBd =>, Rate Law: compartment*k1*IkBd
k1=0.005; k2=2.4E-4 Reaction: IkBd + NIK => IkBd_NIK, Rate Law: compartment*(k1*IkBd*NIK-k2*IkBd_NIK)
k=42.0; Kd=50.0; canon = 1.0 Reaction: => RelB, Rate Law: compartment*k*canon/(Kd+canon)

States:

Name Description
IkBd [Nuclear factor NF-kappa-B p100 subunit]
NIK [Mitogen-activated protein kinase kinase kinase 14]
p100 NIK [Nuclear factor NF-kappa-B p100 subunit; Mitogen-activated protein kinase kinase kinase 14]
p52 [Nuclear factor NF-kappa-B p100 subunit]
IkBd NIK [Nuclear factor NF-kappa-B p100 subunit; Mitogen-activated protein kinase kinase kinase 14]
RelB [Transcription factor RelB]
p100 [Nuclear factor NF-kappa-B p100 subunit]
RelB p52 [Nuclear factor NF-kappa-B p100 subunit; Transcription factor RelB]

Observables: none

BIOMD0000000291 @ v0.0.1

This a model from the article: Mathematical model of binding of albumin-bilirubin complex to the surface of carbon p…

We proposed a mathematical model and estimated the parameters of adsorption of albumin-bilirubin complex to the surface of carbon pyropolymer. Design data corresponded to the results of experimental studies. Our findings indicate that modeling of this process should take into account fractal properties of the surface of carbon pyropolymer. link: http://identifiers.org/pubmed/16307060

Parameters:

Name Description
k6 = 3.226E-7; k8 = 1.011E-7; k9 = 0.01685; k10 = 0.1325; n = 1.0 Reaction: x2 = (k6*x7*x6-k8*x2)+k9*x1*x7^(n+1)+k10*x4*x7, Rate Law: (k6*x7*x6-k8*x2)+k9*x1*x7^(n+1)+k10*x4*x7
k7 = 0.00301; k5 = 0.005489; k9 = 0.01685; n = 1.0 Reaction: x3 = (k5*x7^n*x5-k7*x3)+k9*x1*x7^(n+1), Rate Law: (k5*x7^n*x5-k7*x3)+k9*x1*x7^(n+1)
K_AlB = 95000.0; K_AlB2 = 3000.0; k3 = 5.095E-6; k9 = 0.01685; k10 = 0.1325; k4 = 2.656E-5; n = 1.0 Reaction: x1 = (((K_AlB*k3*x5*x6-K_AlB2*k4*x1*x6)-k3*x1)-k9*x1*x7^(n+1))+k4*x4+k10*x4*x7, Rate Law: (((K_AlB*k3*x5*x6-K_AlB2*k4*x1*x6)-k3*x1)-k9*x1*x7^(n+1))+k4*x4+k10*x4*x7
K_AlB2 = 3000.0; k10 = 0.1325; k4 = 2.656E-5 Reaction: x4 = (K_AlB2*k4*x1*x6-k4*x4)-k10*x4*x7, Rate Law: (K_AlB2*k4*x1*x6-k4*x4)-k10*x4*x7
n = 1.0 Reaction: x7 = (C0-x2)-n*x3, Rate Law: missing

States:

Name Description
x5 [Serum albumin]
x1 [Serum albumin; Bilirubin]
x7 [macromolecule; carbon atom]
x4 [Serum albumin; Bilirubin]
x2 [macromolecule; carbon atom; Bilirubin]
x6 [bilirubin; Bilirubin]
x3 [macromolecule; carbon atom; Serum albumin]

Observables: none

This is a mathematical mechanistic immunobiochemical model that incorporates T cell pathways that control programmed cel…

It was recently reported that acute influenza infection of the lung promoted distal melanoma growth in the dermis of mice. Melanoma-specific CD8+ T cells were shunted to the lung in the presence of the infection, where they expressed high levels of inflammation-induced cell-activation blocker PD-1, and became incapable of migrating back to the tumor site. At the same time, co-infection virus-specific CD8+ T cells remained functional while the infection was cleared. It was also unexpectedly found that PD-1 blockade immunotherapy reversed this effect. Here, we proceed to ground the experimental observations in a mechanistic immunobiochemical model that incorporates T cell pathways that control PD-1 expression. A core component of our model is a kinetic motif, which we call a PD-1 Double Incoherent Feed-Forward Loop (DIFFL), and which reflects known interactions between IRF4, Blimp-1, and Bcl-6. The different activity levels of the PD-1 DIFFL components, as a function of the cognate antigen levels and the given inflammation context, manifest themselves in phenotypically distinct outcomes. Collectively, the model allowed us to put forward a few working hypotheses as follows: (i) the melanoma-specific CD8+ T cells re-circulating with the blood flow enter the lung where they express high levels of inflammation-induced cell-activation blocker PD-1 in the presence of infection; (ii) when PD-1 receptors interact with abundant PD-L1, constitutively expressed in the lung, T cells loose motility; (iii) at the same time, virus-specific cells adapt to strong stimulation by their cognate antigen by lowering the transiently-elevated expression of PD-1, remaining functional and mobile in the inflamed lung, while the infection is cleared. The role that T cell receptor (TCR) activation and feedback loops play in the underlying processes are also highlighted and discussed. We hope that the results reported in our study could potentially contribute to the advancement of immunological approaches to cancer treatment and, as well, to a better understanding of a broader complexity of fundamental interactions between pathogens and tumors. link: http://identifiers.org/pubmed/30745900

Parameters:

Name Description
U_a_k_P = 0.0215814688039663; n_b = 2.0; a_b = 100.0; A_b = 10.0; r_b = 2.0; m_b = 2.0; K_b = 1.0; M_b = 10.0; k_b = 25.0 Reaction: => B; I, C, Rate Law: compartment*(a_b*U_a_k_P^n_b/(A_b^n_b+U_a_k_P^n_b)+k_b*I^m_b/(K_b^m_b+I^m_b))*M_b^r_b/(M_b^r_b+C^r_b)
mu_b = 1.0 Reaction: B =>, Rate Law: compartment*mu_b*B
U_a_k_P = 0.0215814688039663; n_c = 3.0; A_c = 0.01; r_c = 2.0; M_c = 10.0; a_c = 0.75 Reaction: => C; B, I, Rate Law: compartment*a_c*U_a_k_P^n_c/(A_c^n_c+U_a_k_P^n_c)*M_c^r_c/(M_c^r_c+B^r_c+I^r_c+C^r_c)
mu_c = 0.1 Reaction: C =>, Rate Law: compartment*mu_c*C
mu_p = 0.1 Reaction: P =>, Rate Law: compartment*mu_p*P
mu_i = 1.0 Reaction: I =>, Rate Law: compartment*mu_i*I
U_a_k_P = 0.0215814688039663; n_p = 3.0; sigma_p_tilde = 0.1; A_p = 0.1; a_p = 0.75; M_p = 10.0; r_p = 4.0 Reaction: => P; B, Rate Law: compartment*(sigma_p_tilde+a_p*U_a_k_P^n_p/(A_p^n_p+U_a_k_P^n_p))*M_p^r_p/(M_p^r_p+B^r_p)
U_a_k_P = 0.0215814688039663; Q_i = 1.0; n_i = 2.0; a_i = 75.0; k_i = 7.5; m_i = 2.0; q_i = 7.5; s_i = 2.0; sigma_i = 0.3; K_i = 1.0; Phi_L_P = 1.0; A_i = 1.0 Reaction: => I; B, I, Rate Law: compartment*Phi_L_P*(sigma_i+a_i*U_a_k_P^n_i/(A_i^n_i+U_a_k_P^n_i)+k_i*B^m_i/(K_i^m_i+B^m_i)+q_i*I^s_i/(Q_i^s_i+I^s_i))

States:

Name Description
B [PR:000001831]
I [C17926]
C [C26149]
P [PR:000001919]

Observables: none

This is a mathematical model investigating the effects of continuous and intermittent PD-L1 and anti-PD-L1 therapy upon…

The use of immune checkpoint inhibitors is becoming more commonplace in clinical trials across the nation. Two important factors in the tumour-immune response are the checkpoint protein programmed death-1 (PD-1) and its ligand PD-L1. We propose a mathematical tumour-immune model using a system of ordinary differential equations to study dynamics with and without the use of anti-PD-1. A sensitivity analysis is conducted, and series of simulations are performed to investigate the effects of intermittent and continuous treatments on the tumour-immune dynamics. We consider the system without the anti-PD-1 drug to conduct a mathematical analysis to determine the stability of the tumour-free and tumorous equilibria. Through simulations, we found that a normally functioning immune system may control tumour. We observe treatment with anti-PD-1 alone may not be sufficient to eradicate tumour cells. Therefore, it may be beneficial to combine single agent treatments with additional therapies to obtain a better antitumour response. link: http://identifiers.org/doi/10.1080/23737867.2018.1440978

Parameters: none

States: none

Observables: none

Nishio2008 - Design of the phosphotransferase system for enhanced glucose uptake in E. coli.This model is described in t…

The phosphotransferase system (PTS) is the sugar transportation machinery that is widely distributed in prokaryotes and is critical for enhanced production of useful metabolites. To increase the glucose uptake rate, we propose a rational strategy for designing the molecular architecture of the Escherichia coli glucose PTS by using a computer-aided design (CAD) system and verified the simulated results with biological experiments. CAD supports construction of a biochemical map, mathematical modeling, simulation, and system analysis. Assuming that the PTS aims at controlling the glucose uptake rate, the PTS was decomposed into hierarchical modules, functional and flux modules, and the effect of changes in gene expression on the glucose uptake rate was simulated to make a rational strategy of how the gene regulatory network is engineered. Such design and analysis predicted that the mlc knockout mutant with ptsI gene overexpression would greatly increase the specific glucose uptake rate. By using biological experiments, we validated the prediction and the presented strategy, thereby enhancing the specific glucose uptake rate. link: http://identifiers.org/pubmed/18197177

Parameters:

Name Description
fast = 1.0E9 (60*s)^(-1); Kb=40000.0 mol^(-1)*l; one_per_M=1.0 mol^(-1)*l Reaction: CRP + cAMP => CRP_cAMP; CRP, cAMP, CRP_cAMP, Rate Law: cyt*fast*one_per_M*(Kb^2*(CRP*cAMP)^2-CRP_cAMP^2)
kmd=0.0866 (60*s)^(-1) Reaction: mRNA_crr => ; mRNA_crr, Rate Law: cyt*kmd*mRNA_crr
fast = 1.0E9 (60*s)^(-1); Kb=2.0E8 mol^(-1)*l Reaction: Mlc + Mlcsite_ptsGp1 => Mlc_Mlcsite_ptsGp1; Mlc, Mlcsite_ptsGp1, Mlc_Mlcsite_ptsGp1, Rate Law: cyt*fast*(Kb*Mlc*Mlcsite_ptsGp1-Mlc_Mlcsite_ptsGp1)
fast = 1.0E9 (60*s)^(-1); Kb=2.22E7 mol^(-1)*l Reaction: CRP_cAMP + CRPsiteI_crp => CRP_cAMP_CRPsiteI_crp; CRP_cAMP, CRPsiteI_crp, CRP_cAMP_CRPsiteI_crp, Rate Law: cyt*fast*(Kb*CRP_cAMP*CRPsiteI_crp-CRP_cAMP_CRPsiteI_crp)
Kmich=9.61 mol*l^(-1); Q=389.0 (60*s)^(-1) Reaction: IICB + Glc6P => IICB_P + Glucose; IICB, Glc6P, Rate Law: cyt*Q*IICB*Glc6P/(Kmich+Glc6P)
kmd=0.0889 (60*s)^(-1) Reaction: mRNA_ptsH => ; mRNA_ptsH, Rate Law: cyt*kmd*mRNA_ptsH
Q=4800.0 (60*s)^(-1); Kmich=2.0E-5 mol*l^(-1) Reaction: IICB_P + Glucose => IICB + Glc6P; IICB_P, Glucose, Rate Law: cyt*Q*IICB_P*Glucose/(Kmich+Glucose)
km=0.892 (60*s)^(-1); TCRPsite_ptsIp1 = 2.43E-10 mol*l^(-1) Reaction: => mRNA_ptsI; CRP_cAMP_CRPsite_ptsIp1, ptsIp1, CRP_cAMP_CRPsite_ptsIp1, ptsIp1, Rate Law: cyt*km*CRP_cAMP_CRPsite_ptsIp1/TCRPsite_ptsIp1*ptsIp1
Kmich=0.001 mol*l^(-1); Q=100.0 (60*s)^(-1) Reaction: ATP => cAMP; CYA, CYA, ATP, Rate Law: cyt*Q*CYA*ATP/(Kmich+ATP)
TCRPsite_ptsGp2 = 2.43E-10 mol*l^(-1); km=2.0 (60*s)^(-1); TMlcsite_ptsGp2 = 2.43E-10 mol*l^(-1) Reaction: => mRNA_ptsG; CRP_cAMP_CRPsite_ptsGp2, Mlc_Mlcsite_ptsGp2, ptsGp2, CRP_cAMP_CRPsite_ptsGp2, Mlc_Mlcsite_ptsGp2, ptsGp2, Rate Law: cyt*km*CRP_cAMP_CRPsite_ptsGp2/TCRPsite_ptsGp2*(1-Mlc_Mlcsite_ptsGp2/TMlcsite_ptsGp2)*ptsGp2
fast = 1.0E9 (60*s)^(-1); Kb=2430000.0 mol^(-1)*l Reaction: Mlc + Mlcsite_mlcp1 => Mlc_Mlcsite_mlcp1; Mlc, Mlcsite_mlcp1, Mlc_Mlcsite_mlcp1, Rate Law: cyt*fast*(Kb*Mlc*Mlcsite_mlcp1-Mlc_Mlcsite_mlcp1)
Kb=7000000.0 mol^(-1)*l; fast = 1.0E9 (60*s)^(-1) Reaction: IICB + Mlc => IICB_Mlc; IICB, Mlc, IICB_Mlc, Rate Law: cyt*fast*(Kb*IICB*Mlc-IICB_Mlc)
Kb=1350000.0 mol^(-1)*l; fast = 1.0E9 (60*s)^(-1) Reaction: Mlc + Mlcsite_mlcp2 => Mlc_Mlcsite_mlcp2; Mlc, Mlcsite_mlcp2, Mlc_Mlcsite_mlcp2, Rate Law: cyt*fast*(Kb*Mlc*Mlcsite_mlcp2-Mlc_Mlcsite_mlcp2)
fast = 1.0E9 (60*s)^(-1); Kb=1.0E8 mol^(-2)*l^2 Reaction: CYA + IIA_P => IIA_P_CYA; CYA, IIA_P, IIA_P_CYA, Rate Law: cyt*fast*(Kb*CYA*IIA_P^2-IIA_P_CYA)
kx=2.4E8 mol^(-1)*l*(60*s)^(-1) Reaction: IICB_P + IIA => IICB + IIA_P; IIA, IICB_P, Rate Law: cyt*kx*IIA*IICB_P
fast = 1.0E9 (60*s)^(-1); Kb=2700000.0 mol^(-1)*l Reaction: CRP_cAMP + CRPsiteII_crp => CRP_cAMP_CRPsiteII_crp; CRP_cAMP, CRPsiteII_crp, CRP_cAMP_CRPsiteII_crp, Rate Law: cyt*fast*(Kb*CRP_cAMP*CRPsiteII_crp-CRP_cAMP_CRPsiteII_crp)
kx=6.6E8 mol^(-1)*l*(60*s)^(-1) Reaction: IICB + IIA_P => IICB_P + IIA; IICB, IIA_P, Rate Law: cyt*kx*IICB*IIA_P
kp=11.0 (60*s)^(-1) Reaction: => CRP; mRNA_crp, mRNA_crp, Rate Law: cyt*kp*mRNA_crp
kpd=0.1 (60*s)^(-1) Reaction: CRP_cAMP_CRPsite_ptsIp1 => CRPsite_ptsIp1; CRP_cAMP_CRPsite_ptsIp1, Rate Law: cyt*kpd*CRP_cAMP_CRPsite_ptsIp1
kmd=0.0797 (60*s)^(-1) Reaction: mRNA_ptsI => ; mRNA_ptsI, Rate Law: cyt*kmd*mRNA_ptsI
Q=480000.0 (60*s)^(-1); Kmich=0.002 mol*l^(-1) Reaction: EI_P + Pyr => EI + PEP; EI_P, Pyr, Rate Law: cyt*2*Q*EI_P*Pyr^2/(Kmich^2+Pyr^2)
TCRPsite_ptsGp1 = 2.43E-10 mol*l^(-1); km=892.0 (60*s)^(-1); TMlcsite_ptsGp1 = 2.43E-10 mol*l^(-1) Reaction: => mRNA_ptsG; CRP_cAMP_CRPsite_ptsGp1, Mlc_Mlcsite_ptsGp1, ptsGp1, CRP_cAMP_CRPsite_ptsGp1, Mlc_Mlcsite_ptsGp1, ptsGp1, Rate Law: cyt*km*CRP_cAMP_CRPsite_ptsGp1/TCRPsite_ptsGp1*(1-Mlc_Mlcsite_ptsGp1/TMlcsite_ptsGp1)*ptsGp1
km=334.5 (60*s)^(-1) Reaction: => mRNA_crr; crr, crr, Rate Law: cyt*km*crr
kpd=400.0 (60*s)^(-1) Reaction: cAMP => ; cAMP, Rate Law: cyt*kpd*cAMP
kx=3.66E9 mol^(-1)*l*(60*s)^(-1) Reaction: IIA + HPr_P => IIA_P + HPr; IIA, HPr_P, Rate Law: cyt*kx*IIA*HPr_P
kx=4.8E8 mol^(-1)*l*(60*s)^(-1) Reaction: HPr_P + EI => HPr + EI_P; EI, HPr_P, Rate Law: cyt*kx*EI*HPr_P
Q=9000.0 (60*s)^(-1); Kmich=0.001 mol*l^(-1) Reaction: ATP => cAMP; IIA_P_CYA, IIA_P_CYA, ATP, Rate Law: cyt*Q*IIA_P_CYA*ATP/(Kmich+ATP)
kmd=0.3014 (60*s)^(-1) Reaction: mRNA_mlc => ; mRNA_mlc, Rate Law: cyt*kmd*mRNA_mlc
kx=1.2E10 mol^(-1)*l*(60*s)^(-1) Reaction: HPr + EI_P => HPr_P + EI; HPr, EI_P, Rate Law: cyt*kx*HPr*EI_P
fast = 1.0E9 (60*s)^(-1); Kb=6.67E7 mol^(-1)*l Reaction: CRP_cAMP + CRPsite_cyaA => CRP_cAMP_CRPsite_cyaA; CRP_cAMP, CRPsite_cyaA, CRP_cAMP_CRPsite_cyaA, Rate Law: cyt*fast*(Kb*CRP_cAMP*CRPsite_cyaA-CRP_cAMP_CRPsite_cyaA)
km=1.875 (60*s)^(-1); TMlcsite_mlcp2 = 2.43E-10 mol*l^(-1); TCRPsite_mlcp2 = 2.43E-10 mol*l^(-1) Reaction: => mRNA_mlc; CRP_cAMP_CRPsite_mlcp2, Mlc_Mlcsite_mlcp2, mlcp2, CRP_cAMP_CRPsite_mlcp2, Mlc_Mlcsite_mlcp2, mlcp2, Rate Law: cyt*km*CRP_cAMP_CRPsite_mlcp2/TCRPsite_mlcp2*(1-Mlc_Mlcsite_mlcp2/TMlcsite_mlcp2)*mlcp2
fast = 1.0E9 (60*s)^(-1); Kb=1.0E7 mol^(-1)*l Reaction: CRP_cAMP + CRPsite_ptsIp1 => CRP_cAMP_CRPsite_ptsIp1; CRP_cAMP, CRPsite_ptsIp1, CRP_cAMP_CRPsite_ptsIp1, Rate Law: cyt*fast*(Kb*CRP_cAMP*CRPsite_ptsIp1-CRP_cAMP_CRPsite_ptsIp1)
km=17.95 (60*s)^(-1); TCRPsite_ptsHp1 = 2.43E-10 mol*l^(-1) Reaction: => mRNA_ptsH; CRP_cAMP_CRPsite_ptsHp1, ptsHp1, CRP_cAMP_CRPsite_ptsHp1, ptsHp1, Rate Law: cyt*km*CRP_cAMP_CRPsite_ptsHp1/TCRPsite_ptsHp1*ptsHp1
kx=2.82E9 mol^(-1)*l*(60*s)^(-1) Reaction: IIA_P + HPr => IIA + HPr_P; HPr, IIA_P, Rate Law: cyt*kx*HPr*IIA_P
km=6.244 (60*s)^(-1); TCRPsite_ptsIp0 = 2.43E-10 mol*l^(-1); TMlcsite_ptsIp0 = 2.43E-10 mol*l^(-1) Reaction: => mRNA_ptsI; CRP_cAMP_CRPsite_ptsIp0, Mlc_Mlcsite_ptsIp0, ptsIp0, CRP_cAMP_CRPsite_ptsIp0, Mlc_Mlcsite_ptsIp0, ptsIp0, Rate Law: cyt*km*CRP_cAMP_CRPsite_ptsIp0/TCRPsite_ptsIp0*(1-Mlc_Mlcsite_ptsIp0/TMlcsite_ptsIp0)*ptsIp0
km=1.875 (60*s)^(-1); TCRPsite_mlcp1 = 2.43E-10 mol*l^(-1); TMlcsite_mlcp1 = 2.43E-10 mol*l^(-1) Reaction: => mRNA_mlc; CRP_cAMP_CRPsite_mlcp1, Mlc_Mlcsite_mlcp1, mlcp1, CRP_cAMP_CRPsite_mlcp1, Mlc_Mlcsite_mlcp1, mlcp1, Rate Law: cyt*km*(1-CRP_cAMP_CRPsite_mlcp1/TCRPsite_mlcp1)*(1-Mlc_Mlcsite_mlcp1/TMlcsite_mlcp1)*mlcp1
kmd=0.217 (60*s)^(-1) Reaction: mRNA_ptsG => ; mRNA_ptsG, Rate Law: cyt*kmd*mRNA_ptsG
km=71.8 (60*s)^(-1); TCRPsite_ptsHp0 = 2.43E-10 mol*l^(-1); TMlcsite_ptsHp0 = 2.43E-10 mol*l^(-1) Reaction: => mRNA_ptsH; CRP_cAMP_CRPsite_ptsHp0, Mlc_Mlcsite_ptsHp0, ptsHp0, CRP_cAMP_CRPsite_ptsHp0, Mlc_Mlcsite_ptsHp0, ptsHp0, Rate Law: cyt*km*CRP_cAMP_CRPsite_ptsHp0/TCRPsite_ptsHp0*(1-Mlc_Mlcsite_ptsHp0/TMlcsite_ptsHp0)*ptsHp0
Q=108000.0 (60*s)^(-1); Kmich=3.0E-4 mol*l^(-1) Reaction: EI + PEP => EI_P + Pyr; EI, PEP, Rate Law: cyt*2*Q*EI*PEP^2/(Kmich^2+PEP^2)

States:

Name Description
Mlc Mlcsite ptsIp0 [Protein mlc; Protein mlc]
CYA [Adenylate cyclase]
mRNA ptsI [messenger RNA]
HPr P [Phosphocarrier protein HPr]
CRPsite mlcp1 [cAMP-activated global transcriptional regulator CRP; Protein mlc]
CRP cAMP [cAMP-activated global transcriptional regulator CRP; 3',5'-cyclic AMP]
IICB [Fused glucose-specific PTS enzymes: IIB component/IIC component2.7.1.69PTS glucose EIICB componentPTS glucose transporter subunit IIBCPTS glucose-specific subunit IIBCPTS system glucose-specific EIIBC component2.7.1.191PTS system glucose-specific EIICB componentPTS system glucose-specific transporter subunit IIBCPTS system, glucose-specific IIBC componentPtsG]
mRNA ptsH [messenger RNA]
cAMP [3',5'-cyclic AMP]
Mlc Mlcsite ptsGp2 [Protein mlc; Protein mlc]
Mlc Mlcsite ptsGp1 [Protein mlc; Protein mlc]
CRPsiteII crp [cAMP-activated global transcriptional regulator CRP]
CRP cAMP CRPsiteII crp [cAMP-activated global transcriptional regulator CRP; 3',5'-cyclic AMP]
EI [Phosphoenolpyruvate-protein phosphotransferase]
mRNA mlc [messenger RNA]
HPr [Phosphocarrier protein HPr]
Pyr [pyruvic acid]
Mlcsite mlcp1 [Protein mlc]
EI P [Phosphoenolpyruvate-protein phosphotransferase]
IICB Mlc [Fused glucose-specific PTS enzymes: IIB component/IIC component2.7.1.69PTS glucose EIICB componentPTS glucose transporter subunit IIBCPTS glucose-specific subunit IIBCPTS system glucose-specific EIIBC component2.7.1.191PTS system glucose-specific EIICB componentPTS system glucose-specific transporter subunit IIBCPTS system, glucose-specific IIBC componentPtsG; Protein mlc]
CRPsiteI crp [cAMP-activated global transcriptional regulator CRP]
IIA [PTS system glucose-specific EIIA component]
Mlc Mlcsite mlcp2 [Protein mlc; Protein mlc]
Mlc Mlcsite ptsHp0 [Protein mlc; Protein mlc]
IICB P [Fused glucose-specific PTS enzymes: IIB component/IIC component2.7.1.69PTS glucose EIICB componentPTS glucose transporter subunit IIBCPTS glucose-specific subunit IIBCPTS system glucose-specific EIIBC component2.7.1.191PTS system glucose-specific EIICB componentPTS system glucose-specific transporter subunit IIBCPTS system, glucose-specific IIBC componentPtsG]
CRPsite mlcp2 [cAMP-activated global transcriptional regulator CRP; Protein mlc]
CRP [cAMP-activated global transcriptional regulator CRP]
Mlc [Protein mlc]
PEP [phosphoenolpyruvic acid]
CRPsite ptsIp1 [cAMP-activated global transcriptional regulator CRP; Phosphoenolpyruvate-protein phosphotransferase]
mRNA ptsG [messenger RNA]
Mlc Mlcsite mlcp1 [Protein mlc; Protein mlc]
mRNA crr [messenger RNA]
CRP cAMP CRPsite cyaA [cAMP-activated global transcriptional regulator CRP; 3',5'-cyclic AMP]

Observables: none

MODEL8686121468 @ v0.0.1

This a model from the article: A modification of the Hodgkin--Huxley equations applicable to Purkinje fibre action and…

link: http://identifiers.org/pubmed/14480151

Parameters: none

States: none

Observables: none

MODEL0406151557 @ v0.0.1

This a model from the article: A model of sino-atrial node electrical activity based on a modification of the DiFrance…

DiFrancesco & Noble's (1984) equations (Phil. Trans. R. Soc. Lond. B (in the press.] have been modified to apply to the mammalian sino-atrial node. The modifications are based on recent experimental work. The modified equations successfully reproduce action potential and pacemaker activity in the node. Slightly different versions have been developed for peripheral regions that show a maximum diastolic potential near –75 mV and for central regions that do not hyperpolarize beyond –60 to –65 mV. Variations in extracellular potassium influence the frequency of pacemaker activity in the s.a. node model very much less than they do in the Purkinje fibre model. This corresponds well to the experimental observation that the node is less sensitive to external [K] than are Purkinje fibres. Activation of the Na-K exchange pump in the model by increasing intracellular sodium can suppress pacemaker activity. This phenomenon may contribute to the mechanism of overdrive suppression. link: http://identifiers.org/pubmed/6149553

Parameters: none

States: none

Observables: none

This a model from the article: The role of sodium-calcium exchange during the cardiac action potential. Noble D, Nob…

link: http://identifiers.org/pubmed/1785860

Parameters: none

States: none

Observables: none

MODEL1006230089 @ v0.0.1

This a model from the article: Improved guinea-pig ventricular cell model incorporating a diadic space, IKr and IKs, a…

The guinea-pig ventricular cell model, originally developed by Noble et al in 1991, has been greatly extended to include accumulation and depletion of calcium in a diadic space between the sarcolemma and the sarcoplasmic reticulum where, according to contempory understanding, the majority of calcium-induced calcium release is triggered. The calcium in this space is also assumed to play the major role in calcium-induced inactivation of the calcium current. Delayed potassium current equations have been developed to include the rapid (IKr) and slow (IKs) components of the delayed rectifier current based on the data of of Heath and Terrar, along with data from Sanguinetti and Jurkiewicz. Length- and tension-dependent changes in mechanical and electrophysiological processes have been incorporated as described recently by Kohl et al. Drug receptor interactions have started to be developed, using the sodium channel as the first target. The new model has been tested against experimental data on action potential clamp, and on force-interval and duration-interval relations; it has been found to reliably reproduce experimental observations. link: http://identifiers.org/pubmed/9487284

Parameters: none

States: none

Observables: none

MODEL1006230080 @ v0.0.1

This a model from the article: Improved guinea-pig ventricular cell model incorporating a diadic space, IKr and IKs, a…

The guinea-pig ventricular cell model, originally developed by Noble et al in 1991, has been greatly extended to include accumulation and depletion of calcium in a diadic space between the sarcolemma and the sarcoplasmic reticulum where, according to contempory understanding, the majority of calcium-induced calcium release is triggered. The calcium in this space is also assumed to play the major role in calcium-induced inactivation of the calcium current. Delayed potassium current equations have been developed to include the rapid (IKr) and slow (IKs) components of the delayed rectifier current based on the data of of Heath and Terrar, along with data from Sanguinetti and Jurkiewicz. Length- and tension-dependent changes in mechanical and electrophysiological processes have been incorporated as described recently by Kohl et al. Drug receptor interactions have started to be developed, using the sodium channel as the first target. The new model has been tested against experimental data on action potential clamp, and on force-interval and duration-interval relations; it has been found to reliably reproduce experimental observations. link: http://identifiers.org/pubmed/9487284

Parameters: none

States: none

Observables: none

MODEL1006230063 @ v0.0.1

This a model from the article: Improved guinea-pig ventricular cell model incorporating a diadic space, IKr and IKs, a…

The guinea-pig ventricular cell model, originally developed by Noble et al in 1991, has been greatly extended to include accumulation and depletion of calcium in a diadic space between the sarcolemma and the sarcoplasmic reticulum where, according to contempory understanding, the majority of calcium-induced calcium release is triggered. The calcium in this space is also assumed to play the major role in calcium-induced inactivation of the calcium current. Delayed potassium current equations have been developed to include the rapid (IKr) and slow (IKs) components of the delayed rectifier current based on the data of of Heath and Terrar, along with data from Sanguinetti and Jurkiewicz. Length- and tension-dependent changes in mechanical and electrophysiological processes have been incorporated as described recently by Kohl et al. Drug receptor interactions have started to be developed, using the sodium channel as the first target. The new model has been tested against experimental data on action potential clamp, and on force-interval and duration-interval relations; it has been found to reliably reproduce experimental observations. link: http://identifiers.org/pubmed/9487284

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Nogales2008 - Genome-scale metabolic network of Pseudomonas putida (iJN746)This model is described in the article: [A g…

BACKGROUND: Pseudomonas putida is the best studied pollutant degradative bacteria and is harnessed by industrial biotechnology to synthesize fine chemicals. Since the publication of P. putida KT2440's genome, some in silico analyses of its metabolic and biotechnology capacities have been published. However, global understanding of the capabilities of P. putida KT2440 requires the construction of a metabolic model that enables the integration of classical experimental data along with genomic and high-throughput data. The constraint-based reconstruction and analysis (COBRA) approach has been successfully used to build and analyze in silico genome-scale metabolic reconstructions. RESULTS: We present a genome-scale reconstruction of P. putida KT2440's metabolism, iJN746, which was constructed based on genomic, biochemical, and physiological information. This manually-curated reconstruction accounts for 746 genes, 950 reactions, and 911 metabolites. iJN746 captures biotechnologically relevant pathways, including polyhydroxyalkanoate synthesis and catabolic pathways of aromatic compounds (e.g., toluene, benzoate, phenylacetate, nicotinate), not described in other metabolic reconstructions or biochemical databases. The predictive potential of iJN746 was validated using experimental data including growth performance and gene deletion studies. Furthermore, in silico growth on toluene was found to be oxygen-limited, suggesting the existence of oxygen-efficient pathways not yet annotated in P. putida's genome. Moreover, we evaluated the production efficiency of polyhydroxyalkanoates from various carbon sources and found fatty acids as the most prominent candidates, as expected. CONCLUSION: Here we presented the first genome-scale reconstruction of P. putida, a biotechnologically interesting all-surrounder. Taken together, this work illustrates the utility of iJN746 as i) a knowledge-base, ii) a discovery tool, and iii) an engineering platform to explore P. putida's potential in bioremediation and bioplastic production. link: http://identifiers.org/pubmed/18793442

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Nogales2012 - Genome-scale metabolic network of Synechocystis sp. (iJN678)This model is described in the article: [Deta…

Photosynthesis has recently gained considerable attention for its potential role in the development of renewable energy sources. Optimizing photosynthetic organisms for biomass or biofuel production will therefore require a systems understanding of photosynthetic processes. We reconstructed a high-quality genome-scale metabolic network for Synechocystis sp. PCC6803 that describes key photosynthetic processes in mechanistic detail. We performed an exhaustive in silico analysis of the reconstructed photosynthetic process under different light and inorganic carbon (Ci) conditions as well as under genetic perturbations. Our key results include the following. (i) We identified two main states of the photosynthetic apparatus: a Ci-limited state and a light-limited state. (ii) We discovered nine alternative electron flow pathways that assist the photosynthetic linear electron flow in optimizing the photosynthesis performance. (iii) A high degree of cooperativity between alternative pathways was found to be critical for optimal autotrophic metabolism. Although pathways with high photosynthetic yield exist for optimizing growth under suboptimal light conditions, pathways with low photosynthetic yield guarantee optimal growth under excessive light or Ci limitation. (iv) Photorespiration was found to be essential for the optimal photosynthetic process, clarifying its role in high-light acclimation. Finally, (v) an extremely high photosynthetic robustness drives the optimal autotrophic metabolism at the expense of metabolic versatility and robustness. The results and modeling approach presented here may promote a better understanding of the photosynthetic process. They can also guide bioengineering projects toward optimal biofuel production in photosynthetic organisms. link: http://identifiers.org/pubmed/22308420

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This is a reconstruction of the metabolic network of the yeast Saccharomyces cerevisiae as described in the article:…

BACKGROUND: Up to now, there have been three published versions of a yeast genome-scale metabolic model: iFF708, iND750 and iLL672. All three models, however, lack a detailed description of lipid metabolism and thus are unable to be used as integrated scaffolds for gaining insights into lipid metabolism from multilevel omic measurement technologies (e.g. genome-wide mRNA levels). To overcome this limitation, we reconstructed a new version of the Saccharomyces cerevisiae genome-scale model, iIN800 that includes a more rigorous and detailed description of lipid metabolism. RESULTS: The reconstructed metabolic model comprises 1446 reactions and 1013 metabolites. Beyond incorporating new reactions involved in lipid metabolism, we also present new biomass equations that improve the predictive power of flux balance analysis simulations. Predictions of both growth capability and large scale in silico single gene deletions by iIN800 were consistent with experimental data. In addition, 13C-labeling experiments validated the new biomass equations and calculated intracellular fluxes. To demonstrate the applicability of iIN800, we show that the model can be used as a scaffold to reveal the regulatory importance of lipid metabolism precursors and intermediates that would have been missed in previous models from transcriptome datasets. CONCLUSION: Performing integrated analyses using iIN800 as a network scaffold is shown to be a valuable tool for elucidating the behavior of complex metabolic networks, particularly for identifying regulatory targets in lipid metabolism that can be used for industrial applications or for understanding lipid disease states. link: http://identifiers.org/pubmed/18687109

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BIOMD0000000728 @ v0.0.1

A mathematical model of cell cycle progression is presented, which integrates recent biochemical information on the inte…

A mathematical model of cell cycle progression is presented, which integrates recent biochemical information on the interaction of the maturation promotion factor (MPF) and cyclin. The model retrieves the dynamics observed in early embryos and explains how multiple cycles of MPF activity can be produced and how the internal clock that determines durations and number of cycles can be adjusted by modulating the rate of change in MPF or cyclin concentrations. Experiments are suggested for verifying the role of MPF activity in determining the length of the somatic cell cycle. link: http://identifiers.org/pubmed/1825521

Parameters:

Name Description
i = 1.2 Reaction: => C, Rate Law: cell*i
e = 3.46616 Reaction: => M; C, Rate Law: cell*e*C
f = 1.0 Reaction: => M; C, Rate Law: cell*f*C*M^2
g = 10.0 Reaction: M =>, Rate Law: cell*g*M/(M+1)

States:

Name Description
M [MPF complex]
C [Guanidine]

Observables: none

BIOMD0000000107 @ v0.0.1

Novak1993 - Cell cycle M-phase control The model reproduces Figure 9 of the paper. Please note tha