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A kinetic core model of the glucose-stimulated insulin secretion network of pancreatic β cells

The construction and characterization of a core kinetic model of the glucose-stimulated insulin secretion system (GSIS) in pancreatic β cells is described. The model consists of 44 enzymatic reactions, 59 metabolic state variables, and 272 parameters. It integrates five subsystems: glycolysis, the T...

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Detalles Bibliográficos
Autores principales: Jiang, Nan, Cox, Roger D., Hancock, John M.
Formato: Texto
Lenguaje:English
Publicado: Springer New York 2007
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1998884/
https://www.ncbi.nlm.nih.gov/pubmed/17514510
http://dx.doi.org/10.1007/s00335-007-9011-y
Descripción
Sumario:The construction and characterization of a core kinetic model of the glucose-stimulated insulin secretion system (GSIS) in pancreatic β cells is described. The model consists of 44 enzymatic reactions, 59 metabolic state variables, and 272 parameters. It integrates five subsystems: glycolysis, the TCA cycle, the respiratory chain, NADH shuttles, and the pyruvate cycle. It also takes into account compartmentalization of the reactions in the cytoplasm and mitochondrial matrix. The model shows expected behavior in its outputs, including the response of ATP production to starting glucose concentration and the induction of oscillations of metabolite concentrations in the glycolytic pathway and in ATP and ADP concentrations. Identification of choke points and parameter sensitivity analysis indicate that the glycolytic pathway, and to a lesser extent the TCA cycle, are critical to the proper behavior of the system, while parameters in other components such as the respiratory chain are less critical. Notably, however, sensitivity analysis identifies the first reactions of nonglycolytic pathways as being important for the behavior of the system. The model is robust to deletion of malic enzyme activity, which is absent in mouse pancreatic β cells. The model represents a step toward the construction of a model with species-specific parameters that can be used to understand mouse models of diabetes and the relationship of these mouse models to the human disease state. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s00335-007-9011-y) contains supplementary material, which is available to authorized users.