(229u) Parameter Optimization of Insulin Signaling Pathways Model in Adipocyte Cells Using Genetic Algorithm

Diabetes mellitus is an increasing risk to health problem in many countries.T2DM is a complex heterogeneous group of metabolic conditions characterized by increased levels of blood glucose due to impairment in insulin action and/or insulin secretion. Biological action of insulin regulates glucose metabolism and other essential physiological functions .Binding of insulin to its cell surface receptor initiates signal transduction pathways that mediate cellular responses. Thus, it is of great interest, to understand the complexity of signaling pathways and gain a view to mechanisms underlying insulin resistance and its subsequent progression to T2DM. On the other hand, Identifying specific drug targets to restore insulin sensitivity at the cellular level and developing an effective treatment strategy require insight into the whole signaling network response to external cues.In this regard Mathematical models are needed to understand better the glucose-insulin system, to evaluate diagnostic tests, to study and predict drug effects, and to quantify disease progression.

In this study, we used previously validated Sedaghat model. The model is in the form of ordinary differential equations. Differential equations, derived primarily using mass-action principles, were used to describe the time varying concentrations of 21 state variables. In Sedaghat model rate constants and model parameters are constrained by published experimental data .The model is based on limited amounts of experimental data obtained in different experimental systems and settings, with parameter values chosen somewhat arbitrarily, and only a single value for each parameter was examined .Then use of an appropriate method for optimization of parameters can be useful .In this work we used Genetic Algorithm for optimization of parameters. The experimental data that used in this study, were obtained from previous publications. We tried to minimize the differences between experimental values and model predictions as objective functions. Because of insufficient available experimental data, we could optimize only 12 parameter.as expected, the results increase the agreement of model predictions with published experimental data. As a good result, we can say Sedaghat model with this new parameters, is able to satisfy the experimental data perfectly.