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Insulin resistance is the reduced physiological response by cells when presented with the hormone insulin, leading to impaired glucose homeostasis and systemic increases in the blood glucose levels as seen in type 2 diabetes mellitus. It is often attributed to defects in transduction of insulin signalling. As such, understanding the mechanism and variability signal propagation is crucial to the behaviour of the population and unravelling the underlying causes of the disease.
Focusing on adipose tissue, we propose a computational systems wide approach to identifying the signalling causes of insulin resistance. This will involve coupling the top-down network reconstruction method to be further evaluated and characterised by the bottom-up kinetic simulation method. Network reconstruction will be achieved using Bayesian Network analysis of dynamic phospho-proteomic data from both healthy and insulin resistant cell models.
The reconstructed topology was then used as the template for a kinetic model, whose parameters will be tuned by fitting model outputs to time course and steady state dose response data from flow cytometry and total internal reflection fluorescence microscopy experiments.
The aim of this approach will be two-fold: firstly, the experimental data will provide validation for the reconstructed interactions; secondly, the parameterised kinetic model will allow evaluation of the effect of key parameters on cell phenotype and predict the states in which cells become insulin resistant.