Mathematical modeling of the insulin signal transduction pathway for prediction of insulin sensitivity from expression data

Abstract

Mathematical models of biological pathways facilitate a systems biology approach to medicine. However, these models need to be updated to reflect the latest available knowledge of the underlying pathways. We developed a mathematical model of the insulin signal transduction pathway by expanding the last major previously reported model and incorporating pathway components elucidated since the original model was reported. Furthermore, we show that inputting gene expression data of key components of the insulin signal transduction pathway leads to sensible predictions of glucose clearance rates in agreement with reported clinical measurements. In one set of simulations, our model predicted that glycerol kinase knockout mice have reduced GLUT4 translocation, and consequently, reduced glucose uptake. Additionally, a comparison of our extended model with the original model showed that the added pathway components improve simulations of glucose clearance rates. We anticipate this expanded model to be a useful tool for predicting insulin sensitivity in mammalian tissues with altered expression protein phosphorylation or mRNA levels of insulin signal transduction pathway components.

Keywords: Insulin sensitivity; Insulin signal transduction pathway; Mathematical modeling.

Figure 1

Insulin signal transduction pathway. Unphosphorylated proteins and inactive molecules are shown in light gray, and phosphorylated proteins and active molecules are shown in dark gray. A. Insulin signal transduction pathway modeled by Sedaghat et al. (2002). Incompletely elucidated pathway steps are shown as dashed lines. B. Our expanded insulin signaling pathway that is simulated in this study. AS160, RabGDP, and Munc18c-Syn4-SNAP23 complex are the new components introduced to this model.

Figure 2

Comparison of our expanded model (dashed line) and Sedaghat's original model (solid line). A. GLUT4 translocation time response curve comparing the two models with a single insulin dose input of 0.1nM and run time of 60 min B. Glucose uptake rates with a single insulin dose of 0.1nM and run time of 60 min C. Insulin dosage response curve showing expected glucose uptake rates from an insulin dose of 10⁻¹² M to 10⁻⁶ M. Comparison of simulated glucose uptake (solid lines) and experimental glucose uptake (dotted lines) as a result of AS160 overexpression. Experimental results were based on data from Baus et al. study and data points were presented as hollow circles in the figure.

Figure 3

Simulation of the effect of overexpression of PTP (2.8-fold) and PKC (3.0-fold), and underexpression of PI3K ( 2.8-fold). Recorded from our previous microarray data of Gyk KO with respect to Gyk WT mice. A. GLUT4 translocation time response curves, Gyk KO (dashed and dotted lines) vs WT (solid line), with a single insulin dose input of 0.1nM and run time of 60 min. (p<0.05). Dotted lines represent the range of outputs for GLUT4 translocation % of Gyk KO, accounting for the standard error of the data reported in Rahib et al. (2007) B. Glucose uptake level, Gyk KO (dashed and dotted lines) vs WT solid line), with a single insulin dose of 0.1nM and run time of 60 min. (p<0.05). Dotted lines represent the range of outputs for GLUT4 translocation % of Gyk KO, accounting for the standard error of the data reported in Rahib et al. (2007) C. Insulin dosage response curves (Gyk KO vs WT) showing expected glucose uptake levels from insulin dose inputs from 10⁻¹² M to 10⁻⁶ M. (p<0.05)