Model Development and Sensitivity analysis for Blood Glucose Dynamics based on Short Insulin Tolerance Test

Abstract

Biological systems usually consist of large number of components and involve processes at a variety of spatial, temporal and biological scales. Systems biology aims to understand such systems by integrating information from all functional levels into a single dynamic model. This study discusses model development and the use of Global Sensitivity Analysis (GSA) in systems biology modeling and shows how the information content of clinical data from Short Insulin Tolerance Test (SITT) can be handled by optimal model-based estimation techniques. The goal is to develop dynamic model for type II diabetes and estimate set of parameters of the model with greater accuracy and precision. Based on the SITT data, the blood glucose dynamic model was developed as a system of linear differential equations with constant coefficients (parameters). The sensitivity of the parameters was tested using a novel GSA based approach, Derivative-Based Global Sensitivity Measures (DGSM). The proposed approach was implemented in SensSB (a Matlab based toolbox). For the purpose of comparison, the sensitivity of the model was also tested using Sobol’s method and a local approach. The results have shown that the model is less sensitive to the third parameter and fits the SITT data satisfactorily.