Parameter Estimation of S-system by Hybrid Differential Evolution with Data Collocation

Abstract

Modern experimental biology is moving away from analyses of single elements to whole-organism measurements. Such measured time-course data contain a wealth of information about the structure and dynamic of the pathway or network. The dynamic modeling of the whole systems is formulated as a reverse problem, which requires a well-suited mathematical model and a very efficient computational method to identify the model structure and parameters. Numerical integration for differential equations and finding global parameter values are still two major challenges in this field of the parameter estimation of nonlinear dynamic biological systems. In this talk, we will compare three techniques of parameter estimation for nonlinear dynamic biological systems. We will introduce hybrid differential evolution algorithm with modified collocation method to solve reverse problems of biological systems. In the talk, two case studies, a dry lab experiment and a wet lab experiment, will be used to illustrate the effectiveness of the proposed method and to compare with the reported methods.