Parameter Estimation for Nonlinear Biological System Model Based on Global Sensitivity Analysis

Mathematical models of cell signal transduction networks are normally highly nonlinear and complex, which consist of a large number of reaction species and kinetics parameters. An important problem of systems biology is to develop mathematical models of nonlinear biological systems, and to effectively estimate the unknown parameters. In this work, a novel algorithm to estimate parameters based on global sensitivity analysis is proposed, and extended Kalman filter is applied to estimate the unknown sensitive parameters of signaling transduction networks model. Taking an IkappaBalpha
^~
-NF-kappaB signaling pathway model as an example, simulation analysis demonstrates that the algorithm can well estimate the unknown parameters under the disturbs of the noise, and it provides an efficient method for solving the parameters' uncertainty effects of biological pathways.