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 IκBα-NF-κB 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 parametersˇ uncertainty effects of biological pathways.
parameter estiomation signal transduction pathway global sensitivity analysis eztended Kalman filter
Jianfang Jia
School of information and communication engineering North university of China Taiyuan,China
国际会议
北京
英文
1-4
2009-06-11（万方平台首次上网日期，不代表论文的发表时间）