Extended kalman filter for estimation of parameters in nonlinear state-space models of biochemical networks.

Authors

Xiaodian Sun, Laboratory of Theoretical Systems Biology and Center for Evolutionary Biology, School of Life Science and Institute for Biomedical Sciences, Fudan University, Shanghai, China
Li Jin, Laboratory of Theoretical Systems Biology and Center for Evolutionary Biology, School of Life Science and Institute for Biomedical Sciences, Fudan University, Shanghai, China; CAS-MPG Partner Institute of Computational Biology, SIBS, CAS, Shanghai, China
Momiao Xiong, Laboratory of Theoretical Systems Biology and Center for Evolutionary Biology, School of Life Science and Institute for Biomedical Sciences, Fudan University, Shanghai, China; Human Genetics Center, University of Texas Health Science Center at Houston, Houston, Texas, United States of AmericaFollow

Publication Date

11-19-2008

Journal

PLoS ONE. 2008;3(11):e3758. Epub 2008 Nov 19

Abstract

It is system dynamics that determines the function of cells, tissues and organisms. To develop mathematical models and estimate their parameters are an essential issue for studying dynamic behaviors of biological systems which include metabolic networks, genetic regulatory networks and signal transduction pathways, under perturbation of external stimuli. In general, biological dynamic systems are partially observed. Therefore, a natural way to model dynamic biological systems is to employ nonlinear state-space equations. Although statistical methods for parameter estimation of linear models in biological dynamic systems have been developed intensively in the recent years, the estimation of both states and parameters of nonlinear dynamic systems remains a challenging task. In this report, we apply extended Kalman Filter (EKF) to the estimation of both states and parameters of nonlinear state-space models. To evaluate the performance of the EKF for parameter estimation, we apply the EKF to a simulation dataset and two real datasets: JAK-STAT signal transduction pathway and Ras/Raf/MEK/ERK signaling transduction pathways datasets. The preliminary results show that EKF can accurately estimate the parameters and predict states in nonlinear state-space equations for modeling dynamic biochemical networks.

Keywords

Algorithms, Animals, Biochemistry, Computer Simulation, Data Interpretation, Statistical, Humans, Kinetics, Likelihood Functions, MAP Kinase Signaling System, Models, Biological, Models, Theoretical, Regression Analysis, STAT Transcription Factors, Signal Transduction, Software

Comments

PMCID: PMC2582954

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