title: Extended kalman filter for estimation of parameters in nonlinear state-space models of biochemical networks.
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.
authors.affiliation: Laboratory of Theoretical Systems Biology and Center for Evolutionary Biology, School of Life Science and Institute for Biomedical Sciences, Fudan University, Shanghai, China.
authors.forename: Xiaodian
authors.initials: X
authors.lastname: Sun
authors.forename: Li
authors.initials: L
authors.lastname: Jin
authors.forename: Momiao
authors.initials: M
authors.lastname: Xiong
mesh_terms: D000465:Algorithms; D000818:Animals; D001671:Biochemistry / Q000379:methods*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D007700:Kinetics; D016013:Likelihood Functions; D020935:MAP Kinase Signaling System; D008954:Models, Biological; D008962:Models, Theoretical; D012044:Regression Analysis; D050791:STAT Transcription Factors / Q000378:metabolism; D015398:Signal Transduction; D012984:Software
chemical_list: D050791:STAT Transcription Factors