ArticlePloS one2008
Extended kalman filter for estimation of parameters in nonlinear state-space models of biochemical networks.
Article in PloS one, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 103 citations in OpenAlex.
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- Error model of geomagnetic-field measurement and extended Kalman-filter based compensation method.PloS one · 2017Article
- Model reduction and parameter estimation of non-linear dynamical biochemical reaction networks.IET systems biology · 2016Article
- A unified framework for estimating parameters of kinetic biological models.BMC bioinformatics · 2015Article
- Inference of gene regulatory networks incorporating multi-source biological knowledge via a state space model with L1 regularization.PloS one · 2014Article
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- Perturbation biology: inferring signaling networks in cellular systems.PLoS computational biology · 2013Article
- An improved swarm optimization for parameter estimation and biological model selection.PloS one · 2013Article
- Adaptive models for gene networks.PloS one · 2012Article
- Moment fitting for parameter inference in repeatedly and partially observed stochastic biological models.PloS one · 2012Article
- Unscented Kalman filter with parameter identifiability analysis for the estimation of multiple parameters in kinetic models.EURASIP journal on bioinformatics & systems biology · 2011Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 2 countries.
Funding
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.
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