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

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 transd...

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Autores principales: Xiaodian Sun, Li Jin, Momiao Xiong
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Lenguaje:EN
Publicado: Public Library of Science (PLoS) 2008
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spelling oai:doaj.org-article:e05319622df9401d997fc8167c074cfb2021-11-25T06:18:28ZExtended kalman filter for estimation of parameters in nonlinear state-space models of biochemical networks.1932-620310.1371/journal.pone.0003758https://doaj.org/article/e05319622df9401d997fc8167c074cfb2008-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/19018286/?tool=EBIhttps://doaj.org/toc/1932-6203It 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.Xiaodian SunLi JinMomiao XiongPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 3, Iss 11, p e3758 (2008)
institution DOAJ
collection DOAJ
language EN
topic Medicine
R
Science
Q
spellingShingle Medicine
R
Science
Q
Xiaodian Sun
Li Jin
Momiao Xiong
Extended kalman filter for estimation of parameters in nonlinear state-space models of biochemical networks.
description 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.
format article
author Xiaodian Sun
Li Jin
Momiao Xiong
author_facet Xiaodian Sun
Li Jin
Momiao Xiong
author_sort Xiaodian Sun
title Extended kalman filter for estimation of parameters in nonlinear state-space models of biochemical networks.
title_short Extended kalman filter for estimation of parameters in nonlinear state-space models of biochemical networks.
title_full Extended kalman filter for estimation of parameters in nonlinear state-space models of biochemical networks.
title_fullStr Extended kalman filter for estimation of parameters in nonlinear state-space models of biochemical networks.
title_full_unstemmed Extended kalman filter for estimation of parameters in nonlinear state-space models of biochemical networks.
title_sort extended kalman filter for estimation of parameters in nonlinear state-space models of biochemical networks.
publisher Public Library of Science (PLoS)
publishDate 2008
url https://doaj.org/article/e05319622df9401d997fc8167c074cfb
work_keys_str_mv AT xiaodiansun extendedkalmanfilterforestimationofparametersinnonlinearstatespacemodelsofbiochemicalnetworks
AT lijin extendedkalmanfilterforestimationofparametersinnonlinearstatespacemodelsofbiochemicalnetworks
AT momiaoxiong extendedkalmanfilterforestimationofparametersinnonlinearstatespacemodelsofbiochemicalnetworks
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