Model reduction and parameter estimation of non-linear dynamical biochemical reaction networks.
X. Sun, and M. Medvedovic. IET systems biology, 10 (1):
10--16(February 2016)
Abstract
Parameter estimation for high dimension complex dynamic system is a hot topic. However, the current statistical model and inference approach is known as a large p small n problem. How to reduce the dimension of the dynamic model and improve the accuracy of estimation is more important. To address this question, the authors take some known parameters and structure of system as priori knowledge and incorporate it into dynamic model. At the same time, they decompose the whole dynamic model into subset network modules, based on different modules, and then they apply different estimation approaches. This technique is called Rao-Blackwellised particle filters decomposition methods. To evaluate the performance of this method, the authors apply it to synthetic data generated from repressilator model and experimental data of the JAK-STAT pathway, but this method can be easily extended to large-scale cases.
%0 Journal Article
%1 Sun2016Model
%A Sun, Xiaodian
%A Medvedovic, Mario
%D 2016
%J IET systems biology
%K model-reduction parameter-estimation
%N 1
%P 10--16
%T Model reduction and parameter estimation of non-linear dynamical biochemical reaction networks.
%U http://view.ncbi.nlm.nih.gov/pubmed/26816394
%V 10
%X Parameter estimation for high dimension complex dynamic system is a hot topic. However, the current statistical model and inference approach is known as a large p small n problem. How to reduce the dimension of the dynamic model and improve the accuracy of estimation is more important. To address this question, the authors take some known parameters and structure of system as priori knowledge and incorporate it into dynamic model. At the same time, they decompose the whole dynamic model into subset network modules, based on different modules, and then they apply different estimation approaches. This technique is called Rao-Blackwellised particle filters decomposition methods. To evaluate the performance of this method, the authors apply it to synthetic data generated from repressilator model and experimental data of the JAK-STAT pathway, but this method can be easily extended to large-scale cases.
@article{Sun2016Model,
abstract = {Parameter estimation for high dimension complex dynamic system is a hot topic. However, the current statistical model and inference approach is known as a large p small n problem. How to reduce the dimension of the dynamic model and improve the accuracy of estimation is more important. To address this question, the authors take some known parameters and structure of system as priori knowledge and incorporate it into dynamic model. At the same time, they decompose the whole dynamic model into subset network modules, based on different modules, and then they apply different estimation approaches. This technique is called {Rao-Blackwellised} particle filters decomposition methods. To evaluate the performance of this method, the authors apply it to synthetic data generated from repressilator model and experimental data of the {JAK}-{STAT} pathway, but this method can be easily extended to large-scale cases.},
added-at = {2018-12-02T16:09:07.000+0100},
author = {Sun, Xiaodian and Medvedovic, Mario},
biburl = {https://www.bibsonomy.org/bibtex/23ecab416dca37b3f1664ad3714f95ec2/karthikraman},
citeulike-article-id = {13938630},
citeulike-linkout-0 = {http://view.ncbi.nlm.nih.gov/pubmed/26816394},
citeulike-linkout-1 = {http://www.hubmed.org/display.cgi?uids=26816394},
interhash = {185a26d66e5c6b53ad547bcc0ecd878d},
intrahash = {3ecab416dca37b3f1664ad3714f95ec2},
issn = {1751-8849},
journal = {IET systems biology},
keywords = {model-reduction parameter-estimation},
month = feb,
number = 1,
pages = {10--16},
pmid = {26816394},
posted-at = {2016-02-23 09:19:07},
priority = {2},
timestamp = {2018-12-02T16:09:07.000+0100},
title = {Model reduction and parameter estimation of non-linear dynamical biochemical reaction networks.},
url = {http://view.ncbi.nlm.nih.gov/pubmed/26816394},
volume = 10,
year = 2016
}