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Article: Robust state estimation for stochastic genetic regulatory networks

TitleRobust state estimation for stochastic genetic regulatory networks
Authors
KeywordsGenetic regulatory networks
Parameter uncertainty
Robust state estimation
State estimation
Stochastic disturbance
Stochastic systems
Issue Date2010
PublisherTaylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/00207721.asp
Citation
International Journal Of Systems Science, 2010, v. 41 n. 1, p. 47-63 How to Cite?
AbstractIn this article, the state estimation problem is investigated for genetic regulatory networks (GRNs) with parameter uncertainties and stochastic disturbances. To account for the unavoidable modelling errors and parameter fluctuations, the network parameters are assumed to be time-varying but norm-bounded. Furthermore, scalar multiplicative white noises are introduced into both the translation process and the feedback regulation process in order to reflect the inherent intracellular and extracellular noise perturbations. The purpose of the addressed problem is to design a linear state estimator that can estimate the true concentration of the mRNA and the protein of the uncertain GRNs. By resorting to the Lyapunov-Krasovskii functional method combined with the linear matrix inequality (LMI) technique, sufficient conditions are first established for ensuring the stochastic stability of the dynamics of the estimation error, and the estimator gains are then designed in terms of the solutions to some LMIs that can be easily solved by using the standard numerical software. A three-node GRN is presented to show the effectiveness of the proposed design procedures.
Persistent Identifierhttp://hdl.handle.net/10722/124835
ISSN
2015 Impact Factor: 1.947
2015 SCImago Journal Rankings: 1.083
ISI Accession Number ID
Funding AgencyGrant Number
Teaching and Research Fund
Specialized Research Fund for the Doctoral Program of Higher Education200802861044
National Natural Science Foundation of China60804028
RGCHKU 7031/07P
Funding Information:

This work was supported by the Teaching and Research Fund for Excellent Young Teachers at Southeast University of China, the Specialized Research Fund for the Doctoral Program of Higher Education for New Teachers 200802861044, the National Natural Science Foundation of China under Grant 60804028 and RGC HKU 7031/07P.

References

 

DC FieldValueLanguage
dc.contributor.authorLiang, Jen_HK
dc.contributor.authorLam, Jen_HK
dc.date.accessioned2010-10-31T10:56:52Z-
dc.date.available2010-10-31T10:56:52Z-
dc.date.issued2010en_HK
dc.identifier.citationInternational Journal Of Systems Science, 2010, v. 41 n. 1, p. 47-63en_HK
dc.identifier.issn0020-7721en_HK
dc.identifier.urihttp://hdl.handle.net/10722/124835-
dc.description.abstractIn this article, the state estimation problem is investigated for genetic regulatory networks (GRNs) with parameter uncertainties and stochastic disturbances. To account for the unavoidable modelling errors and parameter fluctuations, the network parameters are assumed to be time-varying but norm-bounded. Furthermore, scalar multiplicative white noises are introduced into both the translation process and the feedback regulation process in order to reflect the inherent intracellular and extracellular noise perturbations. The purpose of the addressed problem is to design a linear state estimator that can estimate the true concentration of the mRNA and the protein of the uncertain GRNs. By resorting to the Lyapunov-Krasovskii functional method combined with the linear matrix inequality (LMI) technique, sufficient conditions are first established for ensuring the stochastic stability of the dynamics of the estimation error, and the estimator gains are then designed in terms of the solutions to some LMIs that can be easily solved by using the standard numerical software. A three-node GRN is presented to show the effectiveness of the proposed design procedures.en_HK
dc.languageengen_HK
dc.publisherTaylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/00207721.aspen_HK
dc.relation.ispartofInternational Journal of Systems Scienceen_HK
dc.subjectGenetic regulatory networksen_HK
dc.subjectParameter uncertaintyen_HK
dc.subjectRobust state estimationen_HK
dc.subjectState estimationen_HK
dc.subjectStochastic disturbanceen_HK
dc.subjectStochastic systemsen_HK
dc.titleRobust state estimation for stochastic genetic regulatory networksen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0020-7721&volume=41&issue=1&spage=47&epage=63&date=2010&atitle=Robust+state+estimation+for+stochastic+genetic+regulatory+networksen_HK
dc.identifier.emailLam, J:james.lam@hku.hken_HK
dc.identifier.authorityLam, J=rp00133en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1080/00207720903141434en_HK
dc.identifier.scopuseid_2-s2.0-77649240657en_HK
dc.identifier.hkuros179609en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-77649240657&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume41en_HK
dc.identifier.issue1en_HK
dc.identifier.spage47en_HK
dc.identifier.epage63en_HK
dc.identifier.isiWOS:000273489600005-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridLiang, J=24544407400en_HK
dc.identifier.scopusauthoridLam, J=7201973414en_HK
dc.identifier.citeulike6549496-

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