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Article: Robust state estimation for stochastic genetic regulatory networks
Title | Robust state estimation for stochastic genetic regulatory networks | ||||||||||
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Authors | |||||||||||
Keywords | Genetic regulatory networks Parameter uncertainty Robust state estimation State estimation Stochastic disturbance Stochastic systems | ||||||||||
Issue Date | 2010 | ||||||||||
Publisher | Taylor & 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? | ||||||||||
Abstract | In 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 Identifier | http://hdl.handle.net/10722/124835 | ||||||||||
ISSN | 2023 Impact Factor: 4.9 2023 SCImago Journal Rankings: 1.851 | ||||||||||
ISI Accession Number ID |
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 Field | Value | Language |
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dc.contributor.author | Liang, J | en_HK |
dc.contributor.author | Lam, J | en_HK |
dc.date.accessioned | 2010-10-31T10:56:52Z | - |
dc.date.available | 2010-10-31T10:56:52Z | - |
dc.date.issued | 2010 | en_HK |
dc.identifier.citation | International Journal Of Systems Science, 2010, v. 41 n. 1, p. 47-63 | en_HK |
dc.identifier.issn | 0020-7721 | en_HK |
dc.identifier.uri | http://hdl.handle.net/10722/124835 | - |
dc.description.abstract | In 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.language | eng | en_HK |
dc.publisher | Taylor & Francis Ltd. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/00207721.asp | en_HK |
dc.relation.ispartof | International Journal of Systems Science | en_HK |
dc.subject | Genetic regulatory networks | en_HK |
dc.subject | Parameter uncertainty | en_HK |
dc.subject | Robust state estimation | en_HK |
dc.subject | State estimation | en_HK |
dc.subject | Stochastic disturbance | en_HK |
dc.subject | Stochastic systems | en_HK |
dc.title | Robust state estimation for stochastic genetic regulatory networks | en_HK |
dc.type | Article | en_HK |
dc.identifier.openurl | http://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+networks | en_HK |
dc.identifier.email | Lam, J:james.lam@hku.hk | en_HK |
dc.identifier.authority | Lam, J=rp00133 | en_HK |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1080/00207720903141434 | en_HK |
dc.identifier.scopus | eid_2-s2.0-77649240657 | en_HK |
dc.identifier.hkuros | 179609 | en_HK |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-77649240657&selection=ref&src=s&origin=recordpage | en_HK |
dc.identifier.volume | 41 | en_HK |
dc.identifier.issue | 1 | en_HK |
dc.identifier.spage | 47 | en_HK |
dc.identifier.epage | 63 | en_HK |
dc.identifier.isi | WOS:000273489600005 | - |
dc.publisher.place | United Kingdom | en_HK |
dc.identifier.scopusauthorid | Liang, J=24544407400 | en_HK |
dc.identifier.scopusauthorid | Lam, J=7201973414 | en_HK |
dc.identifier.citeulike | 6549496 | - |
dc.identifier.issnl | 0020-7721 | - |