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Conference Paper: Assessing robust stability properties of uncertain genetic regulatory networks

TitleAssessing robust stability properties of uncertain genetic regulatory networks
Authors
KeywordsConvergence rates
Convex optimization problems
Equilibrium point
Genetic regulatory networks
Globally asymptotically stable
Issue Date2010
PublisherIEEE. The Journal's web site is located at http://www.ieeecss.org
Citation
The 49th IEEE Conference on Decision and Control (CDC 2010), Atlanta, GA., 15-17 December 2010. In Proceedings of 49th CDC, 2010, p. 6882-6887 How to Cite?
AbstractThis paper investigates robust stability properties of genetic regulatory networks (GRNs) affected by uncertainty. Specifically, we consider GRNs with SUMand PROD regulatory functions, where the coefficients are affected polynomially by unknown parameters constrained in a polytope, and where the saturation functions are not exactly known. It is shown that a condition for ensuring that the GRN has a globally asymptotically stable equilibrium point for all admissible uncertainties can be obtained in terms of a convex optimization problem with linear matrix inequalities (LMIs). Moreover, it is shown that a lower bound of the worst-case convergence rate of the trajectories to the equilibrium point over all the admissible uncertainties can be computed by solving a quasi-convex optimization problem with LMIs. The proposed techniques are illustrated by some numerical examples. ©2010 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/135868
ISSN
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorChesi, Gen_HK
dc.date.accessioned2011-07-27T01:49:45Z-
dc.date.available2011-07-27T01:49:45Z-
dc.date.issued2010en_HK
dc.identifier.citationThe 49th IEEE Conference on Decision and Control (CDC 2010), Atlanta, GA., 15-17 December 2010. In Proceedings of 49th CDC, 2010, p. 6882-6887en_HK
dc.identifier.issn0191-2216en_HK
dc.identifier.urihttp://hdl.handle.net/10722/135868-
dc.description.abstractThis paper investigates robust stability properties of genetic regulatory networks (GRNs) affected by uncertainty. Specifically, we consider GRNs with SUMand PROD regulatory functions, where the coefficients are affected polynomially by unknown parameters constrained in a polytope, and where the saturation functions are not exactly known. It is shown that a condition for ensuring that the GRN has a globally asymptotically stable equilibrium point for all admissible uncertainties can be obtained in terms of a convex optimization problem with linear matrix inequalities (LMIs). Moreover, it is shown that a lower bound of the worst-case convergence rate of the trajectories to the equilibrium point over all the admissible uncertainties can be computed by solving a quasi-convex optimization problem with LMIs. The proposed techniques are illustrated by some numerical examples. ©2010 IEEE.en_HK
dc.languageengen_US
dc.publisherIEEE. The Journal's web site is located at http://www.ieeecss.org-
dc.relation.ispartofProceedings of the IEEE Conference on Decision and Controlen_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rightsIEEE Conference on Decision and Control Proceedings. Copyright © IEEE.-
dc.rights©2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.subjectConvergence rates-
dc.subjectConvex optimization problems-
dc.subjectEquilibrium point-
dc.subjectGenetic regulatory networks-
dc.subjectGlobally asymptotically stable-
dc.titleAssessing robust stability properties of uncertain genetic regulatory networksen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0743-1546&volume=&spage=6882&epage=6887&date=2010&atitle=Assessing+robust+stability+properties+of+uncertain+genetic+regulatory+networks-
dc.identifier.emailChesi, G:chesi@eee.hku.hken_HK
dc.identifier.authorityChesi, G=rp00100en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/CDC.2010.5717124en_HK
dc.identifier.scopuseid_2-s2.0-79953135317en_HK
dc.identifier.hkuros187542en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-79953135317&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage6882en_HK
dc.identifier.epage6887en_HK
dc.identifier.isiWOS:000295049107121-
dc.description.otherThe 49th IEEE Conference on Decision and Control (CDC 2010), Atlanta, GA., 15-17 December 2010. In Proceedings of 49th CDC, 2010, p. 6882-6887-
dc.identifier.scopusauthoridChesi, G=7006328614en_HK

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