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Article: Stability analysis of uncertain genetic sum regulatory networks

TitleStability analysis of uncertain genetic sum regulatory networks
Authors
KeywordsGenetic regulatory network
LMI
Lyapunov function
Robust stability
Systems biology
Issue Date2008
PublisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/automatica
Citation
Automatica, 2008, v. 44 n. 9, p. 2298-2305 How to Cite?
AbstractThis paper addresses the problem of establishing robust stability of uncertain genetic networks with sum regulatory functions. Specifically, we first consider uncertain genetic networks where the regulation occurs at the transcriptional level, and we derive a sufficient condition for robust stability by introducing a bounding set of the uncertain nonlinearity. We hence show that this condition can be formulated as a convex optimization through polynomial Lyapunov functions and polynomial descriptions of the bounding set by exploiting the square matricial representation (SMR) of polynomials which allows to establish whether a polynomial is a sum of squares (SOS) via a linear matrix inequality (LMI). Then, we propose a method for computing a family of bounding sets by means of convex optimizations. It is worthwhile to remark that these results are derived in spite of the fact that the variable equilibrium point cannot be computed as being the solution of a system of parameter-dependent nonlinear equations, and is hence unknown. Lastly, the proposed approach is extended to models where the regulation occurs at different levels and both mRNA and protein dynamics are nonlinear. © 2008 Elsevier Ltd. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/58829
ISSN
2023 Impact Factor: 4.8
2023 SCImago Journal Rankings: 3.502
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorChesi, Gen_HK
dc.contributor.authorHung, YSen_HK
dc.date.accessioned2010-05-31T03:37:38Z-
dc.date.available2010-05-31T03:37:38Z-
dc.date.issued2008en_HK
dc.identifier.citationAutomatica, 2008, v. 44 n. 9, p. 2298-2305en_HK
dc.identifier.issn0005-1098en_HK
dc.identifier.urihttp://hdl.handle.net/10722/58829-
dc.description.abstractThis paper addresses the problem of establishing robust stability of uncertain genetic networks with sum regulatory functions. Specifically, we first consider uncertain genetic networks where the regulation occurs at the transcriptional level, and we derive a sufficient condition for robust stability by introducing a bounding set of the uncertain nonlinearity. We hence show that this condition can be formulated as a convex optimization through polynomial Lyapunov functions and polynomial descriptions of the bounding set by exploiting the square matricial representation (SMR) of polynomials which allows to establish whether a polynomial is a sum of squares (SOS) via a linear matrix inequality (LMI). Then, we propose a method for computing a family of bounding sets by means of convex optimizations. It is worthwhile to remark that these results are derived in spite of the fact that the variable equilibrium point cannot be computed as being the solution of a system of parameter-dependent nonlinear equations, and is hence unknown. Lastly, the proposed approach is extended to models where the regulation occurs at different levels and both mRNA and protein dynamics are nonlinear. © 2008 Elsevier Ltd. All rights reserved.en_HK
dc.languageengen_HK
dc.publisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/automaticaen_HK
dc.relation.ispartofAutomaticaen_HK
dc.subjectGenetic regulatory networken_HK
dc.subjectLMIen_HK
dc.subjectLyapunov functionen_HK
dc.subjectRobust stabilityen_HK
dc.subjectSystems biologyen_HK
dc.titleStability analysis of uncertain genetic sum regulatory networksen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0005-1098&volume=44&spage=2298&epage=2305&date=2008&atitle=Stability+Analysis+of+Uncertain+Genetic+SUM+Regulatory+Networksen_HK
dc.identifier.emailChesi, G:chesi@eee.hku.hken_HK
dc.identifier.emailHung, YS:yshung@eee.hku.hken_HK
dc.identifier.authorityChesi, G=rp00100en_HK
dc.identifier.authorityHung, YS=rp00220en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.automatica.2008.01.030en_HK
dc.identifier.scopuseid_2-s2.0-50049106427en_HK
dc.identifier.hkuros151675en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-50049106427&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume44en_HK
dc.identifier.issue9en_HK
dc.identifier.spage2298en_HK
dc.identifier.epage2305en_HK
dc.identifier.eissn1873-2836-
dc.identifier.isiWOS:000259740200010-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridChesi, G=7006328614en_HK
dc.identifier.scopusauthoridHung, YS=8091656200en_HK
dc.identifier.issnl0005-1098-

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