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Article: Robustness analysis of genetic regulatory networks affected by model uncertainty

TitleRobustness analysis of genetic regulatory networks affected by model uncertainty
Authors
KeywordsGenetic regulatory network
Model uncertainty
Robustness
Stability
Issue Date2011
PublisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/automatica
Citation
Automatica, 2011, v. 47 n. 6, p. 1131-1138 How to Cite?
AbstractA fundamental problem in systems biology consists of investigating robustness properties of genetic regulatory networks (GRNs) with respect to model uncertainty. This paper addresses this problem for GRNs where the coefficients are rationally affected by polytopic uncertainty, and where the saturation functions are not exactly known. First, 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), hence generalizing existing results that mainly consider only the case of GRNs where the coefficients are linearly affected by the uncertainty and the regulatory functions are in SUM form. Second, the problem of estimating the worst-case convergence rate of the trajectories to the equilibrium point over all admissible uncertainties is considered, and it is shown that a lower bound of this rate can be computed by solving a quasi-convex optimization problem with LMIs. Third, the paper considers the problem of estimating the set of uncertainties for which the GRN has a globally asymptotically stable equilibrium point. This problem is addressed, first, by showing how one can compute estimates with fixed shape by solving a quasi-convex optimization problem with LMIs, and second, by deriving a procedure for computing estimates with variable shape. Numerical examples illustrate the use of the proposed techniques. © 2010 Elsevier Ltd. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/135120
ISSN
2015 Impact Factor: 3.635
2015 SCImago Journal Rankings: 4.315
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorChesi, Gen_HK
dc.date.accessioned2011-07-27T01:28:32Z-
dc.date.available2011-07-27T01:28:32Z-
dc.date.issued2011en_HK
dc.identifier.citationAutomatica, 2011, v. 47 n. 6, p. 1131-1138en_HK
dc.identifier.issn0005-1098en_HK
dc.identifier.urihttp://hdl.handle.net/10722/135120-
dc.description.abstractA fundamental problem in systems biology consists of investigating robustness properties of genetic regulatory networks (GRNs) with respect to model uncertainty. This paper addresses this problem for GRNs where the coefficients are rationally affected by polytopic uncertainty, and where the saturation functions are not exactly known. First, 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), hence generalizing existing results that mainly consider only the case of GRNs where the coefficients are linearly affected by the uncertainty and the regulatory functions are in SUM form. Second, the problem of estimating the worst-case convergence rate of the trajectories to the equilibrium point over all admissible uncertainties is considered, and it is shown that a lower bound of this rate can be computed by solving a quasi-convex optimization problem with LMIs. Third, the paper considers the problem of estimating the set of uncertainties for which the GRN has a globally asymptotically stable equilibrium point. This problem is addressed, first, by showing how one can compute estimates with fixed shape by solving a quasi-convex optimization problem with LMIs, and second, by deriving a procedure for computing estimates with variable shape. Numerical examples illustrate the use of the proposed techniques. © 2010 Elsevier Ltd. All rights reserved.en_HK
dc.languageengen_US
dc.publisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/automaticaen_HK
dc.relation.ispartofAutomaticaen_HK
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Automatica. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Automatica, 2011, v. 47 n. 6, p. 1131-1138. DOI: 10.1016/j.automatica.2010.10.012-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectGenetic regulatory networken_HK
dc.subjectModel uncertaintyen_HK
dc.subjectRobustnessen_HK
dc.subjectStabilityen_HK
dc.titleRobustness analysis of genetic regulatory networks affected by model uncertaintyen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0005-1098&volume=47&issue=6&spage=1131&epage=1138&date=2011&atitle=Robustness+analysis+of+genetic+regulatory+networks+affected+by+model+uncertainty-
dc.identifier.emailChesi, G:chesi@eee.hku.hken_HK
dc.identifier.authorityChesi, G=rp00100en_HK
dc.description.naturepostprint-
dc.identifier.doi10.1016/j.automatica.2010.10.012en_HK
dc.identifier.scopuseid_2-s2.0-79956213939en_HK
dc.identifier.hkuros187533en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-79956213939&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume47en_HK
dc.identifier.issue6en_HK
dc.identifier.spage1131en_HK
dc.identifier.epage1138en_HK
dc.identifier.isiWOS:000291456100006-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridChesi, G=7006328614en_HK
dc.identifier.citeulike8409716-

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