Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/4704
Title: Synchronization of stochastic genetic oscillator networks with time delays and Markovian jumping parameters
Authors: Wang, Y
Wang, Z
Liang, J
Li, Y
Du, M
Keywords: Genetic oscillator networks;System biology;Stochastic synchrony;Markovian switching;Random perturbation;Linear matrix inequality (LMI)
Issue Date: 2010
Publisher: Elsevier
Citation: Neurocomputing, 73(13-15): 2532-2539, Aug 2010
Abstract: Genetic oscillator networks (GONs) are inherently coupled complex systems where the nodes indicate the biochemicals and the couplings represent the biochemical interactions. This paper is concerned with the synchronization problem of a general class of stochastic GONs with time delays and Markovian jumping parameters, where the GONs are subject to both the stochastic disturbances and the Markovian parameter switching. The regulatory functions of the addressed GONs are described by the sector-like nonlinear functions. By applying up-to-date ‘delay-fractioning’ approach for achieving delay-dependent conditions, we construct novel matrix functional to derive the synchronization criteria for the GONs that are formulated in terms of linear matrix inequalities (LMIs). Note that LMIs are easily solvable by the Matlab toolbox. A simulation example is used to demonstrate the synchronization phenomena within biological organisms of a given GON and therefore shows the applicability of the obtained results.
Description: The official published version of the article can be found at the link below.
URI: http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V10-50CV7WB-6&_user=545641&_coverDate=08%2F31%2F2010&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_searchStrId=1623191011&_rerunOrigin=google&_acct=C000027918&_version=1&_urlVersion=0&_userid=545641&md5=eadffb1287bc65bc8e9d1c8368b76e41&searchtype=a
http://bura.brunel.ac.uk/handle/2438/4704
DOI: http://dx.doi.org/10.1016/j.neucom.2010.06.006
ISSN: 0925-2312
Appears in Collections:Computer Science
Dept of Computer Science Research Papers

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