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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/4919

Title: Robust filtering for stochastic genetic regulatory networks with time-varying delay
Authors: Wei, G
Wang, Z
Lam, J
Fraser, K
Rao, G P
Liu, X
Keywords: Genetic regulatory network
Polytopic-type uncertainty
Decay rate
Time-varying delay
Stochastic disturbance
Publication Date: 2009
Publisher: Elsevier
Citation: Mathematical Biosciences, 220(2): 73-80, Aug 2009
Abstract: This paper addresses the robust filtering problem for a class of linear genetic regulatory networks (GRNs) with stochastic disturbances, parameter uncertainties and time delays. The parameter uncertainties are assumed to reside in a polytopic region, the stochastic disturbance is state-dependent described by a scalar Brownian motion, and the time-varying delays enter into both the translation process and the feedback regulation process. We aim to estimate the true concentrations of mRNA and protein by designing a linear filter such that, for all admissible time delays, stochastic disturbances as well as polytopic uncertainties, the augmented state estimation dynamics is exponentially mean square stable with an expected decay rate. A delay-dependent linear matrix inequality (LMI) approach is first developed to derive sufficient conditions that guarantee the exponential stability of the augmented dynamics, and then the filter gains are parameterized in terms of the solution to a set of LMIs. Note that LMIs can be easily solved by using standard software packages. A simulation example is exploited in order to illustrate the effectiveness of the proposed design procedures.
Description: This is the post print version of the article. The official published version can be obtained from the link - Copyright 2009 Elsevier Ltd
Sponsorship: This work was supported in part by the Biotechnology and Biological Sciences Research Council (BBSRC) of the U.K. under Grants BB/C506264/1 and 100/EGM17735, an International Joint Project sponsored by the Royal Society of the U.K., the Research Grants Council of Hong Kong under Grant HKU 7031/06P, the National Natural Science Foundation of China under Grant 60804028, and the Alexander von Humboldt Foundation of Germany.
URI: http://bura.brunel.ac.uk/handle/2438/4919
DOI: http://dx.doi.org/10.1016/j.mbs.2009.04.002
ISSN: 0025-5564
Appears in Collections:Information Systems and Computing
School of Information Systems, Computing and Mathematics Research Papers

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