Robust  H  ∞  feedback control for uncertain stochastic delayed genetic regulatory networks with additive and multiplicative noise | Zendy

Pan Wei | Zendy; Wang Zidong | Zendy; Gao Huijun | Zendy; Li Yurong | Zendy; Du Min | Zendy

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Robust H ∞ feedback control for uncertain stochastic delayed genetic regulatory networks with additive and multiplicative noise

Author(s) -

Pan Wei,

Wang Zidong,

Gao Huijun,

Li Yurong,

Du Min

Publication year - 2010

Publication title -

international journal of robust and nonlinear control

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.361

H-Index - 106

eISSN - 1099-1239

pISSN - 1049-8923

DOI - 10.1002/rnc.1571

Subject(s) - multiplicative function , control theory (sociology) , controller (irrigation) , noise (video) , multiplicative noise , stochastic control , gene regulatory network , stochastic process , stability (learning theory) , computer science , synthetic biology , process (computing) , control (management) , mathematical optimization , mathematics , optimal control , biology , gene , bioinformatics , artificial intelligence , transmission (telecommunications) , statistics , signal transfer function , analog signal , image (mathematics) , mathematical analysis , gene expression , operating system , telecommunications , biochemistry , machine learning , agronomy

Noises are ubiquitous in genetic regulatory networks (GRNs). Gene regulation is inherently a stochastic process because of intrinsic and extrinsic noises that cause kinetic parameter variations and basal rate disturbance. Time delays are usually inevitable due to different biochemical reactions in such GRNs. In this paper, a delayed stochastic model with additive and multiplicative noises is utilized to describe stochastic GRNs. A feedback gene controller design scheme is proposed to guarantee that the GRN is mean‐square asymptotically stable with noise attenuation, where the structure of the controllers can be specified according to engineering requirements. By applying control theory and mathematical tools, the analytical solution to the control design problem is given, which helps to provide some insight into synthetic biology and systems biology. The control scheme is employed in a three‐gene network to illustrate the applicability and usefulness of the design. Copyright © 2010 John Wiley & Sons, Ltd.

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