State Observer Design for Delayed Genetic Regulatory Networks
Author(s) -
LiPing Tian,
Zhijun Wang,
Amin Mohammadbagheri,
FangXiang Wu
Publication year - 2014
Publication title -
computational and mathematical methods in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.462
H-Index - 48
eISSN - 1748-6718
pISSN - 1748-670X
DOI - 10.1155/2014/761562
Subject(s) - observer (physics) , gene regulatory network , computer science , linear matrix inequality , state (computer science) , control theory (sociology) , genetic network , mathematical optimization , gene , genetics , biology , mathematics , control (management) , artificial intelligence , gene expression , physics , algorithm , quantum mechanics
Genetic regulatory networks are dynamic systems which describe the interactions among gene products (mRNAs and proteins). The internal states of a genetic regulatory network consist of the concentrations of mRNA and proteins involved in it, which are very helpful in understanding its dynamic behaviors. However, because of some limitations such as experiment techniques, not all internal states of genetic regulatory network can be effectively measured. Therefore it becomes an important issue to estimate the unmeasured states via the available measurements. In this study, we design a state observer to estimate the states of genetic regulatory networks with time delays from available measurements. Furthermore, based on linear matrix inequality (LMI) approach, a criterion is established to guarantee that the dynamic of estimation error is globally asymptotically stable. A gene repressillatory network is employed to illustrate the effectiveness of our design approach.
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