A Markovian approach to the control of genetic regulatory networks

Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.biosystems.2006.12.005

Title:	A Markovian approach to the control of genetic regulatory networks
Authors:	Chen, P.C.Y. Chen, J.W.
Keywords:	Genetic networks Heuristic Markov chains Minimum-cost control
Issue Date:	Sep-2007
Citation:	Chen, P.C.Y., Chen, J.W. (2007-09). A Markovian approach to the control of genetic regulatory networks. BioSystems 90 (2) : 535-545. ScholarBank@NUS Repository. https://doi.org/10.1016/j.biosystems.2006.12.005
Abstract:	This paper presents an approach for controlling gene networks based on a Markov chain model, where the state of a gene network is represented as a probability distribution, while state transitions are considered to be probabilistic. An algorithm is proposed to determine a sequence of control actions that drives (without state feedback) the state of a given network to within a desired state set with a prescribed minimum or maximum probability. A heuristic is proposed and shown to improve the efficiency of the algorithm for a class of genetic networks. © 2007 Elsevier Ireland Ltd. All rights reserved.
Source Title:	BioSystems
URI:	http://scholarbank.nus.edu.sg/handle/10635/54331
ISSN:	03032647
DOI:	10.1016/j.biosystems.2006.12.005
Appears in Collections:	Staff Publications

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