Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.biosystems.2006.12.005
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dc.titleA Markovian approach to the control of genetic regulatory networks
dc.contributor.authorChen, P.C.Y.
dc.contributor.authorChen, J.W.
dc.date.accessioned2014-06-16T09:29:59Z
dc.date.available2014-06-16T09:29:59Z
dc.date.issued2007-09
dc.identifier.citationChen, 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
dc.identifier.issn03032647
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/54331
dc.description.abstractThis 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.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/j.biosystems.2006.12.005
dc.sourceScopus
dc.subjectGenetic networks
dc.subjectHeuristic
dc.subjectMarkov chains
dc.subjectMinimum-cost control
dc.typeArticle
dc.contributor.departmentMECHANICAL ENGINEERING
dc.description.doi10.1016/j.biosystems.2006.12.005
dc.description.sourcetitleBioSystems
dc.description.volume90
dc.description.issue2
dc.description.page535-545
dc.description.codenBSYMB
dc.identifier.isiut000250184500024
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