A Markovian approach to the control of genetic regulatory networks.
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.
Chen
Peter C Y
PC
Department of Mechanical Engineering, Faculty of Engineering, National University of Singapore, Singapore. mpechenp@nus.edu.sg
Chen
Jeremy W
JW
D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D006801:Humans; D016013:Likelihood Functions; D008390:Markov Chains; D008954:Models, Biological; D008957:Models, Genetic; D015233:Models, Statistical; D008962:Models, Theoretical; D011336:Probability; D049490:Systems Biology