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An optimal control approach to probabilistic Boolean networks

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  • Liu, Qiuli

Abstract

External control of some genes in a genetic regulatory network is useful for avoiding undesirable states associated with some diseases. For this purpose, a number of stochastic optimal control approaches have been proposed. Probabilistic Boolean networks (PBNs) as powerful tools for modeling gene regulatory systems have attracted considerable attention in systems biology. In this paper, we deal with a problem of optimal intervention in a PBN with the help of the theory of discrete time Markov decision process. Specifically, we first formulate a control model for a PBN as a first passage model for discrete time Markov decision processes and then find, using a value iteration algorithm, optimal effective treatments with the minimal expected first passage time over the space of all possible treatments. In order to demonstrate the feasibility of our approach, an example is also displayed.

Suggested Citation

  • Liu, Qiuli, 2012. "An optimal control approach to probabilistic Boolean networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(24), pages 6682-6689.
    • Handle: RePEc:eee:phsmap:v:391:y:2012:i:24:p:6682-6689
    DOI: 10.1016/j.physa.2012.07.074
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    References listed on IDEAS

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    1. Li, Wei & Li, Jiaorui & Chen, Weisheng, 2012. "The reliability of a stochastically complex dynamical system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(13), pages 3556-3565.
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    Cited by:

    1. Li, Fangfei & Li, Jianning & Shen, Lijuan, 2018. "State feedback controller design for the synchronization of Boolean networks with time delays," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1267-1276.
    2. Liu, Qiuli & He, Yu & Wang, Junwei, 2018. "Optimal control for probabilistic Boolean networks using discrete-time Markov decision processes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 1297-1307.

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