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Optimal Intervention in Semi-Markov-Based Asynchronous Probabilistic Boolean Networks

Author

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  • Qiuli Liu
  • Qingguo Zeng
  • Jinghao Huang
  • Deliang Li

Abstract

Synchronous probabilistic Boolean networks (PBNs) and generalized asynchronous PBNs have received significant attention over the past decade as a tool for modeling complex genetic regulatory networks. From a biological perspective, the occurrence of interactions among genes, such as transcription, translation, and degradation, may require a few milliseconds or even up to a few seconds. Such a time delay can be best characterized by generalized asynchronous PBNs. This paper attempts to study an optimal control problem in a generalized asynchronous PBN by employing the theory of average value-at-risk (AVaR) for finite horizon semi-Markov decision processes. Specifically, we first formulate a control model for a generalized asynchronous PBN as an AVaR model for finite horizon semi-Markov decision processes and then solve an optimal control problem for minimizing average value-at-risk criterion over a finite horizon. In order to illustrate the validity of our approach, a numerical example is also displayed.

Suggested Citation

  • Qiuli Liu & Qingguo Zeng & Jinghao Huang & Deliang Li, 2018. "Optimal Intervention in Semi-Markov-Based Asynchronous Probabilistic Boolean Networks," Complexity, Hindawi, vol. 2018, pages 1-12, September.
    • Handle: RePEc:hin:complx:8983670
    DOI: 10.1155/2018/8983670
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    Cited by:

    1. Bowen Li & Jungang Lou & Yang Liu & Zhen Wang, 2019. "Robust Invariant Set Analysis of Boolean Networks," Complexity, Hindawi, vol. 2019, pages 1-8, February.
    2. Haiyan Wang & Qiuzhen Lin & Jianyong Chen & Jianqiang Li & Jianghua Zhong & Dongdai Lin & Jia Wang & Lijia Ma, 2019. "On Stability of Multi-Valued Nonlinear Feedback Shift Registers," Complexity, Hindawi, vol. 2019, pages 1-11, February.

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