A near-optimal control method for stochastic Boolean networks

Boris Aguilar, Pan Fang, Reinhard Laubenbacher, David Murrugarra

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


One of the ultimate goals in systems biology is to develop control strategies to find efficient medical treatments. One step towards this goal is to develop methods for changing the state of a cell into a desirable state. We propose an efficient method that determines combinations of network perturbations to direct the system towards a predefined state. The method requires a set of control actions such as the silencing of a gene or the disruption of the interaction between two genes. An optimal control policy defined as the best intervention at each state of the system can be obtained using existing methods. However, these algorithms are computationally prohibitive for models with tens of nodes. Our method generates control actions that approximates the optimal control policy with high probability with a computational efficiency that does not depend on the size of the state space. Our C++ code is available at https://github.com/boaguilar/SDDScontrol.

Original languageEnglish
Pages (from-to)67-80
Number of pages14
JournalLetters in Biomathematics
Issue number1
StatePublished - 2020

Bibliographical note

Publisher Copyright:
© 2020, Intercollegiate Biomathematics Alliance. All rights reserved.


  • Approximation Methods
  • Boolean Networks
  • Control Policy
  • Optimal Control
  • Sparse Sampling
  • Stochastic Systems

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology (miscellaneous)
  • Applied Mathematics


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