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Verification and optimal control of context-sensitive probabilistic Boolean networks using model checking and

Koichi Kobayashi1, Kunihiko Hiraishi1

  • 1School of Information Science, Japan Advanced Institute of Science and Technology, Ishikawa 923-1292, Japan.

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|March 4, 2014
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Summary
This summary is machine-generated.

This study introduces a novel control method for gene regulatory networks (GRNs) using context-sensitive probabilistic Boolean networks (CS-PBNs). The approach offers new tools for gene therapy and understanding gene expression control.

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Gene regulatory networks (GRNs) are crucial for cellular functions.
  • Developing effective control theories for GRNs is a significant challenge in systems biology.
  • Current methods for GRN control often involve manipulating gene expression through external stimuli.

Purpose of the Study:

  • To develop and evaluate a novel control method for gene regulatory networks (GRNs).
  • To address the verification and optimal control problems for context-sensitive probabilistic Boolean networks (CS-PBNs).
  • To explore the application of GRN control theory in future gene therapy technologies.

Main Methods:

  • Utilized context-sensitive probabilistic Boolean networks (CS-PBNs) to model GRNs, representing gene expression as binary states (ON/OFF).
  • Employed the probabilistic model checker PRISM for solving the verification problem in CS-PBNs.
  • Applied polynomial optimization techniques to tackle the optimal control problem for CS-PBNs.

Main Results:

  • Proposed a solution method for the verification problem of CS-PBNs using PRISM.
  • Developed a solution method for the optimal control problem of CS-PBNs via polynomial optimization.
  • Demonstrated the applicability of the proposed methods through a numerical example on the WNT5A network associated with melanoma.

Conclusions:

  • The developed methods provide valuable tools for the control theory of gene regulatory networks.
  • This research contributes to advancing the understanding and manipulation of GRNs.
  • The findings have potential implications for the development of gene therapy technologies.