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optPBN: an optimisation toolbox for probabilistic Boolean networks.

Panuwat Trairatphisan1, Andrzej Mizera2, Jun Pang3

  • 1Systems Biology Group, Life Sciences Research Unit, University of Luxembourg, Luxembourg, Luxembourg.

Plos One
|July 2, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces optPBN, a MATLAB toolbox for probabilistic Boolean networks (PBNs), enabling quantitative biological network inference. optPBN facilitates PBN construction and analysis, offering deeper insights into complex biological systems.

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Network Inference

Background:

  • Boolean networks offer limited quantitative insights into biological complexity.
  • Existing computational tools lack detailed quantitative analysis capabilities.

Purpose of the Study:

  • Introduce optPBN, a MATLAB toolbox for optimizing probabilistic Boolean networks (PBNs).
  • Enable quantitative inference and analysis of biological networks beyond qualitative Boolean models.

Main Methods:

  • Develop a MATLAB-based toolbox (optPBN) operating within the BN/PBN framework.
  • Facilitate PBN generation from rule-based Boolean models and integrate multi-experimental data.
  • Generate optimization problems solvable by various optimizers for PBN construction.

Main Results:

  • optPBN successfully infers PBNs by optimizing selection probabilities to fit experimental data.
  • Applied to a large-scale apoptosis model, optPBN quantitatively identified inverse correlations in primary hepatocytes.
  • Elucidated the relevance of crosstalk interactions within the apoptotic network.

Conclusions:

  • optPBN provides a comprehensive pipeline for PBN optimization problem generation.
  • The toolbox is suitable for local or grid-based computational platforms.
  • Applicable to gene regulatory network inference and signal transduction network analysis.