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optPBN: An Optimisation Toolbox for Probabilistic Boolean Networks

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  • Panuwat Trairatphisan
  • Andrzej Mizera
  • Jun Pang
  • Alexandru Adrian Tantar
  • Thomas Sauter

Abstract

Background: There exist several computational tools which allow for the optimisation and inference of biological networks using a Boolean formalism. Nevertheless, the results from such tools yield only limited quantitative insights into the complexity of biological systems because of the inherited qualitative nature of Boolean networks. Results: We introduce optPBN, a Matlab-based toolbox for the optimisation of probabilistic Boolean networks (PBN) which operates under the framework of the BN/PBN toolbox. optPBN offers an easy generation of probabilistic Boolean networks from rule-based Boolean model specification and it allows for flexible measurement data integration from multiple experiments. Subsequently, optPBN generates integrated optimisation problems which can be solved by various optimisers. Summary: The optPBN toolbox provides a simple yet comprehensive pipeline for integrated optimisation problem generation in the PBN formalism that can readily be solved by various optimisers on local or grid-based computational platforms. optPBN can be further applied to various biological studies such as the inference of gene regulatory networks or the identification of the interaction's relevancy in signal transduction networks.

Suggested Citation

  • Panuwat Trairatphisan & Andrzej Mizera & Jun Pang & Alexandru Adrian Tantar & Thomas Sauter, 2014. "optPBN: An Optimisation Toolbox for Probabilistic Boolean Networks," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-15, July.
    • Handle: RePEc:plo:pone00:0098001
    DOI: 10.1371/journal.pone.0098001
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