ASSA-PBN: A Toolbox for Probabilistic Boolean Networks

Andrzej Mizera*, Jun Pang, Cui Su, Qixia Yuan

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

14 Citations (Scopus)

Abstract

As a well-established computational framework, probabilistic Boolean networks (PBNs) are widely used for modelling, simulation, and analysis of biological systems. To analyze the steady-state dynamics of PBNs is of crucial importance to explore the characteristics of biological systems. However, the analysis of large PBNs, which often arise in systems biology, is prone to the infamous state-space explosion problem. Therefore, the employment of statistical methods often remains the only feasible solution. We present ASSA-PBN, a software toolbox for modelling, simulation, and analysis of PBNs. ASSA-PBN provides efficient statistical methods with three parallel techniques to speed up the computation of steady-state probabilities. Moreover, particle swarm optimisation (PSO) and differential evolution (DE) are implemented for the estimation of PBN parameters. Additionally, we implement in-depth analyses of PBNs, including long-run influence analysis, long-run sensitivity analysis, computation of one-parameter profile likelihoods, and the visualization of one-parameter profile likelihoods. A PBN model of apoptosis is used as a case study to illustrate the main functionalities of ASSA-PBN and to demonstrate the capabilities of ASSA-PBN to effectively analyse biological systems modelled as PBNs.

Original languageEnglish
Article number8107541
Pages (from-to)1203-1216
Number of pages14
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume15
Issue number4
DOIs
Publication statusPublished - 1 Jul 2018

Keywords

  • discrete-time Markov chains
  • long-run analysis
  • modelling
  • parameter estimation
  • Probabilistic Boolean networks
  • simulation and analysis of biological networks
  • steady-state analysis

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