Simulation study in Probabilistic Boolean Network models for genetic regulatory networks

Shu Qin Zhang, Wai Ki Ching, Kwok Po NG, Tatsuya Akutsu

Research output: Contribution to journalJournal articlepeer-review

44 Citations (Scopus)


Probabilistic Boolean Network (PBN) is widely used to model genetic regulatory networks. Evolution of the PBN is according to the transition probability matrix. Steady-state (long-run behaviour) analysis is a key aspect in studying the dynamics of genetic regulatory networks. In this paper, an efficient method to construct the sparse transition probability matrix is proposed, and the power method based on the sparse matrix-vector multiplication is applied to compute the steady-state probability distribution. Such methods provide a tool for us to study the sensitivity of the steady-state distribution to the influence of input genes, gene connections and Boolean networks. Simulation results based on a real network are given to illustrate the method and to demonstrate the steady-state analysis.

Original languageEnglish
Pages (from-to)217-240
Number of pages24
JournalInternational Journal of Data Mining and Bioinformatics
Issue number3
Publication statusPublished - 2007

Scopus Subject Areas

  • Information Systems
  • Biochemistry, Genetics and Molecular Biology(all)
  • Library and Information Sciences

User-Defined Keywords

  • Bioinformatics
  • Data mining
  • Genetic regulatory network
  • Markov chains
  • PBN
  • Power method
  • Probabilistic Boolean Network
  • Steady-state probability distribution


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