On computation of the steady-state probability distribution of probabilistic Boolean networks with gene perturbation

Wen Li, Lu Bin Cui, Kwok Po NG*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)

Abstract

Given a Probabilistic Boolean Network (PBN), an important problem is to study its steady-state probability distribution for network analysis. In this paper, we present a new perturbation bound of the steady-state probability distribution of PBNs with gene perturbation. The main contribution of our results is that this new bound is established without additional condition required by the existing method. The other contribution of this paper is to propose a fast algorithm based on the special structure of a transition probability matrix of PBNs with gene perturbation to compute its steady-state probability distribution. Experimental results are given to demonstrate the effectiveness of the new bound, and the efficiency of the proposed method.

Original languageEnglish
Pages (from-to)4067-4081
Number of pages15
JournalJournal of Computational and Applied Mathematics
Volume236
Issue number16
DOIs
Publication statusPublished - Oct 2012

Scopus Subject Areas

  • Computational Mathematics
  • Applied Mathematics

User-Defined Keywords

  • Iterative methods
  • Perturbation bound
  • Probabilistic Boolean networks
  • Steady-state probability distribution
  • Structured matrices

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