An approximation method for solving the steady-state probability distribution of probabilistic Boolean networks | Zendy

WaiKi Ching | Zendy; Shuqin Zhang | Zendy; Michael K. Ng | Zendy; Tatsuya Akutsu | Zendy

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An approximation method for solving the steady-state probability distribution of probabilistic Boolean networks

Author(s) -

WaiKi Ching,

Shuqin Zhang,

Michael K. Ng,

Tatsuya Akutsu

Publication year - 2007

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btm142

Subject(s) - probability distribution , probabilistic logic , stochastic matrix , computation , boolean network , computer science , mathematics , law of total probability , distribution (mathematics) , matrix (chemical analysis) , mathematical optimization , conditional probability , boolean function , algorithm , posterior probability , markov chain , artificial intelligence , statistics , machine learning , mathematical analysis , materials science , composite material , bayesian probability

Probabilistic Boolean networks (PBNs) have been proposed to model genetic regulatory interactions. The steady-state probability distribution of a PBN gives important information about the captured genetic network. The computation of the steady-state probability distribution usually includes construction of the transition probability matrix and computation of the steady-state probability distribution. The size of the transition probability matrix is 2(n)-by-2(n) where n is the number of genes in the genetic network. Therefore, the computational costs of these two steps are very expensive and it is essential to develop a fast approximation method.

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