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

Authors:
Wai-Ki Ching;Shuqin Zhang;Michael K. Ng;Tatsuya Akutsu
Affiliations:
-;-;-;-
Venue:
Bioinformatics
Year:
2007

Citing 0
Cited 7

Learning the structure of dynamic Bayesian networks from time series and steady state measurements

Machine Learning
Context-sensitive probabilistic Boolean networks: steady-state properties, reduction, and steady-state approximation

IEEE Transactions on Signal Processing
Generating probabilistic Boolean networks from a prescribed stationary distribution

Information Sciences: an International Journal
A matrix perturbation method for computing the steady-state probability distributions of probabilistic Boolean networks with gene perturbations

Journal of Computational and Applied Mathematics
Brief paper: Controllability of probabilistic Boolean control networks

Automatica (Journal of IFAC)
On computation of the steady-state probability distribution of probabilistic Boolean networks with gene perturbation

Journal of Computational and Applied Mathematics
Neural networks letter: Controllability and optimal control of a temporal Boolean network

Neural Networks

Quantified Score

Hi-index	3.84

Visualization

Abstract

Motivation: 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 2n-by-2n 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. Results: In this article, we propose an approximation method for computing the steady-state probability distribution of a PBN based on neglecting some Boolean networks (BNs) with very small probabilities during the construction of the transition probability matrix. An error analysis of this approximation method is given and theoretical result on the distribution of BNs in a PBN with at most two Boolean functions for one gene is also presented. These give a foundation and support for the approximation method. Numerical experiments based on a genetic network are given to demonstrate the efficiency of the proposed method. Contact: sqzhang@hkusua.hku.hk Supplementary information: Supplementary data are available at Bioinformatics online.