Simulation study in Probabilistic Boolean Network models for genetic regulatory networks

Authors:
Shu-Qin Zhang;Wai-Ki Ching;Michael K. Ng;Tatsuya Akutsu
Affiliations:
Department of Mathematics, Advanced Modeling and Applied Computing Laboratory, The University of Hong Kong, Pokfulam Road, Hong Kong.;Department of Mathematics, Advanced Modeling and Applied Computing Laboratory, The University of Hong Kong, Pokfulam Road, Hong Kong.;Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong.;Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji-city Kyoto 611-0011, Japan
Venue:
International Journal of Data Mining and Bioinformatics
Year:
2007

Citing 7
Cited 5

Error-free and best-fit extensions of partially defined Boolean functions

Information and Computation
Identification of gene regulatory networks by strategic gene disruptions and gene overexpressions

Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
External Control in Markovian Genetic Regulatory Networks

Machine Learning
External control in Markovian genetic regulatory networks: the imperfect information case

Bioinformatics
Steady-state probabilities for attractors in probabilistic boolean networks

Signal Processing
Estimating gene networks from expression data and binding location data via boolean networks

ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part III
A control model for markovian genetic regulatory networks

Transactions on Computational Systems Biology V

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
Analysis of gene interactions using restricted boolean networks and time-series data

ISBRA'10 Proceedings of the 6th international conference on Bioinformatics Research and Applications
On computation of the steady-state probability distribution of probabilistic Boolean networks with gene perturbation

Journal of Computational and Applied Mathematics
Finding optimal control policy in probabilistic Boolean Networks with hard constraints by using integer programming and dynamic programming

International Journal of Data Mining and Bioinformatics

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.