Article Abstract

	Title:	Simulation study in Probabilistic Boolean Network models for genetic regulatory networks
	Author:	Shu-Qin Zhang, Wai-Ki Ching, Michael K. Ng, Tatsuya Akutsu
	Address:	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
	Journal:	International Journal of Data Mining and Bioinformatics 2007 - Vol. 1, No.3  pp. 217 - 240
	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.
	Keywords:	genetic regulatory networks; probabilistic boolean networks; PBN; steady-state probability distribution; power method; Markov chains; data mining; bioinformatics; simulation; gene connections.
	DOI:	10.1504/IJDMB.2007.011610
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