title: Simulation study in Probabilistic Boolean Network models for genetic regulatory networks.
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.
authors.affiliation: Department of Mathematics, Advanced Modeling and Applied Computing Laboratory, The University of Hong Kong, Pokfulam Road, Hong Kong. sqzhang@hkusua.hku.hk
authors.forename: Shu-Qin
authors.initials: SQ
authors.lastname: Zhang
authors.forename: Wai-Ki
authors.initials: WK
authors.lastname: Ching
authors.forename: Michael K
authors.initials: MK
authors.lastname: Ng
authors.forename: Tatsuya
authors.initials: T
authors.lastname: Akutsu
mesh_terms: D000465:Algorithms; D000818:Animals; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D019143:Evolution, Molecular; D015870:Gene Expression; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks; D005809:Genes, Regulator; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D009010:Monte Carlo Method; D016571:Neural Networks, Computer; D011336:Probability; D015398:Signal Transduction / Q000235:genetics; D014157:Transcription Factors / Q000235:genetics
chemical_list: D014157:Transcription Factors