title: Generating probabilistic Boolean networks from a prescribed transition probability matrix.
abstract: Probabilistic Boolean networks (PBNs) have received much attention in modeling genetic regulatory networks. A PBN can be regarded as a Markov chain process and is characterised by a transition probability matrix. In this study, the authors propose efficient algorithms for constructing a PBN when its transition probability matrix is given. The complexities of the algorithms are also analysed. This is an interesting inverse problem in network inference using steady-state data. The problem is important as most microarray data sets are assumed to be obtained from sampling the steady-state.
authors: The University of Hong Kong, Advanced Modeling and Applied Computing Laboratory, Department of Mathematics, Hong Kong. wkc@maths.hku.hk
authors: W-K
authors: WK
authors: Ching
authors: X
authors: X
authors: Chen
authors: N-K
authors: NK
authors: Tsing
mesh_terms: D000465:Algorithms; D053263:Gene Regulatory Networks; D008390:Markov Chains; D008957:Models, Genetic; D015233:Models, Statistical; D049490:Systems Biology / Q000379:methods*