The University of Hong Kong, Advanced Modeling and Applied Computing Laboratory, Department of Mathematics, Hong Kong.
IET Syst Biol. 2009 Nov;3(6):453-64. doi: 10.1049/iet-syb.2008.0173.
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.
概率布尔网络（PBN）在遗传调控网络建模中受到广泛关注。PBN 可以看作是马尔可夫链过程，其特点是转移概率矩阵。在这项研究中，作者提出了一种当转移概率矩阵给定时构建 PBN 的有效算法，并分析了算法的复杂度。这是使用稳态数据进行网络推断的一个有趣的反问题。该问题很重要，因为大多数微阵列数据集都被假定是从稳态采样获得的。
