Steady-state analysis of genetic regulatory networks modelled by probabilistic boolean networks.
Steady-state analysis of genetic regulatory networks modelled by probabilistic boolean networks.
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DOI：
10.1002/cfg.342
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发表时间：
2003
影响因子：
--
通讯作者：
Zhang, Wei
中科院分区：
文献类型：
--
作者：
Shmulevich, Ilya;Gluhovsky, Ilya;Hashimoto, Ronaldo F;Dougherty, Edward R;Zhang, Wei
概率布尔网络（PBN）最近被引入作为一类有前途的遗传调控网络模型。PBN的动态行为可以在马尔可夫链的背景下进行分析。一个关键目标是通过分析相应的马尔可夫链来确定PBN的稳态（长期）行为。这使得人们可以计算一个基因对另一个基因的长期影响，或者确定几个选定基因的长期联合概率行为。由于基于矩阵的方法很快成为禁止大规模的网络，我们建议使用蒙特卡罗方法。然而，收敛到平稳分布的速度成为一个中心问题。我们讨论了几种方法来确定所需的迭代次数，以实现相应的PBN的马尔可夫链的收敛。使用最近推出的方法的基础上的理论，两个状态马尔可夫链，我们说明了方法的子网络设计的人脑胶质瘤基因表达数据，并确定联合稳态概率为几组基因。
Probabilistic Boolean networks (PBNs) have recently been introduced as a promising class of models of genetic regulatory networks. The dynamic behaviour of PBNs can be analysed in the context of Markov chains. A key goal is the determination of the steady-state (long-run) behaviour of a PBN by analysing the corresponding Markov chain. This allows one to compute the long-term influence of a gene on another gene or determine the long-term joint probabilistic behaviour of a few selected genes. Because matrix-based methods quickly become prohibitive for large sizes of networks, we propose the use of Monte Carlo methods. However, the rate of convergence to the stationary distribution becomes a central issue. We discuss several approaches for determining the number of iterations necessary to achieve convergence of the Markov chain corresponding to a PBN. Using a recently introduced method based on the theory of two-state Markov chains, we illustrate the approach on a sub-network designed from human glioma gene expression data and determine the joint steadystate probabilities for several groups of genes.