Gene perturbation and intervention in probabilistic Boolean networks
Gene perturbation and intervention in probabilistic Boolean networks
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DOI：
10.1093/bioinformatics/18.10.1319
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发表时间：
2002-10-01
期刊：
影响因子：
5.8
通讯作者：
Zhang, W
中科院分区：
文献类型：
--
作者：
Shmulevich, I;Dougherty, ER;Zhang, W
动机：基因调控网络建模的一个主要目标，除了获得更深入的了解遗传调控和控制，是开发计算工具，用于识别和发现潜在的目标，在疾病，如癌症的治疗干预。我们考虑了个体基因对全局动态网络行为的潜在影响的一般问题，无论是从随机基因扰动的角度，还是为了引起期望的网络行为而进行干预的角度。使用最近引入的一类模型，称为概率布尔网络（PBN），本文建立了一个随机基因扰动模型，导出了新PBN模型中转移概率的显式表达式。这一结果提供了一个积木进行模拟和推导有关网络动态的其他结果。最后给出了一个例子来说明如何利用基因扰动模型来计算基因对其他基因的长期影响。在此之后，干预的问题是解决通过几个计算工具的发展的基础上首次通过时间在马尔可夫链。其结果是一种方法，用于找到最佳的基因进行干预，以最有可能实现理想的网络行为。这些想法用几个例子来说明，其中的目标是诱导网络过渡到一个期望的状态，或一组状态。相应的问题，避免不良状态也得到解决。最后，本文转向评估基因扰动对长期网络行为的影响的重要问题。的定态概率上的界的扰动概率。结果表明，在基因扰动存在的情况下，网络的状态更容易从其他状态“到达”。因此，这些被假设为对应于细胞功能状态。
Motivation: A major objective of gene regulatory network modeling, in addition to gaining a deeper understanding of genetic regulation and control, is the development of computational tools for the identification and discovery of potential targets for therapeutic intervention in diseases such as cancer. We consider the general question of the potential effect of individual genes on the global dynamical network behavior, both from the view of random gene perturbation as well as intervention in order to elicit desired network behavior.Results: Using a recently introduced class of models, called Probabilistic Boolean Networks (PBNs), this paper develops a model for random gene perturbations and derives an explicit formula for the transition probabilities in the new PBN model. This result provides a building block for performing simulations and deriving other results concerning network dynamics. An example is provided to show how the gene perturbation model can be used to compute long-term influences of genes on other genes. Following this, the problem of intervention is addressed via the development of several computational tools based on first-passage times in Markov chains. The consequence is a methodology for finding the best gene with which to intervene in order to most likely achieve desirable network behavior. The ideas are illustrated with several examples in which the goal is to induce the network to transition into a desired state, or set of states. The corresponding issue of avoiding undesirable states is also addressed. Finally, the paper turns to the important problem of assessing the effect of gene perturbations on long-run network behavior. A bound on the steady-state probabilities is derived in terms of the perturbation probability. The result demonstrates that states of the network that are more 'easily reachable' from other states are more stable in the presence of gene perturbations. Consequently, these are hypothesized to correspond to cellular functional states.