A comparison study of optimal and suboptimal intervention policies for gene regulatory networks in the presence of uncertainty.

Mohammadmahdi R Yousefi, Edward R Dougherty
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引用次数: 15

Abstract

Perfect knowledge of the underlying state transition probabilities is necessary for designing an optimal intervention strategy for a given Markovian genetic regulatory network. However, in many practical situations, the complex nature of the network and/or identification costs limit the availability of such perfect knowledge. To address this difficulty, we propose to take a Bayesian approach and represent the system of interest as an uncertainty class of several models, each assigned some probability, which reflects our prior knowledge about the system. We define the objective function to be the expected cost relative to the probability distribution over the uncertainty class and formulate an optimal Bayesian robust intervention policy minimizing this cost function. The resulting policy may not be optimal for a fixed element within the uncertainty class, but it is optimal when averaged across the uncertainly class. Furthermore, starting from a prior probability distribution over the uncertainty class and collecting samples from the process over time, one can update the prior distribution to a posterior and find the corresponding optimal Bayesian robust policy relative to the posterior distribution. Therefore, the optimal intervention policy is essentially nonstationary and adaptive.

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存在不确定性的基因调控网络的最优和次优干预政策的比较研究。
对于给定的马尔可夫遗传调控网络,充分了解潜在状态转移概率是设计最佳干预策略的必要条件。然而,在许多实际情况下,网络的复杂性和/或识别成本限制了这种完美知识的可用性。为了解决这个困难,我们建议采用贝叶斯方法,并将感兴趣的系统表示为几个模型的不确定性类,每个模型分配一些概率,这反映了我们对系统的先验知识。我们将目标函数定义为相对于不确定性类别的概率分布的期望成本,并制定了使该成本函数最小化的最优贝叶斯鲁棒干预策略。所得到的策略对于不确定性类中的固定元素可能不是最优的,但是当在不确定性类中平均时,它是最优的。此外,从不确定性类的先验概率分布开始,随着时间的推移从过程中收集样本,可以将先验分布更新为后验分布,并找到相对于后验分布的相应最优贝叶斯鲁棒策略。因此,最优干预策略本质上是非平稳和自适应的。
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