Intervention in Gene Regulatory Networks via a Stationary Mean-First-Passage-Time Control Policy
Intervention in Gene Regulatory Networks via a Stationary Mean-First-Passage-Time Control Policy
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DOI：
10.1109/tbme.2008.925677
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发表时间：
2008-10-01
影响因子：
4.6
通讯作者：
Dougherty, Edward R.
中科院分区：
文献类型：
--
作者：
Vahedi, Golnaz;Faryabi, Babak;Dougherty, Edward R.
遗传调控网络建模的主要目标是识别治疗干预的潜在靶点。到目前为止，最优随机干预已被研究的背景下，概率布尔网络，与控制策略的基础上的转移概率矩阵相关联的IN-Larkov链和动态规划用于找到最优控制策略。动态规划算法由于其高计算复杂度而存在问题。出现的另外两个计算负担问题是控制网络和识别最佳干预基因的可能性。本文提出了一种基于平均首次通过时间的算法，为每个候选基因分配一个稳定的控制策略。它作为一个近似的最优控制策略，由于其降低了计算复杂性，可以用来预测最佳的控制基因。一旦确定了最佳控制基因，就可以导出最优策略，或者在网络规模排除了动态规划算法的直接应用时，简单地利用该基因的近似策略。一个突出的一点是，该算法可以是无模型的。它可以直接从时间过程数据设计，而不必推断网络的转移概率矩阵。
A prime objective of modeling genetic regulatory networks is the identification of potential targets for therapeutic intervention. To date, optimal stochastic intervention has been studied in the context of probabilistic Boolean networks, with the control policy based on the transition probability matrix of the associated IN-larkov chain and dynamic programming used to find optimal control policies. Dynamical programming algorithms are problematic owing to their high computational complexity. Two additional computationally burdensome issues that arise are the potential for controlling the network and identifying the best gene for intervention. This paper proposes an algorithm based on mean first-passage time that assigns a stationary control policy for each gene candidate. It serves as an approximation to an optimal control policy and, owing to its reduced computational complexity, can be used to predict the best control gene. Once the best control gene is identified, one can derive an optimal policy or simply utilize the approximate policy for this gene when the network size precludes a direct application of dynamic programming algorithms. A salient point is that the proposed algorithm can be model-free. It can be directly designed from time-course data without having to infer the transition probability matrix of the network.