External control in Markovian genetic regulatory networks: the imperfect information case
External control in Markovian genetic regulatory networks: the imperfect information case
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DOI：
10.1093/bioinformatics/bth008
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发表时间：
2004-04-12
期刊：
影响因子：
5.8
通讯作者：
Dougherty, ER
中科院分区：
文献类型：
--
作者：
Datta, A;Choudhary, A;Dougherty, ER
概率布尔网络是马尔可夫遗传调控网络的一个子类，最近被引入作为一种基于规则的范例来建模基因调控网络。在早期的一篇论文中，我们将外部控制引入马尔科夫遗传调控网络。更准确地说，给定状态转移概率依赖于外部(控制)变量的马尔可夫遗传调控网络，提出了一种基于动态规划的过程，通过该过程可以选择在有限步数内最小化给定性能指标的控制动作序列。然而，只有当一个人对马尔可夫链的状态有了完美的了解时，那篇论文的控制算法才能实现。本文提出了一种可以在不完全信息情况下实现的控制策略，并利用了假设与潜在马尔可夫链的状态概率相关的可用测量值。
Probabilistic Boolean Networks, which form a subclass of Markovian Genetic Regulatory Networks, have been recently introduced as a rule-based paradigm for modeling gene regulatory networks. In an earlier paper, we introduced external control into Markovian Genetic Regulatory networks. More precisely, given a Markovian genetic regulatory network whose state transition probabilities depend on an external (control) variable, a Dynamic Programming-based procedure was developed by which one could choose the sequence of control actions that minimized a given performance index over a finite number of steps. The control algorithm of that paper, however, could be implemented only when one had perfect knowledge of the states of the Markov Chain. This paper presents a control strategy that can be implemented in the imperfect information case, and makes use of the available measurements which are assumed to be probabilistically related to the states of the underlying Markov Chain.