Intervention in context-sensitive probabilistic Boolean networks revisited.
Intervention in context-sensitive probabilistic Boolean networks revisited.
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DOI：
10.1155/2009/360864
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发表时间：
2009
期刊：
影响因子：
--
通讯作者：
Dougherty ER
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文献类型：
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作者：
Faryabi B;Vahedi G;Chamberland JF;Datta A;Dougherty ER
上下文敏感的概率布尔网络的状态空间的近似表示先前已经被提出并用于设计治疗干预策略。虽然上下文敏感的概率布尔网络的完整状态由网络上下文和基因活性谱组成的有序对指定，但这种近似表示将状态空间单独折叠到基因活性谱上。这种减少产生一个近似的转移概率矩阵，上下文无关，与上下文敏感的概率布尔网络的马尔可夫链。与许多近似方法一样，使用简化的模型表示必须付出代价，即相对于使用完整的状态空间，一些最优性损失。本文探讨了干预性能的影响所造成的减少相对于模型参数的各种值。这个任务是使用一个新的推导上下文敏感的概率布尔网络的转移概率矩阵。这种转移概率分布的表达式与上下文敏感的概率布尔网络的原始定义是一致的。合成网络和真实的案例研究的最佳和近似的治疗策略的性能进行了比较。研究表明，近似表示通过具有相似参数的瞬时随机概率布尔网络描述了上下文敏感概率布尔网络的动力学行为。
An approximate representation for the state space of a context-sensitive probabilistic Boolean network has previously been proposed and utilized to devise therapeutic intervention strategies. Whereas the full state of a context-sensitive probabilistic Boolean network is specified by an ordered pair composed of a network context and a gene-activity profile, this approximate representation collapses the state space onto the gene-activity profiles alone. This reduction yields an approximate transition probability matrix, absent of context, for the Markov chain associated with the context-sensitive probabilistic Boolean network. As with many approximation methods, a price must be paid for using a reduced model representation, namely, some loss of optimality relative to using the full state space. This paper examines the effects on intervention performance caused by the reduction with respect to various values of the model parameters. This task is performed using a new derivation for the transition probability matrix of the context-sensitive probabilistic Boolean network. This expression of transition probability distributions is in concert with the original definition of context-sensitive probabilistic Boolean network. The performance of optimal and approximate therapeutic strategies is compared for both synthetic networks and a real case study. It is observed that the approximate representation describes the dynamics of the context-sensitive probabilistic Boolean network through the instantaneously random probabilistic Boolean network with similar parameters.