Department of Electrical and Computer Engineering, Texas A & M University, College Station, TX 77843, USA.
Bioinformatics. 2009 Aug 15;25(16):2042-8. doi: 10.1093/bioinformatics/btp349. Epub 2009 Jun 8.
A basic problem of translational systems biology is to utilize gene regulatory networks as a vehicle to design therapeutic intervention strategies to beneficially alter network and, therefore, cellular dynamics. One strain of research has this problem from the perspective of control theory via the design of optimal Markov chain decision processes, mainly in the framework of probabilistic Boolean networks (PBNs). Full optimization assumes that the network is accurately modeled and, to the extent that model inference is inaccurate, which can be expected for gene regulatory networks owing to the combination of model complexity and a paucity of time-course data, the designed intervention strategy may perform poorly. We desire intervention strategies that do not assume accurate full-model inference.
This article demonstrates the feasibility of applying on-line adaptive control to improve intervention performance in genetic regulatory networks modeled by PBNs. It shows via simulations that when the network is modeled by a member of a known family of PBNs, an adaptive design can yield improved performance in terms of the average cost. Two algorithms are presented, one better suited for instantaneously random PBNs and the other better suited for context-sensitive PBNs with low switching probability between the constituent BNs.
转化系统生物学的一个基本问题是利用基因调控网络作为一种手段，设计治疗干预策略，以有益地改变网络，从而改变细胞动力学。从控制理论的角度来看，研究的一个方向是通过设计最优马尔可夫链决策过程来解决这个问题，主要是在概率布尔网络（PBN）的框架内。完全优化假设网络被准确地建模，并且在模型推断不准确的程度上，由于基因调控网络的模型复杂性和时间序列数据的缺乏，这是可以预期的，设计的干预策略可能表现不佳。我们希望干预策略不假设准确的全模型推断。
本文证明了在线自适应控制在由 PBN 建模的遗传调控网络中的干预策略中的应用的可行性。通过模拟表明，当网络由已知的 PBN 家族成员建模时，自适应设计可以在平均成本方面提高性能。提出了两种算法，一种更适合于瞬时随机 PBN，另一种更适合于具有低转换概率的上下文敏感 PBN。
