Tan Mehmet, Alhajj Reda, Polat Faruk
Department of Computer Engineering, Middle East Technical University, Ankara 06531, Turkey.
IEEE Trans Syst Man Cybern B Cybern. 2010 Apr;40(2):286-97. doi: 10.1109/TSMCB.2009.2014736. Epub 2009 Oct 23.
Controlling gene regulatory networks (GRNs) is an important and hard problem. As it is the case in all control problems, the curse of dimensionality is the main issue in real applications. It is possible that hundreds of genes may regulate one biological activity in an organism; this implies a huge state space, even in the case of Boolean models. This is also evident in the literature that shows that only models of small portions of the genome could be used in control applications. In this paper, we empower our framework for controlling GRNs by eliminating the need for expert knowledge to specify some crucial threshold that is necessary for producing effective results. Our framework is characterized by applying the factored Markov decision problem (FMDP) method to the control problem of GRNs. The FMDP is a suitable framework for large state spaces as it represents the probability distribution of state transitions using compact models so that more space and time efficient algorithms could be devised for solving control problems. We successfully mapped the GRN control problem to an FMDP and propose a model reduction algorithm that helps find approximate solutions for large networks by using existing FMDP solvers. The test results reported in this paper demonstrate the efficiency and effectiveness of the proposed approach.
控制基因调控网络（GRNs）是一个重要且困难的问题。与所有控制问题一样，维度诅咒是实际应用中的主要问题。在生物体中，可能有数百个基因调控一种生物活性；这意味着即使在布尔模型的情况下，状态空间也非常巨大。文献中也表明，在控制应用中只能使用基因组小部分的模型，这一点很明显。在本文中，我们通过消除指定产生有效结果所需的一些关键阈值对专家知识的需求，增强了我们控制基因调控网络的框架。我们的框架的特点是将因式马尔可夫决策问题（FMDP）方法应用于基因调控网络的控制问题。FMDP是适用于大状态空间的框架，因为它使用紧凑模型表示状态转移的概率分布，从而可以设计出更节省空间和时间的算法来解决控制问题。我们成功地将基因调控网络控制问题映射到一个FMDP，并提出了一种模型约简算法，该算法通过使用现有的FMDP求解器帮助找到大型网络的近似解。本文报告的测试结果证明了所提方法的效率和有效性。
