Chen Peter C Y, Chen Jeremy W
Department of Mechanical Engineering, Faculty of Engineering, National University of Singapore, Singapore.
Biosystems. 2007 Sep-Oct;90(2):535-45. doi: 10.1016/j.biosystems.2006.12.005. Epub 2006 Dec 20.
This paper presents an approach for controlling gene networks based on a Markov chain model, where the state of a gene network is represented as a probability distribution, while state transitions are considered to be probabilistic. An algorithm is proposed to determine a sequence of control actions that drives (without state feedback) the state of a given network to within a desired state set with a prescribed minimum or maximum probability. A heuristic is proposed and shown to improve the efficiency of the algorithm for a class of genetic networks.
本文提出了一种基于马尔可夫链模型控制基因网络的方法，其中基因网络的状态表示为概率分布，而状态转移被视为概率性的。提出了一种算法来确定一系列控制动作，该动作（无状态反馈）以规定的最小或最大概率将给定网络的状态驱动到期望状态集内。提出了一种启发式方法，并证明它能提高一类基因网络的算法效率。
