A Genetic Algorithm for Optimal Control of Probabilistic Boolean Networks
We study the problem of finding optimal control policies for Probabilistic Boolean Networks (PBNs). Boolean Networks (BNs) and PBNs are effective tools for modeling genetic regulatory networks. A PBN is a collection of BNs driven by a Markov chain process. It is well-known that the control/intervention of a genetic regulatory network is useful for avoiding undesirable states associated with diseases like cancer. The optimal control problem can be formulated as a probabilistic dynamic programming problem. However, due to the curse of dimensionality, the complexity of the problem is huge. The main objective of this paper is to introduce a Genetic Algorithm (GA)approach for the optimal control problem. Numerical results are given to demonstrate the efficiency of our proposed GA method.
Boolean Networks Dynamic Programming Genetic Algorithm Intervention Optimal Control Policy Probabilistic Boolean Networks
Wai-Ki Ching Ho-Yin Leung Nam-Kiu Tsing Shu-Qin Zhang
Advanced Modeling and Applied Computing Laboratory, Department of Mathematics,The University of Hong Faculty of Mathematical Sciences, Fudan University, Shanghai, China.
国际会议
The Second International Symposium(OSB08)(第二届国际优化及系统生物学学术会议)
云南丽江
英文
29-35
2008-10-31（万方平台首次上网日期，不代表论文的发表时间）