A Simplified Multivariate Markov Chain Model for the Construction and Control of Genetic Regulatory Networks
The construction and control of genetic regulatory networks using gene expression data is an important research topic in bioinformatics. Probabilistic Boolean Networks (PBNs) have been served as an effective tool for this purpose. However, PBNs are difficult to be used in practice when the number of genes is large because of the huge computational cost. In this paper, we propose a simplified multivariate Markov model for approximating a PBN. The new model can preserve the strength of PBNs and at the same time reduce the complexity of the network and therefore the computational cost. We then present an optimal control model with hard constraints for the purpose of control/intervention of a genetic regulatory network. Numerical experimental examples based on the yeast data are then given to demonstrate the effectiveness of our proposed model and control policy.
Shu-Qin Zhang Wai-Ki Ching Yue Jiao Ling-Yun Wu Raymond, H. Chan
School of Mathematical Sciences, Fudan University, Shanghai,200433, China Advanced Modeling and Applied Computing Laboratory, Department of Mathematics, The University of Hon Institute of Applied Mathematics, Academy of Mathematics and System Sciences, Chinese Academy of Sci Department of Mathematics, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong
国际会议
上海
英文
569-572
2008-05-16（万方平台首次上网日期，不代表论文的发表时间）