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S. Kim, S. Imoto and S. Miyano. Dynamic Bayesian Network and Nonparametric Regression for Nonlinear Modeling of Gene Networks from time Series Gene Expression Data. Proc. 1st Computational Methods in Systems Biology, Lecture Note in Computer Science, 2602: 104-113, 2003.

has been cited by the following article:

• TITLE: Construction and Control of Genetic Regulatory Networks:A Multivariate Markov Chain Approach

  AUTHORS: Shu-Qin Zhang, Ling-Yun Wu, Wai-Ki Ching, Yue Jiao, Raymond, H. Chan

  KEYWORDS: Gene Expression Sequences, Multivariate Markov Chain, Optimal Control Policy, Probabilistic Boolean Networks.

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.1 No.1, June 6, 2008

  ABSTRACT: In the post-genomic era, the construction and control of genetic regulatory networks using gene expression data is a hot research topic. Boolean networks (BNs) and its extension 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, the ability to capture the inter-dependence of the genes in the network, qnd 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 given to demonstrate the effectiveness of our proposed model and control policy.