›› 2010, Vol. 25 ›› Issue (1): 131-153.

• Special Issue on Computational Challenges from Modern Molecular Biology • Previous Articles     Next Articles

Network-Based Predictions and Simulations by Biological State Space Models: Search for Drug Mode of Action

Rui Yamaguchi, Seiya Imoto, and Satoru Miyano   

  1. Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan
  • Received:2009-09-30 Revised:2009-11-21 Online:2010-01-05 Published:2010-01-05
  • About author:
    Rui Yamaguchi is a lecturer of Human Genome Center, Institute of Medical Science, University of Tokyo. He received his Ph.D. degree in science from the Kyushu University in 2003. His current research interests cover high dimensional time-course gene expression data analysis by state space models, biological pathway analysis and data assimilation, and sequence analysis for next generation sequencing data.
    Seiya Imoto is currently an associate professor of Human Genome Center, Institute of Medical Science, University of Tokyo. He received the B.S., M.S., and Ph.D. degrees in mathematics from Kyushu University, Japan, in 1996, 1998 and 2001, respectively. His current research interests cover statistical analysis of high dimensional data by Bayesian approach, biomedical information analysis, microarray gene expression data analysis, gene network estimation and analy-sis, data assimilation in biological networks and computational drug target discovery.
    Satoru Miyano is a professor of Human Genome Center, Institute of Medical Science, University of Tokyo. He received the B.S., M.S. and Ph.D. degrees all in mathematics from Kyushu University, Japan, in 1977, 1979 and 1984, respectively. His research group is developing computational methods for inferring gene networks from microarray gene expression data and other biological data, e.g., protein-protein interactions, promoter sequences. The group also developed a software tool, Cell Illustrator, for modeling and simulation of various biological systems. Currently, his research group is intensively working for developing the molecular network model of lung cancer by time-course gene expression and proteome data. With these technical achievements, his research direction is now heading toward a creation of Systems Pharmacology.

Since time-course microarray data are short but contain a large number of genes, most of statistical models should be extended so that they can handle such statistically irregular situations. We introduce biological state space models that are established as suitable computational models for constructing gene networks from microarray gene expression data. This chapter elucidates theory and methodology of our biological state space models together with some representative analyses including discovery of drug mode of action. Through the applications we show the whole strategy of biological state space model analysis involving experimental design of time-course data, model building and analysis of the estimated networks.

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