›› 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.

[1] Spellman P T, Sherlock G, Zhang M Q, Iyer V R, Anders K, Eisen M B, Brown P O, Botstien D, Futcher B. Comprehensive identification of cell cycle-regulated genes of the Yeast Saccharomyces cerevisiae by microarray hybridization. Molecular Biology of the Cell, 1998, 9(12): 3273-3297.
[2] Friedman N, Linial M, Nachman I, Pe’er D. Using Bayesian network to analyze expression data. J. Comp. Biol., 2000, 7(3/4): 601-620.
[3] Imoto S, Goto T, Miyano S. Estimation of genetic networks and functional structures between genes by using Bayesian network and nonparametric regression. Pacific Symposium on Biocomputing, 2002, 7: 175-186.
[4] Kim S, Imoto S, Miyano S. Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data. Biosystems, 2004, 75(1-3): 57-65.
[5] Murphy K, Mian S. Modelling gene expression data using dynamic Bayesian networks. Technical Report, Computer Science Division, University of California, Berkeley, USA, 1999.
[6] Basso K, Margolin A A, Stolovitzky G, Klein U, Dalla-Favera R, Califano A. Reverse engineering of regulatory networks in human B cells. Nat. Genet., 2005, 3(4): 382-390.
[7] Kitagawa G, Gersch W. Smoothness Priors Analysis of Time Series. New York: Springer-Verlag, 1996.
[8] West M, Harrison J. Bayesian Forecasting and Dynamic Models. Second Edition, New York: Springer-Verlag, 1997.
[9] Hirose O, Yoshida R, Imoto S, Yamaguchi R, Higuchi T, Charnock-Jones S D, Print C, Miyano S. Statistical inference of transcriptional module-based gene networks from time course gene expression profiles by using state space models. Bioinformatics, 2008, 24(7): 932-942.
[10] Yoshida R, Imoto S, Higuchi T. Estimating time-dependent gene networks from time series microarray data by dynamic linear models with Markov switching. In Proc. IEEE Computational Systems Bioinformatics Conference, Stanford, USA, Aug. 8-11, 2005, pp.289-298.
[11] Kojima K, Yamaguchi R, Imoto S, Yamauchi M, Nagasaki M, Yoshida R Shimamura T, Ueno K, Higuchi T, Gotoh N, Miyano S. A state space representation of VAR models with sparse learning for dynamic gene networks. Genome Informatics, 2009, 22: 56-58.
[12] Shumway R H, Stoffer D S. An approach to time series smoothing and forecasting using the EM algorithm. J. Time Series Analysis, 1982, 3(4): 253-264.
[13] Shumway R H. Dynamic mixed models for irregularly observed time series. Resenhas-Reviews of the Institute of Mathematics and Statistics, University of Sao Paulo, Brazil: USP Press, 2000, 4(4): 433-456.
[14] Kalman R E. A new approach to linear filtering and prediction problems. Trans. Amer. Soc. Mech. Eng., J. Basic Engineering, 1960, 82: 35-45.
[15] Yamaguchi R, Yoshida R, Imoto S, Higuchi T, Miyano S. Finding module-based gene networks with state-space models — Mining high-dimensional and short time-course gene expression data. IEEE Signal Processing Magazine, 2007, 24(1): 37-46.
[16] Shimamura T, Yamaguchi R, Imoto S, Miyano S. Weighted lasso in graphical Gaussian modeling for large gene network estimation based on microarray data. Genome Informatics, 2007, 19: 142-153.
[17] Efron B, Hastie T, Johnstone J, Tibshirani R. Least angle regression. Annals of Statistics, 2004, 32(2): 407-499.
[18] Li Z, Shaw S M, Yedwabnick M J, Chan C. Using a state-space model with hidden variables to infer transcription factor activities. Bioinformatics, 2006, 22(6): 747-754.
[19] Wu F X, Zhang A J, Kusalik A J. Modeling gene expression from microarray expression data with state-space equations. Pacific Symposium on Biocomputing, 2004, 9: 581-592.
[20] Rangel C, Angus J, Ghahramani Z, Lioumi M, Sotheran E, Gaiba A, Wild D L, Falciani F. Modeling T-cell activation using gene expression profiling and state-space models. Bioinformatics, 2004, 20(9): 1361-1372.
[21] Beal M J, Falciani F, Ghahramani Z, Rangel C, Wild D L. A Bayesian approach to reconstructing genetic regulatory networks with hidden factors. Bioinformatics, 2005, 21(3): 349- 356.
[22] Boyle E I, Weng S, Gollub J, Jin H, Botstein D, Cherry J M, Sherlock G. GO::TermFinder—Open source software for accessing Gene Ontology information and finding significantly enriched Gene Ontology terms associated with a list of genes. Bioinformatics, 2004, 20(18): 3710-3715.
[23] AffaraM, Dunmore B, Savoie C, Imoto S, Tamada Y, Araki H, Charnock-Jones D S, Miyano S, Print C. Understanding endothelial cell apoptosis: What can the transcriptome glycome and proteome reveal? Philosophical Transactions of Royal Society, 2007, 62(1484): 1469-1487.
[24] Johnson N A, Sengupta S, Saidi S A, Lessan K, Charnock- Jones S D, Scott L, Stephens R, Freeman T C, Tom B D, Harris M, Denyer G, Sundaram M, Sasisekharan R, Smith S K, Print C G. Endothelial cells preparing to die by apoptosis initiate a program of transcriptome and glycome regulation. FASEB J., 2003, 18(1): 188-190.
[25] Carmeliet P. Mechanisms of angiogenesis and arteriogenesis. Nature Medicine, 2000, 6(4): 389-395.
[26] Gerver H P, Hillan K J, Ryan A M, Kowalski J, Keller G A, Rangell L, Wright B D, Radtke F, Aguet M, Ferrara N. VEGF is required for growth and survival in neonatal mice. Development, 1999, 126(6): 1149-1159.
[27] Silverman B W. Density Estimation for Statistics and Data Analysis. London: Chapman & Hall, 1986.
[28] Aggarwal B B. Tumor necrosis factors receptor associated signaling molecules and their role in activation of apoptosis, JNK and NF-κB. Ann. Rheum. Dis., 2000, 59(Suppl. I): i6-i16.
[29] Keifer J A, Guttridge D C, Ashburner B P, Baldwin A S Jr. Inhibition of NF-κB activity by thalidomide through suppression of IκB kinase activity. J. Biol. Chem., 2001, 276(25): 22382-22387.
[30] Schwenzer R. The human tumor necrosis factor (TNF) receptor-associated factor 1 gene (TRAF1) is up-regulated by cytokines of the TNF ligand family and modulates TNFinduced activation of NF-κB and c-Jun N-terminal kinase. J. Biol. Chem., 1999, 274(27): 19368-19374.
[31] Han Y, Weinman S, Boldogh I, Walker R K, Brasier A R. Tumor necrosis factor-α-inducible IκBα proteolysis mediated by cytosolic m-calpain. A mechanism parallel to the ubiquitinproteasome pathway for nuclear factor-κB activation. J. Biol. Chem., 1999, 274(2): 787-794.
[32] Mukherji M, Bell R, Supekova L, Wang Y, Orth A P, Batalov S, Miraglia L, Huesken D, Lange J, Martin C, Sahasrabudhe S, Reinhardt M, Natt F, Hall J, Mickanin C, Labow M, Chanda S K, Cho C Y, Schultz P G. Genome-wide functional analysis of human cell-cycle regulators. Proc. Natl. Acad. Sci. USA, 2006, 103(40): 14819-14824.
[33] Yamaguchi R, Imoto S, Yamauchi M, Nagasaki M, Yoshida R, Shimamura T, Hatanaka Y, Ueno K, Higuchi T, Gotoh N, Miyano S. Predicting differences in gene regulatory systems by state space models. Genome Informatics, 2008, 21: 101-113.
[34] Gupta P K, Yoshida R, Imoto S, Yamaguchi R, Miyano S. Statistical absolute evaluation of gene ontology terms with gene expression data. In Proc. the 3rd Int. Symp. Bioinformatics Research and Applications, Atlanta, USA, May 7-10, 2007, LNCS 4463, Springer, Berlin/Heidelberg, pp.146-157.
[35] Yamaguchi R, Yamamoto M, Imoto S, Nagasaki M, Yoshida R, Tsuiji K, Ishige A, Asou H, Watanabe K, Miyano S. Identification of activated transcription factors from microarray gene expression data of kampo medicine-treated mice. Genome Informatics, 2007, 18: 119-129.
[36] Tamada Y, Imoto S, Araki H, Nagasaki M, Print C, Charnock- Jones D S, Miyano S. Estimating genome-wide gene networks using nonparametric Bayesian network models on massively parallel computers. IEEE/ACM Trans. Computational Biology and Bioinformatics. (in Press)
[37] Nagasaki M, Yamaguchi R, Yoshida R, Imoto S, Doi A, Tamada Y, Matsuno H, Miyan S, Higuchi T. Genomic data assimilation for estimating hybrid functional petri net from time-course gene expression data. Genome Informatics, 2006, 17(1): 46-61.
[38] Cell Illustrator. http://www.cellillustrator.com/, Oct. 1, 2009.
[39] Nagasaki M, Doi A, Matsuno H, Miyano S. Genomic object net: I. a platform for modeling and simulating biopathways. Applied Bioinformatics, 2003, 2(3): 181-184.

No related articles found!
Full text



No Suggested Reading articles found!

ISSN 1000-9000(Print)

CN 11-2296/TP

Editorial Board
Author Guidelines
Journal of Computer Science and Technology
Institute of Computing Technology, Chinese Academy of Sciences
P.O. Box 2704, Beijing 100190 P.R. China
E-mail: jcst@ict.ac.cn
  Copyright ©2015 JCST, All Rights Reserved