Network-Based Predictions and Simulations by Biological State Space Models: Search for Drug Mode of Action
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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.
Keywordsgene networks state space models time-course gene expression data
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- 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.Google Scholar
- 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.Google Scholar
- Murphy K, Mian S. Modelling gene expression data using dynamic Bayesian networks. Technical Report, Computer Science Division, University of California, Berkeley, USA, 1999.Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- Kalman R E. A new approach to linear filtering and prediction problems. Trans. Amer. Soc. Mech. Eng., J. Basic Engineering, 1960, 82: 35–45.Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- 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.CrossRefGoogle Scholar
- 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.Google Scholar
- 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)Google Scholar
- 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.Google Scholar
- Cell Illustrator. http://www.cellillustrator.com/, Oct. 1, 2009.
- 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.Google Scholar