Summary
Understanding responses of the cellular system for a dosing molecule is one of the most important problems in pharmacogenomics. In this chapter, we describe computational methods for identifying and validating drug target genes based on the gene networks estimated from microarray gene expression data. We use two types of microarray gene expression data: gene disruptant microarray data and time-course drug response microarray data. For this purpose, the information of gene networks plays an essential role and is unattainable from clustering methods, which are the standard for gene expression analysis. The gene network is estimated from disruptant microarray data by the Bayesian network model, and then the proposed method automatically identifies sets of genes or gene regulatory pathways affected by the drug. We use an actual example from analysis of Saccharomyces cerevisiae gene expression profile data to express a concrete strategy for the application of gene network information toward drug target discovery.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Akutsu, T., Kuhara, S., Maruyama, O., and Miyano, S. (1998) A system for identifying genetic networks from gene expression patterns produced by gene disruptions and overexpressions. Genome Inform. 9, 151–160.
Akutsu, T., Miyano, S., and Kuhara, S. (1999) Identification of genetic networks from a small number of gene expression patterns under the Boolean network model. Pac. Symp. Biocomput. 4, 17–28.
Shmulevich, I., Dougherty, E. R., Kim, S., and Zhang, W. (2002) Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks. Bioinformatics 18, 261–274.
Chen, T., He, H. L., and Church, G. M. (1999) Modeling gene expression with differential equations. Pac. Symp. Biocomput. 4, 29–40.
De Hoon, M. J. L., Imoto, S., Kobayashi, K., Ogasawara, N., and Miyano, S. (2003) Inferring gene regulatory networks from time-ordered gene expression data of Bacillus subtilis using differential equations. Pac. Symp. Biocomput. 8, 17–28.
Friedman, N., Linial, M., Nachman, I., and Pe’er, D. (2000) Using Bayesian network to analyze expression data. J. Comp. Biol. 7, 601–620.
Hartemink, A. J., Gifford, D. K., Jaakkola, T. S., and Young, R. A. (2001) Using graphical models and genomic expression data to statistically validate models of genetic regulatory networks. Pac. Symp. Biocomput. 6, 422–433.
Pe’er, D., Regev, A., Elidan, G., and Friedman, N. (2001) Inferring subnetworks from perturbed expression profiles. Bioinformatics 17(Suppl. 1), S215–S224.
Imoto, S., Goto, T., and Miyano, S. (2002) Estimation of genetic networks and functional structures between genes by using Bayesian networks and nonparametric regression. Pac. Symp. Biocomput. 7, 175–186.
Imoto, S., Kim, S., Goto, T., et al. (2003) Bayesian network and nonparametric heteroscedastic regression for non-linear modeling of genetic network. J. Bioinform. Comp. Biol. 1, 231–252.
Imoto, S., Savoie, C. J., Aburatani, S., et al. (2003) Use of gene networks for identifying and validating drug targets. J. Bioinform. Comput. Biol. 1, 459–474.
Savoie, C. J., Aburatani, S.,Watanabe, S., et al. (2003) Use of gene networks from full genome microarray libraries to identify functionally relevant drug-affected genes and gene regulation cascades. DNA Res. 10, 19–25.
Tamada, Y., Imoto, S., Tashiro, K., Kuhara, S., and Miyano, S. (2005) Identifying drug active pathways from gene networks estimated by gene expression data. Genome Inform Ser Workshop Genome Inform 16, 182–191.
Aburatani, S., Tashiro, K., Savoie, C. J., et al. (2003) Discovery of novel transcription control relationships with gene regulatory networks generated from multiple-disruption full genome expression libraries. DNA Res. 10, 1–8.
Quackenbush, J. (2002) Microarray data normalization and transformation. Nat. Genet. 32(Suppl.), 496–501.
Maki, Y., Tominaga, D., Okamoto, M., Watanabe, S., and Eguchi, Y. (2001) Development of a system for the inference of large scale genetic networks. Pac. Symp. Biocomput. 6, 446–458.
Konishi, S. (1999) Statistical model evaluation and information criteria, in Multivariate Analysis, Design of Experiments and Survey Sampling (Ghosh, S., ed.), Marcel Dekker, New York, pp. 369–399.
Cooper, G. and Herskovits, E. (1992) A Bayesian method for the induction of probabilistic networks from data. Mach. Learn. 9, 309–347.
Hastie, T. and Tibshirani, R. J. (1990) Generalized Additive Models. Chapman & Hall, London.
De Boor, C. (1978) A Practical Guide to Splines. Springer-Verlag, Berlin.
Imoto, S. and Konishi, S. (2003) Selection of smoothing parameters in B-spline nonparametric regression models using information criteria. Ann. Inst. Stat. Math. 55, 671–687.
Davison, A. C. (1986) Approximate predictive likelihood. Biometrika 73, 323–332.
Tinerey, L. and Kadane, J. B. (1986) Accurate approximations for posterior moments and marginal densities. J. Am. Stat. Assoc. 81, 82–86.
Konishi, S., Ando, T., and Imoto, S. (2004) Bayesian information criteria and smoothing parameter selection in radial basis function networks. Biometrika 91, 27–43.
Neapolitan, R. E. (2004) Learning Bayesian Networks. Peirson Prentice Hall, Upper Saddle River, NJ.
Ott, S., Imoto, S., and Miyano, S. (2004) Finding optimal models for small gene networks. Pac. Symp. Biocomput. 9, 557–567.
Shmulevich, I. and Zhang, W. (2002) Binary analysis and optimization-based normalization of gene expression data. Bioinformatics 18, 555–565.
Imoto, S., Higuchi, T., Kim, S., Jeong, E., and Miyano, S. (2005) Residual bootstrapping and median filtering for robust estimation of gene networks from microarray data, in Proceedings of the 2nd Computational Methods in Systems Biology, Lecture Note in Bioinformatics 3082, Springer-Verlag, Berlin, 149–160.
Imoto, S., Higuchi, T., Goto, T., Tashiro, K., Kuhara, S., and Miyano, S. (2004) Combining microarrays and biological knowledge for estimating gene networks via Bayesian networks. J. Bioinform. Comput. Biol. 2, 77–98.
Tamada, Y., Kim, S., Bannai, H., et al. (2003) Estimating gene networks from gene expression data by combining Bayesian network model with promoter element detection. Bioinformatics 19(Suppl. 2), II227–II236.
Nariai, N., Kim, S., Imoto, S., and Miyano, S. (2004) Using protein-protein interactions for refining gene networks estimated from microarray data by Bayesian networks. Pac. Symp. Biocomput. 9, 336–347.
Nariai, N., Tamada, Y., Imoto, S., and Miyano, S. (2005) Estimating gene regulatory networks and protein-protein interactions of Saccharomyces cerevisiae from multiple genome-wide data. Bioinformatics 21(Suppl.), II206–II212.
Tamada, Y., Bannai, H., Imoto, S., Katayama, T., Kanehisa, M., and Miyano, S. (2005) Utilizing evolutionary information and gene expression data for estimating gene regulations with Bayesian network models. J. Bioinform. Comput. Biol. 3, 1295–1313.
Murphy, K. and Mian, S. (1999) Modelling gene expression data using dynamic Bayesian networks. Technical Report, Computer Science Division, University of California, Berkeley, CA.
Kim, S., Imoto, S., and Miyano, S. (2003) Inferring gene networks from time series microarray data using dynamic Bayesian networks. Brief. Bioinform. 4, 228–235.
Kim, S., Imoto, S., and Miyano, S. (2004) Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data. Biosystems 75, 57–65.
Yoshida, R., Imoto, S., and Higuchi, T. (2005) Estimating time-dependent gene networks from time series microarray data by dynamic linear models with Markov switching. Proc. IEEE 4th Comput. Syst. Bioinform. Conf., 289–298.
Efron, B. and Tibshirani, R. J. (1993) An Introduction to the Bootstrap. Chapman & Hall, New York.
Kamimura, T., Shimodaira, H., Imoto, S., et al. (2003) Multiscale bootstrap analysis of gene networks based on Bayesian networks and nonparametric regression. Genome Inform. 14, 350–351.
Shimodaira, H. (2004) Approximately unbiased tests of regions using multistep-multiscale bootstrap resampling. Ann. Stat. 32, 2616–2641.
Pearl, J. (1988) Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Francisco, CA.
Cooper, G. (1990) The computational complexity of probabilistic inference using bayesian belief networks. Artif. Intell. 42, 393–405.
Lauritzen, S. L. (1996) Graphical Models. Oxford University Press, New York.
Neal, R. M. (1993) Probabilistic inference using Markov chain Monte Carlo methods, Technical Report CRG-TR-93-1, Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.
Acknowledgments
We thank our colleagues and collaborators Hideo Bannai, Michiel de Hoon, Ryo Yoshida, Takao Goto, Sunyong Kim, Naoki Nariai, Sascha Ott, Tomoyuki Higuchi, Hidetoshi Shimodaira, Sachiyo Aburatani, Kousuke Tashiro, and Satoru Kuhara.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Humana Press Inc.
About this protocol
Cite this protocol
Imoto, S., Tamada, Y., Savoie, C.J., Miyanoaa, S. (2007). Analysis of Gene Networks for Drug Target Discovery and Validation. In: Sioud, M. (eds) Target Discovery and Validation Reviews and Protocols. Methods in Molecular Biology™, vol 360. Humana Press. https://doi.org/10.1385/1-59745-165-7:33
Download citation
DOI: https://doi.org/10.1385/1-59745-165-7:33
Publisher Name: Humana Press
Print ISBN: 978-1-58829-656-6
Online ISBN: 978-1-59745-165-9
eBook Packages: Springer Protocols