Akther Shermin
ABSTRACT
The high complexity in the gene regulation mechanism and the prevalent noise in high-throughput detection experiments are considered to be the two major obstacles in discovering transcriptional regulation with high accuracy from experimental gene expression data. In this paper, we study a model based on dynamic Bayesian networks to predict gene regulation by integrating transcription factor binding site data and proteinprotein interaction data with gene expression data. The knowledge of genetic interactions between proteins and the presence of transcription factors binding site at the promoter region of a gene have been used to restrict the number of potential regulators of each gene. We show the effectiveness of combining multiple data sources in the prediction of transcriptional regulation through the analysis of Saccharomyces cerevisiae (Yeast) cell cycle data. Experiments conducted on real microarray datasets show that the proposed model is significantly more efficient and topologically more accurate compared to other existing models based on dynamic Bayesian networks. We also demonstrate the scalability of the proposed model through the analysis of a large dataset with a sustainable performance level.
- Shmulevich, I., Dougherty, E. R., Seungchan, K., and Zhang, W. 2002. Probabilistic Boolean Networks: A Rule-Based Uncertainty Model for Gene Regulatory Networks. Bioinformatics, 18(2), 261--274.Google Scholar
- 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. In Proc. of the Pac.Symp. on Biocomp, 17--28. Google ScholarDigital Library
- Friedman, N., Linial, M., Nachman, I., and Pe'er, D. 2000. Using Bayesian Network to Analyze Expression Data. Computational Biology. 7, 601--20.Google ScholarCross Ref
- Noman, N., and Iba, H., 2007. Inferring Gene Regulatory Networks Using Diffrential Evolution with Local Search Heuristics. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 4, 634--647. Google ScholarDigital Library
- Murphy, K. P., and Mian, S. 1999. Modelling Gene Expression Data using Dynamic Bayesian Networks. Technical Report. MIT Artificial Intelligence Laboratory, Cambridge, USA.Google Scholar
- Shermin, A., and Orgun, M. 2009. Using Dynamic Bayesian Networks to infer Gene Regulatory Networks from Expression Profiles. In Proc. of the ACM Symposium on Applied Computing: Bioinformatics Track. ACM Press, 799--803. Google ScholarDigital Library
- Shermin, A., and Orgun, M., 2009. A 2-stage Approach for Inferring Gene Regulatory Networks using Dynamic Bayesian Networks. In Proc. of the IEEE International Conference on Bioinformatics and Biomedicine. 166--169. Google ScholarDigital Library
- Kim, S., Imoto, S., and Miyano, S. 2004. Dynamic Bayesian Networks and Nonparametric Regression for Nonlinear Modelling of Gene Networks from Time Series Expression Data. Biosystems. 75, 57--65.Google ScholarCross Ref
- Zou, M. and Conzen, S. D. 2005. A New Dynamic Bayesian network (DBN) Approach for Identifying Gene Regulatory Networks from Time Course Microarray Data. Bioinformatics. 21, 71--79. Google ScholarDigital Library
- Koh, C., Wu, F. X., Selvaraj, G., and Kusalik, A. J. 2009. Using a state-space model and location analysis to infer time-delayed regulatory networks. EURASIP J. on Bioinformatics and System Biology. 1--14. Google ScholarDigital Library
- Wentao, Z., Serpedin E., Dougherty E. R. 2006. Inferring gene regulatory networks from time series data using the minimum description length principle. Bioinformatics. 22(17), 2129--2135. Google ScholarDigital Library
- Chaitankar, V., Ghosh Preetam, P., Edward, J., Gong, P., Deng, Y., Zhang, C. 2010. A novel gene network inference algorithm using predictive minimum description length approach. BMC Systems Biology. 4(Suppl 1):S7.Google ScholarCross Ref
- Hartemink, A. J., Gifford, D. K., Jaakkola, T. S., and Young, R. A. 2002. Combining Location and Expression Data for Principled Discovery of Genetic Regulatory Network Models. In Proc. of the Pac. Symp. on Biocomputing. 437--449.Google Scholar
- Tamada, Y., Kim, S., Bannai, H., Imoto, S., Tashiro, K., Kuhara, S., and Miyano, S. 2003. Estimating gene networks from gene expression data by combining BN model with promoter element detection. Bioinformatics. 227--236.Google Scholar
- Segal, E., Yelensky R., and Koller, D. 2003. Genome-wide discovery of transcriptional modules from DNA sequence and gene expression. Bioinformatics. 273--282.Google Scholar
- Spellman, P., Sherlock, G., Zhang, M., Iyer, V., Anders, K., Eisen, M., Brown, P., Botstein, D., and Futcher, B. 1998. Comprehensive Identification of Cell Cycle-Regulated Genes of the Yeast Sacccharomyces Cerevisiae by Microarray Hybridization. Molecular Biology of the Cell. 9, 3273--3297.Google ScholarCross Ref
- Kanehisa, M. 2008. KEGG for linking genomes to life and the environment. Nucleic Acids Res. 36, D480--D484.Google ScholarCross Ref
- Kanehisa, M. 2006. From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res. 34, D354--357.Google ScholarCross Ref
- KEGG: 2000. Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27--30.Google ScholarCross Ref
- Simon, I., Barnett, J., Hannett, N., Harbison, C. T., Rinaldi, N. J., Volkert, T. L., Wyrick, J. J., Zeitlinger, J., Gifford, D. K., Jaakkola, T. S., Young, R. A. 2001. Serial Regulation of Transcriptional Regulators in the Yeast Cell Cycle. Cell. 106, 697--708.Google ScholarCross Ref
- http://www.yeastract.com. (last accessed 15/03/2011)Google Scholar
- Breitkreutz, B. J., Stark, C., Reguly, T., Boucher, L., Breitkreutz, A., Livstone, M., Oughtred, R., Lackner, D. H., Bähler, J., Wood, V., Dolinski, K., and Tyers, M. 2008. The BioGRID Interaction Database: 2008 update. Nucleic Acids Res. D637--640.Google Scholar
- Han J. D. 2008. Understanding biological functions through molecular networks. Cell Res. 18,224--237.Google ScholarCross Ref
- Murphy, K. P. 2002. Bayes Net Toolbox, Technical Report MIT Artificial Intelligence laboratory, Cambridge, USA.Google Scholar
- http://www.r-project.org/. (last accessed 15/03/2011)Google Scholar
- de Lichtenberg, U., Jensen, L. J., Fausboll, A., Jensen, T. S., Bork, P., and Brunak, S. 2005. Comparison of computational methods for the identification of cell cycle-regulated genes. Bioinformatics, 21, 1164--1171. Google ScholarDigital Library
Index Terms
- A scalable approach for inferring transcriptional regulation in the yeast cell cycle
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