Akther Shermin
ABSTRACT
Two major challenges in inferring the sparse topological architecture of Gene Regulatory Networks using computational methods are 1) the low accuracy of predicting connections between genes and 2) the excessive computational cost. In order to address these challenges, we have exploited some biological features of yeast cell cycle. One such feature is that, a high proportion of Cell Cycle Regulated genes are periodically expressed; that is genes are maximally expressed to affect and control the regulation of other genes and on completing certain tasks; they are repressed by some other regulator genes. Thus the whole cell cycle progresses systematically through the successive activation and inactivation of CCR genes. To use this feature, we have calculated the peak time of individual genes which falls into one/more phases of the cell cycle. Therefore, genes that peak in the interval of the same phase of the cell cycle have been grouped together. Finally, we have applied the Dynamic Bayesian Network (DBN) algorithm within distinct phases of genes. As a consequence, both the accuracy and the computational cost of our learning algorithm have been improved in comparison with the existing DBN algorithms.
- Shmulevich I., Dougherty ER, Seungchan K., Zhang W. Probabilistic Boolean Networks: a rule-based uncertainty model for gene regulatory networks. Bioinformatics. 18, pp. 261--274, 2002.Google Scholar
Cross Ref
- Shmulevich I., Gluhovsky I., Hashimoto R., Dougherty ER., Zhang W. Steady-state analysis of genetic regulatory networks modeled by probabilistic Boolean networks. Comparative and Functional Genomics. 4 (6), pp. 601--608, 2003.Google ScholarCross Ref
- Friedman, N., Linial, M., Nachman, I., Pe'er, D. Using Bayesian Network to Analyze Expression Data, J. Computational Biology. 7, pp. 601--620, 2000.Google ScholarCross Ref
- Li, P., Zhang, C., Perkins, E., J., Gong, P., Deng, Y. Comparison of probabilistic Boolean network and dynamic Bayesian network approaches for inferring gene regulatory networks. BMC Bioinformatics. 8(Suppl 7), 2007.Google Scholar
- Murphy, K. P. and Mian, S., Modelling Gene Expression Data using Dynamic Bayesian Networks. Tech. Rep., MIT Artificial Intelligence Laboratory, 1999.Google Scholar
- Ong, I., Glasner, J. D., Page, D. Modelling Regulatory Pathways in E. coli from Time Series Expression Profiles. Bioinformatics, 18(S1), pp. S241-S248, 2002.Google ScholarCross Ref
- Kim, S., Imoto, S., Miyano, S. Dynamic Bayesian Networks and Nonparametric Regression for Nonlinear Modelling of Gene Networks from Time Series Expression Data. Biosystems, 75 (1--3), pp. 57--65, 2003.Google Scholar
- Zou, M., Conzen, S. D. A New Dynamic Bayesian network (DBN) Approach for Identifying Gene Regulatory Networks from Time Course Microarray Data. Bioinformatics, 21, pp. 71--79, 2005. Google ScholarDigital Library
- Kimura, S., Ide, k., Kashihara, A. Kano, M., Hatakeyama, M., Masui, R., Nagakawa, N., Yokoyama, S., Kuramitsu, S., Konagaya, A. Inference of S-System Modeles of Genetic Networks using Cooperative Coevolutionary Algorithm. Bioinformatics, 21, pp. 1154--1163, 2005. Google ScholarDigital Library
- Noman, N., Iba, H. Inferring Gene Regulatory Networks Using Diffrential Evolution with Local Search Heuristics. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 4, 2007. Google ScholarDigital Library
- Pramila, T., Shawna, M., William, S. N., Linda, L. B. The Forkhead Transcription Factor HCm1 Regulates Chromosome Segregation Genes and Fills the S-phase Gap in the Transcriptional Circuitry of the Cell Cycle. Genes & Development, 20, pp. 2266--2278, 2006.Google Scholar
- 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. Serial Regulation of Transcriptional Regulators in the Yeast Cell Cycle. Cell., 106, pp. 697--708 2001.Google ScholarCross Ref
- de Lichtenberg, U., Jensen, L. J., Fausboll, A., Jensen, T. S., Bork, P., Brunak, S., Comparison of computational methods for the identification of cell cycle-regulated genes. Bioinformatics, 21, pp. 1164--1171, 2005. Google ScholarDigital Library
- Spellman, P., Sherlock, G., Zhang, M., Iyer, V., Anders, K., Eisen, M., Brown, P., Botstein, D., Futcher, B. Comprehensive Identification of Cell Cycle-Regulated Genes of the Yeast Sacccharomyces Cerevisiae by Microarray Hybridization. Molecular Biology of the Cell, 9, pp. 3273--3297, 1998.Google ScholarCross Ref
- Murphy, K. P. Bayes Net Toolbox, Tech. Rep., MIT Artificial Intelligence laboratory, 2002.Google Scholar
- Chaturvedi, I., Sakhakar, M., K., Rajapakse, J., C., Validation of Gene Regulatory Networks from Protein-Preotein Interaction Data: Application to Cell-Cycle Regulation. Lecture Notes in Bioinformatics, pp. 300--310, 2007. Google ScholarDigital Library
Index Terms
- Using dynamic bayesian networks to infer gene regulatory networks from expression profiles
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