Abstract
A gene regulatory network (GRN) extracted from microarray data has the potential to give us a concise and precise way of understanding how genes interact under the experimental conditions studied [1, 2]. Learning such networks, and unravelling the knowledge hidden within them is important for drug targets and to understand the basis of disease. In this paper, we analyse microarray gene expression data fromSaccharomyces cerevisiae, to extract Bayesian belief networks (BBNs) which mirror the cell cycle GRN. This is achieved through the use of a novel structure learning algorithm of Taboo search and a novel knowledge extraction technique, target node (TN) analysis. We also show how quantitative and qualitative information captured within the BBN can be used to simulate the nature of interaction between genes. The GRN extracted was validated against literature and genomic databases, and found to be in excellent agreement.
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References
Dewey, T.G.: From microarrays to networks: mining expression time series. Drug Discovery Today 7(20), s170–s175 (2002)
Brazhnik, P., de la Fuente, A., Mendes, P.: Gene networks: how to put the function in genomics. Trends in Biotechnology 20(11), 467–472 (2002)
Lewin, B.: Genes VII. Oxford University Press, Oxford (1999)
Spellman, P.T., et al.: Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Molecular Biology Of The Cell 9(12), 3273–3297 (1998)
Cho, R.J., et al.: A Genome-Wide Transcriptional Analysis of the Mitotic Cell Cycle. Molecular Cell 2(1), 65–73 (1998)
Lu, X., et al.: Statistical resynchronization and Bayesian detection of periodically expressed genes. Nucleic Acids Research 32(2), 447–455 (2004)
Friedman, N., et al.: Using Bayesian networks to analyse expression data. Journal of Computational Biology 7(3/4), 601–620 (2000)
Friedman, N.: Learning Bayesian Networks with Local Structure. In: Proceedings of the Twelfth International Conference on Uncertainty in Artificial Intelligence (1996)
Grunwald, P.: Model Selection Based on Minimum Description Length. Journal of Mathamatical Psychology 1(44), 133–152 (2000)
Fred Glover, M.L.: Tabu Search, p. 408. Kluwer Academic Publishers, Dordrecht (1997)
Donaldson, A.D., Blow, J.J.: The regulation of replication origin activation. Current Opinion in Genetics & Development 9(1), 62–68 (1999)
Tanaka, S., Diffley, J.F.X.: Deregulated G1-cyclin expression induces genomic instability by preventing efficient pre-RC formation. Genes & Development 16(20), 2639–2649 (2002)
Biggins, S., Murray, A.W.: Sister chromatid cohesion in mitosis. Current Opinion In Genetics & Development 9(2), 230–236 (1999)
Mata, J., Nurse, P.: Discovering the poles in yeast. Trends In Cell Biology 8(4), 163–167 (1998)
Cross, F.R.: Two redundant oscillatory mechanisms in the yeast cell cycle. Developmental Cell 4(5), 741–752 (2003)
Schwob, E., Nasmyth, K.: CLB5 and CLB6, a new pair of B cyclins involved in DNA replication in Saccharomyces cerevisiae. Genes & Development 7(7A), 1160–1175 (1993)
Schwob, E., et al.: The B-type cyclin kinase inhibitor p40SIC1 controls the G1 to S transition in S. Cell 79(2), 233–244 (1994)
Surana, U., et al.: The role of CDC28 and cyclins during mitosis in the budding yeast S. Cell 65(1), 145–161 (1991)
Andrews, B., Measday, V.: The cyclin family of budding yeast: abundant use of a good idea. Trends In Genetics: TIG 14(2), 66–72 (1998)
Lew, D.J., et al.: Cell cycle control in Saccharomyces cerevisiae, in Molecular and Cellular Biology of the Yeast Saccharomyces. In: Pringle Jr., B.J., Pringle Jr., J.E. (eds.) Cell Cycle and Cell Biology, pp. 607–695. Cold Spring Harbor Laboratory Press, New York (1997)
Tyers, M.: The cyclin-dependent kinase inhibitor p40SIC1 imposes the requirement for Cln G1 cyclin function at Start. Proceedings Of The National Academy Of Sciences Of The United States Of America 93(15), 7772–7776 (1996)
Valdivieso, M.H., et al.: FAR1 is required for posttranscriptional regulation of CLN2 gene expression in response to mating pheromone. Molecular And Cellular Biology 13(2), 1013–1022 (1993)
Guacci, V., Koshland, D., Strunnikov, A.: A Direct Link between Sister Chromatid Cohesion and Chromosome Condensation Revealed through the Analysis of MCD1 in S. cerevisiae. Cell 91(1), 47–57 (1997)
Habraken, Y., et al.: ATP-dependent assembly of a ternary complex consisting of a DNA mismatch and the yeast MSH2-MSH6 and MLH1-PMS1 protein complexes. The Journal Of Biological Chemistry 273(16), 9837–9841 (1998)
Werner-Washburne, M., et al.: Comparative analysis of multiple genome-scale data sets. Genome Research 12(10), 1564–1573 (2002)
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Kumuthini, J., Jouffe, L., Bessant, C. (2007). A Novel Graph Optimisation Algorithm for the Extraction of Gene Regulatory Networks from Temporal Microarray Data. In: Hochreiter, S., Wagner, R. (eds) Bioinformatics Research and Development. BIRD 2007. Lecture Notes in Computer Science(), vol 4414. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71233-6_3
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DOI: https://doi.org/10.1007/978-3-540-71233-6_3
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