Modeling Stress-Induced Regulatory Cascades with Artificial Neural Networks
Yeast cells live in a constantly changing environment that requires the continuous adaptation of their genomic program in order to sustain their homeostasis, survive and proliferate. Due to the advancement of high throughput technologies, there is currently a large amount of data such as gene expression, gene deletion and protein-protein interactions for S. Cerevisiae under various environmental conditions. Mining these datasets requires efficient computational methods capable of integrating different types of data, identifying inter-relations between different components and inferring functional groups or 'modules' that shape intracellular processes. This study uses computational methods to delineate some of the mechanisms used by yeast cells to respond to environmental changes. The GRAM algorithm is first used to integrate gene expression data and ChIP-chip data in order to find modules of coexpressed and co-regulated genes as well as the transcription factors (TFs) that regulate these modules. Since transcription factors are themselves transcriptionally regulated, a three-layer regulatory cascade consisting of the TF-regulators, the TFs and the regulated modules is subsequently considered. This three-layer cascade is then modeled quantitatively using artificial neural networks (ANNs) where the input layer corresponds to the expression of the up-stream transcription factors (TF-regulators) and the output layer corresponds to the expression of genes within each module. This work shows that (a) the expression of at least 33 genes over time and for different stress conditions is well predicted by the expression of the top layer transcription factors, including cases in which the effect of up-stream regulators is shifted in time and (b) identifies at least 6 novel regulatory interactions that were not previously associated with stress-induced changes in gene expression. These findings suggest that the combination of gene expression and protein-DNA interaction data with artificial neural networks can successfully model biological pathways and capture quantitative dependencies between distant regulators and downstream genes.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1074669Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1198
 Hartwell, L.H., et al., From molecular to modular cell biology. Nature, 1999. 402(6761 Suppl): p. C47-52.
 Wagner, G.P., M. Pavlicev, and J.M. Cheverud, The road to modularity. Nat Rev Genet, 2007. 8(12): p. 921-31.
 Fernandez, A., Molecular basis for evolving modularity in the yeast protein interaction network. PLoS Comput Biol, 2007. 3(11): p. e226.
 Gursoy, A., O. Keskin, and R. Nussinov, Topological properties of protein interaction networks from a structural perspective. Biochem Soc Trans, 2008. 36(Pt 6): p. 1398-403.
 Han, J.D., Understanding biological functions through molecular networks. Cell Res, 2008. 18(2): p. 224-37.
 Ravasz, E., et al., Hierarchical organization of modularity in metabolic networks. Science, 2002. 297(5586): p. 1551-5.
 Zhao, J., et al., Modular co-evolution of metabolic networks. BMC Bioinformatics, 2007. 8: p. 311.
 Segre, D., et al., Modular epistasis in yeast metabolism. Nat Genet, 2005. 37(1): p. 77-83.
 Bruggeman, F.J., J.L. Snoep, and H.V. Westerhoff, Control, responses and modularity of cellular regulatory networks: a control analysis perspective. IET Syst Biol, 2008. 2(6): p. 397-410.
 Tanay, A., et al., Revealing modularity and organization in the yeast molecular network by integrated analysis of highly heterogeneous genomewide data. Proc Natl Acad Sci U S A, 2004. 101(9): p. 2981-6.
 Ihmels, J., et al., Revealing modular organization in the yeast transcriptional network. Nat Genet, 2002. 31(4): p. 370-7.
 Tim Van den Bulcke, K.L., Yves Van de Peer, Kathleen Marchal, Inferring transcriptional networks by mining 'omics' data. Current Bioinformatics, 2006. 1.
 Eisen, M.B., et al., Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci U S A, 1998. 95(25): p. 14863-8.
 Niehrs, C. and N. Pollet, Synexpression groups in eukaryotes. Nature, 1999. 402(6761): p. 483-7.
 Zhao, Y. and G. Karypis, Data clustering in life sciences. Mol Biotechnol, 2005. 31(1): p. 55-80.
 Kerr, G., et al., Techniques for clustering gene expression data. Comput Biol Med, 2008. 38(3): p. 283-93.
 Wei, G.H., D.P. Liu, and C.C. Liang, Charting gene regulatory networks: strategies, challenges and perspectives. Biochem J, 2004. 381(Pt 1): p. 1-12.
 Li, H. and M. Zhan, Unraveling transcriptional regulatory programs by integrative analysis of microarray and transcription factor binding data. Bioinformatics, 2008. 24(17): p. 1874-80.
 Alter, O., P.O. Brown, and D. Botstein, Singular value decomposition for genome-wide expression data processing and modeling. Proc Natl Acad Sci U S A, 2000. 97(18): p. 10101-6.
 Lee, S.I. and S. Batzoglou, Application of independent component analysis to microarrays. Genome Biol, 2003. 4(11): p. R76.
 Segal, E., et al., Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat Genet, 2003. 34(2): p. 166-76.
 Lee, H.G., et al., High-resolution analysis of condition-specific regulatory modules in Saccharomyces cerevisiae. Genome Biol, 2008. 9: p. R2.
 Hu, J., H. Hu, and X. Li, MOPAT: a graph-based method to predict recurrent cis-regulatory modules from known motifs. Nucleic Acids Res, 2008. 36(13): p. 4488-97.
 Kundaje, A., et al., Combining sequence and time series expression data to learn transcriptional modules. IEEE/ACM Trans Comput Biol Bioinform, 2005. 2(3): p. 194-202.
 Imoto, S., et al., Combining microarrays and biological knowledge for estimating gene networks via bayesian networks. J Bioinform Comput Biol, 2004. 2(1): p. 77-98.
 Gao, F., B.C. Foat, and H.J. Bussemaker, Defining transcriptional networks through integrative modeling of mRNA expression and transcription factor binding data. BMC Bioinformatics, 2004. 5: p. 31.
 Xu, X., L. Wang, and D. Ding, Learning module networks from genome-wide location and expression data. FEBS Lett, 2004. 578(3): p. 297-304.
 Maraziotis, I.A., K. Dimitrakopoulou, and A. Bezerianos, An in silico method for detecting overlapping functional modules from composite biological networks. BMC Syst Biol, 2008. 2: p. 93.
 Tornow, S. and H.W. Mewes, Functional modules by relating protein interaction networks and gene expression. Nucleic Acids Res, 2003. 31(21): p. 6283-9.
 Chen, G., S.T. Jensen, and C.J. Stoeckert, Jr., Clustering of genes into regulons using integrated modeling-COGRIM. Genome Biol, 2007. 8(1): p. R4.
 Lemmens, K., et al., Inferring transcriptional modules from ChIPchip, motif and microarray data. Genome Biol, 2006. 7(5): p. R37.
 Li, H. and W. Wang, Dissecting the transcription networks of a cell using computational genomics. Curr Opin Genet Dev, 2003. 13(6): p. 611-6.
 Bar-Joseph, Z., et al., Computational discovery of gene modules and regulatory networks. Nat Biotechnol, 2003. 21(11): p. 1337- 42.
 Gasch, A.P., et al., Genomic expression programs in the response of yeast cells to environmental changes. Mol Biol Cell, 2000. 11(12): p. 4241-57.
 Causton, H.C., et al., Remodeling of yeast genome expression in response to environmental changes. Mol Biol Cell, 2001. 12(2): p. 323-37.
 Bammert, G.F. and J.M. Fostel, Genome-wide expression patterns in Saccharomyces cerevisiae: comparison of drug treatments and genetic alterations affecting biosynthesis of ergosterol. Antimicrob Agents Chemother, 2000. 44(5): p. 1255-65.
 Kwast, K.E., et al., Genomic analyses of anaerobically induced genes in Saccharomyces cerevisiae: functional roles of Rox1 and other factors in mediating the anoxic response. J Bacteriol, 2002. 184(1): p. 250-65.
 Rep, M., et al., The transcriptional response of Saccharomyces cerevisiae to osmotic shock. Hot1p and Msn2p/Msn4p are required for the induction of subsets of high osmolarity glycerol pathwaydependent genes. J Biol Chem, 2000. 275(12): p. 8290-300.
 Gasch, A.P. and M. Werner-Washburne, The genomics of yeast responses to environmental stress and starvation. Funct Integr Genomics, 2002. 2(4-5): p. 181-92.
 Attfield, P.V., Stress tolerance: the key to effective strains of industrial baker's yeast. Nat Biotechnol, 1997. 15(13): p. 1351-7.
 Mager, W.H. and M. Siderius, Novel insights into the osmotic stress response of yeast. FEMS Yeast Res, 2002. 2(3): p. 251-7.
 Tootle, T.L. and I. Rebay, Post-translational modifications influence transcription factor activity: a view from the ETS superfamily. Bioessays, 2005. 27(3): p. 285-98.
 Kel, A., et al., Beyond microarrays: Finding key transcription factors controlling signal transduction pathways. BMC Bioinformatics, 2006. 7 Suppl 2: p. S13.
 Lee, T.I., et al., Transcriptional regulatory networks in Saccharomyces cerevisiae. Science, 2002. 298(5594): p. 799-804.
 Shenton, D., et al., Global translational responses to oxidative stress impact upon multiple levels of protein synthesis. J Biol Chem, 2006. 281(39): p. 29011-21.
 Aragon, A.D., et al., Release of extraction-resistant mRNA in stationary phase Saccharomyces cerevisiae produces a massive increase in transcript abundance in response to stress. Genome Biol, 2006. 7(2): p. R9.
 Molina-Navarro, M.M., et al., Comprehensive transcriptional analysis of the oxidative response in yeast. J Biol Chem, 2008. 283(26): p. 17908-18.
 Qian, N. and T.J. Sejnowski, Predicting the secondary structure of globular proteins using neural network models. J Mol Biol, 1988. 202(4): p. 865-84.
 Bendtsen, J.D., et al., Improved prediction of signal peptides: SignalP 3.0. J Mol Biol, 2004. 340(4): p. 783-95.
 Hart, C.E., E. Mjolsness, and B.J. Wold, Connectivity in the yeast cell cycle transcription network: inferences from neural networks. PLoS Comput Biol, 2006. 2(12): p. e169.
 Huang, J., H. Shimizu, and S. Shioya, Clustering gene expression pattern and extracting relationship in gene network based on artificial neural networks. J Biosci Bioeng, 2003. 96(5): p. 421-8.