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.
[1] Hartwell, L.H., et al., From molecular to modular cell biology.
Nature, 1999. 402(6761 Suppl): p. C47-52.
[2] Wagner, G.P., M. Pavlicev, and J.M. Cheverud, The road to
modularity. Nat Rev Genet, 2007. 8(12): p. 921-31.
[3] Fernandez, A., Molecular basis for evolving modularity in the
yeast protein interaction network. PLoS Comput Biol, 2007. 3(11):
p. e226.
[4] 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.
[5] Han, J.D., Understanding biological functions through molecular
networks. Cell Res, 2008. 18(2): p. 224-37.
[6] Ravasz, E., et al., Hierarchical organization of modularity in
metabolic networks. Science, 2002. 297(5586): p. 1551-5.
[7] Zhao, J., et al., Modular co-evolution of metabolic networks. BMC
Bioinformatics, 2007. 8: p. 311.
[8] Segre, D., et al., Modular epistasis in yeast metabolism. Nat Genet,
2005. 37(1): p. 77-83.
[9] 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.
[10] 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.
[11] Ihmels, J., et al., Revealing modular organization in the yeast
transcriptional network. Nat Genet, 2002. 31(4): p. 370-7.
[12] Tim Van den Bulcke, K.L., Yves Van de Peer, Kathleen Marchal,
Inferring transcriptional networks by mining 'omics' data. Current
Bioinformatics, 2006. 1.
[13] 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.
[14] Niehrs, C. and N. Pollet, Synexpression groups in eukaryotes.
Nature, 1999. 402(6761): p. 483-7.
[15] Zhao, Y. and G. Karypis, Data clustering in life sciences. Mol
Biotechnol, 2005. 31(1): p. 55-80.
[16] Kerr, G., et al., Techniques for clustering gene expression data.
Comput Biol Med, 2008. 38(3): p. 283-93.
[17] 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.
[18] 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.
[19] 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.
[20] Lee, S.I. and S. Batzoglou, Application of independent component
analysis to microarrays. Genome Biol, 2003. 4(11): p. R76.
[21] 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.
[22] Lee, H.G., et al., High-resolution analysis of condition-specific
regulatory modules in Saccharomyces cerevisiae. Genome Biol,
2008. 9: p. R2.
[23] 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.
[24] 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.
[25] 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.
[26] 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.
[27] 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.
[28] 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.
[29] 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.
[30] 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.
[31] Lemmens, K., et al., Inferring transcriptional modules from ChIPchip,
motif and microarray data. Genome Biol, 2006. 7(5): p. R37.
[32] 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.
[33] Bar-Joseph, Z., et al., Computational discovery of gene modules
and regulatory networks. Nat Biotechnol, 2003. 21(11): p. 1337-
42.
[34] 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.
[35] Causton, H.C., et al., Remodeling of yeast genome expression in
response to environmental changes. Mol Biol Cell, 2001. 12(2): p.
323-37.
[36] 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.
[37] 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.
[38] 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.
[39] 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.
[40] Attfield, P.V., Stress tolerance: the key to effective strains of
industrial baker's yeast. Nat Biotechnol, 1997. 15(13): p. 1351-7.
[41] Mager, W.H. and M. Siderius, Novel insights into the osmotic
stress response of yeast. FEMS Yeast Res, 2002. 2(3): p. 251-7.
[42] 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.
[43] Kel, A., et al., Beyond microarrays: Finding key transcription
factors controlling signal transduction pathways. BMC
Bioinformatics, 2006. 7 Suppl 2: p. S13.
[44] Lee, T.I., et al., Transcriptional regulatory networks in
Saccharomyces cerevisiae. Science, 2002. 298(5594): p. 799-804.
[45] 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.
[46] 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.
[47] Molina-Navarro, M.M., et al., Comprehensive transcriptional
analysis of the oxidative response in yeast. J Biol Chem, 2008.
283(26): p. 17908-18.
[48] 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.
[49] Bendtsen, J.D., et al., Improved prediction of signal peptides:
SignalP 3.0. J Mol Biol, 2004. 340(4): p. 783-95.
[50] 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.
[51] 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.
[1] Hartwell, L.H., et al., From molecular to modular cell biology.
Nature, 1999. 402(6761 Suppl): p. C47-52.
[2] Wagner, G.P., M. Pavlicev, and J.M. Cheverud, The road to
modularity. Nat Rev Genet, 2007. 8(12): p. 921-31.
[3] Fernandez, A., Molecular basis for evolving modularity in the
yeast protein interaction network. PLoS Comput Biol, 2007. 3(11):
p. e226.
[4] 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.
[5] Han, J.D., Understanding biological functions through molecular
networks. Cell Res, 2008. 18(2): p. 224-37.
[6] Ravasz, E., et al., Hierarchical organization of modularity in
metabolic networks. Science, 2002. 297(5586): p. 1551-5.
[7] Zhao, J., et al., Modular co-evolution of metabolic networks. BMC
Bioinformatics, 2007. 8: p. 311.
[8] Segre, D., et al., Modular epistasis in yeast metabolism. Nat Genet,
2005. 37(1): p. 77-83.
[9] 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.
[10] 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.
[11] Ihmels, J., et al., Revealing modular organization in the yeast
transcriptional network. Nat Genet, 2002. 31(4): p. 370-7.
[12] Tim Van den Bulcke, K.L., Yves Van de Peer, Kathleen Marchal,
Inferring transcriptional networks by mining 'omics' data. Current
Bioinformatics, 2006. 1.
[13] 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.
[14] Niehrs, C. and N. Pollet, Synexpression groups in eukaryotes.
Nature, 1999. 402(6761): p. 483-7.
[15] Zhao, Y. and G. Karypis, Data clustering in life sciences. Mol
Biotechnol, 2005. 31(1): p. 55-80.
[16] Kerr, G., et al., Techniques for clustering gene expression data.
Comput Biol Med, 2008. 38(3): p. 283-93.
[17] 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.
[18] 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.
[19] 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.
[20] Lee, S.I. and S. Batzoglou, Application of independent component
analysis to microarrays. Genome Biol, 2003. 4(11): p. R76.
[21] 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.
[22] Lee, H.G., et al., High-resolution analysis of condition-specific
regulatory modules in Saccharomyces cerevisiae. Genome Biol,
2008. 9: p. R2.
[23] 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.
[24] 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.
[25] 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.
[26] 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.
[27] 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.
[28] 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.
[29] 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.
[30] 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.
[31] Lemmens, K., et al., Inferring transcriptional modules from ChIPchip,
motif and microarray data. Genome Biol, 2006. 7(5): p. R37.
[32] 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.
[33] Bar-Joseph, Z., et al., Computational discovery of gene modules
and regulatory networks. Nat Biotechnol, 2003. 21(11): p. 1337-
42.
[34] 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.
[35] Causton, H.C., et al., Remodeling of yeast genome expression in
response to environmental changes. Mol Biol Cell, 2001. 12(2): p.
323-37.
[36] 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.
[37] 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.
[38] 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.
[39] 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.
[40] Attfield, P.V., Stress tolerance: the key to effective strains of
industrial baker's yeast. Nat Biotechnol, 1997. 15(13): p. 1351-7.
[41] Mager, W.H. and M. Siderius, Novel insights into the osmotic
stress response of yeast. FEMS Yeast Res, 2002. 2(3): p. 251-7.
[42] 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.
[43] Kel, A., et al., Beyond microarrays: Finding key transcription
factors controlling signal transduction pathways. BMC
Bioinformatics, 2006. 7 Suppl 2: p. S13.
[44] Lee, T.I., et al., Transcriptional regulatory networks in
Saccharomyces cerevisiae. Science, 2002. 298(5594): p. 799-804.
[45] 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.
[46] 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.
[47] Molina-Navarro, M.M., et al., Comprehensive transcriptional
analysis of the oxidative response in yeast. J Biol Chem, 2008.
283(26): p. 17908-18.
[48] 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.
[49] Bendtsen, J.D., et al., Improved prediction of signal peptides:
SignalP 3.0. J Mol Biol, 2004. 340(4): p. 783-95.
[50] 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.
[51] 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.
@article{"International Journal of Chemical, Materials and Biomolecular Sciences:59198", author = "Maria E. Manioudaki and Panayiota Poirazi", title = "Modeling Stress-Induced Regulatory Cascades with Artificial Neural Networks", abstract = "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.
", keywords = "gene modules, artificial neural networks, yeast,stress", volume = "3", number = "4", pages = "214-7", }
