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Dissecting network motifs by identifying promoter features that govern differential gene expression
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Summer Computer Simulation Conference archive
Proceedings of the 2007 summer computer simulation conference table of contents
San Diego, California
SESSION: Bioinformatics/biology: bioinformatics 2 table of contents
Pages 817-826  
Year of Publication: 2007
ISBN:1-56555-316-0
Authors
Oscar Harar  University of Buenos Aires, Buenos Aires, Argentina and University of Granada, Granada, Spain
Igor Zwir  University of Granada, Granada, Spain and Washington University School of Medicine, St. Louis, Missouri
Sponsor
SCS : Society for Modeling and Simulation International
Publisher
Bibliometrics
Downloads (6 Weeks): 6,   Downloads (12 Months): 41,   Citation Count: 0
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ABSTRACT

One of the biggest challenges in genomics is the elucidation of the design principles controlling gene expression. Current approaches examine promoter sequences for particular features, such as the presence of binding sites for a transcriptional regulator, and identify recurrent relationships among these features termed network motifs. To define the expression dynamics of a group of genes, the strength of the connections in a network must be specified, and these are determined by the cis-promoter features participating in the regulation. Approaches that homogenize features among promoters (e.g., relying on consensuses to describe the various promoter features) and even across species hamper the discovery of the key differences that distinguish promoters that are co-regulated by the same transcriptional regulator. Thus, we have developed a an approach based on fuzzy logic expressions to analyze proteobacterial genomes for promoter features that is specifically designed to account for the variability in sequence, location and topology intrinsic to differential gene expression. We applied our method to characterize network motifs controlled by the PhoP/PhoQ regulatory system of Escherichia coli and Salmonella enterica serovar Typhimurium. We identify key features that that enable the PhoP protein to produce differential regulation in target genes, reflecting distinct kinetic patterns even for the same type of network motif. These findings could not have been uncovered just by inspecting network architecture. We show that the same approach can be generalized to model other regulatory systems.


REFERENCES

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1
Winfield, M. D. and E. A. Groisman, Phenotypic differences between Salmonella and Escherichia coli resulting from the disparate regulation of homologous genes. Proc Natl Acad Sci U S A, 2004. 101(49): p. 17162--7.
 
2
Zwir, I., et al., Dissecting the PhoP regulatory network of Escherichia coli and Salmonella enterica. Proc Natl Acad Sci U S A, 2005. 102(8): p. 2862--7.
 
3
Minagawa, S., et al., Identification and molecular characterization of the Mg2+ stimulon of Escherichia coli. J Bacteriol, 2003. 185(13): p. 3696--702.
 
4
Groisman, E. A., The pleiotropic two-component regulatory system PhoP-PhoQ. J Bacteriol, 2001. 183(6): p. 1835--42.
 
5
 
6
Bezdek, J. C., Pattern Analysis, in Handbook of Fuzzy Computation, W. Pedrycz, P. P. Bonissone, and E. H. Ruspini, Editors. 1998, Institute of Physics: Bristol. p. F6.1.1-F6.6.20.
 
7
Gasch, A. P. and M. B. Eisen, Exploring the conditional coregulation of yeast gene expression through fuzzy k-means clustering. Genome Biol, 2002. 3(11): p. RESEARCH0059.
 
8
 
9
Stormo, G. D., DNA binding sites: representation and discovery. Bioinformatics, 2000. 16(1): p. 16--23.
 
10
Tompa, M., et al., Assessing computational tools for the discovery of transcription factor binding sites. Nat Biotechnol, 2005. 23: p. 137--44.
 
11
Barnard, A., A. Wolfe, and S. Busby, Regulation at complex bacterial promoters: how bacteria use different promoter organizations to produce different regulatory outcomes. Curr Opin Microbiol, 2004. 7(2): p. 102--8.
 
12
Lejona, S., et al., Molecular characterization of the Mg2+-responsive PhoP-PhoQ regulon in Salmonella enterica. J Bacteriol, 2003. 185(21): p. 6287--94.
 
13
Collado-Vides, J., B. Magasanik, and J. D. Gralla, Control site location and transcriptional regulation in Escherichia coli. Microbiol Rev, 1991. 55(3): p. 371--94.
 
14
X. Tu, et al., The PhoP/PhoQ two-component system stabilizes the alternative sigma factor RpoS in Salmonella enterica. Proc. Natl. Acad. Sci., 2006. (in press).
 
15
Salgado, H., et al., RegulonDB (version 4.0): transcriptional regulation, operon organization and growth conditions in Escherichia coli K-12. Nucleic Acids Res, 2004. 32(Database issue): p. D303--6.
 
16
Kato, A. and E. A. Groisman, Connecting two-component regulatory systems by a protein that protects a response regulator from dephosphorylation by its cognate sensor. Genes Dev, 2004. 18(18): p. 2302--13.
 
17
McCue, L., et al., Phylogenetic footprinting of transcription factor binding sites in proteobacterial genomes. Nucleic Acids Res, 2001. 29(3): p. 774--82.
 
18
Ruspini, E. H. and I. Zwir. Automated Qualitative Description of Measurements. in Proceedings of the 16th IEEE Instrumentation and Measurement Technology Conf. 1999. Venice, Italy.
 
19
Ruspini, E. H. and I. Zwir, Automated generation of qualitative representations of complex objects by hybrid soft-computing methods, in Pattern recognition: from classical to modern approaches, S. K. Pal and A. Pal, Editors. 2002, World Scientific: New Jersey. p. 454--474.
 
20
Zwir, I., R. R. Zaliz, and E. H. Ruspini, Automated biological sequence description by genetic multiobjective generalized clustering. Ann N Y Acad Sci, 2002. 980: p. 65--82.
 
21
Robison, K., A. M. McGuire, and G. M. Church, A comprehensive library of DNA-binding site matrices for 55 proteins applied to the complete Escherichia coli K-12 genome. J Mol Biol, 1998. 284(2): p. 241--54.
 
22
Bezdek, J. C., S. K. Pal, and IEEE Neural Networks Council, Fuzzy models for pattern recognition: methods that search for structures in data. 1992, New York: IEEE Press. xi, 539.
 
23
Cotik, V., R. R. Zaliz, and I. Zwir, A hybrid promoter analysis methodology for prokaryotic genomes. Fuzzy Sets and Systems, 2005. 152(1): p. 83--102.
 
24
Ishihama, A., Protein-protein communication within the transcription apparatus. J Bacteriol, 1993. 175(9): p. 2483--9.
 
25
Romero Zaliz, R., I. Zwir, and E. H. Ruspini, Generalized analysis of promoters: a method for DNA sequence description, in Applications of Multi-Objective Evolutionary Algorithms, C.a.L. Coello Coello, G., Editor. 2004, World Scientific: Singapore. p. 427--450.
 
26
Benitez-Bellon, E., G. Moreno-Hagelsieb, and J. Collado-Vides, Evaluation of thresholds for the detection of binding sites for regulatory proteins in Escherichia coli K12 DNA. Genome Biol, 2002. 3(3): p. RESEARCH0013.
 
27
Sugeno, M. and T. Yasukama, A Fuzzy-logic-based Approach to Qualitative Modeling. IEEE Transactions on Fuzzy Systems, 1993. 1(1): p. 7--31.
 
28
Herrera, F., M. Lozano, and V. J. L., Tuning fuzzy logic controllers by genetic algorithms. International Journal of Approximate Reasoning, 1995. 12(3): p. 299--315.
 
29
 
30
Bailey, T. L. and C. Elkan. Fitting a mixture model by expectation maximization to discover motifs in biopolymers. in Proceedings of the Second International Conference on Intelligent Systems for Molecular Biology. 1994. Menlo Park, California: AAAI Press.
 
31
Mangan, S. and U. Alon, Structure and function of the feed-forward loop network motif. Proc Natl Acad Sci U S A, 2003. 100(21): p. 11980--5.
 
32
D. C. Grainger, et al., Genomic studies with Escherichia coli MelR protein: applications of chromatin immunoprecipitation and microarrays. J Bacteriol, 2004. 186: p. 6938--43.
 
33
Leung, T. H., A. Hoffmann, and D. Baltimore, One nucleotide in a kappaB site can determine cofactor specificity for NF-kappaB dimers. Cell, 2004. 118(4): p. 453--64.