Fusion of Domain Knowledge for Dynamic Learning in Transcriptional Networks

  • Oscar Harari
  • R. Romero-Zaliz
  • C. Rubio-Escudero
  • I. Zwir
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4224)


A critical challenge of the postgenomic era is to understand how genes are differentially regulated even when they belong to a given network. Because the fundamental mechanism controlling gene expression operates at the level of transcription initiation, computational techniques have been developed that identify cis-regulatory features and map such features into differential expression patterns. The fact that such co-regulated genes may be differentially regulated suggests that subtle differences in the shared cis-acting regulatory elements are likely significant. Thus, we carry out an exhaustive description of cis-acting regulatory features including the orientation, location and number of binding sites for a regulatory protein, the presence of binding site submotifs, the class and number of RNA polymerase sites, as well as gene expression data, which is treated as one feature among many. These features, derived from different domain sources, are analyzed concurrently, and dynamic relations are recognized to generate profiles, which are groups of promoterssharing common features. We apply this method to probe the regulatory networks governed by the PhoP/PhoQ two-component system in the enteric bacteria Escherichia coli and Salmonella enterica. Our analysis uncovered novel members of the PhoP regulon as and the resulting profiles group genes that share underlying biological that characterize the system kinetics. The predictions were experimentally validated to establish that the PhoP protein uses multiple mechanisms to control gene transcription and is a central element in a highly connected network.


Transcriptional Network Dynamic Learn Howard Hughes Medical Institute Position Weight Matrice Control Gene Transcription 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. Cook, D.J., et al.: Structural mining of molecular biology data. IEEE Eng. Med. Biol. Mag. 20(4), 67–74 (2001)CrossRefGoogle Scholar
  2. Cooper, G.F., Herskovits, E.: A Bayesian Method for the Induction of Probabilistic Networks from Data. Machine Learning 9(4), 309–347 (1992)MATHGoogle Scholar
  3. Bezdek, J.C.: Pattern Analysis, in Handbook of Fuzzy Computation. In: Pedrycz, W., Bonissone, P.P., Ruspini, E.H. (eds.) Institute of Physics: Bristol, pp. F6.1.1-F6.6.20 (1998)Google Scholar
  4. Gasch, A.P., Eisen, M.B.: Exploring the conditional coregulation of yeast gene expression through fuzzy k-means clustering. Genome Biol. 3(11) (2002) RESEARCH0059Google Scholar
  5. Ruspini, E.H., Zwir, I.: Automated generation of qualitative representations of complex objects by hybrid soft-computing methods. In: Pal, S.K., Pal, A. (eds.) Pattern recognition: from classical to modern approaches, pp. 454–474. World Scientific, New Jersey (2002)Google Scholar
  6. Deb, K.: Multi-objective optimization using evolutionary algorithms. In: Wiley-Interscience series in systems and optimization, 1st edn., vol. 11, p. 497. John Wiley & Sons. xix, Chichester (2001)Google Scholar
  7. Groisman, E.A.: The pleiotropic two-component regulatory system PhoP-PhoQ. J. Bacteriol 183(6), 1835–1842 (2001)CrossRefGoogle Scholar
  8. Zwir, I., et al.: Dissecting the PhoP regulatory network of Escherichia coli and Salmonella enterica. Proc Natl Acad Sci U S A 102(8), 2862–2867 (2005)CrossRefGoogle Scholar
  9. Beer, M.A., Tavazoie, S.: Predicting gene expression from sequence. Cell 117(2), 185–198 (2004)CrossRefGoogle Scholar
  10. Stormo, G.D.: DNA binding sites: representation and discovery. Bioinformatics 16(1), 16–23 (2000)CrossRefGoogle Scholar
  11. Robison, K., McGuire, A.M., Church, G.M.: A comprehensive library of DNA-binding site matrices for 55 proteins applied to the complete Escherichia coli K-12 genome. J. Mol. Biol. 284(2), 241–254 (1998)CrossRefGoogle Scholar
  12. Barash, Y., Elidan, G., Friedman, N., Kaplan, T.: Dependencies in Protein-DNA Binding Sites. In: RECOMB 2003 (2003)Google Scholar
  13. Salgado, H., et al.: RegulonDB (version 4.0): transcriptional regulation, operon organization and growth conditions in Escherichia coli K-12. Nucleic Acids Res. 32, 303–306 (2004)CrossRefGoogle Scholar
  14. Sugeno, M., Yasukama, T.: A Fuzzy-logic-based Approach to Qualitative Modeling. IEEE Transactions on Fuzzy Systems 1(1), 7–31 (1993)CrossRefGoogle Scholar
  15. Klir, G.J., Folger, T.A.: Fuzzy sets, uncertainty, and information, vol. 6, p. 355. Prentice Hall International, London (1988)MATHGoogle Scholar
  16. Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artificial Intelligence 97(1-2), 273–324 (1997)MATHCrossRefGoogle Scholar
  17. Mitchell, T.M.: Machine learning, p. 414. McGraw-Hill. xvii, New York (1997)MATHGoogle Scholar
  18. Falkenauer, E.: Genetic Algorithms and Grouping Problems. John Wiley & Sons, New York (1998)Google Scholar
  19. Tavazoie, S., et al.: Systematic determination of genetic network architecture. Nat. Genet. 22(3), 281–285 (1999)CrossRefGoogle Scholar
  20. Benitez-Bellon, E., Moreno-Hagelsieb, G., Collado-Vides, J.: Evaluation of thresholds for the detection of binding sites for regulatory proteins in Escherichia coli K12 DNA. Genome. Biol. 3(3) (2002) RESEARCH0013Google Scholar
  21. Barnard, A., Wolfe, A., Busby, S.: Regulation at complex bacterial promoters: how bacteria use different promoter organizations to produce different regulatory outcomes. Curr. Opin. Microbiol. 7(2), 102–108 (2004)CrossRefGoogle Scholar
  22. Minagawa, S., et al.: Identification and molecular characterization of the Mg2+ stimulon of Escherichia coli. J. Bacteriol. 185(13), 702–3696 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Oscar Harari
    • 1
  • R. Romero-Zaliz
    • 1
  • C. Rubio-Escudero
    • 1
  • I. Zwir
    • 1
    • 2
  1. 1.Dept. Computer Science and Artificial IntelligenceUniversity of GranadaSpain
  2. 2.Howard Hughes Medical Institute, Department of Molecular MicrobiologyWashington University School of MedicineSt. LouisUSA

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