Advertisement

A Bayesian Algorithm for Reconstructing Two-Component Signaling Networks

  • Lukas Burger
  • Erik van Nimwegen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4175)

Abstract

We present an algorithm, based on a Bayesian network model, for ab initio prediction of signaling interactions in bacterial two-component systems. The algorithm uses a large training set of known interacting kinase/receiver pairs to build a probabilistic model of dependency between the amino acid sequences of the two proteins and uses this model to predict which pairs interact. We show that the algorithm can accurately reconstruct cognate kinase/receiver pairs across all sequenced bacteria. We also present predictions of interacting orphan kinase/receiver pairs in the bacterium Caulobacter crescentus and show that these significantly overlap with experimentally observed interactions.

Keywords

Histidine Kinase Domain Architecture Receiver Domain Bayesian Network Model Bayesian Algorithm 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Stock, A., Robinson, V., Goudreau, P.: Two-component signal transduction. Annu.Rev.Biochem. 69, 183–215 (2000)CrossRefGoogle Scholar
  2. 2.
    Grebe, T., Stock, J.: The histidine protein kinase superfamily. Advances in Microbial. Physiology 41, 139–227 (1999)CrossRefGoogle Scholar
  3. 3.
    Ausmees, N., Jacobs-Wagner, C.: Spatial and temporal control of differentiation and cell cycle progression in Caulobacter Crescentus. Annu.Rev.Microbiol. 57, 225–247 (2003)CrossRefGoogle Scholar
  4. 4.
    Ramani, A., Marcotte, E.: Exploiting the co-evolution of interacting proteins to discover interaction specificity. J. Mol. Biol. 327, 273–284 (2003)CrossRefGoogle Scholar
  5. 5.
    Bateman, A., Coin, L., Durbin, R., Finn, R., Hollich, V., Griffiths-Jones, S., Khanna, A., Marshall, M., Moxon, S., Sonnhammer, E., Studholme, D., Yeats, C., Eddy, S.: The Pfam protein families database. Nucl. Acids Res. 32, D138–D141 (2004)CrossRefGoogle Scholar
  6. 6.
    Do, C., Mahabhashyam, M., Brudno, M., Batzoglou, S.: Probcons: Probabilistic consistency-based multiple sequence alignment. Genome Research 15, 330–340 (2005)CrossRefGoogle Scholar
  7. 7.
    van Nimwegen, E., Zavolan, M., Rajewsky, N., Siggia, E.D.: Probabilistic clustering of sequences: Inferring new bacterial regulons by comparative genomics. Proc. Natl. Acad. Sci. USA 99, 7323–7328 (2002)MATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Chow, C., Liu, C.: Approximating discrete probability distributions with dependence trees. IEEE Transactions on Information Theory IT-14, 462–467 (1968)MATHCrossRefGoogle Scholar
  9. 9.
    Skerker, J., Laub, M.: Cell-cycle progression and the generation of asymmetry in Caulobacter Crescentus. Nature Reviews Microbiology 3, 325–337 (2004)CrossRefGoogle Scholar
  10. 10.
    Ohta, N., Newton, A.: The core dimerization domains of histidine kinases contain specificity for the cognate response regulator. Journal of Bacteriology 185, 4424–4431 (2003)CrossRefGoogle Scholar
  11. 11.
    Skerker, J., Prasol, M., Perchuk, B., Biondi, E., Laub, M.: Two-component signal transduction pathways regulating growth and cell cycle progression in a bacterium: a systems-level analysis. PLOS Biol. 3, 334 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Lukas Burger
    • 1
  • Erik van Nimwegen
    • 1
  1. 1.BiozentrumUniversity of BaselBaselSwitzerland

Personalised recommendations