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)


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.


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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

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

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