A Prize-Collecting Steiner Tree Approach for Transduction Network Inference

  • Marc Bailly-Bechet
  • Alfredo Braunstein
  • Riccardo Zecchina
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5688)


Into the cell, information from the environment is mainly propagated via signaling pathways which form a transduction network. Here we propose a new algorithm to infer transduction networks from heterogeneous data, using both the protein interaction network and expression datasets. We formulate the inference problem as an optimization task, and develop a message-passing, probabilistic and distributed formalism to solve it. We apply our algorithm to the pheromone response in the baker’s yeast S. cerevisiae. We are able to find the backbone of the known structure of the MAPK cascade of pheromone response, validating our algorithm. More importantly, we make biological predictions about some proteins whose role could be at the interface between pheromone response and other cellular functions.


Steiner Tree Protein Interaction Network Steiner Tree Problem Signal Transduction Network Pheromone Response 
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 2009

Authors and Affiliations

  • Marc Bailly-Bechet
    • 1
    • 2
  • Alfredo Braunstein
    • 1
  • Riccardo Zecchina
    • 1
  1. 1.Microsoft TCI Research, Dipartimento di FisicaPolitecnico di TorinoTorinoItaly
  2. 2.Laboratoire de Biométrie et Biologie EvolutiveUniversité de Lyon, F-69000, Lyon, CNRS, UMR5558VilleurbanneFrance

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