A Prize-Collecting Steiner Tree Approach for Transduction Network Inference

  • Authors:
  • Marc Bailly-Bechet;Alfredo Braunstein;Riccardo Zecchina

  • Affiliations:
  • Microsoft TCI Research, Dipartimento di Fisica, Politecnico di Torino, Torino, Italy 10129 and Laboratoire de Biométrie et Biologie Evolutive, Université de Lyon, F-69000, Lyon, CNRS, UM ...;Microsoft TCI Research, Dipartimento di Fisica, Politecnico di Torino, Torino, Italy 10129;Microsoft TCI Research, Dipartimento di Fisica, Politecnico di Torino, Torino, Italy 10129

  • Venue:
  • CMSB '09 Proceedings of the 7th International Conference on Computational Methods in Systems Biology
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

Abstract

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.