Finding undetected protein associations in cell signaling by belief propagation

Details Finding undetected protein associations in cell signaling by belief propagation Finding undetected protein associations in cell signaling by belief propagation

2011-01-24

arxiv.org/abs/1101.4573v1

Published online before print December 27, 2010, doi: 10.1073/pnas.1004751108 PNAS January 11, 2011 vol. 108 no. 2 882-887

Biology

Quantitative Biology

Molecular Networks

6 pages, 3 figures, 1 table, Supporting Information

Scientific paper

10.1073/pnas.1004751108

External information propagates in the cell mainly through signaling cascades and transcriptional activation, allowing it to react to a wide spectrum of environmental changes. High throughput experiments identify numerous molecular components of such cascades that may, however, interact through unknown partners. Some of them may be detected using data coming from the integration of a protein-protein interaction network and mRNA expression profiles. This inference problem can be mapped onto the problem of finding appropriate optimal connected subgraphs of a network defined by these datasets. The optimization procedure turns out to be computationally intractable in general. Here we present a new distributed algorithm for this task, inspired from statistical physics, and apply this scheme to alpha factor and drug perturbations data in yeast. We identify the role of the COS8 protein, a member of a gene family of previously unknown function, and validate the results by genetic experiments. The algorithm we present is specially suited for very large datasets, can run in parallel, and can be adapted to other problems in systems biology. On renowned benchmarks it outperforms other algorithms in the field.

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