Finding undetected protein associations in cell signaling by belief propagation

TL;DR

This paper introduces a new distributed belief propagation algorithm to identify hidden protein associations in cell signaling, validated by experiments and outperforming existing methods on large datasets.

Contribution

A novel distributed algorithm inspired by statistical physics for detecting protein associations, scalable to large datasets and adaptable to various systems biology problems.

Findings

Identified role of COS8 protein in yeast signaling pathways.

Validated predictions through genetic experiments.

Outperformed existing algorithms on benchmark datasets.

Abstract

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.