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Open Access Highly Accessed Research article

From networks of protein interactions to networks of functional dependencies

Davide Luciani1 and Gianfranco Bazzoni2*

Author Affiliations

1 Unit of Clinical Knowledge Engineering, Mario Negri Institute of Pharmacological Research, Milan, I-20156, Italy

2 Laboratory of Systems Biology, Mario Negri Institute of Pharmacological Research, Milan, I-20156, Italy

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BMC Systems Biology 2012, 6:44  doi:10.1186/1752-0509-6-44

Published: 20 May 2012

Abstract

Background

As protein-protein interactions connect proteins that participate in either the same or different functions, networks of interacting and functionally annotated proteins can be converted into process graphs of inter-dependent function nodes (each node corresponding to interacting proteins with the same functional annotation). However, as proteins have multiple annotations, the process graph is non-redundant, if only proteins participating directly in a given function are included in the related function node.

Results

Reasoning that topological features (e.g., clusters of highly inter-connected proteins) might help approaching structured and non-redundant understanding of molecular function, an algorithm was developed that prioritizes inclusion of proteins into the function nodes that best overlap protein clusters. Specifically, the algorithm identifies function nodes (and their mutual relations), based on the topological analysis of a protein interaction network, which can be related to various biological domains, such as cellular components (e.g., peroxisome and cellular bud) or biological processes (e.g., cell budding) of the model organism S. cerevisiae.

Conclusions

The method we have described allows converting a protein interaction network into a non-redundant process graph of inter-dependent function nodes. The examples we have described show that the resulting graph allows researchers to formulate testable hypotheses about dependencies among functions and the underlying mechanisms.

Keywords:
Protein interaction networks; Biological functions; Markov representations; Peroxisomes; Cell budding; Polarized growth; Saccharomyces cerevisiae