Skip to main content

Simple Topological Features Reflect Dynamics and Modularity in Protein Interaction Networks

Author(s): Pritykin, Y; Singh, Mona

To refer to this page use:
Abstract: The availability of large-scale protein-protein interaction networks for numerous organisms provides an opportunity to comprehensively analyze whether simple properties of proteins are predictive of the roles they play in the functional organization of the cell. We begin by re-examining an influential but controversial characterization of the dynamic modularity of the S. cerevisiae interactome that incorporated gene expression data into network analysis. We analyse the protein-protein interaction networks of five organisms, S. cerevisiae, H. sapiens, D. melanogaster, A. thaliana, and E. coli, and confirm significant and consistent functional and structural differences between hub proteins that are co-expressed with their interacting partners and those that are not, and support the view that the former tend to be intramodular whereas the latter tend to be intermodular. However, we also demonstrate that in each of these organisms, simple topological measures are significantly correlated with the average co-expression of a hub with its partners, independent of any classification, and therefore also reflect protein intra- and inter- modularity. Further, cross-interactomic analysis demonstrates that these simple topological characteristics of hub proteins tend to be conserved across organisms. Overall, we give evidence that purely topological features of static interaction networks reflect aspects of the dynamics and modularity of interactomes as well as previous measures incorporating expression data, and are a powerful means for understanding the dynamic roles of hubs in interactomes.
Publication Date: 10-Oct-2013
Electronic Publication Date: 10-Oct-2013
Citation: Pritykin, Y, Singh, M. (2013). Simple Topological Features Reflect Dynamics and Modularity in Protein Interaction Networks. PLoS Computational Biology, 9 (10.1371/journal.pcbi.1003243
DOI: doi:10.1371/journal.pcbi.1003243
Type of Material: Journal Article
Journal/Proceeding Title: PLoS Computational Biology
Version: Final published version. Article is made available in OAR by the publisher's permission or policy.

Items in OAR@Princeton are protected by copyright, with all rights reserved, unless otherwise indicated.