Email updates

Keep up to date with the latest news and content from BMC Genomics and BioMed Central.

Open Access Research article

Functional organization and its implication in evolution of the human protein-protein interaction network

Yiqiang Zhao1 and Sean D Mooney12*

Author Affiliations

1 Buck Institute for Research on Aging, Novato, California, USA

2 Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA

For all author emails, please log on.

BMC Genomics 2012, 13:150  doi:10.1186/1471-2164-13-150

Published: 24 April 2012



Based on the distinguishing properties of protein-protein interaction networks such as power-law degree distribution and modularity structure, several stochastic models for the evolution of these networks have been purposed, motivated by the idea that a validated model should reproduce similar topological properties of the empirical network. However, being able to capture topological properties does not necessarily mean it correctly reproduces how networks emerge and evolve. More importantly, there is already evidence suggesting functional organization and significance of these networks. The current stochastic models of evolution, however, grow the network without consideration for biological function and natural selection.


To test whether protein interaction networks are functionally organized and their impacts on the evolution of these networks, we analyzed their evolution at both the topological and functional level. We find that the human network is shown to be functionally organized, and its function evolves with the topological properties of the network. Our analysis suggests that function most likely affects local modularity of the network. Consistently, we further found that the topological unit is also the functional unit of the network.


We have demonstrated functional organization of a protein interaction network. Given our observations, we suggest that its significance should not be overlooked when studying network evolution.