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

Yiqiang Zhao,Sean D Mooney

doi:10.1186/1471-2164-13-150

Abstract

BackgroundBased 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.ResultsTo 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.ConclusionWe 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.

Highlights

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
Increasing features of empirical protein-protein interactions (PPI) networks had been captured by current models, all these stochastic models proposed do not require the intervention of natural selection to reproduce the intended topology, nor does it use biological function as a parameter
In many cases analyses and models of the evolution of PPI networks focus on the evolution of network topology

Summary

Introduction

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. One important question to ask is, “How did the human PPI network emerge and evolve?” Given that the most significant property of the network is that the degree distribution follows a power law [4], several evolutionary models have been proposed to account for this attribute. These include the preferential attachment model, which each other in yeast and proposed a new stochastic model which grows the network analogous to the process of growing protein crystals in solution. Increasing features of empirical PPI networks had been captured by current models, all these stochastic models proposed do not require the intervention of natural selection to reproduce the intended topology, nor does it use biological function as a parameter

Methods

Results

Conclusion