Influence of homology and node-age on the growth of protein-protein interaction networks

TL;DR

This paper introduces a statistical model linking the growth of protein-protein interaction networks with the evolution of homology classes, highlighting the importance of age-dependent divergence in reproducing observed network features.

Contribution

The study develops a unified mean-field and simulation framework to model network growth and homology class partitioning, emphasizing the role of node age in network topology.

Findings

Age-dependent divergence is crucial for matching empirical network topologies.

The model successfully reproduces age correlations between interacting proteins.

Insights into joint partition and topology observables are discussed.

Abstract

Proteins participating in a protein-protein interaction network can be grouped into homology classes following their common ancestry. Proteins added to the network correspond to genes added to the classes, so that the dynamics of the two objects are intrinsically linked. Here, we first introduce a statistical model describing the joint growth of the network and the partitioning of nodes into classes, which is studied through a combined mean-field and simulation approach. We then employ this unified framework to address the specific issue of the age dependence of protein interactions, through the definition of three different node wiring/divergence schemes. Comparison with empirical data indicates that an age-dependent divergence move is necessary in order to reproduce the basic topological observables together with the age correlation between interacting nodes visible in empirical data. We also discuss the possibility of nontrivial joint partition/topology observables.