A generative model for bipartite gene-sharing networks

TL;DR

This paper introduces a mechanistic model explaining the degree distributions in bipartite gene-sharing networks, capturing key evolutionary processes like gene transfer, gain, loss, and emergence.

Contribution

It provides an analytical and simulation-based framework that reproduces empirical degree distributions in gene-sharing networks with only two parameters.

Findings

Model accurately fits empirical data from viruses and prokaryotes.

Gene gain dominates viral evolution, as shown by model fit.

Analytical expressions match observed power-law and exponential distributions.

Abstract

Gene-sharing networks provide a powerful framework to study the evolution of viruses and mobile genetic elements. These bipartite networks, which link genes to the genomes that contain them, exhibit characteristic degree distributions: a scale-free distribution for genes and an exponential-like decay for genomes. Here, we propose a mechanistic model that explains these patterns through fundamental evolutionary processes including horizontal gene transfer, capture of new genes, emergence of new genomes, and gene loss. Using a mean-field approximation, we derive analytical expressions for the asymptotic gene and genome degree distributions, recapitulating a power-law distribution for genes and an exponential distribution for genomes. Numerical simulations validate these predictions and yield parameter values that closely fit empirical data from dsDNA viruses, RNA viruses, and prokaryotic pangenomes. This simple model with only two parameters provides a generative framework for bipartite gene-sharing networks, offering qualitative and quantitative insights into the main evolutionary forces driving genome plasticity. Setting the gene loss rate to zero, the gene and genome degree distributions of the model closely fit the empirically observed distributions. Thus, evolution of viruses appears to be dominated by gene gain, in agreement with the results of independent reconstructions of viral evolution.