Universality of fixation probabilities in randomly structured populations

TL;DR

This paper demonstrates that in large, randomly structured populations, the probability that a mutant will fixate is approximately the same as in a well-mixed population, showing a form of universality across various network models.

Contribution

It proves that fixation probabilities in a broad class of random graphs converge to the well-mixed population result, extending the isothermal theorem's robustness.

Findings

Fixation probability approximates the well-mixed population formula in large random graphs.

Simulations confirm similar fixation behavior in perturbed lattices, small-world, and scale-free networks.

The fixation probability is conjectured to be universal across many random graph models.

Abstract

The stage of evolution is the population of reproducing individuals. The structure of the population is know to affect the dynamics and outcome of evolutionary processes, but analytical results for generic random structures have been lacking. The most general result so far, the isothermal theorem, assumes the propensity for change in each position is exactly the same, but realistic biological structures are always subject to variation and noise. We consider a population of finite size $n$ under constant selection whose structure is given by a wide variety of weighted, directed, random graphs; vertices represent individuals and edges interactions between individuals. By establishing a robustness result for the isothermal theorem and using large deviation estimates to understand the typical structure of random graphs, we prove that for a generalization of the Erd\H{o}s-R\'{e}nyi model the fixation probability of an invading mutant is approximately the same as that of a mutant of equal fitness in a well-mixed population with high probability. Simulations of perturbed lattices, small-world networks, and scale-free networks behave similarly. We conjecture that the fixation probability in a well-mixed population, $(1-r^{-1})/(1-r^{-n})$, is universal: for many random graph models, the fixation probability approaches the above function uniformly as the graphs become large.