Suppressors of selection

TL;DR

This paper introduces undirected graph structures that act as suppressors of selection, reducing the fixation probability of advantageous mutants compared to well-mixed populations, and uses computational methods to analyze their effectiveness.

Contribution

First known examples of undirected suppressors of selection for all fitness values greater than one, with analytical and numerical analysis of their fixation probabilities.

Findings

Undirected structures can suppress selection for any fitness r > 1.

Analytical expressions for fixation probabilities of small graphs were derived.

Numerical experiments support the effectiveness of these suppressors on larger graphs.

Abstract

Inspired by recent works on evolutionary graph theory, an area of growing interest in mathematical and computational biology, we present the first known examples of undirected structures acting as suppressors of selection for any fitness value $r > 1$. This means that the average fixation probability of an advantageous mutant or invader individual placed at some node is strictly less than that of this individual placed in a well-mixed population. This leads the way to study more robust structures less prone to invasion, contrary to what happens with the amplifiers of selection where the fixation probability is increased on average for advantageous invader individuals. A few families of amplifiers are known, although some effort was required to prove it. Here, we use computer aided techniques to find an exact analytical expression of the fixation probability for some graphs of small order (equal to $6$, $8$ and $10$) proving that selection is effectively reduced for $r > 1$. Some numerical experiments using Monte Carlo methods are also performed for larger graphs.