Inferring Fitness in Finite Populations with Moran-like dynamics

TL;DR

This paper introduces a Bayesian inference method to estimate biological fitness from population dynamics modeled by Moran-like processes, applicable even with changing population sizes and networks.

Contribution

It develops a robust Bayesian approach to infer fitness from birth-event data in Moran and related models, including dynamic networks and variable population sizes.

Findings

Effective inference of fitness from birth-events in Moran processes

Applicable to populations on dynamic networks

Handles changing population sizes

Abstract

Biological fitness is not an observable quantity and must be inferred from population dynamics. Bayesian inference applied to the Moran process and variants yields a robust inference method that can infer fitness in populations evolving via a Moran dynamic and generalizations. Information about fitness is derived solely from birth-events in birth-death and death-birth processes in which selection acts proportionally to fitness, which allows the method to be applied to populations on a network where the network itself may be changing in time. Populations may also be allowed to change size while still allowing estimates for fitness to be inferred.