Abstract

Using a free boundary approach based on an analogy with ice melting models, we propose a deterministic PDE framework to describe the dynamics of fitness distributions in the presence of beneficial mutations with non-epistatic effects on fitness. Contrarily to most approaches based on deterministic models, our framework does not rely on an infinite population size assumption, and successfully captures the transient as well as the long time dynamics of fitness distributions. In particular, consistently with stochastic individual-based approaches or stochastic PDE approaches, it leads to a constant asymptotic rate of adaptation at large times, that most deterministic approaches failed to describe. We derive analytic formulas for the asymptotic rate of adaptation and the full asymptotic distribution of fitness. These formulas depend explicitly on the population size, and are shown to be accurate for a wide range of population sizes and mutation rates, compared to individual-based simulations. Although we were not able to derive an analytic description for the transient dynamics, numerical computations lead to accurate predictions and are computationally efficient compared to stochastic simulations. These computations show that the fitness distribution converges towards a travelling wave with constant speed, and whose profile can be computed analytically.

Highlights

  • Adaptation under mutation selection and drift is a central process of evolutionary biology

  • Using an analogy with an ice melting problem, we proposed a deterministic PDE framework which captures the entire dynamics of fitness distribution in the presence of beneficial mutations with non-epistatic effect

  • Our approach allows for an approximate treatment of genetic drift, while retaining the useful properties of previous deterministic approximations of these dynamics: a full description of the fitness distribution, at all times, even in the presence of multiple co-segregating alleles

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Summary

Plugging this solution into

Regarding the full asymptotic distribution of fitness, we compared the analytic description (8), with parameters μ and v given by (16)–(17), with empirical distributions obtained by individual-based simulations at time t = 500, averaged over the 100 replicates. The accuracy of the fitness distribution predicted by the free boundary approach (5)–(6) with parameter μ given by (16) is illustrated in Supplementary Video 4; it is compared to an empirical distribution obtained by individual-based simulation, with N = 104 and U = 0.01. The distribution predicted by the standard integro-differential model (1) (same Gaussian kernel as in the individual-based model) is illustrated in Supplementary Video 5 for comparison

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