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Updated: Apr 22, 2026

Daily Transfers, Archiving Populations, and Measuring Fitness in the Long-Term Evolution Experiment with Escherichia coli
Published on: August 18, 2023
Speed of evolution in large asexual populations with diminishing returns
Maria R Fumagalli1, Matteo Osella2, Philippe Thomen3
1Université Pierre et Marie Curie Genomic Physics Group, UMR 7238 "Computational and Quantitative Biology", 15 Rue de l׳École de Médecine, 75006 Paris, France; Dipartimento di Fisica, Università degli Studi di Milano, Via G. Celoria 16, Milano, Italy; Dipartimento di Fisica, Università degli Studi di Torino, Via P. Giuria 1, Torino, Italy; INFN Sezione di Torino, Via P. Giuria 1, Torino, Italy.
Abstract:
The adaptive evolution of large asexual populations is generally characterized by competition between clones carrying different beneficial mutations. Interference slows down the adaptation speed and makes the theoretical description of the dynamics more complex with respect to the successional occurrence and fixation of beneficial mutations typical of small populations. A simplified modeling framework considering multiple beneficial mutations with equal and constant fitness advantage is known to capture some of the essential features of laboratory evolution experiments. However, in these experiments the relative advantage of a beneficial mutation is generally dependent on the genetic background. In particular, the general pattern is that, as mutations in different loci accumulate, the relative advantage of new mutations decreases, a trend often referred to as "diminishing return" epistasis. Here, we propose a phenomenological model that generalizes the fixed-advantage framework to include this negative epistasis in a simple way. We evaluate analytically as well as with direct simulations the quantitative consequences of diminishing returns on the evolutionary dynamics. The speed of adaptation decreases in time and reaches a limit value corresponding to neutral evolution in the long time limit. This corresponds to an increase of the diversity in terms of "classes of mutation" in the population. Finally, we show how the model can be compared with dynamic data on fitness and number of beneficial mutations from laboratory evolution experiments.
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