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Frequent asymmetric migrations suppress natural selection in spatially structured populations.
Abbara, Alia; Bitbol, Anne-Florence.
Afiliación
  • Abbara A; Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland.
  • Bitbol AF; SIB Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland.
PNAS Nexus ; 2(11): pgad392, 2023 Nov.
Article en En | MEDLINE | ID: mdl-38024415
Natural microbial populations often have complex spatial structures. This can impact their evolution, in particular the ability of mutants to take over. While mutant fixation probabilities are known to be unaffected by sufficiently symmetric structures, evolutionary graph theory has shown that some graphs can amplify or suppress natural selection, in a way that depends on microscopic update rules. We propose a model of spatially structured populations on graphs directly inspired by batch culture experiments, alternating within-deme growth on nodes and migration-dilution steps, and yielding successive bottlenecks. This setting bridges models from evolutionary graph theory with Wright-Fisher models. Using a branching process approach, we show that spatial structure with frequent migrations can only yield suppression of natural selection. More precisely, in this regime, circulation graphs, where the total incoming migration flow equals the total outgoing one in each deme, do not impact fixation probability, while all other graphs strictly suppress selection. Suppression becomes stronger as the asymmetry between incoming and outgoing migrations grows. Amplification of natural selection can nevertheless exist in a restricted regime of rare migrations and very small fitness advantages, where we recover the predictions of evolutionary graph theory for the star graph.

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: PNAS Nexus Año: 2023 Tipo del documento: Article País de afiliación: Suiza

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: PNAS Nexus Año: 2023 Tipo del documento: Article País de afiliación: Suiza
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