Mutant fate in spatially structured populations on graphs: Connecting models to experiments
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Date
2024-09-06Type
- Journal Article
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yes
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Abstract
In nature, most microbial populations have complex spatial structures that can affect their evolution. Evolutionary graph theory predicts that some spatial structures modelled by placing individuals on the nodes of a graph affect the probability that a mutant will fix. Evolution experiments are beginning to explicitly address the impact of graph structures on mutant fixation. However, the assumptions of evolutionary graph theory differ from the conditions of modern evolution experiments, making the comparison between theory and experiment challenging. Here, we aim to bridge this gap by using our new model of spatially structured populations. This model considers connected subpopulations that lie on the nodes of a graph, and allows asymmetric migrations. It can handle large populations, and explicitly models serial passage events with migrations, thus closely mimicking experimental conditions. We analyze recent experiments in light of this model. We suggest useful parameter regimes for future experiments, and we make quantitative predictions for these experiments. In particular, we propose experiments to directly test our recent prediction that the star graph with asymmetric migrations suppresses natural selection and can accelerate mutant fixation or extinction, compared to a well-mixed population. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000695095Publication status
publishedExternal links
Journal / series
PLoS Computational BiologyVolume
Pages / Article No.
Publisher
PLOSSubject
Graphs; evolutionary theory; graph theory; natural selection; microbial evolution; antibiotic resistance; antibiotics; animal migrationMore
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ETH Bibliography
yes
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