Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.
PLoS Comput Biol. 2024 Sep 6;20(9):e1012424. doi: 10.1371/journal.pcbi.1012424. eCollection 2024 Sep.
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.
在自然界中，大多数微生物种群具有复杂的空间结构，这些结构会影响它们的进化。进化图论预测，通过将个体置于图的节点上来建模的一些空间结构会影响突变体固定的概率。进化实验开始明确解决图结构对突变体固定的影响。然而，进化图论的假设与现代进化实验的条件不同，这使得理论与实验之间的比较具有挑战性。在这里，我们旨在通过使用我们新的空间结构种群模型来弥合这一差距。该模型考虑了位于图节点上的连接亚种群，并允许不对称迁移。它可以处理大型种群，并明确地对具有迁移的连续传递事件进行建模，从而紧密模拟实验条件。我们根据该模型分析了最近的实验。我们为未来的实验提出了有用的参数范围，并对这些实验进行了定量预测。特别是，我们提出了一些实验来直接测试我们最近的预测，即具有不对称迁移的星形图可以抑制自然选择，并与混合种群相比，可以加速突变体的固定或灭绝。
