ArticlePLoS computational biology2021
Fixation probabilities in graph-structured populations under weak selection.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 42 citations in OpenAlex.
- Multiplayer social dilemmas in completely mixed populations and networks of mixing communities.Journal of mathematical biology · 2026Article
- Mixed updating in structured populations.PLoS computational biology · 2026Article
- Maintaining diversity in structured populations.PNAS nexus · 2025Article
- Graph-structured populations elucidate the role of deleterious mutations in long-term evolution.Nature communications · 2025Article
- Evolutionary graph theory beyond single mutation dynamics: on how network-structured populations cross fitness landscapes.Genetics · 2024Article
- Evolutionary dynamics of mutants that modify population structure.Journal of the Royal Society, Interface · 2023Article
- Mutant fixation in the presence of a natural enemy.Nature communications · 2023Article
- Fixation probability in evolutionary dynamics on switching temporal networks.Journal of mathematical biology · 2023Article
- Self-loops in evolutionary graph theory: Friends or foes?PLoS computational biology · 2023Article
- Fixation dynamics on hypergraphs.PLoS computational biology · 2023Article
- Perturbation theory for evolution of cooperation on networks.Journal of mathematical biology · 2023Article
- Spectral dynamics of guided edge removals and identifying transient amplifiers for death-Birth updating.Journal of mathematical biology · 2023Article
- Suppressors of fixation can increase average fitness beyond amplifiers of selection.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Martingales and the fixation time of evolutionary graphs with arbitrary dimensionality.Royal Society open science · 2022Article
- Martingales and the characteristic functions of absorption time on bipartite graphs.Royal Society open science · 2021Article
- Fixation probabilities in network structured meta-populations.Scientific reports · 2021Article
- Fixation probabilities in graph-structured populations under weak selection.PLoS computational biology · 2021Article
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A population's spatial structure affects the rate of genetic change and the outcome of natural selection. These effects can be modeled mathematically using the Birth-death process on graphs. Individuals occupy the vertices of a weighted graph, and reproduce into neighboring vertices based on fitness. A key quantity is the probability that a mutant type will sweep to fixation, as a function of the mutant's fitness. Graphs that increase the fixation probability of beneficial mutations, and decrease that of deleterious mutations, are said to amplify selection. However, fixation probabilities are difficult to compute for an arbitrary graph. Here we derive an expression for the fixation probability, of a weakly-selected mutation, in terms of the time for two lineages to coalesce. This expression enables weak-selection fixation probabilities to be computed, for an arbitrary weighted graph, in polynomial time. Applying this method, we explore the range of possible effects of graph structure on natural selection, genetic drift, and the balance between the two. Using exhaustive analysis of small graphs and a genetic search algorithm, we identify families of graphs with striking effects on fixation probability, and we analyze these families mathematically. Our work reveals the nuanced effects of graph structure on natural selection and neutral drift. In particular, we show how these notions depend critically on the process by which mutations arise.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.