ArticleNature communications2024
A theory of evolutionary dynamics on any complex population structure reveals stem cell niche architecture as a spatial suppressor of selection.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Mixed updating in structured populations.PLoS computational biology · 2026Article
- Environment heterogeneity creates fast amplifiers of natural selection in graph-structured populations.Nature communications · 2026Article
- Local negative frequency dependence can decrease global coexistence in fragmented populations.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Competition for resources can reshape the evolutionary properties of spatial structure.PLoS computational biology · 2024Article
- Competition for resources can reshape the evolutionary properties of spatial structure.bioRxiv : the preprint server for biology · 2024Article
- Evolutionary graph theory beyond pairwise interactions: Higher-order network motifs shape times to fixation in structured populations.PLoS computational biology · 2024Article
- Self-loops in evolutionary graph theory: Friends or foes?PLoS computational biology · 2023Article
- Categorizing update mechanisms for graph-structured metapopulations.Journal of the Royal Society, Interface · 2023Article
- Fixation probabilities in network structured meta-populations.Scientific reports · 2021Article
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Authors and funding
3 authors.
Funding
Abstract
How the spatial arrangement of a population shapes its evolutionary dynamics has been of long-standing interest in population genetics. Most previous studies assume a small number of demes or symmetrical structures that, most often, act as well-mixed populations. Other studies use network theory to study more heterogeneous spatial structures, however they usually assume small, regular networks, or strong constraints on the strength of selection considered. Here we build network generation algorithms, conduct evolutionary simulations and derive general analytic approximations for probabilities of fixation in populations with complex spatial structure. We build a unifying evolutionary theory across network families and derive the relevant selective parameter, which is a combination of network statistics, predictive of evolutionary dynamics. We also illustrate how to link this theory with novel datasets of spatial organization and use recent imaging data to build the cellular spatial networks of the stem cell niches of the bone marrow. Across a wide variety of parameters, we find these networks to be strong suppressors of selection, delaying mutation accumulation in this tissue. We also find that decreases in stem cell population size also decrease the suppression strength of the tissue spatial structure.
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