Evidence map›Paper›PMID 38241432›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2024

A dynamical limit to evolutionary adaptation.

Matthew J Melissa, Michael M Desai

Open access · greenAbstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.6field-weighted citation impact, top 31% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. Evolutionary dynamics under phenotypic uncertainty.bioRxiv : the preprint server for biology · 2026
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors at 1 institution in 1 country.

Matthew J MelissaDepartment of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138.
Michael M DesaiDepartment of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138.ORCID 0000-0002-9581-1150
U.S. National Science Foundation · US

Funding

Microbial Adaptation and the Statistics of Epistasis and PleiotropyR01GM104239 · NIGMS · HARVARD UNIVERSITY · PI DESAI, MICHAEL M · 2013 to 2025
$4.5M
NIGMS NIH HHS R01 GM104239
6 · The paper itself

Abstract

Natural selection makes evolutionary adaptation possible even if the overwhelming majority of new mutations are deleterious. However, in rapidly evolving populations where numerous linked mutations occur and segregate simultaneously, clonal interference and genetic hitchhiking can limit the efficiency of selection, allowing deleterious mutations to accumulate over time. This can in principle overwhelm the fitness increases provided by beneficial mutations, leading to an overall fitness decline. Here, we analyze the conditions under which evolution will tend to drive populations to higher versus lower fitness. Our analysis focuses on quantifying the boundary between these two regimes, as a function of parameters such as population size, mutation rates, and selection pressures. This boundary represents a state in which adaptation is precisely balanced by Muller's ratchet, and we show that it can be characterized by rapid molecular evolution without any net fitness change. Finally, we consider the implications of global fitness-mediated epistasis and find that under some circumstances, this can drive populations toward the boundary state, which can thus represent a long-term evolutionary attractor.

Indexed as

Mutation RateSelection, GeneticEvolution, MolecularModels, GeneticMutationPopulation Densityevolutionary attractorfitness-mediated epistasisMuller’s ratchetrapid evolution

Identifiers

PMID38241432
PMCPMC10823227
OpenAlexW4391018075

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.