Evidence map›Paper›PMID 22759303›Full record

ArticleEvolution; international journal of organic evolution2012

Interactions between evolutionary processes at high mutation rates.

Thomas E Keller, Claus O Wilke, James J Bull

Abstract read
In one paragraph

Article in Evolution; international journal of organic evolution, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Thomas E KellerThe Institute for Cellular and Molecular Biology, Center for Computational Biology and Bioinformatics, Section of Integrative Biology, The University of Texas at Austin, Austin, Texas 78712, USA. tkeller@mail.utexas.edu
Claus O Wilke
James J Bull

Funding

Experimental viral evolution at high mutation rateR01GM057756 · NIGMS · UNIVERSITY OF TEXAS AUSTIN · PI BULL, JAMES J · 1998 to 2013
$2.9M
NIGMS NIH HHS NIH GM57756NIGMS NIH HHS R01 GM057756
6 · The paper itself

Abstract

Evolution at high mutation rates is minimally affected by six processes: mutation-selection balance, error catastrophes, Muller's Ratchet, robustness and compensatory evolution, and clonal interference. Including all of these processes in a tractable, analytical model is difficult, but they can be captured in simulations that utilize realistic genotype-phenotype-fitness maps, as done here by modeling RNA folding. Subjecting finite, asexual populations to a range of mutation rates revealed simple criteria that predict when particular evolutionary processes are important. Populations were initiated with a genotype encoding the most fit phenotype. When purifying selection was strong relative to mutation, the initial genotype was replaced by one more mutationally robust, and the maximally fit phenotype was maintained in a mutation-selection balance where the deleterious mutation rate determined mean fitness. With weaker purifying selection, the most fit genotypes were lost. Although loss of the best genotype was ongoing and might have led to a progressive fitness decline, continual compensatory evolution led to an approximate fitness equilibration. Per total genomic mutation rate, mean fitness was similar for strong and weak purifying selection. These results represent a first step at separating interactions between evolutionary processes at high mutation rate, but additional theory is needed to interpret some outcomes.

Indexed as

Evolution, MolecularGenetic FitnessMutation RateRNA FoldingComputer SimulationGenotypeModels, GeneticPhenotypeReproduction, Asexual

Identifiers

PMID22759303
PMCPMC3389706

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Registered trials

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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.