↓ Skip to main content

Quasispecies Made Simple

Overview of attention for article published in PLoS Computational Biology, November 2005
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (94th percentile)
  • High Attention Score compared to outputs of the same age and source (83rd percentile)

Mentioned by

blogs
1 blog
twitter
4 X users
wikipedia
2 Wikipedia pages

Readers on

mendeley
296 Mendeley
citeulike
6 CiteULike
connotea
2 Connotea
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Quasispecies Made Simple
Published in
PLoS Computational Biology, November 2005
DOI 10.1371/journal.pcbi.0010061
Pubmed ID
Authors

J J Bull, Lauren Ancel Meyers, Michael Lachmann

Abstract

Quasispecies are clouds of genotypes that appear in a population at mutation-selection balance. This concept has recently attracted the attention of virologists, because many RNA viruses appear to generate high levels of genetic variation that may enhance the evolution of drug resistance and immune escape. The literature on these important evolutionary processes is, however, quite challenging. Here we use simple models to link mutation-selection balance theory to the most novel property of quasispecies: the error threshold-a mutation rate below which populations equilibrate in a traditional mutation-selection balance and above which the population experiences an error catastrophe, that is, the loss of the favored genotype through frequent deleterious mutations. These models show that a single fitness landscape may contain multiple, hierarchically organized error thresholds and that an error threshold is affected by the extent of back mutation and redundancy in the genotype-to-phenotype map. Importantly, an error threshold is distinct from an extinction threshold, which is the complete loss of the population through lethal mutations. Based on this framework, we argue that the lethal mutagenesis of a viral infection by mutation-inducing drugs is not a true error catastophe, but is an extinction catastrophe.

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 296 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 11 4%
Brazil 6 2%
Colombia 2 <1%
Germany 2 <1%
Switzerland 2 <1%
Japan 2 <1%
Mexico 2 <1%
Kenya 1 <1%
Israel 1 <1%
Other 8 3%
Unknown 259 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 74 25%
Researcher 59 20%
Student > Master 29 10%
Professor > Associate Professor 24 8%
Professor 22 7%
Other 57 19%
Unknown 31 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 147 50%
Biochemistry, Genetics and Molecular Biology 30 10%
Physics and Astronomy 15 5%
Immunology and Microbiology 11 4%
Medicine and Dentistry 11 4%
Other 43 15%
Unknown 39 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 13. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 11 December 2022.
All research outputs
#2,738,293
of 25,373,627 outputs
Outputs from PLoS Computational Biology
#2,455
of 8,960 outputs
Outputs of similar age
#8,009
of 159,312 outputs
Outputs of similar age from PLoS Computational Biology
#3
of 18 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,960 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one has gotten more attention than average, scoring higher than 72% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 159,312 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 94% of its contemporaries.
We're also able to compare this research output to 18 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 83% of its contemporaries.