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Evolutionary Accessibility of Mutational Pathways

Overview of attention for article published in PLoS Computational Biology, August 2011
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (91st percentile)
  • High Attention Score compared to outputs of the same age and source (81st percentile)

Mentioned by

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2 blogs
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3 X users

Citations

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169 Dimensions

Readers on

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148 Mendeley
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4 CiteULike
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1 Connotea
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Title
Evolutionary Accessibility of Mutational Pathways
Published in
PLoS Computational Biology, August 2011
DOI 10.1371/journal.pcbi.1002134
Pubmed ID
Authors

Jasper Franke, Alexander Klözer, J. Arjan G. M. de Visser, Joachim Krug

Abstract

Functional effects of different mutations are known to combine to the total effect in highly nontrivial ways. For the trait under evolutionary selection ('fitness'), measured values over all possible combinations of a set of mutations yield a fitness landscape that determines which mutational states can be reached from a given initial genotype. Understanding the accessibility properties of fitness landscapes is conceptually important in answering questions about the predictability and repeatability of evolutionary adaptation. Here we theoretically investigate accessibility of the globally optimal state on a wide variety of model landscapes, including landscapes with tunable ruggedness as well as neutral 'holey' landscapes. We define a mutational pathway to be accessible if it contains the minimal number of mutations required to reach the target genotype, and if fitness increases in each mutational step. Under this definition accessibility is high, in the sense that at least one accessible pathway exists with a substantial probability that approaches unity as the dimensionality of the fitness landscape (set by the number of mutational loci) becomes large. At the same time the number of alternative accessible pathways grows without bounds. We test the model predictions against an empirical 8-locus fitness landscape obtained for the filamentous fungus Aspergillus niger. By analyzing subgraphs of the full landscape containing different subsets of mutations, we are able to probe the mutational distance scale in the empirical data. The predicted effect of high accessibility is supported by the empirical data and is very robust, which we argue reflects the generic topology of sequence spaces. Together with the restrictive assumptions that lie in our definition of accessibility, this implies that the globally optimal configuration should be accessible to genome wide evolution, but the repeatability of evolutionary trajectories is limited owing to the presence of a large number of alternative mutational pathways.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 148 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 4%
Spain 2 1%
United Kingdom 2 1%
France 1 <1%
Austria 1 <1%
Indonesia 1 <1%
Portugal 1 <1%
Germany 1 <1%
Japan 1 <1%
Other 1 <1%
Unknown 131 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 51 34%
Researcher 35 24%
Student > Master 12 8%
Student > Bachelor 8 5%
Student > Doctoral Student 7 5%
Other 22 15%
Unknown 13 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 62 42%
Biochemistry, Genetics and Molecular Biology 25 17%
Physics and Astronomy 14 9%
Computer Science 5 3%
Mathematics 5 3%
Other 20 14%
Unknown 17 11%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 15. 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 25 April 2016.
All research outputs
#2,497,506
of 25,728,855 outputs
Outputs from PLoS Computational Biology
#2,216
of 9,027 outputs
Outputs of similar age
#11,890
of 134,178 outputs
Outputs of similar age from PLoS Computational Biology
#12
of 66 outputs
Altmetric has tracked 25,728,855 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 9,027 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.3. This one has done well, scoring higher than 75% 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 134,178 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 91% of its contemporaries.
We're also able to compare this research output to 66 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.