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The waiting time problem in a model hominin population

Overview of attention for article published in Theoretical Biology and Medical Modelling, September 2015
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#6 of 258)
  • High Attention Score compared to outputs of the same age (95th percentile)

Mentioned by

news
1 news outlet
blogs
2 blogs
twitter
25 tweeters
facebook
2 Facebook pages
googleplus
3 Google+ users
reddit
1 Redditor
q&a
1 Q&A thread
video
3 video uploaders

Citations

dimensions_citation
4 Dimensions

Readers on

mendeley
20 Mendeley
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Title
The waiting time problem in a model hominin population
Published in
Theoretical Biology and Medical Modelling, September 2015
DOI 10.1186/s12976-015-0016-z
Pubmed ID
Authors

John Sanford, Wesley Brewer, Franzine Smith, John Baumgardner

Abstract

Functional information is normally communicated using specific, context-dependent strings of symbolic characters. This is true within the human realm (texts and computer programs), and also within the biological realm (nucleic acids and proteins). In biology, strings of nucleotides encode much of the information within living cells. How do such information-bearing nucleotide strings arise and become established? This paper uses comprehensive numerical simulation to understand what types of nucleotide strings can realistically be established via the mutation/selection process, given a reasonable timeframe. The program Mendel's Accountant realistically simulates the mutation/selection process, and was modified so that a starting string of nucleotides could be specified, and a corresponding target string of nucleotides could be specified. We simulated a classic pre-human hominin population of at least 10,000 individuals, with a generation time of 20 years, and with very strong selection (50 % selective elimination). Random point mutations were generated within the starting string. Whenever an instance of the target string arose, all individuals carrying the target string were assigned a specified reproductive advantage. When natural selection had successfully amplified an instance of the target string to the point of fixation, the experiment was halted, and the waiting time statistics were tabulated. Using this methodology we tested the effect of mutation rate, string length, fitness benefit, and population size on waiting time to fixation. Biologically realistic numerical simulations revealed that a population of this type required inordinately long waiting times to establish even the shortest nucleotide strings. To establish a string of two nucleotides required on average 84 million years. To establish a string of five nucleotides required on average 2 billion years. We found that waiting times were reduced by higher mutation rates, stronger fitness benefits, and larger population sizes. However, even using the most generous feasible parameters settings, the waiting time required to establish any specific nucleotide string within this type of population was consistently prohibitive. We show that the waiting time problem is a significant constraint on the macroevolution of the classic hominin population. Routine establishment of specific beneficial strings of two or more nucleotides becomes very problematic.

Twitter Demographics

The data shown below were collected from the profiles of 25 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 20 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 25%
Student > Doctoral Student 3 15%
Student > Master 3 15%
Researcher 3 15%
Other 2 10%
Other 3 15%
Unknown 1 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 7 35%
Medicine and Dentistry 2 10%
Computer Science 2 10%
Environmental Science 1 5%
Business, Management and Accounting 1 5%
Other 6 30%
Unknown 1 5%

Attention Score in Context

This research output has an Altmetric Attention Score of 49. 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 13 April 2020.
All research outputs
#463,048
of 15,899,576 outputs
Outputs from Theoretical Biology and Medical Modelling
#6
of 258 outputs
Outputs of similar age
#10,195
of 250,779 outputs
Outputs of similar age from Theoretical Biology and Medical Modelling
#1
of 1 outputs
Altmetric has tracked 15,899,576 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 258 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one has done particularly well, scoring higher than 97% 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 250,779 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 95% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them