↓ Skip to main content

The waiting time problem in a model hominin population

Overview of attention for article published in Theoretical Biology and Medical Modelling, September 2015
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#4 of 287)
  • High Attention Score compared to outputs of the same age (97th percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

Mentioned by

news
2 news outlets
blogs
2 blogs
twitter
64 X users
facebook
3 Facebook pages
wikipedia
6 Wikipedia pages
googleplus
3 Google+ users
reddit
1 Redditor
q&a
1 Q&A thread
video
6 YouTube creators

Citations

dimensions_citation
7 Dimensions

Readers on

mendeley
27 Mendeley
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
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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 64 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 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 27 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 22%
Student > Doctoral Student 4 15%
Student > Master 4 15%
Other 3 11%
Researcher 3 11%
Other 6 22%
Unknown 1 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 8 30%
Medicine and Dentistry 3 11%
Computer Science 2 7%
Engineering 2 7%
Linguistics 1 4%
Other 8 30%
Unknown 3 11%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 94. 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 12 March 2024.
All research outputs
#451,964
of 25,464,544 outputs
Outputs from Theoretical Biology and Medical Modelling
#4
of 287 outputs
Outputs of similar age
#6,016
of 284,006 outputs
Outputs of similar age from Theoretical Biology and Medical Modelling
#1
of 7 outputs
Altmetric has tracked 25,464,544 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 287 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.9. This one has done particularly well, scoring higher than 98% 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 284,006 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 97% of its contemporaries.
We're also able to compare this research output to 7 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