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Related Concept Videos

Types of Selection01:46

Types of Selection

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Natural selection influences the frequencies of particular alleles and phenotypes within populations in several different ways. Primarily, natural selection can be directional, stabilizing, or disruptive. Directional selection favors one extreme trait and shifts the population towards that phenotype while selecting against individuals displaying alternate traits. Stabilizing selection favors an intermediate trait with a narrow range of variation. Deviation from the optimal phenotype towards an...
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Mutation, Gene Flow, and Genetic Drift01:09

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In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).
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Diploid organisms have two alleles of each gene, one from each parent, in their somatic cells. Therefore, each individual contributes two alleles to the gene pool of the population. The gene pool of a population is the sum of every allele of all genes within that population and has some degree of variation. Genetic variation is typically expressed as a relative frequency, which is the percentage of the total population that has a given allele, genotype or phenotype.
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Genetics of Speciation02:16

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Speciation is the evolutionary process resulting in the formation of new, distinct species—groups of reproductively isolated populations.
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Evolution of New Traits in Microbes01:24

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Microorganisms evolve rapidly due to their large population sizes and short generation times, often exhibiting measurable changes within days under laboratory conditions. Natural selection acts on standing genetic variation, enabling the retention and amplification of beneficial traits that confer fitness advantages in changing environments.Adaptive Pigment Regulation in RhodobacterIn Rhodobacter, a genus of purple non-sulfur bacteria, light-harvesting pigments such as bacteriochlorophyll and...
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Genetic Drift03:33

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Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.
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Following the Dynamics of Structural Variants in Experimentally Evolved Populations
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Exact solutions for the selection-mutation equilibrium in the Crow-Kimura evolutionary model.

Yuri S Semenov1, Artem S Novozhilov2

  • 1Applied Mathematics-1, Moscow State University of Railway Engineering, Moscow 127994, Russia.

Mathematical Biosciences
|May 26, 2015
PubMed
Summary

This study presents a new method to find the selection-mutation equilibrium distribution in asexual populations. Analytical solutions for steady-state distributions were derived for various fitness landscapes.

Keywords:
Crow–Kimura modelError thresholdQuasispecies modelSelection–mutation equilibriumSingle peaked landscape

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Area of Science:

  • Evolutionary biology
  • Population genetics
  • Mathematical modeling

Background:

  • Understanding the balance between selection and mutation is crucial for predicting evolutionary trajectories.
  • Previous models often relied on numerical simulations, limiting analytical insights.

Purpose of the Study:

  • To reformulate the eigenvalue problem for selection-mutation equilibrium.
  • To develop an analytical approach for determining steady-state distributions in haploid asexual populations.

Main Methods:

  • The study reformulates the eigenvalue problem as an equation for a probability generating function.
  • Analytical solutions are derived in the infinite population size limit.
  • Theoretical findings are validated against numerical calculations.

Main Results:

  • A novel analytical framework for selection-mutation equilibrium was established.
  • Steady-state distributions were obtained for specific fitness landscape models.
  • The approach demonstrated good agreement between theoretical predictions and numerical simulations.

Conclusions:

  • The developed method provides an efficient analytical tool for studying evolutionary dynamics.
  • This approach facilitates a deeper understanding of how selection and mutation interact to shape genetic variation.
  • The findings are applicable to various scenarios in population genetics and evolutionary theory.