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Mutation, Gene Flow, and Genetic Drift

Mutation, Gene Flow, and Genetic Drift

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).

Types of Selection

Types of Selection

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...

Genetic Drift

Genetic Drift

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.

Genetics of Speciation

Genetics of Speciation

Speciation is the evolutionary process resulting in the formation of new, distinct species—groups of reproductively isolated populations.

Gene Flow

Gene Flow

Gene flow is the transfer of genes among populations, resulting from either the dispersal of gametes or from the migration of individuals.

Frequency-dependent Selection

Frequency-dependent Selection

When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.

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Related Experiment Video

Updated: Jul 2, 2025

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Diverse mutant selection windows shape spatial heterogeneity in evolving populations.

Eshan S King¹, Dagim S Tadele^2,3, Beck Pierce⁴

¹Case Western Reserve University School of Medicine, Cleveland, Ohio, United States of America.

Plos Computational Biology

|February 22, 2024

Summary

This summary is machine-generated.

Fitness seascapes offer a robust model for predicting drug resistance by analyzing genotype-specific dose-response data. This approach reveals how drug diffusion and spatial mutant selection windows promote resistant cell proliferation.

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Mutagenesis and Functional Selection Protocols for Directed Evolution of Proteins in E. coli

Mutagenesis and Functional Selection Protocols for Directed Evolution of Proteins in E. coli

Published on: March 16, 2011

Area of Science:

Evolutionary biology
Mathematical modeling
Pharmacology

Background:

Mutant selection windows (MSWs) traditionally compare two subtypes (drug-sensitive vs. drug-resistant).
Existing models like fitness landscapes lack continuous drug response data.
Clinical settings involve diverse drug concentrations selecting for multiple genotypes.

Purpose of the Study:

Develop a more robust model for pathogen response to therapy.
Predict drug resistance and design novel therapeutic strategies.
Incorporate continuous drug response data into evolutionary models.

Main Methods:

Introduced N-allele fitness seascapes to model genotype-by-environment interactions.
Encoded genotype-specific dose-response data for simultaneous MSW comparisons.
Utilized spatial drug diffusion models and agent-based modeling with synthetic and empirical cancer data.

Main Results:

N-allele fitness seascapes allow for N * 2N-1 unique MSW comparisons.
Fitness seascapes reveal spatially heterogeneous MSWs, extending the traditional model.
Drug diffusion and spatial MSWs promote drug-resistant cell proliferation in cyclic drug therapy simulations.

Conclusions:

Fitness seascapes provide a powerful tool for analyzing multiple MSWs simultaneously.
Spatial structure of MSWs significantly influences the evolution of drug resistance.
Dose-dependent fitness landscapes are crucial for understanding and combating drug resistance in evolutionary medicine.