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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.

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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).

What is Population Genetics?

What is Population Genetics?

A population is composed of members of the same species that simultaneously live and interact in the same area. When individuals in a population breed, they pass down their genes to their offspring. Many of these genes are polymorphic, meaning that they occur in multiple variants. Such variations of a gene are referred to as alleles. The collective set of all the alleles within a population is known as the gene pool.

Population Growth

Population Growth

Population size is dynamic, increasing with birth rates and immigration, and decreasing with death rates and emigration. In ideal conditions with unlimited resources, populations can increase exponentially, which plots as a J-shaped growth rate curve of population size against time. This type of curve is characteristic of newly-introduced invasive species, or populations that have suffered catastrophic declines and are rebounding.

Hardy-Weinberg Principle

Hardy-Weinberg Principle

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.

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.

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Home
Stochastic Evolutionary Control In Heterogeneous Populations.

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Stochastic Evolutionary Control In Heterogeneous Populations.

Related Experiment Video

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

Stochastic Evolutionary Control in Heterogeneous Populations.

Peng Chen^1,2, Jonathan Asher Pachter^1,2, Jacob G Scott^1,2,3

¹Department of Physics, Case Western Reserve University, Cleveland, OH 44106.

Biorxiv : the Preprint Server for Biology

|April 10, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

SHEPHERD (Stochastic Heterogeneity-informed Evolutionary Policy Hampering the Expansion of Resistance to Drugs) optimizes adaptive drug policies to reduce disease fitness and mitigate resistance. This approach effectively models heterogeneous populations, outperforming traditional methods for controlling evolving diseases.

Keywords:

Markov Decision Process Wright–Fisher dynamics drug resistance evolutionary control

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Area of Science:

Evolutionary biology
Computational biology
Mathematical modeling

Background:

Therapeutic resistance is a major challenge in treating cancer and infectious diseases.
Existing methods often rely on the strong-selection, weak-mutation assumption, limiting their applicability to homogeneous populations.
Understanding the evolutionary dynamics of heterogeneous populations is crucial for developing effective resistance-mitigation strategies.

Purpose of the Study:

To introduce SHEPHERD (Stochastic Heterogeneity-informed Evolutionary Policy Hampering the Expansion of Resistance to Drugs), a novel framework for designing adaptive drug policies.
To move beyond the strong-selection, weak-mutation assumption by incorporating stochastic heterogeneity in evolutionary modeling.
To reduce the fitness of evolving diseases and mitigate therapeutic resistance.

Main Methods:

Integration of Wright-Fisher (WF) population genetics modeling with Markov Decision Processes (MDPs).
Development of a computational framework (SHEPHERD) to capture full stochastic dynamics of genetically heterogeneous populations.
Simulation using synthetic multi-drug fitness landscapes with varying numbers of genotypes.

Main Results:

The optimized SHEPHERD protocol significantly reduced long-term mean disease fitness compared to single-drug or periodic two-drug regimens in silico.
Demonstrated robustness of SHEPHERD strategies across different levels of genotypic frequency discretization and temporal resolution.
Highlighted strong dependence of strategy effectiveness on the timing of drug updates.

Conclusions:

SHEPHERD provides a foundation for applying MDP optimization to control a broad class of evolving populations beyond the strong-selection, weak-mutation regime.
The approach offers a promising strategy for mitigating therapeutic resistance in complex, heterogeneous disease systems.
Acknowledged the need for empirical multi-genotype, multi-drug measurements to further validate and refine the models.