Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Modeling with Differential Equations

Modeling with Differential Equations

Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...

Background and Environment Affect Phenotype

Background and Environment Affect Phenotype

Although the genetic makeup of an organism plays a major role in determining the phenotype, there are also several environmental factors, such as temperature, oxygen availability, presence of mutagens, that can alter an organism’s phenotype.
An example of how genetic background affects phenotype can be seen in horses. The Extension gene in horses is responsible for their coat color. A wild-type gene (EE) produces black pigment in the coat, while a mutant gene (ee) produces red pigment. A...

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).Mechanisms of Genetic VariationThe original sources of genetic variation are mutations,...

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.Life is not fair. A deer grazing contentedly in a field can have her meal cut tragically short by a bolt of lightning. If the doomed doe is one of only three in the population, 1/3 of the population’s gene pool is lost. Random events like this can...

Evolution of New Traits in Microbes

Evolution of New Traits in Microbes

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...

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Human milk oligosaccharide composition, concentrations and association with maternal factors in the multi-ethnic Asian GUSTO cohort.

Frontiers in nutrition·2026

Same author

Developing a multi-modal neuroimaging-based BrainAge model across childhood.

bioRxiv : the preprint server for biology·2026

Same author

Meteorological, ozone, maternal and individual-level risk factors for childhood diseases in Singapore: A prospective birth cohort study from 2009 to 2019.

Ecotoxicology and environmental safety·2026

Same author

Sex-specific neurodevelopmental pathways to depressive symptoms.

Molecular psychiatry·2026

Same author

Rewiring science diplomacy.

Science (New York, N.Y.)·2025

Same author

Cohort Profile: The New Zealand Best Start study (Kia Tīmata Pai).

International journal of epidemiology·2025

Search research articles

Related Experiment Video

Updated: Jun 12, 2026

Phage Phenomics: Physiological Approaches to Characterize Novel Viral Proteins

Phage Phenomics: Physiological Approaches to Characterize Novel Viral Proteins

Published on: June 11, 2015

A model for phenotype change in a stochastic framework.

Graeme Wake¹, Anthony Pleasants, Alan Beedle

¹National Research Centre for Growth and Development and Institute of Information and Mathematical Sciences, Massey University, Private Bag 102904, Albany, Auckland, New Zealand. g.c.wake@massey.ac.nz

Mathematical Biosciences and Engineering : MBE

|June 29, 2010

Summary

This summary is machine-generated.

Organisms optimize their response time to environmental changes. The optimal waiting time for a population to develop a new phenotype depends on the variance in individual response times, impacting overall fitness.

More Related Videos

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

A Mouse Model for the Transition of Streptococcus pneumoniae from Colonizer to Pathogen upon Viral Co-Infection Recapitulates Age-Exacerbated Illness

A Mouse Model for the Transition of Streptococcus pneumoniae from Colonizer to Pathogen upon Viral Co-Infection Recapitulates Age-Exacerbated Illness

Published on: September 28, 2022

Related Experiment Videos

Last Updated: Jun 12, 2026

Phage Phenomics: Physiological Approaches to Characterize Novel Viral Proteins

Phage Phenomics: Physiological Approaches to Characterize Novel Viral Proteins

Published on: June 11, 2015

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

A Mouse Model for the Transition of Streptococcus pneumoniae from Colonizer to Pathogen upon Viral Co-Infection Recapitulates Age-Exacerbated Illness

A Mouse Model for the Transition of Streptococcus pneumoniae from Colonizer to Pathogen upon Viral Co-Infection Recapitulates Age-Exacerbated Illness

Published on: September 28, 2022

Area of Science:

Ecology
Evolutionary Biology
Mathematical Biology

Background:

Organisms may develop inducible secondary phenotypes after environmental changes.
Individual organisms optimize their response time to environmental shifts, termed 'waiting time'.
Population-level implications of optimal waiting times on mean fitness are significant.

Purpose of the Study:

To propose a stochastic predator-prey model to analyze the impact of waiting time on population fitness.
To investigate how biological variance in response times affects population-level fitness optimization.

Main Methods:

Developed a stochastic predator-prey model with a fixed prey energy budget.
Defined fitness as the product of survival probability and remaining energy for non-defensive functions.
Modeled waiting time as a normally distributed random variable to account for biological variance.

Main Results:

The mean waiting time that maximizes population fitness is linearly dependent on the variance of waiting times.
Increased variance in waiting times influences the optimal mean waiting time for fitness maximization.

Conclusions:

Population-level optimization of inducible phenotypes requires considering the variance in individual response times.
Understanding the interplay between waiting time variance and mean waiting time is crucial for predicting population fitness in changing environments.