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

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,...

Gene Evolution - Fast or Slow?

Gene Evolution - Fast or Slow?

The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...

Gene Evolution - Fast or Slow?

Gene Evolution - Fast or Slow?

The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...

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.In the early 20th century,...

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...

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...

You might also read

Related Articles

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

Sort by

Same author

Temporal signals in dairy cattle slurry and fertilized field soil resistomes and bacterial communities.

Frontiers in microbiology·2026

Same author

Sublethal concentrations of antibiotics enhance transmission of antibiotic resistance genes in environmental <i>Escherichia coli</i>.

Frontiers in microbiology·2025

Same author

Author Correction: Rethinking the words hostspot reservoir and pristine in the environmental dimensions of antimicrobial resistance.

npj antimicrobials and resistance·2025

Same author

Factors that affect the resilience of young adults to depression: a systematic review.

The lancet. Psychiatry·2025

Same author

Multisystemic resilience and its impact on youth mental health: reflections on co-designing a multi-disciplinary, participatory study.

Frontiers in child and adolescent psychiatry·2025

Same author

Rethinking the words hostspot reservoir and pristine in the environmental dimensions of antimicrobial resistance.

npj antimicrobials and resistance·2025

Search research articles

Related Experiment Video

Updated: Jun 16, 2026

Sealable Femtoliter Chamber Arrays for Cell-free Biology

Sealable Femtoliter Chamber Arrays for Cell-free Biology

Published on: March 11, 2015

Stochasticity versus determinism: consequences for realistic gene regulatory network modelling and evolution.

Dafyd J Jenkins¹, Dov J Stekel

¹Centre for Systems Biology, School of Biosciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK. dafyd.jenkins@warwick.ac.uk

Journal of Molecular Evolution

|February 13, 2010

Summary

In silico evolution of gene regulatory networks (GRNs) shows stochastic basal gene expression is vital for efficient biomass production. Non-adaptive models yield bloated networks, highlighting trade-offs between growth and robustness.

More Related Videos

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Continuous Measurement of Biological Noise in Escherichia Coli Using Time-lapse Microscopy

Continuous Measurement of Biological Noise in Escherichia Coli Using Time-lapse Microscopy

Published on: April 27, 2021

Related Experiment Videos

Last Updated: Jun 16, 2026

Sealable Femtoliter Chamber Arrays for Cell-free Biology

Sealable Femtoliter Chamber Arrays for Cell-free Biology

Published on: March 11, 2015

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Continuous Measurement of Biological Noise in Escherichia Coli Using Time-lapse Microscopy

Continuous Measurement of Biological Noise in Escherichia Coli Using Time-lapse Microscopy

Published on: April 27, 2021

Area of Science:

Computational Biology
Evolutionary Biology
Systems Biology

Background:

Gene regulatory networks (GRNs) are crucial for phenotypes and heterogeneity.
Studying GRN evolution is essential but challenging due to timescales.
In silico evolution offers a method to study long-term GRN adaptation.

Purpose of the Study:

To investigate the evolution of GRNs towards biomass production using computational models.
To compare deterministic and stochastic Boolean network paradigms for simulating GRN evolution.
To explore the impact of basal gene expression and evolutionary dynamics on GRN architecture and function.

Main Methods:

Utilized deterministic and stochastic Boolean networks to model GRN evolution.
Incorporated basal gene expression as a factor in simulations.
Focused on the objective of biomass production to simulate adaptive evolution.
Analyzed network architecture, genome size, and trade-offs between efficiency and robustness.

Main Results:

Stochastic basal gene expression drives genome shrinkage due to energetic constraints.
Models without basal expression resulted in bloated networks with non-functional elements.
Stochastic networks, unlike deterministic ones, produced diverse functional solutions under identical conditions.
Observed trade-offs between growth efficiency and mutational robustness.

Conclusions:

Stochasticity and basal gene expression are critical for realistic GRN evolution towards biomass production.
Simulation paradigms must include stochasticity and basal expression to avoid generating non-functional network architectures.
Evolutionary dynamics and biological goals are intrinsically linked, leading to inherent trade-offs.