Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

66.5K
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).
66.5K
Multiple Allele Traits01:49

Multiple Allele Traits

39.3K
The Concept of Multiple Allelism
39.3K
Multiple Allele Traits01:49

Multiple Allele Traits

15.2K
15.2K
Genetic Variation01:25

Genetic Variation

1.6K
Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
Genes exist in different versions called alleles,...
1.6K
Genetic Drift03:33

Genetic Drift

45.7K
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.
45.7K
Combinatorial Gene Control02:33

Combinatorial Gene Control

10.5K
Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
The expression of more than 30,000 genes is controlled by approximately 2000-3000 transcription factors. This is possible because a single transcription factor can recognize more than one regulatory sequence. The specificity in gene...
10.5K

You might also read

Related Articles

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

Sort by
Same author

Deciphering global patterns of marine microbial community assembly and network stability.

mSystems·2026
Same author

Tumor-infiltrating lymphocytes display prognostic signatures associated with chemotherapy response in TNBC patients.

iScience·2026
Same author

UNified FramewOrk for reguLatory Dynamics (UNFOLD): Dissecting robustness, plasticity, evolvability and canalisation of biological function.

PLoS computational biology·2026
Same author

Evaluating Metabolic Support in Pairwise Microbial Communities Using MetQuest.

Methods in molecular biology (Clifton, N.J.)·2026
Same author

Constraint-Based Modeling of Microbial Communities for Metabolite Production.

Methods in molecular biology (Clifton, N.J.)·2026
Same author

Genome-scale metabolic model guided metabolic flux analysis in the endophyte Alternaria burnsii NCIM1409.

Bioprocess and biosystems engineering·2026

Related Experiment Video

Updated: Apr 21, 2026

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

1.4K

Revisiting robustness and evolvability: evolution in weighted genotype spaces.

Raghavendran Partha1, Karthik Raman1

  • 1Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India.

Plos One
|November 13, 2014
PubMed
Summary

Considering mutation probabilities reveals that biological systems may be more robust and less evolvable than previously thought. This study analyzes weighted versus unweighted neutral networks in RNA sequences to understand these intertwined properties.

More Related Videos

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.9K
High-Throughput Live Imaging of Microcolonies to Measure Heterogeneity in Growth and Gene Expression
12:52

High-Throughput Live Imaging of Microcolonies to Measure Heterogeneity in Growth and Gene Expression

Published on: April 18, 2021

5.6K

Related Experiment Videos

Last Updated: Apr 21, 2026

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

1.4K
Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.9K
High-Throughput Live Imaging of Microcolonies to Measure Heterogeneity in Growth and Gene Expression
12:52

High-Throughput Live Imaging of Microcolonies to Measure Heterogeneity in Growth and Gene Expression

Published on: April 18, 2021

5.6K

Area of Science:

  • Computational Biology
  • Evolutionary Biology
  • Molecular Biology

Background:

  • Robustness and evolvability are key intertwined properties of biological systems, influencing their ability to withstand mutations and adapt.
  • Previous computational studies modeled this relationship using RNA neutral networks but often assumed equal mutation likelihoods, ignoring probabilistic mutation effects.

Purpose of the Study:

  • To comparatively analyze weighted and unweighted neutral networks of RNA sequences to explore the relationship between robustness and evolvability.
  • To investigate the impact of mutation probabilities and base composition bias (AU-richness) on these properties.

Main Methods:

  • Comparative analysis of weighted (considering mutation probabilities) and unweighted (equal mutation likelihoods) neutral networks of RNA sequences.
  • Exploration of robustness and evolvability in both sequence (genotype) and structure (phenotype) spaces.
  • Investigation of AU-rich sequence spaces (≥80% AU content) versus normal sequence spaces.

Main Results:

  • Assuming equal mutation likelihoods underestimates robustness and overestimates evolvability.
  • A negative correlation between sequence robustness and sequence evolvability persists, while structure robustness promotes structure evolvability.
  • AU-rich sequence spaces exhibit higher robustness and lower evolvability for both sequences and structures compared to normal spaces.

Conclusions:

  • Incorporating mutation probabilities refines our understanding of the robustness-evolvability trade-off in biological systems.
  • Base composition significantly impacts robustness and evolvability, with AU-rich environments favoring robustness over evolvability and limiting phenotypic variation.
  • The findings highlight the importance of considering mutation biases and probabilities in evolutionary models.