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

Evolution of New Traits in Microbes01:24

Evolution of New Traits in Microbes

150
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...
150
Transduction01:16

Transduction

2.8K
Among the three main modes of HGT—transformation, conjugation, and transduction—transduction is unique in that it is mediated by bacteriophages, or bacterial viruses.Transduction occurs in two ways. Generalized transduction occurs during the lytic cycle of a bacteriophage infection. In this process, bacteriophages infect bacterial cells, replicate within them, and ultimately cause cell lysis, releasing newly assembled virions. Occasionally, random fragments of the bacterial genome...
2.8K
Genetic Drift03:33

Genetic Drift

45.4K
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.
45.4K
Position-effect Variegation02:32

Position-effect Variegation

7.3K
In 1928, a German botanist Emil Heitz observed the moss nuclei with a DNA binding dye. He observed that while some chromatin regions decondense and spread out in the interphase nucleus, others do not. He termed them euchromatin and heterochromatin, respectively. He proposed that the heterochromatin regions reflect a functionally inactive state of the genome. It was later confirmed that heterochromatin is transcriptionally repressed, and euchromatin is transcriptionally active chromatin.
7.3K
Background and Environment Affect Phenotype02:27

Background and Environment Affect Phenotype

8.2K
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...
8.2K
The Ratio of X Chromosome to Autosomes02:45

The Ratio of X Chromosome to Autosomes

10.1K
In most organisms, sex is determined by the ratio of X and Y chromosomes. However, in some organisms, such as Drosophila and C.elegans, sex is determined by the ratio of the number of X chromosomes to the number of sets of autosomes. The Y chromosome in Drosophila is active but does not determine sex. It contains genes responsible for the production of sperms in adult flies.  
Normal male Drosophila has a ratio of one X chromosome to two sets of autosomes. In contrast, normal female...
10.1K

You might also read

Related Articles

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

Sort by
Same author

Nonstandard viral genomes as engines of viral evolution: ecological roles, evolutionary consequences, and insights from mathematical modeling.

Current opinion in virology·2026
Same author

Localized gold nanoparticles-mediated photothermal therapy for head and neck cancer: in vivo proof-of-concept.

International journal of pharmaceutics·2026
Same author

Longitudinal Dynamics of Polyglutamine-Expanded ATXN3 in Biofluids of Spinocerebellar Ataxia Type 3.

Movement disorders : official journal of the Movement Disorder Society·2026
Same author

Life Identification Numbers: A strain nomenclature approach to aid epidemiological surveillance of bacterial pathogens.

PLoS biology·2026
Same author

Systemic and Local Adiposity in the Bone Marrow Microenvironment Associated With Improved Prognosis in Hodgkin Lymphoma: Imaging and Molecular Analysis.

International journal of cancer·2026
Same author

Characterising Epigenetic Tipping Points using a Spectral Dimension Reduction Approach.

Bulletin of mathematical biology·2026

Related Experiment Video

Updated: Apr 11, 2026

Small-Cage Laboratory Trials of Genetically-Engineered Anopheline Mosquitoes
07:45

Small-Cage Laboratory Trials of Genetically-Engineered Anopheline Mosquitoes

Published on: May 1, 2021

3.3K

How Complex, Probable, and Predictable is Genetically Driven Red Queen Chaos?

Jorge Duarte1,2, Carla Rodrigues3, Cristina Januário4

  • 1Department of Mathematics, ISEL - Engineering Superior Institute of Lisbon, Rua Conselheiro Emídio Navarro 1, 1949-014, Lisbon, Portugal. jduarte@deq.isel.ipl.pt.

Acta Biotheoretica
|May 29, 2015
PubMed
Summary
This summary is machine-generated.

Genetically driven Red Queen chaos in food chains, though rare, can be highly unpredictable. This study quantifies the complexity and predictability of this evolutionary phenomenon.

Keywords:
Adaptive dynamicsChaosCoevolutionEcologyPredator-preyPredictabilityRed Queen

More Related Videos

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
09:23

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

8.7K
Measuring Oxidative Stress Resistance of Caenorhabditis elegans in 96-well Microtiter Plates
08:10

Measuring Oxidative Stress Resistance of Caenorhabditis elegans in 96-well Microtiter Plates

Published on: May 9, 2015

16.4K

Related Experiment Videos

Last Updated: Apr 11, 2026

Small-Cage Laboratory Trials of Genetically-Engineered Anopheline Mosquitoes
07:45

Small-Cage Laboratory Trials of Genetically-Engineered Anopheline Mosquitoes

Published on: May 1, 2021

3.3K
Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
09:23

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

8.7K
Measuring Oxidative Stress Resistance of Caenorhabditis elegans in 96-well Microtiter Plates
08:10

Measuring Oxidative Stress Resistance of Caenorhabditis elegans in 96-well Microtiter Plates

Published on: May 9, 2015

16.4K

Area of Science:

  • Ecology
  • Evolutionary Biology
  • Mathematical Biology

Background:

  • Coevolutionary dynamics, particularly the Red Queen hypothesis, are crucial for understanding species adaptation.
  • Previous models focused on ecological or genetic drivers of Red Queen dynamics, often showing oscillations.
  • Recent models introduced genetically driven chaotic Red Queen coevolution in three-species food chains.

Purpose of the Study:

  • To analyze a mathematical model of genetically driven chaotic Red Queen coevolution in a three-species food chain.
  • To investigate the impact of species mutation rates on evolutionary dynamics.
  • To quantify the complexity and predictability of this chaotic coevolutionary system.

Main Methods:

  • Analytical proof of trajectory boundedness for the chaotic attractor.
  • Quantification of dynamical variable coupling using observability indices.
  • Application of symbolic dynamics theory, including topological entropy and Markov partitions, to assess chaos complexity.
  • Construction of bifurcation diagrams from iterated maps.
  • Analysis of chaos predictability across varying mutation rates and parameter spaces.

Main Results:

  • The study analytically proves the boundedness of trajectories within the chaotic attractor.
  • Complexity of the system's dynamics was quantified using observability indices and topological entropy.
  • Genetically driven Red Queen chaos was found to exist in narrow regions of mutation rates.
  • The research quantified the likelihood of chaos across parameter spaces, revealing high unpredictability in chaotic regions.

Conclusions:

  • Genetically driven Red Queen chaos, while confined to specific parameter ranges, exhibits significant unpredictability.
  • The study provides a framework for quantifying evolutionary chaos complexity and predictability.
  • Understanding these dynamics is essential for predicting long-term evolutionary trajectories in multi-species systems.