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

Gene Evolution - Fast or Slow?02:05

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?02:05

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...
Evolution of Microbial Genome01:08

Evolution of Microbial Genome

Microbial genome evolution is a highly dynamic process shaped by continual gene gain and loss across species and strains. This genomic flexibility allows microorganisms to adapt rapidly to environmental pressures and interactions with other organisms. Central to understanding this diversity is the distinction between the core and pan genomes.The core genome comprises the genes shared by all sampled strains of a species, representing essential functions needed for fundamental cellular processes.
Evolution of New Traits in Microbes01:24

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...
Genome Size and the Evolution of New Genes03:21

Genome Size and the Evolution of New Genes

While every living organism has a genome of some kind (be it RNA, or DNA), there is considerable variation in the sizes of these blueprints. One major factor that impacts genome size is whether the organism is prokaryotic or eukaryotic. In prokaryotes, the genome contains little to no non-coding sequence, such that genes are tightly clustered in groups or operons sequentially along the chromosome. Conversely, the genes in eukaryotes are punctuated by long stretches of non-coding sequence.
Genome Size and the Evolution of New Genes03:21

Genome Size and the Evolution of New Genes

While every living organism has a genome of some kind (be it RNA, or DNA), there is considerable variation in the sizes of these blueprints. One major factor that impacts genome size is whether the organism is prokaryotic or eukaryotic. In prokaryotes, the genome contains little to no non-coding sequence, such that genes are tightly clustered in groups or operons sequentially along the chromosome. Conversely, the genes in eukaryotes are punctuated by long stretches of non-coding sequence.

You might also read

Related Articles

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

Sort by
Same author

Reconciling fast Hepatitis B evolutionary rates with ancient co-divergence.

bioRxiv : the preprint server for biology·2026
Same author

Models of microbiome evolution incorporating host resource provisioning.

ISME communications·2025
Same author

Grains, trade and war in the multimodal transmission of Rice yellow mottle virus: An historical and phylogeographical retrospective.

PLoS pathogens·2025
Same author

How fast are viruses spreading in the wild?

PLoS biology·2024
Same author

Modeling the velocity of evolving lineages and predicting dispersal patterns.

Proceedings of the National Academy of Sciences of the United States of America·2024
Same author

Modeling the velocity of evolving lineages and predicting dispersal patterns.

bioRxiv : the preprint server for biology·2024

Related Experiment Video

Updated: Jun 23, 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

Modelling the evolution of protein coding sequences sampled from Measurably Evolving Populations.

Matthew Goode1, Stéphane Guindon, Allen Rodrigo

  • 1The Bioinformatics Institute New Zealand, University of Auckland, Auckland, New Zealand.

Genome Informatics. International Conference on Genome Informatics
|May 9, 2009
PubMed
Summary

This study introduces a new codon-based evolutionary model for Measurably Evolving Populations (MEPs). The model captures dynamic changes in selection intensity and pressures, crucial for analyzing complex evolutionary data.

More Related Videos

Daily Transfers, Archiving Populations, and Measuring Fitness in the Long-Term Evolution Experiment with Escherichia coli
15:00

Daily Transfers, Archiving Populations, and Measuring Fitness in the Long-Term Evolution Experiment with Escherichia coli

Published on: August 18, 2023

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq
07:09

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

Related Experiment Videos

Last Updated: Jun 23, 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

Daily Transfers, Archiving Populations, and Measuring Fitness in the Long-Term Evolution Experiment with Escherichia coli
15:00

Daily Transfers, Archiving Populations, and Measuring Fitness in the Long-Term Evolution Experiment with Escherichia coli

Published on: August 18, 2023

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq
07:09

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

Area of Science:

  • Evolutionary biology
  • Computational biology
  • Genomics

Background:

  • Traditional evolutionary models (homogeneous, stationary Markov processes) are mathematically tractable but oversimplify complex evolutionary dynamics.
  • Next-generation sequencing generates vast datasets, necessitating more sophisticated models, especially for ancient/sub-fossil DNA where evolutionary parameters fluctuate.
  • Measurably Evolving Populations (MEPs) are characterized by detectable substitution accumulation over time, requiring models that account for temporal changes.

Purpose of the Study:

  • To introduce a novel codon-based model of evolution specifically designed for Measurably Evolving Populations (MEPs).
  • To develop a model that accommodates dynamic changes in evolutionary parameters, including selection intensity and site-specific selective pressures.
  • To demonstrate the model's utility in analyzing biological sequence data exhibiting temporal evolutionary shifts.

Main Methods:

  • Development of a new codon-based model of evolution.
  • The model allows for simultaneous changes in the selective regime across all lineages within a serial model framework.
  • Incorporation of distinct selective patterns for different protein regions.

Main Results:

  • The proposed model successfully accommodates dynamic changes in evolutionary parameters, unlike static models.
  • It allows for variations in selection intensity and proportions of sites under different selective pressures over time.
  • The model's application to HIV-1 sequences illustrates its capability to detect and model evolutionary shifts during therapy.

Conclusions:

  • The new codon-based model provides a more realistic framework for studying evolution in Measurably Evolving Populations (MEPs).
  • This model is particularly valuable for analyzing sequence data from temporally sampled populations, such as those undergoing treatment or from ancient remains.
  • The model's flexibility in handling changing selective pressures enhances our understanding of molecular evolution in dynamic biological systems.