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

Evolutionary Relationships through Genome Comparisons

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

Phylogenetic Trees

Phylogenetic Trees

Phylogenetic trees come in many forms. It matters in which sequence the organisms are arranged from the bottom to the top of the tree, but the branches can rotate at their nodes without altering the information. The lines connecting individual nodes can be straight, angled, or even curved.

Phylogeny

Phylogeny

Phylogeny is concerned with the evolutionary diversification of organisms or groups of organisms. A group of organisms with a name is called a taxon (singular). Taxa (plural) can span different levels of the evolutionary hierarchy. For instance, the group containing all birds is a taxon (comprising the class Aves), and the group of all species of daisies (the genus Bellis) is a taxon. Phylogenies can likewise include just one genus (i.e., depict species relationships) or span an entire kingdom.

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for k_a Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...

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...

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.

You might also read

Related Articles

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

Sort by

Same author

Estimating the Potential Burden of Clinically Significant Hantavirus Cases in Argentina.

The Lancet regional health. Europe·2026

Same author

Non-linear age dynamics of malaria infection and fine-scale environmental exposure in rural Uganda.

BMC medicine·2026

Same author

Large-scale genomic surveillance reveals immunosuppression drives mutation dynamics in persistent SARS-CoV-2 infections.

Nature communications·2026

Same author

Shared risk factors for malaria and schistosomiasis co-infection: A systematic review and meta-analysis.

PLoS neglected tropical diseases·2026

Same author

Global approaches to infectious disease surveillance and modeling.

Nature medicine·2026

Same author

Turtle genomic novelty is driven by the evolution of ecological robustness.

iScience·2026

Same journal

Effects of Seasonal Births and Predation on Disease Spread.

Bulletin of mathematical biology·2026

Same journal

Identifiability, Sensitivity, and Genetic Algorithms in Bacterial Biofilm Selection Models.

Bulletin of mathematical biology·2026

Same journal

Slow Evolution Towards Generalism in a Model of Variable Dietary Range.

Bulletin of mathematical biology·2026

Same journal

CBINN: Cancer Biology-Informed Neural Network for Unknown Parameter Estimation and Missing Physics Identification.

Bulletin of mathematical biology·2026

Same journal

A Cost-Sensitive Behavioral Modeling Analysis of the Early Identification and Control of Infectious Diseases.

Bulletin of mathematical biology·2026

Same journal

Tracking Dynamics of Superspreading Through Contacts, Exposures, and Transmissions in Edge-Based Network Epidemics.

Bulletin of mathematical biology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 31, 2025

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

Bayesian Inference of Phylogenetic Distances: Revisiting the Eigenvalue Approach.

Matthew J Penn¹, Neil Scheidwasser², Christl A Donnelly^1,3

¹Department of Statistics, University of Oxford, Oxford, UK.

Bulletin of Mathematical Biology

|January 23, 2025

Summary

This summary is machine-generated.

This study introduces a new Bayesian phylogenetic method to model complex evolutionary rates and variations over time. The approach provides evidence supporting a two-domain theory for the origin of eukaryotic cells.

Keywords:

Bayesian inference GTR Genetic distance Ukaryotes

More Related Videos

A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Published on: August 16, 2017

Related Experiment Videos

Last Updated: May 31, 2025

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Published on: August 16, 2017

Area of Science:

Evolutionary biology
Computational phylogenetics
Molecular evolution

Background:

Phylogenetics commonly uses Markov substitution models to infer evolutionary distances from genetic data, aiding in starting tree selection.
Standard models like the general time reversible (GTR) model handle DNA data well but struggle with complex phenomena like heterotachy (lineage-specific rate variation).
Existing advanced models for heterotachy lack analytical solutions, hindering likelihood calculations essential for Bayesian inference.

Purpose of the Study:

To develop a novel hybrid Bayesian phylogenetic framework that integrates GTR-style modeling with rate variation and heterotachy.
To create a method that is mathematically tractable for optimization and Bayesian inference.
To investigate the evolutionary origins of the eukaryotic cell using this new approach within a universal tree of life context.

Main Methods:

Developed a hierarchical Bayesian GTR-style framework incorporating gamma-distributed rate variation and heterotachy.
Ensured the approach is differentiable, enabling optimization via stochastic gradient descent.
Facilitated Bayesian inference using Hamiltonian Markov Chain Monte Carlo methods.

Main Results:

The proposed hybrid method successfully models complex evolutionary rate variations and heterotachy.
The framework is amenable to modern computational inference techniques like stochastic gradient descent and Hamiltonian Monte Carlo.
Analysis of universal tree of life data provided evidence supporting a two-domain theory for eukaryotic cell origins.

Conclusions:

The novel Bayesian framework offers a powerful tool for phylogenetic analysis incorporating complex evolutionary dynamics.
This method advances the modeling of molecular evolution, particularly for lineage-specific rate variations.
Findings lend support to the two-domain theory regarding the evolutionary emergence of eukaryotic cells.