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

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?

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...

Gene Duplication and Divergence

Gene Duplication and Divergence

The seminal work of Ohno in 1970 popularized the idea of gene duplication and divergence. DNA sequence comparison studies reveal that a large portion of the genes in bacteria, archaebacteria, and eukaryotes was generated by gene duplication and divergence, indicating its critical role in evolution.
The duplicated copies of the gene are called Paralogs. Paralogs with similar sequences and functions form a gene family. Across several species, a large number of gene families are...

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).

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.

You might also read

Related Articles

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

Sort by

Same author

Crick's "Frozen accident theory" of the genetic code explored by Ising models.

Journal of theoretical biology·2025

Same author

Fragile X mental retardation protein modulates translation of proteins with predicted tendencies for liquid-liquid phase separation.

Bio Systems·2025

Same author

A Proposal for the RNAome at the Dawn of the Last Universal Common Ancestor.

Genes·2024

Same author

A Proposal of the Ur-RNAome.

Genes·2023

Same author

Groups of Symmetries of the Two Classes of Synthetases in the Four-Dimensional Hypercubes of the Extended Code Type II.

Life (Basel, Switzerland)·2023

Same author

Decoding viruses: An alternative perspective on their history, origins and role in nature.

Bio Systems·2023

Search research articles

Related Experiment Video

Updated: Feb 19, 2026

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Phenotypic Graphs and Evolution Unfold the Standard Genetic Code as the Optimal.

Gabriel S Zamudio¹, Marco V José²

¹Theoretical Biology Group, Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México, C.P. 04510, Ciudad de México CDMX, Mexico.

Origins of Life and Evolution of the Biosphere : the Journal of the International Society for the Study of the Origin of Life

|October 31, 2017

Summary

The Standard Genetic Code (SGC) evolved through distinct stages, demonstrating optimal error-correction. Network analysis reveals its robustness compared to random codes, highlighting its evolutionary advantage.

Keywords:

Error-tolerance Evolution genetic code Graph theory Network theory Standard genetic code

More Related Videos

Phage Phenomics: Physiological Approaches to Characterize Novel Viral Proteins

Phage Phenomics: Physiological Approaches to Characterize Novel Viral Proteins

Published on: June 11, 2015

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

Published on: November 12, 2012

Related Experiment Videos

Last Updated: Feb 19, 2026

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Phage Phenomics: Physiological Approaches to Characterize Novel Viral Proteins

Phage Phenomics: Physiological Approaches to Characterize Novel Viral Proteins

Published on: June 11, 2015

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

Published on: November 12, 2012

Area of Science:

Biochemistry
Evolutionary Biology
Computational Biology

Background:

The Standard Genetic Code (SGC) is fundamental to all known life.
Understanding the evolutionary pathways leading to the SGC is crucial for deciphering early life processes.

Purpose of the Study:

To investigate the evolutionary trajectory of the Standard Genetic Code (SGC).
To assess the optimality and error-correcting capabilities of the SGC through network theory.

Main Methods:

Utilized network and graph theory to analyze phenotypic graphs representing different genetic codes.
Compared connectivity values of the SGC and intermediate codes against randomized code scenarios.
Defined code optimality based on minimized algebraic connectivity for error correction.

Main Results:

The Standard Genetic Code (SGC) exhibits minimized algebraic connectivity, indicating optimal error-correction.
Connectivity values of the SGC were compared against various randomization scenarios.
The SGC demonstrates superior robustness and error-tolerance compared to random codes.

Conclusions:

The Standard Genetic Code (SGC) is evolutionarily optimized for robustness and error-tolerance.
Network theory provides a quantitative framework for evaluating the optimality of genetic codes.
The study supports the hypothesis of an evolving genetic code with enhanced error-correction properties.