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Related Concept Videos

Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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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...
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Gene Evolution - Fast or Slow?02:05

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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...
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Hardy-Weinberg Principle01:49

Hardy-Weinberg Principle

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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.
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Synteny and Evolution02:31

Synteny and Evolution

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John H. Renwick first coined the term “synteny” in 1971, which refers to the genes present on the same chromosomes, even if they are not genetically linked. The species with common ancestry tend to show conserved syntenic regions. Therefore, the concept of synteny is nowadays used to describe the evolutionary relationship between species.
Around 80 million years ago, the human and mice lineages diverged from the common ancestor. During the course of evolution, the ancestral...
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Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

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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).
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Exon Recombination02:32

Exon Recombination

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The evolution of new genes is critical for speciation. Exon recombination, also known as exon shuffling or domain shuffling, is an important means of new gene formation. It is observed across vertebrates, invertebrates, and in some plants such as potatoes and sunflowers. During exon recombination, exons from the same or different genes recombine and produce new exon-intron combinations, which might evolve into new genes. 
Exon shuffling follows “splice frame rules.” Each exon...
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Procedure for Adaptive Laboratory Evolution of Microorganisms Using a Chemostat
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Evolutionary algorithms simulating molecular evolution: a new field proposal.

James S L Browning1, Daniel R Tauritz1, John Beckmann2

  • 1Department of Computer Science and Software Engineering, Samuel Ginn College of Engineering, 3101 Shelby Center, Auburn, AL 36849-5347, United States.

Briefings in Bioinformatics
|August 12, 2024
PubMed
Summary
This summary is machine-generated.

Researchers are expanding nature's limited protein "vocabulary" using computational evolution. This field merges evolutionary algorithms and machine learning to design novel, customized proteins with potential applications in biotechnology and medicine.

Keywords:
artificial intelligencebiotechnologycomputational biologycomputational evolutionevolutionary algorithmsgenetic programmingmolecular evolutionproteomics

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Related Experiment Videos

Last Updated: Jun 17, 2025

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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

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Area of Science:

  • Computational Biology
  • Biochemistry
  • Evolutionary Biology

Background:

  • DNA encodes proteins, essential for metabolic processes.
  • Genome sequencing reveals protein diversity, yet known functional families are a small fraction of possible sequences.
  • A limited natural protein
  • vocabulary
  • hinders discovery of novel functions.

Purpose of the Study:

  • To explore the expansion of the natural protein repertoire.
  • To design novel, customized proteins beyond existing natural limitations.
  • To introduce a new subfield, Evolutionary Algorithms Simulating Molecular Evolution (EASME).

Main Methods:

  • Integration of evolutionary algorithms, machine learning, and bioinformatics.
  • Application of DNA string representations and biologically accurate molecular evolution.
  • Utilization of bioinformatics-informed fitness functions to guide protein design.

Main Results:

  • Development of a computational framework for designing novel proteins.
  • Demonstration of the potential to create customized proteins with desired functions.
  • Establishment of Evolutionary Algorithms Simulating Molecular Evolution (EASME) as a viable subfield.

Conclusions:

  • Computational evolution offers a powerful approach to expand the protein repertoire.
  • Designer proteins can be created to fulfill specific, potentially extinct or novel, functions.
  • EASME provides a robust methodology for advancing protein engineering and discovery.