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

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...
Genetic Screens02:46

Genetic Screens

Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which result in visible changes...
Bioreactor Controls-III01:22

Bioreactor Controls-III

Strain improvement is a foundational strategy in industrial microbiology aimed at maximizing microbial productivity, particularly because natural isolates typically yield commercially valuable products in very low concentrations. Although optimizing the culture medium and environmental conditions can improve yields, these adjustments are inherently limited by the organism’s genetic potential. As a result, the focus shifts toward genetic modifications to enhance biosynthetic capacity. The...
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...

You might also read

Related Articles

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

Sort by
Same author

Bioinformatic assessment of the potential amyloidogenicity of the human and evolutionarily more ancient proteomes.

The Biochemical journal·2026
Same author

Targeting KRAS codon 13 mutations using direct combination approaches in non-small cell lung cancer.

Cancer discovery·2026
Same author

Rapid antimicrobial susceptibility testing in bloodstream infections: current landscape and emerging technologies.

The Analyst·2026
Same author

Genetic Analysis of Acral Melanomas From Southern African Patients.

Pigment cell & melanoma research·2026
Same author

Surface cues shape procoagulant properties of amyloidogenic microclots.

Cell death & disease·2026
Same author

Methylation profiling of normal tissue adjacent to breast tumors reveals two distinct groups with divergent tumor microenvironment features.

NPJ breast cancer·2026

Related Experiment Video

Updated: Jun 27, 2026

Mutagenesis and Functional Selection Protocols for Directed Evolution of Proteins in E. coli
09:01

Mutagenesis and Functional Selection Protocols for Directed Evolution of Proteins in E. coli

Published on: March 16, 2011

In silico modelling of directed evolution: Implications for experimental design and stepwise evolution.

David C Wedge1, William Rowe, Douglas B Kell

  • 1Manchester Interdisciplinary Biocentre, University of Manchester, 131 Princess Street, Manchester, M1 7ND, UK. david.wedge@manchester.ac.uk

Journal of Theoretical Biology
|December 17, 2008
PubMed
Summary

Directed evolution (DE) in silico performs best with high selection pressure and moderate mutation rates. Evolutionary algorithms outperform model-based methods on complex landscapes.

More Related Videos

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
08:58

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

Published on: October 17, 2025

Directed Evolution Method in Saccharomyces cerevisiae: Mutant Library Creation and Screening
10:50

Directed Evolution Method in Saccharomyces cerevisiae: Mutant Library Creation and Screening

Published on: April 1, 2016

Related Experiment Videos

Last Updated: Jun 27, 2026

Mutagenesis and Functional Selection Protocols for Directed Evolution of Proteins in E. coli
09:01

Mutagenesis and Functional Selection Protocols for Directed Evolution of Proteins in E. coli

Published on: March 16, 2011

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
08:58

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

Published on: October 17, 2025

Directed Evolution Method in Saccharomyces cerevisiae: Mutant Library Creation and Screening
10:50

Directed Evolution Method in Saccharomyces cerevisiae: Mutant Library Creation and Screening

Published on: April 1, 2016

Area of Science:

  • Computational Biology
  • Evolutionary Computation
  • Bioinformatics

Background:

  • Directed evolution (DE) is a powerful method for protein engineering.
  • In silico modeling of DE can optimize experimental design.
  • Understanding the impact of genetic algorithm parameters is crucial for DE success.

Purpose of the Study:

  • To model directed evolution (DE) in silico using genetic algorithms.
  • To analyze the effects of mutation rate, crossover, and selection pressure on DE performance.
  • To compare DE with model-based approaches.

Main Methods:

  • Utilized the NK fitness landscape model to simulate evolutionary processes.
  • Investigated a range of K values representing landscape epistasis.
  • Compared high- and low-throughput evolution modes.
  • Compared genetic algorithms with a literature model-based approach.

Main Results:

  • Optimal DE configuration involves high selection pressure and moderately high mutation rates.
  • Crossover offers benefits primarily on less rugged landscapes.
  • In silico DE performance showed minimal differences across epistasis levels and throughput modes for short runs.
  • Evolutionary techniques outperformed model-based approaches on all but the smoothest landscapes.

Conclusions:

  • In silico directed evolution is robust across various landscape epistasis levels and throughput modes for typical run durations.
  • Specific parameter tuning (selection pressure, mutation rate) is key for optimizing DE performance.
  • Genetic algorithms provide a superior approach to model-based methods for complex optimization problems in silico.