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Related Concept Videos

Conservative Site-specific Recombination and Phase Variation02:53

Conservative Site-specific Recombination and Phase Variation

Because the DNA segments are cut and reorganized in a direction-specific manner, site-specific recombination has emerged as an efficient genetic engineering technique. Flippase and Cyclization recombinases or Flp and Cre, respectively, are two members of the tyrosine recombinase family derived from bacteriophages, that are used to mediate site-specific DNA insertions, deletions, and targeted expression of proteins in mammalian cell lines.
The recognition sites for Cre recombinase called LoxP...
Gene Conversion02:08

Gene Conversion

Other than maintaining genome stability via DNA repair, homologous recombination plays an important role in diversifying the genome. In fact, the recombination of sequences forms the molecular basis of genomic evolution. Random and non-random permutations of genomic sequences create a library of new amalgamated sequences. These newly formed genomes can determine the fitness and survival of cells. In bacteria, homologous and non-homologous types of recombination lead to the evolution of new...
Gene Conversion02:08

Gene Conversion

Other than maintaining genome stability via DNA repair, homologous recombination plays an important role in diversifying the genome. In fact, the recombination of sequences forms the molecular basis of genomic evolution. Random and non-random permutations of genomic sequences create a library of new amalgamated sequences. These newly formed genomes can determine the fitness and survival of cells. In bacteria, homologous and non-homologous types of recombination lead to the evolution of new...
Overview of Transposition and Recombination02:13

Overview of Transposition and Recombination

Transposons make up a significant part of genomes of various organisms. Therefore, it is believed that transposition played a major evolutionary role in speciation by changing genome sizes and modifying gene expression patterns. For example, in bacteria, transposition can lead to conferring antibiotic resistance. Movement of transposable elements within the genetic pool of pathogenic bacteria can aid in transfer of antibiotic-resistant genetic elements. In eukaryotes, transposons can carry out...
Exon Recombination02:32

Exon Recombination

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 has three reading...
Viral Recombination00:57

Viral Recombination

Cells are sometimes infected by more than one virus at once. When two viruses disassemble to expose their genomes for replication in the same cell, similar regions of their genomes can pair together and exchange sequences in a process called recombination. Alternatively, viruses with segmented genomes can swap segments in a process called reassortment.

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Related Experiment Video

Updated: Jun 15, 2026

Preparation of the Mgm101 Recombination Protein by MBP-based Tagging Strategy
11:40

Preparation of the Mgm101 Recombination Protein by MBP-based Tagging Strategy

Published on: June 25, 2013

Geometrical recombination operators for real-coded evolutionary MCMCs.

Mădălina M Drugan1, Dirk Thierens

  • 1Department of Information and Computing Sciences, Utrecht University, 3508 TB Utrecht, The Netherlands. madalina@cs.uu.nl

Evolutionary Computation
|March 10, 2010
PubMed
Summary
This summary is machine-generated.

Evolutionary Markov chain Monte Carlo (EMCMC) algorithms enhance sampling from complex distributions. By using recombination operators, EMCMCs improve information exchange within sample populations, outperforming standard MCMC methods on specific target distributions.

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Subcloning Plus Insertion (SPI) - A Novel Recombineering Method for the Rapid Construction of Gene Targeting Vectors
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Related Experiment Videos

Last Updated: Jun 15, 2026

Preparation of the Mgm101 Recombination Protein by MBP-based Tagging Strategy
11:40

Preparation of the Mgm101 Recombination Protein by MBP-based Tagging Strategy

Published on: June 25, 2013

Subcloning Plus Insertion (SPI) - A Novel Recombineering Method for the Rapid Construction of Gene Targeting Vectors
09:02

Subcloning Plus Insertion (SPI) - A Novel Recombineering Method for the Rapid Construction of Gene Targeting Vectors

Published on: January 8, 2015

Area of Science:

  • Computational statistics
  • Artificial intelligence
  • Machine learning

Background:

  • Markov chain Monte Carlo (MCMC) methods are essential for sampling from complex probability distributions.
  • Standard MCMC algorithms can struggle with multi-dimensional spaces and identifying structures.

Purpose of the Study:

  • To introduce and investigate Evolutionary Markov chain Monte Carlo (EMCMC) algorithms.
  • To improve the efficiency and effectiveness of sampling from multi-dimensional real-coded spaces.

Main Methods:

  • Developing EMCMC algorithms that utilize a population of samples.
  • Implementing recombination operators (e.g., translation, rotation) for information exchange and adaptation.
  • Analyzing the computational complexity of recombination operators.

Main Results:

  • EMCMC algorithms effectively exchange information and preserve commonalities in sample populations.
  • Recombination operators adapt population samples to the proposal distribution by discovering search space structures.
  • EMCMCs demonstrate superior performance compared to standard MCMCs for distributions with inherent dimensional relationships.

Conclusions:

  • EMCMCs offer a novel approach to enhance MCMC sampling in complex, multi-dimensional spaces.
  • Recombination strategies are key to improving sampling efficiency and accuracy in specific scenarios.
  • This work provides a foundation for further research into evolutionary computation for statistical inference.