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Exon Recombination

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...

Conservative Site-specific Recombination and Phase Variation

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...

Overview of Transposition and Recombination

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...

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).Mechanisms of Genetic VariationThe original sources of genetic variation are mutations,...

Gene Conversion

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 Conversion

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...

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Related Experiment Video

Updated: Jun 6, 2026

Molecular Evolution of the Tre Recombinase

Molecular Evolution of the Tre Recombinase

Published on: May 29, 2008

Recombination operators and selection strategies for evolutionary Markov Chain Monte Carlo algorithms.

Madalina M Drugan¹, Dirk Thierens

¹Department of Information and Computing Sciences, Utrecht University, PO. Box 80.089, 3508 TB Utrecht, The Netherlands.

Evolutionary Intelligence

|December 15, 2010

Summary

Evolutionary Computation (EC) techniques enhance Markov Chain Monte Carlo (MCMC) sampling. Population-based Evolutionary MCMC (EMCMC) algorithms outperform standard MCMC by sharing information, but careful design is needed to ensure correct sampling.

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Last Updated: Jun 6, 2026

Molecular Evolution of the Tre Recombinase

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Published on: May 29, 2008

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

Area of Science:

Computational Statistics
Evolutionary Computation
Bayesian Inference

Background:

Markov Chain Monte Carlo (MCMC) methods are crucial for sampling from complex probability distributions.
Population-based MCMC variants aim to improve efficiency by running multiple chains concurrently.
Integrating Evolutionary Computation (EC) principles offers a novel approach to enhance MCMC performance.

Purpose of the Study:

To investigate the design and properties of population-based MCMC algorithms using EC techniques, termed Evolutionary MCMC (EMCMC).
To ensure that EMCMC algorithms correctly sample from the target distribution while potentially improving efficiency.
To analyze the impact of EC operators (recombination, selection) on MCMC convergence and detailed balance.

Main Methods:

Developing Evolutionary MCMC (EMCMC) algorithms by incorporating EC operators like recombination and selection into population-based MCMC.
Analytical investigation of the properties required for recombination and acceptance rules in EMCMC.
Experimental validation using examples from discrete search spaces to compare EMCMC with standard MCMC.
Proving conditions for preserving detailed balance in EMCMC to ensure convergence.

Main Results:

EMCMC algorithms can outperform standard MCMC by exploiting shared structures in high-probability states.
Analytical and experimental evidence demonstrates the effectiveness of EMCMC in specific scenarios.
Identified necessary properties for recombination and selection mechanisms in EMCMC.
Demonstrated that certain EC-inspired rules, like elitist acceptance, can lead to incorrect sampling.

Conclusions:

Evolutionary MCMC (EMCMC) offers a promising framework for improving MCMC sampling efficiency.
Careful consideration of EC operator design is essential to maintain the correctness of the target distribution sampling.
Preserving detailed balance is critical for EMCMC convergence, and not all EC techniques are directly transferable without modification.