Literature DB >> 25767296

Forced evolution in silico by artificial transposons and their genetic operators: The ant navigation problem.

Leonid Zamdborg1, David M Holloway2, Juan J Merelo3, Vladimir F Levchenko4, Alexander V Spirov5.   

Abstract

Modern evolutionary computation utilizes heuristic optimizations based upon concepts borrowed from the Darwinian theory of natural selection. Their demonstrated efficacy has reawakened an interest in other aspects of contemporary biology as an inspiration for new algorithms. However, amongst the many excellent candidates for study, contemporary models of biological macroevolution attract special attention. We believe that a vital direction in this field must be algorithms that model the activity of "genomic parasites", such as transposons, in biological evolution. Many evolutionary biologists posit that it is the co-evolution of populations with their genomic parasites that permits the high efficiency of evolutionary searches found in the living world. This publication is our first step in the direction of developing a minimal assortment of algorithms that simulate the role of genomic parasites. Specifically, we started in the domain of genetic algorithms (GA) and selected the Artificial Ant Problem as a test case. This navigation problem is widely known as a classical benchmark test and possesses a large body of literature. We add new objects to the standard toolkit of GA - artificial transposons and a collection of operators that operate on them. We define these artificial transposons as a fragment of an ant's code with properties that cause it to stand apart from the rest. The minimal set of operators for transposons is a transposon mutation operator, and a transposon reproduction operator that causes a transposon to multiply within the population of hosts. An analysis of the population dynamics of transposons within the course of ant evolution showed that transposons are involved in the processes of propagation and selection of blocks of ant navigation programs. During this time, the speed of evolutionary search increases significantly. We concluded that artificial transposons, analogous to real transposons, are truly capable of acting as intelligent mutators that adapt in response to an evolutionary problem in the course of co-evolution with their hosts.

Entities:  

Keywords:  animats; ant pathfinder; artificial transposons; evolutionary computations; genetic algorithms

Year:  2015        PMID: 25767296      PMCID: PMC4353400          DOI: 10.1016/j.ins.2015.02.012

Source DB:  PubMed          Journal:  Inf Sci (N Y)        ISSN: 0020-0255            Impact factor:   6.795


  19 in total

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Journal:  Nat Rev Genet       Date:  2001-08       Impact factor: 53.242

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Review 5.  Guidelines: From artificial evolution to computational evolution: a research agenda.

Authors:  Wolfgang Banzhaf; Guillaume Beslon; Steffen Christensen; James A Foster; François Képès; Virginie Lefort; Julian F Miller; Miroslav Radman; Jeremy J Ramsden
Journal:  Nat Rev Genet       Date:  2006-08-08       Impact factor: 53.242

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Authors:  J Brosius
Journal:  Science       Date:  1991-02-15       Impact factor: 47.728

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Authors:  A Agrawal; Q M Eastman; D G Schatz
Journal:  Nature       Date:  1998-08-20       Impact factor: 49.962

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Authors:  E R Lozovskaya; D L Hartl; D A Petrov
Journal:  Curr Opin Genet Dev       Date:  1995-12       Impact factor: 5.578

Review 9.  Using evolutionary computations to understand the design and evolution of gene and cell regulatory networks.

Authors:  Alexander Spirov; David Holloway
Journal:  Methods       Date:  2013-05-30       Impact factor: 3.608

10.  Impact of Alu repeats on the evolution of human p53 binding sites.

Authors:  Feng Cui; Michael V Sirotin; Victor B Zhurkin
Journal:  Biol Direct       Date:  2011-01-06       Impact factor: 4.540

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  4 in total

1.  Forced evolution in silico by artificial transposons and their genetic operators: The ant navigation problem.

Authors:  Leonid Zamdborg; David M Holloway; Juan J Merelo; Vladimir F Levchenko; Alexander V Spirov
Journal:  Inf Sci (N Y)       Date:  2015-06-10       Impact factor: 6.795

2.  Evolutionary Design of Gene Networks: Forced Evolution by Genomic Parasites.

Authors:  A V Spirov; E A Zagriychuk; D M Holloway
Journal:  Parallel Process Lett       Date:  2014-06

3.  In silico evolution of gene cooption in pattern-forming gene networks.

Authors:  Alexander V Spirov; Marat A Sabirov; David M Holloway
Journal:  ScientificWorldJournal       Date:  2012-12-25

4.  In vivo, in vitro and in silico: an open space for the development of microbe-based applications of synthetic biology.

Authors:  Antoine Danchin
Journal:  Microb Biotechnol       Date:  2021-09-27       Impact factor: 5.813

  4 in total

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