Naturally selecting solutions The use of genetic algorithms in bioinformatics

For decades, computer scientists have looked to nature for biologically inspired solutions to computational problems; ranging from robotic control to scheduling optimization. Paradoxically, as we move deeper into the post-genomics era, the reverse is occurring, as biologists and bioinformaticians lo...

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Bibliographic Details
Published in:Bioengineered Vol. 4; no. 5; pp. 266 - 278
Main Authors: Manning, Timmy, Sleator, Roy D, Walsh, Paul
Format: Journal Article
Language:English
Published: United States Taylor & Francis 01.09.2013
Landes Bioscience
Subjects:
Algorithms
Computational Biology - methods
Computer Simulation
Crossing Over, Genetic
genetic algorithm
Genetic Fitness
Genotype
Models, Genetic
multiple sequence alignment
optimization
Phenotype
protein structure prediction
Selection, Genetic
Sequence Alignment
genetic algorithm
optimization
protein structure prediction
multiple sequence alignment
ISSN:2165-5979, 2165-5987, 2165-5987
Online Access:Get full text
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Summary:For decades, computer scientists have looked to nature for biologically inspired solutions to computational problems; ranging from robotic control to scheduling optimization. Paradoxically, as we move deeper into the post-genomics era, the reverse is occurring, as biologists and bioinformaticians look to computational techniques, to solve a variety of biological problems. One of the most common biologically inspired techniques are genetic algorithms (GAs), which take the Darwinian concept of natural selection as the driving force behind systems for solving real world problems, including those in the bioinformatics domain. Herein, we provide an overview of genetic algorithms and survey some of the most recent applications of this approach to bioinformatics based problems.
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ISSN:2165-5979
2165-5987
2165-5987
DOI:10.4161/bioe.23041