Genetic Algorithms and Their Application to In Silico Evolution of Genetic Regulatory Networks View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2010-08-17

AUTHORS

Johannes F. Knabe , Katja Wegner , Chrystopher L. Nehaniv , Maria J. Schilstra

ABSTRACT

A genetic algorithm (GA) is a procedure that mimics processes occurring in Darwinian evolution to solve computational problems. A GA introduces variation through "mutation" and "recombination" in a "population" of possible solutions to a problem, encoded as strings of characters in "genomes," and allows this population to evolve, using selection procedures that favor the gradual enrichment of the gene pool with the genomes of the "fitter" individuals. GAs are particularly suitable for optimization problems in which an effective system design or set of parameter values is sought.In nature, genetic regulatory networks (GRNs) form the basic control layer in the regulation of gene expression levels. GRNs are composed of regulatory interactions between genes and their gene products, and are, inter alia, at the basis of the development of single fertilized cells into fully grown organisms. This paper describes how GAs may be applied to find functional regulatory schemes and parameter values for models that capture the fundamental GRN characteristics. The central ideas behind evolutionary computation and GRN modeling, and the considerations in GA design and use are discussed, and illustrated with an extended example. In this example, a GRN-like controller is sought for a developmental system based on Lewis Wolpert's French flag model for positional specification, in which cells in a growing embryo secrete and detect morphogens to attain a specific spatial pattern of cellular differentiation. More... »

PAGES

297-321

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-1-60761-842-3_19

DOI

http://dx.doi.org/10.1007/978-1-60761-842-3_19

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1005294987

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/20835807


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