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

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Computational Biology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 673))

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.

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Abbreviations

CPM:

Cellular Potts model

CRM:

Cis-regulatory module

EC:

Evolutionary computing

GA:

Genetic algorithm

PR:

Gene product (protein or RNA)

GRN:

Genetic regulatory network

TF:

Trans-regulatory factor

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Author information

Authors and Affiliations

  1. Biological and Neural Computation Laboratory and Adaptive Systems Research Group, STRI, University of Hertfordshire, Hatfield, Hertfordshire, UK

    Johannes F. Knabe & Chrystopher L. Nehaniv

  2. Albstadt-Sigmaringen University, Sigmaringen, Germany

    Katja Wegner

  3. Biological and Neural Computation Laboratory, University of Hertfordshire, Hatfield, Hertfordshire, UK

    Maria J. Schilstra

Authors
  1. Johannes F. Knabe
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  2. Katja Wegner
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  3. Chrystopher L. Nehaniv
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  4. Maria J. Schilstra
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Corresponding author

Correspondence to Johannes F. Knabe .

Editor information

Editors and Affiliations

  1. The Rockefeller University, York Avenue 1230, New York, 10065, New York, USA

    David Fenyö

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Knabe, J.F., Wegner, K., Nehaniv, C.L., Schilstra, M.J. (2010). Genetic Algorithms and Their Application to In Silico Evolution of Genetic Regulatory Networks. In: Fenyö, D. (eds) Computational Biology. Methods in Molecular Biology, vol 673. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-60761-842-3_19

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  • DOI: https://doi.org/10.1007/978-1-60761-842-3_19

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  • Publisher Name: Humana Press, Totowa, NJ

  • Print ISBN: 978-1-60761-841-6

  • Online ISBN: 978-1-60761-842-3

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