Computing with artificial gene regulatory networks

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@InCollection{lones2016computing,
  author =       "Michael A. Lones",
  title =        "Computing with artificial gene regulatory networks",
  booktitle =    "Evolutionary Computation in Gene Regulatory Network
                 Research",
  year =         "2016",
  editor =       "Hitoshi Iba and Nasimul Noman",
  chapter =      "15",
  pages =        "398-",
  month =        mar,
  publisher =    "John Wiley \& Sons",
  keywords =     "genetic algorithms, genetic programming, artificial
                 gene regulatory network, biological gene regulatory
                 network, computational models",
  isbn13 =       "978-1-118-91151-8",
  URL =          "http://eu.wiley.com/WileyCDA/WileyTitle/productCd-1118911512,subjectCd-LSG0.html",
  DOI =          "doi:10.1002/9781119079453.ch15",
  size =         "27 pages",
  abstract =     "Gene regulatory networks (GRNs) are the fundamental
                 mechanisms through which biological organisms control
                 their growth, their dynamical behaviour, their
                 interaction with their environment, and which underlie
                 much of the complexity in the biosphere. This chapter
                 reviews current understanding of artificial gene
                 regulatory network (AGRN), discussing what is known
                 about their computational properties, detailing how
                 they have been applied to computational problems, and
                 speculating about how they may be used in the future.
                 It discusses what is known about biological GRNs, and
                 the implications this has for the design of AGRNs. The
                 chapter presents the different motivations behind the
                 development of AGRN models. It also discusses the
                 modelling decisions that have to be made when
                 developing AGRN models. AGRNs give the opportunity to
                 explore analogous behaviors within a more general
                 setting, which, in turn, might lead to a better
                 understanding of the general properties of GRNs.",
}

Genetic Programming entries for Michael A Lones

Citations