Network topology and the evolution of dynamics in an artificial genetic regulatory network model created by whole genome duplication and divergence

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@Article{DwightKuo2006177,
  author =       "P. Dwight Kuo and Wolfgang Banzhaf and Andre Leier",
  title =        "Network topology and the evolution of dynamics in an
                 artificial genetic regulatory network model created by
                 whole genome duplication and divergence",
  journal =      "Biosystems",
  volume =       "85",
  number =       "3",
  pages =        "177--200",
  year =         "2006",
  ISSN =         "0303-2647",
  DOI =          "DOI:10.1016/j.biosystems.2006.01.004",
  URL =          "http://www.sciencedirect.com/science/article/B6T2K-4JVT1VS-1/2/7810c8e58f3e12020b48fe28d9e52097",
  keywords =     "genetic algorithms, genetic programming, Regulatory
                 networks, GRNs, Network motifs, Scale-free,
                 Small-world, Duplication and divergence",
  abstract =     "Topological measures of large-scale complex networks
                 are applied to a specific artificial regulatory network
                 model created through a whole genome duplication and
                 divergence mechanism. This class of networks share
                 topological features with natural transcriptional
                 regulatory networks. Specifically, these networks
                 display scale-free and small-world topology and possess
                 subgraph distributions similar to those of natural
                 networks. Thus, the topologies inherent in natural
                 networks may be in part due to their method of creation
                 rather than being exclusively shaped by subsequent
                 evolution under selection. The evolvability of the
                 dynamics of these networks is also examined by evolving
                 networks in simulation to obtain three simple types of
                 output dynamics. The networks obtained from this
                 process show a wide variety of topologies and numbers
                 of genes indicating that it is relatively easy to
                 evolve these classes of dynamics in this model.",
}

Genetic Programming entries for P Dwight Kuo Wolfgang Banzhaf Andre Leier

Citations