Models in a Multi-Level Biochemical Network Regulating Pea Flowering

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Stolk:2009:modsim,
  author =       "Jacob Stolk and Jim Hanan",
  title =        "Models in a Multi-Level Biochemical Network Regulating
                 Pea Flowering",
  booktitle =    "www.mssanz.org.au/modsim09",
  year =         "2009",
  editor =       "Roger Braddock",
  pages =        "810--816",
  address =      "Cairns, Australia",
  month =        "13-17 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  annote =       "The Pennsylvania State University CiteSeerX Archives",
  bibsource =    "OAI-PMH server at citeseerx.ist.psu.edu",
  language =     "en",
  oai =          "oai:CiteSeerX.psu:10.1.1.526.2306",
  rights =       "Metadata may be used without restrictions as long as
                 the oai identifier remains attached to it.",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.526.2306",
  URL =          "http://www.mssanz.org.au/modsim09/C1/stolk_C1.pdf",
  size =         "7 pages",
  abstract =     "The Emergent Models methodology (EM) is an adaptive
                 computational method for discovering models of complex
                 systems in computer simulations (Stolk 2005). EM uses
                 machine learning and optimisation algorithms such as
                 genetic programming. Stolk and Hanan (2007) used EM to
                 discover genetic regulatory network models of branching
                 in Pisum sativum (pea). Here EM is used to discover
                 models of genetic and metabolic networks regulating
                 flowering in pea. These models describe multiple levels
                 and components in the whole plant complex system,
                 including genes, intercellular signals, modules and
                 phenotype. Flowering in pea is determined by genes and
                 mobile signals, mediating environmental influences such
                 as photoperiod. Models of biochemical mechanisms
                 explaining flowering time of pea studied here
                 incorporate modules such as a circadian clock, signal
                 processors and switching mechanisms. Each module is a
                 combination of chemical reactions. Three hierarchical
                 system levels are involved: the top level of the whole
                 plant (phenotype); a middle level of modules; a bottom
                 level of chemical reactions. It was hypothesised that
                 models describing each level could be automatically
                 discovered by genetic programming, given data on the
                 next higher level. Discovered models should predict
                 experimental data on gene expression and flowering time
                 of wild type and several mutant pea plants. The purpose
                 of this research",
  notes =        "Figure 2 Circadian clock found by genetic programming.
                 ECJ

                 www.mssanz.org.au/modsim09

                 Dione Complex Systems, Auckland, New Zealand

                 The University of Queensland, Centre for Biological
                 Information Technology",
}

Genetic Programming entries for Jacob Stolk Jim Hanan

Citations