Identification of biochemical networks by S-tree based genetic programming

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@Article{Cho:2006:B,
  author =       "Dong-Yeon Cho and Kwang-Hyun Cho and 
                 Byoung-Tak Zhang",
  title =        "Identification of biochemical networks by S-tree based
                 genetic programming",
  journal =      "Bioinformatics",
  year =         "2006",
  volume =       "22",
  number =       "13",
  pages =        "1631--1640",
  month =        jul,
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1367-4803",
  DOI =          "doi:10.1093/bioinformatics/btl122",
  abstract =     "Motivation: Most previous approaches to model
                 biochemical networks have focused either on the
                 characterisation of a network structure with a number
                 of components or on the estimation of kinetic
                 parameters of a network with a relatively small number
                 of components. For system-level understanding, however,
                 we should examine both the interactions among the
                 components and the dynamic behaviours of the
                 components. A key obstacle to this simultaneous
                 identification of the structure and parameters is the
                 lack of data compared with the relatively large number
                 of parameters to be estimated. Hence, there are many
                 plausible networks for the given data, but most of them
                 are not likely to exist in the real system. Results: We
                 propose a new representation named S-trees for both the
                 structural and dynamical modelling of a biochemical
                 network within a unified scheme. We further present
                 S-tree based genetic programming to identify the
                 structure of a biochemical network and to estimate the
                 corresponding parameter values at the same time. While
                 other evolutionary algorithms require additional
                 techniques for sparse structure identification, our
                 approach can automatically assemble the sparse
                 primitives of a biochemical network in an efficient
                 way. We evaluate our algorithm on the dynamic profiles
                 of an artificial genetic network. In 20 trials for four
                 settings, we obtain the true structure and their
                 relative squared errors are less than 5percent
                 regardless of releasing constraints about structural
                 sparseness. In addition, we confirm that the proposed
                 algorithm is robust within 10percent noise ratio.
                 Furthermore, the proposed approach ensures a reasonable
                 estimate of a real yeast fermentation pathway. The
                 comparatively less important connections with non-zero
                 parameters can be detected even though their orders are
                 below 10**2 (??). To demonstrate the usefulness of the
                 proposed algorithm for real experimental biological
                 data, we provide an additional example on the
                 transcriptional network of SOS response to DNA damage
                 in Escherichia coli. We confirm that the proposed
                 algorithm can successfully identify the true structure
                 except only one relation. Availability: The executable
                 program and data are available from the authors upon
                 request.",
  notes =        "C The Author 2006",
}

Genetic Programming entries for Dong-Yeon Cho Kwang-Hyun Cho Byoung-Tak Zhang

Citations