Reverse engineering of biochemical equations from time-course data by means of genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{Sugimoto:2005:BS,
  author =       "Masahiro Sugimoto and Shinichi Kikuchia and 
                 Masaru Tomita",
  title =        "Reverse engineering of biochemical equations from
                 time-course data by means of genetic programming",
  journal =      "Biosystems",
  year =         "2005",
  volume =       "80",
  pages =        "155--164",
  number =       "2",
  abstract =     "Increased research aimed at simulating biological
                 systems requires sophisticated parameter estimation
                 methods. All current approaches, including genetic
                 algorithms, need pre-existing equations to be
                 functional. A generalised approach to predict not only
                 parameters but also biochemical equations from only
                 observable time-course information must be developed
                 and a computational method to generate arbitrary
                 equations without knowledge of biochemical reaction
                 mechanisms must be developed. We present a technique to
                 predict an equation using genetic programming. Our
                 technique can search topology and numerical parameters
                 of mathematical expression simultaneously. To improve
                 the search ability of numeric constants, we added
                 numeric mutation to the conventional procedure. As case
                 studies, we predicted two equations of enzyme-catalyzed
                 reactions regarding adenylate kinase and
                 phosphofructokinase. Our numerical experimental results
                 showed that our approach could obtain correct topology
                 and parameters that were close to the originals. The
                 mean errors between given and simulation-predicted
                 time-courses were 1.6 X 10-5% and 2.0 X 10-3%,
                 respectively. Our equation prediction approach can be
                 applied to identify metabolic reactions from observable
                 time-courses.",
  owner =        "wlangdon",
  URL =          "http://www.sciencedirect.com/science/article/B6T2K-4F1GRJF-1/2/747811cdd13163b25cfb0d65387ea133",
  month =        may,
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1016/j.biosystems.2004.11.003",
}

Genetic Programming entries for Masahiro Sugimoto Shinichi Kikuchia Masaru Tomita

Citations