Integration of Reaction Kinetics Theory and Gene Expression Programming to Infer Reaction Mechanism

Created by W.Langdon from gp-bibliography.bib Revision:1.4031

  author =       "Jason R. White and Ranjan Srivastava",
  title =        "Integration of Reaction Kinetics Theory and Gene
                 Expression Programming to Infer Reaction Mechanism",
  booktitle =    "20th European Conference on the Applications of
                 Evolutionary Computation",
  year =         "2017",
  editor =       "Giovanni Squillero",
  series =       "LNCS",
  volume =       "10199",
  publisher =    "Springer",
  pages =        "53--66",
  address =      "Amsterdam",
  month =        "19-21 " # apr,
  organisation = "Species",
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming, Evolutionary algorithm,
                 Biochemical kinetics, Mechanistic modeling",
  isbn13 =       "978-3-319-55848-6; 978-3-319-55849-3",
  bibdate =      "2017-05-21",
  bibsource =    "DBLP,
  DOI =          "doi:10.1007/978-3-319-55849-3_4",
  abstract =     "Mechanistic mathematical models of biomolecular
                 systems have been used to describe biological phenomena
                 in the hope that one day these models may be used to
                 enhance our fundamental understanding of these
                 phenomena, as well as to optimize and engineer
                 biological systems. An evolutionary algorithm capable
                 of formulating mass action kinetic models of biological
                 systems from time series data sets was developed for a
                 system of n-species. The strategy involved using a gene
                 expression programming (GEP) based approach and
                 heuristics based on chemical kinetic theory. The
                 resulting algorithm was successfully validated by
                 recapitulating a nonlinear model of viral dynamics
                 using only a “noisy” set of time series data. While
                 the system analyzed for this proof-of-principle study
                 was relatively small, the approach presented here is
                 easily parallelizable making it amenable for use with
                 larger systems. Additionally, greater efficiencies may
                 potentially be realized by further taking advantage of
                 the problem domain along with future breakthroughs in
                 computing power and algorithmic advances",
  notes =        "also known as

                 EvoApplications2017 held in conjunction with
                 EuroGP'2017, EvoCOP2017 and EvoMusArt2017

Genetic Programming entries for Jason R White Ranjan Srivastava