Reverse engineering of metabolic pathways from observed data by means of genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.1944

@InProceedings{koza:2000:ICSB,
  author =       "John R. Koza and William Mydlowec and Guido Lanza and 
                 Jessen Yu and Martin A. Keane",
  title =        "Reverse engineering of metabolic pathways from
                 observed data by means of genetic programming",
  booktitle =    "First International Conference on Systems Biology
                 (ICSB)",
  year =         "2000",
  address =      "Tokyo",
  month =        "14-16 " # nov,
  organisation = "Japan Society for Bioinformatics",
  keywords =     "genetic algorithms, genetic programming, Biology,
                 metabolic pathways, reverse engineering",
  URL =          "http://www.genetic-programming.com/jkpdf/icsb2000metabolic.pdf",
  abstract =     "Recent work has demonstrated that genetic programming
                 is capable of automatically creating complex networks
                 and structures (e.g., analog electrical circuits,
                 controllers, and antennas) whose behavior is governed
                 by linear and non-linear differential equations and
                 whose behavior matches prespecified data values. The
                 concentrations of substances (substrates, products, and
                 catalysts) participating in networks of chemical
                 reactions are described by non-linear continuous-time
                 differential equations (e.g., Michaelis-Menten
                 equations). This paper demonstrates that it is possible
                 to automatically create (reverse engineer) a network of
                 chemical reactions from observed time-domain data.
                 Genetic programming starts with observed time-domain
                 concentrations of substances and automatically creates
                 both the topology and sizing (i.e., the rates of each
                 reaction) of a network whose behavior matches observed
                 time-domain data. Specifically, genetic programming
                 automatically created a metabolic pathway involving
                 four chemical reactions that consume glycerol and fatty
                 acids as input, used ATP as a cofactor, and produced
                 diacyl-glycerol as the final product. The metabolic
                 pathway was created from 270 data points. The
                 automatically created metabolic pathway contains three
                 key topological features, including an internal
                 feedback loop, a bifurcation point where one substance
                 is distributed to two different reactions, and an
                 accumulation point where one substance is accumulated
                 from two sources. The topology and sizing of the entire
                 metabolic pathway was automatically created using only
                 the time-domain concentration values of diacyl-glycerol
                 (the final product).",
  notes =        "ICSB-2000 E-CELL. Population size 100000.

                 This paper was subsequently published in 2001 as
                 chapter in book (edited by Kitano)
                 \cite{koza:2001:FSB}",
}

Genetic Programming entries for John Koza William J Mydlowec Guido Lanza Jessen Yu Martin A Keane