Identifying Gene Regulatory Network as Differential Equation by Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.2031

@Article{Sakamoto:2000:GI,
  author =       "Erina Sakamoto and Hitoshi Iba",
  title =        "Identifying Gene Regulatory Network as Differential
                 Equation by Genetic Programming",
  journal =      "Genome Informatics",
  year =         "2000",
  volume =       "11",
  pages =        "281--283",
  keywords =     "genetic algorithms, genetic programming, genome
                 pathway, interaction analysis, inverse problem",
  URL =          "http://www.jsbi.org/journal/GIW00/GIW00P024.pdf",
  abstract =     "1 Introduction

                 This paper proposes an evolutionary method of
                 identifying the gene regulatory network represented as
                 a differential equation system. As the technology in
                 DNA micro arrays has developed, large quantities of
                 gene's expression data are becoming more available. As
                 a result, it is essential to get information as to the
                 gene regulatory network from the observed data of
                 gene's expression. Among many proposed models to
                 describe a gene network, we have chosen the
                 differential equation system since it can represent
                 complex relations among components. In the previous
                 studies \cite{Tominaga:2000:GECCO}, the form of the
                 differential equation is being fixed during the
                 learning so that the ultimate goal of the
                 identification is to optimise parameters, i.e.,
                 coefficients, in the fixed equation. On the other hand,
                 for the sake of the flexibility of the model, we allow
                 an arbitrary form of functions in the right-hand side
                 of the differential equation (eq. (1)).

                 dXi /dt = fi (X1 , X2 , . . . , Xn ) (1)

                 For this purpose, we use Genetic Programming (GP) and
                 establish a GP-based identification of time series in
                 terms of differential equation systems.",
  notes =        "Presented at the Genome Informatics Workshop 2000
                 December 18-19, 2000, Garden Hall, Yebisu Garden Place,
                 Tokyo, Japan

                 Published as: A.K. Dunker, A. Konagaya, S. Miyano, and
                 T.Takagi (eds.) {"}Genome Informatics 2000{"} Universal
                 Academy Press, Tokyo, 2000",
}

Genetic Programming entries for Erina Sakamoto Hitoshi Iba