Inference of gene regulatory networks using S-system: a unified approach

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

@Article{Wang:2010:ietSB,
  author =       "H. Wang and L. Qian and E. Dougherty",
  title =        "Inference of gene regulatory networks using S-system:
                 a unified approach",
  journal =      "IET Systems Biology",
  year =         "2010",
  month =        mar,
  volume =       "4",
  number =       "2",
  pages =        "145--156",
  URL =          "http://wanghaixin.com/papers/ssystem.pdf",
  DOI =          "doi:10.1049/iet-syb.2008.0175",
  ISSN =         "1751-8849",
  abstract =     "With the increased availability of DNA microarray
                 time-series data, it is possible to discover dynamic
                 gene regulatory networks (GRNs). S-system is a
                 promising model to capture the rich dynamics of GRNs.
                 However, owing to the complexity of the inference
                 problem and limited number of available data comparing
                 to the number of unknown kinetic parameters, S-system
                 can only be applied to a very small GRN with few
                 parameters. This significantly limits its applications.
                 A unified approach to infer GRNs using the S-system
                 model is proposed. In order to discover the structure
                 of large-scale GRNs, a simplified S-system model is
                 proposed that enables fast parameter estimation to
                 determine the major gene interactions. If a detailed
                 S-system model is desirable for a subset of genes, a
                 two-step method is proposed where the range of the
                 parameters will be determined first using genetic
                 programming and recursive least square estimation. Then
                 the mean values of the parameters will be estimated
                 using a multi-dimensional optimisation algorithm. Both
                 the downhill simplex algorithm and modified Powell
                 algorithm are tested for multi-dimensional
                 optimisation. A 50-dimensional synthetic model with 51
                 parameters for each gene is tested for the
                 applicability of the simplified S-system model. In
                 addition, real measurement data pertaining to yeast
                 protein synthesis are used to demonstrate the
                 effectiveness of the proposed two-step method to
                 identify the detailed interactions among genes in small
                 GRNs.",
  keywords =     "genetic algorithms, genetic programming,
                 50-dimensional synthetic model, DNA microarray
                 time-series, downhill simplex algorithm, dynamic gene
                 regulatory networks, gene interactions, kinetic
                 parameters, modified Powell algorithm,
                 multi-dimensional optimisation algorithm, parameter
                 estimation, recursive least square estimation,
                 simplified S-system model, two-step method, yeast
                 protein synthesis, genetics, lab-on-a-chip, least
                 squares approximations, molecular biophysics, proteins,
                 recursive estimation",
  notes =        "Also known as \cite{5430862}",
}

Genetic Programming entries for Haixin Wang Lijun Qian Edward R Dougherty

Citations