Gene regulatory network inference: Data integration in dynamic models--A review

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@Article{Hecker200986,
  author =       "Michael Hecker and Sandro Lambeck and 
                 Susanne Toepfer and Eugene {van Someren} and Reinhard Guthke",
  title =        "Gene regulatory network inference: Data integration in
                 dynamic models--A review",
  journal =      "Biosystems",
  volume =       "96",
  number =       "1",
  pages =        "86--103",
  year =         "2009",
  ISSN =         "0303-2647",
  DOI =          "doi:10.1016/j.biosystems.2008.12.004",
  URL =          "http://www.sciencedirect.com/science/article/B6T2K-4V7MSTS-1/2/db669ac3459da19bab3535dc038303d5",
  keywords =     "genetic algorithms, genetic programming, Systems
                 biology, Reverse engineering, Biological modelling,
                 Knowledge integration",
  abstract =     "Systems biology aims to develop mathematical models of
                 biological systems by integrating experimental and
                 theoretical techniques. During the last decade, many
                 systems biological approaches that base on genome-wide
                 data have been developed to unravel the complexity of
                 gene regulation. This review deals with the
                 reconstruction of gene regulatory networks (GRNs) from
                 experimental data through computational methods.
                 Standard GRN inference methods primarily use gene
                 expression data derived from microarrays. However, the
                 incorporation of additional information from
                 heterogeneous data sources, e.g. genome sequence and
                 protein-DNA interaction data, clearly supports the
                 network inference process. This review focuses on
                 promising modelling approaches that use such diverse
                 types of molecular biological information. In
                 particular, approaches are discussed that enable the
                 modelling of the dynamics of gene regulatory systems.
                 The review provides an overview of common modelling
                 schemes and learning algorithms and outlines current
                 challenges in GRN modelling.",
  notes =        "survey",
}

Genetic Programming entries for Michael Hecker Sandro Lambeck Susanne Toepfer Eugene van Someren Reinhard Guthke

Citations