Growing genetic regulatory networks from seed genes

  • Authors:

  • Ronaldo F. Hashimoto;Seungchan Kim;Ilya Shmulevich;Wei Zhang;Michael L. Bittner;Edward R. Dougherty

  • Affiliations:

  • Department of Electrical Engineering, Texas A&M University, College Station, TX, USA 77843, USA,;Translational Genomics Research Institute, Phoenix, AZ, 85004, USA;Cancer Genomics Laboratory, Department of Pathology, University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA;Cancer Genomics Laboratory, Department of Pathology, University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA;Translational Genomics Research Institute, Phoenix, AZ, 85004, USA;Department of Electrical Engineering, Texas A&M University, College Station, TX, USA 77843, USA,

  • Venue:
  • Bioinformatics
  • Year:
  • 2004

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Abstract

Motivation: A number of models have been proposed for genetic regulatory networks. In principle, a network may contain any number of genes, so long as data are available to make inferences about their relationships. Nevertheless, there are two important reasons why the size of a constructed network should be limited. Computationally and mathematically, it is more feasible to model and simulate a network with a small number of genes. In addition, it is more likely that a small set of genes maintains a specific core regulatory mechanism. Results: Subnetworks are constructed in the context of a directed graph by beginning with a seed consisting of one or more genes believed to participate in a viable subnetwork. Functionalities and regulatory relationships among seed genes may be partially known or they may simply be of interest. Given the seed, we iteratively adjoin new genes in a manner that enhances subnetwork autonomy. The algorithm is applied using both the coefficient of determination and the Boolean-function influence among genes, and it is illustrated using a glioma gene-expression dataset. Availability: Software for the seed-growing algorithm will be available at the website for Probabilistic Boolean Networks: http://www2.mdanderson.org/app/ilya/PBN/PBN.htm