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Bioinformatics Advance Access originally published online on February 10, 2004
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Bioinformatics 20(8) © Oxford University Press 2004; all rights reserved.

Growing genetic regulatory networks from seed genes

Ronaldo F. Hashimoto 1,2, Seungchan Kim 3, Ilya Shmulevich 4, Wei Zhang 4, Michael L. Bittner 3 and Edward R. Dougherty 1,4,*

1 Department of Electrical Engineering, Texas A&M University, College Station, TX, USA 77843, USA, 2 Departmento de Ciencia de Computacao, Universidade de Sao Paulo, Sao Paulo, Brazil 05508-090, 3 Translational Genomics Research Institute, Phoenix, AZ, 85004, USA and 4 Cancer Genomics Laboratory, Department of Pathology, University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA

Received on April 22, 2003; revised on November 16, 2003; accepted on November 17, 2003
Advance Access Publication February 10, 2004

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

Contact: e-dougherty{at}tamu.edu

* To whom correspondence should be addressed.


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