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Bioinformatics Advance Access published online on February 10, 2004
Bioinformatics, doi:10.1093/bioinformatics/bth074
Bioinformatics © Oxford University Press 2004; all rights reserved
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Received April 22, 2003
Revised November 16, 2003
Accepted November 17, 2003

Article

Growing genetic regulatory networks from seed genes

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

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

* To whom correspondence should be addressed. E-mail: e-dougherty{at}tamu.edu.


  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, here 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 data set.

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.


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