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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1 Department of Electrical Engineering, Texas A&M University, College Station, TX; Departmento de Ciencia de Computacao, Universidade de Sao Paulo, Sao Paulo, Brazil
* To whom correspondence should be addressed. E-mail: e-dougherty{at}tamu.edu.
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.
Revised November 16, 2003
Accepted November 17, 2003
Article
Growing genetic regulatory networks from seed genes
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
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