Growing genetic regulatory networks from seed genes. Academic Article uri icon


  • 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:

published proceedings

  • Bioinformatics

altmetric score

  • 3.5

author list (cited authors)

  • Hashimoto, R. F., Kim, S., Shmulevich, I., Zhang, W., Bittner, M. L., & Dougherty, E. R.

citation count

  • 82

complete list of authors

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

publication date

  • May 2004