Using a state-space model and location analysis to infer time-delayed regulatory networks

Authors:
Chushin Koh;Fang-Xiang Wu;Gopalan Selvaraj;Anthony J. Kusalik
Affiliations:
Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada;Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK, Canada and Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK, Canada;Plant Biotechnology Institute, National Research Council of Canada, Saskatoon, SK, Canada;Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada and Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK, Canada
Venue:
EURASIP Journal on Bioinformatics and Systems Biology
Year:
2009

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Abstract

Computational gene regulation models provide a means for scientists to draw biological inferences from time-course gene expression data. Based on the state-space approach, we developed a new modeling tool for inferring gene regulatory networks, called time-delayed Gene Regulatory Networks (tdGRNs). tdGRN takes time-delayed regulatory relationships into consideration when developing the model. In addition, a priori biological knowledge from genome-wide location analysis is incorporated into the structure of the gene regulatory network. tdGRN is evaluated on both an artificial dataset and a published gene expression data set. It not only determines regulatory relationships that are known to exist but also uncovers potential new ones. The results indicate that the proposed tool is effective in inferring gene regulatory relationships with time delay. tdGRN is complementary to existing methods for inferring gene regulatory networks. The novel part of the proposed tool is that it is able to infer time-delayed regulatory relationships.