A generic model of transcriptional regulatory networks : application to plants under abiotic stress

DOIResolve DOI: http://doi.org/10.1109/GENSIPS.2013.6735922
AuthorSearch for: ; Search for: ; Search for: ; Search for: ; Search for: ; Search for:
TypeArticle
Conference2013 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS), Nov. 17 to 19, 2013, Houston, Texas
ISBN978-1-4799-3462-1
978-1-4799-3461-4
Pages2831; # of pages: 4
SubjectAbiotic-stress response; Adverse environment; Arabidopsis thaliana; Computational approach; Experimental methods; Mathematical method; Stress-responsive gene; Transcriptional regulatory networks
AbstractUnderstanding the relationships between transcription factors (TFs) and genes in plants under abiotic stress responses, tolerance and adaptation to adverse environments is very important in developing resilient crop varieties. While experimental methods to characterize stress responsive TFs and their targets are highly accurate, identification and characterization of the role of a given gene in a given stress response event are often laborious and time consuming. Computational approaches, on the other hand, offer a platform to identify new knowledge by integrating high throughput omics data and mathematical methods/models. In this research, we have developed a generic linear model of transcriptional regulatory networks (TRNs) and a companion algorithm to identify and to characterize stress responsive genes and their roles in a given stress response event. The proposed methodology was applied to plants, by using Arabidopsis thaliana as an example, under abiotic stress. Well known interactions were inferred as well as putative novel ones that may play important roles in plants under abiotic stress conditions as confirmed by statistical and literature evidences.
Publication date
PublisherIEEE
LanguageEnglish
AffiliationInformation and Communication Technologies; Aquatic and Crop Resource Development; National Research Council Canada
Peer reviewedYes
NRC numberNRC-ACRD-56077
NPARC number21273373
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Record identifiera5216d6e-c708-4ee7-95d5-d33eed3d6fa1
Record created2015-01-08
Record modified2016-05-09
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