Bioinformatics Advance Access originally published online on April 19, 2005
Bioinformatics 2005 21(12):2898-2905; doi:10.1093/bioinformatics/bti440
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Differential network expression during drug and stress response
1Los Alamos National Laboratory PO Box 1663, Mailstop M888, Los Alamos, NM 87545, USA
2Miami Valley Labs, Procter & Gamble PO Box 538707, Cincinnati, OH 45253-8707, USA
*To whom correspondence should be addressed.
Motivation: The application of microarray chip technology has led to an explosion of data concerning the expression levels of the genes in an organism under a plethora of conditions. One of the major challenges of systems biology today is to devise generally applicable methods of interpreting this data in a way that will shed light on the complex relationships between multiple genes and their products. The importance of such information is clear, not only as an aid to areas of research like drug design, but also as a contribution to our understanding of the mechanisms behind an organism's ability to react to its environment.
Results: We detail one computational approach for using gene expression data to identify response networks in an organism. The method is based on the construction of biological networks given different sets of interaction information and the reduction of the said networks to important response sub-networks via the integration of the gene expression data. As an application, the expression data of known stress responders and DNA repair genes in Mycobacterium tuberculosis is used to construct a generic stress response sub-network. This is compared to similar networks constructed from data obtained from subjecting M.tuberculosis to various drugs; we are thus able to distinguish between generic stress response and specific drug response. We anticipate that this approach will be able to accelerate target identification and drug development for tuberculosis in the future.
Contact: chris{at}lanl.gov
Supplementary information: Supplementary Figures 1 through 6 on drug response networks and differential network analyses on cerulenin, chlorpromazine, ethionamide, ofloxacin, thiolactomycin and triclosan. Supplementary Tables 1 to 3 on predicted protein interactions. http://www.santafe.edu/~chris/DifferentialNW
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