Washington: Researchers have developed an algorithm that integrates genomic and metabolic data and enables cell-scale simulations. Researchers at the Institute for Genomic Biology at the University of Illinois have developed a way to harness the prodigious quantities of both genomic and metabolic data being generated with high-throughput genomics and other techniques.The model, called probabilistic regulation of metabolism (PROM), enables researchers to perturb a given regulatory gene or metabolic process and see how that affects the entire network. “PROM provides a platform for studying the behaviours of networks in a wide range of different conditions,” said principal investigator Nathan Price.
Using this model the researchers have created the first genome-scale, regulatory-metabolic network of Mycobacterium tuberculosis. Using E. Coli as a benchmark, Price and Sriram Chandrasekaran showed that PROM was more accurate and comprehensive than the previous model for E. Coli, which had been done by hand and published in 2004.
Price and Chandrasekaran built the algorithm using microarray data, transcription-factor interactions that regulate metabolic reactions, and knockout phenotypes. The method is both accurate and fast. PROM may prove particularly helpful to tuberculosis researchers because, although when tuberculosis is growing it can be killed, the real challenge is to target the bacterium during its dormant or quiescent stage.
PROM may enable researchers to identify and target the pathways keeping the cells alive during dormancy. PROM also represents a major advance because it successfully integrates the statistically derived transcriptional regulatory network with a biochemically derived metabolic network. The results were published online in the journal PNAS. (ANI)
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