Predicting network activity from high throughput metabolomics

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Predicting network activity from high throughput metabolomics

Li, Shuzhao; Park, Youngja; Duraisingham, Sai; Strobel, Frederick H; Khan, Nooruddin; Soltow, Quinlyn A; Jones, Dean P; Pulendran, Bali

PLOS computational biology/PLoS computational biology, 07/2013, Volume: 9, Issue: 7

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The functional interpretation of high throughput metabolomics by mass spectrometry is hindered by the identification of metabolites, a tedious and challenging task. We present a set of computational algorithms which, by leveraging the collective power of metabolic pathways and networks, predict functional activity directly from spectral feature tables without a priori identification of metabolites. The algorithms were experimentally validated on the activation of innate immune cells.

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