Predicting Network Activity from High Throughput Metabolomics

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.

• BA
  Bill and Melinda Gates Foundation
• NI
  National Institutes of Health

  Awards: R37DK057665, UM1AI100663, R37AI48638, AG038746, U19AI090023, ES016731, U19AI057266