Predicting Network Activity from High Throughput Metabolomics

• Type of Material: Article
• Authors: Shuzhao Li, Emory University; Youngja Park, Emory University; Sai Duraisingham, Emory University; Fred Strobel, Emory University; Nooruddin Khan, Emory University; Quinlyn A. Soltow, Emory University; Dean P Jones, Emory University; Bali Pulendran, Emory University
• Language: English
• Date: 2013-07-01
• Publisher: Public Library of Science
• Publication Version: Final Published Version
• Copyright Statement: © 2013 Li et al
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1371/journal.pcbi.1003123
• Title of Journal or Parent Work: PLoS Computational Biology
• ISSN: 1553-734X
• Volume: 9
• Issue: 7
• Start Page: e1003123
• End Page: e1003123
• Grant/Funding Information: The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.; Work supported by grants from US National Institutes of Health AG038746, ES016731 (to DPJ); U19AI090023, U54AI057157, R37AI48638, R37DK057665, U19AI057266, PO1A1096187 (to BP); Scripps CHAVI-ID Award (UM1AI100663 to BP); and the Bill and Melinda Gates Foundation (to BP).
• Keywords: RECONSTRUCTION; SYSTEMS VACCINOLOGY; FUNCTIONAL MODULES; CELLS; MURINE CYTOMEGALOVIRUS; METABOLITE IDENTIFICATION; YELLOW-FEVER VACCINE; Mathematical & Computational Biology; Biochemical Research Methods; MASS-SPECTROMETRY; Biochemistry & Molecular Biology; Science & Technology; DATABASE; Life Sciences & Biomedicine; IMMUNE-RESPONSES
• Research Categories: Health Sciences, Medicine and Surgery; Health Sciences, Public Health

Abstract

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. © 2013 Li et al.