Granger causality in integrated GC–MS and LC–MS metabolomics data reveals the interface of primary and secondary metabolism

• DOI: 10.1007/s11306-012-0470-0
• Publication Date: 2012-10-24T09:04:52Z
• Authors: Hannes Doerfler; David Lyon; Thomas Nägele; Xiaoliang Sun; Lena Fragner; Franz Hadacek; Volker Egelhofer; Wolfram Weckwerth
• Keywords: Metabolic pathway; Metabolome

Metabolomics has emerged as a key technique of modern life sciences in recent years. Two major techniques for metabolomics the last 10 years are gas chromatography coupled to mass spectrometry (GC-MS) and liquid (LC-MS). Each platform specific performance detecting subsets metabolites. GC-MS combination with derivatisation preference small polar metabolites covering primary metabolism. In contrast, reversed phase LC-MS covers large hydrophobic predominant secondary Here, we present an integrative providing mean reveal interaction metabolism plants other organisms. The strategy combines analysis same sample, novel alignment tool MetMAX statistical toolbox COVAIN data integration linkage Granger Causality metabolic modelling. For modelling have implemented combined GC-LC-MS covariance matrix stoichiometric underlying biochemical reaction network. changes regulation expressed differential Jacobian matrices. Applying causality, subset was detected significant correlations such sugars amino acids. These were compiled into N. Using N inverse calculation J from possible. Key points at interface identified.