Profiling the human response to physical exercise: a computational strategy for the identification and kinetic analysis of metabolic biomarkers

In this work we introduce a novel computational strategy that allows to identify and study kinetic changes of putative biomarkers using targeted MS/MS profiling data from time series cohort studies or other cross-over designs. We propose a prioritization model with the objective of classifying biomarker candidates according to their discriminatory ability and couple this discovery step with a novel network-based approach to visualize, review and interpret key metabolites and their dynamic interactions within the network. The application of our method on longitudinal stress test data revealed a panel of metabolic signatures, i.e., lactate, alanine, glycine and the short-chain fatty acids C2 and C3 in trained and physically fit persons during bicycle exercise.We propose a new computational method for the discovery of new signatures in dynamic metabolic profiling data which revealed known and unexpected candidate biomarkers in physical activity. Many of them could be verified and confirmed by literature. Our computational approach is freely available as R package termed BiomarkeR under LGPL via CRAN http://cran.r-project.org/web/packages/BiomarkeR/ webcite.In metabolomics the bioinformatics-driven search for highly-discriminatory biomarker candidates has become a key task in the biomarker discovery process with the objective of introducing novel biomarkers aiding in diagnosis or therapeutic management [1-4].A wide spectrum of feature selection methods including filter, wrapper or embedded algorithms is available for the identification of significant features in biomedical datasets [5-9]. In particular filter algorithms calculate a measure (score), allowing to rank and prioritize putative biomarker candidates according to their predictive value [8]. However, research is still needed to provide bioinformatics methods for the scientific community that address paired/dependent test hypotheses or time series studies. In addition, the quantitative analysis of networks has in