title: Metabolomic fingerprinting: challenges and opportunities.
abstract: Systems biology has primarily focused on studying genomics, transcriptomics, and proteomics and their dynamic interactions. These, however, represent only the potential for a biological outcome since the ultimate phenotype at the level of the eventually produced metabolites is not taken into consideration. The emerging field of metabolomics provides complementary guidance toward an integrated approach to this problem: It allows global profiling of the metabolites of a cell, tissue, or host and presents information on the actual end points of a response. A wide range of data collection methods are currently used and allow the extraction of global or tissue-specific metabolic profiles. The great amount and complexity of data that are collected require multivariate analysis techniques, but the increasing amount of work in this field has made easy-to-use analysis programs readily available. Metabolomics has already shown great potential in drug toxicity studies, disease modeling, and diagnostics and may be integrated with genomic and proteomic data in the future to provide in-depth understanding of systems, pathways, and their functionally dynamic interactions. In this review we discuss the current state of the art of metabolomics, its applications, and future potential.
authors.affiliation: Department of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ.
authors.forename: Alyssa K
authors.initials: AK
authors.lastname: Kosmides
authors.affiliation: Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ.
authors.forename: Kubra
authors.initials: K
authors.lastname: Kamisoglu
authors.affiliation: Department of Surgery, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, New Brunswick, NJ.
authors.forename: Steve E
authors.initials: SE
authors.lastname: Calvano
authors.affiliation: Department of Surgery, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, New Brunswick, NJ.
authors.forename: Siobhan A
authors.initials: SA
authors.lastname: Corbett
authors.affiliation: Department of Surgery, Robert Wood Johnson Medical School, Department of Biomedical Engineering, Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA.
authors.forename: Ioannis P
authors.initials: IP
authors.lastname: Androulakis
mesh_terms: D000465:Algorithms; D000818:Animals; D015415:Biomarkers / Q000378:metabolism; D019295:Computational Biology / Q000379:methods; D003327:Coronary Disease / Q000378:metabolism; D016638:Critical Illness; D016207:Cytokines / Q000378:metabolism; D006801:Humans; D009682:Magnetic Resonance Spectroscopy / Q000379:methods; D013058:Mass Spectrometry / Q000379:methods; D055432:Metabolomics / Q000379:methods* / Q000639:trends; D009369:Neoplasms / Q000378:metabolism; D009765:Obesity / Q000378:metabolism; D010641:Phenotype; D025341:Principal Component Analysis; D012984:Software; D049490:Systems Biology; D018675:Toxicity Tests
chemical_list: D015415:Biomarkers; D016207:Cytokines