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This article is part of the supplement: BioSysBio 2007: Systems Biology, Bioinformatics, Synthetic Biology

Open Access Poster presentation

Analysis of metabolome data by a maximum likelihood approach

Claudia Choi1*, Claudia Hundertmark2, Bernhard Thielen3, Beatrice Benkert1, Richard Münch1, Max Schobert1, Dietmar Schomburg3, Dieter Jahn1 and Frank Klawonn4

Author Affiliations

1 Institut für Mikrobiologie, Technische Universität Braunschweig, Germany

2 Helmholtz-Zentrum für Infektionsforschung GmbH, Germany

3 Institut für Biochemie, Universität zu Köln, Germany

4 Fachbereich Informatik, Fachhochschule Wolfenbüttel, Germany

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BMC Systems Biology 2007, 1(Suppl 1):P20  doi:10.1186/1752-0509-1-S1-P20

The electronic version of this article is the complete one and can be found online at:

Published:8 May 2007

© 2007 Choi et al; licensee BioMed Central Ltd.

Poster presentation

Metabolomics emerges as one key aspect of systems biology, since quantifying the dynamic set of metabolites reveals the effect of altered gene expression and protein pattern and thus complements transcriptomics and proteomics. By high-throughput techniques, such as measuring metabolites by gas chromatography-mass spectrometry (GC-MS), enormous data amounts are produced, that need to be analysed. At present, a variety of methods are available for cluster analysis of metabolome data.

Our maximum likelihood approach identifies significantly altered metabolites between Pseudomonas aeruginosa samples grown under different conditions and measured by GC-MS. P. aeruginosa is a versatile soil bacterium and an important opportunistic pathogen causing persistent infection in immunocompromised patients. This statistical approach estimates the inherent noise of the samples and thereby evaluates the significance of altered metabolite composition. Identified key metabolites with significantly altered pattern under different conditions, will be interesting for further investigation of the metabolic network and flux analysis.