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Open Access

Analysis of metabolome data by a maximum likelihood approach

  • Claudia Choi1Email author,
  • Claudia Hundertmark2,
  • Bernhard Thielen3,
  • Beatrice Benkert1,
  • Richard Münch1,
  • Max Schobert1,
  • Dietmar Schomburg3,
  • Dieter Jahn1 and
  • Frank Klawonn4
BMC Systems Biology20071(Suppl 1):P20

Published: 8 May 2007


Pseudomonas AeruginosaSystem BiologyMetabolic NetworkPersistent InfectionProtein Pattern

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.

Authors’ Affiliations

Institut für Mikrobiologie, Technische Universität Braunschweig, Germany
Helmholtz-Zentrum für Infektionsforschung GmbH, Germany
Institut für Biochemie, Universität zu Köln, Germany
Fachbereich Informatik, Fachhochschule Wolfenbüttel, Germany


© Choi et al; licensee BioMed Central Ltd. 2007

This article is published under license to BioMed Central Ltd.