Prev. Article Next Article Table of Contents

Article

Integrative Top-Down System Metabolic Modeling in Experimental Disease States via Data-Driven Bayesian Methods

Jung-Wook Bang^†, Derek J. Crockford^†, Elaine Holmes^†, Florencio Pazos^‡, Michael J. E. Sternberg^‡, Stephen H. Muggleton^§ and Jeremy K. Nicholson*^†

Department of Biomolecular Medicine, Division of Surgery, Oncology, Reproductive Biology & Anaesthetics, Sir Alexander Fleming Building, Imperial College, London SW7 2AZ, U.K., Structural Bioinformatics Group, Division of Molecular Bioscience, Imperial College, London SW7 2AY, U.K., and Computational Bioinformatics Group, Department of Computing, Imperial College, London SW7 2AZ, U.K.

J. Proteome Res., 2008, 7 (2), pp 497–503

DOI: 10.1021/pr070350l

Publication Date (Web): January 8, 2008

†

Division of Surgery, Oncology, Reproductive Biology & Anaesthetics.

‡

Division of Molecular Bioscience.

Department of Computing.

* To whom correspondence should be addressed. Tel: +44 (0)20 7594 3195 . Fax: +44 (0) 20 7594 3226. E-mail: j.nicholson@imperial.ac.uk.

Abstract

Multivariate metabolic profiles from biofluids such as urine and plasma are highly indicative of the biological fitness of complex organisms and can be captured analytically in order to derive top-down systems biology models. The application of currently available modeling approaches to human and animal metabolic pathway modeling is problematic because of multicompartmental cellular and tissue exchange of metabolites operating on many time scales. Hence, novel approaches are needed to analyze metabolic data obtained using minimally invasive sampling methods in order to reconstruct the patho-physiological modulations of metabolic interactions that are representative of whole system dynamics. Here, we show that spectroscopically derived metabolic data in experimental liver injury studies (induced by hydrazine and α-napthylisothiocyanate treatment) can be used to derive insightful probabilistic graphical models of metabolite dependencies, which we refer to as metabolic interactome maps. Using these, system level mechanistic information on homeostasis can be inferred, and the degree of reversibility of induced lesions can be related to variations in the metabolic network patterns. This approach has wider application in assessment of system level dysfunction in animal or human studies from noninvasive measurements.

View: Full Text HTML | Hi-Res PDF | PDF w/ Links