%0 Journal Article
%T A network perspective on metabolic inconsistency
%A Nikolaus Sonnenschein
%A Jos¨¦ Golib Dzib
%A Annick Lesne
%A Sebastian Eilebrecht
%A Sheerazed Boulkroun
%A Maria-Christina Zennaro
%A Arndt Benecke
%A Marc-Thorsten H¨¹tt
%J BMC Systems Biology
%D 2012
%I BioMed Central
%R 10.1186/1752-0509-6-41
%X Here, we analyze the coherence of gene expression patterns and a reconstruction of human metabolism, using consistency scores obtained from network and constraint-based analysis methods. We find a surprisingly strong correlation between the two measures, demonstrating that a substantial part of inconsistencies between metabolic processes and gene expression can be understood from a network perspective alone. Prompted by this finding, we investigate the topological context of the individual biochemical reactions responsible for the observed inconsistencies. On this basis, we are able to separate the differential contributions that bear physiological information about the system, from the unspecific contributions that unravel gaps in the metabolic reconstruction. We demonstrate the biological potential of our network-driven approach by analyzing transcriptome profiles of aldosterone producing adenomas that have been obtained from a cohort of Primary Aldosteronism patients. We unravel systematics in the data that could not have been resolved by conventional microarray data analysis. In particular, we discover two distinct metabolic states in the adenoma expression patterns.The methodology presented here can help understand metabolic inconsistencies from a network perspective. It thus serves as a mediator between the topology of metabolic systems and their dynamical function. Finally, we demonstrate how physiologically relevant insights into the structure and dynamics of metabolic networks can be obtained using this novel approach.Genomic knowledge allows compiling an inventory of an organism¡¯s enzymes and thus the subsequent reconstruction [1] and simulation of its metabolic system [2] using constraint-based modeling (CBM) techniques [3]. Compensating the lack of detailed information on the systems parameters, e.g., enzyme kinetics, gene regulation etc., CBM has proven to be a valuable tool for genome-scale system analysis. For example, flux balance analysis (FBA) [4]
%U http://www.biomedcentral.com/1752-0509/6/41