Structural analysis of metabolic networks based on flux centrality.
Metabolic reactions are fundamental to living organisms, and a large number of reactions simultaneously occur at a given time in living cells transforming diverse metabolites into each other. There has been an ongoing debate on how to classify metabolites with respect to their importance for metabolic performance, usually based on the analysis of topological properties of genome scale metabolic networks. However, none of these studies have accounted quantitatively for flux in metabolic networks, thus lacking an important component of a cell's biochemistry. We therefore analyzed a genome scale metabolic network of Escherichia coli by comparing growth under 19 different growth conditions, using flux balance analysis and weighted network centrality investigation. With this novel concept of flux centrality we generated metabolite rankings for each particular growth condition. In contrast to the results of conventional analysis of genome scale metabolic networks, different metabolites were top-ranking dependent on the growth condition. At the same time, several metabolites were consistently among the high ranking ones. Those are associated with pathways that have been described by biochemists as the most central part of metabolism, such as glycolysis, tricarboxylic acid cycle and pentose phosphate pathway. The values for the average path length of the analyzed metabolite networks were between 10.5 and 12.6, supporting recent findings that the metabolic network of E. coli is not a small-world network.
Koschützki
Dirk
D
Department of Computer and Electrical Engineering, Furtwangen University of Applied Sciences, Robert-Gerwig-Platz 1, 78120 Furtwangen, Germany. Dirk.Koschuetzki@hs-furtwangen.de
Junker
Björn H
BH
Schwender
Jörg
J
Schreiber
Falk
F
D001669:Biochemical Phenomena / Q000502:physiology*; D016000:Cluster Analysis; D004926:Escherichia coli / Q000254:growth & development / Q000378:metabolism*; D053858:Metabolic Networks and Pathways / Q000502:physiology*; D008954:Models, Biological