Exhaustive identification of steady state cycles in large stoichiometric networks.
BACKGROUND
Identifying cyclic pathways in chemical reaction networks is important, because such cycles may indicate in silico violation of energy conservation, or the existence of feedback in vivo. Unfortunately, our ability to identify cycles in stoichiometric networks, such as signal transduction and genome-scale metabolic networks, has been hampered by the computational complexity of the methods currently used.


RESULTS
We describe a new algorithm for the identification of cycles in stoichiometric networks, and we compare its performance to two others by exhaustively identifying the cycles contained in the genome-scale metabolic networks of H. pylori, M. barkeri, E. coli, and S. cerevisiae. Our algorithm can substantially decrease both the execution time and maximum memory usage in comparison to the two previous algorithms.


CONCLUSIONS
The algorithm we describe improves our ability to study large, real-world, biochemical reaction networks, although additional methodological improvements are desirable.
Wright
Jeremiah
J
Department of Biochemistry, University of Zurich, Zurich, Switzerland. j.wright@bioc.uzh.ch
Wagner
Andreas
A
D000465:Algorithms; D016248:Computer Storage Devices; D004926:Escherichia coli / Q000378:metabolism; D016678:Genome; D016480:Helicobacter pylori / Q000378:metabolism; D006801:Humans; D053858:Metabolic Networks and Pathways; D017021:Methanosarcina barkeri / Q000378:metabolism; D008954:Models, Biological; D012441:Saccharomyces cerevisiae / Q000378:metabolism; D013997:Time Factors