Editorial Technical Support:
D. H. Diaz
M. A. Gomez
Editorial management and production:
SOLGRAF Editora firstname.lastname@example.org
Güell O., Sagués F., Basler G., Nikoloski Z., and Serrano M. A.
Complex networks have been shown to be robust against random structural perturbations, but vulnerable against targeted attacks. Robustness analysis usually simulates the removal of individual or sets of nodes, followed by the assessment of the inflicted damage. For complex metabolic networks, it has been suggested that evolutionary pressure may favor robustness against reaction removal. However, the removal of a reaction and its impact on the network may as well be interpreted as selective regulation of pathway activities, suggesting a tradeoff between the efficiency of regulation and vulnerability. Here, we employ a cascading failure algorithm to simulate the removal of single and pairs of reactions from the metabolic networks of two organisms, and estimate the significance of the results using two different null models: degree preserving and mass-balanced randomization. Our analysis suggests that evolutionary pressure promotes larger cascades of non-viable reactions, and thus favors the ability of efficient metabolic regulation at the expense of robustness.
metabolic networks, robustness, cascading failure, null models.
 ALBERT R, JEONG H & BARABASI AL. 2000. Error and attack tolerance of complex networks. Nature, 406: 378-382.  SZALAY MS, KOVACS IA, KORCSMAROS T, BODE C & CS ERMELY P. 2007. Stress-induced rearrangements of cellular net- works: Consequences for protection and drug design. FEBS Let ters, 581: 3675-3680.
 NEWMAN MEJ, WATTS DJ & STROGATZ SH. 2002. Random graph models of social networks. Proc. Natl. Acad. Sci. USA, 99(suppl. 1): 2566-2572.  GUILLAUME JL & LATAPY M. 2006. Bipartite graphs as models of complex networks. Physica A Statistical and Theoretical Physics, 371: 795-813.