Exhaustive identification of steady state cycles in large stoichiometric networks

Abstract

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.

Organising metabolic networks: Cycles in flux distributions

“…It is important to note that the cycles considered in this study are not stoichiometrically closed. Stoichiometric cycles, which have been described in other works (Schilling et al, 2000;Wright & Wagner, 2008) …”

Generic flux coupling analysis

“…To perform traditional FCA and the test testCoupled B , we use Cplex 12.5 for solving the LPs and MILPs. The internal circuits of the network are computed with a variant of the WW-algorithm [46] using the efmtool by Terzer et al [42]. All the networks analyzed in this study have a low number of internal circuits, which made this approach feasible and easy to implement.…”

Applications of genome‐scale metabolic reconstructions

“…One of the most direct contributions of metabolic GENREs to our understanding of metabolism has been its enabling of the study of otherwise inaccessible network properties. Metabolic properties such as the existence of loops (Kun et al , 2008; Wright and Wagner, 2008), optimal pathway usage (Nishikawa et al , 2008), metabolite connectivity (Becker et al , 2006; Samal et al , 2006; Guimera et al , 2007), and pathway redundancy (Papin et al , 2002b; Mahadevan and Lovley, 2008) have all been studied in metabolic GENREs using computational methods. Many of these network analyses are performed through variants of FBA (see ‘Model building and analysis’ section).…”

“…References for each species in the figure are as follows: M. barkeri (Becker et al , 2006; Feist et al , 2006; Kun et al , 2008; Mahadevan and Lovley, 2008; Wright and Wagner, 2008), R. etli (Resendis‐Antonio et al , 2007), Synechocystis sp. (Kun et al , 2008), M. genitalium (Suthers et al , 2009), H. sapiens (Duarte et al , 2007; Ma et al , 2007; Vo et al , 2007; Shlomi et al , 2008, 2009; Veeramani and Bader, 2009), M. musculus (Sheikh et al , 2005; Quek and Nielsen, 2008; Selvarasu et al , 2009), A. thaliana (Radrich et al , http://hdl.handle.net/10101/npre.2009.3309.1), H. influenza (Edwards and Palsson, 1999; Papin et al , 2002a; Papin et al , 2002b; Price et al , 2002; Price et al , 2003; Schilling and Palsson, 2000), H. pylori (Price et al , 2002, 2003; Schilling et al , 2002; Papin et al , 2002b; Thiele et al , 2005; Becker et al , 2006; Guimera et al , 2007; Kun et al , 2008; Wright and Wagner, 2008), M. tuberculosis (Beste et al , 2009; Beste et al , 2007; Jamshidi and Palsson, 2007; Kun et al , 2008), N. meningitides (Baart et al , 2007a, 2007b), P. aeruginosa (Oberhardt et al , 2008), S. aureus (Becker and Palsson, 2005; Becker et al , 2006; Heinemann et al , 2005; Kun et al , 2008; Samal et al , 2006), S. typhimurium (Abuoun et al , 2009; Raghunathan et al , 2009), Y. pestis (Navid and Almaas, 2009), P. gingivalis (Mazumdar et al , 2009), L. major (Chavali et al , 2008b), C. reinhardtii (Boyle and Morga...…”