Elementary flux modes, computational

Considering a trade-off between knowledge that is required prior to the analysis and predictive power, stoichiometric network analysis must be regarded as the most successful computational approach to large-scale metabolic networks to date. It is computationally feasible even for large-scale networks, and it is nonetheless far more predictive that a simple graph-based analysis. Stoichiometric analysis has resulted in a vast number of applications [35,67,70 74], including quantitative predictions of metabolic network function [50, 64]. The two most well-known variants of stoichiometric analysis, namely, flux balance analysis and elementary flux modes, constitute the topic of Section V. 

Equation (64) justifies the application of flux-balance analysis even in the face of (i) fast short-term fluctuations and (ii) periodic long term for example, circadian variability. The steady state balance condition restricts the feasible steady-state flux distributions to the flux cone P = v° G IRr IVv0 = 0. The reduction of the admissible flux space, with some of its algebraic properties already summarized in Section III.B, is exploited by several computational approaches, most notably Flux Balance Analysis (FBA) [61, 71, 235] and elementary flux modes (EFMs) [96, 236 238],... 

The concept of elementary flux modes has resulted in a vast number of applications to analyze and predict the functionality of metabolic networks [64, 65, 138, 241 243]. Software resources that allow for the computation of elementary flux modes are fisted in Table V [178, 224], It should be noted that, due to their definition as an exhaustive enumeration of possible flux distributions,... 

J., and Schuster, S. (2009) EFMEvolver computing elementary flux modes in genome-scale metabolic networks, in Proceedings of the German Conference on Bioinformatics, Lecture Notes in Informatics (LNI) P-1S7, Gesellschaft fiir Informatik, Bonn, pp. 179-190. 

David, L. and Bockmayr, A. (2014) Computing elementary flux modes involving a set of target reactions. lEEE/ACM Trans. Comput. 

Terzer, M. and Stelling, J. (2008) Large-scale computation of elementary flux modes with bit pattern trees. Bioinformatics, 24 (19), 2229-2235, doi 10.1093/bioinformatics/btn401. 

Gerstl, M.P., Jungreuthmayer, C., and Zanghellini, J. (2015) tEFMA computing thermodynamically feasible elementary flux modes in metabolic networks. Bioinformatics, 31 (13), 2232-2234, doi 10.1093/bioinformatics/btvl 11. 

As stated above. Table 2.1 shows a list of different software packages that have been developed to compute EMs and ExPas. The fastest software to compute elementary modes to date is efmtool that employs the bit pattern trees algorithm (Terzer and Stelling 2008). Different techniques have also been developed to compute EMs for a large-scale metabolic network based on determination of the K-shortest elementary flux mode(s) (de Figueiredo et al. 2009), elementary flux patterns (Kaleta et al. 2009), and parallel computation (Gentler et al. 2010 Jevremovic et al. 2011). 

Schuster S, Hilgetag C, Woods JH, Fell DA (1994) Elementary modes of functioning in biochemical networks. In Cuthbertson R, Holcombe M, Paton R (eds) Computation in cellular and molecular biological systems. World Scientific, Singapore, pp 151-165 Schuster S, Dandekar T, Fell DA (1999) Detection of elementary flux modes in biochemical networks a promising tool for pathway analysis and metabolic engineering. Trends Biotechnol 17 53-60... 

Csete ME, Doyle JC (2002) Reverse engineering of biological complexity. Science 295 1664-1669 De Figueiredo LF, Podhorski A, Rubio A, Kaleta C, Beasley JE, Schuster S, Planes EJ (2009) Computing the shortest elementary flux modes in genome-scale metabolic networks. Bioinformatics 25 3158-3165... 

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