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Table 1 Classification of methods for EFM reduction

From: Projection to latent pathways (PLP): a constrained projection to latent variables (PLS) method for elementary flux modes discrimination

Principle Method Data required References
Network connectivity and stoichiometry K-shortest EFM: Enumerates the EFM in increasing order of number of reactions.
Yield Analysis: Excludes EFM with negligible contribution to convex hull in yield space.
Parameter free [11]
[12]
Thermodynamics Fractional contributions of EFM: Estimates the EFM Coefficients based on calculated EFM thermodynamic properties.
Maximum Entropy Principle: Calculates the EFM Coefficient by maximizing Shannon's entropy, which is an indirect measure of system complexity.
Thermodynamic data [13]
[14]
(Non)linear programming α-spectrum: Uses linear optimization to maximize and minimize the weightings of each metabolic pathway that produces steady state flux distributions.
Flux regulation coefficients: Estimates the EFM coefficients that optimize a given performance function (e.g. minimum error in flux or yield prediction).
Quadratic program: Calculates the weights for a large set of EFM by using quadratic program to reconstruct flux distributions from subsets of EFM.
'-omics' data can be used to shrink the α-spectrum.
Fluxomics and possibly other omic datasets
[15, 16, 38]
[18]
[17]
Enzyme kinetics Quantitative elementary mode analysis of metabolic pathways: Combines structural and kinetic modelling to assess the effect of changes in enzyme kinetics on the usage of EFM. Enzyme kinetic parameters [19]