Table 1 Classification of methods for EFM reduction
| 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] |
