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 | [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] |