Enzymeconstrained

Enzymeconstrained refers to a family of constraint-based metabolic models that explicitly incorporate enzyme abundance and catalytic properties to bound the fluxes of metabolic reactions. Also known as enzyme-constrained genome-scale metabolic models (ecGEMs), these models aim to more accurately reflect cellular resource allocation and enzymatic capacity than traditional stoichiometric models.

In enzyme-constrained models, each reaction flux is limited by the catalytic capacity of the enzyme(s) that catalyze the reaction. A common representation is v_i ≤ E_i × kcat_i, where E_i is the amount of the enzyme (or enzyme pool) available for reaction i and kcat_i is the turnover number. For reactions with multiple isoenzymes or complexes, the bound can be expressed as sums of the contributions from each enzyme. Some approaches also impose a global protein or proteome constraint, reflecting the finite cellular budget for enzyme production and maintenance, so the total enzyme usage must not exceed a defined cellular capacity.

Data requirements include enzyme abundances from proteomics, enzyme kinetic rates (kcat values) from curated databases or experiments, and gene–protein–reaction associations to map enzymes to metabolic steps. Because kcat values can vary with conditions and measurement gaps exist, ecGEMs rely on data curation and, sometimes, estimator approaches.

Applications of enzyme-constrained modeling include improved predictions of growth rates, flux distributions, and metabolite production yields, as well as aids in metabolic engineering and strain design. Limitations involve incomplete kcat data, condition-dependent enzyme activity, and the complexity of integrating dynamic regulation into static models. Notable implementations and frameworks include those that integrate enzyme constraints into genome-scale models to produce ecGEMs.