ecFBA

ecFBA, or enzyme-constrained Flux Balance Analysis, is a computational framework that extends traditional flux balance analysis by explicitly accounting for enzyme kinetics and proteome allocation. It integrates constraints that reflect how scarce cellular resources are used to support metabolic reactions, providing more biologically realistic predictions of flux distributions than standard FBA.

In ecFBA, each reaction’s flux is limited by the amount of the catalyzing enzyme, typically expressed as v_j ≤ kcat_j × E_j, where kcat_j is the enzyme’s catalytic turnover number and E_j is the enzyme abundance. A secondary constraint represents the proteome budget, often modeled as the sum of enzyme abundances weighted by their molecular weights not exceeding a total protein capacity. The optimization objective remains similar to FBA, usually maximizing biomass production or another biologically relevant target, subject to stoichiometric balance and the enzyme- and proteome-related constraints.

Inputs for ecFBA include a genome-scale metabolic model, mappings from reactions to their corresponding enzymes, kcat values (or approximations), and enzyme abundance data from proteomics. The framework can also incorporate uncertain data through bounds or regularization. By linking enzyme investment to flux, ecFBA can reveal trade-offs between growth and enzyme usage, helping to explain phenotypes that standard FBA cannot.

Applications span metabolic engineering and systems biology, including prediction of flux under proteome-limited conditions, identification of bottlenecks, and design of strains with optimized enzyme allocation. Limitations include reliance on accurate kcat and enzyme data, handling of isoenzymes and multi-subunit complexes, and potential data gaps that affect predictive power. ecFBA is commonly implemented within COBRA-based workflows and related modeling tools.