Construction of an Enzyme-Constrained Model for Escherichia coli Using GECKO Toolbox 3.0

Genome-scale metabolic models (GEMs) are key computational tools in systems biology and metabolic engineering, yet GEMs often neglect constraints imposed by enzyme catalytic capacities. Enzyme-constrained models (ecModels) address this limitation by incorporating enzyme abundance and catalytic turnover rates (k_cat), thereby improving predictive accuracy. This chapter provides a streamlined workflow for constructing a high-quality enzyme-constrained model (ecModel) of Escherichia coli using the GECKO Toolbox 3.0, including software setup, conversion of GEM to ecModel structure, integration of k_cat values from BRENDA, DLKcat deep learning model, and in vivo catalytic rates (k_max), as well as model calibration and validation. Targeted adjustment of k_cat values for key transport and metabolic reactions resolved abnormal pyruvate overflow in the initial model and restored acetate as the primary overflow metabolism, yielding simulations consistent with experimental data. Overall, this work provides a concise, reproducible framework for building and calibrating ecModels, supporting future applications in metabolic engineering and systems biology.