ecGEM

ecGEM stands for enzyme-constrained genome-scale metabolic model. It is a type of metabolic model that extends standard genome-scale metabolic models by incorporating enzyme kinetics and abundance information to constrain reaction fluxes. In an ecGEM, each reaction is limited not only by stoichiometry and mass balance but also by the catalytic capacity of the involved enzyme, reflecting the protein cost of metabolism.

Methodologically, ecGEMs attach constraints to reactions based on enzyme data. For a reaction catalyzed by enzyme i, the flux v_j is constrained by v_j ≤ kcat_ij × E_i, where kcat is the turnover number and E_i represents the amount of enzyme available. Often, these models include a representation of enzyme allocation and a protein-budget constraint to account for the cellular cost of producing enzymes. Building an ecGEM requires mapping genes to enzymes and reactions, gathering kinetic parameters (primarily kcat) from databases, and obtaining enzyme abundances through proteomics or literature. When data are incomplete, approximations or group-based estimates are used.

Applications of ecGEMs include more realistic predictions of growth rates and metabolic capacities, assessment of trade-offs between growth and enzyme investment, and improved guidance for metabolic engineering and strain design. They are used to explore how enzyme limitations shape flux distributions and to identify bottlenecks in pathways.

Limitations and challenges include incomplete kinetic data for many enzymes, condition-dependent kcat values, variability in enzyme levels, and increased model complexity. Despite these limitations, ecGEMs have been developed for several organisms, notably bacteria such as Escherichia coli and yeast such as Saccharomyces cerevisiae, and are supported by frameworks like GECKO for their construction.