RESUMEN
Genome-scale metabolic network models have been successfully applied to guide metabolic engineering. However, the conventional flux balance analysis only considers stoichiometry and reaction direction constraints, and the simulation results cannot accurately describe certain phenomena such as overflow metabolism and diauxie growth on two substrates. Recently, researchers proposed new constraint-based methods to simulate the cellular behavior under different conditions more precisely by introducing new constraints such as limited enzyme content and thermodynamics feasibility. Here we review several enzyme-constrained models, giving a comprehensive introduction on the biological basis and mathematical representation for the enzyme constraint, the optimization function, the impact on the calculated flux distribution and their application in identification of metabolic engineering targets. The main problems in these existing methods and the perspectives on this emerging research field are also discussed. By introducing new constraints, metabolic network models can simulate and predict cellular behavior under various environmental and genetic perturbations more accurately, and thus can provide more reliable guidance to strain engineering.