Enzyme-constrained metabolic model and metabolic engineering of for the development of sustainable production processes.

Constraint-based genome-scale models (GEMs) of microorganisms provide a powerful tool for predicting and analyzing microbial phenotypes as well as for understanding how these are affected by genetic and environmental perturbations. Recently, MATLAB and Python-based tools have been developed to incorporate enzymatic constraints into GEMs. These constraints enhance phenotype predictions by accounting for the enzyme cost of catalyzed model´s reactions, thereby reducing the space of possible metabolic flux distributions. In this study, enzymatic constraints were added to an existing GEM of , a model acetogenic bacterium, by including its enzyme turnover numbers (s) and molecular masses, using the Python-based AutoPACMEN approach. When compared to the metabolic model iHN637, the enzyme cost-constrained model (ec_iHN637) obtained in our study showed an improved predictive ability of growth rate and product profile. The model ec_iHN637 was then employed to perform metabolic engineering of , by using the OptKnock computational framework to identify knockouts to enhance the production of desired fermentation products. The metabolic engineering was geared towards increasing the production of fermentation products by , with a focus on the utilization of synthesis gas and CO. This resulted in different engineering strategies for overproduction of valuable metabolites under different feeding conditions, without redundant knockouts for different products. Importantly, the results of the engineering results indicated that the mixotrophic growth of is a promising approach to coupling improved cell growth and acetate and ethanol productivity with net CO fixation.