Model-Driven Metabolic Engineering of Escherichia coli for Improving Conversion of Lignocellulose-Derived Sugars to Ethanol

Computational models of genome-scale metabolic networks have been successfully used to study and engineer microbial metabolism for production of valuable chemicals. Constraint-based approaches such as flux balance analysis can predict metabolic flux distributions in genetically perturbed strains, and they have been used to identify novel metabolic engineering strategies. 

We used genome-scale metabolic models of Escherichia coli to identify gene knockout strategies to improve co-utilization of glucose and xylose, which are major sugars in lignocellulosic hydrolysate. We constructed the gene knockout mutants and inserted Zymomonas mobilis pyruvate decarboxylase and alcohol dehydrogenase genes to increase ethanol production. 

The constructed E. coli mutants co-utilized glucose and xylose anaerobically in minimal media, but the growth and glucose uptake rates were much slower than wildtype E. coli strain. The mutants were adaptively evolved in minimal media containing glucose, xylose, or both. The evolved mutants were able to simultaneously convert glucose and xylose into ethanol when grown in synthetic hydrolysate.