In silico target discovery for aromatic amino acid production in Escherichia coli
Aromatic amino acids (AAA) and derived compounds are rising on global market demand, which leads to the need of better production means. These compounds are usually produced by means of microbial cell factories. However, bacterial metabolism is complex and highly regulated, which complicates the discovery of new higher yield strains, optimized for the production of these compounds. Dynamic modelling has the capability to solve this problem by predicting metabolic behavior and helping to find targets for strain design. In this paper, a new Escherichia coli dynamic model is presented. Evolutionary algorithm processes were used in order to find targets for metabolic engineering strategies for AAA increased production. The solutions obtained are compatible with strategies found in literature, while at the same time presenting new possible targets, not yet reported in literature.