Metabolic modeling and response surface analysis of an Escherichia coli strain engineered for shikimic acid...

Classic metabolic engineering strategies often induce significant flux imbalances to microbial metabolism, causing undesirable outcomes such as suboptimal conversion of substrates to products. Several mathematical frameworks have been developed to understand the physiological and metabolic state of production strains and to identify genetic modification targets for improved bioproduct formation. In this work, a modeling approach was applied to describe the physiological behavior and the metabolic fluxes of a shikimic acid overproducing Escherichia coli strain lacking the major glucose transport system, grown on complex media.

The obtained flux distributions indicate the presence of high fluxes through the pentose phosphate and Entner-Doudoroff pathways, which could limit the availability of erythrose-4-phosphate for shikimic acid production even with high flux redirection through the pentose phosphate pathway. In addition, highly active glyoxylate shunt fluxes and a pyruvate/acetate cycle are indicators of overflow glycolytic metabolism in the tested conditions. The analysis of the combined physiological and flux response surfaces, enabled zone allocation for different physiological outputs within variant substrate conditions. This information was then used for an improved fed-batch process designed to preserve the metabolic conditions that were found to enhance shikimic acid productivity. This resulted in a 40% increase in the shikimic acid titer (60 g/L) and 70% increase in volumetric productivity (2.45 gSA/L*h), while preserving yields, compared to the batch process.

The combination of dynamic metabolic modeling and experimental parameter response surfaces was a successful approach to understand and predict the behavior of a shikimic acid producing strain under variable substrate concentrations. Response surfaces were useful for allocating different physiological behavior zones with different preferential product outcomes. Both model sets provided information that could be applied to enhance shikimic acid production on an engineered shikimic acid overproducing Escherichia coli strain.