Rapid Evaluation of Itaconic Acid Production Strategies in Saccharomyces Cerevisiae

The recent explosion of strain optimization algorithms based on genome-scale metabolic models (GSMM) allows for rapid generation of a large number of metabolic engineering strategies for maximizing metabolite production. However, implementation and validation of these strategies remains a bottleneck in the design-build-test-learn cycle. Here we demonstrate a method for targeted high-throughput strain transformation using exchangeable building blocks. Using this method and a high-throughput analysis platform, we evaluated multiple itaconic acid producing strategies simultaneously in Saccharomyces cerevisiae wild type strains. The initial design included differences in compartmental strategy, a set of gene variants, differences in promoter strength and two strain backgrounds. The results revealed further potential for improving the productivity of itaconic acid. Overall, we demonstrate that rapid prototyping by targeted engineering has become a valuable tool to quickly explore metabolic engineering scenario’s including host strains for the production of a heterologous product.