Genome-Scale Modeling Coupled with Crispri for Improving Biofuel Production in Cyanobacteria

Cyanobacteria are promising hosts for production of low-value products such as biofuels. However, competitive rates, yields and titers have not been reached to date. We are combining genome-scale modeling and CRISPR/Cas to create highly modified cyanobacteria cell factories. We used constraint-based modeling and optimization algorithms on a genome-scale model (GEM) of Synechocystis PCC6803 to predict ways to improve productivity of 1-butanol and other biofuels. We found that a combination of gene knockouts could couple biofuel production to cell growth; such strains are predicted to give high biofuel productivities and titers. High productivity of fermentation-based 1-butanol requires elimination of NADH sinks, while fatty-acid and terpene based products require creating imbalances in intracellular ATP and NADPH production and consumption. We are currently implementing these knockouts using CRISRP/Cas and characterizing mutant strains. Integration of transcriptomics and proteomics data from wild-type and mutant strains into the GEM will allow us to further refine our engineering strategies. Combining genome-scale modeling with CRISPR/Cas can lead a new era of metabolic engineering in cyanobacteria.