Model-Guided Engineering of Cyanobacteria for Improved Biofuel Production | AIChE

Model-Guided Engineering of Cyanobacteria for Improved Biofuel Production


Purdy, H. M. - Presenter, University of Wisconsin-Madison
Reed, J. L., University of Wisconsin-Madison
Cyanobacteria are promising hosts for fuel and chemical production as their main metabolic inputs are minimal and renewable (i.e. light, carbon dioxide, water, and a nitrogen source). However, cyanobacteria engineered to produce compounds of interest frequently suffer from low yields and/or are genetically unstable. As such, metabolically coupling chemical production to growth may be important in engineering cyanobacteria because it can generate robust production characteristics. Additionally, a growth-coupled production approach may help to circumvent gaps in our knowledge about cyanobacterial metabolic regulation, which likely factors into the poor production characteristics that are often reported relative to heterotrophic strains. In this work, a genome-scale metabolic model is being used to guide engineering of the cyanobacterium Synechococcus sp. PCC 7002 for growth-coupled overproduction of short- to mid-chain alcohols (e.g. n-butanol) that are potential biofuels. PCC 7002 is of particular interest as a cyanobacterial production strain due to its relatively rapid growth-rate and tolerance to saline and high-light conditions. Metabolic engineering strategies for improving the production of various alcohols in PCC 7002 were investigated via the metabolic model iSyp708, which was previously developed by our lab. Using this approach, a potential strategy for high-yield growth-coupled production of n-butanol and 2-methyl-1-butanol in PCC 7002 was successfully identified. This strategy hinges on coupling production of these alcohols to nitrate assimilation by rewiring PCC 7002’s native NADH-cycling pathways. Strains are currently being constructed to test this approach. If a strongly growth-coupled production strain is successfully engineered, it will be adaptively evolved to further improve its alcohol production characteristics and genetic changes in these evolved strain(s) will be identified and analyzed. This study demonstrates that genome-scale metabolic models are useful in guiding cyanobacterial metabolic engineering and will help identify genetic alterations for optimizing the production of biofuel compounds in cyanobacteria.