(697a) Flux Balance Analysis and Crispr/Cas9-Facilitated Genetic Modification for the Increased Production of Beta-Carotene in Recombinant Saccharomyces Cerevisiae
AIChE Annual Meeting
2016
2016 AIChE Annual Meeting
Food, Pharmaceutical & Bioengineering Division
Metabolic and Process Engineering for Sustainable Food and Biochemical Production
Thursday, November 17, 2016 - 12:30pm to 12:48pm
First, the substrate utilization and beta-carotene production through the use of a metabolic flux model of S. cerevisiae is examined3. This model is modified to include the additional beta-carotene-producing reactions as well as experimentally determined substrate utilization. Flux variance analysis is used to further refine the model to accurately depict the glucose and ethanol metabolism in the yeast strain. The results from this model, along with previously obtained experimental bioreactor data, will be used to determine the system state that exhibits the highest production of beta-carotene. In addition, this refined intracellular model is combined with a previously developed extracellular kinetic model leading to dynamic predictions of all intracellular metabolite fluxes.
A second strategy looks to improve beta-carotene productivity through the targeted deletion of cellular pathways. Utilizing the OptKnock functionality of the COBRA Matlab toolbox in conjunction with the modified metabolic flux model, it is possible to identify individual genes and groups of genes whose absence could lead to higher beta-carotene productivity4. Potential knockout genes targeted by OptKnock include several genes involved in the conversion of pyruvate and acetate, key intermediates in both the growth and beta-carotene production pathways. In order to confirm these results experimentally, several of these genes are disrupted using a CRISPR/Cas-9 method5. The effects of these genetic disruptions, particularly regarding cellular growth and beta-carotene productivity will be emphasized.
1Verwaal et al., App. and Env. Microbiology, 73, 4342-4350 (2007)
2Reyes et al., Metabolic Engineering, 21, 26-33 (2014)
3Herrgård et al, Nat Biotechnol., 26, 1155-1160 (2008)
4Burgard et al., Biotech. and Bioeng., 84, 647-657 (2003)
5Bao et al., ACS Synthetic Biology, 4, 585-594 (2015)