Prediction of gene knockout strategies using python's optknock linear programming in Iyali4 of Yarrowia Lipolytica for lycopene production (Journal SCIENCEVOLUTION, 2024)
In this study, the linear programming algorithm OptKnock was applied in Python to the GEM model iYali4 of Yarrowia lipolytica to predict gene knockout strategies and optimize lycopene production. Since this yeast does not naturally produce lycopene, heterologous pathways were additionally incorporated. The metabolic engineering analysis packages used included Cameo and COBRApy; constraint-based metabolic models, specifically FBA, were applied for optimization. Phenotypic phase plane plots (production envelopes) were generated to visualize various phases of optimal growth with different usages of two substrates, oxygen and nitrogen. The optimization also involved analyzing the deletion of the PGM2 gene (phosphoglucomutase), predicted by OptKnock. The FBA results indicated a lycopene production rate of 0,0567 mmol/gDCW/h upon deletion of the PGM2 gene, demonstrating that the predicted gene deletion approach was suitable for simulating and enhancing lycopene production using the iYali4 model of this yeast. However, biomass production was compromised, reducing the microorganism’s growth rate to near zero.
Keywords: Yarrowia lipolytica; OptKnock; FBA; Metabolic engineering; Lycopene; COBRApy; Cameo; Gene knockout; Production envelopes; GEM; iYali4; Linear programming; PGM2