Putting algae on the map: Improving lipid productivity of Nannochloropsis oceanica through genome-scale metabolic modelling

Summary

The increasing demands of lipid resources for food, feed, and fuels, are putting a significant pressure on the environment. Alternative lipid sources have to be developed to achieve a bio-based economy. Microalgae show promise, owing to their ability to fixate CO2 into valuable biochemicals using harnessed energy from light. Nannochloropsis oceanica stands out for storing over half its dry weight as oil and thriving in various conditions. To make N. oceanica a cost-effective oil source, enhancing its productivity is key, achievable through strain engineering.
However, algal strain engineering is time-consuming and costly, and the complex oil synthesis process complicates identifying effective genetic targets. This thesis focuses on building and using a genome-scale constrained-based metabolic model (GEM) to identify ways to boost oil production efficiently through metabolic engineering. By understanding how to optimize N. oceanica's lipid metabolism, we can advance its role as a reliable source for lipid-based products, contributing to a more sustainable future.