Metabolic modeling and engineering of Nannochloropsis microalgae for improved lipid production

Background

Microlgae show great potential for replacing fossil fuels in the near future. However, strains and technologies must be improved in order to reach an economically feasible bulk production. Nannochloropsis oceanica is a microalgal specie with high natural lipid production and accumulation (50-60% DW), in addition to producing high contents of the omega-3 fatty acid Eicosapentaenoic acid (EPA, 7-10% of total lipids). 

Recent efforts in understanding lipid accumulation in Nannochloropsis provided omics data (e.g. transcriptomics, lipidomics) and physiological data for various conditions, which can now be combined into a metabolic model. In addition, molecular biology tools are already available for N. oceanica (e.g. CRISPR and transformation techniques). Metabolic modeling of microalgal metabolism is key to understanding carbon partitioning mechanisms, and provides insights for strain engineering. Metabolic engineering based on the model predictions can then steer the microalgae to form specific products and lipid compositions at increased production rates.

Aim

Developing and testing a metabolic model of Nannochloropsis oceanica, to improve understanding of lipid metabolism and to further advance Nannochloropsis as cell factory for specific lipid products.

Approach

In collaboration with the laboratory of Systems and Synthetic Biology (SSB), a Genome-scale Metabolic model will be developed and tested taking into account available omics and physiological data. Using the model, predictions will be made of lipid compositions and production rates in various growth conditions. In addition, using the model, knock-out simulations will be performed to predict targets for metabolic engineering. Finally, mutants will be created using novel strain engineering techniques such as CRISPR-Cas and tested to validate the model.

Thesis project

Within this project there are many possibilities for MSc and BSc students interested in either bio-informatics, molecular biology and/or process engineering. Feel free to contact us and ask us about possible thesis projects!