Sanchez Tarre, Victor; (2021) A combined modelling and experimental characterisation of Chlamydomonas reinhardtii under monochromatic LED illumination. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

Industrial biotechnology is currently synonymous with heterotrophic processes that rely on bacterial, yeast, insect or mammalian cells to biosynthesise products of interest. Microalgae are of substantial biotechnological interest due their polyphyletic nature which grants them access to a wide array of high-value metabolites and their ability to grow under a variety of trophic strategies, including phototrophy. Despite significant process development and optimisation efforts, the full potential of these photosynthetic organisms has yet to be realised. One of the most impactful process parameters when cultivating microalgae is light. It is essential for phototrophic growth and remains highly influential on mixotrophic growth. Indoor cultivations relying on artificial light allow full control of illumination conditions. The advent of LED lights has lowered the costs and improved the flexibility of such installations. Specifically, the spectral composition of LED lights can be accurately and dynamically tailored to the needs of the culture. Spectral composition is known to exert regulatory control over the cell cycle and can affect the cell’s biochemical make up. The effects of illumination strategy on the model microalgae Chlamydomonas reinhardtii were characterised at three different levels (a) growth kinetics, (b) biochemical composition and, (c) transcriptional activity at key carbon nodes. To obtain the transcriptional data, RNA extraction protocols were compared and optimised. Additionally, a suite of candidate reference genes was validated to ensure accurate gene expression normalisation was possible in reverse transcriptase quantitative real-time polymerase chain reaction (RT-qPCR) studies. The growth kinetics and biochemical composition data obtained served as inputs for a previously published genome scale metabolic model. An algorithm was developed to approximate the default biomass composition in the model to experimental data in an effort to increase the fidelity of the simulations. The flux distributions obtained thereafter helped to describe the distinct metabolic fingerprints created under different trophic and illumination strategies.

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