Development of a biochemically based structured model for algal growth in a photobioreactor.
There have been significant advances in the understanding of the metabolism of microalgae in the past decades and there is now a huge amount of data available on the subject. There is, however, a gap of knowledge as to how these organisms can adapt so well to their changing environment. Most recent mathematical models try to focus on some of the aspects but do not always include data from both the metabolism and engineering.
The project aims to develop and refine a mathematical model capable of predicting the growth of the microalga Chlamydomonas reinhardtii, which can reliably represent the adaptation of metabolism within changing environment in photobioreactors (PBRs). The model will be biochemically structured, meaning it will explicitly express energy-yielding and energy-requiring metabolic processes, which ought to be taken into account in the overall biomass growth process. In order to do so, prior knowledge of the metabolic network and energetics coupling is required, requiring specific metabolic investigations that will consider the microalga as a “cell factory” capable of transforming and storing energy under various forms.
The model will be compared to experiments at the population scale in PBRs, where the growth parameters can be controlled: light, temperature, pH, dissolved gases being the most common. Because light is the main energy source for growth under autotrophic conditions, light availability within PBR ought to be fully characterized and analyzed during culturing so that photon uptake rate can be used as model input. Using the PBR as a platform, the model parameters will be assessed experimentally and/or identified in order to make the model as robust as possible. The objective is to develop a tool that would be as deterministic/generic as possible, in order to extend it to other microalgal species (eg: Phaeodactylum tricornutum) in the future.