Tools and applications to assess yeast physiology and robustness in bioprocesses: Lab-scale methods from single cells to populations

Doctoral thesis, 2024

Bioprocesses enable the efficient production of valuable chemicals by microorganisms such as the yeast Saccharomyces cerevisiae. Predictable and stochastic perturbations affect microbial performance in an industrial-scale bioreactor. Because some of these complex and dynamic perturbations are difficult to mimic at a small scale, strains selected and developed in the lab might underperform in industrial settings, creating challenges during scale-up. Moreover, the ability of a system to maintain a stable performance, defined as microbial robustness, has been overlooked owing to a scarcity of suitable quantification methods.

This thesis describes novel approaches for characterising industrially relevant microorganisms at laboratory scale. The developed methods and techniques were applied to one laboratory and two industrial yeast strains predominantly in the context of second-generation biofuel production. Yeast physiology was explored by both canonical methods and real-time monitoring of eight intracellular parameters using the ScEnSor Kit. To complement physiology, the concept of robustness was explained and elaborated. A recently formulated method for quantifying robustness was applied to physiological data to determine the stability of cell performance and expand the concept of robustness itself. Lastly, the physiology and robustness of yeast cells exposed to rapid feast-starvation oscillations were investigated using dynamic microfluidics single-cell cultivation. This technique proved instrumental in mimicking, at a laboratory scale, the fast dynamics encountered within large-scale bioreactors.

In summary, the tools presented in this thesis address some of the challenges associated with the scaling up of bioprocesses. Owing to the multilevel resolution, ranging from populations to single cells, the developed techniques have the potential to advance our understanding of microbial performance and robustness, ensuring more efficient and reliable industrial applications of engineered microorganisms.