Bayesian experimental design for optimizing medium composition and biomass formation of tobacco BY-2 cell suspension cultures in stirred-tank bioreactors.

INTRODUCTION: The plant suspension cell culture Bright Yellow-2 (BY-2) can be an economic platform for producing complex biopharmaceuticals like cytokines and antibodies at small- and medium-scale because of potentially reduced cultivation and purification costs compared to mammalian cells. This is especially relevant for rare diseases. However, the productivity is currently low in terms of biomass formation in a typical batch fermentation. A potential reason might be that the standard BY-2 cultivation medium, as it is used under laboratory shake-flask cultivation conditions, has not yet been comprehensively optimized and tested in industrial bioreactor settings, addressing all four macronutrients relevant for biomass formation (i.e., sucrose, ammonium, nitrate, and phosphate) in parallel. In this article, we therefore propose a multi-variate, multi-objective, and batch-wise Bayesian experimental design (BED) approach for optimally parameterizing macronutrient supply in the cultivation medium, promoting the fresh mass (FM) increase (i.e., growth rate) and final FM (i.e., biomass). METHODS: We performed a sequential and adaptive experimentation utilizing the BED to optimize the cultivation medium in four iterations with four different media each and confirmed the results in two additional experimentation rounds. RESULTS: Our results show that while nitrate and phosphate can be used to adjust the growth rate (i.e., reaching up to 40 g/L × d FM), it is possible to reduce sucrose and ammonium without impacting the growth rate and only affecting the final biomass yield (i.e., reaching up to 300 g/L FM). Thereby, we improved the overall productivity of biomass formation (i.e., as a ratio between nutrient and FM input and FM output) for this batch fermentation process by 36%. DISCUSSION: The results demonstrate the advantages of BED to generate new medium compositions (i.e., macronutrient concentrations) by unbiasedly varying the design space. Thereby, the discovery of new process insights (i.e., impact of individual macronutrient concentrations on the growth rate and biomass formation) is facilitated. Moreover, we show that our data-efficient and black-box BED approach represents a promising alternative to traditional design of experiments (DoE) and mechanistic white-box modeling. Established for the plant suspension cell line BY-2, our BED approach might also apply to yeast and mammalian cells.