Multi-cycle high-throughput growth media optimization using batch Bayesian optimization

Abstract

BACKGROUND

The optimization of growth media is a crucial step in enhancing microbial growth and product formation in industrial bioprocesses. While Design of Experiments (DoE) is widely used for this purpose, it has inherent limitations, particularly in capturing complex interactions between media components and managing the high number of required experiments without overlooking important factors or interactions. This study evaluates batch Bayesian optimization (BBO) as an alternative approach by applying it in simulations and in wet-lab experiments, using Sporosarcina pasteurii as a model organism, to demonstrate its effectiveness.

RESULTS

In silico, BBO significantly outperformed DoE on average (+14%), although it was not superior in all test cases, highlighting the need for further investigation. In automated high-throughput microbioreactor experiments with S. pasteurii, BBO led to a 28% increase in maximum backscatter value, indicating a higher biomass titer compared to a DoE-optimized medium. BBO enabled a more targeted optimization of media components, allowing broader exploration when needed and focusing on specific adjustments where appropriate. The BBO-optimized growth medium, designed in small-scale experiments, also outperformed the DoE-optimized medium at a 2 L bioreactor scale.

CONCLUSION

In our use cases, BBO offers a more effective and targeted approach for growth media optimization than traditional DoE. Placing our results in the context of other studies, BBO shows promise for outperforming DoE in bioprocess optimization tasks. The ability of BBO to streamline the optimization process could enhance yields and reduce costs in industrial bioprocesses. © 2025 The Author(s). Journal of Chemical Technology and Biotechnology published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry (SCI).