Multi-fidelity Gaussian Process for Biomanufacturing Process Modeling with Small Data
This addresses the problem of high data acquisition costs and uncertainty in bioprocess modeling for biomanufacturing, though it appears incremental as it applies an existing statistical method to a new domain.
The paper tackles the challenge of modeling complex biomanufacturing processes with limited data by proposing a multi-fidelity Gaussian process approach, demonstrating its efficacy on real-world datasets for bioreactor scale-up and knowledge transfer across cell lines.
In biomanufacturing, developing an accurate model to simulate the complex dynamics of bioprocesses is an important yet challenging task. This is partially due to the uncertainty associated with bioprocesses, high data acquisition cost, and lack of data availability to learn complex relations in bioprocesses. To deal with these challenges, we propose to use a statistical machine learning approach, multi-fidelity Gaussian process, for process modelling in biomanufacturing. Gaussian process regression is a well-established technique based on probability theory which can naturally consider uncertainty in a dataset via Gaussian noise, and multi-fidelity techniques can make use of multiple sources of information with different levels of fidelity, thus suitable for bioprocess modeling with small data. We apply the multi-fidelity Gaussian process to solve two significant problems in biomanufacturing, bioreactor scale-up and knowledge transfer across cell lines, and demonstrate its efficacy on real-world datasets.