  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.
