  Biomanufacturing innovation relies on an efficient Design of Experiments
(DoEs) to optimize processes and product quality. Traditional DoE methods,
ignoring the underlying bioprocessing mechanisms, often suffer from a lack of
interpretability and sample efficiency. This limitation motivates us to create
a new optimal learning approach for digital twin model calibration. In this
study, we consider the cell culture process multi-scale mechanistic model, also
known as Biological System-of-Systems (Bio-SoS). This model with a modular
design, composed of sub-models, allows us to integrate data across various
production processes. To calibrate the Bio-SoS digital twin, we evaluate the
mean squared error of model prediction and develop a computational approach to
quantify the impact of parameter estimation error of individual sub-models on
the prediction accuracy of digital twin, which can guide sample-efficient and
interpretable DoEs.
