Digital Twin Calibration for Biological System-of-Systems: Cell Culture Manufacturing Process
<|reference_start|>Digital Twin Calibration for Biological System-of-Systems: Cell Culture Manufacturing Process: 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.<|reference_end|>
@article{cheng2024digital,
    title={Digital Twin Calibration for Biological System-of-Systems: Cell Culture
  Manufacturing Process},
    author={Fuqiang Cheng, Wei Xie, Hua Zheng},
    journal={arXiv preprint arXiv:2405.03913},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.03913},
    primaryClass={q-bio.QM cs.LG stat.ML}
}