Digital Twin Calibration for Biological System-of-Systems: Cell Culture Manufacturing Process

• URL: http://arxiv.org/abs/2405.03913v2
• Date: Fri, 28 Jun 2024 15:13:15 GMT
• Title: Digital Twin Calibration for Biological System-of-Systems: Cell Culture Manufacturing Process
• Authors: Fuqiang Cheng, Wei Xie, Hua Zheng,
• Abstract summary: 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.
• Score: 3.0790370651488983
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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