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.

翻译：生物制造创新依赖于高效实验设计以优化工艺与产品质量。传统实验设计方法忽略底层生物加工机制，常面临可解释性与样本效率不足的问题。这一局限促使我们为数字孪生模型校准创建新的最优学习方法。本研究采用细胞培养过程多尺度机理模型，即生物系统之系统模型。该模块化设计模型由多个子模型构成，使我们能够整合跨生产流程的数据。为校准生物系统之系统数字孪生，我们评估模型预测的均方误差，并开发计算方法以量化各子模型参数估计误差对数字孪生预测精度的影响，从而指导样本高效且可解释的实验设计。