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

TL;DR

This paper introduces a novel calibration method for biological digital twins, specifically for cell culture manufacturing, enhancing interpretability and sample efficiency in process optimization.

Contribution

It develops a new optimal learning approach for calibrating multi-scale biological system digital twins with modular sub-models, improving interpretability and data efficiency.

Findings

Quantifies the impact of sub-model parameter errors on digital twin accuracy

Provides a computational method to guide sample-efficient experiments

Enhances interpretability of bioprocess models

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.