Search

Mammalian cell cultures make a significant contribution to the pharmaceutical industry. They produce many of the biopharmaceuticals obtaining FDA-approval each year. Motivated by quality-by-design principles, various modelling methodologies are frequently trialled to gain insight into these bioprocesses. However, these systems are highly complex and uncertain, involving dynamics at different scales, both in time and space, making them challenging to model in a comprehensive and fully mechanistic manner. This study develops a machine-learning-supported multiscale modelling framework of cell cultures, linking the macroscale bioprocess dynamics to the microscale metabolic flux distribution. As a relevant biopharmaceutical case study, we consider the production of Trastuzumab by Chinese Hamster Ovary (CHO) cells in batch. A macroscale hybrid model is constructed by integrating macro-kinetic and machine-learning approaches. Enzyme-constrained Dynamic Metabolic Flux Analysis (ecDMFA) is adopted to calculate flux distributions based on the dynamic predictions of the hybrid model. Uncertainty estimation of the multiscale model is conducted through bootstrapping. Judging from experimental data, our hybrid model can reduce the modelling error of the macroscale dynamics to 8.0%; a 70% reduction from the purely mechanistic model. In addition, the predicted dynamic flux distribution aligns with observations seen in literature, highlighting important metabolic changes throughout the process. Model uncertainty is maintained at a low level, demonstrating the trustworthiness of the predictions. Overall, our comprehensive modelling framework has the potential to facilitate the development of digital twins in the biopharmaceutical industry., Competing Interests: Declaration of competing interest The authors declare that they have no competing interest., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Biotechnology offers many opportunities for the sustainable manufacturing of valuable products. The toolbox to optimize bioprocesses includes extracellular process elements such as the bioreactor design and mode of operation, medium formulation, culture conditions, feeding rates, and so on. However, these elements are frequently insufficient for achieving optimal process performance or precise product composition. One can use metabolic and genetic engineering methods for optimization at the intracellular level. Nevertheless, those are often of static nature, failing when applied to dynamic processes or if disturbances occur. Furthermore, many bioprocesses are optimized empirically and implemented with little-to-no feedback control to counteract disturbances. The concept of cybergenetics has opened new possibilities to optimize bioprocesses by enabling online modulation of the gene expression of metabolism-relevant proteins via external inputs (e.g., light intensity in optogenetics). Here, we fuse cybergenetics with model-based optimization and predictive control for optimizing dynamic bioprocesses. To do so, we propose to use dynamic constraint-based models that integrate the dynamics of metabolic reactions, resource allocation, and inducible gene expression. We formulate a model-based optimal control problem to find the optimal process inputs. Furthermore, we propose using model predictive control to address uncertainties via online feedback. We focus on fed-batch processes, where the substrate feeding rate is an additional optimization variable. As a simulation example, we show the optogenetic control of the ATPase enzyme complex for dynamic modulation of enforced ATP wasting to adjust product yield and productivity., (© 2023 The Authors. Biotechnology and Bioengineering published by Wiley Periodicals LLC.)