Dynamic Multiscale Hybrid Modelling of a CHO cell system for Recombinant Protein Production

Multiscale hybrid modelling of biosystems utilises advantageous aspects of several modelling approaches, from the physical interpretations of kinetic modelling to the power of a data-driven Artificial Neural Network (ANN). This study implements multiscale modelling to gain insight into the productio...

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Bibliographic Details
Published inComputer Aided Chemical Engineering Vol. 53; pp. 43 - 48
Main Authors Pennington, Oliver, Ríos, Sebastián Espinel, Sebastian, Mauro Torres, Dickson, Alan, Zhang, Dongda
Format Book Chapter
LanguageEnglish
Published 2024
Subjects
Kinetic modelling
Machine learning
Metabolic flux analysis
Multiscale modelling
Multiscale modelling
Kinetic modelling
Metabolic flux analysis
Machine learning
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Summary:Multiscale hybrid modelling of biosystems utilises advantageous aspects of several modelling approaches, from the physical interpretations of kinetic modelling to the power of a data-driven Artificial Neural Network (ANN). This study implements multiscale modelling to gain insight into the production of Trastuzumab (Herceptin) from Chinese Hamster Ovary (CHO) cells under challenging dynamics. A reduced metabolic network is subject to enzyme constraints with a Dynamic Metabolic Flux Analysis (ecDMFA) approach and integrated within a macro-scale hybrid kinetic model. The model can simulate batch processes with defined initial conditions and provide insight into the systems behaviour as a response to changes in the cell culture medium. On the intracellular level, the influence from extracellular perturbations can be observed, in addition to giving an estimated production rate of unmeasured by-products. Overall, this model can be potentially used as a reliable digital twin to estimate the underlying fedbatch process dynamics for future model predictive control and process optimisation.
ISBN:9780443288241
0443288240
ISSN:1570-7946
DOI:10.1016/B978-0-443-28824-1.50008-9