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Mammalian cells produce up to 80 % of the commercially available therapeutic proteins, with Chinese Hamster Ovary (CHO) cells being the primary production host. Manufacturing involves a train of reactors, the last of which is typically run in fed-batch mode, where cells grow and produce the required protein. The feeding strategy is decided a priori, from either past operations or the design of experiments and rarely considers the current state of the process. This work proposes a Model Predictive Control (MPC) formulation based on a hybrid kinetic-stoichiometric reactor model to provide optimal feeding policies in real-time, which is agnostic to the culture, hence transferable across CHO cell culture systems. The benefits of the proposed controller formulation are demonstrated through a comparison between an open-loop simulation and closed-loop optimization, using a digital twin as an emulator of the process.
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Mariana Monteiro
Sarah Fadda
Cleo Kontoravdi
SHILAP Revista de lepidopterología
Computational and Structural Biotechnology Journal
Imperial College London
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Monteiro et al. (Sun,) studied this question.
www.synapsesocial.com/papers/69d916e77fca1f84ab6840d5 — DOI: https://doi.org/10.1016/j.csbj.2023.07.003
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