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Related Experiment Video

Updated: Jul 20, 2025

Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology
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Towards advanced bioprocess optimization: A multiscale modelling approach.

Mariana Monteiro1, Sarah Fadda1, Cleo Kontoravdi1

  • 1Department of Chemical Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom.

Computational and Structural Biotechnology Journal
|July 31, 2023
PubMed
Summary

This study introduces a novel real-time feeding strategy for Chinese Hamster Ovary (CHO) cell cultures using Model Predictive Control (MPC). This adaptable method optimizes protein production by responding to the current process state, improving efficiency in biopharmaceutical manufacturing.

Keywords:
Bioprocess controlCHO cellsDigital twinMetabolic optimizationProcess systems

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Area of Science:

  • Biotechnology
  • Bioprocess Engineering
  • Cell Culture Technology

Background:

  • Mammalian cell culture, particularly using Chinese Hamster Ovary (CHO) cells, is crucial for producing therapeutic proteins, accounting for up to 80% of commercial supply.
  • Current fed-batch processes rely on predetermined feeding strategies, often neglecting real-time process conditions, which can limit optimal protein yield and quality.
  • The need for adaptive and efficient biomanufacturing strategies is paramount to meet the growing demand for biopharmaceuticals.

Purpose of the Study:

  • To develop and validate a Model Predictive Control (MPC) formulation for optimizing feeding policies in CHO cell fed-batch cultures.
  • To create a culture-agnostic control strategy that is transferable across different CHO cell culture systems.
  • To demonstrate the efficacy of real-time, state-aware feeding control compared to traditional open-loop methods.

Main Methods:

  • Development of a hybrid kinetic-stoichiometric reactor model to capture cell growth and protein production dynamics.
  • Implementation of a Model Predictive Control (MPC) framework utilizing the developed model for real-time optimization of feeding rates.
  • Validation using a digital twin as a process emulator to compare closed-loop MPC control against open-loop simulations.

Main Results:

  • The MPC formulation successfully generated optimal feeding policies in real-time, adapting to the dynamic state of the cell culture.
  • Simulations demonstrated significant improvements in process performance under closed-loop MPC control compared to open-loop strategies.
  • The proposed control strategy proved to be agnostic to specific culture conditions, indicating broad applicability across CHO cell manufacturing.

Conclusions:

  • Model Predictive Control offers a powerful approach for real-time optimization of fed-batch CHO cell cultures.
  • The developed culture-agnostic MPC strategy enhances biopharmaceutical production efficiency and robustness.
  • This advanced control methodology holds promise for improving the manufacturing of therapeutic proteins.