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<p>The multi-parameter intricate process control of bioreactor systems poses an urgent challenge to cell culture. It is feasible to simulate and analyze the implications of each parameter on the culture process, comprehend the correlation between key variables involved in the cell culture process, and establish the framework for multi-parameter collaborative regulation through the utilization of bioreactor modeling. This paper reviews the approaches for model implementation of multi-parameter process control, along with the analysis and optimization techniques of mathematical models related to physiological processes, culture environments, and bioreactor structures involved in bioengineering, tissue engineering, or hepatocyte culture processes. It then covers remaining obstacles and potential for using the digital twin. In view of this, the review anticipates process optimization and control of the bioreactor hepatocyte <i>in vitro</i> culture system and the bioartificial liver clinical support system, intending to enhance the understanding of the hepatocyte culture process and meet the requirements associated with bioartificial liver therapy for the amount and quality of hepatocytes.</p>

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Advances in modeling analysis for multi-parameter bioreactor process control

  • 期刊论文

  • 发表日期：

    2025年1月14日

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  • Xin Lin,
  • Ke Li,
  • Changzhe Wu,
  • Cheng Zhang,
  • Guanghao Zhang,
  • Xiaolin Huo

摘要

The multi-parameter intricate process control of bioreactor systems poses an urgent challenge to cell culture. It is feasible to simulate and analyze the implications of each parameter on the culture process, comprehend the correlation between key variables involved in the cell culture process, and establish the framework for multi-parameter collaborative regulation through the utilization of bioreactor modeling. This paper reviews the approaches for model implementation of multi-parameter process control, along with the analysis and optimization techniques of mathematical models related to physiological processes, culture environments, and bioreactor structures involved in bioengineering, tissue engineering, or hepatocyte culture processes. It then covers remaining obstacles and potential for using the digital twin. In view of this, the review anticipates process optimization and control of the bioreactor hepatocyte in vitro culture system and the bioartificial liver clinical support system, intending to enhance the understanding of the hepatocyte culture process and meet the requirements associated with bioartificial liver therapy for the amount and quality of hepatocytes.