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Multi-Scale Hybrid Modeling to Predict Cell Culture Process With Metabolic Phase Transitions.

Keqi Wang1, Sarah W Harcum2, Wei Xie1

  • 1Department of Mechanical and Industrial Engineering, Northeastern University, Boston, Massachusetts, USA.

Biotechnology and Bioengineering
|May 13, 2026
PubMed
Summary
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This study introduces a multi-scale hybrid model to predict Chinese Hamster Ovary (CHO) cell culture dynamics and reduce batch variability. The framework accurately forecasts culture behavior using readily available data, enhancing biomanufacturing stability.

Area of Science:

  • Biotechnology
  • Cellular Metabolism
  • Process Analytics

Background:

  • Cell culture processes exhibit batch-to-batch variability, impacting biomanufacturing.
  • Understanding dynamic metabolic phase transitions in cell cultures is crucial for process optimization.

Purpose of the Study:

  • To develop a multi-scale hybrid modeling framework for simulating and predicting Chinese Hamster Ovary (CHO) cell culture dynamics.
  • To account for variability in single-cell metabolic phases and population heterogeneity.
  • To enable accurate long-term forecasting of culture behavior and batch-to-batch variation.

Main Methods:

  • Integration of a stochastic mechanistic model for single-cell metabolism.
  • Incorporation of a probabilistic model for metabolic phase transitions.
Keywords:
cell classificationcell metabolismmetabolic flux dynamics and analysismetabolic phase shiftmulti‐scale kinetic modelingstochastic molecular reaction network

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  • Utilizing a macro-kinetic model for heterogeneous population dynamics, leveraging online and offline measurements.

    • Main Results:

    • The framework accurately predicts multivariate culture dynamics using limited online measurements and initial conditions.
    • Provides uncertainty-aware estimates of batch-to-batch variation.
    • Demonstrates flexibility in representing diverse process trajectories.

    Conclusions:

    • The developed modeling framework is robust for bioprocess analytics and digital twin platforms.
    • It supports systematic experimental design and process control strategies.
    • Potential to improve yield and production stability in biomanufacturing.