Work

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

Year: 2026

Type: article

Abstract: To advance understanding of cellular metabolism and reduce batch-to-batch variability in cell culture processes, this study introduces a multi-scale hybrid modeling framework designed to simulate and predict the dynamic behavior of CHO cell cultures undergoing metabolic phase transitions. The model captures dependencies across molecular, cellular, and macro-kinetic levels, accounting for variability in single-cell metabolic phases. It integrates three components: (i) a stochastic mechanistic model of single-cell metabolic networks, (ii) a probabilistic model of phase transitions, and (iii) a macro-kinetic model of heterogeneous population dynamics. This modular architecture enables flexible representation of process trajectories under diverse conditions and incorporates heterogeneous online (e.g., oxygen uptake, pH) and offline measurements (e.g., viable cell density, metabolite concentrations). Leveraging these data and single-cell insights, the framework predicts culture dynamics using only readily available online measurements and initial conditions, delivering accurate long-term forecasts of multivariate culture behavior and uncertainty-aware estimates of batch-to-batch variation. Overall, this work establishes a robust modeling framework relevant to the development of digital twin platforms and predictive bioprocess analytics, with the potential to support systematic experimental design and future process control strategies aimed at improving yield and production stability in biomanufacturing.

Source: Biotechnology and Bioengineering

Authors: Keqi Wang, Sarah W. Harcum, Wei Xie

Institutions: Northeastern University, Clemson University

Language: English

Topic: Viral Infectious Diseases and Gene Expression in Insects

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: hybrid

Estimated APC paid: $5,060

Funders: National Science Foundation, National Institute of Standards and Technology, Division of Civil, Mechanical and Manufacturing Innovation

Awards: IUCRC Phase II+ Clemson University: Advanced Mammalian Biomanufacturing Innovation Center (AMBIC), RII Track-2 FEC: Advanced Biomanufacturing: Catalyzing Improved Host Development and High Quality Medicines through Genome to Phenome Predictions, CAREER: Mechanism-Informed AI for Biological Systems-of-Systems to Accelerate Biomanufacturing Systems Integration and Innovations, THE NATIONAL INSTITUTE FOR INNOVATION IN MANUFACTURING BIOPHARMACEUTICALS (NIIMBL), THE NATIONAL INSTITUTE FOR INNOVATION IN MANUFACTURING BIOPHARMACEUTICALS (NIIMBL)