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

TL;DR

This paper presents a multi-scale hybrid modeling framework that predicts CHO cell culture dynamics during metabolic phase transitions, integrating molecular, cellular, and population data for improved bioprocess control.

Contribution

It introduces a novel modular hybrid model combining stochastic, probabilistic, and macro-kinetic components to accurately forecast cell culture behavior from online measurements.

Findings

Accurately predicts culture dynamics and variability.

Integrates multi-level data for comprehensive process understanding.

Supports digital twin development for biomanufacturing.

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 foundation for digital twin platforms and predictive bioprocess analytics, supporting systematic experimental design and process control to improve yield and production stability in biomanufacturing.