GlycoMAC: A Multiscale Metabolic-Glycosylation Framework for Predicting Glycosylation Across Conditions in Mammalian Cell Cultures

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Original abstract

Antibody productivity and glycosylation quality in CHO cultures arise from a dynamically evolving metabolic environment, yet models often work in isolation or at a single scale. Here, we present a multiscale mechanistic framework linking molecular, cellular, and process levels to predict how inputs shape bioprocess trajectories. The framework is grounded on a single-cell kinetic model that couples metabolic and glycosylation networks governing yield and critical quality attributes (CQAs). A stochastic single-cell model describes environment-dependent transitions among growth, production, and decline, capturing population heterogeneity. We further introduce cumulative variation in the oxygen uptake rate, integrating total metabolic adjustment over time, as a compact biomarker for predicting metabolic shifts. Unlike population-averaged approaches, the model propagates cell-resolved metabolic states (including ammonia-regulated Golgi pH, nucleotide sugar availability, manganese cofactors, and synthesis rates) into glycan processing. The framework was evaluated using CHO-K1 fed-batch cultures producing VRC01 IgG1 under targeted ammonia stress, matched control conditions, and a pyramid-feeding strategy with tighter control. It accurately predicts trajectories of cell density, metabolites, productivity, and glycosylation, including increased G0F and reduced galactosylation under ammonia stress, and quantifies how metabolic heterogeneity drives variability in productivity and CQAs. This work provides a unified foundation for predictive biomanufacturing and advanced process control.

Links and sources

• Official / arXiv page
• PDF from original source

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