GlycoMAC: A Multiscale Metabolic-Glycosylation Framework for Predicting Glycosylation Across Conditions in Mammalian Cell Cultures
Abstract
Antibody productivity and glycosylation quality in CHO cell cultures emerge from a dynamically evolving metabolic environment, yet existing models often work in isolation or at a single scale. Here, we present a multiscale mechanistic framework linking molecular, cellular, and process scales to predict how inputs shape bioprocess trajectories. The framework combines a single-cell kinetic model of metabolism and glycosylation with a stochastic population model that captures environment-dependent transitions among growth, production, and decline states. To characterize metabolic adaptation, we introduce the cumulative variation in oxygen uptake rate, a trajectory-based biomarker that quantifies the total metabolic adjustment experienced during culture. 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 reproduced trajectories of cell growth, metabolites, productivity, and harvest glycosylation, including increased G0F abundance and reduced galactosylation under ammonia stress. By mechanistically linking process conditions to cell-state dynamics and glycosylation outcomes, the framework provides a unified foundation for digital bioprocessing, predictive biomanufacturing, and advanced process control.
Versions
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BibTeX
@misc{zeng2026glycomac,
title = {{GlycoMAC: A Multiscale Metabolic-Glycosylation Framework for Predicting Glycosylation Across Conditions in Mammalian Cell Cultures}},
author = {Yuming Zeng and Sarah W. Harcum and Jinxiang Pei and Wei Xie},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2607.01725},
eprint = {2607.01725},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2607.01725},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - GlycoMAC: A Multiscale Metabolic-Glycosylation Framework for Predicting Glycosylation Across Conditions in Mammalian Cell Cultures
AU - Yuming Zeng
AU - Sarah W. Harcum
AU - Jinxiang Pei
AU - Wei Xie
PY - 2026
DA - 2026/09/01
PB - arXiv
DO - 10.48550/arxiv.2607.01725
UR - https://arxiv.org/abs/2607.01725
AB - Antibody productivity and glycosylation quality in CHO cell cultures emerge from a dynamically evolving metabolic environment, yet existing models often work in isolation or at a single scale. Here, we present a multiscale mechanistic framework linking molecular, cellular, and process scales to predict how inputs shape bioprocess trajectories. The framework combines a single-cell kinetic model of metabolism and glycosylation with a stochastic population model that captures environment-dependent transitions among growth, production, and decline states. To characterize metabolic adaptation, we introduce the cumulative variation in oxygen uptake rate, a trajectory-based biomarker that quantifies the total metabolic adjustment experienced during culture. 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 reproduced trajectories of cell growth, metabolites, productivity, and harvest glycosylation, including increased G0F abundance and reduced galactosylation under ammonia stress. By mechanistically linking process conditions to cell-state dynamics and glycosylation outcomes, the framework provides a unified foundation for digital bioprocessing, predictive biomanufacturing, and advanced process control.
ER -