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Updated: Aug 15, 2025

Rapid Antibody Glycoengineering in Chinese Hamster Ovary Cells
Published on: June 2, 2022
CHOGlycoNET: Comprehensive glycosylation reaction network for CHO cells
Pavlos Kotidis1, Roberto Donini2, Johnny Arnsdorf3
1Sargent Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London, London, SW7 2AZ, UK.
Chinese hamster ovary (CHO) cells are crucial for glycoprotein therapeutics. This study introduces CHOGlycoNET, a comprehensive reaction network model, to better understand and predict protein glycosylation for improved biomanufacturing.
Area of Science:
- Biotechnology
- Glycobiology
- Systems Biology
Background:
- Chinese hamster ovary (CHO) cells are vital for producing glycoprotein therapeutics.
- N-linked glycans are critical quality attributes influencing therapeutic protein activity and immunogenicity.
- Current glycosylation models are often product-specific, limiting broader application.
Purpose of the Study:
- To reconstruct a comprehensive glycosylation reaction network for CHO cells.
- To investigate enzyme distribution and identify key reactions in protein glycosylation.
- To facilitate the development of predictive glycomodels and glycoengineering strategies.
Main Methods:
- Compilation of 200 glycan datasets from CHO cell lines.
- Reconstruction of a comprehensive reaction network (CHOGlycoNET) from minimal reaction networks.
- Application of machine learning and dimensionality reduction techniques.
Main Results:
- Development of CHOGlycoNET, a unified glycosylation reaction network for CHO cells.
- Identification of key enzymatic reactions and Golgi apparatus enzyme distribution.
- Demonstration of CHOGlycoNET's utility in predicting glycoengineering outcomes.
Conclusions:
- CHOGlycoNET provides a robust framework for understanding CHO cell glycosylation.
- The model accelerates the development of glycomodels and aids in designing glycoengineering strategies.
- CHOGlycoNET is available as an SBML file for integration with other biological models.
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