Imperial College London
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Professor Cleo Kontoravdi

Faculty of Engineering, Department of Chemical Engineering

Professor of Biological Systems Engineering
 
 
 
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Contact

 

+44 (0)20 7594 6655cleo.kontoravdi98 Website

 
 
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Location

 

310ACE ExtensionSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Kotidis:2023:10.1016/j.ymben.2022.12.009,
author = {Kotidis, P and Donini, R and Arnsdorf, J and Hansen, AH and Voldborg, BGR and Chiang, AWT and Haslam, SM and Betenbaugh, M and del, Val IJ and Lewis, NE and Krambeck, F and Kontoravdi, C},
doi = {10.1016/j.ymben.2022.12.009},
journal = {Metabolic Engineering},
pages = {87--96},
title = {CHOGlycoNET: comprehensive glycosylation reaction network for CHO cells},
url = {http://dx.doi.org/10.1016/j.ymben.2022.12.009},
volume = {76},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Chinese hamster ovary (CHO) cells are extensively used for the production of glycoprotein therapeutics proteins, for which N-linked glycans are a critical quality attribute due to their influence on activity and immunogenicity. Manipulation of protein glycosylation is commonly achieved through cell or process engineering, which are often guided by mathematical models. However, each study considers a unique glycosylation reaction network that is tailored around the cell line and product at hand. Herein, we use 200 glycan datasets for both recombinantly produced and native proteins from different CHO cell lines to reconstruct a comprehensive reaction network, CHOGlycoNET, based on the individual minimal reaction networks describing each dataset. CHOGlycoNET is used to investigate the distribution of mannosidase and glycosyltransferase enzymes in the Golgi apparatus and identify key network reactions using machine learning and dimensionality reduction techniques. CHOGlycoNET can be used for accelerating glycomodel development and predicting the effect of glycoengineering strategies. Finally, CHOGlycoNET is wrapped in a SBML file to be used as a standalone model or in combination with CHO cell genome scale models.
AU  - Kotidis,P
AU  - Donini,R
AU  - Arnsdorf,J
AU  - Hansen,AH
AU  - Voldborg,BGR
AU  - Chiang,AWT
AU  - Haslam,SM
AU  - Betenbaugh,M
AU  - del,Val IJ
AU  - Lewis,NE
AU  - Krambeck,F
AU  - Kontoravdi,C
DO  - 10.1016/j.ymben.2022.12.009
EP  - 96
PY  - 2023///
SN  - 1096-7176
SP  - 87
TI  - CHOGlycoNET: comprehensive glycosylation reaction network for CHO cells
T2  - Metabolic Engineering
UR  - http://dx.doi.org/10.1016/j.ymben.2022.12.009
UR  - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000927440500001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://www.sciencedirect.com/science/article/pii/S1096717622001586?via%3Dihub
UR  - http://hdl.handle.net/10044/1/102682
VL  - 76
ER  -
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