A Markov chain model for N-linked protein glycosylation – towards a low-parameter tool for model-driven glycoengineering

Glycosylation is a critical quality attribute of most recombinant biotherapeutics. Consequently, drug development requires careful control of glycoforms to meet bioactivity and biosafety requirements. However, glycoengineering can be extraordinarily difficult given the complex reaction networks unde...

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Bibliographic Details
Published in:Metabolic engineering 2016-01, Vol.33, p.52-66
Main Authors: Spahn, Philipp N., Hansen, Anders H., Hansen, Henning G., Arnsdorf, Johnny, Kildegaard, Helene F., Lewis, Nathan E.
Format: Article
Language:English
Subjects:
Animals
CHO Cells
Computer Simulation
Cricetulus
Flux-balance analysis
Glycoengineering
Glycoproteins - chemistry
Glycoproteins - genetics
Glycoproteins - metabolism
Glycosylation
Markov Chains
Metabolic Engineering - methods
Metabolic Flux Analysis - methods
Models, Biological
Models, Statistical
Polysaccharides - chemistry
Polysaccharides - genetics
Polysaccharides - metabolism
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Summary:Glycosylation is a critical quality attribute of most recombinant biotherapeutics. Consequently, drug development requires careful control of glycoforms to meet bioactivity and biosafety requirements. However, glycoengineering can be extraordinarily difficult given the complex reaction networks underlying glycosylation and the vast number of different glycans that can be synthesized in a host cell. Computational modeling offers an intriguing option to rationally guide glycoengineering, but the high parametric demands of current modeling approaches pose challenges to their application. Here we present a novel low-parameter approach to describe glycosylation using flux-balance and Markov chain modeling. The model recapitulates the biological complexity of glycosylation, but does not require user-provided kinetic information. We use this method to predict and experimentally validate glycoprofiles on EPO, IgG as well as the endogenous secretome following glycosyltransferase knock-out in different Chinese hamster ovary (CHO) cell lines. Our approach offers a flexible and user-friendly platform that can serve as a basis for powerful computational engineering efforts in mammalian cell factories for biopharmaceutical production. •A non-kinetic low-parameter Markov model for N-glycosylation is presented.•The model is fit to experimental glycoprofiles.•The model can be used to predict the effect of glycosyltransferase knock-downs.•We show validation data on IgG, EPO and the secretome.
ISSN:1096-7176
1096-7184
DOI:10.1016/j.ymben.2015.10.007