West, Ben, Wood, A Jamie orcid.org/0000-0002-6119-852X and Ungar, Daniel orcid.org/0000-0002-9852-6160 (2021) Computational Modeling of Glycan Processing in the Golgi for Investigating Changes in the Arrangements of Biosynthetic Enzymes. Methods in Molecular Biology. pp. 209-222. ISSN 1064-3745
Abstract
Modeling glycan biosynthesis is becoming increasingly important due to the far-reaching implications that glycosylation can exhibit, from pathologies to biopharmaceutical manufacturing. Here we describe a stochastic simulation approach, to overcome the deterministic nature of previous models, that aims to simulate the action of glycan modifying enzymes to produce a glycan profile. This is then coupled with an approximate Bayesian computation methodology to systematically fit to empirical data in order to determine which set of parameters adequately describes the organization of enzymes within the Golgi. The model is described in detail along with a proof of concept and therapeutic applications.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022. Springer Science+Business Media, LLC, part of Springer Nature. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details |
Keywords: | Bayes Theorem,Computer Simulation,Glycosylation,Golgi Apparatus/metabolism,Polysaccharides/metabolism |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Biology (York) The University of York > Faculty of Sciences (York) > Mathematics (York) |
Funding Information: | Funder Grant number BBSRC (BIOTECHNOLOGY AND BIOLOGICAL SCIENCES RESEARCH COUNCIL) BB/T016965/1 |
Depositing User: | Pure (York) |
Date Deposited: | 20 Jul 2022 15:20 |
Last Modified: | 02 Apr 2025 23:24 |
Published Version: | https://doi.org/10.1007/978-1-0716-1685-7_10 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1007/978-1-0716-1685-7_10 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:189298 |
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