Literature DB >> 18636599

A mathematical model of N-linked glycoform biosynthesis.

P Umaña1, J E Bailey.   

Abstract

Metabolic engineering of N-linked oligosaccharide biosynthesis to produce novel glycoforms or glycoform distributions of a recombinant glycoprotein can potentially lead to an improved therapeutic performance of the glycoprotein product. Effective engineering of this pathway to maximize the fractions of beneficial glycoforms within the glycoform population of a target glycoprotein can be aided by a mathematical model of the N-linked glycosylation process. A mathematical model is presented here, whose main function is to calculate the expected qualitative trends in the N-linked oligosaccharide distribution resulting from changes in the levels of one or more enzymes involved in the network of enzyme-catalyzed reactions that accomplish N-linked oligosaccharide biosynthesis. It consists of mass balances for 33 different oligosaccharide species N-linked to a specified protein that is being transported through the different compartments of the Golgi complex. Values of the model parameters describing Chinese hamster ovary (CHO) cells were estimated from literature information. A basal set of kinetic parameters for the enzyme-catalyzed reactions acting on free oligosaccharide substrates was also obtained from the literature. The solution of the system for this basal set of parameters gave a glycoform distribution consisting mainly of complex-galactosylated oligosaccharides distributed in structures with different numbers of antennae in a fashion similar to that observed for various recombinant proteins produced in CHO cells. Other simulations indicate that changes in the oligosaccharide distribution could easily result from alteration in glycoprotein productivity within the range currently attainable in industry. The overexpression of N-acetylglucosaminyltransferase III in CHO cells was simulated under different conditions to test the main function of the model. These simulations allow a comparison of different strategies, such as simultaneous overexpression of several enzymes or spatial relocation of enzymes, when trying to optimize a particular glycoform distribution.

Entities:  

Year:  1997        PMID: 18636599     DOI: 10.1002/(SICI)1097-0290(19970920)55:6<890::AID-BIT7>3.0.CO;2-B

Source DB:  PubMed          Journal:  Biotechnol Bioeng        ISSN: 0006-3592            Impact factor:   4.530


  35 in total

Review 1.  Integration of systems glycobiology with bioinformatics toolboxes, glycoinformatics resources, and glycoproteomics data.

Authors:  Gang Liu; Sriram Neelamegham
Journal:  Wiley Interdiscip Rev Syst Biol Med       Date:  2015-04-13

Review 2.  Systems glycobiology: biochemical reaction networks regulating glycan structure and function.

Authors:  Sriram Neelamegham; Gang Liu
Journal:  Glycobiology       Date:  2011-03-24       Impact factor: 4.313

3.  Animal Cell Expression Systems.

Authors:  M Butler; U Reichl
Journal:  Adv Biochem Eng Biotechnol       Date:  2021       Impact factor: 2.635

4.  Glycosylation Network Analysis Toolbox: a MATLAB-based environment for systems glycobiology.

Authors:  Gang Liu; Apurv Puri; Sriram Neelamegham
Journal:  Bioinformatics       Date:  2012-12-10       Impact factor: 6.937

Review 5.  Understanding glycomechanics using mathematical modeling: a review of current approaches to simulate cellular glycosylation reaction networks.

Authors:  Apurv Puri; Sriram Neelamegham
Journal:  Ann Biomed Eng       Date:  2011-11-17       Impact factor: 3.934

Review 6.  Bioinformatics and molecular modeling in glycobiology.

Authors:  Martin Frank; Siegfried Schloissnig
Journal:  Cell Mol Life Sci       Date:  2010-04-04       Impact factor: 9.261

7.  Systems-level modeling of cellular glycosylation reaction networks: O-linked glycan formation on natural selectin ligands.

Authors:  Gang Liu; Dhananjay D Marathe; Khushi L Matta; Sriram Neelamegham
Journal:  Bioinformatics       Date:  2008-10-07       Impact factor: 6.937

8.  A mathematical model to derive N-glycan structures and cellular enzyme activities from mass spectrometric data.

Authors:  Frederick J Krambeck; Sandra V Bennun; Someet Narang; Sean Choi; Kevin J Yarema; Michael J Betenbaugh
Journal:  Glycobiology       Date:  2009-06-08       Impact factor: 4.313

9.  An HPLC-MALDI MS method for N-glycan analyses using smaller size samples: application to monitor glycan modulation by medium conditions.

Authors:  Michael P Gillmeister; Noboru Tomiya; Scott J Jacobia; Yuan C Lee; Stephen F Gorfien; Michael J Betenbaugh
Journal:  Glycoconj J       Date:  2009-12       Impact factor: 2.916

10.  Optimisation of the cellular metabolism of glycosylation for recombinant proteins produced by Mammalian cell systems.

Authors:  M Butler
Journal:  Cytotechnology       Date:  2006-06-09       Impact factor: 2.058

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