What can mathematical modelling say about CHO metabolism and protein glycosylation?-Reference-Cited by-同舟云学术

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What can mathematical modelling say about CHO metabolism and protein glycosylation?

Published:2017 Issue: Volume:15 Page:212-221
ISSN:2001-0370
Container-title:Computational and Structural Biotechnology Journal
language:en
Short-container-title:Computational and Structural Biotechnology Journal

Author:

Galleguillos Sarah N.,Ruckerbauer David,Gerstl Matthias P.,Borth Nicole,Hanscho Michael,Zanghellini Jürgen

Funder

Austrian BMWFW

BMVIT

SFG

Standortagentur Tirol

Government of Lower Austria

Business Agency Vienna

Publisher

Elsevier BV

Subject

Computer Science Applications,Genetics,Biochemistry,Structural Biology,Biophysics,Biotechnology

Reference117 articles.

1. Analysis of the landscape of biologically-derived pharmaceuticals in Europe: dominant production systems, molecule types on the rise and approval trends;Kyriakopoulos;Eur J Pharm Sci,2013

2. Engineering mammalian cells in bioprocessing–current achievements and future perspectives;Lim;Biotechnol Appl Biochem,2010

3. Recombinant protein therapeutics from CHO cells–20 years and counting;Jayapal;Chem Eng Prog,2007

4. Production of recombinant proteins in Chinese hamster ovary cells using a protein-free cell culture medium;Zang;Nat Biotechnol,1995

5. 1.4 Aspects of gene transfer and gene amplification in recombinant mamman cells;Wurm;Mamm Cell Biotechnol Protein Production,1997

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1. Building blocks needed for mechanistic modeling of bioprocesses: A critical review based on protein production by CHO cells;Metabolic Engineering Communications;2024-06

2. Deciphering Metabolic Pathways in High-Seeding-Density Fed-Batch Processes for Monoclonal Antibody Production: A Computational Modeling Perspective;Bioengineering;2024-03-28

3. Advancements in CHO metabolomics: techniques, current state and evolving methodologies;Frontiers in Bioengineering and Biotechnology;2024-03-27

4. COSMIC-dFBA: A novel multi-scale hybrid framework for bioprocess modeling;Metabolic Engineering;2024-03

5. Applications of machine learning in antibody discovery, process development, manufacturing and formulation: Current trends, challenges, and opportunities;Computers & Chemical Engineering;2024-03

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