Towards rational glyco-engineering in CHO: from data to predictive models
| Autor: | Jürgen Zanghellini, Nicole Borth, David E. Ruckerbauer, Diana Széliová, Jerneja Štor |
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| Rok vydání: | 2021 |
| Předmět: |
0106 biological sciences
Protein glycosylation Glycosylation Computer science Molecular Networks (q-bio.MN) Biomedical Engineering Bioengineering Machine learning computer.software_genre Models Biological 01 natural sciences 03 medical and health sciences 010608 biotechnology Quantitative Biology - Molecular Networks Research question Selection (genetic algorithm) 030304 developmental biology 0303 health sciences Glyco engineering business.industry Estimation theory Biomolecules (q-bio.BM) Kinetics Quantitative Biology - Biomolecules Research Design FOS: Biological sciences Artificial intelligence Experimental methods Focus (optics) business computer Biotechnology |
| Zdroj: | Current Opinion in Biotechnology. 71:9-17 |
| ISSN: | 0958-1669 |
| Popis: | Metabolic modeling strives to develop modeling approaches that are robust and highly predictive. To achieve this, various modeling designs, including hybrid models, and parameter estimation methods that define the type and number of parameters used in the model, are adapted. Accurate input data play an important role so that the selection of experimental methods that provide input data of the required precision with low measurement errors is crucial. For the biopharmaceutically relevant protein glycosylation, the most prominent available models are kinetic models which are able to capture the dynamic nature of protein N-glycosylation. In this review we focus on how to choose the most suitable model for a specific research question, as well as on parameters and considerations to take into account before planning relevant experiments. 15 pages, 2 figures, 63 references |
| Databáze: | OpenAIRE |
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