Your browser doesn't support javascript.
loading
Towards rational glyco-engineering in CHO: from data to predictive models.
Stor, Jerneja; Ruckerbauer, David E; Széliová, Diana; Zanghellini, Jürgen; Borth, Nicole.
Afiliación
  • Stor J; Department of Biotechnology, University of Natural Resources and Life Sciences Vienna, A-1190 Vienna, Austria; acib - Austrian Centre of Industrial Biotechnology, A-8010 Graz, Austria.
  • Ruckerbauer DE; acib - Austrian Centre of Industrial Biotechnology, A-8010 Graz, Austria; Department of Analytical Chemistry, University of Vienna, A-1090 Vienna, Austria.
  • Széliová D; acib - Austrian Centre of Industrial Biotechnology, A-8010 Graz, Austria; Department of Analytical Chemistry, University of Vienna, A-1090 Vienna, Austria.
  • Zanghellini J; acib - Austrian Centre of Industrial Biotechnology, A-8010 Graz, Austria; Department of Analytical Chemistry, University of Vienna, A-1090 Vienna, Austria. Electronic address: juergen.zanghellini@univie.ac.at.
  • Borth N; Department of Biotechnology, University of Natural Resources and Life Sciences Vienna, A-1190 Vienna, Austria; acib - Austrian Centre of Industrial Biotechnology, A-8010 Graz, Austria. Electronic address: nicole.borth@boku.ac.at.
Curr Opin Biotechnol ; 71: 9-17, 2021 10.
Article en En | MEDLINE | ID: mdl-34048995
Metabolic modelling strives to develop modelling approaches that are robust and highly predictive. To achieve this, various modelling 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.
Asunto(s)
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Proyectos de Investigación / Modelos Biológicos Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Curr Opin Biotechnol Asunto de la revista: BIOTECNOLOGIA Año: 2021 Tipo del documento: Article País de afiliación: Austria Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Proyectos de Investigación / Modelos Biológicos Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Curr Opin Biotechnol Asunto de la revista: BIOTECNOLOGIA Año: 2021 Tipo del documento: Article País de afiliación: Austria Pais de publicación: Reino Unido