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<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:dc="http://purl.org/dc/elements/1.1/"><rdf:Description rdf:about="https://repozitorij.uni-lj.si/IzpisGradiva.php?id=109719"><dc:title>Fermentation of CHO cells for biomass component quantification</dc:title><dc:creator>Knez,	Špela	(Avtor)
	</dc:creator><dc:creator>Narat,	Mojca	(Mentor)
	</dc:creator><dc:creator>Borth,	Nicole	(Komentor)
	</dc:creator><dc:subject>Biotechnology</dc:subject><dc:subject>bioprocessing</dc:subject><dc:subject>CHO</dc:subject><dc:subject>modeling</dc:subject><dc:subject>biomass</dc:subject><dc:description>Chinese Hamster Ovary (CHO) cells are the main production host for
recombinant therapeutic proteins. They have desirable process performance

characteristics such as high expression rates, rapid growth and required post-
translational modification. To further exploit its potential, genome scale

metabolic model was made for CHO-K1 cell line. Since model is lacking
correct biomass values of CHO-K1, which could improve model predictions,
focus of this study was to determine biomass and biomass components during
a batch process in exponential phase for producing and non producing cell
line, grown with and without 8 mM Glutamine. For that, bioprocesses of each
condition were performed and sampled 3 times per day to obtain data for
growth characteristics, extracellular metabolites and to determine biomass
composition. Biomass equation and biomas formula were calculated for each
day and condition. Data show cell dry mass varies between strains, yet
differences are not significant. Biomass compositional analysis revealed only
small differences in amino acid composition between different CHO cell lines,
but the composition changes over the time course. As the macromolecular
composition changes over the batch, so must the energy requirements to
synthesize these macromolecules. While results presented in this thesis may
show a need to determine only proteins content for each cell line to improve
cell specific predictions, further studies as energy maintenance and
uptake/secretion rates are still needed to improve model predictions.</dc:description><dc:publisher>[Š. Knez]</dc:publisher><dc:date>2019</dc:date><dc:date>2019-09-07 07:45:53</dc:date><dc:type>Magistrsko delo/naloga</dc:type><dc:identifier>109719</dc:identifier><dc:language>sl</dc:language></rdf:Description></rdf:RDF>
