Published on 2018/9/11 11:50:30
test It is known that glycosylation is influenced by many factors, such as variations in culturing conditions, host glycosyltransferase expression levels, genetic mutations as well as the secondary and tertiary protein structure around the glycosylation site. However, the causalities of these influencing factors are still largely unknown, hampering the implementation of the ‘Quality by Design’ principle in pharmaceutical manufacturing. Rational glycol engineering would be greatly facilitated if predictive computational tools could be leveraged to predict optimal culturing conditions and host genome engineering efforts to produce desired glycol patterns. The development of these computational models is being enabled by recent experimental, analytical, and computational advances in systems glycol biology including novel modeling tools.
The table is overview of models with potential application in glycoengineering, only the principal and most distinctive input is listed.
Spahn, Philipp N., and Nathan E. Lewis. "Systems glycobiology for glycoengineering." Current opinion in biotechnology 30 (2014): 218-224.