  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.
