Dynamic modelling plays a crucial role in life science research. A key feature of models is parameter uncertainty, arising from biological variation, or a lack of knowledge. It is generally hard to foresee how parameter uncertainty results in variation in the model predictions, especially when a model contains a lot of complex interactions. Some predictions may be sharp, others highly uncertain. Some parameter uncertainties make all predictions uncertain, whereas others may have no influence at all.
The purpose of this course is to make the participants familiar with general statistical concepts describing uncertainty, and methods to compute prediction uncertainty and sensitivity coming from uncertain parameter values. Methods are presented to obtain parameter uncertainty noisy measurement data. The methods are illustrated with realistic examples.