A workflow for incorporating cross-sectional data into the calibration of dynamic models
AO_SCPLOWBSTRACTC_SCPLOWMathematical modelling and dynamic simulations are commonly used in systems medicine to investigate the interactions between various biological entities in time. The level of model complexity is mainly restricted by the number of model parameters that can be estimated from available experimental data and prior knowledge. The calibration of dynamic models usually requires longitudinal data from multiple individuals, which is challenging to obtain and, consequently, not always available. On the contrary, the collection of cross-sectional data is often more feasible. Here, we demonstrate how the parameters of individual dynamic models can be estimated from such cross-sectional data using a Bayesian updating method. We illustrate this approach on a model for puberty in girls with cross-sectional hormone measurement data.