Likelihood based observability analysis and confidence intervals for predictions of dynamic models
05.09.2012
Kreutz C, Raue A, Timmer J.
BMC Syst Biol. 2012;6(1):120
Predicting a system’s behavior based on a mathematical model is a primary task in Systems Biology. If the model parameters are estimated from experimental data, the parameter uncertainty has to be translated into confidence intervals for model predictions. For dynamic models of biochemical networks, the nonlinearity and the large number of parameters renders classical approaches as hardly feasible. In this article reliable confidence intervals are calculated based on the prediction profile likelihood.
