A consensus approach for estimating the predictive accuracy of dynamic models in biology
We propose a method for quantifying the quality of predictions in dynamic models. The approach helps in deciding which additional measurements are more useful. Meta-parameters are used for accelerating parameter estimation and reducing the risk of over-fitting. An ensemble of models is built, and consensus among their predictions is used as an indication of reliability. The method is demonstrated on a metabolic model of Chinese Hamster Ovary cells (CHO).
consensus dynamic modelling systems biology metabolic engineering cell line engineering ensemble modelling