Fig. 4

Strategies for predicting ligand-induced phenotypic response. Based on the receptor expression of individual cancer cell lines, either a univariate or multivariate approach can be used to predict the phenotypic response to ligand stimulation. a Univariate approaches relate the respective receptor expression to the observed ligand induced proliferation for each of the four ligands separately. b–c Multivariate approaches such as bagged decision trees (BDTs) relate high-dimensional feature sets to the observed phenotype. b In this case the feature set consists of the five receptor surface levels as well as information about the respective ligand stimulation and mutation status. c The calibrated and validated signaling model allows to simulate the expected signaling dynamics for each individual cell line based on its receptor expression and ligands present. Based on the mechanistic knowledge that the signaling model incorporates, it can expand the initial five-dimensional feature set to a 12-dimensional feature set. This expanded feature set, together with information about mutation status is now connected to the observed growth responses by a bagged decision tree