Diverse cell stimulation kinetics identify predictive signal transduction models

Abstract

The drive to understand cell signaling responses to environmental, chemical and genetic perturbations has produced outstanding fits of computational models to increasingly intricate experiments, yet predicting quantitative responses for new biological conditions remains challenging. Overcoming this challenge depends not only on good models and detailed experimental data but perhaps more so on how well the two are integrated. Our quantitative, live single-cell fluorescence imaging datasets and computational framework to model generic signaling networks show how different changing environments (hereafter kinetic stimulations) probe and result in distinct pathway activation dynamics. Utilizing multiple diverse kinetic stimulations better constrains model parameters and enables predictions of signaling dynamics that would be impossible using traditional step-change stimulations. To demonstrate our approachs generality, we use identified models to predict signaling dynamics in normal, mutated, and drug-treated conditions upon multitudes of kinetic stimulations and quantify which proteins and reaction rates are most sensitive to which extracellular stimulations.