Deciphering the network structure of signaling dynamics

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Project Summary/Abstract The signaling network involving Ras GTPases and their downstream effectors, particularly the PI3K and MAPK/ERK pathways, plays important roles in diverse cellular processes including proliferation, metabolism, migration, and survival. Derangements of the signaling network leads to diseases such as developmental anomalies, metabolic disorders, and cancer. Despite its clinical importance, targeting the Ras signaling network for disease treatment has been challenging due to an incomplete understanding of its complex regulation. Recent studies of the Ras signaling dynamics at the single-cell level revealed fascinating properties with important functional implications. In particular, we demonstrated that the Ras signaling network displays hallmarks of excitable systems such as stochastic activation, traveling waves, and all-or-none activation. The excitability of the Ras-PI3K-ERK signaling network plays important roles in cell motility and integration of chemical and mechanical stimuli that regulate cell proliferation. However, the overall structure of the Ras signaling network that encodes the excitable dynamics is not known. The purpose of this application is to analyze the structure of the Ras signaling network by systematically perturbing individual nodes and studying the effects on the excitable dynamics of the network. To this end we will develop a method based on fluorescent live cell imaging to simultaneously track a large number of signaling activities. We will use this method to monitor the excitable responses of ~30 signaling activities when each activity is pharmacologically inhibited. The effects of perturbations will provide insight into the regulatory relationship between the signaling activities. We will also carry out network analysis on different cell types to understand the basis of their distinct responses to small molecule inhibitors. These studies will pave the way for quantitative models containing sufficient details of the network to make accurate predictions of cellular responses.