Systems Pharmacology of Cell-Signaling Networks in Human Disease

Cell-signaling networks are fascinatingly complex communication systems that integrate a diverse array of extracellular cues and appropriately modulate cellular responses. Deregulation of these networks can result in various human diseases, from cancer to Alzheimer’s disease. A systems-level understanding of cell-signaling networks will improve our understanding of normal physiology and disease states, and most importantly, improve our ability to pharmacologically treat disease.
In this dissertation, we develop and implement experimental and computational approaches to study human cell-signaling networks. This work is comprised of four aims: 1) to develop an experimental platform to measure receptor expression, 2) to study the effect of local pathway topology on directed perturbations, 3) to apply data-driven modeling to a host-pathogen dataset to discover signaling subnetworks, and 4) to develop a high-throughput multiplex experimental assay to measure dynamic information flow in cell-signaling networks.
Many signal transduction cascades are initiated by receptors that sense extracellular stimuli and catalyze downstream signaling events. Receptor expression is therefore critical in defining cellular responsiveness. We began by developing a high-throughput qRT-PCR-based platform to measure expression of 194 signaling receptors. We then leveraged the high-throughput capabilities of the assay to probe receptor expression patterns in 40 cancer cell lines. Downstream of receptors, pathway structure is critical for processing and propagating cellular information. We next built 95 computational models to study the effect of local pathway connectivity on drug and RNAi targeting. The models revealed an important role for pathway structure in determining levels of pathway inhibition. Pathways are further interconnected with other pathways to integrate numerous cues and regulate cellular processes. We next applied data-driven modeling to study cell signaling during Coxsackievirus B3 infection of cardiomyocytes. The model implicated the ERK1/2, ERK5, and p38 pathways in regulating apoptosis and necrosis during infection. Follow-up experiments revealed dual ERK1/2 and p38 inhibition dramatically reduces cell death and virus release. In order to measure dynamic information flow throughput the network in multiple pathways simultaneously, we describe the design of a high-throughput kinase activity assay. Together these aims provide a framework to study cell-signaling networks and their pharmacological inhibition at a systems level.