PhD candidate, MIT, Biological Engineering Department
MS, Biological Engineering, MIT 2006
BA, Biological Sciences, University of Chicago, 2003
The cellular genome is continuously exposed to various sources of DNA damage. Cells respond to DNA damage using extensive surveillance and repair mechanisms mediated largely by protein kinases and phosphatases. In multi-cellular organisms, the accumulation of DNA damaged cells is avoided either through the activation of cell cycle checkpoints allowing for DNA repair or through the elimination of damaged cells by apoptosis. To date, the study of the signaling mechanisms that govern the DNA damage response have focused on delineating the molecules involved in specific DNA damage response pathways. However, to develop a comprehensive understanding of how the cell responds to DNA damage, dynamic measurements of signals within each specific pathway and across pathways, encompassing the entire relevant signal transduction network, is necessary.
In this work we will focus on gaining insight into the relationship between the molecular signaling and cellular response to a therapeutically relevant DNA lesion, the DNA double strand break (DSB).
Our goal is to develop novel assays using In-cell Western and automated immunofluorescence (auto IF) technology that may be used to collect quantitative, dynamic, high throughput signaling information spanning the signaling pathways most relevant to DSB signaling. This signaling data will be complemented by data representing a dense sampling of the components of the extended DNA damage network, to be monitored using high-throughput kinase assays and quantitative immunoblotting.
Taking advantage of this larger signaling data set, including cellular response measurements taken in parallel, our goal is to use data-driven modeling approaches to develop models of the DNA damage signal transduction network that will i) provide insight into the process by which cells decide their fate following induction of DSB, serving as a rich source for mechanistic hypothesis generation, and ii) be able to quantitatively predict apoptosis following novel perturbations to the signaling network.