Thesis: Ph. D., Massachusetts Institute of Technology, Department of Biological Engineering, 2016. / Cataloged from PDF version of thesis. / Includes bibliographical references. / In recent years, the field of systems biology rapidly expanded in both basic and translational science. This method of investigation revolves around an iterative cycle of observing a system, making predictions about its behavior using a model, and testing these hypotheses with further experiments. Though computational approaches have achieved astonishing sophistication, these models are fundamentally limited in their predictive power by the quality of data they are given. Thus, the lack of tools to capture information-rich data has become a bottleneck for our ability to predict and perturb biological systems. This thesis focuses on developing tools to collect data that captures the complexity of signaling networks to deepen our understanding of the mechanistic processes occurring inside the cell. In particular, we present a method capable of measuring phosphorylation changes in the cell with 10-second resolution. One of the best-characterized proteins in biology is the Epidermal Growth Factor Receptor (EGFR), which has long been associated with diseases including cancer. Despite development of several EGFR inhibitors, the clinical efficacy of targeting this receptor has been minimal. This shortcoming is attributable primarily to the incredible complexity of the EGFR signaling network, which includes hundreds of proteins throughout the cell. In this thesis, we use EGFR as a model to demonstrate the utility of measuring phosphorylation dynamics with high temporal resolution. We present an extensive characterization of EGFR signaling behavior across a range of growth factor concentrations, exposing distinct regimes of network activation. Bioinformatic analysis uncovers unexpected relationships within the data that uncover previously obscured biological distinctions within the system. This information is used to generate and test specific mechanistic hypotheses using broad and targeted perturbations. We explore the relationship between phosphorylation and complex formation of receptors and adaptors, finding evidence for distinct recruitment mechanisms for Shc and Gab1. Inhibition of phosphatase activity in the system shows unexpected behaviors in the form of specific phosphatase activity against sites on EGFR and Gab1 and ligand-independent activation of ERK. Examination of the data suggests a connection with Src family kinases as contributors to EGFR signaling. Further exploration with targeted inhibition of Src and P13K create a quantitative mechanistic explanation for EGFR signaling. Lastly, inhibition of the network with clinically relevant tyrosines kinase inhibitors reveals temporally distinct effects of inhibitors in early signaling. Combination of broad kinase and phosphatase inhibition produces unusual results that raise further questions of EGFR signaling. Together, the tools presented here for studying early signaling events at the systems level will contribute to our understanding of complex biological systems. / by Raven Jon Reddy. / Ph. D.
| Identifer | oai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/107315 |
| Date | January 2016 |
| Creators | Reddy, Raven Jon |
| Contributors | Forest M. White., Massachusetts Institute of Technology. Department of Biological Engineering., Massachusetts Institute of Technology. Department of Biological Engineering. |
| Publisher | Massachusetts Institute of Technology |
| Source Sets | M.I.T. Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | 166 pages, application/pdf |
| Rights | MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission., http://dspace.mit.edu/handle/1721.1/7582 |
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