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Quantitative analysis of the cytokine-mediated apoptosis-survival cell decision process

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dc.contributor.advisor Douglas A. Lauffenburger. en_US
dc.contributor.author Janes, Kevin A. (Kevin Allyn) en_US
dc.contributor.other Massachusetts Institute of Technology. Biological Engineering Division. en_US
dc.date.accessioned 2006-08-25T18:52:31Z
dc.date.available 2006-08-25T18:52:31Z
dc.date.copyright 2005 en_US
dc.date.issued 2005 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/33868
dc.description Thesis (Ph. D.)--Massachusetts Institute of Technology, Biological Engineering Division, 2005. en_US
dc.description Includes bibliographical references (p. 119-134). en_US
dc.description.abstract How do cells sense their environment and decide whether to live or to die? This question has drawn considerable interest since 1972, when it was first discovered that cells have an intrinsic ability to self-destruct through a process called apoptosis. Since then, apoptosis has been shown to play a critical role in both normal physiology and disease. In addition, many of the basic molecular mechanisms that control apoptosis have been revealed. Yet despite the known list of interactions and regulators, it remains difficult to inspect the network of apoptosis-related proteins and predict how cells will behave. The challenge is even greater when one considers interactions with other networks that are anti-apoptotic, such as growth-factor networks. In this thesis, we develop an approach to measure, analyze, and predict how complex intracellular signaling networks transduce extracellular stimuli into cellular fates. This approach entails three interrelated aims: 1) to develop high-throughput, quantitative techniques that measure key nodes in the intracellular network; 2) to characterize the quantitative changes in network state and cell behavior by exposing cells to diverse fate-changing stimuli; and 3) to use data-driven modeling approaches that analyze large signaling-response datasets to suggest new biological hypotheses. en_US
dc.description.abstract (cont.) These aims were focused on an apoptosis-survival cell-fate decision process controlled by one prodeath cytokine, tumor necrosis factor (TNF), and two prosurvival stimuli, epidermal growth factor (EGF) and insulin. We first developed radioactive- and fluorescence-based high-throughput assays for quantifying activity changes in the kinases that catalyze key phosphorylation events downstream of TNF, EGF, and insulin. By combining these assays with techniques measuring other important posttranslational modifications, we then compiled over 7000 individual protein measurements of the cytokine-induced network. The signaling measurements were combined with over 1400 measurements of apoptotic responses by using partial least squares (PLS) regression approaches. These signaling-apoptosis regression models predicted apoptotic responses from cytokine-induced signaling patterns alone. Furthermore, the models helped to reveal the importance of previously unrecognized autocrine cytokines in controlling cell fate. This thesis has therefore shown how cell decisions, like apoptosis-versus-survival, can be understood and predicted from the quantitative information contained in the upstream signaling network. en_US
dc.description.statementofresponsibility by Kevin A. Janes. en_US
dc.format.extent 140 leaves en_US
dc.format.extent 10117645 bytes
dc.format.extent 10123512 bytes
dc.format.mimetype application/pdf
dc.format.mimetype application/pdf
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. en_US
dc.rights.uri http://dspace.mit.edu/handle/1721.1/7582
dc.subject Biological Engineering Division. en_US
dc.title Quantitative analysis of the cytokine-mediated apoptosis-survival cell decision process en_US
dc.type Thesis en_US
dc.description.degree Ph.D. en_US
dc.contributor.department Massachusetts Institute of Technology. Biological Engineering Division. en_US
dc.identifier.oclc 66296823 en_US


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