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dc.contributor.advisorDouglas A. Lauffenburger.en_US
dc.contributor.authorStrasser, Samantha Dale.en_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2019-11-04T20:22:00Z
dc.date.available2019-11-04T20:22:00Z
dc.date.copyright2019en_US
dc.date.issued2019en_US
dc.identifier.urihttps://hdl.handle.net/1721.1/122741
dc.descriptionThesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 107-121).en_US
dc.description.abstractProtein phosphorylation is a process central to cellular signaling. Consequently, targeted interruption or alteration of this process in the context of disease holds the potential for new treatments. Phosphoproteomic data generated by global mass spectrometry (MS) contain high-content information on protein phosphorylation. Nevertheless, the biological function of the vast majority of phosphorylation sites remains unknown. This poses a challenge in drawing meaningful, actionable biological conclusions from this datatype. This thesis presents the development of Substrate-based Kinase Activity Inference, SKAI, a methodology to infer kinase activity from phosphoproteomic data. SKAI first draws upon prior knowledge of kinase-substrate interactions to construct custom lists of kinases and their respective substrate sites, termed kinase-substrate sets. These sets integrate prior knowledge originating from studies of a variety of different organisms (e.g., human, mouse, and rat). This results in more informative and organism specific sets. Kinase inference is carried out by using these sets within the Gene Set Enrichment Analysis (GSEA) framework. To demonstrate its utility, SKAI is applied to global phosphoproteomic data from two disease systems, i.e., in vivo mouse models of inflammatory bowel disease (IBD) and mutant RAS cancers. Results to date have provided new hypotheses for potential therapeutic development, illustrating the utility of SKAI in elucidating new drug target leads for the treatment of disease.en_US
dc.description.statementofresponsibilityby Samantha Dale Strasser.en_US
dc.format.extent121 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT 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.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titlePhosphoproteomic data interpretation applied to studies of cancer and inflammatory diseasesen_US
dc.typeThesisen_US
dc.description.degreePh. D.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.identifier.oclc1124762001en_US
dc.description.collectionPh.D. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Scienceen_US
dspace.imported2019-11-04T20:21:59Zen_US
mit.thesis.degreeDoctoralen_US
mit.thesis.departmentEECSen_US


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