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dc.contributor.advisorForest M. White.en_US
dc.contributor.authorCurran, Timothy Gordonen_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Biological Engineering.en_US
dc.date.accessioned2014-09-19T19:38:24Z
dc.date.available2014-09-19T19:38:24Z
dc.date.copyright2014en_US
dc.date.issued2014en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/89866
dc.descriptionThesis: Ph. D., Massachusetts Institute of Technology, Department of Biological Engineering, 2014.en_US
dc.descriptionThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.en_US
dc.descriptionCataloged from student-submitted PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references.en_US
dc.description.abstractMass spectrometry has become the tool of choice for proteomics. Its unrivaled coverage and reproducibility has positioned it head and shoulders above competing techniques for analyzing protein expression post-translational modification. With the increased popularity comes a flood of new research applications, each with its own biological motivations and goals. To ensure that mass spectrometry-based proteomics can be useful to as many biological questions as possible, it is of utmost importance to ensure high data quality. This research focuses on two general stages of the typical proteomics workflow and introduces tools to facilitate effective target screening, follow-up analysis, as well as more precise measurements. This new pipeline is then demonstrated in a case study of Epidermal Growth Factor Receptor (EGFR) signaling and phenotype prediction. The quantity of proteomic mass spectrometry data available from a single analysis has increased exponentially as new generations of instruments become quicker and more sensitive. This deluge of data leaves many tempted to forego time-intensive manual validation of database identified targets in favor of global data set quality statistics. Particularly in the realm of post-translational modifications, long lists of putative matches are often reported with little or no scan-specific validation. Such practices no longer provide assurance that any single identified target is indeed correct, leaving researchers vulnerable to expending vast resources chasing false positives. The argument is that manual validation is too time-intensive to be carried out for each and every identification. To remedy this problem we have introduced the Computer Assisted Manual Validation (CAMV) software package to expedite the procedure by preprocessing the database results so as to remove the tedious steps associated with the validation task and only recruit human judgment for the final quality decision. This approach has drastically decreased the time required for manual validation; a task that used to take weeks now is completed in hours. Another focus of this research is the development of a multiplex, multisite absolute quantification method, which has improved the quality of quantitative proteomic mass spectrometry data. Absolute site-specific data allows many more biological hypotheses to be directly tested with a single mass spectrometry experiment, including phosphorylation stoichiometry. This technique has been applied to the EGFR system to better understand signaling downstream of three distinct ligands. These ligands all bind the same receptor yet elicit different phenotypes, suggesting differential information processing. The analysis showed unique patterns of receptor phosphorylation present following sub-saturating ligand treatment. However, at saturating doses the same pattern of phosphorylation is produced regardless of ligand, but the magnitude of that pattern is still ligand-dependent. In this regime, the adaptor proteins were still able to retain ligand-specific phosphorylation patterns presumably responsible for differential phenotypes. The data set also permitted the identification of signals important for the regulation of only one of the two phenotypes examined.en_US
dc.description.statementofresponsibilityby Timothy Gordon Curran.en_US
dc.format.extent163 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.subjectBiological Engineering.en_US
dc.titleTools for investigating cellular signaling networks by mass spectrometryen_US
dc.typeThesisen_US
dc.description.degreePh. D.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Biological Engineering.en_US
dc.identifier.oclc890197298en_US


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