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dc.contributor.advisorAviv Regev.en_US
dc.contributor.authorDixit, Atray (Atray Chitanya)en_US
dc.contributor.otherHarvard--MIT Program in Health Sciences and Technology.en_US
dc.date.accessioned2018-09-17T15:49:03Z
dc.date.available2018-09-17T15:49:03Z
dc.date.copyright2018en_US
dc.date.issued2018en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/117897
dc.descriptionThesis: Ph. D. in Medical Engineering and Medical Physics, Harvard-MIT Program in Health Sciences and Technology, 2018.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references.en_US
dc.description.abstractComplex hierarchical structures are a hallmark of life. Within multicellular organisms, the building blocks of these structures are cells; within cells, they are genes. The interdependence of these building blocks is difficult to measure but is integral to the biological processes of health and disease, which emerge from the dynamism of thousands of interacting genes. This cooperativity manifests in particular mutations which accumulate over the course of cancer progression, gender-specific medical conditions, and transcription factor cocktails used to reprogram differentiated cells into stem cells. However, it is experimentally intractable to test the significance of perturbing every unique combination of genes. Instead, we explore gross features of this interaction space to determine how prevalent these synergies are. We take a top-down approach, creating new methods to measure the effects of removing genes from the full set. In the first, we develop a method to measure the transcriptional response to genetic perturbations across hundreds of thousands of cells revealing opposing classes of transcription factors regulating the immune response of dendritic cells. In the second, we create a method to measure how millions of combinations of genetic perturbations impact the growth rate of cancer cell lines.en_US
dc.description.statementofresponsibilityby Atray Dixit.en_US
dc.format.extent152 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.subjectHarvard--MIT Program in Health Sciences and Technology.en_US
dc.titleMethods for bounding genetic nonlinearitiesen_US
dc.typeThesisen_US
dc.description.degreePh. D. in Medical Engineering and Medical Physicsen_US
dc.contributor.departmentHarvard--MIT Program in Health Sciences and Technology.en_US
dc.contributor.departmentHarvard University--MIT Division of Health Sciences and Technology
dc.identifier.oclc1051214946en_US


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