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dc.contributor.advisorPaul C. Blainey.en_US
dc.contributor.authorKehe, Jared Scott.en_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Biological Engineering.en_US
dc.date.accessioned2020-10-08T21:28:50Z
dc.date.available2020-10-08T21:28:50Z
dc.date.copyright2020en_US
dc.date.issued2020en_US
dc.identifier.urihttps://hdl.handle.net/1721.1/127886
dc.descriptionThesis: Ph. D., Massachusetts Institute of Technology, Department of Biological Engineering, May, 2020en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 203-216).en_US
dc.description.abstractReductionist biology of the 20th century rooted pure culture methods and antibiotics as pillars of humankind's interaction with microbiology, igniting a revolution in medicine and biotechnology. The revolution was not without cost. By overlooking complex biological interactions, it introduced new problems--from the sharp rise in immune disorders to the antibiotic resistance crisis--that 21st century tools must address. While 'omics methods have fundamentally expanded our understanding of biological complexity, we lack a generalized method for measuring how the parts of a complex system, such as the individual strains of a microbial community, interact with each other. In this thesis, I present kChip, a new platform for constructing massively parallel combinatorial arrays of these parts in order to measure their interactions directly. I describe how kChip has been used to reveal patterns in microbial community assembly, unearth minimal microbial combinations with desirable functions, and screen for compounds that potentiate antibiotic activity. I demonstrate how kChip can advance the development of new technologies like microbial consortia and combinatorial drug therapies.en_US
dc.description.statementofresponsibilityby Jared Scott Kehe.en_US
dc.format.extent216 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectBiological Engineering.en_US
dc.titleMassively parallel combinatorial microbiologyen_US
dc.typeThesisen_US
dc.description.degreePh. D.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Biological Engineeringen_US
dc.identifier.oclc1197071541en_US
dc.description.collectionPh.D. Massachusetts Institute of Technology, Department of Biological Engineeringen_US
dspace.imported2020-10-08T21:28:49Zen_US
mit.thesis.degreeDoctoralen_US
mit.thesis.departmentBioEngen_US


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