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Massively parallel combinatorial microbiology

Author(s)
Kehe, Jared Scott.
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Massachusetts Institute of Technology. Department of Biological Engineering.
Advisor
Paul C. Blainey.
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MIT 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. http://dspace.mit.edu/handle/1721.1/7582
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Abstract
Reductionist 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.
Description
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Biological Engineering, May, 2020
 
Cataloged from PDF version of thesis.
 
Includes bibliographical references (pages 203-216).
 
Date issued
2020
URI
https://hdl.handle.net/1721.1/127886
Department
Massachusetts Institute of Technology. Department of Biological Engineering
Publisher
Massachusetts Institute of Technology
Keywords
Biological Engineering.

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