Massively parallel combinatorial microbiology
Name
1197071541-MIT.pdf
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31.28 MB
Format
Adobe PDF
Checksum (MD5)
b2970bd3cd3d9630c9b88d7e8b143730
Author(s)
Kehe, Jared Scott.
Advisor(s)
Paul C. Blainey.
Date Issued
2020
Publisher
Massachusetts Institute of Technology
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).
Subjects
Biological Engineering.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
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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.
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