Microbial communities as predictors of outcomes in industrial and clinical applications
Name
1003292884-MIT.pdf
Description
Full printable version
Size
11.53 MB
Format
Adobe PDF
Checksum (MD5)
0cad6be8179bf88d2a8ceb5a08002a6e
Author(s)
Perrotta, Allison Rose
Advisor(s)
Eric J. Alm.
Date Issued
2017
Publisher
Massachusetts Institute of Technology
Abstract
Environmental and host associated microbial communities provide an extensive reservoir of genetic and functional diversity. This diversity represents a wealth of potential for applications in many fields. To harness this potential for engineering applications, the impact of temporal dynamics need to be better understood. Yet most of the data we have are in the form of static surveys of diversity. In this thesis, I use 16S rRNA sequencing analysis to measure community composition across time series to predict outcomes for three applications: bioreactor function; a non-invasive diagnostic of endometriosis; and commercial chicken rearing. I identify bacteria that exhibit distinct temporal dynamics within each application, and discuss the implications of those dynamics in the context of each application. Despite the diverse communities covered in this work, temporal dynamics emerge as a common theme that can impact these engineering applications which rely on stable and predictable community performance.
Description
Thesis: Ph. D. in Environmental Microbiology, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2017.
Cataloged from PDF version of thesis.
Includes bibliographical references.
Subjects
Civil and Environmental Engineering.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Terms of Use
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