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Quantitative modeling for microbial ecology and clinical trials

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dc.contributor.advisor Eric J. Alm. en_US Olesen, Scott Wilder en_US
dc.contributor.other Massachusetts Institute of Technology. Department of Biological Engineering. en_US 2017-03-10T14:19:23Z 2017-03-10T14:19:23Z 2016 en_US 2016 en_US
dc.description Thesis: Ph. D., Massachusetts Institute of Technology, Department of Biological Engineering, 2016. en_US
dc.description This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. en_US
dc.description Cataloged from student-submitted PDF version of thesis. en_US
dc.description Includes bibliographical references. en_US
dc.description.abstract Microbial ecology has benefited from the decreased cost and increased quality of next-generation DNA sequencing. In general, studies that use DNA sequencing are no longer limited by the sequencing itself but instead by the acquisition of the samples and by methods for analyzing and interpreting the resulting sequence data. In this thesis, I describe the results of three projects that address challenges to interpreting or acquiring sequence data. In the first project, I developed a method for analyzing the dynamics of the relative abundance of operational taxonomic units measured by next-generation amplicon sequencing in microbial ecology experiments without replication. In the second project, I and my co-author combined a taxonomic survey of a dimictic lake, an ecosystem-level biogeochemical model of microbial metabolisms in the lake, and the results of a single-cell genetic assay to infer the identity of taxonomically-diverse, putatively-syntrophic microbial consortia. In the third project, I and my co-author developed a model of differences in the efficacy that stool from different donors has when treating patients via fecal microbiota transplant. We use that model to compute statistical powers and to optimize clinical trial designs. Aside from contributing scientific conclusions about each system, these methods will also serve as a conceptual framework for future efforts to address challenges to the interpretation or acquisition of microbial ecology data. en_US
dc.description.statementofresponsibility by Scott Wilder Olesen. en_US
dc.format.extent 179 pages en_US
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights MIT 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.uri en_US
dc.subject Biological Engineering. en_US
dc.title Quantitative modeling for microbial ecology and clinical trials en_US
dc.type Thesis en_US Ph. D. en_US
dc.contributor.department Massachusetts Institute of Technology. Department of Biological Engineering. en_US
dc.identifier.oclc 972737461 en_US

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