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Massachusetts Institute of Technology

Quantitative modeling for microbial ecology and clinical trials

Abstract

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.

Degree

thesis:*
Department dc:contributor.department
Massachusetts Institute of Technology. Department of Biological Engineering.
Grantor dc:publisher
Massachusetts Institute of Technology
Year dc:date.issued
2016

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Olesen, Scott Wilder
Advisor dc:contributor.advisor
  • Eric J. Alm.

Subjects

dc:subject × 1

Rights

dc:rights
Statement 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.
Language dc:language.iso
eng

Identifiers

dc:identifier.*
Handle dc:identifier.uri
http://hdl.handle.net/1721.1/107277
OAI identifier oai:identifier
oai:dspace.mit.edu:1721.1/107277

Chain of custody

source
Harvested from
MIT
Base URL
dspace.mit.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Olesen, Scott Wilder. Quantitative modeling for microbial ecology and clinical trials. Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/107277