Data mining and modeling of the human microbiome [electronic resource]
- Tomer Altman.
- Physical description
- 1 online resource.
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|3781 2015 A||In-library use|
- Altman, Tomer.
- Dill, David L. primary advisor.
- Relman, David A., primary advisor.
- Brutlag, Douglas L., advisor.
- Stanford University. Program in Biomedical Informatics.
- The human gut microbiome teems with a dizzying array of microbial flora. Recent studies have demonstrated a causal relationship between microbial diversity and clinical phenotypes such as obesity and cardiovascular disease. Current analytical methods in the field excel at answering "who is there" in terms of the microbial population, yet are limited in answering "what are they doing". In particular, for dysbiosis of the human distal gut, we desire an understanding of microbial community structure at the biochemical level. My approach to revealing the biochemistry of the human microbiome consists of: 1) enhancing single-cell isolate genomes with metagenomic reads, 2) generating hundreds of microbiome-wide metabolic reconstructions spanning thousands of enzymatic reactions, and 3) creating a new modeling framework for complex microbial communities. I will first discuss a new method for recovering a complete genome from a single-cell experiment when there is insufficient coverage from the single-cell reads alone. This new method, called "Barrel of Monkeys", enables the efficient combination of both single-cell and metagenomic reads, with an accuracy of 99%. I demonstrate this method on four isolates of the enigmatic phylum TM7. I then annotate and perform metabolic reconstruction on novel genomes, to shed light on the biology of these organisms. Next, I will review a pipeline for comparative analysis of microbiome metabolic reconstructions, termed the GutCyc Collection. The utility of GutCyc will be motivated in a few vignettes, ranging from applications in metabolic route discovery in cardiovascular disease to pharmaco-microbiomics. Finally, I have modeled the human gut as a set of distinct microbial guilds in order to understand their biochemical thermodynamics. Guilds, or trophic species, are microbial equivalence classes where guild members share the same carbon source, nitrogen source, electron donor, and electron acceptor. I will review the details of a novel methodology for guild identification from reference microbial genomes, a pipeline for normalized abundance computation, and formulation of multi-guild biochemical mass balance equations. I will demonstrate the utility of this approach with applications in data mining and in thermodynamic modeling of an obesity microbiome dataset.
- Publication date
- Submitted to the Program in Biomedical Informatics.
- Thesis (Ph.D.)--Stanford University, 2015.
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