Probabilistic metabolic modeling of microbial communities
Bernstein, David Bedig
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Microbial communities (microbiomes) comprise a vast component of life on our planet. They are involved in many fundamental processes, ranging from balancing global biogeochemical cycles to influencing human health. Recently, advances in genome sequencing technologies have allowed us to explore the genetic diversity of microbiomes in high-throughput, cataloging hundreds of thousands of microbial species and millions of genes. As genomic data is accumulating, the challenge remains: to translate genome sequences into functional predictions of relevant phenotypes. A promising approach to address this challenge is the annotation of genomic data to a metabolic network (referred to as genome-scale metabolic model reconstruction), which can then be analyzed to simulate metabolic phenotypes. Although this approach has provided valuable insight into microbial phenotypes, there are many sources of uncertainty in both reconstruction and analysis of genome-scale metabolic networks that currently limit their application. The development of improved reconstruction and analysis methods, and additional sources of data, that further address this uncertainty would facilitate our understanding of microbial community function. The first section of this dissertation is a review that outlines the major uncertainties along a general pipeline for genome-scale metabolic model reconstruction and analysis, and highlights existing approaches for addressing them. An emphasis is placed on probabilistic and ensemble based methods that can be used to formally represent uncertainty and facilitate the crystallization of metabolic network knowledge. The second section of this dissertation introduces a new probabilistic genome-scale metabolic model analysis method, inspired by percolation theory, to quantify the biosynthetic capabilities of microbial organisms in uncertain environments. This method was applied to microbial organisms from the human oral microbiome, providing broad insight into the structure of this microbial community. The third section of this dissertation describes the development of an experimental device to facilitate the collection of data related to metabolic interactions between microbes. The data collected with this device was probabilistically integrated with a mechanistic metabolic model to gain quantitative insight into the syntrophic interaction between an engineered E. coli auxotroph pair. Together, the work described in this dissertation introduces several novel probabilistic methods for metabolic modeling of microbial communities, and sets the stage for future work that can further improve our understanding of these important biological systems.
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