Omics-driven and constraint-based modelling of microbial community metabolism
type: CreativeWork
description: As microbial ecology research has benefited from advancements in omics technologies, the increasing complexity and volume of generated data has in turn stimulated developments in computational biology approaches for analysis and interpretation. This is where my research lies: at the intersection of microbial ecology, metagenomics, and metabolic modelling. More specifically, this thesis outlines the development and applications of omics-driven metabolic modelling approaches to understand microbial community metabolism in diverse ecological contexts. I begin by providing background on systems biology applications of microbial ecology, metagenomic methods, and genome-scale metabolic modelling approaches. Next, I outline the development of metaGEM, a workflow designed to reconstruct context-specific genome-scale metabolic models (GEMs) via metagenome-assembled genomes (MAGs) and predict nutritional dependencies within communities directly from shotgun metagenomic samples. I applied this workflow to five datasets: synthetic lab cultures, human gut, plant associated, bulk soil, and ocean metagenomes, reconstructing over 14,000 MAGs and corresponding GEMs. Having developed this omics-driven metabolic modelling approach, I applied these methods to three case studies including: i) the distribution of auxotrophies across microbial communities, ii) the role of microbial interactions in cheese flavour formation, and iii) the pathogen-suppressing ability of a defined gut sub-community. In the first case study, I discuss two collaborations aiming to understand the prevalence, role, and origin of auxotrophies across microbes. In the first collaboration, I observed a high frequency of predicted amino acid auxotrophs across microbial communities in a large dataset, with a higher percentage of total auxotroph relative abundance in host-associated compared to free-living samples, as well as evidence for higher drug tolerance in auxotrophs. In the second collaboration, I analysed data …
abstract: As microbial ecology research has benefited from advancements in omics technologies, the increasing complexity and volume of generated data has in turn stimulated developments in computational biology approaches for analysis and interpretation. This is where my research lies: at the intersection of microbial ecology, metagenomics, and metabolic modelling. More specifically, this thesis outlines the development and applications of omics-driven metabolic modelling approaches to understand microbial community metabolism in diverse ecological contexts. I begin by providing background on systems biology applications of microbial ecology, metagenomic methods, and genome-scale metabolic modelling approaches. Next, I outline the development of metaGEM, a workflow designed to reconstruct context-specific genome-scale metabolic models (GEMs) via metagenome-assembled genomes (MAGs) and predict nutritional dependencies within communities directly from shotgun metagenomic samples. I applied this workflow to five datasets: synthetic lab cultures, human gut, plant associated, bulk soil, and ocean metagenomes, reconstructing over 14,000 MAGs and corresponding GEMs. Having developed this omics-driven metabolic modelling approach, I applied these methods to three case studies including: i) the distribution of auxotrophies across microbial communities, ii) the role of microbial interactions in cheese flavour formation, and iii) the pathogen-suppressing ability of a defined gut sub-community. In the first case study, I discuss two collaborations aiming to understand the prevalence, role, and origin of auxotrophies across microbes. In the first collaboration, I observed a high frequency of predicted amino acid auxotrophs across microbial communities in a large dataset, with a higher percentage of total auxotroph relative abundance in host-associated compared to free-living samples, as well as evidence for higher drug tolerance in auxotrophs. In the second collaboration, I analysed data …
author: Zorrilla, Francisco
language: eng
keywords: microbial ecology, metabolism, microbiome, metagenomics, omics, constraint-based metabolic modelling, genome-scale metabolic models
license: https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/77845619-7761-4cce-a85f-f497ca5978d0/download
license: https://creativecommons.org/licenses/by-nc-nd/4.0/
date published: 2024-06-17
date modified: 2026-07-22T22:24:28Z
provider: Cambridge University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Cambridge

PropertyValue
property ID: DOI
value: 10.17863/CAM.114621