Exploring networks of the human microbiota in health and disease
File(s)
Author(s)
Fairclough, Virginia
Type
Thesis
Abstract
The human microbiota changes throughout our lives and is crucial to our health. However, some perturbations to the microbiota can have life-changing implications for its host. We are still in the early stages of understanding the complexities of these crucial microbial communities; as research into the human microbiota progresses, more methods will be required to explore dependencies between constituents of the microbiota, with the aim of understanding how they interact with each other and their host.
In this thesis, I present new methods and tools for creating and analysing networks of the human microbiota. I present a new method for analysing and comparing microbial dependencies in disease cases when compared to controls. This method is applied to real data taken from patients, and I draw inferences from the results, which agree well with the existing scientific literature.
This analysis is followed by the generation of weighted metagenome-scale metabolic networks. This novel approach presents the metabolic capabilities of the microbiota in a new manner, whilst providing a basis for future comparisons between the metabolic profiles found within the microbiota of cases and controls.
As well as presenting the metabolic capabilities of the community as a whole, it would be beneficial to be able to model a particular metabolic symbiosis. However, the current bottleneck in this area of research is due to slow curation of genome-scale metabolic models. Therefore, this thesis ends with the presentation of a new tool to aid the curation of genome-scale metabolic models and its application, with the hope that it may be used to improve existing models prior to the creation of models of metabolic symbiosis.
In this thesis, I present new methods and tools for creating and analysing networks of the human microbiota. I present a new method for analysing and comparing microbial dependencies in disease cases when compared to controls. This method is applied to real data taken from patients, and I draw inferences from the results, which agree well with the existing scientific literature.
This analysis is followed by the generation of weighted metagenome-scale metabolic networks. This novel approach presents the metabolic capabilities of the microbiota in a new manner, whilst providing a basis for future comparisons between the metabolic profiles found within the microbiota of cases and controls.
As well as presenting the metabolic capabilities of the community as a whole, it would be beneficial to be able to model a particular metabolic symbiosis. However, the current bottleneck in this area of research is due to slow curation of genome-scale metabolic models. Therefore, this thesis ends with the presentation of a new tool to aid the curation of genome-scale metabolic models and its application, with the hope that it may be used to improve existing models prior to the creation of models of metabolic symbiosis.
Version
Open Access
Date Issued
2016-09
Date Awarded
2017-06
Copyright Statement
Attribution NoDerivatives 4.0 International Licence (CC BY-ND)
Advisor
Pinney, John
Sponsor
Imperial College London
Publisher Department
Life Sciences
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)