In Search of a Host - Using Machine Learning for Host Attribution of Salmonella Typhimurium and Host Specificity of Escherichia Coli Bacteriophages - Doctor of Philosophy (PhD) Thesis

Aug 2, 2026

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This doctoral thesis advances computational approaches to support zoonotic outbreak investigation by predicting bacterial host origins from genomic signals. Salmonella Typhimurium’s broad host range creates challenges for source tracing, where closely related lineages can still select different hosts. The work focuses on machine-learning-based host attribution using genomic features, and extends analysis to host specificity in Escherichia coli bacteriophages. Results are presented with supporting bioinformatic pipeline development, phylogenetic context, and interpretation of predictive performance for outbreak-relevant inference.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages241

Reading Time10 h 7 min

AccessFree to download

Declaration of Originality

Lay Summary

This thesis has been submitted in fulfilment of the requirements for a postgraduate degree (e. g. PhD, MPhil, DClinPsychol) at the University of Edinburgh. Please note the following terms and conditions of use: • This work is protected by copyright and other intellectual property rights, which are retained by the thesis author, unless otherwise stated. • A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. • This thesis cannot be reproduced or quoted extensively from without first obtaining permission in writing from the author. • The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the author. • When referring to this work, full bibliographic details including the author, title, awarding institution and date of the thesis must be given. In Search of a Host: Using Machine Learning for Host Attribution of Salmonella Typhimurium and Host Specificity of Escherichia Coli Bacteriophages Antonia Chalka A thesis submitted for the degree of Doctor of Philosophy (PhD) College of Medicine and Veterinary Medicine The University of Edinburgh 1. Declaration of Originality I declare that the thesis has been composed by myself and that the work has not been submitted for any other degree or professional qualification. I confirm that the work submitted is my own, except where work which has formed part of jointly-authored publications has been included. My contribution and those of the other authors to this work have been explicitly indicated below. I confirm that appropriate credit has been given within this thesis where reference has been made to the work of others. The work presented in Chapter 4 was previously published in Microbial Genomics as ‘The advantage of intergenic regions as genomic features for machine- learning- based host attribution of Salmonella Typhimurium from the USA’ by Antonia Chalka; Prerna Vohra; Mark P. Stevens; Tim J. Dallman; David L. Gally. I, Antonia Chalka am the first author and the author of this declaration; Mark P. Stevens; Tim J. Dallman; David L. Gally are supervisors of this thesis. I carried out the majority of the work under the supervision of David Gally, Tim Dallman and Mark Stevens, including gathering the sequencing, subsequent bioinformatic analysis including pipeline development and deployment, and interpretation and visualisation of the results. I also worked together with the other authors in writing and editing the paper. Part of the work presented in Chapter 6 used data generated by Marianne Keith, Alba Park dela Torriente and is undergoing peer review, with the title of ‘Predictive phage therapy for Escherichia coli urinary tract infections: cocktail selection for therapy based on machine learning model’ by Marianne Keith; Alba Park de la Torriente; Antonia Chalka; Adriana VallejoTrujillo; Sean P. McAteer; Gavin K. Paterson; Alison S. Low; David L. Gally, and is also available on bioRxiv as a pre-print under the same title and authors. David L. Gally is a supervisor of this thesis. I performed all of the machine learning analysis presented in the thesis, as well as part of the phylogenetic analysis (contributing to the code used to generate the trees and the visualisation) and worked together with the other authors in writing and editing the paper (limited to only sections of the introduction, methods and results relating to the machine learning aspect of the work) . Antonia Chalka, 2023 2. Lay Summary Salmonella enterica is an infamous bacterial pathogen, associated with gastrointestinal disease and found all over the world. Salmonella is both promiscuous and diverse, as among its over 2600 antigenically distinct serovars (i.e. distinct strains), some, such as Typhi, are limited to a single host, whereas others, such as Typhimurium, are considered generalists, meaning they able to infect a wide variety of animals. Similar to many other dis

FAQs

What problem does the thesis address?

The thesis addresses the difficulty of tracing zoonotic outbreaks caused by pathogens with broad host ranges, focusing on how to infer likely hosts from genomic information.

How does the thesis support outbreak source tracing?

It applies machine-learning methods to identify genomic features that relate to host selection, leveraging genetic neighborhoods such as SNP-based similarities to guide predictions.

What additional biological system is studied beyond Salmonella?

The thesis also examines host specificity of Escherichia coli bacteriophages, connecting predictive modeling to phage therapy-related host targeting.

Aug 2, 2026

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