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Bacterial diseases have historically accounted for a high burden of morbidity and mortality worldwide. While antibiotic therapy and immunisations have significantly improved clinical outcomes in the last couple of centuries, the rate at which new treatments and vaccines are being developed has slowed down. One way to accelerate innovation in the field is by understanding the host-bacteria interactions that take place in vivo and affect the clinical outcome of the disease, the response to treatment and the immunological response following vaccination. Mechanistic mathematical models are being increasingly used to advance our understanding about unobservable, host-pathogen interactions in vivo. These models allow us to develop hypotheses about the underlying biological mechanisms responsible for an observable and measurable outcome (e.g. the number of bacteria in a tissue of interest) and use them to try to match what has been experimentally or clinically observed. In this thesis, I have developed mechanistic models and used them in conjunction with experimental data on systemic murine Salmonella Typhimurium infections to: 1. characterise the effects of different vaccine preparations on the immune system of the host and its response to a secondary bacterial challenge, 2. compare the effects of different antibiotic classes on bacterial populations residing in different organs, and predict whether the residual bacteria following antibiotic treatment differ in their proliferative capacity, 3. develop a new, flexible methodological tool for a priori experimental design in studies of bacterial dynamics at the level of the population within the host., Newnham College Major Studentship Cambridge Trust Studentship

Antibiotic therapy has drastically reduced the mortality and sequelae of bacterial infections. From naturally occurring to chemically synthesized, different classes of antibiotics have been successfully used without detailed knowledge of how they affect bacterial dynamics in vivo. However, a proportion of patients receiving antimicrobial therapy develop recrudescent infections post-treatment. Relapsing infections are attributable to incomplete clearance of bacterial populations following antibiotic administration; the metabolic profile of this antibiotic-recalcitrant bacterial subpopulation, the spatio-temporal context of its emergence and the variance of antibiotic-bacterial interactions in vivo remain unclear. Here, we develop and apply a mechanistic mathematical model to data from a study comparing the effects of ciprofloxacin and ampicillin on the within-host dynamics of Salmonella enterica serovar Typhimurium in murine infections. Using the inferential capacity of our model, we show that the antibiotic-recalcitrant bacteria following ampicillin, but not ciprofloxacin, treatment belong to a non-replicating phenotype. Aligning with previous studies, we independently estimate that the lymphoid tissues and spleen are important reservoirs of non-replicating bacteria. Finally, we predict that post-treatment, the progenitors of the non-growing and growing bacterial populations replicate and die at different rates. Ultimately, the liver, spleen and mesenteric lymph nodes are all repopulated by progenitors of the previously non-growing phenotype in ampicillin-treated mice.

The management of bacterial diseases calls for a detailed knowledge about the dynamic changes in host–bacteria interactions. Biological insights are gained by integrating experimental data with mechanistic mathematical models to infer experimentally unobservable quantities. This inter-disciplinary field would benefit from experiments with maximal information content yielding high-precision inference. Here, we present a computationally efficient tool for optimizing experimental design in terms of parameter inference in studies using isogenic-tagged strains. We study the effect of three experimental design factors: number of biological replicates, sampling timepoint selection and number of copies per tagged strain. We conduct a simulation study to establish the relationship between our optimality criterion and the size of parameter estimate confidence intervals, and showcase its application in a range of biological scenarios reflecting different dynamics patterns observed in experimental infections. We show that in low-variance systems with low killing and replication rates, predicting high-precision experimental designs is consistently achieved; higher replicate sizes and strategic timepoint selection yield more precise estimates. Finally, we address the question of resource allocation under constraints; given a fixed number of host animals and a constraint on total inoculum size per host, infections with fewer strains at higher copies per strain lead to higher-precision inference.

Fbxo7 is the substrate-recognition subunit of an SCF-type ubiquitin E3 ligase complex. It has physiologically important functions in regulating mitophagy, proteasome activity and the cell cycle in multiple cell types, like neurons, lymphocytes and erythrocytes. Here, we show that in addition to the previously known Parkinsonian and hematopoietic phenotypes, male mice with reduced Fbxo7 expression are sterile. In these males, despite successful meiosis, nuclear elongation and eviction of histones from chromatin, the developing spermatids are phagocytosed by Sertoli cells during late spermiogenesis, as the spermatids undergo cytoplasmic remodeling. Surprisingly, despite the loss of all germ cells, there was no evidence of the symplast formation and cell sloughing that is typically associated with spermatid death in other mouse sterility models, suggesting that novel cell death and/or cell disposal mechanisms may be engaged in Fbxo7 mutant males. Mutation of the Drosophila Fbxo7 ortholog, nutcracker (ntc) also leads to sterility with germ cell death during cytoplasmic remodeling, indicating that the requirement for Fbxo7 at this stage is conserved. The ntc phenotype was attributed to decreased levels of the proteasome regulator, DmPI31 and reduced proteasome activity. Consistent with the fly model, we observe a reduction in PI31 levels in mutant mice; however, there is no alteration in proteasome activity in whole mouse testes. Our results are consistent with findings that Fbxo7 regulates PI31 protein levels, and indicates that a defect at the late stages of spermiogenesis, possibly due to faulty spatial dynamics of proteasomes during cytoplasmic remodeling, may underlie the fertility phenotype in mice.

The 2018 Varsity Medical Ethics debate convened upon the motion: "This house believes that the constant monitoring of our health does more harm than good". This annual debate between students from the Universities of Oxford and Cambridge is now in its tenth year. This year's debate was hosted at the Oxford Union on 8th of February 2018, with Oxford winning for the Opposition, and was the catalyst for the collation and expansion of ideas in this paper.New technological devices have the potential to enhance patient autonomy, improve patient safety, simplify the management of chronic diseases, increase connectivity between patients and healthcare professionals and assist individuals to make lifestyle changes to improve their health. However, these are pitted against an encroachment of technology medicalising the individual and home, an exacerbation of health inequalities, a risk to the security of patient data, an alteration of the doctor-patient relationship dynamic and an infringement on individual self-identity. This paper will draw upon and develop these concepts, while contending arguments for and against constant health monitoring. This is not a review of medical devices and health monitoring, but a reflective development and more detailed elaboration of the main points highlighted in the 2018 Varsity Medical Ethics debate.