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  • Project No: #OxKEN-2022/16
  • Intake: OxKEN 2022


COVID-19 pathologies result from inappropriate inflammatory immune responses to the infection rather than the virus infection per say, leading to patient morbidity and mortality. Even for patients that recover from severe COVID infection long term COVID pathologies can persist having significant effects on well-being and capacity to work. Understanding the molecular and cellular mechanisms driving the interlinked pathological events in severe COVID-19 will help stratify patient treatment and provide new insights into novel potential therapeutic approaches for these patients including drug repurposing and timing of therapeutic delivery.  In Oxford a large scale collaboration between investigators has led to the formation of the Oxford COMBAT dataset that contains deep phenotyping on COVID-19 patients including tracking clinical parameters with scRNAseq, ATACseq, genetics, high dimensional cytometry, antibody profiles, and functional assays. Although machine learning and topological data analytical based techniques have provided key insights from these datasets into disease mechanisms, translating these insights into new clinical treatments and novel patient stratification is more limited due the challenge of capturing time in datasets a key factor dictating clinical outcomes. Mathematical (ordinary and partial differential equations (ODE/PDE)) and computational (agent based models (ABM)) mechanistic simulations can be used to address temporal questions about how pathways identified from high dimensional data analysis impact on human pathology. 

Initial analysis of these datasets are consistent with changes to innate immune cell production and function, is different between mild and severe patients which correlates with change in iron and oxygen levels, the mechanisms driving these pathological events and how the intersection between different biological systems lead to severe outcomes is unclear.  Based on analysis of the COMBAT datasets we hypothesise that interplay between hypoxia, iron metabolism, inflammatory cytokines and activation of complement drive a set of intersecting autocrine and paracrine inflammatory loops leading to emergence of immature neutrophils from the bone marrow and hyperinflammation driving damage to secondary tissues leading severe COVID symptoms and development of long term pathology.  Mechanistic mathematical biology built on well understand molecular and cellular pathways will permit analysis of the contribution of individual pathways and how the intersections between these different cellular and molecular pathways can drive pathological autocrine and paracrine feedback loops. This will permit exploration of potential therapeutic approaches to complex disease pathologies.

In this PhD project we will bring together a team of mathematicians, immunologists and clinical expertise to address this key problem in a project with three key aims: 1) to develop multiple different mathematical models of individual inflammatory/pathological events (e.g. emergence of immature neutrophils), 2) to link model parameters in these models to the data from the COMBAT dataset, 3) analyse how the intersection between the different models might synergise to drive severe pathology.

This project will develop the mathematical models that can provide a basis for models to accelerate stratification of patients at higher risk of severe pathology and identify potential therapeutic combinations that can be applied to virtual clinical trials accelerating and de-risking clinical development of new therapies to prevent formation of long-term patient pathologies.


COVID-19, Mathematical Modelling, Systems Immunology, Inflammation


The student will gain training in computational and systems immunology including modelling methodologies and modelling tools including Matlab and other programming environments (e.g. Python or C++).  The student will be undertaking an interdisciplinary project brining together mathematics, systems biology and immunology.


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  2. Aschenbrenner D, Quaranta M, Banerjee S, Ilott N, Jansen J, Steere B, Chen YH, Ho S, Cox K, Arancibia-Cárcamo CV, Coles M, Gaffney E, Travis SP, Denson L, Kugathasan S, Schmitz J, Powrie F, Sansom SN, Uhlig HH. Deconvolution of monocyte responses in inflammatory bowel disease reveals an IL-1 cytokine network that regulates IL-23 in genetic and acquired IL-10 resistance, Gut. 2020 Oct 9:gutjnl-2020-321731. doi: 10.1136/gutjnl-2020-321731A covid
  3. Croft AP, Campos J, Jansen K, Turner JD, Marshall J, Attar M, Savary L, Perlman H, Barone F, McGettrick HM, Fearon DT, Wei K, Raychaudhuri S, Lorsunsky I, Brenner MB, Coles M, Sansom SN, Filer A, Buckley CD, Pathologically distinct fibroblast subsets drive inflammation and tissue damage in arthritis, Nature. 2019 Jun;570(7760):246-251. doi: 10.1038/s41586-019-1263-7
  4. Abnormal morphology biases hematocrit distribution in tumor vasculature and contributes to heterogeneity in tissue oxygenation, Proc Natl Acad Sci U S A,  2020 Nov 10;117(45):27811-27819. doi: 10.1073/pnas.2007770117.
  5. Bull JA, Mech F, Quaiser T, Waters SL, Byrne HM, Mathematical modelling reveals cellular dynamics within tumour spheroids, PLoS Comput Biol, 2020 Aug 18;16(8):e1007961. doi: 10.1371/journal.pcbi.1007961


Mark Coles

Helen Byrne