IMMU Subgroup Contributed Talks

Macauley Locke

University of Leeds

"Novel Stochastic Models of type 1 interferon inhibition by Ebola Virus VP35"

The 2014 West Africa Ebola virus (EBOV) epidemic resulted in an increased desire and urgency to identify the mechanisms explored by EBOV to subvert immune responses; in particular that of type I interferon, which is a prototype innate immune response to a viral infection. This family of cytokines is important in the early stages of infection and key to inducing antiviral states within infected cells. There exists ample experimental evidence of the role that the EBOV 'multi-function'' protein VP35 has in promoting antagonism in a number of antiviral signalling pathways. We have developed novel stochastic models of VP35 antagonismin the type I interferon induction pathway based on current empirical evidence.Making use of approximate Bayesian computation, the mathematical modelsand experimental data sets, we have carried out model selection (to test differentmolecular hypotheses) and parameter calibration. Experimentaldata from the EBOV animal model of in vivo infection of rhesus monkeys.With a wish to gain further understanding into early time dynamics of type I interferon production during EBOV infections (or other viral infections, such as SARS-CoV-2), we hope that these models can be further extended to other viruses and their methods of innate immuneinhibition.

Christopher Rowlatt

University of St Andrews

"Modelling the within-host spread of SARS-CoV-2 infection, and immune response, using a multi-scale individual-based model"

The COVID-19 pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has affected millions of people worldwide. Although the majority of cases present asymptomatic or mild symptoms that do not require hospitalisation, many cases can develop into severe disease (such as acute respiratory disease syndrome (ARDS)) requiring hospitalisation, ventilation and may result in death. A hyper-active or dysfunctional immune response (such as increased monocyte/macrophage and neutrophil infiltration) coupled with an excessive pro-inflammatory cytokine response (such as high levels of interleukin-6) are believed to play a prominent role in the development of severe disease. However, the precise mechanisms that lead to severe disease remain unclear. In this talk, we employ a hybrid multi-scale individual-based model to study the spread of SARS-CoV-2 on an epithelial monolayer. We focus our attention on the early dynamics of the host innate immune response and the immune cell cross-talk, as well as the interaction with secreted cytokines.

Giulia Belluccini

University of Leeds

"Multi-stage model of cell proliferation and death"

Many biological processes are modelled using Markov chains; thus, the inter-event times are assumed to be exponentially distributed. This hypothesis fails when cell proliferation plays a key role in the system of interest. Indeed, the history-dependence nature of the cell cycle breaks the Markov property. Our description of a population of stimulated cells preserves some of the convenient properties of a Markov process. A cell's time to division is a random variable with an Erlang distribution; its time to death has an exponential distribution. The underlying idea is to divide the cell cycle into a number of stages, each exponentially distributed and independent of the others. We can then consider cell generations in the model; that is all the cells that have divided the same number of times. Hence, the number of divisions that the cell has undergone is tracked, and this makes the model parameterisation feasible. The parameters are inferred using an Approximate Bayesian Computation approach based on sequential Monte Carlo methods with CFSE data of human and murine T lymphocytes. We calibrated the exponential model and the multi-stage one, and compared them using the corrected Akaike Information Criterion to prove statistically the better fit of the multi-stage model.

Daniel Luque Duque

University of Leeds

"Multivariate competition for survival stimulus of T cell clonotypes in homeostasis"

A mechanism used to maintain the naive T cell repertoire is competition for homeostatic proliferation stimuli. We propose a multivariate competition model to study the dynamics of n clonotypes competing for proliferation stimulus provided by self peptides bound to Major Histocompatibility Complexes (self pMHC). We assume the population of self pMHC to be at steady state (thus providing constant stimulus) and study the long-term behaviour of the system by analysing: (i) dynamics in the long-term before extinction through the quasi-stationary distribution; (ii) time to extinction of the first clonotype, probabilities of extinction for each clonotype, and size of the surviving ones when extinction occurs. Additionally we analyse the distribution of the number of divisions of a given clonotype before its extinction.