IMMU Subgroup Contributed Talks

Girma Mesfin Zelleke

AIMS-Cameroon

"A Mathematical Understanding of the Dynamic Regulation of the Complement System on bacterial infection."

The innate immune system responds to bacterial infections first by activating the fastest defense mechanism called the complement system. This system is controlled by more than 30 different proteins that work as a team to damage the invader and to alert other immune systems. However, this defense mechanism is perilous if the activation is abnormal, inefficient, and overstimulated, or if there are deficiencies in a surface-bound with receptors of the invaders. It is therefore vital that the complement system binds with an invading bacterium successfully allowing the cascade of events that will enable a proper stimulation of the defensive mechanism by the complement system. Here, we propose a mathematical model which describes the dynamics of the complement system against bacterial infection. We further investigate the mathematical and numerical analysis of the model which generates and explains conditions for normal, efficient, and properly stimulated concentration of the complement system proteins. We also perform sensitivity analysis to identify the critical parameters that affect the direct action of the complement system on the bacterial infection.

Solveig A. van der Vegt

Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, UK

"Mathematical modelling of autoimmune myocarditis and the effects of immune checkpoint inhibitors"

Autoimmune myocarditis, or inflammation of cardiac muscle tissue, is a rare but potentially fatal side effect of cancer treatment with immune checkpoint inhibitors. Of patients receiving this type of treatment, approximately 1% develop myocarditis, and the disease proves to be fatal in about 25-50% of these cases. Despite the severity of this side effect and the large volume of cancer patients eligible for treatment with immune checkpoint inhibitors, no preclinical assay currently exists that tests new compounds for myocarditis-related cardiotoxicity. Our aim is to use mathematical modelling to develop a better understanding of the immune cell types and mechanisms involved in the development and progression of autoimmune myocarditis and the effects that immune checkpoint inhibitors have on this. To this end, we have developed the first mathematical model of this disease. By employing parameter sensitivity methods and examining the bifurcation structures in the model, we aim to pinpoint the critical cell types that have to be included in the preclinical test for it to reflect well the mechanisms involved in the development of drug-induced autoimmune myocarditis in vivo.

Martin Lopez-Garcia

Department of Applied Mathematics, University of Leeds

"A stochastic multi-scale model of Francisella tularensis infection"

We present a multi-scale model of the within-phagocyte, within-host and population-level infection dynamics of Francisella tularensis, which extends the mechanistic one proposed by Wood et al. (2014). Our multi-scale model incorporates key aspects of the interaction between host phagocytes and extracellular bacteria, accounts for inter-phagocyte variability in the number of bacteria released upon phagocyte rupture, and allows one to compute the probability of response, and mean time until response, of an infected individual as a function of the initial infection dose. A Bayesian approach is applied to parameterize both the within-phagocyte and within-host models using infection data. Finally, we show how dose response probabilities at the individual level can be used to estimate the airborne exposure to Francisella tularensis in indoor settings (such as a microbiology laboratory) at the population level, by means of a deterministic zonal ventilation model.

Mohammad Aminul Islam

University at Buffalo, The State University of New York

"Modeling the Progression of Fibrosis with Dysregulation of ACE2 in COVID19 Patients"

The severity of the COVID19 pandemic creates an emerging need to investigate the long-term effect of infection on healing patients. Many individuals are at the risk of suffering pulmonary fibrosis due to pathogenesis of lung injury and impairment in the healing mechanism. SARS-CoV-2 enters the host cells via binding its spike protein with the ACE2 receptor which is a key component in modulating the balance of the renin-angiotensin system (RAS). The dysregulation of ACE2 by the viral infection can shift the balance of RAS towards pro-inflammation and pro-fibrosis. We developed a multiscale agent-based model to investigate the dynamics of viral infection, immune cell response, and fibrosis in lung tissue. The model can simulate the dynamics of ACE2 and collagen deposition in the 2D lung tissue at different severity of infections. We use the ACE2 dynamics as input in a separate model of RAS to predict the change in ANGII, which is a mediator for pro-inflammation and collagen deposition which is a mediator for pro-fibrosis from homeostasis for normotensive and hypertensive patients. Our model also reveals that the variation in available ACE2 due to age and gender can lead to significant change in inflammation, tissue damage, and fibrosis.