MS04 - IMMU-01

New approaches to infectious disease immunity for model-informed vaccine development (Part 1)

Tuesday, July 15 at 4:00pm

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Note: this minisymposia has multiple sessions. The other session is: and Part-2.

Terry Easlick (Univeristé de Montréal/Centre de recherche Azrieli du CHU Sainte-Justine), Morgan Craig, Univeristé de Montréal/Centre de recherche Azrieli du CHU Sainte-Justine

Description:

This session will bring together leading researchers to discuss innovative mathematical modelling approaches for studying immune responses to infectious diseases for the establishment of robust vaccination strategies. The objective is to foster interdisciplinary dialogue, showcasing novel methods and their application to key areas such as antigen-specific responses, humoral and cell-mediated immunity, vaccine dose optimization, and addressing challenges posed by waning immunity and pathogen diversity. This minisymposium will present complementary approaches to studying within-host immune responses to infections and vaccines. Topics will include capturing population-level dynamics, accounting for biological variability using stochastic models, simulating cell-to-cell interactions using agent-based models (ABMs), and extracting complex patterns from large immunological datasets using machine learning techniques. In particular, we will highlight how individual-level diversity (i.e., sex, age, comorbidities, genetics, etc.) affect immune and vaccine responses. By bridging diverse perspectives and methodologies, this minisymposium will contribute to innovation in model-informed vaccine development by promoting cutting-edge approaches to mathematical immunology that advance our fundamental understanding of individual immunity to bring necessary improvements to the vaccine development pipeline.

Jane Heffernan

York University

"The Malaria Parasite Life-Cycle"

We have developed a model of the malaria parasite life-cycle in the blood stage. A system of partial differential equations is employed and models maturation and differentiation of five phases within this stage. The model is used to study possible therapeutic vaccine targets for effective parasite control and eradication.

Solène Hegarty-Cremer

Université de Montréal

"Analysing Immune Dysregulation in Vitamin A Deficient Mice During Influenza A Infection"

Influenza virus results in varied infection outcomes and causes significant mortality and morbidity worldwide. Vitamin A deficiency (VAD) is common in both developed and developing countries and has recently been discovered as a comorbidity of influenza A. During influenza infection, VAD mice exhibit dysregulated immune function, with increased viral titers, delayed viral clearance, and elevated levels of inflammatory cytokines. Understanding the interactions between the immune response and vitamin A is critical in addressing this comorbidity. Mathematical models are effective tools to understand and disentangle the complex dynamics of within-host immune responses to respiratory infections. Models for the response to influenza have identified nonlinear relationships between infected cell clearance by CD8+ T cells and infected cell density as well as factors influencing time to recovery. From viral titers, CD8+ T cell counts, and weight loss data in VAD and control murine models during influenza infection, we calibrated a within-host mechanistic mathematical that considers the nonlinear relationships between viral load, infected cells, and effector and memory CD8+ T cells. Through parameter estimation and sensitivity analyses, we found that differences in viral dynamics arise through reduced T cell recruitment and proliferation in VAD mice, as well as an impaired innate immune response. This new mechanistic understanding of the links between retinol and the immune response to influenza will allow for a clearer understanding of VAD and its comorbidity mechanisms and thus enhance our ability to forecast disease progression and combat acute illness from influenza, with potential impacts on other viral infections.

Stanca M. Ciupe

Virginia Tech

"How much data is needed to validate a multiscale model of viral infection?"

Uncertainty in parameter estimates from fitting mathematical models to empirical data limits the model’s ability to uncover mechanisms of interaction. Understanding the effect of model structure and data availability on model predictions is important for informing model development and experimental design. To address sources of uncertainty in parameter estimation, I will present several methodologies that can help determine when a model can reveal its parameters. I will apply them in the context of virus infections in animals and humans at within-host, population, and multiscale levels. Using these approaches, I will provide insight into the sources of uncertainty and provide guidelines for the types of model assumptions, optimal experimental design, and biological information needed for improved predictions.

Terry Easlick

Université de Montréal

"Stochastic Methods for modelling antigen-specific cell-mediated immune response"

Cell-mediated immune responses to antigenic stimulation involve a range of stochastic processes, from individual cell fate decisions to population-level dynamics. These responses are shaped by randomness at multiple biological scales, motivating the use of probabilistic models to capture variability and rare events. We will consider how stochastic methods can be used in modelling antigen-specific immunity. The focus is on understanding how noise and structure interact in shaping immune outcomes.

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Annual Meeting for the Society for Mathematical Biology, 2025.