MS08 - IMMU-04 Part 2 of 2

Multiscale modelling in infectious diseases (Part 2)

Friday, July 18 at 10:20am

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Note: this minisymposia has multiple sessions. The other session is: and Part-1.

Dr Macauely Locke (Los Alamos National Laboratory), Dr Jasmine Kreig, Dr Aurelien Marc, Los Alamos National Laboratory

Description:

Part of the complexity of biological and epidemiological systems arises from interactions that occur on different scales. This mini-symposium explores available data scales and how models can be formulated to represent them, with applications to viral dynamics, immune responses, and epidemiology. In addition to showcasing current work by presenters, this session encourages discussion of further development on and novel approaches to multiscale modelling of infectious diseases. This session aims to gather speakers from diverse backgrounds, including early and late-career scientists and post-docs, different fields of expertise such as viral dynamics, immunology (T-cell and antibody) and epidemiology and cover a range of institutions (such as Academia and National labs).

Jasmine A.F. Kreig

Los Alamos National Laboratory

"A stochastic model of HIV viral rebound after treatment interruption"

Human Immunodeficiency Virus (HIV) infections can be effectively controlled with the use of antiretroviral therapy (ART), which keep viral loads below detectable levels. Currently, individuals with HIV must adhere to treatment for the rest of their lives to manage the virus. This is due to the existence of the HIV reservoir – a population of cells that are latently infected by HIV – which can reactivate and cause viral rebound in individuals who stopped ART. Interestingly, the time to viral rebound is variable from weeks (in most individuals) to years. Mechanisms behind viral rebound or factors that could influence the timing of viral rebound remain largely misunderstood. We have developed a simplified model that simulates the seeding of the reservoir and viral rebound after treatment interruption. Using this model, we explore different factors that could be associated with extending the time to viral rebound.

Sarafa Adewale Iyaniwura

Fred Hutch

"Understanding the effectiveness of a capsid assembly modulator (CAM) in the treatment of chronic HBV infection"

Chronic hepatitis B virus (HBV) infection is strongly associated with increased risk of liver cancer and cirrhosis. While existing treatments effectively inhibit the HBV life cycle, viral rebound frequently occurs following treatment interruption. Consequently, functional cure rates of chronic HBV infection remain low and there is increased interest in a novel treatment modality, capsid assembly modulators (CAMs). We developed a multiscale mathematical model of CAM treatment in chronic HBV infection. By fitting the model to participant data from a phase I trial of the first-generation CAM, vebicorvir, we estimate the drug's dose-dependent effectiveness and identify the physiological mechanisms that drive the observed biphasic decline in HBV DNA and RNA, and mechanistic differences between HBeAg-positive and -negative infection.

Paolo Bosetti

Institut Pasteur

"Accounting for epidemic reintroductions in infectious disease modelling"

Infectious disease models frequently assume a closed epidemic within a defined geographic area, such as a country or region. However, neglecting to account for epidemic reintroductions from external areas can introduce bias in the estimation of key epidemiological parameters, such as the basic reproduction number, and distort our understanding of epidemic dynamics, especially in the early stages. Moreover, incorporating reintroduction events into models can provide a more accurate depiction of transmission and improve the spatiotemporal characterization of epidemics. In this presentation, I will illustrate these concepts through two case studies related to cholera epidemics in France. The first focuses on the spatiotemporal characterization of the historical cholera epidemic of 1892. The second presents a modelling framework that accounts for multiple reintroductions of the pathogen during the recent cholera outbreak on Mayotte Island.

Mason Lacy

Queensland University of Technology

"Modelling T cell expansion in immune cell-mimicking scaffolds for adoptive cell therapy"

T cells are immune cells that are known to be effective at killing cancer cells, however in normal immune responses, there is often an insufficient amount of effective tumour-specific T cells to eliminate the cancer or control its growth. Adoptive cell therapy aims to mitigate this issue by activating and expanding highly effective T cells ex vivo before injecting them back into the body to employ their cancer-killing functions. Expansion is often achieved by facilitating interactions between T cells and artificial particles that mimic the activating functionality of other immune cells. An especially promising approach involves using tunable micro-rods which efficiently imitate T cell-activating immune cells and form fluid scaffolds for cell interaction. In this talk, I will present a stochastic agent-based model used to model T cell expansion in these scaffolds. This model includes activation and expansion of T cells through interactions between T cells and micro-rods and reproduces key features observed in laboratory experiments. The continuum limit of this stochastic model is used to analyse the average behaviour of T cells within micro-rod scaffolds under varying scaffold structures, and a mean-field approximation is used to justify the importance of micro-rod and T cell locality during activation. Stochastic and deterministic simulations reveal the underlying processes that drive experimental observations, including the notion that the gradual release of a T cell growth factor from micro-rods is important for prolonged T cell expansion. These models are used to inform alterations to micro-rods that will likely improve the speed and efficiency of T cell expansion for adoptive cell therapy.

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Annual Meeting for the Society for Mathematical Biology, 2025.