Organized by: Veronika I. Zarnitsyna (Emory University), Esteban Hernandez Vargas, University of Idaho

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

1. Macauley Locke Los Alamos National Laboratory
   "Quantification of Type I Interferon Inhibition by Viral Proteins: Ebola Virus as a Case Study"
Type I interferons (IFNs) are cytokines with both antiviral properties and protective roles in innate immune responses to viral infection. They induce an antiviral cellular state and link innate and adaptive immune responses. Yet, viruses have evolved different strategies to inhibit such host responses. One of them is the existence of viral proteins which subvert type I IFN responses to allow quick and successful viral replication, thus, sustaining the infection within a host. We propose mathematical models to characterise the intra-cellular mechanisms involved in viral protein antagonism of type I IFN responses, and compare three different molecular inhibition strategies. We study the Ebola viral protein, VP35, with this mathematical approach. Approximate Bayesian computation sequential Monte Carlo, together with experimental data and the mathematical models proposed, are used to perform model calibration, as well as model selection of the different hypotheses considered. Finally, we assess if model parameters are identifiable and discuss how such identifiability can be improved with new experimental data.
2. Jane Marie Heffernan York University
   "COVID-19 Vaccination in HIV+ Individuals"
The immune response to vaccination is highly heterogeneous across individuals, and emerges from an intricate time-evolving interplay between humoral and cellular immune components. We have employed machine learning to study immune system heterogeneity in HIV individuals after multiple COVID-19 vaccinations. We employ a random forest (RF) approach to classify informative differences in immunogenicity between older people living with HIV (PLWH) on ART and an age-matched control group who received up to five SARS-CoV-2 vaccinations over a period of 104 weeks. An extension of our study uses supervised and unsupervised Machine Learning methods to produce physiologically accurate synthetic datasets that enable data-driven hypothesis testing and model validation. We have found that immunological variables of importance in determining different immunological outcomes from vaccination include cytokine-based features in combination with post-booster saliva IgA measures. In total, nine important features are identified from 63 possible measures included in the dataset.
3. Jason E. Shoemaker University of Pittsburgh
   "A More Severe Influenza Infection in Female Mice Relative to Male is Characterized by Early Viral Production and Increased Innate Immune Activity"
In humans, differences in the immune response between males and females greatly influence influenza virus infection outcomes. During the 2009 H1N1 pandemic, females were at greater risk than their male, age-matched counterparts for hospitalization and death by a ratio of nearly 3:2. The innate immune response has been implicated as a factor of these sex differences in influenza pathogenesis, with sex hormones considered an important component of innate immune regulation. Together with our collaborators at the University of Wisconsin, Madison, we have completed experiments on male and female mice infected with CA04 H1N1 influenza infection. The results of these experiments show that the female mice have increased viral production at 36 hours post infection, resulting in early and excessive innate immune activation characterized by the proinflammatory cytokine profiles. Immune cell counts show that alveolar macrophages have increased depletion in female mice, while exfiltrating macrophage cell counts are higher at 3 days post infection in female mice: both observations have been associated with increased disease severity. Finally, histopathology of the lung cells shows very few lesions in the male mice compared to female mice. These lesions are present in the alveolar region of the female mice, but not male, indicating that influenza virus penetrates more deeply in the female lungs. While the experimental data points to certain cytokine/chemokines and immune cells as potential factors influencing severity, mathematical modeling can further contribute to our understanding of increased disease severity in females by identifying sex-specific rates within the immune response to infection that differs between males and females. We are currently developing mathematical models to identify mechanism(s) responsible for the observed increases in disease severity in the female mice. We will present work recently published comparing male and female infection, discuss the caveats and challenges, and introduce solutions to these challenges going forward.
4. Veronika I. Zarnitsyna Emory University School of Medicien
   "Challenges in Evaluating Vaccine-Induced Protection Against Severe Disease"
Understanding the full scope of vaccine-induced protection necessitates distinguishing between a vaccine’s ability to prevent infection (first line of protection) and its capacity to mitigate disease severity (second line of protection) in those who do become infected. While much of the empirical focus has traditionally centered on vaccine effectiveness against infection, protection against progression to severe disease is equally crucial—especially for vaccine impact modeling and assessing the overall burden of a pandemic. Despite its importance, estimating this secondary layer of protection presents significant analytical challenges. Analysis of empirical data from the COVID-19 pandemic shows that estimates of vaccine effectiveness against severe disease progression can appear to rise from 0% to over 70% within months—changes unlikely to reflect true biological effects. Using mathematical modeling, we explore how such patterns can arise in settings with heterogeneous immune responses. Our findings highlight the challenges in isolating vaccine effects on disease progression and emphasize the need for refined methods that adjust for shifting risk profiles among the infected.

Organized by: 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

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

1. 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.
2. 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.
3. 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. 
4. 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.

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

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

1. Aurelien Marc Los Alamos National Laboratory
   "Modeling suggests that the HCV polymerase inhibitor bemnifosbuvir has two modes of action: inhibiting intracellular viral replication and virion assembly/secretion"
Chronic hepatitis C virus (HCV) infection remains a significant global health challenge, with an estimated 50 million people affected and 1 million new cases annually as of 2022. The majority of HCV-related deaths are due to severe liver damage and hepatocellular carcinoma. This highlights the urgent need to optimize treatment strategies and develop next-generation antivirals that can enhance cure rates and shorten therapy, especially for patients with advanced liver disease. In this study, we developed a joint pharmacokinetic and multiscale viral dynamic model to characterize the antiviral effects of the HCV polymerase inhibitor bemnifosbuvir (BEM) currently in clinical trials. Fitting our model to clinical data suggests that BEM acts via a dual mechanism, effectively inhibiting both viral replication as expected for a polymerase inhibitor and, unexpectedly, virion assembly/secretion. These effects were consistent across multiple genotypes and in patients with cirrhosis, highlighting BEM’s potential as a new effective, multi-targeting antiviral agent for chronic HCV infection.
2. Nathanael Hoze IAME
   "Integrating multiscale mathematical modeling and serology to unravel antibody dynamics and infection risk"
: Understanding infectious disease dynamics requires connecting processes across biological scales, from individual immune responses to population-level transmission. Serological data—particularly age-stratified antibody measurements—are a rich resource for inferring historical pathogen circulation and quantifying infection risk, especially in settings lacking continuous surveillance. In this talk, I will present a suite of Bayesian statistical models that leverage serological data to infer key epidemiological and immunological parameters. I begin with models analyzing cross-sectional seroprevalence data, where age profiles inform the reconstruction of time-varying force of infection. These serocatalytic models account for features such as lifelong or waning immunity, heterogeneous exposure, and imperfect diagnostics. Inference is performed via Hamiltonian Monte Carlo using Stan, and the framework is implemented in a dedicated R package, Rsero, which includes tools for model fitting, diagnostics, and model selection using LOOIC and DIC. Analysis of serological data is often dogged by complex immune mechanisms that makes their interpretation difficult. Antibody cross-reactivity, especially relevant in arbovirus serology, happens when viruses can generate antibodies with similar responses. Determining which virus generated the response is challenging. I developed hierarchical Bayesian models that integrate individual-level quantitative titer data with transmission dynamics and prior knowledge on cross-reactive immune responses. These models estimate individual infection histories, pathogen-specific forces of infection, and cross-reactivity structures, using latent variable methods implemented in rstan. The framework accommodates diverse data types, including spatial and demographic covariates. I will also present ongoing work on Enterovirus EV-A71, the main causative agent of hand-foot-and-mouth disease, based on a yearly repeated dataset of age-stratified neutralizing antibody titers collected over 18 years in Malaysia. This model extends classical serocatalytic approaches by modeling full titer distributions across age and time, incorporating mechanisms for infection-induced boosting, waning, and identifying protection due to antibody and number of infections.
3. Quiyana Murphy Virginia tech
   "Understanding antibody durability and magnitude following vaccination against SARS-CoV-2"
Vaccination against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) results in transient antibody response against the spike protein. The individual immune status at the time of vaccination influences the response. Using mathematical models of antibody decay, we determined the dynamics of serum immunoglobulin G (IgG) and serum immunoglobulin A (IgA) over time. Data fitting to longitudinal IgG and IgA titers was used to quantify differences in antibody magnitude and antibody duration among infection-naïve and infection-positive vaccinees. We found that prior infections result in more durable serum IgG and serum IgA responses, with prior symptomatic infections resulting in the most durable serum IgG response and prior asymptomatic infections resulting in the most durable serum IgA response. These findings can guide vaccine boosting schedules.
4. Grant Lythe University of Leeds
   "Models of bursting and budding"
When are infectious viruses or bacteria released from infected cells? We consider two types of mathematical models. In the first, “budding'', infected cells are assumed to release new infectious particles at a constant rate (that is, constant probability per unit time). No description of the intracellular dynamics is needed; the mean number of new infectious particles released per infected cell is simply the rate of release multiplied by the mean lifetime of an infected cell. In the second, “bursting'', infectious particles accumulate inside a host cell until the cell dies and the intracellular load is released at once. At the stochastic level of an individual cell, the simplest budding models have two types of events (release of an infectious particle and death of the infected cell), and the mathematics is consistent with the assumption that events are independent. In bursting, however, release of infectious particles and death of the host cell are not independent events: they occur simultaneously.

Organized by: Veronika I. Zarnitsyna (Emory University), Esteban Hernandez Vargas, University of Idaho

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

1. Grant Lythe University of Leeds
   "TCR repertoire and cross-reactivity"
There are approximately 400000000000 naive CD4 T cells in your body, about the same as the number of stars in our galaxy. On the other hand, the number of cells of one TCR clonotype is a small integer that increases or decreases by one cell at a time, when cells divide or die. New clonotypes are released from the thymus and compete with other clonotypes in the periphery for specific and non-specific resources. Mean clonal sizes can therefore be calculated from mean clonal lifetimes. For example, if the ratio of thymic production to peripheral division is four percent, then the number of distinct T-cell clonotypes in the human body is about nine percent of the total number of (naive CD4) T cells. In mice, most TCR clonotypes may consist of just one or two T cells. TCRs recognize peptides (or epitopes), typically 8¬14 amino acids long, bound to MHC molecules on antigen-presenting cells. There cannot only exist a single TCR which recognizes a given peptide because the possible number of peptides is far greater than the number of T cells in a mouse or in one person. Therefore, individual TCRs must recognize multiple peptides if a mammal's T cell repertoire is to be capable of providing coverage against the majority of new pathogens a host might encounter in its lifetime. Patterns of recognition of epitopes by T cell clonotypes (a set of cells sharing the same T cell receptor) are represented as edges on a bipartite network. We introduce a circular space of epitopes, so that T cell cross-reactivity is a quantitative measure of the overlap between clonotypes that recognize similar (that is, close in epitope space) epitopes.
2. Dylan Hull-Nye Washington State University
   "Derivation of mathematical relationship between cytotoxic T lymphocyte (CTL) and antibody production rates for immune control in lentiviral infection"
Understanding immune responses to lentiviruses such as HIV and Equine Infectious Anemia Virus (EIAV) creates hope for a potential vaccine. We analyze a within-host model of EIAV infection with antibody and cytotoxic T lymphocyte (CTL) responses. In this model, the stability of the endemic equilibrium, characterized by the coexistence of long-term antibody and CTL levels, relies upon the balance between CTL and antibody production rates. We derive a mathematical relationship between CTL and antibody production rates to explore the bifurcation curve that leads to coexistence. The focus of this talk is on the mathematical two-parameter analysis that was developed for the numerical identification of the parameter ranges that drive the system towards immune system control of virus infection and on the extensions of this analysis.
3. Alexis Erich S. Almocera University of the Philippines Mindanao
   "Hopf Bifurcations Unravel Complex Antibody Dynamics in COVID-19 Patients"
The introduction of vaccines during the later phases of the 2019-2022 coronavirus pandemic (COVID-19) emphasized the value of understanding the dynamics of immune responses. In this talk, we will present a model that previously concentrated on viral replication and T-cells to illustrate the antibody dynamics seen in COVID-19 patients. Our analysis revealed the existence of Hopf bifurcations, where changes in viral clearance rates by IgM and IgG can affect a shift between oscillating viral loads and a stable, steady state. When T-cell immunity is compromised, resulting in the emergence of the virus-positive equilibrium point (VPE), moderate antibody levels can facilitate pathways to manage prolonged infections through an unstable VPE. Our findings suggest a more complex immune response than suggested by our earlier model: while T-cells can still eliminate the infection by achieving a stable virus-free equilibrium, antibody responses become valuable when SARS-CoV-2 overwhelms the T-cells.
4. Reagan Johnson University of Idaho
   "Modeling Rhinovirus mediated protection against lethal influenza"
Influenza continues as a global issue, resulting in approximately one million deaths each year. Clinical studies have found that coinfections with other pathogens can occur, the impact of which varies. While some lead to an exasperated infection, others appear to reduce influenza’s severity and confer protection. However, the mechanisms behind this virus mediated protection have yet to be fully understood. Experimental results have shown complete survival of mice that are inoculated with Rhinovirus two days before receiving a lethal inoculation of Influenza-A. These studies have provided viral titers and immune cell counts which suggest an earlier innate immune response and faster clearance of the virus. We hypothesize that Rhinovirus is promoting earlier, and effective innate immunity to Influenza through its induction of type one Interferon. An important cytokine upstream of much of the innate immune response. Utilizing the viral-data available, preliminary modeling results have supported that this coinfection cannot be well-captured by a simple target cell model, which does not consider an innate immune response. In this talk we will discuss our work in extending these differential models to investigate how Rhinovirus’s early impact on the immune system may be providing protection.

Organized by: Esteban A. Hernandez-Vargas (University of Idaho), Veronika Zarnitsyna, Emory University

1. Esteban Hernandez Vargas University of Idaho
   "CrossLabFit: Enhancing parameter fitting in viral dynamics through cross-laboratory qualitative integration"
Accurate parameter estimation is critical for predictive modeling in viral dynamics, yet it remains a major bottleneck due to sparse, heterogeneous, and often qualitative data. Traditional fitting approaches typically rely on rich quantitative datasets from a single lab—an impractical constraint for many real-world biological systems. In this talk, I will introduce CrossLabFit, a new framework that enables parameter fitting by integrating qualitative trends from multiple experimental sources. Instead of requiring high-frequency, high-resolution data from a single experiment, our method leverages categorical and trend-based insights collected across labs. These are encoded as 'qualitative windows'—adaptive constraints that guide model behavior without demanding precise point-wise agreement. We implement this approach using a GPU-accelerated differential evolution algorithm, allowing us to efficiently explore parameter spaces constrained by both quantitative data and distributed qualitative insights. Applied to viral dynamics models, CrossLabFit not only improves fit accuracy but also enhances parameter identifiability in settings where conventional methods struggle. By enabling collaborative data use across labs, this method offers a scalable, realistic path to better modeling of complex infectious diseases. I will conclude by discussing applications to current viral systems and how this approach opens doors for broader integration in systems biology. Funding: Research reported in this publication was supported by the National Institute of General Medical Sciences of the National Institutes of Health under award numbers R01GM152736.
2. Hana Dobrovolny Texas Christian University
   "Time-varying viral production in virus dynamics models"
Mathematical modeling of viral kinetics can be used to gain further insight into the viral replication cycle and virus-host interactions. However, many virus dynamics models assume that viral production occurs at a constant rate over the lifespan of the infected cell. In fact, virus yield is time-dependent, which could alter the time course of the viral infection. We used measurements of viral yield from single cells infected with vesicular stomatitis virus (VSV) to determine the cumulative distribution of virus produced by a single cell. We then incorporate the distribution into an integro-differential equation model of viral infection that allows for time-dependent viral production, allowing us to determine how time-dependent viral production changes the predictions of viral kinetics models.
3. Timon Kapischke University Medicine Greifswald Greifswald
   "Mathematical Modeling and Analysis of Factors Influencing the Intracellular Replication of SARS-CoV-2"
The emergence of SARS-CoV-2 underscored the critical need to understand the molecular mechanisms governing viral replication and host response. While mathematical models have provided valuable insights into viral dynamics, detailed mechanistic models of SARS-CoV-2 intracellular replication remain scarce. Here, we present a comprehensive model that captures the key stages of the intracellular viral life cycle and enables quantitative analysis of host-virus interactions. Methods: We developed a mechanistic model encompassing viral entry, replication, protein synthesis, and virion release, implemented within the Data2Dynamics framework. The model was calibrated using high-resolution 24-hour kinetic data, including measurements of viral RNA, proteins, and virion concentrations, to ensure accurate parameter estimation and robust validation. Results: The model successfully recapitulates the intracellular dynamics of SARS-CoV-2 and identifies key regulatory points that serve as potential therapeutic targets. We validated these predictions through drug response experiments targeting distinct stages of the replication cycle. Furthermore, comparative analysis of replication kinetics across SARS-CoV-2 variants reveals mechanistic insights into observed differences in replication efficiency. Ongoing work focuses on extending the model to include the RIG-I/JAK-STAT signaling pathway, aiming to link intracellular viral dynamics with innate immune responses. Funding: DFG, project number 462165342
4. Lubna Pinky Meharry Medical College
   "Modeling how virus-virus interference can affect population-level transmission dynamics"
Certain viruses demonstrate capacity to inhibit the replication of competing pathogens during concurrent infection within hosts - a phenomenon termed viral interference that has been observed among respiratory viruses, including SARS-CoV-2 and its co-circulating counterparts. Using a compartmental epidemiological framework, we investigate how this host-level viral suppression translates to population-scale transmission dynamics. Our findings reveal that viral interference manifests as reduced infections of the suppressed virus across the population, with significant effect observed when competing viruses possess comparable epidemiological parameters. Simulating co-circulation scenarios between SARS-CoV-2 and three common respiratory pathogens, we demonstrate that RSV co-circulation produces the most substantial suppression of SARS-CoV-2 transmission. Interestingly, while SARS-CoV-2 epidemics remain largely unaltered during co-circulation with either influenza or rhinovirus, these interactions induce temporal shifts in the epidemic curves of the latter viruses - highlighting asymmetric interference effects that depend on specific viral pairings. These results suggest how molecular-level viral competition shapes broader epidemic patterns and transmission trajectories of respiratory infections.

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

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

1. 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.
2. 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.
3. 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.
4. 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.

Organized by: 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

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

1. Mélanie Prague Université de Bordeaux/INRIA
   "Mechanistic Model of initial and persisting antibody response following Ebola vaccination: application to the PREVAC trial."
Antibody response dynamics following Ebola vaccination remain incompletely understood, particularly regarding the continuum of initial induction to long-term persistence. This study developed a mechanistic model of B-cell stimulation post-vaccination able to infer antigen presentation kinetics and propose an identifiable model based solely on anti-ZEBOV IgG levels. This study was based on the PREVAC randomized placebo-controlled trial (NCT02876328), which enrolled healthy adults and children, from Sierra Leone, Mali, guinea and Liberia, to evaluate the safety and immune responses of three vaccine strategies: Ad26.ZEBOV followed by MVA-BN-Filo 56 days later (the Ad26-MVA group, 799 participants), rVSV∆G-ZEBOV-GP followed by placebo 56 days later (the rVSV group, 802 participants), and rVSV∆G-ZEBOV-GP followed by rVSV∆G-ZEBOV- GP 56 days later (the rVSV–booster group, 399 participants). Our model was modified from Clairon et al. (2022, PLOS Comp. Biol.), which assumes that antigen stimulates the differentiation of naive B cells into long-lived and short-lived antibody-secreting cells. The two groups were modeled following similar pipelines. For both vaccine groups, a bell-shaped curve best described the dynamics of antigen presentation according to model information criterion indicating a long antigen presentation regardless of replication competence of the vaccine. Longer presentation times were found for rVSV (half-life t1/2=45 days at first dose and t1/2=1 days at second dose) than for Ad26–MVA (t1/2=35 days for Ad26.ZEBOV and t1/2=7 days for MVA-BN-Filo). We as well quantified effect of sex, age and geography on humoral dynamics. This work provides a foundation for in silico simulations of vaccination clinical trials, with the objective of optimizing booster strategies to ensure the long-term maintenance of immunogenicity in target populations according to patients characteristics, revaccination timing and vaccination strategy.
2. Elizabeth Amona Virginia Commonwealth University
   "Studying Disease Reinfection Rates, Vaccine Efficacy and the Timing of Vaccine Rollout in the context of Infectious Diseases"
The COVID-19 pandemic has highlighted the intricate nature of disease dynamics, extending beyond transmission patterns to the complex interplay of intervention strategies. In the post-COVID-19 era, reinfection has emerged as a critical factor, shaping how we model disease progression, evaluate immunity, and assess the effectiveness of public health interventions. This research uniquely explores the varied efficacy of existing vaccines and the pivotal role of vaccination timing in the context of COVID-19. Departing from conventional modeling, we introduce two models that account for the impact of vaccines on infections, reinfections, and deaths. We estimate model parameters under the Bayesian framework, specifically utilizing the Metropolis-Hastings Sampler. The study conducts data-driven scenario analyses for the State of Qatar, quantifying the potential duration during which the healthcare system could have been overwhelmed by an influx of new COVID-19 cases surpassing available hospital beds. Additionally, the research explores similarities in predictive probability distributions of cumulative infections, reinfections, and deaths, employing the Hellinger distance metric. Comparative analysis, utilizing the Bayes factor, underscores the plausibility of a model assuming a different susceptibility rate to reinfection, as opposed to assuming the same susceptibility rate for both infections and reinfections. Results highlight the adverse outcomes associated with delayed vaccination, emphasizing the efficacy of early vaccination in reducing infections, reinfections, and deaths. Our research advocates prioritizing early vaccination as a key strategy in effectively combating future pandemics, thereby providing vital insights for evidence-based public health interventions.
3. Cailan Jeynes-Smith University of Tennessee Health Science Centre
   "Dissecting Cytokine Production: Integrating Subset-Specific Data into Immunological Models"
Understanding cytokine regulation and its impact on the cellular response to infection is challenging. Immunological models often rely on implicit assumptions about cytokine production, as direct quantification of cytokine-producing cell subsets is uncommon. However, only a fraction of cells may be actively producing cytokines, and their dynamics frequently diverge from those of the broader population. To address this, we developed a mechanistic model of IFN-γ production during influenza A virus infection, integrating cell abundance data with integrated median fluorescence intensity (iMFI) measurements. This framework allowed us to quantify the relative contributions and nonlinear regulation in addition to demonstrating the necessity of using the iMFI to define the balance between production and uptake to explain observed IFN-γ levels. Our findings highlight the importance of incorporating both cell subset data and functional intensity (iMFI) into cytokine modeling, enabling more accurate inference of production mechanisms and improved model predictions.
4. Jonah Hall University of British Columbia/BC Children's Hospital Research Institute
   "Optimization of Pertussis Immunization Using Mathematical Modeling"
Pertussis disease (whooping cough), caused by the bacteria Bordetella pertussis, is most severe in young infants, with the majority of deaths occurring among unvaccinated children aged <3 months. Pertussis vaccination is a safe and effective approach for prevention of pertussis, with the DTaP (priming) and TdaP (booster) vaccine series. TdaP is given during pregnancy and DTaP is given in the first year of childhood. The phenomenon of immunomodulation, however, is known to dampen the IgG response of infants born following pertussis vaccination during pregnancy. We hypothesize that the timing of the vaccination series, while not the immunological cause, could be modified so as to decrease the effect of immunomodulation and thus increase the efficacy of childhood pertussis vaccination. While attempting to empirically test many different schedules would be ineffective, using mathematical modeling to evaluate several schedules simultaneously could be extremely useful in determining a more effective schedule. We will use a combination of a mathematical model of pertussis vaccination and an experimental mouse model of pertussis vaccination to identify the optimal immunization schedule for pregnancy and infancy. We will immunize pregnant and infant mice, according to a murine analog of the conventional vaccination schedule, with TdaP (pregnant) and DTaP (infant) pertussis vaccine doses. These data will form an input to our working mathematical model. Using the data, we will estimate experimentally inaccessible parameters that govern the mechanisms of the immune system. Once parametrized, we will use the mathematical model to propose immunization schedules that maximize the infant antibody response. We will test our proposed schedules via a second mouse experiment, comparing the immune responses between the two experiments to evaluate the efficacy of the mathematically-optimized schedule. Possible mechanisms of immunomodulation can be evaluated in the mathematical model, using the data collected in both experiments to ensure model accuracy.

1. Daniel Rüdiger Max Planck Institute Magdeburg
   "The secrets of “OP7”, an influenza DIP: mathematical model, impact of mutations and antiviral mechanisms"
Defective interfering particles (DIPs) are mutated, replication-incompetent virions that can inhibit their corresponding standard virus (STV). Previous studies have shown the effectiveness of DIPs against various virus species, highlighting them as promising broad-spectrum antivirals. OP7, an influenza DIP with 37 nucleotide substitutions in its segment 7 (S7) vRNA, has been found to suppress STV replication more effectively than conventional DIPs. However, the effects of these mutations on the replication of OP7 and its mechanism of interference with the STV remained unclear. In this study, we investigated the infection dynamics during a coinfection of influenza STV and OP7 in cell culture. We monitored the dynamics of viral RNAs, assessed viral protein levels, and determined virus titers. With these experimental results, we developed a mathematical model to simulate the coinfection of STV and OP7. Subsequently, we used this model to explore various hypotheses about the impact of mutations on virus replication and to predict the suppression of STV by OP7 in passaging experiments. In vitro experiments show that S7-OP7 surpasses the levels of all STV genome segments. Model simulations suggest this is induced by a significantly increased rate of replication, attributed to mutations in S7-OP7 inducing a “superpromoter”. Additionally, simulations predicted a notable reduction in viral mRNA transcription for S7-OP7, which was later validated experimentally. Moreover, we deduce that the M1 protein derived from S7-OP7 mRNA is likely defective. Lastly, the model accurately predicts the spread of OP7 and the suppression of STV in infected cell cultures over multiple passages under various initial conditions. In summary, we developed a mathematical model that enables a thorough examination of STV and OP7 coinfection, improves our understanding of DIP interference mechanisms, and supports the development of antiviral therapies.
2. Ying Xie Kyoto University
   "Antihistamine Efficacy in Relation to the Morphology of Skin Eruptions in Chronic Spontaneous Urticaria"
Chronic spontaneous urticaria (CSU) is a persistent skin disorder characterized by red, itchy eruptions of various shapes, known as wheals. These wheals appear and disappear daily, persisting for months or even decades, and severely impact patients' quality of life. The standard treatment for CSU primarily consists of second-generation H1 antihistamines, often administered at higher-than-usual doses. However, approximately 30% of patients remain symptomatic despite these conventional therapies. On the other hand, our previous mathematical modeling and clinical studies have identified five distinct types of wheal shapes through the development of clinical criteria for eruption geometry (EGe Criteria) and have shown how the characteristics of each wheal type are involved in the pathophysiology of CSU. These findings suggest that CSU may be classified into five medical subtypes based on wheal morphology. Thus, in this study, we explore the effectiveness of antihistamines based on wheal shape. We first evaluate the efficacy of antihistamines in silico using three key measures: wheal area, itching severity, and wheal expansion dynamics, across the five identified wheal types. Additionally, we validate some of our theoretical observations using clinical data from patients. By elucidating the relationships among the key networks involved in CSU pathophysiology, wheal morphology, and drug efficacy, we can enhance the development of more accurate diagnostic tools and treatment strategies in clinical settings.
3. Madeleine Gastonguay Institute for Computational Medicine, Johns Hopkins University
   "Viral rebound kinetics following single and combination immunotherapy for HIV/SIV"
Combination antiretroviral therapy (ART) can treat but not cure HIV, motivating the development of therapies that stimulate the immune system to control or eliminate infection. Two such immunotherapies- a TLR7 agonist and a therapeutic vaccine - were previously tested in SIV-infected rhesus macaques. Animals received ART alone or with concurrent single or combination immunotherapy, and viral rebound was monitored after treatment interruption. Many treated animals exhibited altered rebound kinetics, and a subset achieved either complete viral suppression or immune control after an initial rebound. However, the mechanisms driving these effects are unknown: do these therapies deplete the latent reservoir or enhance antiviral immunity, and do they act synergistically? To investigate the effects of immunotherapy, we built a mathematical model of viral dynamics incorporating latent cell reactivation and a generalized immune response. We confirmed the model could reproduce the range of rebound trajectories seen in the data, and examined whether parameters could be reliably estimated from the available data. Using nonlinear mixed-effects modeling, we quantified interindividual variability and identified significant differences in model parameters between treatment groups. Our results indicate that the vaccine alone reduces latent virus reactivation and enhances immune response avidity. The TLR7 agonist, when administered after late ART initiation, increases target cell availability and reduces the latent reservoir. We found that regardless of ART initiation, the two therapies act synergistically to further enhance immune response avidity. Immune avidity appeared to increase with later ART initiation, although whether this effect is specific to TLR7 treatment is unclear. Our model provides mechanistic insight into immunotherapeutic control of viral rebound and can be adapted to predict their impact in controlling HIV, guiding future therapeutic design and clinical trials.

1. Hwai-Ray Tung University of Utah
   "Missed an antibiotic dose - what to do?"
What should you do if you miss a dose of antibiotics? Despite the prevalence of missed antibiotic doses, there is vague or little guidance on what to do when a dose is forgotten. In this paper, we consider the effects of different patient responses after missing a dose using a mathematical model that links antibiotic concentration with bacteria dynamics. We show using simulations that, in some circumstances, (a) missing just a few doses can cause treatment failure, and (b) this failure can be remedied by simply taking a double dose after a missed dose. We then develop an approximate model that is analytically tractable and use it to understand when it might be advisable to take a double dose after a missed dose.
2. Montana Ferita University of Utah
   "Surfing the Actin Wave: Mathematical Modeling of Natural Killer Cell Synapse Formation"
Natural killer (NK) cells are members of the innate immune system and are proving to be a lethal weapon against cancer. To unlock the full power of NK cells, we must first address the central question: How does an NK cell recognize a malignant cell? To assess a target cell, an NK cell forms an immunological synapse, which is the interaction zone between the two cells. Ligand-receptor binding within the synapse triggers downstream activating and inhibitory signaling pathways that integrate to control the actin cytoskeleton network. Dominating activating signals causes the NK cell’s actin network to reorganize which transports more receptors to the synapse, thereby generating a positive feedback loop. Mechanistically, activating signals lead to the activation of the Arp2/3 complex which creates a branched actin network. In return, the flow of this network drives the centripetal transport of receptors to the synapse. We propose an advection-diffusion model to capture this phenomenon. Furthermore, we test what ligand-receptor densities permit synapse formation.
3. Madeleine Gastonguay Johns Hopkins University
   "Quantifying the dynamics of Kaposi’s sarcoma-associated herpesvirus persistence"
Kaposi’s sarcoma-associated herpesvirus (KSHV) is a causative agent of several lymphoproliferative diseases, particularly in immunocompromised individuals. These malignancies originate from latently infected B cells, where KSHV persists as extrachromosomal episomes. While the viral protein LANA is essential for viral maintenance during latency, the mechanisms enabling lifelong persistence remain unclear. To quantify episome dynamics, we developed a mathematical model of latent KSHV replication and segregation during cell division, and a statistical framework to infer viral dynamics from fluorescent microscopy images. We built a Gibbs sampler to extract episome counts from imperfectly resolved images of pre- and post-division cells. Using these counts, we estimate the efficiency of replication and segregation, propagating imaging uncertainty into our parameter estimates. Our framework, validated on synthetic data, provided similar estimates of replication efficiency (78%, 95% CI [53%, 90%]) and segregation efficiency (91% [78%, 100%]) when applied to fixed and live images of cells transfected with either full-length KSHV or a minimal plasmid capable of episome maintenance. Simulations of a dividing cell population showed that imperfect replication and segregation preclude decades-long persistence without the assistance of additional mechanisms such as cell-survival benefits to infection or occasional lytic replication. We also modeled KSHV-dependent malignancies to evaluate episome replication and segregation as targets to control tumor growth. Simulations revealed that reducing replication effectively disrupts tumor growth, with the required reduction dependent on cell division kinetics. Our results suggest that KSHV employs a partitioning mechanism, as opposed to random segregation, though replication and segregation are imperfect. Furthermore, targeting episome replication may offer a viable strategy to reduce tumor burden in KSHV-associated malignancies.
4. Kathryn Lynch University of Utah
   "Genetic regulation of vibrio vulnificus hemolysin drives population heterogeneity"
Individual bacterium make decisions at a genetic level as a result of various types of gene regulation; this process plays out on a population level to inform colony growth. Vibrio vulnificus is an opportunistic Gramnegative marine pathogen with a limiting growth factor of iron. Compared to other foodborne pathogens, Vibrio vulnificus has a high mortality rate and relatively poorly understood virulence mechanisms. When inside a human host, this bacteria utilizes heme as a source of iron, necessitating the ability to turn pieces of the heme acquisition system off and on in response to various environmental signals. As establishment of infection depends on Vibrio vulnificus’s ability to change from a marine to human environment, the ability to switch on the heme-intake system is an important part of establishment of initial infection. One such part of this system is the hemolysin VvhA. This toxin is excreted by the bacterium to lyse erythrocytes, thereby releasing heme into the extracellular environment where the bacteria can use it as a source of iron. This toxin is regulated by a complex set of factors including nutrient availability and quorum sensing. Exploring this gene regulatory network via bifurcation analysis reveals a complex bifurcation structure. These dynamics allow an individual bacterium to integrate a variety of signals in response to a changing environment. In particular, bistability in the system points to the likelihood of a heterogenous bacterial colony, where many bacteria benefit from a smaller number of hemolysin producers. This allows for modeling both a heterogeneous population and incorporation of the physiological mechanism by which cells make the decision to switch states. The interdependence between toxin production, nutrient availability, and colony growth result in interplay between the bacteria and their environment, allowing for insights into the overall course of infection.

1. Jonah Hall UBC
   "Optimization of Pertussis Immunization Using Mathematical Models"
Pertussis (whooping cough), caused by Bordetella pertussis, is most severe in infants, with most deaths occurring in unvaccinated infants under three months of age. Vaccination with the DTaP (priming) and TdaP (booster) immunizations is effective, with TdaP given during pregnancy and DTaP in infancy. However, immunomodulation can dampen the IgG response in infants born to vaccinated mothers. We hypothesize that adjusting the vaccination schedule could reduce immunomodulation and enhance vaccine efficacy. Since empirically testing multiple schedules is impractical, we propose using mathematical modeling alongside two experimental mouse models to determine an optimal schedule. Pregnant and infant mice will be immunized following a murine analog of standard vaccination. These data will inform our model, allowing us to estimate key immune parameters. Once parametrized, our model will propose schedules that maximize infant antibody response. A second mouse experiment will test these schedules, comparing immune responses to assess their efficacy. This approach will help evaluate immunomodulation mechanisms and refine vaccination strategies. The mechanistic evaluation of immunomodulation is of significance due to its lack of effective investigations to date.
2. Adnan Khan Lahore University of Management Sciences
   "Antibiotic Resistance and Dosing in Bacterial Biofilms"
In this talk, we will present effective antibiotic regimens in the presence of drug-resistant bacteria in biofilms. We begin by discussing models of in-vivo antimicrobial resistance transfer within bacterial biofilms, focusing on various one-dimensional biofilm models. Our approach includes modeling resistance acquisition through horizontal gene transfer between resistant and susceptible strains while also accounting for the role of persistor cells. We examine the effects of periodic antibiotic dosing at a constant level, showing that it may not always lead to bacterial eradication. To address this challenge, we utilize a numerical optimization algorithm to determine the optimal antibiotic dosing strategy. Additionally, we analyze how changes in different model parameters impact the qualitative behavior of the optimal dosing regimen.
3. Peter Rashkov Institute of Mathematics and Informatics, Bulgarian Academy of Science, Sofia, Bulgaria
   "Towards a mathematical model of the methotrexate effect on immunogenicity to adalimumab in axial spondyloarthritis"
Axial spondyloarthritis (SpA) is a chronic inflammatory disease impacting the joints in the axial skeleton (e.g. chest, spine, pelvis). Tumor necrosis factor inhibitors (TNFi), such as the monoclonal antibody adalimumab, are used to treat severe cases, but therapy is often discontinued due to loss of efficacy, not least resulting from immunogeniocity and development of anti-drug antibodies (ADA). The disease-modifying drug methotrexate (MTX) has shown potential in reducing the formation of ADA to various TNFi in rheumatoid arthritis (Krickaert et al, 2012), but little is known about its mode of action. We adapt a mechanistic mathematical model for immunogencity towards adalimumab based on Chen et al. 2014 to describe the impact of MTX in reducing immunogenicity, and parametrise it based on patient data from a multicentric randomised trial (Ducoureau et al, 2020). ODEs describe the pharmacokinetics and pharmacodynamics of the therapeutic compounds (adalimumab only or adalimumab and MTX, depending on patient cohort), the dynamics of T and B lymphocytes, antigen presenting cells, and some relevant cytokines for the disease. Due to the large size of this model, we employ several reduced models to estimate some of the parameter values. The model is used to simulate several scenarios in order to elucidate the most likely modes of action of MTX to reduce immunogenicity by comparing the simulated and measured ADA titres along 5 hospital visits. This is joint work with Sara Sottile (Bologna, Italy) and Denis Mulleman (Tours, France). This work is based upon work from COST Action ENOTTA (CA21147), supported by COST (European Cooperation in Science and Technology), and Contract KP-06-DKOST-13 of the Bulgarian Fund for Scientific Research.