Organized by: Jared Barber (Indiana University Indianapolis), Luoding Zhu

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

1. Anotida Madzvamuse University of British Columbia
   "A geometric bulk-surface PDE approach for modelling single and collective cell migration"
In this talk, I will present a geometric bulk-surface PDE approach for modelling single cell migration. First, I will discuss a geometric-surface PDE approach where cell migration is described by a force balance equation posed only on the cell plasma membrane, under a sharp interface formulation. The evolution law for the cell plasma membrane is discribed through forces acting at each material point, in the normal direction. These forces include (but are not limited to): actomyosin forces for cell polarisation, driven by molecular species resident on the plasma membrane and these obey a surface reaction-diffusion system; forces describing the energetic nature of the plasma membrane (e.g. surface tension, bending energy, etc); forces associated with volume constraint and external forces (including cell-to-cell interactions, cell-to-obstacle interactions), and so forth. By introducing bulk dynamics associated with the bulk-surface wave-pinning model, we will demonstrate the generalisation to a geometric bulk-surface modelling approach. To support the modelling approach, numerical examples will be exhibited based on evolving bulk-surface finite elements to model single and collective cell migration through stationary and deformable extracellular matrices as well as cell migration through confined spaces, reminiscent of microfluidic devices.
2. Jared Barber Indiana University Indianapolis
   "Admissible behaviors for a model of actin filaments pushing the cell forward"
During cell migration across a 2D surface, cells develop a flat protrusive structure called a lamellipodium (“sheet-like foot”). Actin (protein) filaments form inside of this structure and push at the leading edge of the cell in order to propel the cell forward. While there are various complexities associated in this process, in this talk, we explore a simple version of the “Filament-Based Lamellipodium Model (FBLM)”. In this version, filaments are represented by multiple line segments that are relatively short, parallel to each other, and perpendicular to the front of the cell/lamellipodium. The model includes frictional forces as well as forces that tend to keep the filaments approximately equally spaced from each other, the front of the cell, and the side of the lamellipodium. Such forces are derived by defining corresponding energies and then using variational techniques. We study this system near equilibrium to better understand what solutions are admissible and share numerical representations of such solutions. Such information informs us about the variation that may arise when actin filament networks act to push forward the lamellipodium during cell migration.
3. Jianda Du University of Florida
   "Effect of Curvature in a Cell Migration Model"
Cell migration is essential for processes such as tissue development, wound healing, and cancer metastasis. For instance, during gastrulation—an early stage of embryonic development—cell migration is crucial for the formation of germ layers that eventually develop into tissues and organs. We extend a previously established continuum mechanical model of cell migration by introducing curvature as a key factor. We investigate how curvature influences cell migration in spreading embryonic tissues of two species: the aquatic frog Xenopus laevis and the axolotl salamander Ambystoma mexicanum. Simulations are conducted with various initial tissue shapes to assess the impact of curvature. Sensitivity analysis and approximate Bayesian computation with sequential Monte Carlo (ABC-SMC) are used to evaluate the importance of incorporating curvature and to additionally determine the form of curvature dependence that best reflects the experimental data.
4. David Odde University of Minnesota
   "Modeling the mechanics of glioblastoma progression and treatment."
Effector CD8+ T cells must make cell-to-cell contacts (TCR-MHC-antigenic-peptide-complex) to identify and eliminate cancer cells selectively. This requirement could become a make-or-break factor in the clearance of solid tumors such as glioma, which we focus on in this study, where T cells have to actively search for the cancer cells in the tissue. Several immunotherapies, such as checkpoint blockade and adoptive T cell therapy, have been proposed; however, all of these essentially aim to make T cells better killers, not migrators. In this study, we recognize an equally important factor crucial for their success, i.e., their migration in the tissue. T cells have been assumed to be optimal navigators based on evolutionary reasons, an idea we challenge in this study. Using a combination of ex vivo brain tissue and in vitro assays, we found that T cells, on average, migrated slower than reported in the literature (0.5-2 μm/min, 0.1-1 μm/min vs 6-10 μm/min, 10-30 μm/min) and only modestly faster than cancer cells in a similar setting (0.1-0.2 μm/min), suggesting the need for improvement for effective immune response and immunotherapy. Strikingly, for T cells, the best description was not a single, homogeneous population of superdiffusive walks as previously found but a mix of comparable numbers of sub, normal, and superdiffusive walks, especially at longer time scales. This heterogeneity is advantageous for finding targets of a range of sizes but worse than the single superdiffusive population for finding a fixed target such as a glioma. We investigated the reason for such slow migration. Our T cells, consistent with previous studies, showed evidence of a 'stop-and-go' pattern. We found that hyper adhesive interactions with the perivascular space of blood vessels, the entry point of T cells into the brain, microglia, a major antigen-presenting cell in the brain, and hyaluronic acid, a major ECM protein in the brain, all could explain many, but not all, of the 'stops”. Reducing these 'stops' could increase net T cell migration, potentially an improvement enough to stop the inevitable GBM recurrence under current standard therapy regimens. Next, we used drug-perturbation experiments and high-resolution imaging to unravel the biomechanics of CD8+ T cell migration. We discovered that these T cells are capable of using multiple modes, highlighting their adaptive nature, but often use the familiar motor-clutch mode of cell migration usually reported for cancer cells, but with altered, faster protrusion and focal-adhesion dynamics. To capture these dynamics we developed a momentum-conserving model for hybrid bleb-adhesion-based rapid T cell migration. Together, these results advance our fundamental understanding of T cell migration in the brain, which may inspire better immunotherapies in the future that are focused on making T cells both powerful killers and adept at rapidly locating target cancer cells.

Organized by: Daozhou Gao (Cleveland State University), Xingfu Zou, University of Western Ontario

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

1. Wenxian Shen Auburn University
   "Front Propagation Dynamics in Fisher KPP Equations on Unbounded Metric Graphs"
This talk is concerned with front propagation dynamics in Fisher KPP equations on unbounded metric graphs. Such equations can be used to model the evolution of populations living in environments with network structure. There are several studies on front propagation phenomenon in bistable equations on unbounded metric graphs. It is known that, in such equations, the network structure of the underlying environment may block the propagation of the fronts. It will be shown in this talk that the network structure of the environments does not block the propagation of the fronts in Fisher-KPP equations. In particular, it will be shown that the Fisher-KPP equation on an unbounded graph with finite many edges has the same spreading speed $c^*$ as the Fisher KPP equation on the real line $mathbb{R}$ and has a generalized traveling wave connecting the stable positive constant solution and the trivial solution with averaged speed $c$ for any $c > c^∗$.
2. Rachidi Salako University of Nevada, Las Vegas
   "On a Cross-diffusive SIS Epidemic Model with Singular Sensitivity"
We investigate the dynamics of solutions to a repulsive chemotaxis SIS (susceptible-infected-susceptible) epidemic model with logarithmic sensitivity and with mass-action transmission mechanism. Under suitable regular assumptions on the initial data, we firstly assert the global existence and boundedness of smooth solutions to the corresponding no-flux initial boundary value problem in the spatially one-dimensional setting. Second, we investigate the effect of strong chemotaxis sensitivity on the dynamics of solutions through extensive numerical simulations. Our studies on the asymptotic profiles of the endemic equilibrium indicate that the susceptible populations move to low-risk domains whereas infected individuals become spatially homogeneous when the repulsive-taxis coefficient is large. Additionally, our numerical simulations suggest that the susceptible population with larger chemosensitivity, tends to respond better to the infected population, revealing the effect of strong chemotaxis sensitivity coefficient on the dynamics of the disease.
3. Yun Kang Arizona State University
   "Migration Dynamics and Collective Decision-Making in Social Insect Colonies"
Social insects are among the most ecologically and evolutionarily successful organisms on Earth, known for exhibiting robust collective behaviors that emerge from local interactions among individuals. Colony migration is a particularly striking example of collective decision-making in these systems. In this talk, we introduce a piecewise dynamical model of colony migration incorporating recruitment switching to investigate the underlying mechanisms and synergistic effects of colony size and quorum thresholds on decision outcomes. Our theoretical findings suggest that larger colonies are more likely to successfully emigrate to a new site. Notably, the model also reveals several intriguing behaviors: (a) the system may exhibit oscillatory dynamics when the colony size falls below a critical threshold; and (b) it may display bistability, where the colony either migrates to a new site or remains at the original nest, depending on the initial distribution of recruiters. Bifurcation analysis further highlights how variations in colony size and quorum thresholds critically influence the overall system behavior. These results underscore the importance of distinguishing between different recruiter populations in modeling and offer valuable insights into how simple, local interactions can lead to complex and coordinated migratory behavior in social insect colonies.
4. Carolin Grumbach Osnabrück University
   "Allee Pits in Metapopulations: When Increasing Dispersal Can Backfire"
Habitat fragmentation divides populations into smaller subpopulations, while the Allee effect diminishes the viability of small populations. Together, these processes can synergistically amplify negative impacts on spatially structured populations. Conservation strategies often aim to counteract these effects by enhancing connectivity between subpopulations, for example, through corridors or stepping stones. However, increasing connectivity does not always lead to the desired positive outcomes. In this talk, I will demonstrate that due to the Allee effect, low connectivity leads to a decline in the asymptotic total population size, which we call the 'Allee pit'. However, increased connectivity facilitates the rescue effect, wherein a persistent subpopulation in one patch can save an extinction-prone subpopulation in another patch, ultimately increasing the total population size. Using simulations based on a generic discrete-time patch model with positively density-dependent growth, I will explore how enhanced connectivity influences a fragmented population subject to the Allee effect. Our results highlight that conservation strategies must carefully consider dispersal dynamics. Simply increasing connectivity is not enough; ensuring dispersal rates exceed a critical threshold is essential for achieving long-term benefits.

Organized by: Zhisheng Shuai (University of Central Florida), Junping Shi, College of William & Mary; Yixiang Wu, Middle Tennessee State University

Note: this minisymposia has multiple sessions. The other sessions are: Part-1, and Part-2.

1. Yuanwei Qi University of Central Florida
   "Mathematical Analysis of a Cancer Invasion Model"
In this talk I shall present some recent results on Global Existence, Stability of various equilibrium points as well as existence of traveling wave to a well established reaction-diffusion system modeling cancer invasion. This is a joint work with Xinfu Chen of University of Pittsburgh, Xueyan Tao and Shulin Zhou of Peking University.
2. Chunhua Shan University of Toledo
   "Transmission dynamics and bifurcations of a diffusive epidemic model with a nonlinear recovery rate"
In this talk we study the disease transmission dynamics of a diffusive epidemic model with a nonlinear recovery rate. The Hopf bifurcation and Bogdanov-Taken bifurcation are first considered for the corresponding ODE model. Then we analyze the Turing instability and the Turing-Hopf bifurcation. Numerical simulations and biological interpretation are also provided.
3. Yixiang Wu Middle Tennessee State University
   "Analysis of a parabolic-hyperbolic hybrid population model: an integrated semigroup approach"
We consider the global dynamics of a hybrid parabolic-hyperbolic model describing populations with distinct dispersal and sedentary stages. We first establish the global well-posedness of solutions, prove a comparison principle, and demonstrate the asymptotic smoothness of the solution semiflow. Through the spectral analysis of the linearized system, we derive and characterize the net reproductive rate $mathcal{R}_{0}$. Furthermore, an explicit relationship between $mathcal{R}_{0}$ and the principal eigenvalue of the linearized system is analyzed. Under appropriate monotonicity assumptions, we show that $mathcal{R}_{0}$ serves as a threshold parameter that completely determines the global stability of the system. This is a joint work with Qihua Huang and Mingling Wang.
4. Poroshat Yazdanbakhsh Rollins College
   "A Novel Approach to Understanding Disease Spread and Population Persistence in Heterogenous Environments"
How do infectious diseases or species populations evolve in heterogeneous environments? This has been one of the most sought-after questions in the field of mathematical biology. In this talk, we aim to address this question by introducing a new measure called the network heterogeneity index, denoted by H. We offer a fresh perspective to better understand how diseases spread and populations persist in heterogeneous environments. Our analysis shows that how H is influenced by several key factors, including the structure of such networks, regional disease or population dynamics, and the movements between regions. To highlight the importance of H, we conclude our talk by exploring its applications in epidemiology and ecology across various heterogeneous settings.

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: Folashade B. Agusto (University of Kansas), Chidozie Williams Chukwu

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

1. Blessing Emerenini Rochester Institute of Technology, USA
   "Integrative Triple Therapy Against Bacterial Infections: Exploring Synergistic Dynamics"
Due to their adaptive resistance mechanisms against phages, immune responses, and antibiotics, bacterial biofilms pose considerable challenges to effective treatment, necessitating the development of innovative therapeutic strategies. In this work, we present a comprehensive triple combination therapy model that integrates bacteriophages, innate immune responses, and antibiotics within a highly nonlinear, deterministic, and spatiotemporal mathematical framework. We investigate a range of clinically relevant parameters, including antibiotic dosage and timing, phage administration strategies, and immune response intensity. The formulated model provides mechanistic insights into phage-bacteria dynamics, elucidates post-treatment biofilm structures, and informs precision treatment strategies, particularly for clinically accessible biofilm infections. By bridging clinical applications with advanced mathematical modeling, this work contributes to the development of more effective therapeutic interventions.
2. Olusegun Otunuga Augusta University, USA
   "Stochastic Modeling and First-Passage-Time Analysis of Oncological Time Metrics with Dynamic Tumor Barriers"
The first-passage-time (FPT) that a tumor size reaches a particular barrier is important in evaluating the efficacy of anti-cancer therapies and understanding certain oncological time occurrences. For certain verified stochastic models describing the volume of a tumor, a moving barrier for the tumor size in which an explicit solution of an FPT probability density function (PDF) exists for the first time the tumor size reaches the moving barrier is obtained in this work. The stochastic tumor dynamics incorporate anti-cancer therapies/treatments that are administered at varying rates. The first-passage-time density (FPTD) is derived and utilized to determine the time at which the tumor volume first reaches the moving barrier, providing a framework for analyzing various oncological time metrics. These metrics include key time measurements used to characterize tumor progression, evaluate treatment response, and capture recurrence patterns in cancer dynamics. The treatment effort needed to cause reduction in tumor size is also obtained. This work is applied to experimental data including the Murine Lewis Lung Carcinoma cells originally derived from a spontaneous tumor in twenty control mice. The time at which the volume of the tumor of each mouse doubles in size is estimated using the results obtained in this study.
3. Nourridine Siewe Rochester Institute of Technology, USA
   "A mathematical model of obesity-induced type 2 diabetes and efficacy of anti-diabetic weight reducing drug"
The dominant paradigm for modeling the obesity-induced T2DM (type 2 diabetes mellitus) today focuses on glucose and insulin regulatory systems, diabetes pathways, and diagnostic test evaluations. The problem with this approach is that it is not possible to explicitly account for the glucose transport mechanism from the blood to the liver, where the glucose is stored, and from the liver to the blood. This makes it inaccurate, if not incorrect, to properly model the concentration of glucose in the blood in comparison to actual glycated hemoglobin (A1C) test results. In this paper, we develop a mathematical model of glucose dynamics by a system of ODEs. The model includes the mechanism of glucose transport from the blood to the liver, and from the liver to the blood, and explains how obesity is likely to lead to T2DM. We use the model to evaluate the efficacy of an anti-T2DM drug that also reduces weight.
4. Joan Ponce University of Arizona, USA
   "Impact of DARC Polymorphism on P. vivax Transmission Dynamics"
The malarial parasite emph{Plasmodium vivax} has co-evolved with human populations for millennia. Genetic variants such as the FY(^*)O allele in the Duffy Antigen Receptor for Chemokines (DARC) and the sickle cell allele (HbS) have been naturally selected in malaria-endemic regions because they confer partial resistance to infection, enhancing the survival and reproductive success of carriers. As protective alleles rise in frequency, malaria incidence declines, reducing the selective pressure for further resistance. In this work, we develop a seasonally forced model that couples malaria transmission dynamics with the evolution of DARC genotype frequencies, using fast-slow analysis to capture the multiscale nature of these processes. We derive the basic reproduction number (R_0) and interpret it as a weighted sum of contributions from infected individuals of each genotype. Using data from the Amazonas region of Brazil---where DARC polymorphism remains prevalent and emph{P. vivax} cases persist---we calibrate the model and explore how changes in DARC genotype frequencies impact malaria burden. Our analysis determines the threshold proportion of Duffy-negative individuals required to achieve population-wide protection against emph{P. vivax}, and quantifies how varying levels of Duffy negativity affect monthly incidence patterns.

Organized by: Abba Gumel (University of Maryland), Alex Safsten, Alice Oveson (both University of Maryland)

Note: this minisymposia has multiple sessions. The other sessions are: Part-1, and Part-2.

1. Jacques Bélair Université de Montréal
   "Knowledge as an Infection: Modeling Variable Compliance with Non-Pharmaceutical Interventions (NPIs)"
Management of the COVID-19 pandemic required, during its early stages, the deployment of non pharmaceutical interventions (NPIs) [social isolation, physical distancing, mask-wearing, hand-washing], and then, as they became available, administration of repeated doses of vaccine. We are interested in the consequences, for the dynamics of the disease, of variable adherence to these measures, and the motivation generating the lack thereof. We present two modeling approaches to represent this evolution of behaviour. First, a basic SEIRS model is expanded by introducing a structure in the infectious class, to reflect the variable severity of symptoms and the presence of asymptomatic cases considering the population divided into two classes according to their degree of adherence to the NPIs. Then, from a different perspective, we focus on the health literacy level in a population and the consequences, for the disease dynamics, of both knowledge dissemination and its integration in behaviour.
2. Asa Rishel University of Maryland
   "Mind Over Matter: Balancing the Benefits of COVID Lockdowns with Their Cost on Mental Health"
The COVID-19 pandemic took its toll not only on the physical health of those who lived through it, but also on their mental health. I will present a model of the direct and indirect effects of COVID-19 and the associated public policies on mental health. This is an SIRS model of COVID-19, with compartments for mild, acute, and chronic COVID-19 infections and additional compartments for populations with mental health symptoms. Parameters are determined based on fitting from the first wave of COVID-19 in the New York state population, which includes several changes in local government policy, e.g, lockdown orders, which have an effect on the rate at which mental health systems develop. Finally, an additional “delay” term is included in the model to account for the delay between lockdowns going into effect and individuals developing mental health symptoms. The goal of our analysis is to understand how government policy in response to a pandemic can seek to maximize the population's quality-adjusted life years (QALY), which is a measure not only of lifespan, but also the quality of the years lived. I will present some preliminary results suggesting the optimal timing and strength of government lockdown mandates.
3. Bryce Morsky Florida State University
   "Social Dynamics, Information Spread, and Behavioral Responses in Epidemic Modeling"
Social dynamics and the spread of information are critical factors in the spread of disease, influencing contact rates, behavior, and beliefs. This talk presents behavioral-epidemiological models featuring tipping-point dynamics for the uptake of vaccines or non-pharmaceutical interventions (NPIs), driven by real and perceived infection risks and social norms. These dynamics create collective action problems, leading to cycles of protective behavior and infections, with nonlinear responses to epidemiological parameters. The role of information dissemination is explored, particularly the roles of broadly shared information along with information bubbles.
4. Claus Kadelka Iowa State University
   "Adaptive Human Behavior and Delays in Information Availability Autonomously Modulate Epidemic Waves"
The recurrence of epidemic waves has been a hallmark of infectious disease outbreaks. Repeated surges in infections pose significant challenges to public health systems, yet the mechanisms that drive these waves remain insufficiently understood. Most prior models attribute epidemic waves to exogenous factors, such as transmission seasonality, viral mutations, or implementation of public health interventions. We show that epidemic waves can emerge autonomously from the feedback loop between infection dynamics and human behavior. Our results are based on a behavioral framework in which individuals continuously adjust their level of risk mitigation subject to their perceived risk of infection, which depends on information availability and disease severity. We show that delayed behavioral responses alone can lead to the emergence of multiple epidemic waves. The magnitude and frequency of these waves depend on the interplay between behavioral factors (delay, severity, and sensitivity of responses) and disease factors (transmission and recovery rates). Notably, if the response is either too prompt or excessively delayed, multiple waves cannot emerge. Our results further align with previous observations that adaptive human behavior can produce non-monotonic final epidemic sizes, shaped by the trade-offs between various biological and behavioral factors – namely, risk sensitivity, response stringency, and disease generation time. Interestingly, we found that the minimal final epidemic size occurs on regimes that exhibit a few damped oscillations. Altogether, our results emphasize the importance of integrating social and operational factors into infectious disease models, in order to capture the joint evolution of adaptive behavioral responses and epidemic dynamics.

Organized by: Amy Hurford (Memorial University), Michael Li, Public Health Agency of Canada

Note: this minisymposia has multiple sessions. The other sessions are: Part-1, and Part-3.

1. Sally Otto University of British Columbia
   "Coupled dynamics and the challenge of estimating Rt in small jurisdictions"
During the COVID-19 pandemic, estimates of the reproductive number (Rt) or growth rate (r) from different jurisdictions were often surprisingly similar, given expected variation in contact rates. In this talk, I discuss how signatures of growth can be misleading in areas small jurisdictions and what we can learn from considering coupled dynamics with migration among areas.
2. Julien Arino University of Manitoba
   "Introduced cases and spread of infection in a community"
The recent COVID-19 pandemic made it clear that governments the world over would not hesitate to take public health measures of consequence to curtail the spread of pathogens. Among the myriad of measures used, travel interruptions, enhanced border control and quarantine, targeted specifically spatio-temporal spread. Sometimes, these travel measures were demonstrably useful, but altogether, the overall benefits remain debated. In order to quantify the effect of these measures, it is important to understand how disease introductions unfold in locations from which they are at that point absent. In particular, gaining some sense of the relative contributions of externally and locally generated cases is critical. To do this, we explore numerically a continuous-time Markov chain derived from a simple deterministic metapopulation model for case introduction.
3. Jude Kong University of Toronto
   "Human Behavior and Epidemic Dynamics: Adaptive vs. Robust Control Strategies in Shaping Outbreak Outcomes"
Human behavior significantly influences epidemic dynamics through complex interactions driven by risk perception and public health interventions. In this talk, we use a model of epidemic spread to examine how adaptive control strategies, where individuals dynamically adjust behaviors in response to trends like case doubling rates or awareness campaigns, influence disease transmission compared to robust control strategies that enforce fixed reductions in risky activities. We equally explore how behavioral adaptations, such as risk compensation—where perceived lower risks lead to increased risky behaviors—or risk homeostasis, where individuals maintain a constant level of acceptable risk, can undermine these control efforts. Our findings suggest that adaptive control strategies, by leveraging responsive behavioral changes, may offer a more effective approach to mitigating epidemic spread. These insights highlight the critical role of understanding and harnessing human behavioral dynamics in designing effective public health strategies for outbreak management.
4. Pouria Ramazi Brock University
   "Modeling Behavioral Heterogeneity to Optimize Vaccine Uptake Through Tailored Communication"
This talk explores how heterogeneity in individual decision-making influences vaccine uptake and how understanding this variation can enhance public health strategies. We distinguish between two primary behavioral types: evidence-based learners, who base their decisions on immediate personal payoff, and social-based learners, who are influenced by the observed experiences of others. The relative proportions of these two groups in a population fundamentally shape uptake dynamics. Through a mechanistic modeling framework and identifiability analysis, we demonstrate that these group proportions are not only theoretically identifiable but also practically estimable from vaccine uptake data. Our results show significant variation in these proportions across jurisdictions, suggesting that a one-size-fits-all communication strategy may be suboptimal. Tailoring messages to target specific behavioral profiles can more effectively promote vaccination and improve overall public health outcomes.

Organized by: Hye-Won Kang (University of Maryland Baltimore County), Arnab Ganguly, Louisiana State University, aganguly@lsu.edu

Note: this minisymposia has multiple sessions. The other sessions are: Part-1, and Part-2.

1. Suzanne Sindi University of California Merced
   "Scalable Bayesian Discovery of Chemical Reaction Networks from Fully Observed Stochastic Dynamics"
We present a Bayesian framework for inferring chemical reaction networks (CRNs) from fully observed state-transition data, using spike-and-slab priors to jointly model reaction rates and network structure with uncertainty-aware sparsity. Building on previous work, we leverage likelihood decomposition to enable scalable inference, and demonstrate practical identifiability in three-species networks where the full reaction set is combinatorially large. Our method captures nontrivial posterior structure even in intermediate data regimes, where traditional MLE-based sparse regression methods may fail due to over-penalization of small but important reactions. Unlike point-estimate approaches, our model returns full posterior distributions, allowing principled model selection. We show that our approach generalizes to higher-dimensional systems by exploiting structural sparsity and decomposition strategies, providing a tractable path toward Bayesian inference in large, complex reaction networks.
2. Muruhan Rathinam University of Maryland Baltimore County
   "Stochastic Filtering of Reaction Networks"
We consider the problem of inferring states and/or parameters from exact observations of a subset of states of a stochastic reaction network. We present two particle filtering methods for the computation of the conditional distribution of the state and/or parameters, one for the case of continuous in time observations and the other for the case of observations in discrete snapshots of time. In addition to presenting theoretical justification, we also provide numerical examples to illustrate the applicability of these methods.
3. Arnab Ganguly Louisiana State University
   "Multiscale Enzyme Kinetic Reactions: Stochastic Averaging and Statistical Inference"
We study a stochastic model of multistage enzyme kinetics of the Michaelis–Menten (MM) type, where substrate molecules are converted into product through a sequence of intermediate species. The reaction network is both high-dimensional and multiscale, posing substantial computational challenges, particularly in estimating reaction rates. These challenges are compounded when direct observations of the system's states are unavailable and only random samples of product formation times are accessible. To address this, we adopt a two-stage approach. In the first stage, under technical assumptions similar to those in the Quasi-Steady-State Approximation (QSSA) literature, we establish two asymptotic results: a stochastic averaging principle that reduces the model’s dimensionality, and a functional central limit theorem that characterizes the resulting fluctuations. In the second stage, we consider the problem of estimating parameters of the system from data consisting of a sample of product-formation times. Note that such a dataset does not allow reconstruction of temporal paths of species rendering any trajectory- based inference method categorically inapplicable. To address this, we develop a novel inference framework based on an interacting particle system (IPS) that approximately captures the dynamics the reduced-order model at a molecular level. The crux of our approach is a propagation of chaos result that leads to an asymptotically exact product-form expression for the likelihood function. Numerical examples are presented to demonstrate the effectiveness of the proposed approach. This is a joint work with Wasiur R. KhudaBukhsh.
4. Boseung Choi Korea University Sejong Campus
   "Statistical Inference Method for Identifying the Stochastic Chemical Kinetics Using Logistic Regression"
Identifying network structures and inferring parameters are challenging tasks in the modeling of chemical reaction networks. This study presents likelihood-based methods that utilize logistic regression to derive these components from complete time-series data of stochastic chemical reaction networks. When full trajectories of molecular counts for all species are available, the stoichiometries can be identified, provided that each reaction occurs at least once during the observation period. However, determining which species act as catalysts is more difficult since their molecular counts do not change with the occurrence of reactions. We demonstrate the effectiveness of logistic regression in identifying the entire network structure, including stoichiometric information, using three stochastic models that incorporate catalytic reactions. Additionally, we investigate Bayesian logistic regression approaches for estimating model parameters using real epidemic data. To tackle the challenges presented by data observed from only a subset of populations, we propose a method that combines Bayesian logistic regression with differential equations to infer parameters in the SIR model, utilizing COVID-19 infection data. Our findings emphasize the potential of straightforward likelihood-based methods, such as logistic regression, to extract valuable modeling insights from both synthetic and real-time series data.

Organized by: Jinsu Kim (POSTECH), Eric Foxall (The University of British Columbia - Okanagan Campus), and Linh Huynh (Dartmouth College)

Note: this minisymposia has multiple sessions. The other sessions are: Part-1, Part-2, and Part-3.

Note: this minisymposia has been accepted, but the abstracts have not yet been finalized.

Organized by: Cody FitzGerald (Northwestern University), Rainey Lyons (CU Boulder), Nora Heitzman-Breen (CU Boulder), Susan Rogowski (NCSU)

Note: this minisymposia has multiple sessions. The other sessions are: Part-1, Part-2, and Part-3.

Note: this minisymposia has been accepted, but the abstracts have not yet been finalized.

Organized by: James Osborne (University of Melbourne), James Glazier (Indiana University) Yi Jiang (Georgia State University)

Note: this minisymposia has multiple sessions. The other sessions are: Part-1, and Part-2.

1. James Glazier Indiana University, USA
   "OpenVT--Towards Making Virtual Tissue Models FAIR - Opportunities and Challenges"
Multi-scale, Multicellular Agent-Based Virtual-Tissue models built using modeling frameworks like CompuCell3D, Morpheus, Artistoo, CHASTE or PhysiCell are versatile tools for exploring the complex interactions between intracellular signaling and gene-regulatory networks, inter-cellular signaling through contact and diffusible signals, and force generation, cell migration and shape change. They can play a crucial role in helping to interpret and design more informative experiments, in particular in in vitro to in vivo extrapolation. However, Virtual Tissues currently lack the model-specification standards, support for modular architectures and annotation, cross-compatible tools for graphical model specification, visualization and analysis and accepted model sharing infrastructure that have enabled the rapid developing of systems biology network modeling as a core technology in modern biology and the regulatory acceptance of these approaches. Comparable infrastructure is essential for Virtual Tissues to move from academic one-offs for discovery science to truly progressive mainstream technologies in biomedicine, technology and regulation. Because Virtual Tissues are substantially more complex and structurally and functionally diverse than network models, standardization and modularization, graphical specification and distribution are all more challenging. I will consider some of the variety of Virtual Tissue applications, frameworks and modeling approaches and some of the challenges and opportunities we face in developing an effective ecosystem of tools and standards. I will also discuss how the NSF-funded OpenVT project is working to build community to address these challenges.
2. TJ Sego University of Florida, USA
   "Quantitative Reproducibility at Scale: A Federated, Standardizable Approach"
Stochastic simulations are commonly used to quantitatively or semi-quantitatively describe the dynamics of biological systems. Proving reproducibility of simulation results is critical to establishing the credibility of a model. However, reproducibility of stochastic simulation is difficult for numerous reasons. For example, under-sampling produces insufficient information to allow conclusive findings from independent reproducibility studies. Hence, along with measures to compare results, reproducible stochastic simulation as a community-level practice requires measures of when results can be verified as meaningfully reproduced in independent study, and data formats for facilitating information exchange. This session presents the Empirical Characteristic Function Equality Convergence Test (EFECT), a data-driven method to quantify the reproducibility of stochastic simulation results. EFECT provides a quantitative measure of the reproducibility of stochastic results, called the EFECT Error, for modelers to determine a sample size that allows independent reproducibility studies. EFECT also provides a statistical test for performing reproducibility studies with an a priori significance, enabling modeling communities to develop standards and best practices. To this end, EFECT defines the minimum necessary information, called an EFECT Report, to facilitate exchange between modelers for reproducibility studies. The session surveys numerous applications that demonstrate EFECT enabling reproducible stochastic simulation with a variety of modeling methodologies, including ordinary differential equations with stochastic parameter sampling, stochastic differential equations, agent-based models, and uncertainty quantification in physics-informed neural networks. The session concludes with a detailed look at how a Python library implementation of EFECT, called libSSR, is enabling quantitative reproducibility in multicellular modeling as a federated, community-level activity.
3. Eran Agmon University of Connecticut, USA
   "Multicellular Schema with Vivarium"
As models of multicellular biology grow in complexity, there is a critical need for modular, extensible frameworks that can manage diverse biological processes across scales. This talk introduces a compositional schema approach, a methodology for constructing simulations of multicellular systems through modular, interoperable components. Central to this approach is Vivarium, an open-source software platform designed to integrate heterogeneous modeling formalisms—including ODEs, stochastic kinetics, constraint-based models, agent-based systems, and rule-based logic—into unified, hybrid simulations. I will highlight how Vivarium supports schema-driven composition of cells, their interactions, and their environments, enabling scalable simulations of tissues, microbial communities, and synthetic consortia. A flagship example is the integration of diverse mechanistic submodels in a whole-cell simulation of Escherichia coli, demonstrating Vivarium’s ability to orchestrate biological complexity through standardized schemas and modular interfaces. I will discuss design principles behind Vivarium, share emerging tools for building multicellular models, and outline future directions for collaborative, open-ended systems biology.
4. James Osborne University of Melbourne, AUSTRALIA
   "Multicellular Model Reproducibility: A case study, results from the Open VT hackathon"
Multicellular development is a key area of ongoing research, focussing on how tissues and organs develop and function, particularly how underlying processes fail. The last decade has seen remarkable progress in experimental studies of tissue and organ development, leading to the development of more advanced mathematical models and increased computational power. This has enabled the increased adoption of multicellular approaches to modelling the self-organisation of cells within tissues. Multicellular simulations have become indispensable in understanding complex biological phenomena, from tissue development to disease progression. However, the diversity in simulation methods, such as agent-based models, lattice-free models, and stochastic particle simulations, poses challenges in terms of reproducibility, modularity, reusability, and integration within multi-scale multicellular simulations. To address these challenges, we organised a workshop titled “Community Development of Multicellular Virtual Tissues: The OpenVT project” on the 13th of July 2025, as part of the 2025 SMB meeting. We held a Reproducibility Hackathon during the workshop to test model reproducibility and specification. In this talk, we provide an overview of the workshop and present our findings on reproducibility in multicellular simulation.

Organized by: Richard Bertram (Florida State University), Yangyang Wang, Brandeis University

1. Na Yu Toronto Metropolitan University
   "Exploring the Roles of Noise and Coupling Strength in the Emergent Dynamics of Clustered Neural Motifs"
Understanding how neural networks process information involves exploring how their structure and external influences shape collective dynamics. Research has shown that neuronal networks are not always randomly connected; instead they are organized into highly clustered motifs that act as fundamental building blocks. These motifs play a critical role in shaping collective dynamics such as synchrony and coherence. In this study, we modeled a neural network composed of six representative types of clustered motifs and examined how key factors, including intrinsic noise, inter-motif connectivity, network size, and coupling strength, affect the emergence of synchrony and coherence at both the motif and network levels. Our results reveal that synchrony is optimized when noise intensity and inter-motif connectivity are at intermediate levels. We also find that network performance is enhanced when the ratio of intra- to inter-motif coupling strength is within a specific range.  These results reveal how the interplay between structure and noise shapes coherent neural dynamics.
2. Amin Akhshi McGill University
   "From Chaos to Neural Code: Exploring the Role of Gamma-Frequency Burst Oscillations in Sensory Pyramidal Cells"
Pyramidal cells in the electrosensory lateral line lobe (ELL) of weakly electric fish fire action potentials in the form of bursts (i.e., repeated clusters of spikes) through a mechanism known as ghostbursting. This mechanism enables ELL pyramidal cells to fire gamma-frequency burst oscillations (20–80 Hz) in response to constant applied current, characterized by progressively increasing spike frequencies within bursts. To efficiently investigate the emergence of these complex burst dynamics, we developed a phenomenological model based on the Hindmarsh-Rose (HR) formalism by incorporating dual adaptation variables and an extracellular noise term representing random excitatory and inhibitory inputs. Using parameter optimization against in vivo recordings, we demonstrated the model reliably reproduced key spiking features of ELL pyramidal cells, including chaotic burst firing patterns comparable to experimental observations. Subsequently, bifurcation analysis confirmed that chaotic burst firing is an intrinsic property of the model, consistent with the behavior of ELL pyramidal cells, while parameter sensitivity analysis revealed that external stochastic input modulates spiking activity, collectively shaping the firing patterns observed in vivo. Finally, to investigate the implications of our model for population coding, we performed network simulations in response to electrocommunication stimuli and found that burst firing significantly enhances synchronization among model neurons receiving common input. In this talk, I will provide an overview of these results.
3. Adam Stinchcombe University of Toronto
   "Modelling Insights into Two Behaviour Rhythm Phenomena"
Ultradian behavioural rhythms are highly-flexible oscillations in goal-directed behaviour with periods shorter than a day. They remain mysterious in both their biochemical mechanisms and their functional significance, but are generally believed to be a reflection of neural dynamics. We propose that D2 autoreceptor-dependent dopamine self-regulation in the midbrain-striatal synapses gives rise to ultradian rhythmicity. We express this hypothesis in an ordinary differential equation based mathematical model in a dual-negative feedback-loop structure. Numerical integration and bifurcation analysis shows that the oscillations have a flexible and parameter-sensitive period in agreement with experimental observation. The model also demonstrates the masking-entraining effects of circadian (approximately 24 hour) regulation on ultradian rhythms and the rapid-resetting effect of transient excitation. This reveals the crucial role of circadian-ultradian interaction in consolidating behavioural activity and coordinating the motivation to engage in recurring, albeit not highly predictable events, such as social interactions.
4. Richard Bertram Florida State University
   "Dynamic Homeostasis in Relaxation and Bursting Oscillations"
Homeostasis is typically thought of as the invariance of an equilibrium across a range of input values. However, many biological systems oscillate, and dynamic homeostasis refers to the invariance of some feature of the oscillation across a range of input values. In this presentation, we demonstrate that in fast/slow systems, in which the variables can be partitioned into those that change rapidly and those that change slowly, invariance can be produced in the mean value of a slow variable responsible for driving the oscillations. We use fast/slow analysis to explain this form of dynamic homeostasis and consider the effects of noise. The biological relevance lies in the fact that the slow variable driving the relaxation or bursting oscillation is often a quantity, such as the intracellular calcium or ATP concentration, that plays many roles in a cell and for which homeostasis is advantageous to cell behavior or health.

Organized by: Guillermo Lorenzo (University of A Coruna (Spain)), Chengyue Wu (The University of Texas MD Anderson Cancer Center, US), Ernesto A. B. F. Lima (The University of Texas at Austin, US)

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

1. Heber L. Rocha Indiana University
   "Agent-Based Modeling of Cancer Drug Response with PKPD Calibration Challenges and Personalized Modeling"
In this talk, I will present an agent-based model developed in PhysiCell that integrates transmembrane diffusion and pharmacokineticpharmacodynamic (PKPD) processes to simulate drug responses in cancer cell cultures. While the model is designed to handle a range of therapeutic scenarios, I will focus on its application to AU565 breast cancer cells, using a previously published dataset of in vitro area cultures. Before calibration, we perform a local sensitivity analysis to identify key parameters influencing cell behavior and to reduce dimensionality in the inference process. We then apply Approximate Bayesian Computation (ABC) to calibrate the model using experimental data, highlighting the challenges posed by limited temporal resolution and spatial variability in the measurements. I will discuss how these limitations lead to parameter non-identifiability, and share insights into how experimental design and model assumptions interact to shape the reliability of inference. This work not only demonstrates the potential of integrating ABMs with data-driven calibration, but also underscores the need for careful consideration of data quality when advancing toward more personalized modeling frameworks.
2. Marianna Cerasuolo University of Sussex
   "Mathematical and Statistical Insights into Gut Microbiota–Phytocannabinoid–Diet Interplay in Prostate Cancer Progression in Mice"
This study presents a data-driven framework for modelling host-microbiome-tumour interactions in obesity-associated prostate cancer (PCa). We quantitatively investigated PCa progression in the TRAMP mouse model, focusing on the interplay between dietary fat intake, phytocannabinoid therapy, and gut microbiota composition. Mice on regular or high-fat diets (HFD) were treated with enzalutamide, cannabidiol and cannabigerol, alone or in combination. While combination therapy proved most effective across both dietary groups, a high-fat diet alone was associated with tumour acceleration and significant microbiome dysbiosis. To get further insight into the underlying dynamics, we used statistical analyses to characterise microbial community structure and its modulation by treatment. Using Granger causality and generalised linear models, we inferred predictive relationships among microbial taxa over time. These were complemented by support vector regression and network-theoretic measures to characterise microbial ecosystems under perturbation. Statistical modelling revealed that HFD promotes a more clustered, less modular microbiome with altered predictive relationships between bacterial taxa. Under combination therapy, these patterns were partially reversed, including restoration of Bacteroidetes abundance and damping of pro-tumour microbial signals. Building upon these findings, we developed a dynamical framework to simulate the co-evolution of microbial communities and tumour burden under varied interventions. This model is based on established ecological principles to capture the temporal dynamics of microbial interactions within the gut environment. The framework simulates microbiome-mediated tumour progression and therapeutic responses by integrating host, microbial, and therapeutic variables. Our results support using microbiome-informed digital twins in obesity-associated PCa, highlighting the therapeutic value of integrated, data-driven modelling.
3. Guillermo Lorenzo University of A Coruna
   "Patient-specific forecasting of prostate cancer progression to higher-risk disease during active surveillance"
Prostate cancer (PCa) usually exhibits low or intermediate risk at diagnosis, for which active surveillance (AS) is an established clinical option. Patients in AS are monitored via serum Prostate Specific Antigen (PSA), multiparametric magnetic resonance imaging (mpMRI), and biopsies. If these exams indicate tumor progression to higher-risk disease, curative treatment is typically recommended (e.g., surgery, radiotherapy). Hence, AS combats overtreatment of indolent PCa, thereby avoiding unnecessary treatment that can induce side effects reducing quality of life (e.g., incontinence, impotence) but without prolonging longevity. However, monitoring protocols for AS rely on an observational and population-based approach that does not account for the heterogeneous nature of PCa dynamics and cannot provide an early identification of progressing patients. To address these two critical limitations, we propose using personalized predictions of tumor progression based on biomechanistic features that describe the heterogeneous disease dynamics for each patient (e.g., tumor cell density, proliferation activity). To this end, we first calculate these patient-specific features from MRI-informed, organ-scale predictions of a biomechanistic model of prostate cancer growth. Then, a generalized logistic classifier is leveraged to map these features to risk groups. Since our PCa growth predictions are spatiotemporally-resolved, we can calculate the biomechanistic features describing PCa dynamics and the tumor risk over time, thus enabling the calculation of time to progression for each patient. Here, we present a preliminary study of our approach in which we demonstrate the accuracy of our personalized growth and progression predictions in a small patient cohort. Although further improvement and testing in larger cohorts are required, we believe that our predictive technology could be leveraged to inform clinical decision-making and personalize AS protocols for PCa patients.
4. David A. Hormuth, II The University of Texas at Austin Texas
   "Image-based habitat dynamics in patients with head and neck cancer undergoing radiotherapy"
Intratumoral hypoxia in head and neck cancer plays a crucial role in radiotherapy (RT) response. Accurate prediction of the extent of hypoxia could enable personalized RT planning to target resistant lesions. Multiparameteric MRI (mpMRI) methods have been developed that are sensitive to the underlying tumor biology and microenvironment enabling longitudinal characterization of the dynamics of tumor heterogeneity. Integration of mpMRI methods with biology-based mathematical modeling could enable the prediction of treatment outcomes. We developed a RT response model informed from each patient’s (N = 20) own imaging data to forecast their response at week 4 of RT for both primary and nodal tumors in human papillomavirus-associated oropharyngeal head and neck cancer. Dynamic contrast-enhanced MRI and oxygen-enhanced MRI was collected before and during the delivery of RT. These data were then analyzed to derive physiological parameters describing hypoxia status, perfusion, and cellularity allowing for clustering tumor regions into four distinct habitats. The time course of each habitat’s volume was then tracked to assess tumor dynamics. A four-compartment mathematical model was implemented to describe tumor habitat changes and RT response. Model parameters were optimized using cross-validation and tested on unseen data. Predictions for primary tumor volumes showed strong correlation (Pearson correlation coefficient ; PCC > 0.85) and agreement (concordance correlation coefficient; CCC > 0.78) with measured volumes, with an error of only 5.3% in hypoxic volume at week 4. Predictions for nodal tumors exhibited higher error (16.7%), with moderate correlation (PCC > 0.80) and agreement (CCC > 0.66). By leveraging MRI-derived habitat information, the model provides accurate, patient-specific forecasts of tumor response. These findings support the potential of MRI-based modeling in guiding personalized RT, helping to refine treatment strategies for head and neck cancer.

Organized by: Stacey Smith? (The University of Ottawa)

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

1. Michael Kelly Transylvania University
   "Beyond an introduction: advanced interdisciplinary data science in the liberal arts."
With data playing an increasingly central role in shaping society, it is essential for students not only to interpret and analyze data, but to apply advanced techniques across disciplines with purpose and ethical awareness. As part of Transylvania University’s new interdisciplinary data analytics minor, this talk will be a discussion of the two upper-level courses developed (and recently taught) to build on foundational skills through project-based learning, critical thinking, and domain-specific applications. Topics include machine learning, data wrangling, text mining, and data ethics, all grounded in liberal arts inquiry. This talk will discuss the development and implementation of these advanced courses. In addition, assessment data will be shared from across the minor to reflect on student outcomes, successes, and ongoing challenges.
2. Meredith Greer Bates College
   "Assessment Strategies in the Teaching of Mathematical Biology"
Students have great fun learning about mathematical models in biology, and many faculty love to create these experiences. However, it can be challenging to develop meaningful ways to assess student progress. Exams in a modeling class may be an option, but the full modeling process can be messy, and modeling requires the sort of iteration that does not fit well into a timed test. Homework, papers, and projects all have potential advantages, such as allowing plenty of time for thinking, revision, and perhaps teamwork. Yet grading these assignments takes a long time in a way that does not scale up well to large courses, and faculty want to make sure the work is done by students, not generative AI. In-class activities, possibly in groups, and complete with deliverables, provide yet another possibility to consider. In this session we discuss multiple possibilities in greater detail. Time-permitting, we also open up discussion to the audience, to share more ideas and build a conversation about feasible and productive ways to assess student work in mathematical biology courses.
3. Reginald McGee Haverford University
   "Probabilistic models and methods course"
'Life is what you make it, I hope you make a movement' -Nipsey Hussle. This talk is the next installment after my 2019 and 2023 reflections from my first and fifth years teaching full-time at a private liberal arts college. We will discuss recent moves to better align assessments and activities with stated course objectives. In particular, we consider tasks that give students agency in showing mastery of core concepts in the context of their own interests, and a first attempt at a class journal club.
4. Elissa Schwartz Washington State University
   "Educational outreach via international workshops"
For the graduates of our master’s and Ph.D. programs, future careers are likely to involve professional communication skills. Yet, traditional curricula seldom include formal coursework in writing. Furthermore, many students in graduate programs in mathematical biology have arrived on this career path due to their proficiency in mathematics or interdisciplinary applications, without a focus on training in written or oral communication. Incorporation of training on writing has begun in some graduate programs to benefit their thesis and dissertation writing, manuscript submissions, grant proposal writing, and beyond. In this talk, data and experiences will be shared from a pilot course on professional communication for graduate students in STEM. Topics covered in the course included grant proposal writing, poster presentation, the three-minute thesis (3MT), writing assistance from large language models, and other topics in professional development.

Organized by: Marissa Renardy (Quantitative Systems Pharmacology, GSK), Kathryn G. Link, Quantitative Systems Pharmacology, Pfizer Inc.

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

1. Farrah Sadre-Marandi qPharmetra
   "Navigating pediatric dose selection: Insights from nifurtimox for Chagas disease"
Pediatric dose selection presents unique mathematical and pharmacological challenges due to developmental changes in drug absorption, distribution, metabolism, and excretion (ADME). In the context of Chagas disease—a neglected tropical disease affecting children across Latin America—this talk presents a quantitative framework used to support the regulatory approval of nifurtimox in pediatric populations. This talk will focus on the mathematical modeling strategies that informed dose selection, highlighting the integration of population pharmacokinetics, allometric scaling, and exposure-response analysis. We explore the nonlinear mixed effects modeling approach applied to sparse clinical data across a wide pediatric age and weight range, and we demonstrate how simulation-based techniques were used to evaluate dose-exposure relationships and optimize weight-band dosing strategies.
2. Sarah Minucci Certara
   "A Quantitative Systems Pharmacology Platform Model of Alzheimer's Disease for Reducing Amyloid Plaque and Tau"
Alzheimer's Disease (AD) is a neurogenerative disease that progressively impacts cognitive function, characterized by the buildup of amyloid-beta (Aβ) and tau into plaques and neurofibrillary tangles (NFTs), respectively.  AD is currently ranked as the seventh leading cause of death in the United States. It has been shown that the removal of Aβ plaques as well as the reduction in tau biomarkers can reduce cognitive decline in AD patients. Furthermore, anti-Aβ treatments have shown effects on tau pathology, indicating a complex interplay between these two proteins and their role in the development of AD. Quantitative systems pharmacology (QSP) models have been developed in the field of AD to understand the impact of plaques and NFTs in disease progression and their responses to therapeutic modulation. We developed a platform QSP model of Aβ and tau pathology in AD and the effects of anti-Aβ and anti-tau therapies on various biomarkers in order to better understand the interplay of Aβ and tau and test potential strategies for mono- and combination anti-Aβ and anti-tau therapies. Current hypotheses regarding tau pathology spreading, NFT formation, and amyloid re-accumulation after depletion were tested throughout model development. The model was calibrated/benchmarked to >70 clinical biomarker datasets to capture disease progression as well as PK and Aβ/tau biomarker changes in response to anti-Aβ mAbs lecanemab, gantenerumab, donanemab, and aducanumab as well as tau-targeting ASO BIIB080. Model simulations were in good agreement to Aβ, tau, and PK data, including plaque reaccumulation after treatment and the effects of anti-Aβ therapy on tau pathology.
3. Sarah Minucci, Olivia Walch, Farrah Sadre-Marandi, Marissa Renardy Certara, Arcascope, qPharmetra, GSK
   "Industry Panel Discussion"
Quantitative systems pharmacology (QSP) combines mathematical and computational modeling tools with mechanistic understanding of biology and pharmacology to guide drug discovery and development. QSP is used in the pharmaceutical industry to accelerate and de-risk drug discovery and development across multiple stages, from target discovery/validation to clinical trial design to lifecycle management. In recent years, QSP has been increasingly used in regulatory submissions for clinical trials across many therapeutic areas (PMID: 34734497). In this session, speakers will present recent advances and perspectives in the field of QSP. The second session will be composed of two technical talks and an industry panel discussion with prepared and audience-driven questions.