Organized by: Mitchel J. Colebank (University of South Carolina), Vijay Rajagopal, The University of Melbourne, Australia

Note: this minisymposia has multiple sessions. The other sessions are: Part-2, Part-3, and Part-4.

1. Vijay Rajagopal University of Melbourne
   "Calcium-dependent regulation of physiological vs pathological cardiomyoctre hypertrophy"
Cardiomyocyte hypertrophic growth contributes to the adaptative response of the heart to meet sustained increases in hemodynamic demand. While hypertrophic responses to physiological cues maintains or enhances cardiac function, when triggered by pathological cues, this response is maladaptive, associated with compromised heart function, although initially, this response maybe adaptive with preserved function. Since cues and activated pathways associated with both forms of hypertrophy overlap, the question arises as to the mechanism that determines these different outcomes. Here we evaluate the hypothesis that cardiomyocyte Ca2+ signalling – a regulator of pathological hypertrophy - also signals physiological hypertrophy. We discuss how different Ca2+ profiles, in distinct subcellular organelles/microdomains, and interacting with other signalling pathways, provides a mechanism for Ca2+ to be decoded to induce distinct hypertrophic phenotypes. We discuss how integration of computational with rich structural and functional cellular measurements can be used to decipher the role of Ca2+ in hypertrophic gene programming.
2. Karin Leiderman University of North Carolina at Chapel Hill
   "A discrete platelet-bonding model for simulating platelet aggregation under flow"
Hemostasis is the healthy clotting response to a blood vessel injury. A major component of clotting is platelet aggregation, which involves the formation of platelet-platelet and platelet-wall bonds between platelet receptors (GPVI and GP1b), and platelet integrins ($alpha_2beta_1$ and $alpha_{IIb}beta_3$) with plasma-borne molecules (von Willebrand factor and fibrinogen) and wall adherent collagen. There are platelet disorders that decrease the number and/or functionality of $alpha_{IIb}beta_3$, which results in excessive bleeding. Current treatments exist but are not evidence based and are not always successful in restoring hemostasis. In the cases where hemostasis is restored, the aggregation mechanism without $alpha_{IIb}beta_3$ remains speculative. Our long-term goal is to uncover this mechanism with a mathematical and computational approach. As a first step, we simulated platelet aggregation using the molecular dynamics software, LAMMPS. We considered individual platelets and tracked the platelet-platelet and platelet-wall bonds that formed during aggregation. Currently, the strength of the bonds depends on the local shear rate of a prescribed background flow. Simulations show stable aggregation for healthy platelets under flow. Future work is to improve our modeling framework by parameterizing with experimental measurements and computationally coupling our platelet model to a dynamic flow.
3. Pradeep Keshavanarayana University College London, London, UK
   "Combination of shear stress and hydrostatic pressure dictates the temporal behaviour of vasculature permeability"
ndothelial cells form the inner lining of blood vessels, and their dysfunction, particularly at VE-cadherinbased cell-cell junctions, is associated with several life-threatening diseases. These cells are simultaneously exposed to various mechanical and chemical stimuli, with pathological conditions altering the balance of these stimuli, disrupting mechano-chemical equilibrium and cellular functions. Key mechanical stimuli include extracellular matrix (ECM)-dependent traction forces, shear stress from blood flow, and hydrostatic pressure within blood vessels. The simultaneous action of these forces disrupts cell-cell junctions, leading to changes in endothelial permeability. Increased permeability is not only linked to cardiovascular diseases but also impacts organs like the eyes and brain through the blood-retinal and blood-brain barriers. To investigate the effects of multiple mechanical stimuli on the endothelium, we developed a continuum model of an endothelial cell incorporating a strain-rate dependent active stress model. VE-cadherins, which connect neighbouring endothelial cells, are modelled using a traction-separation law. As traction forces on cell-cell junctions increase, the cohesive bonds weaken, resulting in loss of contact between cells. Our model considers both planar and cylindrical monolayers, revealing that monolayer geometry, in addition to mechanical stimuli, influences permeability. Recent in vitro studies have identified piezo-1 as a mechanotransduction pathway that regulates endothelial cell responses by altering cytoplasmic calcium concentration. Using a phenomenological law linking mechanical stimuli to calcium concentration and active stress, we demonstrate that endothelial permeability depends on shear stress and hydrostatic pressure magnitudes, and the duration of its application. Simulations show that permeability evolves over time based on shear stress magnitude. Under hydrostatic pressure, low shear stress initially results in lower permeability compared to high shear stress. However, over time, permeability under low shear stress surpasses that of high shear stress. This suggests that low shear stress is initially atheroprotective but becomes atheroprone over time, while high shear stress transitions from being atheroprone to relatively atheroprotective. Additionally, we analysed contact forces between endothelial cells under varying mechanical stimuli. For low shear stress, the median contact force is higher at the start than at the end, whereas for high shear stress, the median is higher at the end than at the start. These findings indicate that changes in shear stress magnitude affect VE-cadherin distribution and mechanical equilibrium. In vitro experiments further show that the morphology of VE-cadherin junctions—whether finger-like projections or smooth—depends on the magnitude and duration of mechanical stimuli. Furthermore, we expand the model to examine how increased vascular permeability influences diabetic macular oedema. Our findings indicate that the spatiotemporal progression of oedema is governed by the patientspecific distribution of retinal vasculature. Thus, our model provides insights into how multiple mechanical stimuli influence endothelial permeability and regulates tissue behaviour in physiological and pathological conditions.
4. Pim Oomen University of California, Irvine
   "One Size Does Not Fit All: Systems Biology Modeling of Sex-Specific Cardiac Remodeling"
While all hearts share the same fundamental properties and functions, no two hearts are truly the same. These differences are especially evident between female and male hearts. Interestingly, infant female hearts are initially slightly larger, with male hearts exceeding female heart size only after puberty. These relative changes coincide with major hormonal transitions during puberty and menopause, indicating a pivotal role for sex hormones in cardiac growth and remodeling. Yet, the precise mechanisms through which sex hormones such as estradiol and testosterone influence cardiac growth and remodeling remain elusive. Due to the complexity and intricate interplay of processes involved in cardiac growth and remodeling, computational models have proven useful in quantifying and analyzing these dynamics. However, there remains a critical need for models that consider sex hormones as biological variable. This critical gap prevents us from understanding the mechanisms behind sex-specific cardiac growth and remodeling and limits the effectiveness of using computational models to inform personalized therapies. In this talk, we will discuss how we used publicly available data to develop a multi-scale systems biology model of the interplay of sex hormones and cardiac remodeling. We use this model to understand the mechanisms that drive sex differences in cardiac remodeling, and demonstrate how these insights can be translated into personalized therapeutic approaches tailored to each patient, ultimately advancing the field toward precision cardiovascular medicine.

Organized by: Tharana Yosprakob (University of Alberta)

1. Michael Hendzel University of Alberta
   "Nuclear Microenvironments and Intranuclear Transport"
There is a poorly-defined transition in the size-dependent transport properties of molecules in the nucleoplasm. The most studied molecules, proteins and protein complexes, are small enough to diffuse freely through the nucleoplasm. That is not true of larger molecules but the transition between these two states and the underlying reason is poorly understood. Of particular interest is pre-mRNA and mRNA, which are significantly larger than most protein/protein complexes and must be trafficked to the nuclear pore for export. We have been studying size-dependent transport of small molecules, RNA, and particles of defined diameters to define the transport properties of the nucleoplasm and nuclear compartments. We confirm that mRNA transport is discontinuous and that mRNAs frequently become transiently trapped within the nucleoplasm. These transport properties are very similar to what is observed with 40 nm fluorescent particles microinjected into nuclei suggesting that this reflects a sharp size- dependent transition to obstructed diffusion characterized by transient caging. In comparing two cell lines, one cancer (U2OS) and a normal cell line from mouse (C2C12). These differ in their spatial organization and local densities of chromatin and, remarkably, show an order of magnitude difference in both the confinement volumes and the diffusion coefficients observed between the two cell lines. The cancer cell line showed much more rapid transport properties. Since most transport studies have been performed in cancer cell lines, this raises the possibility that find dramatic differences in the transport of both mRNAs and fluorescent beads. In this presentation, I will review the transport properties of molecules through the nucleoplasm and its compartments and discuss our new results that suggest a surprising range of biophysical properties of the nucleoplasm across cell types.
2. Kelsey Gasior University of Notre Dame
   "Molecular Interactions and Intracellular Phase Separation"
Found in both the nucleus and the cytoplasm, intracellular phase separation allows for the formation of liquidlike droplets that localize molecules, such as proteins and RNAs. Many RNA- binding proteins interact with different RNA species to create compartments necessary for cellular function, such as polarity and nuclear division. Additionally, the proteins that promote phase separation are frequently coupled to multiple RNA binding domains and several RNAs can interact with a single protein, leading to a large number of potential multivalent interactions. This work focuses on a multiphase, Cahn-Hilliard (CH) diffuse interface model to examine the RNA- protein interactions and competition driving intracellular phase separation. By combining the CH approach with a Flory-Huggins free energy scheme, biologically-relevant mass action kinetics, and phase-dependent diffusion, this model explores how molecular dynamics control droplet- scale phenomena. In-depth analysis using numerical simulations and combined sensitivity techniques, such as Morris Method Screening and Sobol’, shows the depth of complications underlying even the simplest droplet field properties, such as the time of separation and composition of the droplet field. These results show that while specific mathematical parameters can be set to push a system to phase separate, it shares control of the droplet field with the rates at which the protein and RNA can interact. Ultimately, this targeted and thorough approach to intracellular condensates begins to peel back the layers of complex molecular dynamics governing the formation and evolution of these droplets that contribute to cellular function.
3. Justin Knechtel Cross Cancer Institute, University of Alberta
   "Single Molecule Tracking of KMT5C in Chromatin Compartments"
Our genome is packaged into chromatin, a dynamic DNA-protein complex organized into distinct functional states defined by epigenetic modifications. These states give rise to spatially segregated chromatin compartments, such as euchromatin and heterochromatin, which differ in molecular composition and biophysical properties. KMT5C, a histone lysine methyltransferase that specifically targets histone H4 lysine 20 (H4K20), shows a striking pattern of chromatin compartmentalization: while it is both highly enriched and mobile within heterochromatin, it exhibits minimal exchange with the neighboring euchromatin. To investigate this behavior, we performed single particle tracking of KMT5C in living cells and quantitatively characterized its kinetic properties within and between chromatin compartments. We applied Hidden Markov Modeling to resolve discrete states of motion and leveraged our tracking data as a microrheological tool to assess the physical state of chromatin. These findings provide new insight into how the material properties and molecular organization of chromatin regulate protein dynamics within the nucleus.
4. Tharana Yosprakob University of Alberta
   "Spatial Organization and Dynamics of Nuclear Proteins"
The protein KMT5C regulates gene transcription and maintains genome integrity. It interacts with CBX5 proteins and is enriched within chromocenters, which are distinct regions in the nucleus where chromatin serves as an organizing scaffold. Although both proteins are similar in size and co-localize in chromocenters, fluorescence recovery after photobleaching (FRAP) reveals different mobility characteristics: CBX5 moves freely within and between chromocenters, whereas KMT5C is limited to movement within chromocenters. To understand these differences, we developed a reaction-diffusion model that incorporates diffusion and binding/unbinding dynamics between KMT5C, CBX5, and chromatin. Using multiple-timescale analysis, we show that the correct diffusion equation for this situation differs from Fick’s Law and predicts a non-uniform steady state concentration, which results in regions of condensation rather than a uniform distribution. Simulated bleaching experiment using this model is consistent with experimental FRAP result, indicating that differential enrichment of KMT5C and CBX5 arises primarily from their binding and unbinding interactions.

Organized by: Tin Phan (Los Alamos National Laboratory), Yun Kang (Arizona State University); Tracy Stepien (University of Florida)

Note: this minisymposia has multiple sessions. The other sessions are: Part-2, Part-3, and Part-4.

1. Yun Kang Arizona State University
   "Recognizing and Honoring Yang Kuang’s Contributions to Mathematical Biology"
This presentation is dedicated to celebrating the extraordinary career of Professor Yang Kuang, whose pioneering contributions have left a lasting impact on the field of mathematical biology. Professor Kuang's research, spanning ecological stoichiometry, delay differential equations, partial differential equations, and data-driven modeling, has shaped critical directions in both theoretical and applied biosciences. Beyond his influential scientific achievements, Dr. Kuang is widely recognized for his collaborative spirit and unwavering dedication to mentoring. Over the course of his career, he has guided 29 Ph.D. students and mentored numerous postdoctoral fellows, master’s students, and undergraduates, many of whom have gone on to make significant contributions to academia, industry, and government. In this talk, we will share personal reflections, quotes, and experiences collected from Dr. Kuang’s former students, postdoctoral scholars, and collaborators. Through their stories, we aim to highlight not only his profound academic influence but also his remarkable legacy as a mentor, role model, and community builder. This celebration honors both the depth of Dr. Kuang’s scholarship and the far-reaching impact he has had in shaping the next generation of mathematical biologists.
2. Jiaxu Li University of Louisville
   "A class of delay differential equation system and its applications"
Time delays are inherent in biological systems, appearing in processes such as physiological feedback loops, drug delivery, therapeutic interventions, and the cyclical harvesting of fish and restocking of fry in aquaculture operations. Numerous delay differential equation (DDE) models have been developed to study these systems. However, many of these models fall short in fully capturing the dynamics and delayed effects of interventions that are administered at discrete time intervals and gradually absorbed by the system. Despite advances in artificial intelligence (AI), modeling complex biological systems—such as glucose-insulin regulation—remains a significant challenge. Personalized algorithms often face limitations due to insufficient training data, while delay-induced uncertainties (DIUs) can lead to chaotic behavior, further complicating the development of effective control strategies. A deep understanding of these dynamic behaviors and their implications is essential for designing accurate and robust interventions. In this talk, we present a novel modeling framework that accounts for both the intrinsic time delays in biological systems and the delayed effects of time-distributed interventions, with the goal of improving system effectiveness and sustainability. Applications include artificial pancreas systems for single-hormone (insulin) and dual-hormone (insulin and glucagon) delivery, tumor treatment strategies, and population dynamics models incorporating optimized intermittent restocking and harvesting to promote ecological balance.
3. Bingtuan Li University of Louisville
   "Forced Traveling Waves in a Reaction-Diffusion Equation with a Strong Allee Effect and Shifting Habitat"
Renewed interest in spatial ecology has emerged, largely due to the threats posed by global change. Shifts in habitat suitability for many species have already occurred and are expected to continue, profoundly affecting invasion dynamics. In this study, we consider a reaction-diffusion equation modeling the growth of a population subject to a strong Allee effect within a bounded habitat that shifts at a constant speed c. We demonstrate that the existence of forced positive traveling waves depends on the habitat size L and on c∗, the wave speed for the corresponding reaction-diffusion equation defined over an unbounded spatial domain with the same growth function. Specifically, we show that when c∗>c>0, there exists a positive threshold L∗(c) such that two positive traveling waves exist if L>L∗(c), while no positive traveling wave exists if Lc∗, then for any L>0, no positive traveling wave exists. These theoretical results are complemented by numerical simulations that explore the equation’s dynamics in greater detail.
4. Clay Prater University of Arkansas
   "I get by with a little help from my friends: Adventures in stoichiometric modeling of a mathematically challenged empirical ecologist"
Organisms interact with their environments through the exchange of elements and energy. However, predicting the effects of insufficient supplies of these resources on organismal growth has been a longstanding challenge. To this end, we developed a conceptual framework, the growth efficiency hypothesis, which posits strong mechanistic relationships among organismal resource contents, use efficiencies, and growth rate. We tested this hypothesis by exposing consumers to multiple forms of resource limitation, which resulted in unique differences in their resource composition. These differences reflected physiological changes serving to optimize resource use efficiencies and were used to generate accurate predictions of consumer growth rate. Our findings demonstrate the growth efficiency hypothesis to be a powerful framework for understanding the multivariate nature of resource limitation.

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-2, and Part-3.

1. Christopher Heggerud University of California, Davis
   "The many mechanisms behind regime shifts and tools to predict them"
Transient dynamics and regime shifts pose unique challenges when dealing with predictions and management of ecological systems yet little headway has been made on understanding when an ecological system might be in a transient state, or if a regime shift is imminent. In particular, given an ecological timeseries, it is difficult to detect the underlying mechanism causing a regime shift, or if one is occurring at all. Through a series of simplifications, we analyze synthetic data known to exhibit crawl-by type transient dynamics or that undergo some nonlinear excursion through state space that appears as a transient dynamic. Using dynamical systems theory, we create metrics that predict transient dynamics and furthermore to understand useful characteristics of the regime shift. These new metrics are additionally compared to typical early warning signals in ecology and the utility of both are discussed.
2. Tao Feng Yangzhou University
   "Modeling Collective Foraging Dynamics in Social Insect Colonies: Deterministic Structures and Stochastic Transitions"
In this talk, we explore the collective foraging dynamics of social insect colonies through mathematical models. Starting from a classical framework, we incorporate nonlinear recruitment and recruiter interference, and analyze how these factors influence system bistability and bifurcation behavior. To enhance analytical tractability, we introduce a reduced two-dimensional model that preserves key features of the original system. We then examine the impact of environmental stochasticity arising from multiple ecological processes—including recruitment efficiency and mortality rates—on foraging state transitions, critical thresholds, and colony resilience. Our results reveal how both intrinsic mechanisms and extrinsic variability shape the robustness of collective foraging behavior.
3. Amy Veprauskas University of Louisiana at Lafayette
   "Examining the impact of periodicity on population dynamics: with applications to agroecosystems and conservation science"
Population responses to repeated environmental or anthropogenic disturbances are shaped by complex interactions among disturbance patterns, population structure, and stage-specific vulnerability. Here, we introduce a matrix-based modeling framework designed to capture these dynamics and identify critical population thresholds. To demonstrate the versatility of our approach, we apply the framework to two distinct scenarios, one rooted in agroecosystem management and the other in conservation biology. By conducting sensitivity analyses across both cases, we reveal how variations in disturbance intensity and pre-disturbance demographic composition can lead to markedly different outcomes. The contrasting outcomes between these applications underscores the importance of incorporating demographic detail into ecological risk assessments.
4. Zhian Wang Hong Kong Polytechnic University
   "Global dynamics on the persistence and extinction of a periodic diffusive consumer-resource model"
we consider a reaction-diffusion model describing the consumer-resource interactions, where the resource's input rate may be temporally periodic and spatially heterogeneous. By employing the parabolic comparison principle, method of super-lower solutions for the mixed-quasilinear monotone system, theory for asymptotically periodic systems, uniform persistence theory for infinite-dimensional dynamical systems, and principal eigenvalue theory, we classify the persistence and extinction dynamics of the consumer population in terms of dispersal rates and relaxation time classified by the mortality rate of the consumer. Furthermore, we derive the asymptotic profiles of positive periodic solutions as the resource's dispersal rate is sufficiently small or large. Our results elucidate how the consumer's mortality rate, the relaxation time, the spatiotemporal heterogeneity of the resource's input, and the dispersal rates affect the global dynamics of consumer and resource populations. In particular, our analytical results derive the following implications:  (a) the resource's decay is the dominant factor that can prevent the resource abundance from blowing up; (b) the consumer’s mortality rate is a key factor determining the persistence and extinction for the consumer population; (c) the temporally homogeneous resource  input may be more beneficial to the consumer's persistence  than the temporally varying input when the consumer’s mortality is moderate.

Organized by: Daniel Cooney (University of Illinois Urbana-Champaign), Olivia Chu (Bryn Mawr College) and Alex McAvoy (University of North Carolina, Chapel Hill)

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

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

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: Zhilan Feng (National Science Foundation), John W Glasser, The US Centers for Disease Control and Prevention (CDC)

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

1. John W Glasser The US Centers for Disease Control and Prevention (CDC)
   "Validating a SARS-CoV-2 transmission model"
During the COVID-19 pandemic, we endeavored to keep pace with understanding of biological phenomena that might affect SARS-CoV-2 transmission by modifying SEIR metapopulation models structured via age, location, or strain. With probabilities of infection on contact and initial conditions from a serial, cross-sectional survey of antibodies to nucleocapsid protein among commercial laboratory clients throughout the United States and all save one other parameter from the literature, our age- and location-structured model reproduced seroprevalence from this and another nationwide survey, of antibodies to spike as well as nucleocapsid protein among blood-donors, remarkably well. Because fit parameters are conditional on model formulae and other parameter values, we recommend that mechanistic modelers base theirs on first principles, estimate them from accurate independent observations, or source them from the primary, not the modeling literature. In this talk, I will describe our descriptive model of seroprevalence by age and time and then our calculation, via first principles, of the age-specific forces of infection, attack rates and -- given information from a contact study -- probabilities of infection on contact. Because those parameters were not estimated by fitting our transmission model to any observations, others could use them too.
2. Wendy S Parker Virginia Tech
   "Testing the adequacy-for-purpose of dynamical models"
Dynamical models, especially mechanistic ones, are often viewed as “hypotheses” about the workings of a target system. Such hypotheses, however, are often known to be false from the outset, insofar as models are known to involve various simplifications, idealizations, and omissions. A more coherent perspective instead views scientific models as representational tools, the evaluation of which is concerned with their adequacy or fitness for particular purposes of interest. Adopting this perspective, stringent testing is still an aim of model evaluation, but what is ultimately tested is not the model itself, but a hypothesis about its adequacy- or fitness-for-purpose. Ideally, model evaluation is carried out such that, if the model is inadequate for the purpose of interest, then the testing procedure is very likely to reveal that inadequacy.
3. Michael Y. Li University of Alberta
   "Why do models calibrated with data need to be validated?"
Mechanistic models based on dynamical system theory are natural for making predictions. There is a general belief that these models are constructed based on the best available science, they are inherently valid. But are they? Reliable quantitative model predictions rely on both the model structure (mechanisms incorporated) and the model parameters. Models with the same structure but different parameter values can make different finite-time quantitative predictions. Model parameter values are critical for accurate predictions. When parameter values of an epidemic model using the trusted SEIAR structure for COVID-19 are estimated from fitting model outputs to COVID-19 data, and these parameter values allow an excellent fitting between model outputs and the data, would this mean the calibrated model is validated, can be trusted for scenario analysis, and for making recommendations to public health decision makers? I will use examples to show that epidemic models calibrated from data are prone to the following failures: (1) fail the cross-validation test, (2) suffer from over-fitting, and (3) over-project the final size. I will provide some of the underlying reasons for these failings. I will also present a study on estimating the proportion of infected population of COVID-19, using identified-case data for model training and reserving the seroprevalence data for model validation.
4. Marie Betsy Varughese Institute of Health Economics
   "Real-time Validation of Model Projections of Seasonal Influenza in Alberta"
Modelling efforts during the COVID-19 pandemic highlighted the challenges that arose with making accurate and validating projections. The difficulty or near impossibility to accurately predict the peak time and other epidemic indicators using standard mathematical models with constate rate parameters have been stated previously in literature. This talk will describe an age-stratified Susceptible-Infectious-Recovered (SIR) deterministic model used to describe influenza transmission dynamics in Alberta. We will describe our validation approach and compare the performance of making accurate model projections based on our assumptions of case detection when calibrating to surveillance data between 2016 and 2019. In addition, we will present more recent real-time influenza model projections for cases and hospitalizations for the 2023-2024 respiratory virus seasons and discuss how we present these findings to decision and policy makers.

Organized by: Iain Moyles (York University), Rebecca Tyson, University of British Columbia Okanagan

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

1. Iain Moyles York University
   "Fear dynamics in a mathematical model of disease transmission"
We explore a mathematical model of disease transmission with a fearful compartment. Susceptible individuals become afraid by either interacting with individuals who are already afraid or those who are infected. Individuals who are afraid take protective measures via contact reductions to reduce risk of transmission. Individuals can lose fear naturally over time or because they see people recovering from the disease. We consider two scenarios of the model, one where fear is obtained at a slower rate than disease spread and one where it is comparable. In the former we show that behavioural change cannot impact disease outcome, but in the latter, we observe that sufficient behavioural intervention can reduce disease impact. However, response to recovery can induce a bifurcation where contact reduction cannot mitigate disease spread. We identify this bifurcation and demonstrate its implication on disease dynamics and final size.
2. Md. Mijanur Rahman University of British Columbia Okanagan
   "The role of opinion dynamics in generating multiple epidemic waves"
We develop and rigorously analyze a coupled opinion-disease framework in which the population is partitioned into two susceptible classes that differ in their infection rates and are linked by opinion switching. We show that the model preserves classic SIR-type dynamics for the total susceptible population but embeds a feedback loop through a time-varying effective transmission rate that depends on the opinion proportions. We define an effective reproduction rate based on the transmission rate and establish explicit criteria for epidemic peaks in terms of its sign changes. Two asymptotic regimes are examined using the scaled base opinion-switching rate. In the slow switching limit, opinion exchange freezes and guarantees at most one infection wave. In the fast switching limit, the opinion distribution equilibrates instantaneously to a quasi-steady state, which again leads to a single wave. Extending the model to Hill-type, infection-dependent switching rates yields the same one-wave result in both asymptotic limits. These findings imply that neither vanishingly slow nor extremely rapid opinion change, as modelled here, can sustain recurrent outbreaks. Repeated waves in this framework must arise from intermediate switching speeds or necessitate the inclusion of additional mechanisms not considered in these asymptotic limits. The work highlights the speed of opinion change as a potential public health leverage point.
3. Azadeh Aghaeeyan Brock University
   "Understanding the Decision-Making of Late COVID-19 Vaccine Adopters"
Individuals responded differently to the COVID-19 vaccination campaign: some were early adopters, others delayed vaccination, and some refused it altogether. Despite the important role of late adopters in pandemic control, their behaviour remains understudied. We propose a mechanistic model that divides late adopters based on their decision-making strategies into two types: success-based learners, who are influenced by others’ vaccination experiences, and myopic rationalists, who receive their shots when the perceived benefit of vaccination outweighs the cost. The model also accounts for possible shifts in vaccination perception triggered by impactful events. Using a Bayesian framework, we fit the model to weekly COVID-19 vaccine uptake data from U.S. states, stratified by age and sex. Our results suggest that late adopters mainly behaved as success-based learners and that perception shifts varied across events—some increased and others reduced the perceived value of vaccination. These findings are a step toward tailoring vaccine promotion communication strategies to late adopters.
4. Bouchra Nasri University of Montreal
   "Mathematical Modelling of Pregnant Women Co-infected with HIV and ZIKV: A Case Study in Endemic Latin American and Caribbean Countries"
Co-infection with HIV and Zika virus (ZIKV) in pregnant women remains under-documented, and its dynamics and impact on neonatal health are understudied. This gap raises public health concerns, particularly in Latin America and the Caribbean, where ZIKV vectors remain active. We conducted a cross-sectional ecological study using aggregated data (2015-2023). A compartmental model was developed: an SIR compartment for pregnant women (HIV and ZIKV), SI compartments for newborns and mosquito vectors. The basic reproduction number R0 for co-infection was estimated. Sensitivity analysis was used to identify influential parameters. The effect of different control measures (personal and sexual protection, ZIKV treatment, antiretrovirals) was simulated to assess their efficacy on neonatal health. As results, we found R0 ranging from 0.09 to 1.29 depending on the country and was most sensitive to mosquito-biting rates and mortality in pregnant women. ZIKV infection had a greater impact on neonatal complications than HIV infection. The introduction of ZIKV into this population resulted in a significant increase in adverse neonatal outcomes. Control strategies were most effective when combined and maintained over time, with ZIKV treatment having the least impact. It is important to improve prenatal care for women living with HIV, who are vulnerable to other infections such as ZIKV. Prevention of sexual transmission of HIV and better surveillance are also essential to protect maternal and child health. This is a joint work with: Sika-Rose Coffi, Jhoana P. Romero-Leiton, Idriss Sekak and Rado Ramasy

Organized by: Kang-Ling Liao (University of Manitoba), Wenrui Hao, Pennsylvania State University

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

1. Weitao Chen University of California, Riverside
   "A Mechanochemical Coupled Model to Understand Budding Behavior in Aging Yeast"
Cell polarization, in which a uniform distribution of substances becomes asymmetric due to internal or external stimuli, is a fundamental process underlying cell mobility and cell division. Budding yeast provides a good system to study how biochemical signals and mechanical properties coordinate with each other to achieve stable cell polarization and give rise to certain morphological change in a single cell. Recent experimental data suggests yeast budding develops into two trajectories with different bud shapes as mother cells become old. We first developed a 2D model to simulate biochemical signals on a shape-changing cell and investigated strategies for robust yeast mating. Then we extended and coupled this biochemical signaling model with a 3D subcellular element model to take into account cell mechanics, which was applied to investigate how the interaction between biochemical signals and mechanical properties affects the cell polarization and budding initiation. This 3D mechanochemical model was also applied to predict mechanisms underlying different bud shape formation due to cellular aging.
2. Harsh Jain University of Minnesota Duluth
   "Looking Beyond Data: Simulating Treatment Outcomes for Unobserved Heterogeneous Populations Using Preclinical Insights"
Developing new cancer drugs involves significant investments of time and resources, yet many promising candidates fail during clinical trials. One potential reason for this failure is that preclinical testing typically relies on genetically identical animals and uniform cell lines, which do not reflect the diversity found in actual patient populations. Additionally, preclinical data is often presented in aggregated form, masking important individual-level differences that could inform clinical predictions. In this talk, I will present a case study of non-small cell lung cancer xenograft treatment with radiation to introduce our Standing Variations Modeling approach, which addresses these issues in two main steps. First, we deconstruct aggregated preclinical data – specifically, average tumor volume time-courses and Kaplan-Meier survival curves – to recover individual-level variation, uncovering hidden differences among study subjects (“who’s in”). Second, we use these insights to simulate treatment outcomes for broader, more diverse virtual populations through computational modeling (“who’s out”). A key innovation in our method is the assignment of a personalized survival probability to each virtual participant, explicitly linked to their unique disease dynamics. This mechanistic connection allows us to capture inter-individual variability and supports meaningful extrapolation to unobserved populations. By moving beyond aggregate data and homogeneous preclinical models, this approach offers a more nuanced and practical path to clinical translation.
3. Leili Shahriyari University of Massachusetts Amherst
   "Data Driven QSP Modeling of Cancer: A Step Toward Personalized Treatment"
Our work explores the possibility of creating personalized mathematical models for cancer to better understand the progression of an individual's cancer. By simulating the unique characteristics of each tumor and its response to treatments, we aim to offer insights into personalized cancer care. Our method combines elements of mechanistic modeling and machine learning techniques to create individualized predictions. A central aspect of our approach is the use of a mechanistic model based on quantitative systems pharmacology (QSP). QSP is a computational method used to analyze drug interactions and effects, and it plays a crucial role in our project. The model includes a large system of nonlinear equations modeling both bio-chemical and bio-mechanical integrations in the tumors. We acknowledge that a common challenge in QSP modeling is accurately determining parameters, especially since traditional models often assume a general uniformity across different patients' diseases. This assumption can lead to limitations when calibrating parameters using varied data sources. Our objective is to build a more personalized mathematical framework by concentrating on individual patient data for parameter estimation. We adjust the QSP model parameters for each patient based on their unique data. Through detailed sensitivity analysis and uncertainty quantification, we identify key interactions in the model and define the range of our predictions. By integrating this tailored QSP model with insights into cellular and molecular interactions, we hope to better predict how cancer evolves and responds to specific treatments. We are excited about the potential this has for advancing personalized cancer therapy, though we are aware of the challenges and complexities involved in this endeavor.
4. Nourridine Siewe Rochester Institute of Technology
   "Osteoporosis Induced by Cellular Senescence: A Mathematical model"
Osteoporosis is a disease characterized by loss of bone mass, where bones become fragile and more likely to fracture. Bone density begins to decrease at age 50, and a state of osteoporosis is defined by loss of more than 25%. Cellular senescence is a permanent arrest of normal cell cycle, while maintaining cell viability. The number of senescent cells increase with age. Since osteoporosis is an aging disease, it is natural to consider the question to what extend senescent cells induce bone density loss and osteoporosis. In this paper we use a mathematical model to address this question. We determine the percent of bone loss for men and women during age 50 to 100 years, and the results depend on the rate η of proliferation of senescent cell, with η=1 being the average rate. In the case η=1, the model simulations are in agreement with empirical data. We also consider senolytic drugs, like fisetin and quercetin, that selectively eliminate senescent cells, and assess their efficacy in terms of reducing bone loss. For example, at η=1, with estrogen hormonal therapy and early treatment with fisetin, bone density loss for women by age 75 is 23.4% (below osteoporosis), while with no treatment with fisetin it is 25.8% (osteoporosis); without even a treatment with estrogen hormonal therapy, bone loss of 25.3% occurs already at age 65.

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-2, Part-3, and Part-4.

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-2, and Part-3.

1. Yi Jiang Georgia State University, USA
   "Multicellular Modelling of Collective Cancer Invasion"
Collective invasion enhances cancer metastasis. However, the mechanisms underlying cancer collective invasion remains poorly understood. I will discuss two types of collective invasion, one with leaders and followers that engage in mutualistic social interactions, the other involves hypoxia induced secretome. We use cell-based multiscale models to elucidate the physical mechanisms for the emergence of collective behavior. In the leader-follower invasion, cell-cell adhesion and cell migration are the key drivers of migration patterns, while in the microenvironment-induced invasion, protrusion force and adhesion together give rise to symmetry breaking and directional migration. The results from the experimental and computational models combined provide new insights into tumor metastasis in terms of tumor heterogeneity and cellular response to microenvironmental stimuli
2. Jupiter Algorta University of British Columbia, CANADA
   "Simulating Cell Decisions and Embryo Structure with Morpheus"
One of the most fascinating aspects of early embryonic development is how a single cell gives rise to a structured, multicellular organism. In mammals, this process begins with a few identical cells that divide and gradually acquire distinct identities. These identities not only depend on each cell’s own internal machinery but also emerge from how cells interact with their neighbors and their environment. In this project, we modeled two critical stages of this developmental process. First, cells differentiate between becoming part of the outer layer (which contributes to the placenta) or the inner group (which forms the embryo proper). This initial decision is governed by how cells contact one another. Second, cells in the inner group further specialize, influenced by a signaling molecule called FGF4, which spreads through the surrounding space and nudges cells toward one of two fates: forming the future embryo (epiblast) or a supporting layer (primitive endoderm). To capture this complex cascade of decisions, we translated two detailed models by De Mot et al. (2016) and Cang et al. (2021) into a unified spatial simulation using Morpheus, a platform for multiscale modeling. These existing models describe how genes interact within each cell through systems of equations, while our goal was to bring them into a spatial context, allowing cells to move, divide, and interact in space, while still carrying out their internal decision-making logic. While the original models include dozens of interacting components and parameters, Morpheus’ design made it possible to integrate these internal processes with the physical layout and behaviour of the cells. Each simulated cell runs its own internal 'program' while also communicating with others through contact or diffusing signals in the surrounding space. The platform’s graphical interface and modular setup made this a manageable task, even for undergraduate researchers. Our resulting simulation recreates known patterns of cell arrangement and fate specification seen in real embryos. More importantly, this case study illustrates how modeling tools like Morpheus can help translate complex biological mechanisms into testable, visual models, even when starting from dense theoretical descriptions.
3. Andreas Buttenschoen University of Massachusetts, USA
   "Robust Numerical Methods for cells invading extracellular matrix: Adaptive Time-stepping and preconditioning for reproducible multicellular models"
Cell migration through extracellular matrix (ECM) environments represents a fundamental biological process essential for development, immune response, wound healing, and cancer metastasis. This migration presents significant physical challenges as cells must simultaneously use the ECM as a substrate for force transmission while overcoming its role as a mechanical barrier. In this talk, I will present a physics-based computational model that elucidates how cells employ three primary 'space negotiation' strategies to navigate dense ECM: (1) adaptive cellular deformation, (2) mechanical remodeling of surrounding matrix, and (3) enzymatic degradation via matrix metalloproteinases (MMPs). Our model captures the essential mechanical interactions between deformable rod-shaped cells and a viscoelastic fiber network, with cells extending filopodia that establish adhesion sites and generate traction forces. Through systematic computational analysis, we demonstrate that cellular migration efficiency exhibits a biphasic response to ECM density, with optimal migration occurring at intermediate pore sizes that match nuclear dimensions. We further show that different microenvironmental contexts necessitate distinct combinations of space negotiation strategies - while ECM degradation is dispensable in pre-formed tracks, it becomes essential in dense matrices where nuclear size represents the primary migration-limiting factor. In the final portion of this talk, I will discuss the numerical methods that enable robust and reproducible simulation of these complex multicellular systems. Specifically, I will present our implementation of adaptive time-stepping using embedded Runge-Kutta methods that allow users to specify absolute and relative error tolerances, ensuring reliable integration of agent-based models. Additionally, I will describe graph-based preconditioning techniques for efficiently solving the overdamped Langevin dynamics with anisotropic friction tensors, and discuss memory management strategies using smart pointers and double buffering that significantly improve computational performance. These numerical advances enable simulation of larger multicellular collectives while maintaining mechanistic fidelity at the single-cell level.
4. Rajendra Singh Negi Syracuse University, USA
   "Multicellular modeling of how myosin localization impacts symmetry-breaking in zebrafish embryonic development"
A fundamental question is how organisms control cell and organ morphology during development, and we address this question using Kupffer’s Vesicle (KV), the left-right organizer in zebrafish, as a simple model organ. Both the cells that comprise the KV, and the organ itself, change shape in a stereotyped manner that is important for organ function. While multiple mechanisms have been proposed to govern these shape changes, recent studies combining 3D simulations with laser ablation experiments have shown that the slow movement of KV through the surrounding tailbud tissue generates dynamic forces that alter the organ and cell shape [1]. To understand the molecular mechanisms that affect this motion, we have developed an experimental protocol to perturb myosin activity in a localized region of interest in the tailbud, using an optically controlled rho-kinase inhibitor. We implement a 3D vertex-based simulation framework that captures the multicellular dynamics of KV migration. Our model incorporates key mechanical interactions: posterior traction from migratory cells, anterior pushing by the notochord using conversion-extension, and viscoelastic responses from surrounding tailbud tissue. We model the effect of the caged rho-kinase inhibitor as a diffusing patch of signal that emanates from a region of interest below the tailbud, which alters both the dynamic forces applied to KV as well as the mechanics of the tailbud tissue. This approach allows us to investigate how localized molecular perturbations propagate through tissue to influence organ motion and morphology. Preliminary results show that such perturbations can alter the motion and shape of KV, revealing how spatially confined molecular changes can drive large-scale morphogenetic transformations. [1] Manna et al. bioRxiv, https://arxiv.org/pdf/2407.07055 This work was supported by NIH R01HD099031.

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-2.

1. Thomas E. Yankeelov The University of Texas at Austin
   "A practical computational framework for systematically investigating alternative treatment strategies for cancer"
Over the last decade our team has developed a set of partial differential equations that capture key features of tumors growth and treatment response related to tumor cell migration, proliferation, treatment response, and tissue mechanical properties. These models can be calibrated with widely-available medical imaging data to accurately predict the spatio-temporal changes of solid tumors in response to both radiation and systemic therapies. We will briefly summarize those results for cancers of the brain and breast, and then show how these models power digital twins to identify alternative therapeutic regimens that are hypothesized to outperform the standard-of-care interventions. In the case of high-grade gliomas, we will show how our digital twins can be used to identify personalized treatment plans predicted to reduce tumor burden 24% more than the standard-of-care approach one month after treatment (n = 15), while maintaining toxicity in the organs-at-risk within acceptable clinical limits. In the case of breast cancer, we have used digital twins to personalize neoadjuvant chemotherapy schedules (n = 105) to yield therapeutic strategies that are hypothesized to increase the pathological complete response rate by at least 20%. Furthermore, we have used our digital twin formalism to virtually recapitulate the results of three key clinical trials that led to the current backbone for neoadjuvant therapy. While the results we will present will focus only on cancers of the brain and breast, we emphasize that since our digital twins are based on key underlying biology and physics features of cancer, they are applicable to any solid tumor for which the requisite imaging data is available.
2. Maximilian Strobl Cleveland Clinic
   "What pre-clinical experiments can teach us about digital twins for personalized cancer treatment scheduling"
Cancers are complex and evolving diseases. To tackle this complexity there has been growing interest in developing “digital twins” – personalized computational tumor models – to better inform when and how to treat to reduce toxicity and maximize tumor control. As this idea finds traction, the crucial question is how do we ensure efficacy and safety as we translate from bench to bedside? In this study, we test the digital twin approach to treatment scheduling in vitro, in the context of EGFR+ non-small cell lung cancer. Using fluorescent, time-lapse microscopy we characterize the evolutionary dynamics of co-cultures of Gefitinib-sensitive and paired resistant cell lines (PC9) across four different treatment schedules: i) continuous therapy, ii) intermittent therapy (on/off), iii) intermittent therapy (off/on), iv) continuous therapy at half the full dose. Our results demonstrate that both the dose and the frequency of treatment influence evolutionary dynamics. Intermittent therapy minimizes final resistant cell and total cell count after six treatment changes (18 days total), across four dose levels examined (2uM, 200nM, 100nM, 20nM Gefitinib). Moreover, the off/on intermittent schedule outperforms the on/off schedule, suggesting a role for spatial competition in suppressing resistant cells. Next, we test how well three commonly used mathematical models of sensitive-resistant dynamics can predict the observed dynamics: 1) A simple exponential model, 2) A logistic model which accounts for spatial competition, and 3) A 3-population model which includes an additional subpopulation of drug-tolerant cells in the “sensitive” population. While Models 1 and 2 can capture the dynamics under continuous treatment, the more complex Model 3 is required to predict the outcomes of intermittent treatment. Our work illustrates how in vitro experiments can support the development of digital twins, and how this process can uncover new insights into drug resistance evolution in cancer.
3. Renee Brady-Nicholls H. Lee Moffitt Cancer Center & Research Institute
   "Investigating Response Differences between African American and European American Prostate Cancer Patients Through an In Silico Study"
African American (AA) men have the highest incidence and mortality rates of prostate cancer (PCa) compared to any other racial group. The increased incidence as well as mortality are likely due to socioeconomic factors, environmental exposure, access to care, and biologic variations. Deciphering the specific drivers of increased incidence and mortality is difficult due to a scarcity in available data from AA patients. Mathematical modeling offers the opportunity to run in silico studies to investigate treatment responses in a larger cohort of virtual patients. Here, we investigate response differences between AA and European American (EA) prostate cancer patients receiving hormone therapy. We simulate prostate-specific antigen (PSA) dynamics, using a mathematical model of interactions between PCa stem cells and differentiated cells. We use propensity score matching to identify 15 EA patients that most closely matched the 10 AA patients. Bayesian calibration is used to determine plausible parameter sets that accurately describe longitudinal PSA dynamics on a per-patient basis. Model parameters are compared between AA and EA patients to determine potential drivers of resistance. Our findings show that the initial PSA levels, stem-cell self-renewal, and PSA production rates significantly differ between AA and EA patients. Using the plausible parameter sets drawn from the calibration, we simulate adaptive therapy as a potential strategy to maximally delay progression. Our findings show that both patient groups receive benefit from adaptive therapy when compared to continuous, with AA patients receiving a significantly greater advantage. Our study presents an important step in identifying race-specific, patient-specific treatment options that can be used to maximally delay time to progression.
4. Fatemeh Beigmohammadi Université de Montréal
   "Efficient methods for generating virtual patient cohorts using trajectory-matching ABC-MCMC"
Virtual patient cohorts (VPCs) are computer-generated representations of patients that mirror real-world populations. VPCs use mechanistic mathematical models to establish the effects of inter-patient variability on disease and treatment outcomes, thereby allowing for the comprehensive exploration of disease mechanisms and therapeutic strategies at low-cost and without burden to patients. Further, they may aid the development and validation of mechanistic mathematical models that aim to capture the underlying mechanisms of biological systems and their responses to drug treatments. Thus, the effective generation of virtual populations is critical. While several methods exist to create VPCs, there is a growing need for more computationally efficient techniques. Previous work combined Approximate Bayesian Computation (ABC) with Markov Chain Monte Carlo (MCMC) to generate heterogeneity in model parameters and predicted outcomes. Unfortunately, because the generated samples must simultaneously meet the ABC and MCMC acceptance criteria, this approach has a very high rejection rate. To reduce this computational burden, we developed a model-based technique we call Trajectory Matching ABC-MCMC (TM-ABC-MCMC). TM-ABC-MCMC modifies the acceptance criteria in ABC-MCMC to only require that model trajectories fall within observed bounds. In this talk, I will discuss the application of TM-ABC-MCMC to three existing mechanistic models of varying complexity and will show that it has a lower rejection rate compared to ABC-MCMC and maintains high fidelity with previous results. Thus, our approach significantly decreases computational costs, boosting the efficiency of virtual patient cohort generation.

Organized by: Mohit Kumar Jolly (Indian Institute of Science), Sarthak Sahoo (Indian Institute of Science)

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

1. Rebecca A Bekker University of Southern California
   "Modeling Cell-State Dynamics to Unravel and Counteract Immune Suppression in Breast Cancer Immunotherapy"
HER2+ breast cancer is aggressive and has historically had poor outcomes. Despite therapeutic advances, such as the development and approval of HER2 targeted therapies and the associated improved outcomes, resistance invariably develops. However, recent preclinical and clinical evidence suggest patients may benefit from combination therapies that include immunotherapy. The PANACEA trial, which investigated the efficacy of the combination of the HER2 monoclonal antibody trastuzumab and the PD-1 inhibitor pembrolizumab in HER2+ breast cancer, reported a 15% response rate in patients with PD-L1+ tumors. This unexpectedly low response rate may be the result of a highly tumor-engineered immune-suppressive niche containing MDSCs, Tregs, and TAMs. Understanding the interplay between pro- and anti-tumor immune subsets and HER2+ breast cancer cells is crucial for improving responses to immunotherapy and identifying novel therapeutic strategies. To this end, we developed an agent-based model (ABM) of tumor-immune interactions, which is initialized with digitized multiplex immunohistochemistry slides of untreated spontaneous lung metastases from the NT2.5LM HER2+ murine model. The ABM includes tumor evolution via mutations that affect PD-L1 expression, chemotherapy sensitivity, and proliferation speed. We explore how treatment regimens, combining chemotherapy, anti-PD-L1 and anti-CTLA-4, impact immune composition and suppression, and tumor evolution. The model provides a framework to explore how immune plasticity and tumor adaptation may co-evolve under therapy, and to generate hypotheses about potential mechanisms of resistance and strategies to counteract immunosuppression.
2. James Greene Clarkson University
   "Understanding therapeutic tolerance through a mathematical model of drug-induced resistance"
Resistance to chemotherapy is a major impediment to successful cancer treatment that has been studied over the past three decades. Classically, resistance is thought to arise primarily through random genetic mutations, after which mutated cells expand via Darwinian selection. However, recent experimental evidence suggests this evolution to resistance need not occur randomly, but instead may be induced by the application of the drug. In this work, we present a mathematical model that describes induced resistance. We utilize our mathematical model to study control-theoretic questions with respect to different clinical treatment protocols, and study the effect of different therapies across parameter regimes (e.g. we investigate patient-specific responses). An extended model is then fit to time-resolved in vitro experimental data. From observational data of total numbers of cells, the model unravels the relative proportions of sensitive and resistance subpopulations and quantifies their dynamics as a function of drug dose; the predictions are then validated using data on drug doses that were not used when fitting parameters. Optimal control techniques are then utilized to discover dosing strategies that could lead to better outcomes as quantified by lower total cell volume. These results are reported in J.L Gevertz, J.M Greene, S. Prosperi, N. Comandante-Lou, and E.D. Sontag. Understanding therapeutic tolerance through a mathematical model of drug-induced resistance. npj Systems Biology and Applications, 11, 2025
3. Sara Hamis Uppsala University
   "Growth rate-driven modelling elucidates phenotypic adaptation in BRAFV600E-mutant melanoma"
Phenotypic adaptation, the ability of cells to change phenotype in response to external pressures, has been identified as a driver of drug resistance in cancer. To quantify phenotypic adaptation in BRAFV600E-mutant melanoma, we develop a theoretical model that emerges from data analysis of WM239A-BRAFV600E cell growth rates in response to drug challenge with the BRAF-inhibitor encorafenib. Our model constitutes a cell population model in which each cell is individually described by one of multiple discrete and plastic phenotype states that are directly linked to drug-dependent net growth rates and, by extension, drug resistance. Data-matched simulations reveal that phenotypic adaptation in the cells is directed towards states of high net growth rates, which enables evasion of drug-effects. The model subsequently provides an explanation for when and why intermittent treatments outperform continuous treatments in vitro, and demonstrates the benefits of not only targeting, but also leveraging, phenotypic adaptation in treatment protocols.
4. Sarthak Sahoo Indian Institute of Science
   "Mathematical modelling of multi-axis plasticity in ER+ breast cancer"
Resistance of anti-estrogen therapy is a major clinical challenge in treating estrogen receptor positive (ER+) breast cancer. Recent studies highlight the role of non-genetic adaptations in drug tolerance, yet the mechanisms remain unclear. One of the key processes that underlies enhanced drug tolerance as well as metastatic potential is the process of epithelial-mesenchymal plasticity (EMP). Furthermore, lineage switching, and acquisition of immunosuppressive traits are also commonly observed. We investigate the role of genes involved in EMP to elucidate origins of such multi-axis cellular plasticity by mathematical modelling of underlying gene regulatory networks. Specifically, we show that six co-existing phenotypes are enabled by underlying gene regulatory network, with epithelial-sensitive and mesenchymal-resistant being dominant. Population dynamics analysis demonstrates how phenotypic plasticity promotes survival among sensitive and resistant cells, suggesting that mesenchymal-epithelial transition inducers could enhance anti-estrogen therapy effectiveness. Additionally, hybrid epithelial/mesenchymal (E/M) phenotypes, which exhibit high PD-L1 levels, contribute to immune evasion and enhanced metastatic fitness, obviating the need for a full EMT. Finally, multi-modal transcriptomic data analysis shows associations between EMT and luminal-basal plasticity, linking luminal breast cancer with epithelial states and basal breast cancer with hybrid E/M phenotypes and higher heterogeneity. Our mechanistic modelling of ER+ breast cancer recapitulates observed clinical and pre-clinical findings in spatial transcriptomic datasets, thus offering a predictive framework for characterizing intra-tumor heterogeneity and potential therapeutic interventions given a spatially heterogenous tissue sample. These insights underscore the importance of understanding phenotypic plasticity and non-genetic heterogeneity and its integration with spatial transcriptomics to improve treatment strategies for ER+ breast cancer.

Organized by: Ashlee N. Ford Versypt (University at Buffalo, State University of New York), John Metzcar, University of Minnesota

1. Ashlee N. Ford Versypt University at Buffalo, State University of New York
   "Agent-Based Modeling of the Transwell Migration Assay to Inform Tumor-Immune Microenvironment Simulations"
Cell migration in tumor microenvironments is crucial in disease progression and treatment efficacy. Recent experimental studies reported variations in chemotactic migration of cancer and immune cells. A popular method to study chemotaxis is the transwell migration assay. To complement the in vitro experiments to characterize tumor and immune cells in this assay, we developed a 3D agent-based model with Compucell3D to simulate the effects of random and directed cell migration in response to chemokines. To accommodate various cell lines, we categorized targeted cell lines into 6 groups based on their size and adhesion to the membrane. The model shows a 3D column space of the transwell device with 400 moving agents and periodic boundary conditions applied to vertical surfaces of the domain to simulate the dynamics of the in vitro transwell experiments where cells have realistic biomechanics of neighboring cells and tissue-mimic biomaterials. A solid plane contains randomly distributed pores that mimic the structure of the collagen-coated membrane with the same level of pore density. Chemokines are initiated from the bottom half of the assay below the membrane and can diffuse upwards to generate a concentration gradient. Several parameters, including chemical concentrations, diffusion coefficients, chemotactic potential coefficient, an external potential energy term, and a contact energy term are included with a direct connection to published data. The randomized external potential energy simulates the intrinsic Brownian motion of cells and drives cells to move through the membrane in the negative control group without chemokines. Smaller contact energy between cells and the membrane mimics stronger cell-collagen adhesion. The chemotactic energy term and heterogeneous chemical field regulate directional chemotaxis. We observed that larger external potential energy can induce more cells to migrate through the membrane. Thus, we calibrate this energy term with negative control group data from different cell lines (e.g., Panc1, MiaPaCa2, HCT116, U937, and THP-1). Our simulated results also predicted variations in cell migration with cell density and pore density of the membrane in the negative control groups. We employed various methods to generate Brownian motion and analyzed the resulting trajectories with their velocity profiles and mean squared displacements (MSD), finding these methods affected cell persistence, average velocity, and diffusivity. Next, we are extending the model to investigate the effects of chemokine concentrations and diffusion. In the future, we will implement these validated mechanisms and physiological properties into larger systems of agent-based models to simulate cancer pathology and therapy inside the body, considering cells, chemokines, and tumor immune microenvironments in various tissues. Acknowledgments: This work was supported by the National Institutes of Health grant R35GM133763 and the University at Buffalo. Co-author MBD is supported in part by R01 CA226279.
2. Aaron Meyer University of California, Los Angeles
   "Bridging single cell features to the tissue and patient scale with tensor modeling"
High-dimensional single-cell measurements have revolutionized our ability to study the variation within and between heterogeneous cell populations. As single-cell RNA-sequencing (scRNAseq) and similar technologies have become increasingly accessible, these technologies have extended to studies including multiple experimental conditions, samples, or subjects. Multi-condition single-cell experiments can evaluate how heterogeneous cell populations behave across patients according to disease pathology. Analyzing multi-condition single-cell datasets to link cellular heterogeneity to patient-level features like disease state is crucial, but challenged by the limited sample sizes and the inherent misalignment of cell states across individuals. To overcome these hurdles, we developed ULTRA (Unaligned Low-rank Tensor Regression with Attention). ULTRA leverages a tensor framework to model the multi-way data structure and employs an attention mechanism to derive interpretable, cell-specific gene signatures associated with patient features, crucially without requiring prior cell population alignment across samples. In ULTRA, a fit gene signature scores each cell within a sample; an attention mechanism weights cells based on their expression of this signature. The aggregated cells are then regressed against the patient feature of interest. Importantly, despite using attention, a well-known mechanism enabling transformer models, ULTRA is otherwise a linear model, maximizing data use and interpretability. We applied ULTRA across several scRNAseq datasets, demonstrating its consistently superior ability to identify associative signatures with patient-level features. As one example, I will show how ULTRA identifies features of the tumor microenvironment that are predictive of immunotherapy response across several cancers. A key challenge in deriving associations between single cell measurements and patient-level characteristics is that, while there is an abundance of data, these datasets typically include only a few samples due to cost. Therefore, we used the ULTRA model to optimize future single cell profiling experiments. I will cover several lessons regarding optimal cell numbers and read depths for improving the insights from single cell studies at reduced cost.
3. Erzsébet Ravasz Regan The College of Wooster
   "Cell Interrupted — Modular Boolean Modeling of the Coordination between Mitochondrial Dysfunction-Associated Senescence, Cell Cycle Control and the Epithelial to Mesenchymal Transition"
The steady accumulation of senescent cells with aging creates tissue environments that aid cancer evolution. Aging cell states are highly heterogeneous. 'Deep senescent' cells rely on healthy mitochondria to fuel a strong proinflammatory secretome, including cytokines, growth and transforming signals. Yet, the physiological triggers of senescence, such as reactive oxygen species (ROS), can also trigger mitochondrial dysfunction. This energy deficit alters the secretome of these cells and causes chronic oxidative stress – a state termed Mitochondrial Dysfunction-Associated Senescence (MiDAS). Here, we offer a mechanistic molecular model for MiDAS in the form of  a Boolean regulatory network that qualitatively captures key aspects of mitochondrial dynamics during cell cycle progression (hyper-fusion at the G1/S boundary, fission in mitosis), apoptosis (fission and dysfunction) and glucose starvation (reversible hyperfusion), as well as MiDAS in response to SIRT3 knockdown or oxidative stress. We offer testable predictions about the growth factor- and glucose-dependence of MiDAS and its reversibility at different stages of reactive oxygen species (ROS)-induced senescence. We then link this model to our large modular model of mechano-sensitive Epithelial to Myesenhynal Transition, and show that EMT is incompatible with MiDAS. Our models provide mechanistic insights into the distinct stages of DNA-damage induced senescence, the relationship between senescence and epithelial-to- mesenchymal transition in cancer and offers a foundation for building multiscale models of tissue aging.
4. Stacey D. Finley University of Southern California
   "Systems biology modeling and analyses of metabolic phenotypes in the tumor microenvironment"
Colorectal cancer (CRC) remains one of the most prevalent and lethal malignancies worldwide, ranking third in cancer incidence and second in mortality. Despite advances in targeted therapies and immunotherapy, clinical outcomes often remain poor, largely due to the complex nature of the disease that extends beyond just the malignant cells themselves. Like all solid tumors, CRC develops as an ecosystem rather than a simple mass of cancer cells. As tumors progress, metabolic interactions between tumor and stromal cells in the tumor microenvironment (TME) lead to altered cellular growth and contribute to invasion, metastasis, and drug resistance. The complexity of metabolic interactions in the TME requires computational approaches that can capture system-level behaviors. We have developed genome-scale metabolic models of cancer cells, cancer-associated fibroblasts, and macrophages. We applied the models to predict the flux distributions and compare the cells’ metabolic phenotypes in the TME. We also apply graph theoretical methods to analyze the structure and organization of the cells’ metabolic networks. The integration of genome-scale metabolic modeling and graph theory produces new insights into metabolism in CRC and identifies strategies to exploit metabolic crosstalk to inhibit tumor progression.

Organized by: Ioana Bouros (University of Oxford), Alexander Browning, University of Melbourne

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

1. Yurij Salmaniw University of Oxford
   "Structural identifiability of linear-in-parameter parabolic PDEs through auxiliary elliptic operators"
In this talk, I will discuss results appearing in a recent manuscript (collaboration with Dr. Alexander P. Browning, Melbourne) under the same title (arXiv: 2411.17553). In it, we develop a relatively elementary approach to establishing parameter identifiability in parabolic partial differential equations and systems under an assumption of 'perfect' data observation. Key to this approach is an assumption of linearity in the parameters, which allows one to reduce the problem of parameter identifiability to a problem of identification of the kernel of a related elliptic operator. We will discuss our notion of parameter distinguishability, and how this connects to the more commonly used notion of parameter identifiability. We will appeal to several common examples from ecological modelling literature to clearly illustrate our results. These insights highlight the intimate connection between idenfiability, the influence of boundary conditions, and the role played by certain eigenfunctions and the linear dependence between low and higher order terms. Despite an ideal assumption of perfect data observation, these insights have consequences for parameter identifiability in practice, which we will also discuss. We will conclude briefly with some future directions, challenges, and open problems.
2. Dasuni Salpadoru Queensland University of Technology
   "Parameter estimation and identifiability analysis of bistable ecosystems"
Bistable ecosystems, such as lacustrine ecosystems, exhibit two stable equilibria: one representing a healthy equilibrium (oligotrophic) and the other representing an unhealthy equilibrium (eutrophic). Environmental perturbations can push a bistable ecosystem beyond a critical threshold, triggering a shift between these equilibria. Understanding bistable dynamics for ecosystem management requires these thresholds to be identified, as these sudden behavioural changes can potentially lead to irreversible damage. However, a key challenge in studying bistable ecosystems is determining if typical phosphorus monitoring data are sufficient for an ecological model parameter to be identifiable. Although parameter identifiability is important, it has been largely overlooked in bistable ecosystem studies. In this work, we use a profile likelihood approach, which is well-suited for assessing parameter identifiability by quantifying uncertainty and detecting potential non-identifiability issues. This method is applied to estimate parameters and analyse the practical identifiability of key parameters and critical thresholds for the Carpenter Lake eutrophication model in a range of different monitoring scenarios. Understanding the effects of different monitoring strategies on parameter identifiability can inform risk assessment and management plans to maintain water quality in a lake and prevent irreversible degradation. Beyond lake ecosystems, our analysis is generalisable to other bistable ecosystems that may be targets of conservation management. Keywords: Bistable ecosystems, Parameter estimation, Identifiability analysis, Profile likelihood, Lake eutrophication
3. Liam O'Brien Ohio State University
   "Structural causes of pattern formation and its breakdown - through model independent bifurcation analysis"
During development, precise cellular patterning is essential for the formation of functional tissues and organs. These patterns arise from conserved signaling networks that regulate communication both within and between cells. Here, we develop and present a model-independent ordinary differential equation (ODE) framework for analyzing pattern formation in a homogeneous cell array. In contrast to traditional approaches that focus on specific equations, our method relies solely on general assumptions about global intercellular communication (between cells) and qualitative properties of local intracellular biochemical signaling (within cells). Prior work has shown that global intercellular communication networks alone determine the possible emergent patterns in a generic system. We build on these results by demonstrating that additional constraints on the local intracellular signaling network lead to a single stable pattern which depends on the qualitative features of the network. Our framework enables the prediction of cell fate patterns with minimal modeling assumptions, and provides a powerful tool for inferring unknown interactions within signaling networks by analyzing tissue-level patterns.
4. Ioana Bouros University of Oxford
   "A retrospective analysis of the robustness of existing compartmental models for modelling future pandemics"
Background & aims of study For the duration of the Covid pandemic, the UK government consulted a number of mathematical models of transmission dynamics to help to guide policy response. Several of these epidemiological models use compartments to sort the population into, and ODEs to describe the infection dynamics. However, these models rely on a number of modelling assumptions about the disease, which sacrifice accuracy for model tractability. These differences in turn impact the forecasts of the epidemic trajectory and may lead to incongruent recommendations to policy makers. In this talk, we conduct a retrospective analysis of the performance of three models used for modelling the rapid progression of the Covid pandemic in the UK to test the robustness of the results and whether they can be used interchangeably to inform policy response: the “Cambridge-PHE”, the 'Warwick Household model”, and the “Roche model”. Methods & Results For each model, we produce forecasts for cases, deaths and inferred instantaneous reproduction number trajectories both in the actual and in the unmitigated epidemic scenario, by fitting to the same early 2020 UK epidemic death dataset. We identified that each of the three considered models produced very different death and case trajectories in the counterpart scenario, i.e. when no non-pharmaceutical interventions are put in place and contacts are maintained at the same rates throughout the simulation - which suggests that we cannot substitute the conclusions of on of these models for the other. Additionally, we analysed how the time of application of NPIs impacts the model outcomes. Finally, we include a sensitivity analysis to assess robustness to parameter changes of the three models. Implications This work highlights the pitfalls of relying on individual models to inform policy responses for future epidemics and pandemics, as well as the need for a more in-depth study of the impact of modelling assumptions on the quality of model outputs.