Organized by: Ning Wei (Purdue University)

1. Igor Vorobyov University of California, Davis
   "Digital twins for cardiac safety pharmacology and neuromodulation: from the atom to the rhythm"
It is increasingly clear that individual variability may be a key factor in determining the emergence of rare disease phenotypes in the setting of inherited and acquired disease. To begin to address personalized susceptibility to disease and drug responses, we have been working to develop a transformative experimentally informed and validated digital twin technology for patient-specific prediction of physiological processes and pharmacological interventions. Here we will describe such digital twins approach for prediction of the cardiotoxicity of drugs and efficacy of neuromodulation therapy in individuals. We established atomic-protein-structure digital twins of the cardiac ion channels including hERG, a major drug anti-target, which plays a critical role in the cardiac action potential. We used multiple machine learning based molecular modeling approaches including AlphaFold for predictions of physiologically and pharmacologically important conformational states of the hERG channel and its state-specific drug interactions. We used enhanced sampling molecular dynamics (MD) simulations to estimate hERG - drug binding affinities and rates, which were used to parameterize new digital twin representations at the cardiac protein, cell and tissue function scales to predict emergent drug-induced arrhythmia risks. Recently we expanded this multiscale digital twins pipeline to include multi-protein drug effects and acute effects of sex hormones on cardiac ion channel – drug interactions for more accurate predictions of arrhythmogenesis. We used a similar multiscale digital twins approach for the prediction of the autonomic nervous system stimulation effects to combat arrhythmia in the diseased heart tissue as an alternative to anti-arrhythmic medications. At the molecular level we focused on beta-adrenergic receptor – neurotransmitter interactions, a key event in the sympathetic nervous system stimulation. As a result of our studies, we aim to develop robust and efficient experimentally validated multiscale digital twins pipeline for an accurate prediction of arrhythmia risks starting from drug chemical structures and patients’ genetic information.
2. Karli Gillette University of Utah
   "Generation of cardiac digital twins of whole-heart electrophysiology under normal sinus rhythm"
Introduction: Personalized medicine using cardiac digital twins of cardiac electrophysiology has shown great promise for enhancing diagnostics and therapy planning for cardiac arrhythmias. Whole-heart cardiac digital twins, however, are challenging to personalize in terms of both anatomy and function. We present a novel computational pipeline for generating single snapshots of cardiac digital twins of whole-heart electrophysiology based on non-invasive clinical imaging and 12 lead electrocardiogram (ECG) data. Methods: Our computational pipeline produces anatomically highly detailed heart-torso models of patient hearts from clinical cardiac magnetic resonance images and calibrates their electrophysiological model properties to replicate the measured 12 lead ECGs. Efficient modeling pipelines in the atria and ventricles are deployed with modifications for atrioventricular entities. We utilize a novel optimization approach termed Geodesic-BP to infer ventricular activation during normal sinus rhythm based on the QRS complex. T-wave morphology is based on ventricular repolarization gradients related to activation, and the P-wave depends on fitted atrial electrophysiology through electrophysiological parameters. The method is demonstrated for two healthy subjects under normal sinus rhythm. Results: The novel computational pipeline can generate cardiac digital twins of whole-heart electrophysiology at scale within clinical time frames under 10 hours. Segmentation and optimization of the ventricular activation constituted the highest temporal costs. Simulated 12 lead ECGs are high fidelity with a mechanistic basis, especially in the QRS complex. Discussion: Our robust and non-invasive computational pipeline facilitates the generation of cardiac digital twins based on non-invasive clinical data. The method is scalable for additional subjects. In future work, we aim to generate time-integrated cardiac digital twins and apply the cardiac digital twins across various cardiac arrhythmias. Depending on the application, a detailed His-Purkinje system must be incorporated, and further optimization of atrial parameters may be needed.
3. Trine Krogh-Madsen Weill Cornell Medical College
   "Population modeling to explain heterogeneity of single stem cell-derived cardiomyocytes"
Human induced pluripotent stem cell derived cardiomyocytes (iPSC-CMs) are a promising tool to study arrhythmia-related factors, but the variability of action potential (AP) recordings from these cells limits their use as an in vitro model. We have recently developed an efficient voltage clamp protocol to quantify the relative size of key ionic currents within a single cardiomyocyte. Applying this protocol to tens of cells, correlating features of the recorded current to AP recordings from the same cells, and using computational models, we can generate mechanistic insights into the ionic currents contributing to AP heterogeneity.
4. Ning Wei Purdue University
   "The impact of ephaptic coupling and ionic electrodiffusion on arrhythmogenesis in the heart"
Cardiac myocytes synchronize through electrical signaling to contract heart muscles, facilitated by gap junctions (GJs) in the intercalated disc (ID). GJs provide low-resistance pathways for electrical impulse propagation between myocytes, serving as the primary mechanism for electrical communication in the heart. However, research indicates that conduction can persist without GJs. For instance, GJ knockout mice still exhibit slow, discontinuous electrical propagation, suggesting alternative communication mechanisms. Ephaptic coupling (EpC) serves as an alternative way for cell communication, relying on electrical fields within narrow clefts between neighboring myocytes. Studies show that EpC can enhance conduction velocity (CV) and reduce conduction block (CB), especially when GJs are compromised. Reduced GJs and significant electrochemical gradients are prevalent in various heart diseases. However, existing models often fail to capture their combined influence on cardiac conduction, which limits our understanding of both the physiological and pathological aspects of the heart. Our study aims to address this gap by developing a two-dimensional (2D) multidomain electrodiffusion model that incorporates EpC. This is the first model to capture the dynamics of all ions across multiple domains, enabling us to reveal the impact of EpC in the heart. In particular, we investigated the interplay between ionic electrodiffusion and EpC on action potential propagation, morphology, electrochemical properties and arrhythmogenesis in both healthy and ischemic hearts. Our findings indicate that ionic electrodiffusion enhances CV and reduces CB under strong EpC. Specifically, the electrodiffusion of Ca$^{2+}$ and K$^+$ intensifies the effects of EpC on action potential morphology, whereas Na$^+$ diffusion mitigates these effects. Ionic electrodiffusion also facilitates action potential propagation into ischemic regions when EpC is substantial. Moreover, strong EpC can effectively terminate reentry, prevent its initiation, and lower the maximum dominant frequency (max DF), irrespective of GJ functionality. However, weak EpC may help counteract proarrhythmic effects when GJ coupling is slightly to moderately reduced, contributing to the stabilization of conduction patterns. Additionally, strong EpC notably alters ionic concentrations in the cleft, significantly increasing [K$^+$] and nearly depleting [Ca$^{2+}$], while causing moderate changes in [Na$^+$]. This multidomain electrodiffusion model sheds light on the mechanisms of EpC in the heart.

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-1, Part-2, and Part-4.

1. Bruce Pell Lawrence Technological University
   "Stability Switching Induced by Cross-Immunity in a Two-Strain Virus Competition Model with Wastewater Data Validation"
We study a two-strain virus competition model incorporating temporary immunity through a discrete delay. After reducing and analyzing the system, we identify stability switching phenomena at the strain-1-only equilibrium, including the occurrence of Hopf bifurcations. A detailed characterization of the stability dynamics at this equilibrium is provided. We further validate the model using wastewater surveillance data and apply it to investigate the viral shedding behavior of recovered individuals.
2. Erica Rutter University of California, Merced
   "Methods for Modeling and Estimating Treatment Heterogeneity in Tumors"
Heterogeneity in biological populations, from cancer to ecological systems, is a fundamental characteristic that can significantly affect outcomes. Despite this, many mathematical models in population biology do not account for inter- or intra-individual heterogeneity. In systems such as cancer, this means assuming cellular homogeneity and deterministic phenotypes, even though heterogeneity is thought to play a crucial role in therapy resistance. In this talk, I will discuss several innovative approaches towards incorporating and estimating cellular heterogeneity in models of tumor growth. I will focus on random differential equations to model treatment heterogeneity and the Prohorov metric framework for estimating parameter distributions from aggregate data (e.g., tumor volume). We validate our method on synthetic and in vitro tumor volume data.
3. Kyle Nguyen Sandia National Laboratory
   "Mathematical modeling of multicellular tumor spheroids quantifies inter-patient and intra-tumor heterogeneity"
In the study of brain tumors, patient-derived three-dimensional sphere cultures provide an important tool for studying emerging treatments. The growth of such spheroids depends on the combined effects of proliferation and migration of cells, but it is challenging to make accurate distinctions between increase in cell number versus the radial movement of cells. To address this, we formulate a novel model in the form of a system of two partial differential equations (PDEs) incorporating both migration and growth terms, and show that it more accurately fits our data compared to simpler PDE models. We show that traveling-wave speeds are strongly associated with population heterogeneity. Having fitted the model to our dataset we show that a subset of the cell lines are best described by a “Go-or-Grow”-type model, which constitutes a special case of our model. Finally, we investigate whether our fitted model parameters are correlated with patient age and survival.
4. Kevin Flores North Carolina State University
   "Biologically-informed neural networks for modeling of BKV infection dynamics in renal transplant patients"
BK virus (BKV) nephropathy is a significant cause of kidney transplant failure, with no effective antiviral treatments currently available. Clinicians manage BKV by adjusting immunosuppressive medications, balancing the risks of infection progression and transplant rejection. To support clinical decision-making, we propose a biologically-informed neural network (BINN) model for predicting BKV infection dynamics. Our approach integrates patient data from electronic health records, including BKV levels, creatinine, vital signs, lab results, demographics, and medication dosage. A key challenge in modeling BKV infection is the lack of mechanistic detail in existing equations, particularly for creatinine levels. To address this, we applied BINNs to refine a previously validated differential equation model of BKV infection; in particular, the functional form for the equation used to describe creatinine was learned from time series data. Additionally, we used symbolic regression to extract simpler, interpretable mathematical expressions from the learned neural network-based function. Our study shows how machine learning can enhance the accuracy of mechanistic models, thereby enabling future clinical applicability and a personalized predictive framework for optimizing BKV management in kidney transplant patients.

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

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

Organized by: Nir Gavish (Technion)

1. Nir Gavish Technion Israel Institute of Technology
   "Optimal vaccination for contagious diseases with seasonal transmission"
We consider epidemiological models with seasonality and ask what the optimal temporal vaccination profile is that minimizes the basic reproduction number defined over a season, given a constraint on the total annual number of vaccinations. We do not impose any a priori assumptions about the structure or regularity of the optimal vaccination profile. In particular, we allow the vaccination profile to include delta functions corresponding to pulse vaccination. To do so, we consider a periodic optimal control problem over a measure space. Using non-standard tools that do not rely on Pontryagin's theorem for optimal control problems, we characterize the solution to the problem. In addition, we develop an efficient numerical scheme for computing the optimal vaccination profile over time. This is a joint work with Guy Katriel
2. Amit Huppert Tel Aviv University
   "Modeling Predation in Bacterial Interactions"
We developed a mathematical model to explore density-dependent predation in microbial systems. Density dependence is a fundamental ecological mechanism that influences population dynamics and regulation. While mathematical modeling is a valuable tool for quantifying predator-prey interactions, a research gap exists in exploring the nature of predator growth's dependence on prey density through a combination of empirical and mathematical approaches within the prey-predator framework. We developed mathematical ODE models and fitted them to experimental time-series data from microbial predator-prey systems. These models incorporated different functional responses—specifically Holling types I, II, and III—which describe the relationship between prey density and predator foraging. Parameter inference was performed using a Bayesian approach with the Markov Chain Monte Carlo (MCMC) technique, adapting the framework to handle multi-replicate time-series data. We employed two distinct modeling approaches: Single Interval Modeling, which fits one model to the entire dataset, and Phased Interval Modeling, which divides the 96-hour period into three distinct phases (0-12, 12-48, and 48-96 hours) and fits separate models to each. Model selection in both approaches was based on likelihoods, AIC, and BIC. The study revealed distinct dynamics in each phase. In Phase I, the predator's per-capita growth rate (PGR) was density independent, and a simple model assuming the predator's total time was spent handling prey provided a good fit with an analytical solution. Phase II exhibited density-dependent dynamics, where the predator's PGR changed with prey density; the best model for this phase was an ODE model with a Holling type-III functional response. In Phase III, following prey depletion, the predator population showed exponential death, and its PGR was again density independent.
3. Byul Nim Kim Kyung Hee University
   "Empirical and Spatiotemporal Approaches to Effective Reproduction Number Estimation: Insights from Network and Mobility Models in South Korea"
Understanding the effective reproduction number (R_t) is crucial for real-time epidemic assessment and public health intervention. This presentation introduces and synthesizes three advanced approaches for estimating Rt during the COVID-19 pandemic in South Korea, highlighting their methodological innovations and practical implications. First, the Transmission Potential (TP) model integrates household vs. non-household transmission, mobility patterns (via Google data), and social distancing behaviors to estimate Reff in real time. By distinguishing the impact of mobility changes on different transmission settings, this model provides dynamic and context-aware Reff estimates, outperforming conventional methods like Cori's Rt in sensitivity and short-term prediction​. Second, an empirical infection network-based R_t model uses real-world infector-infectee pair data to directly compute R_t without relying on assumptions of homogeneous mixing. This network approach captures the heterogeneity of regional and demographic transmission, especially during superspreading events and early epidemic phases, where traditional models often underperform​. Third, a multi-patch SEIIR model with mobility-informed regional Reff quantifies both local and interregional transmission. Using high-resolution mobility data and compartmental modeling across 17 regions, it reveals Seoul and Gyeonggi as dominant transmission hubs. The study emphasizes phase-specific and region-targeted mobility interventions as more effective than uniform national policies​. Together, these studies highlight the need for adaptive, data-driven R_t estimation frameworks that incorporate real-time behavior, mobility, and infection network structures. The integration of these methods advances epidemic modeling and supports refined public health strategies tailored to regional and temporal dynamics.
4. Kyeongah Nah National Institute for Mathematical Sciences
   "Age-structured modeling of tuberculosis in South Korea and insights for national control strategies"
Despite improvements in its national tuberculosis (TB) control program and rapid economic growth, South Korea continues to report the highest TB incidence among OECD countries. Addressing this challenge requires an understanding of the changing trends of TB burden across different age groups and a long-term evaluation of policies aimed at TB control. This study introduces an age-structured population dynamics to analyze the dynamics of TB transmission under national control in Korea. We perform retrospective assessments and future projections to assess the impact of Public-Private Mix (PPM) strategies on TB incidence. Additionally, we explore how this model could be extended to provide insights for designing effective TB control policies.

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-3.

1. Mallory Harris University of Maryland
   "Risk (Mis)estimation and Population Heterogeneity Shape Infectious Disease Dynamics"
Models of human behaviour during infectious disease outbreaks often assume that people perfectly assess the risks associated with infection and become more cautious when risk is high. However, prior work showed that people tended to misestimate the risk of Covid-19 exposure at events of different sizes (Sinclair et al 2021, PNAS). The effects of event risk estimation have not been studied at population level, a critical gap given potential for nonlinear and emergent dynamics in infectious disease systems. Here, we build an agent-based model to capture feedback between infectious disease dynamics, risk perception, and behavior in the context of event attendance. At each time step, individuals decide whether to attend an event based on their assessed exposure risk, a function of event size and prevalence calibrated to actual risk assessments collected from 11,169 individuals across the United States between September 2021 and August 2022 (Sinclair et al 2023, PLoS One). We show that risk misestimation substantially worsens epidemic burden compared to what it would be if people estimated risk perfectly. Behavioural interventions to improve risk estimation reduce but do not completely eliminate this effect. We also compare strategies for deploying behavioural interventions across a heterogeneous population where certain subgroups are more likely to underestimate risk. This work underscores the importance of considering risk misestimation in mathematical models of infectious diseases and demonstrates benefits of behavioural interventions to improve individual decision-making and reduce disease transmission. Joint work with Shu Yuan Shi and Joshua Weitz.
2. Christian Parkinson Michigan State University
   "Optimal Control of a Reaction-Diffusion Epidemic Model with Noncompliance"
We consider an optimal distributed control problem for a reaction-diffusion-based SIR epidemic model with human behavioral effects. We develop a model wherein non-pharmaceutical intervention methods are implemented, but a portion of the population does not comply with them, and this noncompliance affects the spread of the disease. Drawing from social contagion theory, our model allows for the spread of noncompliance parallel to the spread of the disease. Control variables affect the infection rate among the compliant population, the rate of spread of noncompliance, and the rate at which non-compliant individuals return to a compliant state. We prove the existence of global-in-time solutions for fixed controls and study the regularity properties of the resulting control-to-state map. We establish the existence of optimal controls for a fairly general class of objective functions and present a first-order stationary system which is necessary for optimality. Finally, we present simulations with various parameters values to demonstrate the behavior of the model.
3. Zitao He University of Waterloo
   "From Sentiment to Spread: Homophily and Early Warnings in Epidemic Dynamics"
Understanding the interplay between social activities and disease dynamics is crucial for effective public health interventions. While many coupled behavior-disease models assume homogeneous populations, real-world social structure is often heterogeneous. In this talk, we present a model that divides the population into social media users and non-users to investigate the impact of homophily (the tendency for individuals to associate with similar others) and online events on disease dynamics. We find that homophily slows down the spread of vaccinating strategies, pushing the system closer to a tipping point where vaccine uptake collapses and an endemic equilibrium emerges. Online events also play an important role, with early social media discussions acting as warning signs of upcoming outbreaks. Building on these insights, we also discuss a data-driven approach that uses deep learning to detect early warning signals from vaccine-related social media time series. Specifically, we train LSTM and ResNet classifiers on simulated data from a stochastic behavior-disease model with additive Lévy noise, capturing heavy-tailed real-world fluctuations. These classifiers consistently outperform conventional indicators such as variance and lag-1 autocorrelation, offering clearer and more interpretable signals. Together, these studies underscore the importance of incorporating social structure and real-time data in predictive models for proactive public health response.
4. Alice Oveson University of Maryland
   "Modeling Racial and Age-Structured Transmission Dynamics with Empirical Contact Data"
I present a compartmental infectious disease model structured by both race and age, incorporating empirically derived contact matrices to represent heterogeneity in social behavior. The model captures interactions across twelve demographic subgroups and enables the study of how behavioral mixing patterns shape disease transmission. While the inclusion of contact data explains a substantial portion of variation in group-level transmission dynamics, our results indicate that racial disparities persist beyond what can be attributed to behavioral contact patterns alone. This suggests the influence of unmeasured structural factors, such as differential susceptibility, healthcare access, or baseline risk. Our approach highlights the utility of structured modeling frameworks for uncovering the multi-layered mechanisms underlying population-level disparities in disease burden.

Organized by: Meredith Greer, Prashant Kumar Srivastava, Michael Robert (Bates College), Prashant Kumar Srivastava (Indian Institute of Technology, Patna) and Michael Robert (Virginia Tech)

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

1. Lihong Zhao Kennesaw State University
   "Modeling the Dynamics of Legionnaries' Disease and Management Strategies"
Some pathogens can survive and replicate in abiotic environment outside the host systems and rely on the interaction with an environmental reservoir to transmit and infect hosts. Mathematical modeling can provide insights into the complex and often unknown dynamics of environmentally transmitted diseases. One such pathogen is the bacteria Legionella, the inhalation of this bacteria suspended in aerosolized water can lead to an atypical pneumonia which is known as the Legionnaries' disease (LD). In 2018, nearly 10,000 LD cases were reported in the United States. The true incidence should be higher as LD is underdiagnosed and underreported. In this talk, we will present the model we developed to examine the factors that may have contributed to the increase in LD outbreaks, and the insights into management strategies using control theory.
2. Tinashe Byron Gashirai (Postdoctoral Fellow) University of Idaho
   "A theory of risk perception in shaping human behavior to policy compliance during outbreaks"
The interplay of perceived risk of infection and protective behavior of the host in response to an emerging infection is complex and difficult to abstract. We therefore present a simple human behavior model based on the hypothesis that the human host engages in positive adaptive behavior when the disease prevalence reaches a certain threshold. Our mathematical analysis shows that the recruitment rate of susceptible individuals and the prevalence that triggers protective behavior influence the persistence or extinction of the disease. Moreover, abrupt changes in the transmission rate due to risk perception modulated host behavior may result in backward bifurcation. This complicates the control of the disease since the basic reproduction number fails to predict the occurrence of an epidemic. This study highlights the importance of understanding the role of complacency in engaging human adaptive response and risk perception in combating disease spread.
3. Claudia Pio Ferreira Unesp, IBB
   "Mathematical epidemiology and control of hospital-associated infections"
Healthcare-associated infections cause significant patient morbidity and mortality, and contribute to growing healthcare costs. Active surveillance systems, hospital staff compliance, including hand hygiene, and a rational use of antimicrobials are among the important measures to mitigate the spread of healthcare-associated infection within and between hospitals. Focusing on the role of patient movement within and between hospitals on the transmission and incidence of enterobacteria producing the K. pneumoniae Carbapenemase, we developed a metapopulation model where the connections among hospitals are made using a theoretical hospital network based on Brazilian hospital sizes and locations. The pathogen reproductive number, R_0 was calculated in different scenarios defined by both the links between hospital environments and between different hospitals. Furthermore, the efficacy of infection prevention and control on several hospital networks is assessed. Overall, the obtained results emphasize the importance of data collection on infection transmission and patient transfers, and show that the allocation of control units based on the R_0 of the hospitals may work better than the network-topology-based allocations.

Organized by: Stuart Johnston (The University of Melbourne), Matthew Faria

1. Irina Kareva Northeastern University
   "From pre-clinical data to first in human dose projections: a different puzzle every time"
Identification and pre-clinical testing to determine dose-response relationships for new compounds is only one step in the drug discovery and development process. Translation of pre-clinical data into efficacious and safe human doses is a separate process, which may rely on assessing target occupancy, biomarker modulation, benchmarking against competitor compounds, and other detective work, to identify compound-specific criteria to predict safety and efficacy criteria in humans. In this talk, I will review the general approaches that help us make these predictions and then go through a few examples to describe how we solved the dose prediction puzzle for each individual case.
2. Yun Min Song Institute for Basic Science
   "Beyond FDA Guidance: Enhancing Accuracy in Predicting Drug-Drug Interactions"
The U.S. Food and Drug Administration (FDA) guidance recommends several model-based approaches to assess potential drug-drug interactions (DDIs) mediated by enzyme induction or inhibition. However, the FDA-recommended equation for predicting DDIs mediated by induction is known to have low accuracy. Moreover, a systematic and reliable method for accurately estimating inhibition constants—key parameters for predicting DDIs mediated by inhibition—remains lacking, with considerable inconsistencies reported across studies. In this talk, we demonstrate that the inaccuracy of the FDA-recommended equation stems from a fundamental limitation of the Michaelis-Menten equation, and we propose an improved model that considerably enhances predictive accuracy (~2-fold). Furthermore, we introduce a novel, optimized method for estimating inhibition constants, termed 50-BOA, which substantially reduces the number of required experiments (>75%)% while maintaining high precision and accuracy.
3. Stuart Johnston The University of Melbourne
   "Quantifying biological heterogeneity in nano-engineered particle-cell interaction experiments"
Nano-engineered particles are a promising tool for medical diagnostics, biomedical imaging, and targeted drug delivery. Fundamental to the assessment of particle performance are in vitro particle-cell interaction experiments. These experiments can be summarised with key parameters that facilitate objective comparisons across various cell and particle pairs, such as the particle-cell association rate. Previous studies often focus on point estimates of such parameters and neglect heterogeneity in routine measurements. In this study, we develop an ordinary differential equation-based mechanistic mathematical model that incorporates and exploits the heterogeneity in routine measurements. Connecting this model to data using approximate Bayesian computation parameter inference and prediction tools, we reveal the significant role of heterogeneity in parameters that characterise particle-cell interactions. We then generate predictions for key quantities, such as the time evolution of the number of particles per cell. Finally, by systematically exploring how the choice of experimental time points influences estimates of key quantities, we identify optimal experimental time points that maximise the information that is gained from particle-cell interaction experiments.
4. Thibault Delobel Institut Curie
   "Integrating glioblastoma plasticity into combination treatment design: a quantitative systems pharmacology and machine learning approach"
Glioblastoma (GBM) is the most aggressive primary brain tumor in adults, with a median survival below 18 months and no curative treatment currently available. To investigate resistance mechanisms to temozolomide (TMZ), the standard-of-care chemotherapy, we generated perturbed proteomic data (with and without TMZ) for 12 patient-derived cell lines (PDCLs). Pathways enrichment and independent component analysis revealed a high inter-patient heterogeneity. Commonly dysregulated proteins across PDCLs were matched to pharmacological compounds targeting them using a new pipeline. These molecules are currently being evaluated in a dedicated drug screening. The next step is to build a digital twin for each PDCL, enabling personalized prediction of combination therapies. A previously developed quantitative systems pharmacology (QSP) model of TMZ pharmacokinetics-pharmacodynamics serves as a foundation. To initiate model individualization, we developed a method to personalize model parameters using publicly available multi-omics and TMZ cytotoxicity data. Current work focuses on integrating proteomics-derived key species into the core model, via network reconstruction methods, to obtain PDCL-specific digital twins and infer personalized treatment by combining QSP and machine learning. This integrative approach, combining data analysis, network reconstruction, and mechanistic modeling, opens the path for efficient patient specific therapies in GBM.

Organized by: Wenjun Zhao (UBC/Wake Forest University), Khanh Dao Duc (UBC)

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

1. Ben Cardoen University of Birmingham
   "Shape discovery of functional interaction between proteins and organelles in the presence of weak oracle distances in superresolution microscopy"
Novel superresolution microscopy (SRM) allows mesoscale (5-150 nm) discovery in situ, in both live and fixed cells. Unlike EM based approaches, it is less costly, less invasive, and enables tagging of individual targets with fluorescence, at a cost of lower precision. Multichannel SRM enables the study of interacting organelles and protein complexes with use cases such as : ER-Mitochondria contacts, HIV ingress, HIV coat forming, protein complex formation dynamics, chromatic dynamics, and neurotransmitter patterns. Interaction at mesoscale is defined as distance mediated state change. Where EM based analysis is ideally placed to reconstruct stable structure, SRM can describe equilibria and diversity. However, SRM is characterized by complex non-additive noise, and localizes objects with an uncertainty and precision that can be as high as the size of or distance between objects. In other words, SRM interaction analysis works with weak distance oracles. Second, the physics at the mesoscale are decidedly non-linear, calling for algorithms that leverage these factors. Finally, a number of underappreciated SRM specific confounding factors can disrupt downstream analysis. In this talk I will give an overview of those challenges, and review how current methods elucidate interaction patterns from SRM data. Using a new computational paradigm to formalize interaction mathematically, I will review underappreciated confounding factors that can comprise SRM interaction analysis. Finally, using in silico data I will illustrate the potential and limitations of current computational techniques to recover distance mediated state change from SRM data. We will measure if we can detect pentagonal versus hexagonal protein conformations, typical in membrane coat function to form spherical structures, or in the capsid coat of the HIV1 virus.
2. Ashok Prasad Colorado State University
   "Static Shapes and Dynamic Networks: Morphological Analysis of Cellular Identity"
Cell morphology offers a powerful and underutilized lens for understanding cellular identity, behavior, and state. While transcriptomic and proteomic profiling have revolutionized our capacity to characterize cells, quantitative morphological features can provide complementary insights into cell state and function. In this talk, I will present our recent work demonstrating that cells can be robustly classified using a range of morphological metrics derived from microscopy images. I will also discuss our ongoing efforts to develop morphological features that are sensitive to the dynamics of intracellular structures, such as the actin cytoskeleton and other dynamic polymer networks. We simulate the actin cytoskeleton, incorporating the action of molecular motors and cross-linkers, and look for features that are sensitive to different initial conditions and differences in temporal dynamics. Ultimately, we seek to build a framework in which cellular morphology is treated as a high-dimensional, information-rich signature of cell state. This work contributes to a broader vision of morphology-based phenotyping as a bridge between structure and function in living systems.
3. Felix Zhou UT Southwestern
   "Methods to identify causal links between morphology and cell signaling"
Form is function. Just as Darwin’s finches have beaks adapted to their ecological niche, so too do cell morphology associate with its function. Indeed, cell shape changes are widely used as a first clinical indicator of disease. Conventionally, we have thought of shape as downstream of a cell’s molecular processes. However, recently we have found that the shape of protrusions on cell surfaces might also directly drive signaling whereby changes to their properties, such as curvature and thickness dynamically in time, modify signaling cascades and ultimately affect fate. For example, we found a previously undescribed role of blebbing - dynamic hemispherical protrusions in melanoma cells to activate prosurvival signals and avoid the normal checkpoint program of programmed cell death – a prerequisite step for cancer metastasis. Causal investigation of shape and signaling is notoriously difficult due to the intricate feedback between the two. Notably, shape changes are a product of molecular signals. Consequently, we have been developing statistical causal inference techniques to systematically test for causal links between 3D cell shapes segmented from microscopy videos with jointly measured molecular signal intensities from fluorescent biosensors. Here, I will talk about 3 general computational frameworks we have developed to enable this: u-Segment3D to segment the 3D surface, leveraging pretrained generalist 2D segmentation models; u-Unwrap3D to bidirectionally map the segmented 3D surface to a 2D image; and u-InfoTrace to adapt 1D causal measures and test spatiotemporal causality in the 2D unwrapped images. I will demonstrate example application to diverse videos of 3D cell blebbing, cancer-immune cell interaction, and organoids.
4. Joe Kileel UT Austin
   "Method of moments for determining macromolecular shapes in cryo-EM"
In this talk, I will present method of moments based approaches for 3D reconstruction of molecular conformations from datasets of noisy 2D images in cryo-electron microscopy. I will present progress both theoretically and computationally for these methods, in particular leveraging prior and side information to improve the cryo-EM reconstruction. Method of moments based solvers also provide a more general methodology, and may be applicable to other inverse problems involving shape 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-3, and Part-4.

1. Ellen Baake Bielefeld University
   "Evolving genealogies in cultural evolution"
We consider a Moran-type model of cultural evolution, which describes how traits emerge, are transmitted, and get lost in populations. Our analysis focuses on the underlying cultural genealogies; they were first described by Aguilar and Ghirlanda (2015) and are closely related to the ancestral selection graph of population genetics, wherefore we call them emph{ancestral learning graphs}. We investigate their dynamical behaviour, that is, we are concerned with emph{evolving genealogies}. In particular, we consider the total length of the genealogy of a sample of individuals from a stationary population as a function of the (forward) time at which the sample is taken. This quantity shows a sawtooth-like dynamics with linear increase interrupted by collapses to near-zero at random times. We relate this to the metastable behaviour of the stochastic logistic model, which, in our context, describes the evolution of the number of ancestors, or equivalently, the number of descendants of a given sample. This is joint work with Joe Wakano (Tokyo), Hisashi Ohtsuki (Hayama), and Yutaka Kobayashi (Kochi).
2. Linh Huynh Dartmouth College
   "Spin glass model for Large Language Models and evolution"
In recent years, Large Language Models (LLMs) have revolutionized Natural Language Processing with their ability to generate human-like texts. However, a fundamental challenge remains in understanding the underlying mechanisms driving their emergent behaviors, particularly the randomness in their outputs. In this talk, I will discuss the application of spin glass theory as a mathematical framework to quantify the uncertainty of LLMs. By making connections between LLMs and spin glass models, which are traditionally used in statistical physics and probability to describe disordered networks with random interactions and frustrations (conflicting constraints), we can gain insights into the high-dimensional optimization landscapes of LLMs, the uncertainty in their outputs, and the role of noise in their learning process. I will conclude by making a connection to evolution.
3. Samuel Isaacson Boston University
   "Coarse-grained limits of particle-based stochastic reactive-transport models"
In many applications, both spatial transport and stochasticity in chemical reaction processes play critical roles in system dynamics. Particle-based stochastic reaction diffusion (PBSRD) models have been successfully use to study a variety of such reaction processes, particularly at the single-cell scale. However, as commonly used, they typically assume overdamped transport, ignoring inertial forces. In this talk we investigate how to construct more microscopic particle-based reactive Langevin Dynamics (PBRLD) models that include inertial forces, formulating models that are consistent with detailed balance of reaction fluxes at equilibrium. We show via asymptotic analysis that with appropriate scaling assumptions for the dependence of reaction kernels on friction/mass, PBRLD models converge to common PBSRD models in the overdamped limit. Finally, we identify and prove the large population mean-field limit of the new PBRLD models, obtaining systems of nonlocal kinetic reaction-diffusion equations.
4. Clément Soubrier University of British Columbia
   "Modeling the meiotic spindle using a spatial birth-death process."
In eukaryotes, during the second phase of meiosis, the two chromatids of each chromosomes are separated to form haploid gametes. This segregation is driven by a bi-polar mechanical and dynamical structure, the spindle, primarily composed of microtubules. Spindle defects, such as loss or split of a pole, lead to failure of the mitosis or to aneuploid gametes. In this talk, we model the spindle stability using a spatial birth-death process representing the position of micro-tubules attached to the spindle pole. In particular, we study the first transition of the process to a multipolar state. We define this state as having a large spatial gap between two consecutive micro-tubules. Our main result is an asymptotic estimate of the first passage time of the multipolar state, as a function of the spindle creation rate and spatial gap.

Organized by: Margherita Maria Ferrari (University of Manitoba), Daniel Cruz, University of Florida

1. Stacey Smith? The University of Ottawa
   "The implications of micro-host--pathogen co-evolutionary outcomes on macro-epidemics"
Host defence and pathogen virulence both interplay and mutually influence the evolutionary processes of each another. Host–pathogen co-evolutionary outcomes have potentially significant impacts on population dynamics and vice versa. To investigate host–pathogen interactions and explore the impact of micro-level co-evolutionary outcomes on macro-level epidemics, we develop a co-evolutionary model with a mixed host-defence strategy. Our results illustrate that host–pathogen co-evolution may induce infection cycling and lead to the vanishing of the disease-induced hydra effect, whereas pathogen mono-evolution strengthens the hydra effect in both range and magnitude. As the recovery rate increases, we find a counter-intuitive effect of increased disease prevalence due to host–pathogen co-evolution: the disease is first highly infectious and lethal, then highly infectious but with low lethality. Such diverse outcomes suggest that this combined co-evolutionary and epidemiological framework holds great promise for a better understanding of disease infection.
2. Morgan Craig Université de Montréal
   "Age-related variability in antibody responses to the mRNA COVID-19 vaccine primary series"
Immunological heterogeneity, driven by a variety of factors including e.g., age and sex, heavily influences vaccine outcomes. To better understand this variability, we recently developed a mechanistic mathematical model describing the generation and maintenance of humoral immunity after the mRNA COVID-19 vaccine primary series. By fitting our model to a clinical cohort of younger health care workers and seniors, we disentangled the mechanisms driving weaker antibody responses and faster antibody waning in older adults. Based on these results, we outlined vaccine strategies tailored to key characteristics driving outcomes using an approach rooted in computational immunology.
3. Chris Soteros University of Saskatchewan
   "Lattice polygon models of DNA topology"
The field of DNA Topology includes the study of DNA geometry (supercoiling) and topology (knots and links) and their effects on DNA in vitro and in vivo. Statistical physics-based lattice models of DNA have proved useful for addressing many questions arising from DNA topology experiments. In this talk I will review recent advances we have made using lattice polygon models to address questions related to the knot and link statistics of DNA in vitro either subject to varying salt conditions or under nanochannel-like confinement.
4. Margherita Maria Ferrari University of Manitoba
   "Discrete models for DNA-RNA complexes"
R-loops are three-stranded structures formed by a DNA-RNA hybrid and a single strand of DNA, often appearing during transcription. Experimental works show that R-loops can threaten genome integrity, while also playing regulatory roles in biological processes. In this talk, we introduce a model for R-loops based on formal grammars, that are systems to generate words widely applied in molecular biology. The model is trained on experimental data and, despite not including topological information, it accurately predicts R-loop formation on plasmids with varying starting topologies.

Organized by: Annequa Sundus (Indiana University Bloomington), Elmar Bucher (Indiana University Bloomington), Paul Macklin (Indiana University Bloomington)

1. Harsh Jain University of Minnesota Duluth
   "The SMoRe-verse: A novel method for ABM parametrization and uncertainty quantification"
Agent-based models (ABMs) are widely used to study complex biological systems where emergent behaviors arise from individual-level interactions. Understanding the influence of input parameters on model output is essential for interpreting results and improving predictive power, but global sensitivity analysis (GSA) remains computationally prohibitive for many ABMs due to their complexity and high simulation costs. This talk presents SMoRe GloS (Surrogate Modeling for Recapitulating Global Sensitivity), a novel, computationally efficient method for performing GSA on ABMs. SMoRe GloS leverages explicitly formulated surrogate models to approximate ABM outputs, enabling thorough exploration of parameter space and quantification of uncertainty with significantly reduced computational demands. We demonstrate the method’s flexibility and accuracy using two case studies: a 2D cell proliferation assay and a 3D vascular tumor growth model. In both settings, SMoRe GloS produced sensitivity indices consistent with established methods such as Morris one-at-a-time and eFAST, while achieving substantial reductions in computation time. Importantly, the method also captures sensitivities for parameters associated with processes not explicitly included in the surrogate model. These results highlight the potential of SMoRe GloS to extend the accessibility of GSA for computationally intensive ABMs and to support more robust model-based inference in complex systems.
2. Atul Deshpande Johns Hopkins University
   "Deriving Spatially-Resolved Descriptors from Omics Data to Inform Agent-Based Models"
This talk will highlight spatial bioinformatics methodologies that directly inform the design and enhance the calibration of Agent-Based Models (ABMs). I will introduce novel computational methods developed in our group for analyzing spatial omics data, focusing on their application in deriving spatially-resolved quantitative descriptors of tumor organization and oncogenic signaling. For instance, SpaceMarkers quantifies the spatial overlap between distinct cell types in spatial transcriptomics and identifies the molecular changes associated with these interactions. Similarly, FunCN models cellular influences on individual cells to define functional neighborhoods (CNs), allowing us to map abundant CNs in tissue samples. The descriptors from these methods are crucial not only as direct inputs for defining agent properties, interaction rules, and environmental conditions in ABMs, but also for precise model parameter calibration. I will showcase our integrated approach, applying these bioinformatics methods on spatial data from biospecimens and ABM simulations. This synergy enables advanced model calibration and validation, surpassing traditional coarse-grained techniques, and we are applying it to power virtual trials of combination therapies for hepatocellular carcinoma.
3. Eric Cramer Oregon Health & Science University
   "Mapping Parameter Space to Emergent Tumor Microenvironment States in Agent-Based Models"
The tumor microenvironment (TME) is a dynamic ecosystem of malignant, immune, and stromal cells whose interactions shape tumor progression and therapeutic response. Agent-based models (ABMs) are a powerful tool for simulating such complexity, yet systematically linking model parameters to emergent TME states remains challenging. Here, we present a computational framework to systematically map ABM parameter space to phenotypic TME states and their transitions. Using the PhysiCell modeling framework, we developed an ABM of triple negative breast cancer featuring malignant epithelial cells, CD8+ T cells, and macrophages with different functional polarities. We explored the parameter space of the model and applied spatial and temporal analysis to identify distinct TME states and their transition dynamics. We found that specific parameter combinations – such as macrophage polarization, cell adhesion, inflammatory signaling, and CD8+ T cell cytotoxicity – strongly influence the likelihood of state transitions. These insights suggest strategies to steer TME evolution toward more favorable immune-activated states and highlight potential intervention points. Our approach demonstrates how integrating parameter space exploration with clinically relevant spatial metrics analyses can reveal actionable patterns in TME dynamics and support hypothesis generation for therapeutic strategy design.

Organized by: Tomas Helikar (University of Nebraska - Lincoln), Juilee Thakar (Juilee_Thakar@URMC.Rochester.edu) - University of Rochester Medical Center James Glazier (jaglazier@gmail.com) - Indiana University

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

1. Reinhard Laubenbacher University of Florida
   "Immune Digital Twins: Foundational Mathematical Challenges"
The digital twin concept has its origins in industry. One industrial equipment manufacturer advertised its digital twin capabilities to its customers as ”No unplanned downtime” for its products. There is a compelling aspirational analog in healthcare: “No unplanned doctor visits.' Of course, the challenges of building digital twins for human patients are incomparably greater than for machinery. Nonetheless, there are now several instances of what might be called digital twins in medicine, and many more ongoing development projects. Aside from our incomplete understanding of human biology, relative sparseness of data characterizing human patients, and logistical difficulties in implementing computational models in healthcare, there are many mathematical and computational problems that need to be solved. Examples include calibration and validation of multiscale, hybrid, stochastic computational models, forecasting algorithms, and optimal control methods. This talk will describe some of these problems and outline a mathematical research program for the field.
2. Josh Loecker University of Nebraska-Lincoln
   "Adaptive Analysis of Mechanistic Models using Large Language Models"
Large language models (LLMs) hold immense potential for revolutionizing biomedical research and personalized medicine, but their application to mechanistic modeling and immune digital twins (IDTs) remains largely unexplored. This work proposes a novel framework integrating LLMs with mechanistic models to address two critical gaps: (1) translating complex model outputs into actionable insights for patients and clinicians, and (2) automating the analysis and interpretation of large-scale mechanistic models. Our framework leverages a comprehensive library of “Action Intents,” enabling LLMs to interact with and manipulate models, perform complex analyses, and generate human-readable explanations. We will develop novel LLM-driven algorithms for tasks such as parameter sensitivity analysis, critical node identification, and emergent behavior prediction. Furthermore, we will establish robust evaluation metrics to assess LLM performance in this domain, encompassing both quantitative measures of accuracy and qualitative assessments of clinical utility. This framework will empower patients with personalized, understandable insights derived from their Personalized Digital Twin, fostering greater autonomy in healthcare decisions. Simultaneously, it will provide researchers with powerful tools to accelerate the analysis and interpretation of complex biological models, ultimately advancing our understanding of the immune system and accelerating the development of novel therapeutic strategies. This innovative approach promises to bridge the gap between complex biological models and their practical application in personalized medicine, paving the way for more effective and patient-centered healthcare.
3. Heber L. Rocha Indiana University
   "Multiscale Modeling of Immune Surveillance for Cancer Patient Digital Twins"
Tumors are complex ecosystems characterized by heterogeneous cellular behaviors, intercellular interactions, and stochastic processes, which collectively challenge the development of personalized cancer therapies due to unpredictable therapeutic responses. Digital twins—computational representations of individual patients—offer a transformative approach to simulate and predict treatment outcomes, enabling precision oncology. This presentation describes an multiscale agent-based model, developed using the PhysiCell framework, to investigate immune surveillance in micrometastases, early metastatic clusters critical to cancer progression. Through high-throughput simulations of over 100,000 virtual patient trajectories, our model revealed a spectrum of outcomes, ranging from tumor proliferation to immune-mediated eradication. These analyses identified critical parameters, such as immune cell functionality and tumor immunogenicity, that govern these divergent dynamics. These findings provide a robust foundation for constructing cancer patient digital twins to optimize therapeutic strategies. To enhance model reliability, our ongoing efforts focus on uncertainty quantification, employing sensitivity analysis and parameter calibration to address inherent biological variability and epistemic uncertainties, thereby advancing the development of clinically actionable digital twins.
4. James A. Glazier Indiana University, Bloomington
   "Medical Digital Twins: Addressing Simulation Equivalence Challenges in Virtual-Tissue Models"
Developing closed-loop Medical Digital Twins requires multiple tools—both physical (sensors/actuators) and computational—to support the cycle of measurement, forecasting, divergence assessment, anomaly detection, data assimilation, and action selection. While significant progress has been made in predictive modeling and data assimilation, comparing simulation states presents unique challenges, particularly for agent-based spatial Virtual-Tissue models. When working with scalar quantities like blood oxygenation, comparing measured and forecast values is straightforward. However, for Virtual-Tissue models, determining whether two simulation states derive from the same underlying model becomes complex. Implementation differences across software frameworks create substantial numerical variations (inter-simulation variability), while stochasticity within single implementations produces multiple potential phenotypes (intra-simulation variability). To address these reproducibility and interoperability challenges, I present three methodologies for determining simulation state equivalence despite phenotypic differences: 1) A neural-network image classifier that learns features of equivalent model configurations robust to both intra- and inter-simulation variability. This classifier also supports developing generative AI surrogates of mechanistic agent-based models for Medical Digital Twin applications. 2) AI/ML approaches to cluster and classify synthetic images generated by agent-based models of cell sorting and angiogenesis. 3) Leveraging the classification techniques to solve the inverse problem of inferring model parameters from images, enabling parameter identification in complex systems. The presentation concludes with proposed next steps for advancing these techniques in the Digital Twin ecosystem.

Organized by: Yangjin Kim (Brown University and Konkuk University), Magdalena Stolarska at University of St. Thomas

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

1. Donggu Lee Konkuk University
   "Asthma-mediated control of optic glioma growth via T cell-microglia interactions: Mathematical model"
Optic glioma, a slow-growing tumor, is associated with Neurofibromatosis type 1 (NF1) mutations and increased midkine (MDK) production. To elucidate the role of asthma in regulation of glioma formation, we investigated the role of T cells and the subsequent pathways in regulation of microglia, a key player in tumor microenvironment (TME). While asthma is often linked to chronic inflammation, our mathematical analysis and experimental evidence suggest that it can play a significant role in suppressing proliferation of optic glioma cells via immune reprogramming of T cells and delicate control of signaling network in microglia. The mathematical model unveil the complex interaction between brain tumor and immune cells in the brain. Our results indicate that asthma-induced T cell reprogramming inhibit tumor growth by promoting the release of Decorin, which leads to a chain of suppression of CCR8 and NFkB in microglia and CCL5 production. These findings highlight the potential of leveraging asthma-induced immune regulation as a novel mechanism for glioma suppression and demonstrate the power of mathematical modeling in uncovering complex tumor-immune interactions.
2. Ji Young Yoo University of Texas Health Science Center at Houston
   "Reshaping the Tumor Microenvironment by targeting IGF2-IGF1R signaling: Enhancing Viro-Immunotherapy"
The FDA approval of oncolytic herpes simplex-1 virus (oHSV) therapy for melanoma patients underscores its therapeutic promise as a cancer immunotherapy. However, despite this promise only a small subset of patients respond favorably in the clinic. Oncolytic virotherapies work both through direct oncolysis of infected cancer cells and by inducing of inflammatory response and concurrent activation of anti-tumor immunity through the release of tumor antigens from the lysed cancer cells, a phenomenon referred to as viro-immunotherapy. However, the induction of an immunosuppressive tumor microenvironment (TME) by the tumor both before and shortly after therapy poses the greatest hurdle to lasting efficacy and viro-immunotherapy. Our work centers on understanding the oHSV therapy resistance mechanism and characterizing the impact of viro-immunotherapy to design a better viro-immunotherapy to broaden their applications in the clinic. RNA-Seq analysis of oHSV-infected glioblastoma (GBM) and breast cancer (BC) cells identified Insulin-like growth factor 2 (IGF2) as one of the top 10 secreted proteins following infection. Moreover, IGF2 expression was significantly upregulated in 10 out of 14 recurrent GBM patients treated with oHSV, rQNestin34.5v.2 (71.4%) (p=0.0020) (ClinicalTrials.gov, NCT03152318), highlighting its clinical relevance. IGF2 is upregulated in tumor malignancies and its overexpression is associated with resistance to chemotherapy and radiation therapy, worse prognosis, anti-tumor immune suppression in the TME, and cancer metastasis. In order to mitigate oHSV therapy-induced IGF2 and improve the therapeutic efficacy of oHSV, we designed a novel oHSV construct, oHSV-D11mt, which integrates a secretable modified IGF2R domain 11 into the parental oHSV genome that serves as an IGF2 decoy receptor. The secreted IGF2RD11mt selectively binds to IGF2, effectively blocking oHSV-induced IGF2-IGF1R signaling, which lead to enhanced tumor cell cytotoxicity, reduced oHSV-induced neutrophils/PMN-MDSCs infiltration, reduced secretion of immunosuppressive/proangiogenic cytokines, and increased Cytotoxic T lymphocytes (CTLs) infiltration. These effects resulted in enhanced survival of both GBM and BC brain metastasis (BCBM) tumor-bearing mice, abrogating the resistance conferred by IGF2 secretion. Collectively, our findings suggest that oHSV-induced secreted IGF2 exerts a critical role in resistance to oHSV therapy and our novel viral construct represents a promising therapeutic for enhanced viro-immunotherapy.
3. Alexandra Shyntar University of Alberta
   "Mathematical Modelling of Microtube-Driven Regrowth of Glioma After Local Resection"
In this talk, I will first summarize the results from the paper Weil et al. (2017) “Tumor microtubes convey resistance to surgical lesions and chemotherapy in gliomas.” In this paper, they perform a series of experiments on glioblastoma tumors motivated by the discovery of tumor microtubes (TMs). TMs are thin long protrusions extending from the glioblastoma cell body which help the cancer grow, spread, and facilitate communication between glioma cells. Weil et al. (2017) implanted glioblastoma tumors into mice and tested various treatments (surgery, surgery with targeted therapy, surgery with anti-inflammation treatment, and chemotherapy). They find that TMs help with the faster and denser tumor repopulation in the lesion area. Furthermore, inhibiting the TMs slows down tumor growth significantly. In the second part of the talk, I will show how the experiments outlined in the paper can be modelled with partial differential equations. The effects from the wound healing response and TMs are simplified but are accounted for in the model. The numerical simulations reveal good agreement with the experimental observations and can capture the experimental trends after treatment application. Based on these results, the wound healing mechanisms as well as TM dynamics are key in explaining the experimental observations.
4. Sean Lawler The Warren Alpert Medical School, Brown University
   "Remodeling the Tumor Microenvironment to Facilitate Glioblastoma Therapy"
Glioblastoma(GBM) is the most common malignant brain tumor and is extremely challenging to treat effectively. Standard of care therapy involves surgical resection followed by radiation and alkylating chemotherapy. This results in a median survival of only 15 months, with resistance to therapy rapidly emerging. Immune checkpoint blockade has not yet shown efficacy in GBM. The GBM tumor microenvironment (TME)is complex and is thought to make a major contribution to resistance to both standard of care and immunotherapies. The GBM TME is characterized by infiltration of suppressive myeloid cells, and microglia, the presence of Tregs and lack of CD8+ T cells. In addition, tumor cells interact with neurons, astrocytes and with one another, subverting neurological signaling mechanisms to drive tumor progression and therapeutic resistance mechanisms. Understanding how to effectively modulate the GBM TME by targeting cellular interactions is essential to provide new therapeutic approaches. In this work, I will discuss approaches being developed in my lab to model these interactions, and the effects of immunomodulatory drugs on the TME that may facilitate responses to immune checkpoint blockade and other therapies, for improved outcomes in this devastating tumor type.

Organized by: Sungrim Seirin-Lee (Kyoto University/Graduate School of Medicine), Jaekyoung Kim (KAIST), So Miyoshi (Pfizer)

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

1. So Miyoshi Pfizer
   "Transforming Drug Research and Development: The Paradigm Shift Driven by Mathematical Models"
Mathematical modeling and simulation technologies are playing a critical role in revolutionizing pharmaceutical research and development. Model-Informed Drug Development (MIDD), built upon the foundations of pharmacometrics and quantitative systems pharmacology (QSP), has emerged as a powerful approach to streamline the drug development process. It enables quantitative decision-making for optimizing clinical trial designs, improving dosing strategies, and ultimately accelerating the delivery of new therapies to patients. In particular, the integration of mechanistic modeling through QSP has demonstrated its value in recent real-world applications. As the pharmaceutical industry embraces MIDD, the roles of pharmacometricians, clinical pharmacologists, and systems modelers are becoming increasingly prominent across academia, regulatory agencies, and industry. This evolution calls for deeper interdisciplinary collaboration, especially with the mathematical biology community, to address the complexity of human disease and treatment responses. In this presentation, I will provide an overview of the current landscape and future directions of MIDD, illustrating its impact with practical examples from pharmaceutical development. We can shape a future in which mathematical models serve as a bridge from data to decision, accelerating the creation of innovative therapies across various disease areas.
2. Nessy Tania Pfizer
   "Advancing Quantitative Systems Pharmacology Model for Inflammatory Bowel Disease for Clinical Efficacy Predictions in Ulcerative Colitis"
Inflammatory Bowel Disease (IBD) is a chronic autoimmune disease associated with gastrointestinal inflammation. While therapeutic options for the disease have expanded, patient response to these treatments can be highly variable. In this presentation, I will present a mechanistic Quantitative Systems Pharmacology Model for IBD that can be connected to clinical endpoint, specifically Mayo endoscopic score for Ulcerative Colitis. As a specific case study, the application of the model and virtual population simulations to predict the effect of a novel target combination (p40 and TL1A) will be discussed. In the future, the model can be further developed to account for additional mechanisms and utilized to predict biomarker response and efficacy for novel IBD therapies.
3. Eamonn Gaffney University of Oxford
   "Modelling immunological systems, as exemplified by Short Peptide Vaccinations Simulations for Immuno-oncology"
A prospective immunologically-based cancer treatment is multi-peptide vaccination, targeting multiple tumour-associated peptides. However, a recent multiple short-peptide vaccination trial for renal cell carcinoma failed to show benefit, with many patients responding to only one of the administered peptides. An in-silico model is considered to enable an exploration of the determinants of the initial immunological response following multiple short peptide vaccination, suggesting mechanisms for the observed lack of benefit in the recent clinical trial. These insights may also used to suggest possible improvements to the trial design and more generally illustrate one means by which in silico studies can be used to test and improve the design of clinical trials. Further recent work investigating the modelling of immunological systems will also be surveyed.
4. Brian Corrigan Metrum
   "Superconvergence: Charting the Course from Lab to Global Health Outcomes in Translational Clinical Sciences for the Next Decade"
The presentation will highlight the important roles that various disciplines in Translational Clinical Sciences will play in bringing new medicines to patients over the next decade. It will examine the convergence of advances in genetics, biotechnologies, and AI on medicines development, and how these changes will impact our roles throughout the research spectrum, from non-clinical to human, from patients to practice, and from practice to our impact on population health. It will highlight the impact of new data sources, analytic and decision-making techniques, and explore patient centric approaches to Medicine development that increase trial accessibility and broaden representation in our clinical trials.

Organized by: Veronica Ciocanel (Duke University), Hye-Won Kang, University of Maryland Baltimore County

1. Jae Kyoung Kim Korea Advanced Institute of Science & Technology
   "Advancing Static and Time-series data: Random Matrix Theory, Causal Inference and Mathematical Modeling"
In this talk, I will discuss methods for extracting meaningful information from static and time-series data. For static data, Principal Component Analysis (PCA) is widely used to detect signals in noisy datasets. However, determining the appropriate number of signals often relies on subjective judgment. I will introduce an approach based on random matrix theory to objectively select the optimal number of signals. For time-series data, causal inference techniques such as Granger causality are commonly employed. Unfortunately, these methods often yield high false-positive rates. I will present a novel mathematical model-based approach to causal inference.
2. Janet Best The Ohio State University
   "Energy Allocation and Sleep Homeostasis"
The upregulation of diverse functions, including memory consolidation and restorative processes, suggests sleep is a time for specialized energy use. While sleep was long considered an energy conservation strategy, the modest calculated savings led to skepticism that energy conservation is the function of sleep, particularly given sleep’s inherent costs in vulnerability. This talk will present a mathematical model based in an evolutionary perspective on the function and timing of sleep.
3. Punit Gandhi Virginia Commonwealth University
   "Using transformation information to characterize symmetry transitions"
Transformation information (TI) provides a versatile, entropy-based method for identifying approximate symmetries by quantifying deviations from exact symmetry with respect to a parametrized family of transformations. We define notions of approximate symmetry and maximal asymmetry in terms of critical points in TI as a function of a transformation parameter. This framework allows us to characterize transitions in symmetry by tracking qualitative changes with respect to these critical points. We apply TI to mathematical models inspired by developmental biology and actual biological images. Our analysis of the qualitative changes in symmetry properties indicates a potential pathway toward a general mathematical framework for characterizing symmetry transitions akin to bifurcation theory for dynamical systems.
4. Anastasios Matzavinos Pontifical Catholic University of Chile
   "Chemotaxis and Stochastic Gradient Ascent: Fractional Brownian Motion in Optimization and Biological Models"
Chemotaxis, the directed movement of cells and microorganisms in response to chemical signals, is a fundamental biological process. Modern modeling approaches often combine Brownian motion with gradient-driven motility, drawing parallels to stochastic gradient ascent algorithms used in optimization. At its core, chemotaxis can be viewed as a natural optimization process that steers cells toward regions of higher chemoattractant concentration. However, recent experimental findings challenge this classical view. In the absence of chemotactic cues, many cell types exhibit motility patterns that resemble fractional Brownian motion, with correlated increments that differ fundamentally from those of Brownian motion. This shift in perspective has important implications for how we understand and model cell migration. In this talk, we present computational evidence showing that cells with positively correlated movement patterns explore their environment more effectively and are better equipped to handle fluctuations in the chemotactic landscape. We also discuss the broader relevance of these findings in contexts such as tumor-induced angiogenesis and developmental processes. This work was supported in part by ANID FONDECYT Regular grant No. 1221220.