Organized by: Fabian Spill (University of Birmingham), Anotida Madzvamuse, University of British Columbia

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

1. Alex Grigas Syracuse University
   "Modeling fluidity in stellate mesenchymal tissues"
In many developmental and disease processes, tissues shift from solid-like to fluid-like mechanical behavior to enable large-scale tissue flows. A key unresolved question is how different organisms regulate this transition by controlling cell-scale properties. In both zebrafish and chick, a fluid-to-solid transition occurs in the presomitic mesoderm, the driving force behind posterior body axis elongation. In zebrafish, this transition is well explained by a soft particle model that undergoes a jamming/unjamming transition, driven by small changes in global volume fraction and active fluctuations, without considering cell shape or deformation. However, the tissue architecture in chick is distinct from zebrafish, with large extracellular gaps and stellate cells with distinct arm junctions, indicating that even closely related species may have evolved different mechanisms to cross a fluid/solid transition. Here, we develop a computational model to understand the essential features needed to predict the unique properties of low density, but highly connected, stellate tissues, which tissue rounding experiments demonstrate are fluid-like on long timescale. We compare short-time retraction velocities and tissue relaxation due to laser ablation between experiment and simulation to determine whether the mesenchyme is under tension. Additionally, we propose novel glassy dynamics can be controlled not via density changes but instead by cell-cell adhesion unbinding kinetics coupled with contact inhibition of locomotion, and propose new experiments to test these ideas.
2. Sharon Minsuk Indiana U., Bloomington
   "The Role of Embryo, Tissue, and Cell Shape in Morphogenesis: Modeling the Cellular Dynamics of Tissue Deformation"
Morphogenesis of embryonic tissues involves complex and extreme deformations in response to intra- and intercellular forces; and is profoundly dependent on the geometry not only of the deforming tissue itself, but of the environment in which that tissue finds itself. Epiboly in zebrafish, the spreading of an epithelial sheet in response to external tension, to cover and engulf the rest of the spherical embryo, requires deformation of a shallow spherical cap into a full sphere, without tearing or buckling, accommodated by cell rearrangement as well as cell shape change. We built a computational model of epiboly. Rearrangement of mechanically coupled model cells is achieved by allowing those couplings to dynamically break and re-form; broken couplings in a tissue under tension risk tearing, which we prevent by adding a constraint on cell packing geometry. The straightening of the leading edge of the expanding tissue, as observed in living embryos, arises emergently and robustly from our model, and is associated with rapid cell rearrangement (tissue fluidization). Changes in cell shape and packing geometry have been implicated in promoting fluidization, suggesting they may play a role in facilitating both tissue deformation and edge straightening. I will briefly describe and demonstrate the model, with special emphasis on the interplay between embryo, tissue, and cell geometry, and the dynamics of morphological transformation.
3. Margherita De Marzio Harvard Medical School and Brigham and Women’s Hospital
   "Understanding the Role of Surface Curvature on Epithelial Plasticity"
To heal, remodel, or invade, the epithelial tissue transitions from a state that is sedentary and quiescent to one that is strikingly migratory and dynamic. This phenotypic switch is known as the epithelial unjamming transition (UJT). Previous theoretical models have characterized the UJT in flat epithelial layers. By contrast, the epithelium in vivo often resides on highly curved structures like pulmonary alveoli, airways, and intestines. How surface curvature, and the resulting topological defects and out-of-plane forces, impact epithelial plasticity remains poorly understood. In this talk, I will present our recent findings on the role of geometry on the migratory phenotype in vivo. Using a 2D spherical vertex model, we investigated the UJT within physiological ranges of cell density and surface curvature. I will show that increasing curvature promotes tissue fluidization and migration. At higher curvatures, cell rearrangements become energetically advantageous, leading to cellular configurations that are more malleable and migratory. I will demonstrate that this effect is not due to changes in the local mechanism of cell intercalation, which is independent of curvature. Instead, it stems from changes in the global structure of the cell-junction network, which becomes less tensed as curvature increases. Together, these results reveal curvature-induced unjamming as a novel mechanism of epithelial fluidization, offering insights into how surface geometry drives tissue malleability, remodeling, and stabilization.
4. Padmini Rangamani UCSD
   "Nanoscale curvature of the plasma membrane regulates mechanoadaptation through nuclear deformation and rupture"
Nuclear translocation of the transcription regulatory proteins YAP and TAZ is a critical readout of cellular mechanotransduction. Recent experiments have demonstrated that cells on substrates with well-defined nanotopographies demonstrate mechanoadaptation through a multitude of effects - increased integrin endocytosis as a function of nanopillar curvature, increased local actin assembly on nanopillars but decreased global cytoskeletal stiffness, and enhanced nuclear deformation. How do cells respond to local nanotopo-graphical cues and integrate their responses across multiple length scales? This question is addressed using a biophysical model that incorporates plasma membrane (PM) curvature-dependent endocytosis, PM curvature-sensitive actin assembly, and stretch-induced opening of nuclear pore complexes (NPCs) in the nuclear envelope (NE). This model recapitulates lower levels of global cytoskeletal assembly on nanopillar substrates, which can be partially compensated for by local actin assembly and NE indentation, leading to enhanced YAP/TAZ transport through stretched NPCs. Using cell shapes informed by electron micrographs and fluorescence images, the model predicts lamin A and F-actin localization around nanopillars, in good agreement with experimental measurements. Finally, simulations predict nuclear accumulation of YAP/TAZ following rupture of the NE and this is validated by experiments. Overall, this study indicates that nanotopography tunes mechanoadaptation through both positive and negative feedback on mechanotransduction.

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

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

1. Calina Copos Northeastern University
   "Migration modes of small cell groups: which forces govern their emergent movement?"
Collective cell migration is essential to many physiological and pathological processes, yet its classification remains incomplete. Focusing on cohesive cell pairs migrating on flat substrates, we identified two motility modes: the individual contributor (IC) mode, where each cell generates its own traction force dipole, and the supracellular (S) mode, characterized by a single dipole across the pair. Amoeboid Dictyostelium discoideum (Dd) cells predominantly adopt the IC mode, while mesenchymal Madin-Darby canine kidney (MDCK) cells favor the S mode. A two-dimensional biophysical model incorporating cell-cell and cell-matrix adhesions, along with boundary contractility, recapitulated these patterns. The IC mode emerged in Dd-like cells with balanced traction, whereas S mode dominated in asymmetric or MDCK-like pairs, often driven from the rear. Increasing cell-matrix adhesion promoted the IC mode in amoeboid chains but favored the S mode in MDCK-like cells. The model, extended to longer chains, offers a novel theoretical framework to study diverse collective migration behaviors.
2. Yuehui Xu Indiana University Indianapolis
   "A 3D Viscoelastic Model of Cell Migration with Mechanical and Adhesive Forces"
Gaining a deeper understanding of cell migration can aid in the development of treatments for a wide range of diseases in which it plays a major role, including infection and cancer. To investigate the mechanisms of cell migration and identify key factors that influence migratory behavior, we developed a three-dimensional mathematical model of an HEK 293 cell migrating unidirectionally on a flat substrate. The cell is represented as a network of viscoelastic elements, while focal adhesions are modeled as points on the cell membrane that connect to the substrate using elastic fibers. The model includes forward pushing forces that are typically generated by actin filaments and cause the cell to protrude in the migratory direction. It also includes an internal interconnected set of elements that represent the internal cell structure. We share how our approach is capable of producing results that agree qualitatively with experiment and vary simulation parameters to examine how the cell responds to changes in membrane stiffness, substrate stiffness, internal element elasticity, the number of focal adhesions, and frictional forces. Results suggest the model can be used to consider more physiologically relevant questions in the future such as the effects of different component properties on overall cell migration and forces.
3. John Dallon Brigham Young University
   "Modeling differential cell motion in the Dictyostelium discoideum slug"
Differential cell motion plays an important role in the front to back pattern formed during the slug stage of the organism Dictyostelium discoideum (Dd). The slug has at least two cell types: prespore cells and prestalk cells. As the slug moves the prestalk cells aggregate to the front of the moving slug while the prespore cells aggregate to the rear. In this talk I will discuss a force based mathematical model where cells attach and detach to one another via discrete adhesions with stochastic dynamics. Using simulations, different strategies that cells could employ are explored which cause differential cell motion leading to this front to back pattern.
4. David Odde University of Minnesota
   "Modeling the mechanics of glioblastoma progression and treatment."
Effector CD8+ T cells must make cell-to-cell contacts (TCR-MHC-antigenic-peptide-complex) to identify and eliminate cancer cells selectively. This requirement could become a make-or-break factor in the clearance of solid tumors such as glioma, which we focus on in this study, where T cells have to actively search for the cancer cells in the tissue. Several immunotherapies, such as checkpoint blockade and adoptive T cell therapy, have been proposed; however, all of these essentially aim to make T cells better killers, not migrators. In this study, we recognize an equally important factor crucial for their success, i.e., their migration in the tissue. T cells have been assumed to be optimal navigators based on evolutionary reasons, an idea we challenge in this study. Using a combination of ex vivo brain tissue and in vitro assays, we found that T cells, on average, migrated slower than reported in the literature (0.5-2 μm/min, 0.1-1 μm/min vs 6-10 μm/min, 10-30 μm/min) and only modestly faster than cancer cells in a similar setting (0.1-0.2 μm/min), suggesting the need for improvement for effective immune response and immunotherapy. Strikingly, for T cells, the best description was not a single, homogeneous population of superdiffusive walks as previously found but a mix of comparable numbers of sub, normal, and superdiffusive walks, especially at longer time scales. This heterogeneity is advantageous for finding targets of a range of sizes but worse than the single superdiffusive population for finding a fixed target such as a glioma. We investigated the reason for such slow migration. Our T cells, consistent with previous studies, showed evidence of a 'stop-and-go' pattern. We found that hyper adhesive interactions with the perivascular space of blood vessels, the entry point of T cells into the brain, microglia, a major antigen-presenting cell in the brain, and hyaluronic acid, a major ECM protein in the brain, all could explain many, but not all, of the 'stops”. Reducing these 'stops' could increase net T cell migration, potentially an improvement enough to stop the inevitable GBM recurrence under current standard therapy regimens. Next, we used drug-perturbation experiments and high-resolution imaging to unravel the biomechanics of CD8+ T cell migration. We discovered that these T cells are capable of using multiple modes, highlighting their adaptive nature, but often use the familiar motor-clutch mode of cell migration usually reported for cancer cells, but with altered, faster protrusion and focal-adhesion dynamics. To capture these dynamics we developed a momentum-conserving model for hybrid bleb-adhesion-based rapid T cell migration. Together, these results advance our fundamental understanding of T cell migration in the brain, which may inspire better immunotherapies in the future that are focused on making T cells both powerful killers and adept at rapidly locating target cancer cells.

Organized by: Valeria GIunta (Swansea University), Yurij Salmaniw - University of Oxford

1. Raluca Eftimie Université de Franche-Comté, France
   "Mathematical models for non-local cell-cell interactions in health and disease"
Non-local cell-cell interactions via long cellular protrusions seem to be more and more prevalent in cell biology: from airineme-mediated inter-cellular communication between different skin cells in zebrafish, to cytoneme-mediated cell-cell interactions between keratinocytes in epidermal remodelling, and even tunnelling nanotubes-mediated interactions between cancer cells and surrounding non-tumour cells. In this talk, we will present a class of non-local mathematical models developed to investigate normal and abnormal wound healing processes such as keloids (these abnormal processes lead to tissue overgrowth, remodelling and invasion similar to those observed in benign tumours). The models account for non-local cell-cell and cell-matrix interactions via different signalling molecules as well as long-distance cell protrusions. We will discuss various analytical and numerical aspects associated with these non-local models from the perspective of biological applications.
2. Junping Shi College of William & Mary, Williamsburg, USA
   "Biological Aggregations from Spatial Memory and Nonlocal Advection"
We present a nonlocal single-species reaction-diffusion-advection model that integrates the spatial memory of previously visited locations and nonlocal detection in space, resulting in a coupled PDE-ODE system reflective of several existing models found in spatial ecology. We prove the existence and uniqueness of a Hölder continuous weak solution in one spatial dimension under some general conditions, allowing for discontinuous kernels such as the top-hat detection kernel. A robust spectral and bifurcation analysis is also performed, providing the rigorous analytical study not yet found in the existing literature. In particular, the essential spectrum is shown to be entirely negative, and we classify the nature of the bifurcation near the critical values obtained via a linear stability analysis.
3. Sara Bernardi Politecnico di Torino, Italy
   "Variations in nonlocal interaction range lead to emergent chase-and-run in heterogeneous populations"
In a chase-and-run dynamic, the interaction between two individuals is such that one moves towards the other (the chaser), while the other moves away (the runner). This interaction is observed in various biological systems, including cells and animals. In this talk, I will explore the behaviors that can emerge at the population level in a heterogeneous group containing subpopulations of chasers and runners. A wide variety of patterns can form, ranging from stationary patterns to oscillatory and population-level chase-and-run, with the latter describing a synchronized collective movement of the two populations. A key aspect of our study is the role of interaction ranges—the distances over which cells or organisms can sense one another’s presence. I will show that robust population-level chase-and-run emerges when the interaction range of the chaser is sufficiently larger than that of the runner. Our findings are contextualized with examples from cellular dynamics, specifically neural crest and placode cell populations, and offer insights into similar phenomena observed in ecological systems. This talk will aim to provide a deeper understanding of chase-and-run dynamics within nonlocal advection-diffusion models and contribute to the broader understanding of how simple individual interactions can lead to complex, coordinated behaviors at the population level.
4. Jun Jewell University of Oxford, UK
   "Long-ranged interactions shape populations and patterns in biology"
Movement shapes how populations distribute across space and evolve over time. Across biological scales, individuals move in response to interactions that are often long-ranged (nonlocal). Animals use scent cues to establish territorial boundaries, predators pursue prey based on sight or sound, and cells can aggregate by extending pseudopodia toward distant neighbours. We explore these processes using nonlocal advection-diffusion models, analysing their bifurcations to gain insight into emergent spatial and temporal dynamics. A key result is that, unlike in local models (e.g. Fickian diffusion), Turing bifurcations in these nonlocal systems fundamentally depend on spatial dimension. For example, purely repulsive interactions cannot generate spatial patterns in one spatial dimension, but can in two. Additionally, even simple interactions, such as attraction and logistic growth within a single species, can produce spatio-temporal oscillations that exhibit signs of chaos. This provides an example of spatio-temporal complexity of relevance to ongoing debates on how common chaos is in ecosystems. We also explore more complex mechanisms like chiral movement, which is often exhibited by cells and also used by prey to evade predators. We show how it can suppress oscillations, and instead promote stationary patterns. Finally, we highlight cautionary cases where linear stability analysis fails to predict long-term behaviour, including populations with a Turing instability that forms patterns only transiently before collapsing to extinction. These results emphasise the need for analytical tools which go beyond local linear stability analyses in order to understand complex biological systems in the long-term.

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

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

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

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

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

1. Sarah Machado-Marques York University
   "Considering the effects of pair formation dynamics on mpox and HIV co-infection in the gbMSM community"
There is a growing need to explicitly consider how behaviour plays a role in the spread of diseases transmitted through close, prolonged contact. In particular, the duration individuals spend single or in relationships has yet to be incorporated into co-infection models, potentially underestimating the protective effects of stable partnerships. We propose an mpox and HIV co-infection model that explicitly incorporates the formation of pairs between individuals. We demonstrate that considering pair formation and dissolution rates are critical in determining outbreak potential and severity. These considerations remain important beyond the initial stages of the outbreak and can lead to more accurate predictions. Our work highlights that the particular pairing context and serological status of the population should always be carefully considered prior to intervention on behavioural patterns.
2. Bridgette Amoako University of Guelph
   "Sexual Behaviour and Mpox Transmission in an Agent Based Model"
We present an agent-based framework that uses a dynamic signaling game to model mpox transmission in a population of gay and bisexual men who have sex with men (gbMSM). The model focuses on how agents' beliefs about a partner’s infection status influence their decisions to pursue or abstain from casual sexual encounters. Agents are subject to mpox’s typical incubation and infectious periods and become immune upon recovery. Immunity could also be obtained through vaccination. Within this framework, each agent updates their risk perception based on both individual encounters and broader disease prevalence, then selects a strategic response to potential partners. By examining how risk perception interacts with behavior, as well as how the timing and efficacy of vaccination factor into disease spread, we aim to provide insights into which conditions are most conducive to large outbreaks or successful containment. This approach highlights the role of dynamic, feedback-driven behaviors in shaping the course of mpox epidemics and can help inform strategies for more effective vaccination and public health interventions.
3. Pouria Ramazi Brock University
   "Enough but Not Too Many: Modeling and Dynamics of Bi-Threshold Social Behavior"
This work introduces and analyzes bi-threshold models of social behavior, where individuals adopt actions only when their prevalence lies between a personal lower and upper threshold. This captures real-world dynamics like fashion trends or information spread, where behaviors are appealing when moderately common but lose appeal if too rare or too widespread. Theoretical results show that while small populations may exhibit persistent fluctuations, these fade as population size increases, with dynamics converging to equilibrium. Empirical validation on social media data confirms the model’s superior predictive power over traditional threshold models, particularly in capturing phases of behavioral decline.
4. Clark KendrickGo Ateneo de Manila University
   "Exploring Mathematical Techniques in Collective Behaviour and Decision Making in Animal Groups"
Collective behaviour in animal groups are coordinated movements and interactions among members that aim to achieve a common goal. Whether these goals are for allocation of resources or defence from predators, the collective behaviour appears to be largely a group activity initiated by a member, known as the leader. In the absence of high-resolution spatio-temporal data, various qualitative studies offer a glimpse of how leader-follower interactions take place. For example, Nagy, et al., studied the average delay in response when pigeons change the direction inflight. Next, Bourjade, et al., studied the first mover and the succeeding order of movements of Przewalski's Horses. Furthermore, various studies on the collective motion in the animal kingdom offer mathematical models and infer how the interactions and decision making take place. Important questions arise during an event of coordinated motion in animals. During such an event, do individuals move according to a certain set of natural rules? Or certain patterns form due to the influence of a leader? How is this influence measured? Finally, how is influence transferred to other members of the group? In this study, we discuss the role of information theory to quantitatively uncover leader-follower relationship in a horse group. Specifically, we introduce concepts from information theory, specifically global and local transfer entropy being applied to a harem of horses. We will discuss their definitions, and how these key concepts are used to support causation in events. We will then discuss some important implications on how this technique can be used to analyse collective motion where data is scarce.

Organized by: Abba Gumel (University of Maryland), Alex Safsten, Arnaja Mitra (both University of Maryland)

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

1. Katharine Gurski Howard University
   "Building a Model for Seasonal Malaria Chemoprevention and Drug Resistance"
Seasonal malaria chemoprevention has been shown to cut clinical malaria episodes by up to 75% in high-risk areas. However, when chemoprevention is given in an area with drug-resistant parasites, there is a risk of the long-term growth of drug-resistance outweighing the benefits of the immediate reduction in deaths of children. We aim to study this situation with data driven pharmacokinetics and pharmacodynamics, experimental data on gametocyte growth within an infected human, gametocyte decay within a treated human, and the probabilities of infecting a mosquito who bites either an infected or treated human by modeling gametocyte transmission. We formulate a model by considering arbitrarily distributed sojourn for various disease stages and chemoprevention. We consider the lessened effectiveness of treatment on drug-resistant parasites. With the use of gamma distributions fit to data, the system can be reduced to a system of ODEs, with non-trivial characteristics which are only captured by non-exponential distributions for disease stages and susceptibility.
2. Yves Dumont French Agricultural Research Centre for International Development
   "Reducing nuisances or minimizing epidemiological risks: which is the best choice with the Sterile Insect Technique?"
The sterile Insect Technique (SIT) is a promising biological control method against vectors of human diseases, like mosquitoes. SIT can be used either to reduce the nuisance (mosquito bites), or to reduce the epidemiological risk. Depending on the objective, the releases strategy is not the same. Since SIT is an autocidal method, it takes time to notice any effect. Reducing nuisances requires a significant reduction in the wild mosquito population. This generally requires mass releases and, consequently, the production of large numbers of sterile mosquitoes, and, time. When SIT is used to reduce the epidemiological risk, it is preferable to release sterile males only because sterile females may transmit viruses during blood meal on humans. Even if sexing methods have become increasingly efficient, allowing males to be separated from females, it is important to estimate the maximum number of sterile females per release, without, however, increasing the epidemiological risk. In this presentation, I will present some theoretical results and illustrate some of them with numerical simulations in order to discuss the best strategies depending on whether we want to reduce the nuisance, or the epidemiological risk, with SIT.
3. Alex Safsten University of Maryland
   "Leveraging inter-species competition to improve the effectiveness of the sterile insect technique"
Mosquitos top the list of the deadliest animals in the world due to the diseases they carry and transmit to humans, including malaria, West Nile virus, and dengue, with malaria being the most important vector-borne disease of mankind. Existing methods of mosquito control heavily rely on using chemical insecticides to kill them. Unfortunately, however, in the context of malaria for instance, the heavy and widespread use of these insecticides in endemic areas has resulted in widespread resistance to all the chemical compounds currently used in vector control. This necessitates the use of alternative methods for vector control. The sterile insect technique (SIT), which entails the periodic mass release of sterilized male mosquitoes into an environment where adult female mosquitoes are abundant, is one of the main promising approaches being proposed. The eggs laid by females that mated with sterile male mosquitoes will not hatch, thereby potentially reducing the population of the wild mosquitoes in the environment. I will present an ODE model of SIT and several strategies eliminating disease-carrying mosquitoes including using optimal and feedback control for adjusting the rate of release of sterile males as the wild population is reduced and leveraging interspecies competition from less-harmful species. I will also present a PDE model of SIT which demonstrates the spatio-temporal dynamics of SIT and allows for the development of strategies for, e.g., inducing counter invasions of non-disease-carrying mosquitoes that have recently been pushed out of their historical ranges by their disease-carrying cousins.
4. Zhuolin Qu University of Texas San Antonio
   "Multistage spatial model for informing release of Wolbachia-infected mosquitoes as disease control"
Malaria remains one of the leading causes of infectious disease mortality worldwide, disproportionately affecting young children and vulnerable populations. Its transmission is shaped by complex interactions between host immunity, vector dynamics, and environmental seasonality. In this talk, I will introduce a mathematical modeling framework that captures age-structured malaria transmission in both year-round and highly seasonal settings. The model integrates the development of immunity through repeated exposure and accounts for the nonlinear feedback between immunity and disease spread. I will discuss how we use this framework to explore the timing and design of vaccination strategies, particularly in environments with strong seasonal variation. Along the way, I’ll highlight key modeling challenges, share insights from our sensitivity analysis, and reflect on how mathematical tools can inform more effective and context-specific malaria control interventions.

Organized by: Yun Kang (Arizona State University), Tao Feng, Yangzhou University & University of Alberta

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

1. Matthew Wheeler University of Florida
   "Linking Network Architecture to Dynamic Behavior"
Modularity is a key feature of biological systems that is well accepted and studied in biology. However, from a mathematical standpoint, it remains poorly defined. In previous work, we developed a decomposition theory based on feedback loops, linking network structure to the organization of its dynamics. We went on to propose that an appropriate definition for a module of a network are the irreducible objects of this decomposition theory.  In this talk, we present a categorical framework for dynamical systems that significantly broadens the scope of our original approach. This generalization extends the decomposition theory to a wider class of systems, providing deeper insight into the structure-dynamics relationship and offering powerful new tools for analyzing complex biological networks.
2. Xingfu Zou University of Western Ontario
   "Infection forces mediated by behaviour changes with demonstration by a DDE  model"
In this talk, we will revisit the notion of infection force from a new angle which can offer a new perspective to motivate and justify some infection force functions. Our approach not only can explain  many existing infection force functions in the literature, it can also motivate new forms of infection force functions, particularly infection forces depending on disease surveillance of the past. As a demonstration, we propose an SIRS model with delay. We comprehensively investigate the disease dynamics represented by this model, particularly focusing on the local bifurcation caused by the delay and another parameter that reflects the weight of the past epidemics in the infection force.  We confirm Hopf bifurcations both theoretically and numerically. The results show that depending on how recent the disease surveillance data are, their assigned weight may have a different impact on disease control measures.
3. Daniel B. Reeves Fred Hutchinson Cancer Center
   "Modeling HIV reservoir ecology and selection through the lens of CD4+ T cell kinetics"
The latent reservoir of HIV persists for decades in people living with HIV (PWH) on antiretroviral therapy (ART). To determine if persistence arises simply from natural behaviors of CD4+ T cells harboring HIV proviruses, we use ecological models to contrast the clonal dynamics of HIV vs memory CD4+ T cell sequences from the same PWH. We show HIV reservoirs are more clonal than general CD4+ T cells and that increasing reservoir clonality over time with decay of intact proviruses cannot be explained by CD4+ T cell kinetics alone. We develop a stochastic multitype branching process model that describes the dynamics of CD4+ T cells, some of which harbor HIV proviruses. We test nearly 1000 combinations of model mechanisms against a broad range of experimental observations, finding that weak selection against intact proviruses (s~0.06) is a parsimonious explanation for all data. These results help to understand the long-term dynamics of HIV reservoirs in PWH on ART and may inform immunotherapies for HIV cure.
4. Tianxu Wang University of Alberta
   "Cognitive Movement Strategies in a Food-Threat Dilemma"
Dilemmas involving trade-offs between acquiring food and avoiding threats are unavoidable in animals' daily lives. To enhance survival, they must continually adjust their movement in response to environmental cues, while internal hunger levels also strongly influence their decision-making processes. In this study, we develop a general coupled PDE–ODE model that integrates movement strategies driven by internal hunger dynamics. Even in the same environment, different species or even individuals may prioritize different types of information. We investigate how various cognitive movement strategies affect species survival and extinction outcomes in simplified food–threat dilemmas. Specifically, we compare five commonly observed strategies using local, nonlocal maximum, global maximum, nonlocal aggregate, and global aggregate information. We prove the well-posedness of the model, where a general function of ODE variable appears in the taxis sensitivity function in PDE equation, and conduct stability and bifurcation analyses around arbitrary equilibria. These results are then used to determine survival and extinction conditions under each strategy. Numerical simulations show that the effectiveness of movement strategies highly depends on environmental context. The global maximum strategy enables survival with minimal foraging and threat avoidance effort in simple dilemmas, but in complex multimodal settings, the local strategy achieves the highest survival, while the global maximum performs worst.

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

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

1. Ruth J Williams University of California San Diego
   "Stochastic Analysis of Markov Chain Models for Chromatin Dynamics"
Stochastic dynamics and time-scale differences between establishment and erasure processes in chromatin modifications (such as histone modifications and DNA methylation) have been seen in simulations to have a critical effect on maintaining and switching cell types through generations of cell division. It has been further observed that cross-catalysis between repressive histone modifications and DNA methylation can quickly silence a gene, and protein-mediated positive autoregulation can alleviate this silencing. In this talk, we provide a rigorous mathematical framework to validate, explain and extend these insights. We introduce stochastic models of chromatin modification circuits as singularly perturbed, continuous-time Markov chains with a small parameter epsilon capturing the time scale separation. We characterize the limiting stationary distribution as epsilon goes to zero in terms of a reduced Markov chain. We also show that protein-mediated positive autoregulation can monotonically alleviate cross-catalytic silencing caused by two types of repressive modifications. The theoretical tools developed not only provide a solid mathematical foundation for previous computational and experimental findings, emphasizing the role of chromatin modification dynamics and protein-mediated autoregulation, but also have broader applications to singularly perturbed continuous time Markov chains, particularly those associated with chemical reaction networks. Based on joint work with S. Bruno, Felipe Campos, D. Del Vecchio, Y. Fu.
2. Grzegorz Rempala Ohio State University
   "Likelihood Functions for Individual-Level Chemical Reaction Models"
When analyzing chemical reaction systems, it is often valuable to track the behavior of individual molecules over time. In such settings, one can construct an individual-level likelihood function—a statistical tool that quantifies how well a specific parametric reaction model explains observed data. Such likelihood functions are particularly useful when applied to time series data that capture the trajectories of chemical reaction networks. In this talk, I will introduce the concept of individual-level likelihoods, highlight their key applications, and discuss practical approximations, especially in the context of mass-transfer models. A central example will be the stochastic SIR model, though similar constructions apply more broadly across biological and chemical systems.
3. Mark Flegg Monash University
   "Stochastic Simulation of Reaction Networks with Well-Mixed Clustered Agents"
In this talk we will explore the suitability of simulating reaction networks at the level of local clusters rather than individuals for improvements in efficiency and reduction of complexity. This approach asserts an approximation with how a local cluster of reactants evolve and specifically how the components of this cluster interact with the larger network. We explore the method in the context of simple population models of a disease. Here, clusters represent the efficient disease interactions within households and approximations are made with how individuals of a household contribute to spreading the disease in the rest of the community. In biochemical systems a cluster is more complicated and constitutes efficient molecular mechanisms of multiple components embedded in a larger chemical network.
4. Hye-Won Kang University of Maryland Baltimore County
   "Multiscale Approximation and Parameter Estimation in Stochastic Models of the Glycolytic Pathway"
In this talk, I will introduce a glycolytic pathway that includes multiple enzyme-catalyzed reactions. We focus on the part involving the phosphofructokinase (PFK) reaction as a case study in stochastic modeling. Using model reduction techniques, we show how to derive a simplified model and use it to estimate parameters from partially observed data. Previous studies modeled this pathway deterministically and employed a quasi-steady-state approximation to reduce its complexity. In contrast, we assume that some enzymes are present in low copy numbers and thus adopt a continuous-time Markov chain framework to capture stochastic effects. To further reduce network complexity, we apply a multiscale approximation method and derive a reduced ODE model that describes the system's behavior on a slow timescale. The reduced model focuses on two key species: fructose-6-phosphate (F6P) and adenosine diphosphate (ADP). It not only captures the essential dynamics of the full network but also provides insights into key parameters. The equations in the reduced model contain fewer parameters—expressed as functions of those in the full model--which facilitates more tractable parameter estimation. Assuming that only the reduced species are observable, we generate synthetic data from the full model and use it to estimate the parameters in the reduced model. This approach demonstrates how time-series data from a subset of species can enable effective estimation of composite parameters in a reduced system. This is joint work with Arnab Ganguly.

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

Note: this minisymposia has multiple sessions. The other sessions are: Part-1, Part-2, and Part-4.

1. David Bortz CU Boulder
   "Weak form Scientific Machine Learning"
The creation and inference of mathematical models is central to modern scientific discovery in the life sciences. As more realism is demanded of models, however, the conventional framework of biology-guided model proposal, discretization, parameter estimation, and model refinement becomes unwieldy, expensive, and computationally daunting. Recent advances in Weak form-based Scientific Machine Learning (WSciML) allow for the creation and inference of interpretable models directly from data via advanced numerical functional analysis, computational statistics, and numerical linear algebra techniques. This class of methods completely bypasses the need for forward-solve numerical discretizations and yields both parsimonious mathematical models and efficient parameter estimates. These methods are orders of magnitude faster and more accurate than traditional approaches and far more robust to the high noise levels common to data in the biological sciences. The combination of these features in a single framework provides a compelling alternative to both traditional modeling approaches as well as modern black-box neural networks. In this talk, I will present our weak form approach, describing our equation learning (WSINDy) and parameter estimation (WENDy) algorithms. I will demonstrate these performance properties via applications to several canonical problems in structured population modeling, cell migration, and mathematical epidemiology.
2. Alasdair Hastewell NITMB
   "Discovering dynamical models from partial biological observations with degeneracy-robust algorithms."
Complex multi-component biological, chemical, and physical systems are often only partially observable despite rapid advancements in experimental measurement techniques. Discovering predictive nonlinear dynamical models and their parameters directly from incomplete, noisy experimental observations is essential for understanding such systems. A key challenge introduced by partial observations is model degeneracy, where multiple structurally distinct models match observed variables. By mapping a large class of partially observed ordinary differential equations to a simpler model form and using differential equations sensitivity analysis, we can account for symmetries that lead to degenerate models and efficiently use the resulting structure to develop a dynamical systems inference framework that is robust both to noise and partial observations. After validating our method on the FitzHigh-Nagumo oscillator and a model of cell signaling, we will demonstrate the framework’s broad applicability to various experimental datasets from biological oscillators to animal locomotion.
3. Xiaojun Wu University of Southern California
   "Data-driven model discovery and model selection for noisy biological systems"
Biological systems exhibit complex dynamics that differential equations can often adeptly represent. Ordinary differential equation models are widespread; until recently their construction has required extensive prior knowledge of the system. Machine learning methods offer alternative means of model construction: differential equation models can be learnt from data via model discovery using sparse identification of nonlinear dynamics (SINDy). However, SINDy struggles with realistic levels of biological noise and is limited in its ability to incorporate prior knowledge of the system. We propose a data-driven framework for model discovery and model selection using hybrid dynamical systems: partial models containing missing terms. Neural networks are used to approximate the unknown dynamics of a system, enabling the denoising of the data while simultaneously learning the latent dynamics. Simulations from the fitted neural network are then used to infer models using sparse regression. We show, via model selection, that model discovery using hybrid dynamical systems outperforms alternative approaches. We find it possible to infer models correctly up to high levels of biological noise of different types. We demonstrate the potential to learn models from sparse, noisy data in application to a canonical cell state transition using data derived from single-cell transcriptomics. Overall, this approach provides a practical framework for model discovery in biology in cases where data are noisy and sparse, of particular utility when the underlying biological mechanisms are partially but incompletely known.
4. Nora Heitzman-Breen CU Boulder
   "Weak-form parameter inference of epidemiological systems"
Compartmental modeling of epidemiological systems provides insights into biological and behavioral interactions that cannot be observed directly. Accurate and robust parameter estimation is critical to applying such models as decision-making tools for public health interventions. A common approach to parameter estimation is nonlinear least squares using a forward solver. However, as model size and noise in data increase these methods become computationally expensive to maintain accuracy in parameter estimates. In this work, we establish a weak-form based method of parameter estimation for systems with unobserved variables by generating weak-form input-output equations using differential algebra techniques. We show that practical identifiability of the weak-form system can be assessed more quickly in comparison to output error based parameter estimation using criteria informed by the additive error in observed variables. Finally, we demonstrate that using a weak-form based method to estimate transmission rates is computationally efficient and robust to noise, even for complex variations of the classical SIR model.

Organized by: Maria Kleshnina (Queensland University of Technology), Mason Lacy (Queensland University of Technology), Luke Filippini (Queensland University of Technology)

1. Luke Filippini Queensland University of Technology
   "Data-informed stochastic frameworks of anisotropic movement in the brain"
Neurological diseases and disorders are the subject of an extensive area of research that is of significant importance to the scientific community and wider population. Notable examples include autism, multiple sclerosis, and nervous system cancers, such as glioblastoma, which currently remain incurable. This is primarily due to the structural complexity of the nervous system and the impracticalities of surgical examination and/or resection. Hence, indirect methods, such as magnetic resonance imaging and mathematical modelling, are frequently relied upon to yield meaningful insight into the physiological processes that drive disease progression. In this talk, we discuss methods for deriving data-informed stochastic models from deterministic frameworks of anisotropic particle diffusion, motivated by applications to neurological diseases and disorders. We consider on-lattice stochastic models derived from a finite volume discretisation of the diffusion equation coupled with diffusion tensor imaging data. Furthermore, we discuss the limitations of using a traditional square or rectangular lattice, in terms of obtaining non-negative transition probabilities, and present a more promising approach using a hexagonal lattice. Most notably, the latter approach yields non-negative transition probabilities for any valid diffusion tensor.
2. Moriah Echlin Tampere University
   "Using Single-Cell Data-driven Boolean Network Models to Analyze Prostate Cancer Progression"
Cancer is a multifaceted disease, with many unique drivers; yet all cancers have a common foundation – the abnormal and malignant behavior of the body’s cells. Broadly, cellular behaviors result from the dynamics of the gene regulatory network (GRN) and genetic mutations can force the GRN into irregular dynamics. Thus, cells can exhibit the pathological properties associated with cancer: unchecked growth, immune evasion, and metastasis. To understand the origins and ramifications of malignant changes to the GRN, we combine clinically relevant single-cell transcriptomic data with a dynamical systems theoretical framework. This approach takes advantage of the system-wide gene correlations and cell state heterogeneity captured in single-cell ‘omics and the temporal and functional structure provided by dynamical systems models. Specifically, we use a Boolean network architecture to convert distinct cellular profiles to dynamical states. Our work focuses on the conversion of single-cell transcriptomic data to informative Boolean states and their subsequent analysis with the aim of identifying disease-relevant genes, inter-gene dependencies, and cell state dynamics that would not be evident in the original unstructured data. In particular, we highlight changes to the cell state structure that occur as cancer progresses from a primary indolent tumor to metastatic treatment-resistant disease.
3. Louise Spekking TU Delft
   "Improving cancer therapy through migrastatics and estimating tumor composition"
Adaptive therapy, which anticipates and forestalls the evolution of resistance in cancer cells, has gained significant traction, especially following the success of the Zhang et al.'s protocol in treating metastatic castrate-resistant prostate cancer. While several adaptive therapies have now advanced to clinical trials, none currently incorporates migrastatics, i.e. treatments designed to inhibit cancer cell metastasis. In this study, we propose the integration of migrastatics into adaptive therapy protocols and an evaluation of the potential benefits of employing a game-theoretic spatial model. Our results demonstrate that the combination of adaptive therapy with migrastatics effectively delays the onset of metastasis and reduces both the number and size of metastases across the majority of cancer scenarios . This approach not only extends the time to the first metastasis but also enhances the overall efficacy of adaptive therapies. Our findings suggest a promising new direction for cancer treatment, where adaptive therapy, in conjunction with migrastatic agents, can target both the evolution of resistance and the metastatic spread of cancer cells. In treatment of cancers, success strongly depends on our ability to capture how the disease evolves in response to treatment, both in terms of the size and composition. Understanding the changes in these compositions and the composition of the tumor will aid in developing new therapies in the future. In the second part of the talk, we will assess different machine learning methods on the deconvolution of cells based on microarray RNA sequencing data of glioblastoma organoids. Here, we show that the proportion of cell types changes over time with treatment and that these changes differ between organoids. We believe that this methodology can help in designing better therapies through testing evolutionary responses in patient-derived organoids, while in parallel the ecological response can be tracked through serum biomarkers and imaging in the corresponding patients. This will improve the adoption of adaptive therapies in clinical practice. Joint work with Jan Brábek, Joel S. Brown, Rachel Cavill, Robert A. Gatenby, Christopher Hubert, Weronika Jung, Christer Lohk, Barbora Peltanová, Daniel Rösel, Katharina Schneider, Maikel Verduin, Marc Vooijs, Sepinoud Azimi and Kateřina Staňková.
4. Noa Levi University of Melbourne
   "Leveraging algebraic approaches to inform therapeutic intervention"
The propensity for biological systems to exhibit adaptation is thought to play an important role in many treatment failures, especially in the context of cancer, since the underlying signalling networks under which cancer thrives are frequently able to adapt to the therapy. Here we present a general mathematical framework to study the effect of targeted pharmacological intervention in intracellular signalling networks which exhibit adaptation. This framework combines methods from graph theory and algebraic geometry to explain why treatment often fails, while illuminating alternative treatment strategies which may offer more success.

Organized by: Jana Gevertz (The College of New Jersey), Thomas Hillen (University of Alberta), Linh Huynh (Dartmouth College)

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

1. Thomas E. Yankeelov The University of Texas at Austin
   "Integrating mechanism-based and data driven modeling to predict breast cancer response to neoadjuvant chemotherapy"
While neoadjuvant chemotherapy (NAC) is the standard-of-care for treatment of patients with triple-negative breast cancer (TNBC), only about half of patients attain a pathological complete response (pCR). The addition of immunotherapy increases the pCR rate to about two thirds, but also increases toxicity. Thus, to improve patient outcomes, it is essential to develop methods that can predict and optimize patient response early during NAC. Previously, we showed that a biology-based model calibrated to pre- and on-treatment patient specific MRI data from the ARTEMIS trial (NCT02276443) can accurately predict patient response to NAC. This model describes cell movement and drug induced death globally over the tumor and cell proliferation locally on a voxel-by-voxel basis. We explore two approaches to extend this model. First, we train a convolutional neural network to predict the calibrated model parameters using only pre-treatment MRI data as input. Second, we apply k-means clustering to the longitudinal MRI data to segment tumors into distinct regions called habitats. Then, rather than calibrating cell proliferation locally, we calibrate one proliferation per habitat. To evaluate these models, we use the total tumor cellularity after the first course of NAC in a receiver operating characteristic (ROC) curve analysis to predict pCR status at the end of NAC. For the first method, we obtain an area under the ROC curve (AUC) of 0.72 for predicting the eventual response to NAC before initiating NAC. For the second method, we obtain AUC values of 0.79 and 0.77 for the habitat-informed and voxel-by-voxel proliferation rate calibrations, respectively, for 101 patients. Thus, we can make reasonably accurate predictions of pCR before initiating NAC with our CNN approach and attain comparable accuracy to a local calibration using our habitats approach, which requires fewer model parameters.
2. Lena Podina University of Waterloo
   "Universal Physics-Informed Neural Networks and Their Applications"
Differential equations are widely used to model systems such as predator-prey interactions, and the effect of chemotherapy on cancer cells. However, in order to construct these models, assumptions must be made about the behaviour of these systems, which may require significant manual distillation of the literature if the model is large. In this talk, I will discuss Universal Physics-Informed Neural Networks (UPINNs), and show how UPINNs can be used to learn unknown terms in ordinary and partial differential equations from sparse and noisy data. This approach allows one to use machine learning to identify the best way to model a system, rather than relying on prior assumptions. Physics Informed Neural Networks (PINNs) have been very successful in a sparse data regime (reconstructing entire ODE solutions using only a single point or entire PDE solutions with very few measurements of the initial condition). The Universal PINN approach (UPINN) adds a neural network that learns a representation of unknown hidden terms in the differential equation. These hidden term neural networks can then be converted into symbolic equations using symbolic regression techniques like AI Feynman. In our work, we demonstrate strong performance of UPINNs even when provided with very few measurements of noisy data in both the ODE and PDE regime. We apply UPINNs to learning predator-prey interaction in the Lotka-Volterra model, chemotherapy drug action terms in a model of cancer cell growth, and terms in Burgers’ PDE. UPINNs could be instrumental to paving the way to allow machine learning to help applied mathematicians model systems in a more automatic, data-driven way even when observations are sparse.
3. Adam L. MacLean University of Southern California
   "Dynamic rewiring of cell-cell interaction networks in metastatic TMEs to empower checkpoint inhibition"
Tumors grow, evolve, and metastasize as a result of an intricate set of interactions between the numerous cell types that comprise the tumor microenvironment (TME). Many of these interactions remain poorly understood, even in the absence of therapy, and the size of the networks that must be considered necessitates a systems biology approach. Immune checkpoint inhibition combined with entinostat, a histone deacetylase inhibitor, has been shown to promote durable responses in a minority of patients with metastatic, triple negative breast cancer. But the mechanisms of action of entinostat at metastatic sites has not been investigated. We measured the molecular properties of the metastatic TME in high resolution via scRNA-seq, quantifying 39 cell states across six treatment arms. Entinostat treatment led to an increase in stemness in tumor cells and decreased mesenchymal gene expression. To study the wiring of cell-cell interaction networks with and without treatments we inferred small cell circuits that are over-represented in the full networks. Top ranked cell circuits comprised myeloid and T cell subtypes: interactions between which were dramatically affected by combination therapy. Top pathways contributing to these interactions included chemokine, galectin, and ICAM pathways, which we tested via targeting specific ligand-receptor pairs in functional suppression assays, coupled to predictions from mathematical models to simulate therapeutic responses for a given treatment intervention. Beyond the clinical impact of this work, our results offer a framework with which to decompose large, complex TMEs to infer multiscale networks mediating treatment effects and then to infer the tumor response dynamics via mathematical modeling.
4. Venkata Manem Centre de Recherche du CHU de Québec; Université Laval, Canada Université Laval, Canada
   "Beyond the One-Size-Fits-All Paradigm: Leveraging Bioinformatics and AI to Advance Biomarker-Guided Oncology."
In the era of precision oncology, the convergence of high-throughput technologies and artificial intelligence (AI) is transforming how we understand and treat cancer. Traditional 'one-size-fits-all' regimens are being replaced by biomarker-driven strategies that tailor therapies to individual patients. However, the complexity of tumor genomics and microenvironments limits the effectiveness of many biomarkers, underscoring the need for biologically informed approaches. This talk will explore the intersection of bioinformatics and AI in uncovering biological insights for personalized cancer care. The first part will focus on breast cancer, emphasizing the critical role of epithelium-stroma crosstalk utilizing transcriptomics data to uncover the dynamic interactions within the tumor microenvironment. The second part will address the discovery of biomarkers in non-small cell lung cancer patients treated with immunotherapy, leveraging medical imaging data. Together, these efforts demonstrate how AI and bioinformatics bridge the gap between molecular complexity and actionable insights, paving the way for patient-specific decision-making in oncology.

Organized by: Joanna Wares (University of Richmond), Luis Melara, Shippensburg University

1. Luis Melara Shippensburg University
   "Optimal Bandwith Selection in Bio-FET Measurements"
The use of stochastic regression to separate signal from noise produced by Bio-FETs will be discussed in this talk. The noise realized by BioFETs interferes with quantitative and qualitative analysis, thus determining optimal bandwidth associated with experimental Bio-FET data measurements is an important task. Presented results suggest consistent across aspect rations and a choice of stochastic regression kernel function and yield what appear to be good results.
2. Joanna R. Wares University of Richmond
   "Comparison of Virtual Clinical Trial Techniques"
Virtual clinical trials (VCTs) are growing in popularity as a tool for quantitatively predicting heterogeneous treatment responses across a population. In the context of a VCT, a plausible patient is an instance of a mathematical model with parameter (or attribute) values chosen to reflect features of the disease and response to treatment for that particular patient. In a previous work, we rigorously quantified the impact that VCT design choices have on VCT prediction. We found that the prior distribution, rather than the inclusion/exclusion criteria, has a larger impact on the heterogeneity of the plausible population. Yet, the percent of treatment responders in the plausible population was more sensitive to the inclusion/exclusion criteria utilized. Here I discuss past results and preview a new study that seeks to understand how the underlying complexity of the chosen mathematical model affects the results of virtual clinical trials.

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

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

1. Hyukpyo Hong University of Wisconsin–Madison
   "Inferring delays in partially observed gene regulation processes"
Cell function is regulated by gene regulatory networks (GRNs) defined by protein-mediated interaction between constituent genes. Despite advances in experimental techniques, we can still measure only a fraction of the processes that govern GRN dynamics. To infer the properties of GRNs using partial observation, unobserved sequential processes can be replaced with distributed time delays, yielding non-Markovian models. Inference methods based on the resulting model suffer from the curse of dimensionality. We develop a simulation-based Bayesian MCMC method for the efficient and accurate inference of GRN parameters when only some of their products are observed. We illustrate our approach using a two-step activation model: An activation signal leads to the accumulation of an unobserved regulatory protein, which triggers the expression of observed fluorescent proteins. Our method is scalable and can be used to analyze other non-Markovian models with hidden components. References [1] Hyukpyo Hong, Mark Jayson Cortez, Yu-Yu Cheng, Hang Joon Kim, Boseung Choi, Krešimir Josić, Jae Kyoung Kim, Inferring delays in partially observed gene regulation processes, Bioinformatics, 39 (11): btad670, 2023.
2. Hui Jia Farm University of Oxford
   "Ensuring parameter identifiability in cardiac cell models is an essential prerequisite for reliable prediction"
Computational modelling of heart cells, especially the binding of drugs to the ion channel, is now an essential part of the drug development process, aiming to predict a drug’s risk to the heart from channel-level reactions. The ion channel controls the flow of ions across the cell membrane and the beating of the heart. The ion channel is susceptible to drug inhibition which can disrupt the heart’s beating cycle and can be fatal. Some drugs can get “trapped” within the channel, meaning they are unable to unbind from the channel while it is closed, and this is believed to increase the risk they pose. The trapping component introduced in a popular model of the drug-binding mechanism (the ORd-CiPAv1 model) has a limited effect on the beating cycle of heart cells, running counter to the claim that a drug’s risk depends on its trapping behaviour. We show that this limited effect of the trapping component is due to the non-identifiability of its parameters which stems from the insignificant contribution of the trapping component to the current. We propose two alternative drug-binding models which do not suffer from the problem of non-identifiability of the trapping component. With fewer parameters and/or constraints, the alternative models are more interpretable and/or more identifiable. Despite not having an explicit drug-trapping component, our proposed models can capture the drug-trapping phenotype observed in the experimentally-measured current. Even though all drug-binding models have limitations, one of our alternative models can replicate the risk categorisation of drugs predicted by the ORd-CiPAv1 model. We conclude that the trapping component defined in the ORd-CiPAv1 model is not necessary for the risk categorisation of drugs. Moreover, a model with identifiable and interpretable components each of which has an impact on model predictions would be preferable over complex models that contain more components but where those additional components have little to no effect.
3. Harsh Jain Swenson College of Science and Education
   "Parameter Estimation in ABMs: A Surrogate Modeling Approach"
Validated mathematical models of complex biological phenomena are increasingly recognized as invaluable for uncovering mechanisms that underlie real-world (experimental or clinical) observations. Agent-based models (ABMs) have emerged as a natural formulation of choice in such models, providing a logical structure for capturing the multiple time and spatial scales associated with complex biology. However, the inherent stochasticity and heavy computational requirements of ABMs are significant obstacles for data-driven parameterization and for conducting parameter space exploration and sensitivity analyses. Further, experimental or clinical data used for parameterization is typically limited, coarse-grained and may lack spatial resolution, resulting in issues of parameter identifiability. There is hence a need for developing new theoretical and computational frameworks that can bridge the current divide between ABM parameters and real-world data. In this talk I will present a novel approach — Surrogate Modeling for Reconstructing Parameter Surfaces (SMoRe ParS) — that uses explicitly formulated surrogate models (SMs) to bridge ABM simulations and experimental data. Our approach quantifies the relationship between parameter values across the ABM and SM, and between SM parameters and experimental data. Thus, SM parameters act as interlocutors between ABM inputs and data that can be used for calibration and uncertainty quantification of ABM parameters.
4. Tyler Cassidy University of Leeds
   "Parameter estimation and identifiability from clinical data in viral dynamics models"
Mathematical models have been instrumental in our understanding of viral kinetics. These models have identified important portions of the viral life cycle in many infections, like HIV and HBV, and are increasingly used to understand data from clinical trials of new treatments for these infections. I'll discuss some recent work focused on understanding how we can leverage analytical approximations and hierarchical parameter estimation techniques to identify model parameters from participants in early-stage clinical trials.

Organized by: David Sabin-Miller (University of Michigan)

1. Heather Zinn Brooks Harvey Mudd College
   "An opinion reproduction number for infodemics in a bounded-confidence content-spreading process on networks"
We study the spreading dynamics of content on networks. Our content-spreading model, which one can also interpret as an independent-cascade model, introduces a twist into bounded-confidence models of opinion dynamics by using bounded confidence for the content spread itself. We define an analog of the basic reproduction number from disease dynamics that we call an opinion reproduction number. A critical value of the opinion reproduction number indicates whether or not there is an “infodemic” (i.e., a large content-spreading cascade) of content that reflects a particular opinion. By determining this critical value, one can determine whether or not an opinion dies off or propagates widely as a cascade in a population of agents. Using configuration-model networks, we quantify the size and shape of content dissemination by calculating a variety of summary statistics, and we illustrate how network structure and spreading-model parameters affect these statistics.
2. Olivia Chu Bryn Mawr College
   "Adaptive network models and the dynamics of political polarization and social activism"
The formation of activist groups can spark social movements, coalitions, and revolutions. The creation of such groups can be influenced by social ties, network structure, ideology and culture, and the institutional environment. Still, the relative importance of these factors, the mechanisms through which individuals develop or lose their commitment to various causes, and the channels through which like-minded individuals find each other and establish social connections are not thoroughly understood. In this work, we develop a theory that begins to explain two phenomena: 1) how a potential activist's conviction co-evolves with their social network, and 2) how 'socially-mobilizable activist networks' tend to arise or disappear based on the distribution of potential activists and overall environment. We illustrate this theory by modifying the adaptive voter model (AVM) with a conviction variable, which represents the strength with which an individual holds on to their beliefs and the comfort of holding on to them in their surroundings, encapsulating the co-evolutionary dynamics of networks and attitudes. As is expected from empirical evidence, we find that activists are systematically discouraged by exposure to disengaged individuals. However, some situations with increased interaction payoffs and strong homophily preferences favor the formation and persistence of activist networks.
3. Alexandria Volkening Purdue University
   " Forecasting U.S. elections with compartmental models of infection"
Election dynamics are a rich complex system, and forecasting U.S. elections is a high-stakes problem with many sources of subjectivity and uncertainty. In this talk, I take a dynamical-systems perspective on election forecasting, with the goal of helping to shed light on choices in this process and raising questions for future work. By adapting a Susceptible-Infected-Susceptible model to account for interactions between voters in different states, I will show how to combine a compartmental approach with polling data to produce forecasts of senatorial, gubernatorial, and presidential elections at the state level. Our results for the last two decades of U.S. elections are largely in agreement with those of popular analysts, and we correctly called all of the state-level outcomes of the 2024 U.S. presidential race. We use our modeling framework to determine how weighting polling data by polling organization affects our forecasts, and explore how our forecast accuracy changes in time in the months leading up to each election.
4. David Sabin-Miller University of Michigan
   "Data-driven modeling of US information-ideological dynamics"
We may view the ideological ecosystem as an interplay between individuals’ acceptance and rejection of political ideas, and the algorithmically-mediated information environment which supplies those ideas according to each individual’s preference. This framework may help us make sense of the frustrating coexistence of seemingly contradictory worldviews in today’s polarized ideological climate; each may seem totally nonsensical or irrational to the opposing side, leaving little room for productive discourse or compromise. However, with fresh eyes and an interdisciplinary mindset, it is possible to make useful progress on this classically social-science domain by seeking an underlying dynamical model supported by data. This talk will present recent empirical results from a purpose-built ideological survey which find robust and seemingly universal patterns in individual-level political reasoning, a quantitative estimate of the political information landscape, and the implications of dynamically connecting the two. These efforts point to further illuminative data-gathering possibilities, laying the groundwork for a theory-experiment loop towards accurately understanding this powerful aspect of modern society.

Organized by: Professor James Glazier (Indiana University Bloomington), Hayden Fennell, Indiana University Bloomington

1. James Glazier Indiana University Bloomington
   "Using CompuCell3D as a Platform to Construct Multi-Scale, Multicellular, Agent-Based Virtual-Tissue Simulations: Exploring the Interactions between Intracellular Pathways, Cell Behaviors, Tissue-Level Signaling, and Whole-Body Context"
Multi-scale, Multicellular Agent-Based Virtual-Tissue models built using modeling frameworks like CompuCell3D are versatile tools for exploring the complex interactions between intracellular signaling and gene-regulatory networks, inter-cellular signaling through contact and diffusible signals, force generation, cell migration, and shape change. This session will provide a hands-on introduction to using the open-source CompuCell3D modeling environment to construct and execute compact and sharable multiscale, multicellular Virtual-Tissue models for developmental, cancer, toxicology, and infectious-disease applications. Members of the audience will build and explore how CompuCell3D can simplify the construction of complex, extensible and reusable Virtual Tissue models. Key topics will include: installing and running CompuCell3D, principles of CompuCell3D Physics, representation of cell behaviors and interactions as effective energies and constraints, CC3DML and Python model-specification basics, and representing complex network models in CompuCell3D. Several hands-on exercises will be incorporated to provide practice with the software. For more information or to download, please visit: www.compucell3d.org.