Submitted Contributed Talks & Posters

All submitted contributed talks & posters

  • CARD (Cardiovascular Modelling)
  • CDEV (Cell and Developmental Biology)
  • ECOP (Population Dynamics & Ecology)
  • EDUC (Education)
  • IMMU (Immunobiology and Infection)
  • MEPI (Mathematical Epidemiology)
  • MFBM (Methods for Biological Modeling)
  • NEUR (Mathematical Neuroscience)
  • ONCO (Mathematical Oncology)
  • OTHE (Other / Misc.)
CARD-1:
Mathematical model of blood flow in the brain after a major arterial occlusion.

  1. JaredBarber IU Indianapolis
    "Mathematical model of blood flow in the brain after a major arterial occlusion."
    2. Blood vessel adaptation plays an important role in maintaining healthy and well-oxygenated tissue throughout the body. This is especially true for the brain. To better characterize how blood flow changes when the brain suffers a major arterial occlusion (e.g. during a stroke) and to identify major factors that may affect flow restoration to downstream regions, we created a mathematical model of blood flow in the brain. The network is modeled as multiple larger vessels interconnected with multiple compartments of smaller vessels with each compartment consisting of identical vessels situated in parallel. The model further includes vessel adaptation in response to changes in pressure (myogenic response), wall shear stress (shear response), and oxygen saturation (metabolic response). By varying tissue oxygen demand and incoming pressure, we are able to identify that the number of collateral vessels moving flow from unobstructed to obstructed regions is a major factor. We also predicted a loss of normal function particularly reflected by a shift in the “autoregulation curve”, a curve that reflects the ability of vessels to reasonably respond to increases in pressure. Such results were consistent with experiment and reinforce the appropriateness of treatments that raise flow and oxygenation by minimizing tissue oxygen demand and raising vascular pressure.

CARD-2:
Testing hypotheses of glomerular capillary development with geometric and topological data analysis

  1. CoryBrunson University of Florida
    "Testing hypotheses of glomerular capillary development with geometric and topological data analysis"
    2. Blood filtration occurs in renal capillary tufts called glomeruli, the internal structure of which bears on questions of function, development, evolution, and pathology. Due to the low resolution and labor-intensity of imaging technology, only a handful of studies reaching back decades have examined the spatial structure of glomerular capillaries. Several common features have been described, including lobular topology, plausibly associated with robustness to vascular damage, and circuitous geometry, hypothesized to ensure consistent filtration. However, these properties have been neither mathematically defined nor statistically confirmed. Recent developments in serial scanning electron microscopy and virtual reality enabled us to reconstruct the capillary networks of twelve murine glomeruli and trace spatial graph models. We used circuit analysis to represent these as Reeb graphs, the fundamental theorem of calculus to describe a mean trajectory and its curvature, and principal components analysis to reveal lateral and transverse symmetry. Separately, we built a non-spatial random graph growth model based on two mechanisms, angiogenesis and intussusception, which provided evidence that both contribute to development. We then introduced several topological measures of lobularity and found, surprisingly, that empirical glomeruli tend to be less lobular than those generated by our model. Ongoing work focuses on simulation-based attack tolerance and the development of a spatial growth model.

CARD-3:
Modeling the effects of vascular impairments on blood flow autoregulation in the retinal microcirculation

  1. BrendanFry Metropolitan State University of Denver
    "Modeling the effects of vascular impairments on blood flow autoregulation in the retinal microcirculation"
    2. The retinal microcirculation supplies blood and oxygen to the cells responsible for vision, and vascular impairments – including compromised flow regulation, reduced capillary density, and elevated intraocular pressure – are involved in the progression of eye diseases such as glaucoma. Here, an established theoretical model of a retinal microvascular network will be presented and extended to investigate the effects of these impairments on retinal blood flow and oxygenation as intraluminal pressure is varied. A heterogeneous description of the arterioles based on confocal microscopy images is combined with a compartmental representation of the downstream capillaries and venules. A Green’s function method is used to simulate oxygen transport in the arterioles, and a Krogh cylinder model is used in the capillary and venular compartments. Acute blood flow autoregulation is simulated in response to changes in pressure, shear stress, and metabolism. The model predicts that impaired flow regulation mechanisms, decreased capillary density, and increased intraocular pressure all cause a loss in the autoregulation plateau over the baseline range of intraluminal pressures (meaning that blood flow is not maintained constant over those pressures), leading to a corresponding decrease in oxygenation in that range. Small impairments in capillary density or intraocular pressure are predicted to mostly be offset by functional flow regulation; however, larger changes and/or combinations of vascular impairments lead to a significant decrease in oxygenation. Clinically, since poor retinal tissue oxygenation could lead to vision loss in advanced glaucoma, model results suggest early identification of vascular changes to prevent these impairments from progressing.

CARD-4:
From Discrete to Continuous Structured Modelling of Macrophage Populations in Early Atherosclerotic Plaque

  1. EleonoraAgostinelli University of Oxford
    "From Discrete to Continuous Structured Modelling of Macrophage Populations in Early Atherosclerotic Plaque"
    2. Atherosclerosis is a chronic inflammatory disease characterised by lipid accumulation within arterial walls and driven by macrophage interactions with extracellular material, particularly lipid. In this work, we use mathematical modelling to investigate the dynamics of the macrophages in early atherosclerosis. We develop a discrete, lipid-structured mathematical model that accounts for cell proliferation and crowding, and also extracellular material uptake and offloading. With this model we are able to describe the dynamics of key biophysical quantities in the plaque, in particular the total number of macrophages and the total amount of intracellular material contained within the macrophages. Moreover, we rigorously derive a continuum approximation of the discrete model using the method of discrete multiple scales and asymptotic analysis techniques. In this way, we systematically derive a partial differential equation that accurately describes the distribution of macrophage content at leading order. We take advantage of the continuum form to analyse the mathematical system and understand its biological implications, such as the effects of proliferation and crowding on plaque composition. We also investigate the important spatio-temporal regions that appear degenerate but can be understood via boundary layer analysis.

CDEV-1:
Travelling waves in age-structured collective cell migration

  1. StéphanieAbo University of Oxford
    "Travelling waves in age-structured collective cell migration"
    2. This work examines the interplay between age-structure and migration dynamics in collective cell behaviour. We focus on the integration of cell cycle dynamics with spatial migration, particularly examining the 'go-or-grow' hypothesis in the context of age-dependent processes. Our framework extends classical travelling wave theory to account for the age structure of cell populations, offering new insights into how cell cycle phases influence moving fronts and invasion dynamics. We analyse wave speed characteristics and front dynamics in age-structured systems, addressing a significant gap in current mathematical biology literature. The research provides a novel theoretical foundation for understanding how cell-cycle dependent proliferation and migration behaviours contribute to collective cell dynamics.

CDEV-10:
Mechanotransducing structures promote self-driven cell surface patterning

  1. Gordon R.McNicol University of Waterloo
    "Mechanotransducing structures promote self-driven cell surface patterning"
    2. Cells respond to their local environment through mechanotransduction, converting mechanical signals into a biological response (e.g. cell growth, proliferation or differentiation). The cell cytoskeleton, particularly actomyosin stress fibres (SFs), and focal adhesions (FAs), which bind the cytoskeleton to the extra-cellular matrix (ECM), are central to this process, activating intracellular signalling cascades in response to deformation. We present a novel two-dimensional bio-chemo-mechanical model to describe the development of these structures, coupled through a positive feedback loop, and the associated cell deformation. Building on our previous one-dimensional approach, we similarly employ reaction-diffusion-advection equations to describe the evolution of key scaffolding and signalling proteins, and connect their concentrations to a viscoelastic description of the cell cytoplasm, ECM and adhesions. Further, we now incorporate other key mechanotransducing structures including the stiff cell nucleus, and plasma and cortical membranes. Working in an axisymmetric framework, we employ this model to explain how, dependent upon the mechanical properties of the surrounding ECM, non-uniform patterns of cell striation develop, leading to FA and SF localisation at the cell periphery. Moreover, a linear stability analysis reveals the stability of the axisymmetric configuration to various normal modes of deformation. By identifying non-axisymmetric modes with positive growth rates our model demonstrates a possible mechanism for self-driven surface patterning of cells in vitro.

CDEV-11:
Modeling mechanisms of microtubule dynamics and polarity in neurons

  1. AnnaNelson University of New Mexico
    "Modeling mechanisms of microtubule dynamics and polarity in neurons"
    2. The stability and polarity of the microtubule cytoskeleton is required for long-range, sustained transport within neuronal cellsl. In particular, the healthy microtubule cytoskeleton is comprised of tubulin protein and is stable with a particular orientation. However, when injured, these microtubules are dynamic, rearrange their orientation, and the appearance of microtubules is upregulated. It is unknown what mechanisms are involved in this balance between dynamic rearrangement and sustained function. Using a stochastic mathematical model that incorporates experimental data, we seek to understand how nucleation can impact microtubule dynamics in dendrites of fruit fly neurons. In the stochastic model, we assume two mechanisms limit microtubule growth: limited tubulin availability and the dependence of shrinking events on microtubule length. To better understand our stochastic model, we develop a partial differential equation (PDE) model that describes microtubule growth and nucleation dynamics, and we compare analytical results to results from the complex stochastic model. Insights from these models can then be used to understand what mechanisms are used organize into polarized structures in neurons, and how microtubule dynamics, like nucleation, may impact cargo localization post-injury.

CDEV-12:
Mechanochemical pattern formation in Hydra

  1. DietmarOelz The University of Queensland
    "Mechanochemical pattern formation in Hydra"
    2. Tissue morphogenesis involves the self-organized creation of patterns and shapes. In many cases details of underlying mechanisms are elusive, yet an increasing amount of experimental data suggests that chemical morphogens and mechanical processes are strongly coupled. Here, we develop and simulate a minimal model for the emergence of asymmetry in aggregates of the Hydra polyp based on mechanochemical coupling of surface stiffness and a morphogen concentration. We contrast this model with the classical morphogen patterning mechanisms based on Turing type reaction diffusion systems. In analogy to this classical mechnism, we carry out the stability analysis of the lower dimensional toy model and identify minimal conditions for symmetry breaking. Our results suggest that mechanochemical pattern formation underlies symmetry breaking in Hydra.

CDEV-13:
Intermittent attractions lead to emergent material properties in migrating cell aggregates

  1. Devi PrasadPanigrahi University College London
    "Intermittent attractions lead to emergent material properties in migrating cell aggregates"
    2. Cells migrate in response to gradients in extra-cellular chemical signals in a process known as chemotaxis. Recent experiments on the model microorganism Dictyostelium discoideum have shown that dense aggregates of cells collectively undergoing chemotaxis exhibit emergent fluid-like properties such as viscosity and surface tension. In this work, we use simulations to explain how active interactions between cells give rise to these emergent phenomena. We propose an agent-based model for intermittent cell-cell attachments and show that it gives rise to emergent fluid-like behavior for an aggregate of cells. We generalize this model to include cell-surface attachments, and show that surface-associated aggregates display properties similar to a liquid droplet resting on a surface. Furthermore, we study the situation where cells self-generate and respond to a chemical gradient by consuming an externally supplied chemoattractant. Our simulations reveal how individual cells move inside the swarm as the cells move as a collective. Finally, we predict some of the key cellular processes that are responsible for this collective behavior, and provide hypotheses to be tested in future experimental studies.

CDEV-14:
Motility-Induced Patterning in Signalling Bacteria

  1. WesleyRidgway University of Oxford
    "Motility-Induced Patterning in Signalling Bacteria"
    2. Chemical signaling, or quorum sensing (QS), promotes collective behaviour in bacteria, from biofilm formation to swarming. By coupling QS systems with genes that control motility, bacteria can be engineered to generate tunable spatio-temporal patterns in vitro. However, it is not well-understood in general how the type of gene-regulatory network affects emergent population-level patterning. In this talk, we investigate the effect of the gene-regulatory network on emergent patterning in a population of motile bacteria that interact via QS. By formally upscaling a cell-level model in a biologically relevant scaling regime, we derive a continuum model that explicitly accounts for genetic regulation of motility and signal production through chemical structuring. Using a WKBJ-like framework, we derive criteria for the onset of two types of emergent patterning for a canonical QS circuit. We also uncover a new route to the well-known phenomenon of motility-induced phase separation (MIPS) through genetic regulation of tumbling frequency. Lastly, we discuss generalisations of our WKBJ-like analysis to more complex gene-regulatory networks that exhibit bistability.

CDEV-15:
The bacterial dimeric transcription factor NsrR: a case study of a regulatory protein with a large number of states

  1. MarcRoussel University of Lethbridge
    "The bacterial dimeric transcription factor NsrR: a case study of a regulatory protein with a large number of states"
    2. In a number of bacteria, nitric oxide (NO) is converted to nitrate by an enzyme called Hmp. In emph{Streptomyces coelicolor}, synthesis of Hmp is in turn controlled by an iron-sulfur protein called NsrR. NsrR represses the transcription of two copies of the emph{hmp} gene in the emph{S. coelicolor} genome, but reaction of NsrR's iron-sulfur cluster with NO causes NsrR to dissociate from the emph{hmp} promoter, thus allowing Hmp to be expressed. While this is a straightforward control mechanism, NsrR is a dimer, and the iron-sulfur cluster in each monomer of NsrR can react with NO several times. Eventually, a repair system restores the NO-damaged iron-sulfur clusters of the dimers. But given that a single reaction with NO is sufficient to cause the NsrR dimer to dissociate from the emph{hmp} promoter, do we need to model the complex chemistry of the dimer, or is a highly simplified model that considers a single NsrR unit and its iron-sulfur cluster sufficient to capture the dynamics of this control system?

CDEV-16:
mRNA Translation Stalling in Single-Codon Resolution Monte-Carlo Ribosome Flow Model Simulations

  1. KatrinSchröder Goethe University
    "mRNA Translation Stalling in Single-Codon Resolution Monte-Carlo Ribosome Flow Model Simulations"
    2. Ribosomal stalling during translation of mRNA can result for example from oxidative conditions surrounding the site of translation. It impacts the cellular protein production machinery and therefore decrease cell proliferation. Accordingly, the rate of protein synthesis (R) can be considered as a hallmark of ribosomal stalling. In vivo experiments can determine protein content of a cell and differences in ribosomal density for different stalling scenarios. We employ the Ribosome Flow Model (RFM) coupled with Monte Carlo simulations to quantitatively establish the implications of three stalling patterns motivated by biological processes: We consider (1) the overall frequency of stalling sides as defined by harmful mRNA modification, (2) the degree of the reduction of the translocation rate λ reflecting the severity of mRNA transcriptional impairments, as well as (3) the effect of clustering, chain and gap impairments, as well as cluster locality of these anomalies. Each of these stalling patterns impacts protein synthesis rate and ribosomal density differently. We show how quantitative prediction of the impact of each and combinations of these patterns can be used as to study and predict mRNA stalling. Major findings of our analysis are, that for a given severity of mRNA damage, the equilibrium rate of protein synthesis R* does not depend on impairment locality, and is not related to the ribosomal density. In contrast, ribosomal density is strongly dependent on the locality of impairment clustering.

CDEV-17:
Quantifying the roles of drift and selection in spermatogonial stem cell dynamics

  1. ConnorShrader University of Utah
    "Quantifying the roles of drift and selection in spermatogonial stem cell dynamics"
    2. Stem cells maintain and repair our tissues, but not all stem cells are identical. As organisms age, distinct stem cell 'clones' can begin to dominate the cell population. While this behavior has been observed across multiple species and organs, the mechanisms and consequences of stem cell clonality are still poorly understood. We have developed a novel experimental approach using a CRISPR-Cas9 system to uniquely “barcode” spermatogonial stem cell clones in the testes of male zebrafish. Once these fish reach sexual maturity, we sample sperm each month to determine the contribution of each stem cell clone to the sperm pool over time. The observed clonal dynamics may be driven by factors such as genetic drift, selection, or sampling error. We hypothesize that a small number of clones are under positive selection, resulting in their eventual dominance in the sperm pool. To bridge the gap between theory and data, we have developed stochastic models of stem cell dynamics in the testis. These models are formulated as hidden Markov models that describe rules for the division and differentiation of stem cells within the testis. We first evaluate our ability to estimate model parameters on simulated data. Then, we apply our model to the experimental data to quantify evidence for genetic drift and selection. Our models provide insight into how individual stem cell behavior can lead to population-level clonality.

CDEV-18:
Cell fate through the lens of random dynamical systems

  1. AdrianaZanca The University of Melbourne
    "Cell fate through the lens of random dynamical systems"
    2. How pluripotent cells give rise to progressively more specialised cells over multiple cell divisions, known as cell fate, remains one of the mysteries of systems biology. During development, it is of the utmost importance that cells uphold certain division regimes for an organism to survive and thrive. Beyond development, cell fate perturbations can result in cancer and other pathological conditions. The theoretical and mathematical biology community has been making contributions to our understanding of cell fate including by quantifying Waddington’s seminal landscape using dynamical systems, performing statistical trajectory inference on single-cell sequencing data, or considering geometric and algebraic approaches to cell fate. In this talk, I will present a random dynamical systems interpretation of cell fate. This approach is, arguably, a generalisation of existing models of cell fate that may be able to provide new perspectives into cell fate.

CDEV-19:
Mathematical Modelling of Tumour Dynamics in Hypoxic Environments

  1. maryamalka University of Birmingham
    "Mathematical Modelling of Tumour Dynamics in Hypoxic Environments"
    2. Understanding tumour dynamics under hypoxic conditions is critical for optimising cancer therapies, particularly with chemotherapeutic agents like Paclitaxel. This study presents a refined mathematical model of tumour growth that incorporates Paclitaxel effects and hypoxia-driven resistance using a system of nonlinear ordinary differential equations (ODEs). We employ the Metropolis-Hastings Markov Chain Monte Carlo (MH MCMC) algorithm for Bayesian inversion and parameter estimation, providing a probabilistic framework to capture uncertainties. Sensitivity analysis is conducted using the multiple shooting method, which enhances the stability and accuracy of local sensitivity estimates across time intervals. The simulation results demonstrate that cell viability is reduced under moderate hypoxia when treated with Paclitaxel, which is consistent with experimental data from HCC1806 breast cancer cell lines. This agreement between model predictions and experimental outcomes supports the model’s validity in capturing key biological mechanisms. Future work will extend the model using Physics-Informed Neural Networks (PINNs) to improve computational efficiency and explore advanced inverse problem-solving techniques for robust cancer treatment optimisation.

CDEV-2:
Quantitative understanding of bone loss mechanism in mice using mathematical analysis

  1. MarwaAkao Nagoya university
    "Quantitative understanding of bone loss mechanism in mice using mathematical analysis"
    2. Osteoporosis is a disease that affects more than 200 million people all over the world. Although its underlying mechanisms are gradually being revealed, effective treatments or preventive measures have not been established yet. This study focused on age-related osteoporosis by measuring bone mass and bone metabolism markers in mice from 4 to 52 weeks of age. We developed mathematical models describing bone metabolism and analyzed experimental data. From the result of data analysis, we quantitatively elucidated the mechanisms of bone loss. Furthermore, we conducted treatment intervention simulations by changing parameter values in mathematical models to identify effective bone metabolism pathways for increasing bone mass and new potential therapeutic strategies.

CDEV-20:
Multi-Scale Analysis of Spatial Clustering Methods for Tissue Domains with Persistent Homology

  1. PerryBeamer North Carolina State University
    "Multi-Scale Analysis of Spatial Clustering Methods for Tissue Domains with Persistent Homology"
    2. Spatial gene-expression data can be clustered to segment a tissue into distinct spatial domains representing tissue structure. Though clustering algorithms are limited to a single fixed scale (by choice of a resolution hyperparameter k), we develop new methods from topological data analysis to analyze patterns in clusters across multiple scales. Zero-dimensional persistent homology analyzes the connectivity of data by tracking changes in homology groups across a filtered simplicial complex. We build a new filtration scheme to analyze similarity between clusters generated from multiple choices of scale parameter k, where persistent components represent clusters which exist across scales. We apply these results to select optimal scale parameters for spatial gene-expression clustering. These results have potential clinical application in tumor identification, where the size and scale of cancerous domains within healthy tissue is not known a priori.

CDEV-21:
Graph-based, Dynamics-Preserving Reduction of Chemical Systems using Thomas-Style Qualitative Stability Analysis

  1. BentaraDe Silva University of Lethbridge
    "Graph-based, Dynamics-Preserving Reduction of Chemical Systems using Thomas-Style Qualitative Stability Analysis"
    2. Abstract A biochemical system includes a network of chemical reactions often exhibiting complex behaviors such as oscillations, spatial patterns, and multistability. The parameter values of these models are often unknown or difficult to measure, and even some details of the reaction networks may be uncertain. Since these models tend to be large and complex, it is useful to create a simplified version of these models. However, traditional model-reduction methods depend on knowledge of parameter values which make them difficult to apply. Qualitative stability analysis methods provide an alternative approach without necessarily requiring parameter values. When reducing models with non-trivial dynamics arising from an instability, one must ensure that the conditions for instability are preserved, which depend mainly on the presence of circuits, and their signs. Roussel and Soares presented dynamics-preserving reductions based on Ivanova's qualitative conditions for instabilities (J. Math. Biol. 89, 42). The main objective of this research is to implement a similar framework based on the concepts outlined in that paper. However, instead of using Ivanova's conditions for instability, we will apply the Thomas qualitative stability analysis method to preserve the structures in the interaction graph that generate instability. An Oregonator-class model for oscillations in the photosensitive Belousov-Zhabotinsky (BZ) reaction due to Amemiya and coworkers is used in an initial exploration of possible reduction rules in interaction graphs. Given that the interaction graph discards information about the kinetics of a reaction, some attention will have to be given to the potential loss of important nonlinear terms while implementing the new method.

CDEV-22:
Modelling Filopodia Dynamics for Cell Patterning in Drosophila

  1. Nneka KarenEnumah Clarkson University
    "Modelling Filopodia Dynamics for Cell Patterning in Drosophila"
    2. Repeated patterns such as bristles and hair follicles play an important role in epithelia, which sense the environment. Optimal organization of patterns contributes to normal tissue function and gives organisms a spatial and temporal mapped-out input of their environmental stimuli. Although many local (e.g., cell- cell) signaling mechanisms are understood, some gaps still exist in our understanding of long-distance signaling via cell protrusions such as filopodia and cytoneme. The sensory bristles of the fruit fly Drosophila Melanogaster are a genetically tractable system for studying the formation of repeating patterns and invariably long-range cell signaling via cell protrusions. One critical feature of the sensory bristle spot pattern is the presence of long-range lateral inhibition, a mechanism that relies on forming actin-based cell protrusions – filopodia. We develop a mathematical model to describe filopodia dynamics and their role in determining cell fate during patterning.

CDEV-23:
Mesoscale Simulation of Sheet-to-Tubule Transformation in the Endoplasmic Reticulum by Curvature-Promoting Proteins

  1. EmadGhazizadeh University of Alberta/Department of mechanical engineering
    "Mesoscale Simulation of Sheet-to-Tubule Transformation in the Endoplasmic Reticulum by Curvature-Promoting Proteins"
    2. The endoplasmic reticulum (ER) is a highly dynamic organelle that undergoes contin- uous remodeling between tubular and sheet-like structures, driven by the Rtn and Reep protein families. Understanding the physical principles underlying these transitions is cru- cial for elucidating the ER’s role in cellular homeostasis and disease. In this study, we em- ploy mesoscale simulations to investigate the mechanisms by which curvature-promoting proteins regulate ER morphology. Specifically, we explore the influence of protein in- trinsic curvature, protein concentration, and protein sti!ening on tubulation dynamics. Our results indicate that increasing the intrinsic curvature of proteins lowers the pro- tein coverage threshold required for tubulation, while enhanced membrane sti!ness facil- itates curvature propagation at lower protein coverage. A phase diagram is constructed to map the conditions necessary for membrane remodeling, identifying critical curvature and protein coverage thresholds that drive ER transformation. These findings establish a quantitative framework for ER shape regulation, shedding light on the interplay between protein-membrane interactions and mechanical properties in ER morphogenesis. By inte- grating computational predictions, this study advances our understanding of ER structural dynamics and its implications for cellular function.

CDEV-24:
A multiscale model on hair follicle bulb replenishment

  1. RoseJohnson-Leiva UC Riverside
    "A multiscale model on hair follicle bulb replenishment"
    2. Hair follicles (HFs) are mini-organs in skin who undergo cyclic growth. During the anagen phase, the hair shaft is produced from the bottom part of a HF, referred to as the hair bulb. Proper regulations of the HF bulb cell fate decisions are crucial to maintain an anagen HF, therefore guaranteeing the continuous production of hair. Recent experiments have provided evidence on how the HF bulb is replenished during anagen. In this paper, we develop a hybrid multiscale computational model on HF bulb, integrating an agent-based submodel of cell divisions and movement, and a reaction-advection-diffusion equation submodel of diffusive signaling dynamics. Using our model, we investigate the HF bulb replenishment dynamics driven by different cell dividing strategies, showing that signaling-driven cell division may lead to efficient replenishment dynamics.

CDEV-25:
Quantifying the Effect of Space on Antibiotic Resistance Evolution.

  1. Induni Uresha DiasKariyawasam Majuwana Gamage Clarkson Univeristy
    "Quantifying the Effect of Space on Antibiotic Resistance Evolution."
    2. Antibiotics, which can be defined as substances that work against bacteria, are one of the most useful agents used in healthcare. As a result, they serve to treat and prevent many bacterial infections. However, due to the emergence of antibiotic resistance, where bacteria develop a mechanism to defend themselves against antibiotics, managing infections has become increasingly challenging. Antibiotic resistance in bacteria arises through genetic mutations or horizontal gene transfer. Spatial heterogeneity in antibiotic concentration has a potential to affect this bacterial evolution. For example, compared to a well mixed population, in a highly structured population, increased phenotypic and genotypic diversity, as well as slower adaptation, is expected. Here, we are studying the bacterial evolution under the stochastic processes of division, which is influenced by the availability of food sources in the culture, as well as by mutations and migration. As division reaction is time dependent, this chemical system is non-homogeneous and non-stationary. In this scenario, continuous time Markov processes can not be applied as chemical reactions are non-homogeneous and non-stationary. In this study, an expression was formulated to determine the time until the next reaction occurs, given the current state of the system, by considering the combined effects of division, migration, and mutation.

CDEV-26:
Stressed out: Probing DNA replication stress and the role of G-quadruplexes via stochastic process approaches

  1. MirandaLynch Univ. at Buffalo/Hauptman-Woodward Institute
    "Stressed out: Probing DNA replication stress and the role of G-quadruplexes via stochastic process approaches"
    2. Replication stress refers to the impeding of DNA copying and the slowing or arresting of replication forks during DNA synthesis. It arises due to a number of exogenous and endogenous agents such as reactive oxygen species (ROS), radiation-induced DNA lesions, and noncanonical folded DNA species such as G-quadruplexes. Replication stress can give rise to chromosomal missegregation in anaphase, DNA breakage, or faulty rearrangements. In this work, we take a stochastic process approach to modeling replication stress, using a coupled system of point processes to capture replication fork distribution and characterization of replication origin licensing, and Poisson process modeling of origin activation. Recent work in yeast has demonstrated the appropriateness of the Poisson model for capturing the stochastic multiple activation process under replication stress. Finally we focus particularly on the role of G-quadruplexes (G4), which are guanine (G)-rich regions of DNA that form noncanonical quadruple-stranded structures that are implicated in replication stress. We discuss how the different topologies of G4 potentially influence the origin activation process modeled in this work.

CDEV-3:
Towards a Vertex Modeling Framework for Simulating Bristle Cell Patterning in Fruit Flies

  1. WilliamAnnan Clarkson University
    "Towards a Vertex Modeling Framework for Simulating Bristle Cell Patterning in Fruit Flies"
    2. Repeating patterns, such as hair follicles and bristles play important roles in the lives of animals. These structures help animals to optimally sense their environment. Notch signaling is known to control these patterns. Primarily, Notch signaling is a local communication between neighboring cells in contact (signal-sending and signal-receiving cells). The local communication between cells in contact is not able to explain all the complex biological patterns observed. Further studies reveal long-range communication between cells using actin-based filopodia called cytonemes. The precise understanding of how the dynamics of filopodia such as protrusion and retraction lead to notch-delta activation remains unclear. In this work, we develop a mathematical model to help unravel the mystery of this long-range communication between cells. Student: William Ebo Annan Advisors: Prof. Emmanuel O.A. Asante & Prof. Ginger Hunter.

CDEV-4:
Understanding the role of hydration in aging of skin epidermis through a modeling cell-cell communication

  1. SupriyaBidanta Indiana University
    "Understanding the role of hydration in aging of skin epidermis through a modeling cell-cell communication"
    2. Advances in cell type and gene expression mapping have significantly enhanced our understanding of the human body. However, comprehending interactions at cellular and tissue levels is equally critical for unveiling mechanisms underlying health and aging. This project leverages data from the Human BioMolecular Atlas Program (HuBMAP) and Human Cell Atlas (HCA) to explore cellular functionality within functional tissue units (FTUs) of the skin epidermis. Using HuBMAP and HCA single-cell RNA sequencing (scRNA-seq) and transcriptomics data, we identify key cell types acting as chemical secretors (ligands) and receivers (receptors) in healthy and diseased tissue. We employ PhysiCell, an agent-based modeling platform, to construct a 3D computational cellular environment. The workflow involves preprocessing the transcriptomics data into a machine-readable format and generating chemical communication graphs that capture th ofe dynamic interplay signaling molecules between secretor and receiver cells. By combining biological data with multiscale ABM, we aim to visually and quantitatively model the chemical interactions within the epidermal FTUs of human skin tissue. The overarching goal is to develop a mathematical model elucidating how hydration-mediated cellular communication impacts tissue homeostasis and delays aging processes. This research has the potential to provide new insights into the mechanisms of skin aging and inform strategies for promoting tissue health through hydration management.

CDEV-5:
Exploring the role of Ca2+ influx in controlling competing oscillatory mechanisms in T cells using ODEs

  1. PacoCastaneda Ruan The University of Auckland
    "Exploring the role of Ca2+ influx in controlling competing oscillatory mechanisms in T cells using ODEs"
    2. Across the spectrum of cell types, the concentration of calcium controls a wide array of cellular functions. These calcium signals, usually in the form of periodic oscillations, play a paramount role in correct cellular activity. T cells are fundamental to the correct behaviour of the immune system. These cells have recently been shown to exhibit two competing oscillatory mechanisms, depending on the influx of extracellular Ca2+. Ca2+ influx is controlled by two molecules, STIM1 and STIM2. When both STIMs are present, T cells showcase sinusoidal Ca2+ oscillations on a raised baseline, but when one of them is absent the nature of the oscillation changes to a mix of Ca2+ spikes and bursting periods. In this talk, we will present an ODE that attempts to explain how these two molecules control the nature of these oscillations in T cells

CDEV-6:
A tale of trafficking: On prolactin receptor localization in pancreatic β-cells

  1. LynneCherchia University of Southern California
    "A tale of trafficking: On prolactin receptor localization in pancreatic β-cells"
    2. The prolactin receptor (PRLR) is a single-pass transmembrane receptor driving pancreatic β-cell proliferation via JAK/STAT signaling activation. This signal transduction pathway enables insulin-secreting β-cells to adapt to metabolic stress; however, the precise mechanisms underlying the pathway’s proliferative effect remain ill-defined. Here we implement a pipeline that uses live-cell fluorescence imaging, reconstitution approaches, and fluorescence correlation spectroscopy (FCS) to inform a mathematical model of PRLR signaling in β-cells and build a quantitative, mechanistic understanding of the signaling network. PRLR signaling is dynamic, involving changes in the spatial organization of signaling molecules. We have observed PRLR undergoing rapid internalization, a behavior that has been shown and modeled in other signaling pathways but has not been considered in a mathematical model of PRLR signaling. Such a model is useful for predicting strategies to modulate β-cell function. PRLR internalization is observed in both our minimal engineered PRLR expression system and in native pancreatic tissue, while FCS and chemigenetic labeling with SNAP-tag confirm the presence of a low concentration plasma membrane pool of PRLR. Our imaging data are used to integrate PRLR trafficking dynamics into an ordinary differential equation (ODE) model of PRLR signaling. We employ the ODE model to test hypotheses targeting how the spatial heterogeneity of PRLR signaling dynamics affects downstream signaling outcomes. Our data underscore the versatility of building a generalizable modeling-imaging framework to quantitatively understand signal transduction in and beyond β-cells.

CDEV-7:
Travelling waves of phenotypically structured cell populations migrating into extracellular matrix

  1. RebeccaCrossley University of Oxford
    "Travelling waves of phenotypically structured cell populations migrating into extracellular matrix"
    2. Collective cell migration plays a crucial role in numerous biological processes, including cancer growth, wound healing, and the immune response. Often, the migrating population consists of cells with various different phenotypes. This study derives a general mathematical framework for modelling cell migration into the micro-environment, which is coarse-grained from an underlying individual-based model that captures some of the dynamics of cell migration that are influenced by the phenotype of the cell, such as: random movement, proliferation, phenotypic transitions, and interactions with the external environment. The resulting model provides a continuum, macroscopic description of cell invasion, which represents the phenotype of the cell as a continuous variable and is much more amenable to simulation and analysis than its individual-based counterpart when considering a large number of phenotypes. The results highlight how phenotypic structuring impacts the spatial and temporal dynamics of cell populations, demonstrating that different environmental pressures and phenotypic transition mechanisms significantly influence invasion patterns.

CDEV-8:
Matching data from multiple yeast cell cycle experiments to a genetic network model

  1. BreschineCummins Montana State University
    "Matching data from multiple yeast cell cycle experiments to a genetic network model"
    2. Modeling biological systems holds great promise for speeding up the rate of discovery in systems biology by predicting experimental outcomes and suggesting targeted interventions. However, this process is dogged by an identifiability issue, in which network models and their parameters are not sufficiently constrained by coarse and noisy data to ensure unique solutions. In this work, we evaluated the capability of a simplified yeast cell-cycle network model to reproduce multiple observed transcriptomic behaviors under genomic mutations. We matched time-series data from both cycling and checkpoint arrested cells to model predictions using an asynchronous multi-level Boolean approach. We showed that this single network model, despite its simplicity, is capable of exhibiting dynamical behavior similar to the datasets in most cases, and we demonstrated the drop in severity of the identifiability issue that results from leveraging multiple datasets.

CDEV-9:
Mathematical Optimisation of Actin-Driven Protrusion Formation in Eukaryotic Chemotaxis

  1. SamuelJohnson University of Oxford
    "Mathematical Optimisation of Actin-Driven Protrusion Formation in Eukaryotic Chemotaxis"
    2. In eukaryotic chemotaxis, cells extend and retract transient actin-driven protrusions at their membrane. These protrusions facilitate both the detection of external chemical gradients and directional movement via the formation of focal adhesions with the extracellular matrix. While extensive experimental work has characterised how protrusive activity varies with a range of environmental parameters, the mechanistic principles governing these relationships remain poorly understood. Here, we model the extension of actin-based protrusions in chemotaxis mathematically as an optimisation problem, wherein cells must balance the detection of external chemical gradients with the energetic cost of protrusion formation. The model highlights energetic efficiency in movement as a major predictor of phenotypic variation amongst motile cell populations, successfully reproducing experimentally observed but previously non-understood patterns of protrusive activity across a range of biological systems. Additionally, we leverage the model to generate novel predictions regarding cellular responses to environmental perturbations, providing testable hypotheses for future experimental work.

ECOP-1:
Quantifying the Impact of Forest Harvesting on Chum and Pink Salmon Populations in Coastal BC

  1. Maria Kuruvilla University of Victoria
    "Quantifying the Impact of Forest Harvesting on Chum and Pink Salmon Populations in Coastal BC"
    2. Forest harvesting in coastal British Columbia (BC) has altered watersheds, impacting salmon habitat by increasing sediment, reducing riparian cover, and altering hydrology. These changes can affect the survival and growth of salmon through mechanisms like reduced egg-to-fry survival, increased stream scour, increased thermal stress, and loss of stream complexity which is essential for salmon rearing. Despite numerous localized studies, no comprehensive analysis has examined the population-level effects of forestry on salmon across BC. After assembling forest harvest histories for 1,746 salmon-bearing watersheds (1883-2022) and salmon abundance data (1950-2022), we used stock-recruit models (Ricker and Beverton-Holt) in a hierarchical Bayesian framework to test the effects of forestry metrics (Equivalent Clearcut Area, Cumulative Percent Disturbed) on chum and pink salmon productivity. Our results show a strong negative effect of forestry on chum productivity (e.g. 25% equivalent clearcut area reduces productivity by more than 20%) and a negligible effect on pink salmon. This highlights forestry’s significant role in the decline of chum salmon populations over recent decades.

ECOP-10:
Exploring the population impacts of climate change effects on the mean, variance and autocorrelation of temperature using thermal performance curves.

  1. KimCuddington University of Waterloo
    "Exploring the population impacts of climate change effects on the mean, variance and autocorrelation of temperature using thermal performance curves."
    2. Climate change is altering the mean, variance and autocorrelation of temperature. However, linear approaches to incorporating these temperature impacts in simple population models do not provide realistic predictions regarding climate change impacts. For example, simple degree days approaches or using a linear function of temperature to alter the density-independent population growth rate will not account for the sometimes catastrophic decrease in performance with high temperatures. We use an extremely simple population model coupled to nonlinear thermal performance curves to explore the simultaneous impact of changes to temperature mean, variance and autocorrelation. The realized density-independent population growth rate is given by three types of thermal performance curves that correspond to published data. We find relatively small impacts on established population dynamics when realistic changes in temperature sequences are used, suggesting that many populations may be quite robust to temperature-driven climate change impacts in the near term. The most extreme right-skewed performance curves are most likely to result in species extinctions, even though these curves have higher optimal temperatures.

ECOP-11:
Understanding the cyclic populations of the short-tailed field vole in the UK using long term experimental data

  1. MattDopson Newcastle University
    "Understanding the cyclic populations of the short-tailed field vole in the UK using long term experimental data"
    2. The short-tailed field vole (microtus agrestis) is the most abundant mammal in the UK, with populations reaching up to 80 million individuals. However, voles experience huge fluctuations in population numbers with up to a tenfold change over the course of regular 3.5 year cycles. Previous research has aimed to understand the mechanics behind these oscillations, but most of this work focuses on tundra regions. The ongoing Glen Finglas grazing experiment spans over 20 years, focusing on how managing grazing pressures affects various groups of species - including voles - in the more temperate upland acid grasslands of Scotland. Here, I will first present new data analysis on the Glen Finglas experiment, in particular the relationship between voles and the vegetation they use as a food source and shelter. I will then show how this data can be used to create and fit a mathematical model, capturing the vole's complex life history and interactions. Understanding these small animals is important as they are a key prey species for many predators and can also cause massive damage to plants and tree saplings. This mathematical model furthers our understanding of vole dynamics in temperate regions.

ECOP-12:
About the fight against the oriental fruit fly using a combination of non-chemical control tools - Mathematical strategy versus field strategy

  1. YvesDumont CIRAD/University of Pretoria
    "About the fight against the oriental fruit fly using a combination of non-chemical control tools - Mathematical strategy versus field strategy"
    2. The oriental fruit fly, Bactrocera dorsalis, is a serious threat to crops and orchards in many places around the World, and in particular in Réunion island, where it was first detected in 2017. Since then, this pest has invaded the whole island and displaced established fruit fly populations. Since Réunion island is a hot spot of diversity, appropriate control tools have to be deployed to eliminate or reduce the wild population. I will present recent results that study the combination of the Sterile Insect Technique, entomopathogen fungi, and also pheromone traps. In particular, we will show how the spatial component and the orchards connectivity can drastically change the releases strategy, as well as the critical amount of sterile insects to release. We discuss (optimal) strategies obtained with our models versus realistic strategies that can actually be developed in the field. Our approach being generic, it can be adapted to other pests and disease vectors, such as mosquitoes. This works stands within the AttracTIS project, funded by Ecophyto 2021-2022.

ECOP-13:
Understanding self-organisation in nature: Patterns and Bifurcations in Nonlocal Advection-Diffusion Models

  1. ValeriaGiunta Swansea University
    "Understanding self-organisation in nature: Patterns and Bifurcations in Nonlocal Advection-Diffusion Models"
    2. Understanding the mechanisms behind the spatial distribution, self-organisation and aggregation of organisms is a central issue in both ecology and cell biology. Since self-organisation at the population level emerges from individual behaviour, a mathematical approach is essential to elucidate these dynamics. In nature, individuals - whether cells or animals - inspect their environment before moving. This process is typically nonlocal, meaning that individuals gather information from a part of their environment rather than just their immediate location. Empirical research increasingly highlights nonlocality as a key aspect of movement, while mathematical models incorporating nonlocal interactions have gained attention for their ability to describe how interactions shape movement, reproduction and well-being. In this talk, I will present a study of a class of advection-diffusion equations that model population movement driven by nonlocal species interactions. Using a combination of analytical and numerical tools, I will show that these models support a wide range of spatio-temporal patterns, including segregation, aggregation, time-periodic behaviour, and chase-and-run dynamics. I will also discuss the existence of parameter regions with multiple stable solutions and hysteresis phenomena. Overall, I will explore various methods for analysing the bifurcation and pattern formation properties of these models, which provide essential mathematical tools for understanding the many aggregation phenomena observed in nature.

ECOP-14:
Can tourism drive effective coral reef management? A modelling study.

  1. ArielGreiner University of Oxford
    "Can tourism drive effective coral reef management? A modelling study."
    2. Coral reefs are some of the most threatened ecosystems on the planet but also some of the most important, hosting upwards of 25% of marine biodiversity while also providing food and livelihood to almost 1 billion people. Coral reefs are also connected together into reef networks by coral larval dispersal, meaning that management or damage at one reef may have consequences for any reefs it is connected to. For this reason, coral reef management is of interest to many industries (e.g., fisheries, tourism) and governments. The potential impact of tourism in the context of coral reef management is unclear, as tourism is a source of damage for reefs but may also be a source of income that motivates conservation actors to keep reefs healthy for future tourists. Tourism groups also often focus on a subset of coral reefs, meaning that any management initiatives driven by tourism income would also only be focused on a subset of coral reefs in a reef network. We develop a socio-ecological model composed of a system of differential equations. This model represents a network of coral reefs visited by tourists to determine whether tourism income could help sustain a healthy network of coral reefs into the future. We explore this question under a variety of different tourism paradigms, management methods (coral restoration, fisheries management) and network types. Overall, we find that management funded by tourism can help counteract tourism damage, but is unable to save reefs that are unhealthy (low initial coral cover, high fishing). Management also has limited potential to help connected reefs in the network. This study demonstrates the limited effectiveness of tourism to drive coral reef conservation and instead encourages active investment in management methods that focus on the entire reef network.

ECOP-15:
Allee Pits in Metapopulations: When Increasing Dispersal Can Backfire

  1. CarolinGrumbach Osnabrueck University
    "Allee Pits in Metapopulations: When Increasing Dispersal Can Backfire"
    2. Habitat fragmentation divides populations into smaller subpopulations, while the Allee effect diminishes the viability of small populations. Together, these processes can synergistically amplify negative impacts on spatially structured populations. Conservation strategies often aim to counteract these effects by enhancing connectivity between subpopulations, for example, through corridors or stepping stones. However, increasing connectivity does not always lead to the desired positive outcomes.In this talk, I will demonstrate that due to the Allee effect, low connectivity leads to a decline in the asymptotic total population size, which we call the 'Allee pit'. However, increased connectivity facilitates the rescue effect, wherein a persistent subpopulation in one patch can save an extinction-prone subpopulation in another patch, ultimately increasing the total population size. Using simulations based on a generic discrete-time patch model with positively density-dependent growth, I will explore how enhanced connectivity influences a fragmented population subject to the Allee effect.Our results highlight that conservation strategies must carefully consider dispersal dynamics. Simply increasing connectivity is not enough; ensuring dispersal rates exceed a critical threshold is essential for achieving long-term benefits.

ECOP-16:
The site frequency spectrum of an exponentially growing population: Theory and evolutionary history inference

  1. Einar BjarkiGunnarsson Science Institute, University of Iceland
    "The site frequency spectrum of an exponentially growing population: Theory and evolutionary history inference"
    2. The site frequency spectrum (SFS) is a popular summary statistic of genomic data. In population genetics, the SFS has provided a simple means of inferring the rate of adaptation of a population and for distinguishing between neutral evolution and evolution under selection. The rapidly growing amount of cancer genomic data has attracted interest in the SFS of an exponentially growing population. In this talk, we discuss recent results on the expected value of the SFS of a population that grows according to a stochastic branching process, as well as (first-order) almost sure convergence results for the SFS in the large-time and large-population limits. Our results show that while the SFS depends linearly on the mutation rate, the branching process parameters of birth and death control the fundamental shape of the SFS at the low-frequency end. For the special case of a birth-death process (binary branching process), our results give rise to statistically consistent estimators for the mutation rate and extinction probability of the population, which stands in contrast to previous work which has indicated the need for additional data to decouple these two parameters. Overall, our work shows how single timepoint data on the SFS of an exponentially growing population can be used to infer important evolutionary parameters.

ECOP-17:
Quantifying the fires of the future: Modelling and inference of wildfire spread dynamics.

  1. Axa-MariaLaaperi Newcastle University
    "Quantifying the fires of the future: Modelling and inference of wildfire spread dynamics."
    2. Wildfires disrupt ecosystems, with climate change exacerbating vulnerability in regions poorly adapted to such disturbances. These events are driven by complex, multi-scale interactions where small perturbations in environmental factors can trigger large-scale shifts, complicating prediction efforts. We propose a coupled convection-reaction-diffusion system as a framework for modelling wildfire spread dynamics. This system integrates spatial and temporal variability to identify thresholds for spread and quantify the impact of abrupt environmental changes on burnt areas and rates of propagation. Incorporating environmental, meteorological, and historical fire record data from the Global Wildfire Information System, the Department for Environment, Food and Rural Affairs (UK), and drone footage of heather burning. Bayesian inference and Monte Carlo methods are employed for parameter estimation and uncertainty quantification, ensuring robust model validation against unseen data. Recent wildfire events around the globe highlight the need for actionable insights into environmental vulnerability, property loss, and infrastructure risk. By enabling near-real-time simulations, this model aims to provide a computational tool for emergency response, long-term management strategies, and assessments of climate change-induced outlier weather patterns influencing fire behaviour. This work highlights the potential of mathematical modelling to advance understanding and management of critical ecological disturbances.

ECOP-18:
Exploring Climate-Driven Population Changes in Aster Leafhoppers Using Age-Structured Models

  1. MorganLavenstein Bendall University of California, Merced
    "Exploring Climate-Driven Population Changes in Aster Leafhoppers Using Age-Structured Models"
    2. Due to their diversity and abundance, insects play essential ecological roles, including crop pollination, nutrient cycling, and serving as a food source for other species. However, climate change is predicted to heavily impact insect populations, with some expected to decline by up to 18% globally by the end of the 2020s, raising concerns about the future health of the bioeconomy. To investigate these impacts, we conducted a temperature study on Aster leafhoppers (Hemiptera: Cicadellidae: Macrosteles quadrilineatus). Using five temperature conditions, we collected physiological data over a month to assess the impact of temperature on survival, maturation, and egg production. We then developed an age-structured population model to explore how environmental temperature influences insect fitness and mortality rates. Our model is parameterized with experimental data across various climate change scenarios, providing insights into the effects of rising temperatures on insect survival and population dynamics. This work highlights the cascading effects of climate change on ecological networks and emphasizes the importance of understanding insect responses to environmental stressors.

ECOP-19:
Modeling photo-fermentative bacteria evolution for H2 production in a bio-reactor

  1. VincenzoLuongo University of Naples Federico II
    "Modeling photo-fermentative bacteria evolution for H2 production in a bio-reactor"
    2. We propose a mathematical model describing the dynamics of photo fermentative bacteria leading to hydrogen production and polyhydroxybutyrate accumulation in an engineered environment. The model is derived from mass balance principles and consists of a system of differential equations describing the biomass growth, the substrate degradation and conversion into hydrogen and other catabolites, such as intracellular polyhydroxybutyrate, a precursor for bioplastics. The model accounts for crucial inhibiting phenomena and catabolic interactions affecting the evolution of the process. The study of the model has been performed also in terms of calibration with real experimental data related to specific photo-fermentative species, and it is supported by a sensitivity analysis study. The effective application of photo-fermentation for the concomitant hydrogen production and polyhydroxybutyrate accumulation was investigated.

ECOP-2:
Management of an EBHSV-infected lagomorph invasion in some Italian regions using Z-type control method

  1. FrancescaAcotto Università di Torino, Italy
    "Management of an EBHSV-infected lagomorph invasion in some Italian regions using Z-type control method"
    2. The introduction of eastern cottontail (Sylvilagus floridanus) for hunting purposes has influenced the local predator-prey dynamics of native European hares (Lepus europaeus) and red foxes (Vulpes vulpes) in some northern and central regions of Italy. No direct competition seems to occur between the two lagomorphs, but invasive cottontails cause hyperpredation of red foxes on native prey and are also carriers of viruses and parasites which can potentially affect European hares. Specifically, eastern cottontails may be infected by the European Brown Hare Syndrome Virus (EBHSV) which causes a disease characterized by acute necrotizing hepatitis, which infects European hares and leads to high mortality. We focus on the situation in which EBHSV-infected eastern cottontails are introduced in a virus-free region of European hares and red foxes. To possibly give indications to ecosystem managers to mitigate the negative effects of this lagomorph invasion, we look at two possible biological control actions using the Z-type control method on a four-population reference system. In particular, we consider an indirect control of invasive prey acting on predators and a combination of this indirect control with direct control of native prey.

ECOP-20:
Detecting selection with a null model of gene order evolution

  1. AlexanderMoffett Northeastern University
    "Detecting selection with a null model of gene order evolution"
    2. Recent progress in genome assembly techniques has led to an explosion in chromosome-length genome sequences. These unfragmented assemblies have enabled biologists to study molecular evolution at unprecedented scales, providing insight into the evolution of genome architecture. Microsynteny, the conservation of gene order, has proven to be a key concept in our understanding of genome evolution. However, it remains unclear when microsynteny occurs due to random chance or selection. Here, we develop a mathematical model to discriminate between these two cases. Our model describes the dynamics of synteny block size distributions in the absence of selection or other biases. By fitting this null model to data from a comparative analysis of mammalian genomes, we identify synteny blocks larger than expected in the absence of selection. This approach allows us to rigorously determine which sets of genes are likely to have selection on their ordering in a lineage-specific manner. Our model presents a powerful tool for uncovering functional relationships between genes based on their ordering and for understanding the evolution of gene co-regulation.

ECOP-21:
Two sides of the same coin: Euler-Lotka and R0

  1. KaanÖcal University of Melbourne
    "Two sides of the same coin: Euler-Lotka and R0"
    2. Two fundamental quantities in population biology, the reproductive number R0 and the growth rate, are intimately linked, but the exact nature of their relationship is somewhat obscure. Models of microbial growth typically have R0=2, but estimating their growth rate, and hence fitness, requires solving the famous Euler-Lotka equation. Conversely, in epidemiology one typically measures how quickly the infected population grows, but it is the reproductive number R0 that sets the threshold for an epidemic breakout and for herd immunity. In this talk, we use statistical techniques based on large deviations theory to clarify how exactly the population growth rate and R0 are connected. Building an analogy to classical thermodynamics, we show that the long-term behaviour of a population is encoded in a single convex function that relates growth rate, R0, and the statistics of intergeneration times in lineages. As an application, we derive a general formulation of the Euler-Lotka equation and explain why it is almost always appears as an implicit equation.

ECOP-22:
Forecasting Mosquito Population in Maricopa County Using Climate Factors and Filtering Techniques

  1. KayodeOshinubi Northern Arizona University
    "Forecasting Mosquito Population in Maricopa County Using Climate Factors and Filtering Techniques"
    2. Mosquito-borne diseases pose a significant public health challenge, and effective prevention requires accurate forecasting of mosquito populations. In this study, we developed a statistical forecasting framework that leverages climate factors, such as temperature and precipitation, to improve mosquito population predictions in Maricopa County, Arizona. Our approach combines adaptive modeling techniques and filtering methods to infer precise model parameters and address previously observed limitations, particularly the inability to capture spring dynamics. By incorporating an Ensemble Kalman Filter (EnKF) method, we estimated time-varying parameters (baseline population growth rate) and static parameters while resolving the spring problem observed in prior models. Using Generalized Additive Models (GAMs), we forecasted the baseline population growth rate on a weekly basis, integrating precipitation and temperature data as covariates. These forecasts were further used to run a mechanistic ordinary differential equation (ODE) model to predict mosquito abundance and estimate associated uncertainties. Our iterative framework was applied weekly over a 52-week period, successfully capturing seasonal variations in mosquito populations from 2014 to 2015. The EnKF demonstrated superior performance compared to traditional Markov Chain Monte Carlo (MCMC) approaches for fitting mosquito abundance data. This enhanced methodology provides actionable insights for public health decision-makers, supporting resource allocation and improving outcomes in mosquito-borne disease prevention. Our findings underscore the value of integrating climate data and adaptive filtering techniques to address forecasting challenges, ultimately enabling more effective responses to emerging or reemerging pathogens of mosquito-borne disease risks, which can be driven by human behavior to become a pandemic.

ECOP-23:
Modelling electrostatic sensory interactions between plants and polinators: a guide from AAA to Bee

  1. RyanPalmer University of Bristol, UK
    "Modelling electrostatic sensory interactions between plants and polinators: a guide from AAA to Bee"
    2. Plant-arthropod relationships are crucial to the health of global ecosystems and food production. Through co-evolution, arthropods have acquired a variety of novel senses in response to the emergence of floral cues such as scent, colour and shape. The recent discovery that several terrestrial arthropods can sense electrical fields (e-fields) motivates the investigation of floral e-fields as part of their wider sensory ecology. That is, how does a flower's morphology and material properties produce and propagate detectable, ecologically relevant electrical signals? To investigate this, we modelled the e-field interior and exterior of a flower using a novel modification of the popular AAA-least squares algorithm, extending it to two domain boundary value problems. Physically, flowers typically act as dielectrics that inductively charge in the presence of a background electrical field, e.g. charged pollinators or the Earth's atmospheric potential gradient. We therefore present the development and application of this new method for these cases and discuss the biological relevance of the results for sensory and ecological studies. Our adapted AAA algorithm gives accurate and rapid results dependent on only three parameters: the relative permittivity of the flower, flower shape and the location of the pollinator(s). The results show how flowers display distinct information about their morphology, pollen availability and nearby pollinators, at distance, through the perturbed e-field. As well as how predators, such as the crab spider, can use flowers to mask their own electrical presence and draw in unsuspecting prey. The results of the two-dimensional AAA method also shows good qualitative agreement with equivalent three-dimensional finite element models. Biologically, our results highlight the significant role floral electrics may play in plant-pollinator and predator-prey relationships, unveiling previously unstudied facets.

ECOP-24:
Epistasis and the Emergence of Evolutionary Capacitance

  1. SwatiPatel Oregon State University
    "Epistasis and the Emergence of Evolutionary Capacitance"
    2. In the 90s, several experiments suggested a hypothesis that certain genes function to mask or buffer the effects of mutations, thereby allowing them to accumulate and be stored. These were termed evolutionary capacitors and addressed the fundamental evolutionary problem of how populations optimize fitness in one environment while maintaining variation to adapt to another. However, more recent experiments support an alternative hypothesis that such buffering of mutations is a natural and unsurprising outcome of epistasis and the mutation-selection process. To quantitatively test this hypothesis, we develop a mathematical framework that extends a classical partial differential equation of the mutation-selection process to account for epistasis. Using a perturbation method on steady state solutions, we show that certain types of epistatic interactions and selection pressures will lead to the emergence of the evolutionary capacitance phenomena.

ECOP-25:
Impacts of Social Organization and Competition on Social Insect Population Dynamics

  1. TamanthaPizarro Arizona State University
    "Impacts of Social Organization and Competition on Social Insect Population Dynamics"
    2. In this study, we utilize the species Pogonomyrmex californicus, a type of queen ant that exhibits two distinct behavioral subtypes, as inspiration for our mathematical model. The first, known as solitary queens, establish colonies independently and represent the ancestral lineage. The second subtype, cooperative queens, form groups that collectively found a single colony—an evolutionary adaptation. Laboratory experiments have revealed that these queen types display distinct behavioral traits, or personalities. To better understand the ecological implications of these differences, we develop an ordinary differential equations (ODE) model that incorporates the effects of resource availability and social organization among adult ants, particularly in relation to brood care and foraging behaviors. Our model allows for Hopf bifurcations, enabling us to analyze the conditions under which colony coexistence is promoted or collapses. With this framework, we seek to address the following key questions: How does dependency on resource availability impact the survival of each queen type? How do social organization and resource dependency together influence queen survival, and what new conditions must be met for their persistence?

ECOP-26:
Evolutionary dynamics at the leading edge of biological invasions

  1. SilasPoloni University of Victoria
    "Evolutionary dynamics at the leading edge of biological invasions"
    2. Empirical evidence shows that evolution may take place during species' range expansion. Indeed, dispersal ability tends to be selected for at the leading edge of invasions, ultimately increasing a species' spreading speed. However, for organisms across many different taxa, higher dispersal comes at the cost of fitness, producing evolutionary trade-offs at the leading edge. Using reaction-diffusion equations and adaptive dynamics, we provide new insights on how such evolutionary processes take place. We show how evolution may drive phenotypes at the leading edge to maximize the asymptotic spreading speed, and conditions under which phenotypic plasticity in dispersal is selected for under different dispersal-reproduction trade-off scenarios. We provide some possible future research directions and other systems where the framework can be applied.

ECOP-27:
Connectivity, conservation, and catch: understanding the effects of dispersal between harvested and protected patches

  1. FemkeReurik Osnabrueck University
    "Connectivity, conservation, and catch: understanding the effects of dispersal between harvested and protected patches"
    2. Overharvesting is a pressing global problem, and spatial management, such as protecting designated areas, is one proposed solution. This talk examines how dispersal between protected and harvested areas affects the asymptotic total population size and the asymptotic yield, which are key questions for conservation management and the design of protected areas. We utilize a two-patch model with heterogeneous habitat qualities, symmetric dispersal and density-dependent growth functions in both discrete and continuous time. One patch is subject to proportional harvesting, while the other one is protected. Our results demonstrate that increased dispersal does not always increase the asymptotic total population size or the asymptotic yield. Depending on the circumstances, dispersal enables the protected patch to rescue the harvested patch from overexploitation, potentially increasing both total population size and yield. However, high levels of dispersal can also lead to a lower total population size or even cause extinction of both patches if harvesting pressure is strong. The population in the protected patch needs to have high reproductive potential and the patch needs to be the effectively larger patch in order to benefit monotonically from increased dispersal. These findings provide a fundamental understanding of how dispersal influences dynamics in fragmented landscapes under harvesting pressure.

ECOP-28:
A Qualitative Analysis Exploring the Hidden Threats of Methane to Ecosystems.

  1. PranaliRoy Chowdhury University of Alberta, Edmonton, Canada
    "A Qualitative Analysis Exploring the Hidden Threats of Methane to Ecosystems."
    2. Methane, a potent greenhouse gas (GHG), is now driving climate change at an unprecedented rate. With a warming potential greater than carbon dioxide, it poses a substantial threat to the functioning of ecosystems. Despite its importance, studies investigating its direct impact on species interactions within ecosystems are rare. This growing concern highlights the need for a comprehensive understanding of the factors that could disrupt food chains, ultimately impacting ecosystem stability and resilience. In this talk, I will address this gap by developing a mechanistic model that integrates methane dynamics with the populations of species and detritus. This novel approach offers a framework for understanding how gaseous pollutants like methane influence trophic interactions. The model is studied for a range of concentrations of methane. Our findings reveal that low concentrations of methane can benefit species growth as an alternative carbon source. However, moderate to high levels induce sub-lethal to lethal effects. Further, analyzing the mechanisms for long transients in the fast-intermediate-slow formulation of the model, I will discuss how faster methane accumulation in water can result in slower species growth.

ECOP-29:
Modeling biofilm growth and microbially induced corrosion in wastewater concrete pipes: a double free boundary problem

  1. FabianaRusso University of Naples Federico II
    "Modeling biofilm growth and microbially induced corrosion in wastewater concrete pipes: a double free boundary problem"
    2. Microbially induced corrosion (MIC) is a significant global issue impacting infrastructure, economies, and environment. In wastewater systems, MIC is primarily associated with biofilm formation on concrete sewer pipes, leading to severe degradation due to microbial metabolic activity. The proliferation of sewer biofilms occurs in both submerged and unsubmerged conditions, leading to distinct microbial communities. Commonly, these biofilms host microorganisms such as fermentation bacteria, hydrogen-producing acetogens, denitrifying bacteria, sulfate-reducing bacteria, sulfur-oxidizing bacteria, and methanogens. In particular, sulfur-oxidizing bacteria play a crucial role in corrosion, as they oxidize hydrogen sulfide from wastewater effluents, generating sulfuric acid that accelerates concrete deterioration. A one-dimensional model with double free boundaries has been developed to investigate the proliferation of biofilms and the related corrosion process in wastewater concrete pipes. The domain is composed of two free boundary regions: a biofilm that grows towards the interior cavity of the pipe, sitting on a gypsum layer formed by corrosion, which penetrates the concrete pipe. Diffusion-reaction equations govern the transport and the metabolic production or consumption of dissolved substances, such as hydrogen sulfide, oxygen, and sulfuric acid within the biofilm layer. The biofilm free boundary tracks the growth of the microbial community, regulated by microbial metabolic activity and detachment phenomena. The corrosion process is incorporated into the model through a Stefan-type condition, which drives the advancement of the gypsum free boundary into the concrete pipe, governed by microbial production of sulfuric acid. Numerical simulations have been carried out to investigate the model behavior, encompassing the development and progression of the biofilm as well as the corrosion advancement, with the aim of elucidating the key factors governing both phenomena.

ECOP-3:
Stoichiometric theory in optimal foraging strategy

  1. ShohelAhmed University of Alberta
    "Stoichiometric theory in optimal foraging strategy"
    2. Understanding how organisms make choices about what to eat is a fascinating puzzle explored in this study, which employs stoichiometric modeling and optimal forag- ing principles. The research delves into the intricate balance of nutrient intake with foraging strategies, investigating quality and quantity-based food selection through mathematical models. The stoichiometric models in this study, encompassing pro- ducers and a grazer, unveils the dynamics of decision-making processes, introducing fixed and variable energetic foraging costs. Analysis reveals cell quota-dependent pre- dation behaviors, elucidating biological phenomena such as “compensatory foraging behaviors” and the “stoichiometric extinction effect”. The Marginal Value Theorem quantifies food selection, highlighting the profitability of prey items and emphasizing its role in optimizing foraging strategies in predator–prey dynamics. The environ- mental factors like light and nutrient availability prove pivotal in shaping optimal foraging strategies, with numerical results from a multi-species model contributing to a comprehensive understanding of the intricate interplay between organisms and their environment.

ECOP-30:
Decoding the spatial spread of cyanobacterial blooms in an epilimnion

  1. JacobSerpico University of Alberta
    "Decoding the spatial spread of cyanobacterial blooms in an epilimnion"
    2. Cyanobacterial blooms (CBs) pose significant global challenges due to their harmful toxins and socio-economic impacts, with nutrient availability playing a key role in their growth, as described by ecological stoichiometry (ES). However, real-world ecosystems exhibit spatial heterogeneity, limiting the applicability of simpler, spatially uniform models. To address this, we develop a spatially explicit partial differential equation model based on ES to study cyanobacteria in the epilimnion of freshwater systems. We establish the well-posedness of the model and perform a stability analysis, showing that it admits two linearly stable steady states, leading to either extinction or saturation. We use the finite elements method to numerically solve our system on a real lake domain derived from Geographic Information System (GIS) data and realistic wind conditions extrapolated from ERA5-Land. Our numerical results highlight the importance of lake shape and size in CB monitoring, while global sensitivity analysis using Sobol Indices identifies light attenuation and intensity as primary drivers of bloom variation, with water movement influencing early bloom stages and nutrient input becoming critical over time. This model supports continuous water-quality monitoring, informing agricultural, recreational, economic, and public health strategies for mitigating CBs.

ECOP-31:
Environmental “Knees” and “Wiggles” as Stabilizers of Species Range Limits Set by Interspecific Competition

  1. FarshadShirani Emory University
    "Environmental “Knees” and “Wiggles” as Stabilizers of Species Range Limits Set by Interspecific Competition"
    2. Whether interspecific competition is a major contributing factor in setting species' range limits has been debated for a long time. Theoretical studies using evolutionary models have proposed that the interaction between interspecific competition and disruptive gene flow along an environmental gradient can halt range expansion of ecologically related species where they meet. However, the stability of such range limits has not been well addressed. In this talk, I present our work on investigating the stability of competitively formed range limits using a deterministic model of adaptive range evolution. We show that the range limits are unlikely to be evolutionarily stable if the environmental optima for fitness-related traits vary linearly in space. However, we demonstrate that environmental nonlinearities such as “knees” and “wiggles”, wherein an isolated sharp change or a step-like change occurs in the steepness of a trait optimum, can strongly stabilize the range limits. We show that the stability of the range limits established at such nonlinearities is robust against moderate environmental disturbances. Although strong climatic changes can still destabilize the range limits, such destabilization depends on how the relative dominance of the competing species changes across the environmental nonlinearity. Therefore, our results highlight the importance of measuring the competitive ability of species when predicting their response to climate change.

ECOP-32:
A modified May Holling Tanner Model: the role of dynamic alternative resources on species' survival

  1. AnurajSingh ABV-IIITM Gwalior, India
    "A modified May Holling Tanner Model: the role of dynamic alternative resources on species' survival"
    2. The present paper investigates the dynamical behavior of the modified May Holling Tanner model in the presence of dynamic alternative resources. We study the role of dynamic alternative resources on the survival of the species when there is prey rarity. Detailed mathematical analysis and numerical evaluations, including the situation of ecosystem collapsing, have been presented to discuss the coexistence of species', stability, occurrence of different bifurcations (saddle-node, transcritical, and Hopf) in three cases in the presence of prey and alternative resources, in the absence of prey and in the absence of alternative resources. It has been obtained that the multiple coexisting states and their stability are outcomes of variations in predation rate for alternative resources. Also, the occurrence of Hopf bifurcation, saddle-node bifurcation, and transcritical bifurcation are due to variations in the parameters of dynamic alternative resources. The impact of dynamic alternative resources on species' density reveals the fact that if the predation rate for alternative resources increases, then the prey biomass increases (under some restrictions), and variations in the predator's biomass widely depend upon the quality of food items. This study also points out that the survival of predators is possible in the absence of prey. In the theme of ecological balance, the present study suggests some theoretical points of view for the eco-managers.

ECOP-33:
Should I stay, or should I go: Sex ratio response drives a diverse range (anti-)correlated intra-species behaviours

  1. BethStokes University of Bath
    "Should I stay, or should I go: Sex ratio response drives a diverse range (anti-)correlated intra-species behaviours"
    2. The decision of an individual or group to leave its current environment may be influenced by various factors. These include external or inter-species factors such as the presence of predators or food availability, and also intra-species dynamics like mate searching or the strength of social ties within a group. Understanding the consequences of these behaviours on the population level dynamics is non-trivial. In this study, we explore a stochastic model describing the movement of males and females of a species between localised patches, in which the movement rates are dependent on the sex ratio within the patch. By deriving a system of stochastic differential equations governing the fluctuations in these patches we can model a diverse range of intra-species behaviours driven solely by an individual's response to local sex ratio. We subsequently uncover and explore how different individual behaviours can give rise to large scale (anti-)correlated movements between the sexes.

ECOP-34:
Towards Quantitative and Predictive Models of Tumour Ecology: A Framework for Calibrating Evolutionary Game Theory with Experimental Data

  1. MaximilianStrobl Cleveland Clinic
    "Towards Quantitative and Predictive Models of Tumour Ecology: A Framework for Calibrating Evolutionary Game Theory with Experimental Data"
    2. Tumours are complex ecosystems where diverse cancer cell subpopulations interact with each other and with non-cancer cells around them. Evolutionary game theory (EGT) has established itself as a powerful mathematical framework to study the implications of such ecological interactions, demonstrating an important role in shaping oncogenesis and treatment response. However, much of this work has been theoretical using parameters that are only loosely grounded in biological data. To move towards quantitative and predictive models of tumour ecology it is crucial to develop theoretical and experimental methodology to empirically calibrate and validate EGT models. We present an in silico study to optimize the 'Game Assay' for measuring ecological interactions between cancer cell populations in vitro. This assay, originally developed by Kaznatcheev et al (2017), involves co-culturing populations at different ratios, monitoring growth rates via time-lapse microscopy, and inferring frequency-dependent interactions. We begin by characterizing the accuracy and precision of this assay in a simulation study in which we use the replicator equation as the “ground truth”. Our simulations reveal potential biases in estimating fitness differences and interaction parameters, highlighting the need for careful experimental design. We provide guidelines for optimizing seeding ratios, number of replicates, and frequency of measurements, and present a new analysis techniques to improve the accuracy and precision of interaction measurements. Finally, we apply our optimized protocol to quantify interactions between 4 drug-sensitive and resistant lung cancer cell lines, revealing diverse ecological dynamics. This work demonstrates the power of integrating mathematical modeling with experimental approaches to develop robust empirical protocols and gain a quantitative understanding of tumour ecology.

ECOP-35:
Phototaxis-Driven Dynamics in Phototrophic Biofilms: Modeling Invasion and Light-Dependent Behavior of Planktonic Cells

  1. AlbertoTenore Department of Mathematics and Applications, University of Naples Federico II, Italy
    "Phototaxis-Driven Dynamics in Phototrophic Biofilms: Modeling Invasion and Light-Dependent Behavior of Planktonic Cells"
    2. Phototaxis, the ability of microorganisms to move in response to light, plays a crucial role in shaping the dynamics of phototrophic biofilms. While sessile cells remain typically embedded within the extracellular polymeric matrix, planktonic cells can navigate through the biofilm’s porous structure, adjusting their position in response to light cues. This directed movement optimizes exposure to favorable light conditions while avoiding harmful intensities, influencing the spatial organization and development of the biofilm community. In this talk, I will present a mathematical model for planktonic cell invasion in biofilms, where phototaxis acts as a driver of directed motility. The model incorporates a volume-filling term into the transport equation for planktonic cells, enabling the representation of phototactic behavior. A light-dependent sensitivity function captures both positive and negative phototaxis, governing cell movement toward favorable light conditions and away from excessive illumination. The biofilm is modeled as a homogeneous, viscous, incompressible fluid, with velocity described by Darcy’s law. The governing equations are solved numerically to explore the role of phototaxis in shaping biofilm dynamics. Numerical simulations reveal that motile cells accumulate in well-lit regions, enhancing sessile phototrophic growth and promoting biofilm development. The distribution of phototrophic biomass results from the interplay between random diffusion and phototactic movement. Under high-light stress conditions, photoinhibition reduces phototrophic growth and reverses phototaxis, slowing overall biofilm growth. Additionally, biofilm density modulates light penetration, either limiting phototrophic growth or providing protection against excessive exposure. These findings offer valuable insights into biofilm behavior in natural environments and can guide the optimization of biofilm-based processes in fields like wastewater treatment and bioremediation.

ECOP-36:
The Role of Environmental Stress in Promoting Mutators Through Evolutionary Rescue

  1. MarwaTuffaha York University
    "The Role of Environmental Stress in Promoting Mutators Through Evolutionary Rescue"
    2. Evolutionary rescue occurs when a population facing environmental stress avoids extinction by rapidly acquiring beneficial mutations. While higher mutation rates can enhance rescue, the role of mutators—genotypes with elevated mutation rates—remains unclear. We develop a theoretical framework and use stochastic simulations to investigate how mutators emerge and fix under selective pressure. Our results show that mutators cannot persist in stable environments but are favored when environmental deterioration occurs, with their fixation probability influenced by the speed of environmental change and wildtype mutation rates. Pre-existing mutators further increase rescue likelihood, particularly under rapid environmental shifts. These findings provide insights into antibiotic resistance, cancer evolution, and adaptation to climate change by highlighting how environmental stress shapes mutation rate evolution.

ECOP-37:
Mathematical Model for Gene Drive Mosquito Releae On Principe Island

  1. SureniWickramasooriya University of California - Davis
    "Mathematical Model for Gene Drive Mosquito Releae On Principe Island"
    2. Genetically engineered mosquitoes (GEMs) offer a promising malaria control strategy, yet their ecological interactions, dispersal, and long-term effects remain uncertain. Accurate modeling is essential to optimize GEM release strategies and assess their effectiveness in natural ecosystems. This study presents a high-performance, exascale agent-based model (ABM) simulating gene drive dynamics in wild mosquito populations. Incorporating mosquito population dynamics, spatial ecology, and genotype inheritance, the model provides insights into optimizing release timing, locations, and dispersal strategies. Our findings indicate that under optimal dispersal conditions, GEMs can achieve a 95% prevalence in wild populations within 112 days. Furthermore, our findings indicate that strategically coordinating GEM releases across multiple sites does not significantly impact gene drive establishment on the island. By capturing mosquito behaviors and movement in heterogeneous environments, this ABM serves as a powerful tool for evaluating GEM interventions, supporting evidence-based malaria control strategies, and enhancing ecological understanding of gene drive propagation..

ECOP-38:
Dynamical Analysis of Additional Food Models with Mutual Interaction in Predator-Prey Systems for Pest Control

  1. SureniWickramasooriya Univeresity of California - Davis
    "Dynamical Analysis of Additional Food Models with Mutual Interaction in Predator-Prey Systems for Pest Control"
    2. The supplementation of additional food (AF) to introduced predators has been explored as a strategy to enhance pest control. However, AF models with prey-dependent functional responses can lead to unbounded predator growth. To address this, an AF model incorporating mutual interference has been proposed, demonstrating that pest eradication is feasible when the AF quantity ξξ exceeds a threshold function of the interference parameter ϵϵ. In this study, we revisit and extend this model, uncovering novel dynamical behaviors. We show that pest eradication occurs within a narrower AF range and can be bi-stable or globally attracting, arising through a saddle-node bifurcation. Additionally, we identify Hopf and global homoclinic bifurcations, revealing a unique dynamic where the pest extinction state becomes an 'almost' global attractor. This is the first analytical proof of such a structure in AF models, providing insights into bio-control strategies under varying predator interference conditions.

ECOP-39:
Cyanobacteria Hot Spot Detection Integrating Remote Sensing Data with Convolutional and Kolmogorov-Arnold Networks

  1. BrianZambrano University of Alberta
    "Cyanobacteria Hot Spot Detection Integrating Remote Sensing Data with Convolutional and Kolmogorov-Arnold Networks"
    2. Monitoring cyanobacterial blooms promptly and accurately is crucial for public health management and understanding aquatic ecosystem dynamics. Remote sensing, particularly satellite observations, offers a viable approach for continuous monitoring. This study utilizes multispectral images from the Sentinel-2 satellite constellation in conjunction with ERA5-Land data to facilitate broad-scale data collection. We proposed a simple deep convolutional neural network (CNN) architecture to analyze cyanobacteria (CB) concentration dynamics in Pigeon Lake, Canada, over a five-year period. Utilizing the Local Getis-Ord statistic, we identified and analyzed trends in hot and cold spots under the null hypothesis of random distribution. We observed changes in the distribution and median CB concentration in hot spots over time. Additionally, we trained a Kolmogorov-Arnold Network (KAN) to classify segments of the lake shoreline into hot and non-hot spots using the Dynamic World dataset within a 500-meter radius of the lake.

ECOP-4:
Personality-Driven Consumer-Resource Dynamics

  1. ShohelAhmed University of Alberta
    "Personality-Driven Consumer-Resource Dynamics"
    2. To comprehend the mechanisms driving biodiversity and ecosystem resilience in a rapidly changing world, it is essential to explore the behavioral diversity among individuals in greater depth. Consistent individual differences in behavior, often referred to as animal personality, play a crucial role in shaping ecological and evolutionary dynamics, particularly in foraging behavior. Traditional approaches in behavioral and evolutionary ecology typically focus on average behavior, neglecting the significance of individual variability. This study explores the influence of consumer personality on ecological dynamics, specifically examining how variations in food availability affect behavioral strategies and ecosystem functioning. We develop a resource-consumer model that incorporates personality-dependent saturating attack rates based on the mean-field ratio of resources to consumers. The well-posedness of the model is established, and we analyze the existence and stability of all steady-state solutions. Through bifurcation analysis, we identify critical transition parameters and describe the nonlinear phenomena induced by personality-dependent attack rates. Our findings demonstrate that boldness in consumers enhances their persistence, particularly under low levels of boldness, where populations can survive even with moderate or high food supply, which was not captured in classical frameworks.

ECOP-40:
Experimental and theoretical investigations of rotating algae biofilm reactors (RABRs): Areal productivity, nutrient recovery, and energy efficiency

  1. JiaZhao University of Alabama
    "Experimental and theoretical investigations of rotating algae biofilm reactors (RABRs): Areal productivity, nutrient recovery, and energy efficiency"
    2. Microalgae biofilms have been demonstrated to recover nutrients from wastewater and serve as biomass feedstock for bioproducts. However, there is a need to develop a platform to quantitatively describe microalgae biofilm production, which can provide guidance and insights for improving biomass areal productivity and nutrient uptake efficiency. In this talk, I will introduce a unified experimental and theoretical framework to investigate algae biofilm growth on a rotating algae biofilm reactor (RABR). Experimental laboratory setups are used to conduct controlled experiments on testing environmental and operational factors for RABRs. We propose a differential–integral equation‐based mathematical model for microalgae biofilm cultivation guided by laboratory experimental findings. The predictive mathematical model development is coordinated with laboratory experiments of biofilm areal productivity associated with ammonia and inorganic phosphorus uptake by RABRs. The unified experimental and theoretical tool is used to investigate the effects of RABR rotating velocity, duty cycle (DC), and light intensity on algae biofilm growth, areal productivity, nutrient uptake efficiency, and energy efficiency in wastewater treatment.

ECOP-41:
Matched asymptotic analysis of the Luria–Delbrück distribution in a reversible fluctuation assay

  1. PavolBokes Comenius University
    "Matched asymptotic analysis of the Luria–Delbrück distribution in a reversible fluctuation assay"
    2. We study a fluctuation test where cell colonies grow from a single cell to a specified population size before being treated. During growth, cells may acquire resistance to treatment and pass it to offspring, with a small probability. Unlike the classical Luria–Delbrück test, we allow the resistant state to revert to a drug-sensitive state, motivated by recent research on drug tolerance in cancer and microbes. This modification does not change the central part of the Luria–Delbrück distribution, where the Landau probability density function approximation still applies. However, the right tail of the distribution deviates from the power law of the Landau distribution, with the correction factor equal to the Landau cumulative distribution function. We use singular perturbation theory and asymptotic matching to derive uniformly valid approximations and describe tail corrections for populations with different initial cell states.

ECOP-42:
SAMtasia: A Transformer-based Pipeline for Automatic Data Acquisition

  1. PabloCuriel University of California, Merced
    "SAMtasia: A Transformer-based Pipeline for Automatic Data Acquisition"
    2. As temperatures rise, photosymbiotic marine species are presented with unique challenges. Exaptasia diaphana is a highly adaptable model organism for studying these challenges. Current methods for acquiring experimental data are expensive and require sacrificing the organism. This work focuses on the development of computational tools that will reduce cost and automate the acquisition of data. A pipeline consisting of a convolutional neural network and a transformer-based model is used to accomplish this task. Given input images of aiptasia colonies, this pipeline automatically produces accurate segmentations of aiptasia that can be used for experimental data acquisition (e.g. obtaining counts, measuring oral disk size and color information, etc.).

ECOP-43:
Kelly bets and optimal information processing in biological systems

  1. AndrewEckford York University
    "Kelly bets and optimal information processing in biological systems"
    2. In an information-processing investment game, such as the growth of a population of organisms in a changing environment, Kelly betting maximizes the expected log rate of growth. In this work, we show that Kelly bets are closely related to optimal single-letter codes (i.e., they can achieve the rate-distortion bound with equality). Thus, natural information processing systems with limited computational resources can achieve information-theoretically optimal performance. We show that the rate-distortion tradeoff for an investment game has a simple linear bound, and that the bound is achievable at the point where the corresponding single-letter code is optimal. This interpretation has two interesting consequences. First, we show that increasing the organism's portfolio of potential strategies can lead to optimal performance over a continuous range of channels, even if the strategy portfolio is fixed. Second, we show that increasing an organism's number of phenotypes (i.e., its number of possible behaviours in response to the environment) can lead to higher growth rate, and we give conditions under which this occurs. Examples illustrating the results in simplified biological scenarios are presented.

ECOP-44:
A Bayesian framework to model transmissible cancer dynamics within Mya arenaria populations

  1. CarissaMayo University of Washington
    "A Bayesian framework to model transmissible cancer dynamics within Mya arenaria populations"
    2. Bivalve Transmissible Neoplasia (BTN) is an increasingly prevalent cancer spreading among bivalve species worldwide. BTN dynamics introduce complexities not common in many other infectious diseases due to its marine environment. Therefore, little is known about its transmission dynamics and population effects. This study develops a Bayesian framework to model BTN spread within Mya arenaria populations to address key gaps in our understanding, with a focus on statistical methodology for parameter inference and model development. We use a Bayesian compartmental modeling approach to infer and refine model parameters and leverage controlled laboratory and survey data. Laboratory data provide information on cancer cell emission, disease progression and environmental factor effects on the disease. Survey data from the field includes samples from East Coast sites that are used for fitting the model to disease progression over time in its natural environment. To capture the intricacies of BTN, our model framework builds on the traditional Susceptible-Exposed-Infectious (SEI) epidemiological model by incorporating cancer particle survivability components and the environmental effects of temperature. The Bayesian approach in our model development, implemented in STAN, provides parameter inferences and quantifies uncertainty in the results amidst limited or noisy ecological data. Future work will validate the model by comparing its predictions with 2025 survey data and conducting sensitivity analyses to identify key parameters. This statistical framework not only advances our understanding of BTN, but also demonstrates the applicability of Bayesian modeling in developing complex ecological and epidemiological compartmental models.

ECOP-45:
Predicting enhanced wetland greenhouse gas emissions in response to climate change

  1. Gordon R.McNicol University of Waterloo
    "Predicting enhanced wetland greenhouse gas emissions in response to climate change"
    2. Wetlands are characterised by the interaction of soil with seasonal or permanent water bodies and serve several crucial ecological functions including flood prevention and water filtration. Moreover, serving as a boundary between land and aquatic environments, they provide diverse ecosystems for a variety of plants, animals and microbes. However, the ability of wetlands to sequester and store carbon also secures their place as the largest natural source of methane emissions. These emissions are strongly dependent on both the relative depth of the water table to the soil and the soil temperature, with submerged warm soil providing favourable anaerobic conditions for methanogenesis by microbes and detrimental to methane consumption through oxidation. Hence, wetland methane emissions are strongly susceptible to climate change, particularly changes in rainfall and temperature. We present a mathematical model to describe the stochastic movement of the water table coupled to a simple set of ODEs describing methane production, oxidation, and emission, parameterised by this water table depth. We employ this model to predict how the inherent variations in water table depth due to the soil profile leads to changing emission profiles across individual wetlands. Moreover, by exploring the sensitivity of these emissions to rainfall and temperature changes, we demonstrate during wetland conservation efforts the need to consider how climate change will influence emissions.

ECOP-46:
Modeling a Novel Gene Drive Targeting Immune Responses to Increase Confidence in Local Confinement

  1. CaseyO'Brien NCSU
    "Modeling a Novel Gene Drive Targeting Immune Responses to Increase Confidence in Local Confinement"
    2. Gene drive technologies hold promise for controlling invasive pests, mitigating disease transmission, and protecting local ecosystems and agriculture. However, their deployment hinges on resolving safety concerns, particularly the risk of unintended spread into non-target populations. Current confinement strategies rely largely on invasion thresholds which take advantage of unstable equilibrium points in allele frequency, below which the drive will not spread. This maintains local confinement by preventing migrants from spreading the drive in surrounding populations. While this is an effective strategy for gene drives meant to introduce a trait to a population, its success has been more limited in suppression gene drives. We circumvent this issue by designing a novel suppression drive system that targets the immune response of an organism to a local stressor (i.e., endemic virus, fungus, or a specialized parasitoid). The drive system increases the target organism’s susceptibility to the stressor by increasing the likelihood of acquiring the infection or the impact of infection on the organism. This means that the drive system’s fitness cost is dependent on the abundance of the stressor. We model several drive systems to consider the efficacy of the system in different settings.

ECOP-47:
Modeling the spread of Hemlock Woolly Adelgid

  1. KwameOsei Bonsu Eastern Connecticut State University
    "Modeling the spread of Hemlock Woolly Adelgid"
    2. In this paper, a mathematical model is proposed to explain the interaction between Eastern Hemlock Trees and the invasive species Hemlock Woolly Adelgid. A system of reaction diffusion equations is used for this modeling exercise. There are at most three (3) steady states for the system of which the coexistent state is the only stable steady states for some parameter values. The model dynamics show that the solutions exhibit traveling wave solutions. In addition, a sensitivity analysis is conducted to determine the impact of model parameters. Sensitivity analysis suggests that the mortality rate of Eastern Hemlock Trees and the predation intensity of Hemlock Woolly Adelgid drive the dynamics of the interaction. Since Eastern Hemlock trees are foundation tress that provide shelter for several species they need to be protected for as long as possible. Based on the model dynamics and sensitivity analysis, it is postulated that selective and strategic removal of these trees will help curtail their destruction.

ECOP-48:
Assessing Biological Control through Additional Food and Harvesting in Cannibalistic Natural Enemy-Pest Model

  1. DeepakTripathi ABV-Indian Institute of Information Technology and Management Gwalior, M.P., India
    "Assessing Biological Control through Additional Food and Harvesting in Cannibalistic Natural Enemy-Pest Model"
    2. In the present work, using the theory of dynamical system, we discuss the dynamics of a cannibalistic predator prey model in the presence of linear harvesting schemes in prey (pest) population and provision of additional food to predators (natural enemies). A detailed mathematical analysis and numerical evaluations have been presented to discuss the pest free state, coexistence of species, stability, occurrence of different bifurcations (saddle-node, transcritical, Hopf, Bogdanov-Takens) and the impact of additional food and harvesting schemes on the dynamics of the system. Interestingly, we also observe that the pest population density decreases immediately even when small amount of harvesting is implemented. Also the eradication of pest population (stable pest free state) could be achieved via variation in the additional food and implemented harvesting schemes. The individual effects of harvesting parameters on the pest density suggest that the linear harvesting scheme is more effective to control the pest population rather than constant and nonlinear harvesting schemes.

ECOP-49:
Predator-prey dynamics in patchy forests subject to fire and forestry disturbances

  1. ChristianWiewelhove UBC Okanagan
    "Predator-prey dynamics in patchy forests subject to fire and forestry disturbances"
    2. Our work focuses on modelling how predator-prey dynamics are affected by the frequency and impact of forest disturbances such as forestry and wildfires and the subsequent regeneration of the forest. Our focus is on the Goshawk- Pine Squirrel predator-prey system. The coastal subspecies of the Goshawk is listed as endangered and their preferred habitat, mature, dense coniferous forests, is commonly disturbed. Climate change is increasing the frequency and severity of wildfires as well as periods of drought and heavy rainfall, leading to new challenges for sustaining Goshawk habitat. We present a modelling approach examining the dynamics of Goshawks and Squirrels within a disturbed landscape, and determine how different types of disturbance affect persistence of both species. An additional complicating factor that we take into account is the territoriality of Squirrels and Goshawks. Ultimately, we hope that the final model will inform conservation efforts for the Goshawk and, more generally, for predator-prey systems that rely on mature forest.

ECOP-5:
Effect of Climate Warming on Mosquito Population Dynamics in Newfoundland

  1. JosephBaafi Memorial University of Newfoundland
    "Effect of Climate Warming on Mosquito Population Dynamics in Newfoundland"
    2. Mosquitoes are key vectors of several infectious diseases affecting humans and animals. In North America, Culex mosquitoes are primary vectors of West Nile virus, St. Louis encephalitis, and Japanese encephalitis, as well as viral diseases in birds and horses. The Culex mosquito life cycle consists of four stages: eggs, larvae, pupae, and adults, each with unique development and mortality rates. Only active (non-diapausing) adults can reproduce, and environmental factors such as temperature, photoperiod, and rainfall influence population dynamics and stage-specific abundances. We develop a data-driven, stage-structured model that incorporates experimental data to describe how key climate variables regulate life history parameters. Specifically, egg laying rates depend on temperature, while maturation and survival rates are influenced by both temperature and rainfall. Mortality is temperature-dependent, and diapause induction and reactivation rates in adults are driven by temperature and photoperiod. Unlike many previous models that focus on tropical mosquitoes, our study explicitly includes diapause, a dormancy period in adult Culex mosquitoes essential for accurate modelling of temperate mosquito populations. Our results show that mosquito populations peak during summer months when temperatures exceed 10°C. Seasonal fluctuations in abundance highlight the need for adaptive vector control strategies. Since control measures often target specific life stages, such as larvicides for larvae or insecticides for adults, our findings suggest that optimal intervention strategies should vary by season to effectively reduce mosquito populations and disease risk.

ECOP-6:
Estimating potential myrtle rust impacts to carbon sequestration in Australia

  1. ChrisBaker The University of Melbourne
    "Estimating potential myrtle rust impacts to carbon sequestration in Australia"
    2. The impacts of invasive alien pests and diseases are routinely estimated and measured in the context of agriculture, but less so in the context of biodiversity and ecosystem services. In this study, we developed a new 'contribution modelling' approach to systematically estimate the impacts of pests and diseases at a continent scale. We developed this method using a case study of myrtle rust in Australia. We estimated the potential reduction of carbon sequestration in Australia due to myrtle rust using various national and scientific ecological datasets. We found that myrtle rust could lead to over 3% loss in national annual carbon sequestration if it were to spread across Australia, or over $700 million AUD value loss. While developed using a case study, this model is designed to be readily adaptible to other species and their impact on other environmental assets. Our work shows the need to systematically compile the potential impacts and costs of invasive pests and diseases to the environment and ecosystem services globally, to support both biosecurity decision-making and climate-change related initiatives such as net-zero emissions targets and reforestation efforts.

ECOP-7:
Effect of productivity and seasonal variation on phytoplankton intermittency in a microscale ecological study using closure approach

  1. SandipBanerjee Indian Institute of Technology Roorkee
    "Effect of productivity and seasonal variation on phytoplankton intermittency in a microscale ecological study using closure approach"
    2. A microscale ecological study using the closure approach to understand the impact of productivity controlled by geographical and seasonal variations on the intermittency of phytoplankton will be presented in this talk. Using this approach for a nutrient–phytoplankton model with Holling type III functional response, it has been shown how the dynamics of the system can be affected by the environmental fluctuations triggered by the impact of light, temperature, and salinity, which fluctuate with regional and seasonal variations. Reynold’s averaging method in space, which results in expressing the original components in terms of its mean (average value) and perturbation (fluctuation) has been used to determine the impact of growth fluctuation in phytoplankton distribution and in the intermittency of phytoplankton spreading (variance). Parameters are estimated from the nature of productivity and spread of phytoplankton density during field observation done at four different locations of Tokyo Bay. The model validation shows that our results are in good agreement with the field observation and succeeded in explaining the intermittent phytoplankton distribution at different locations of Tokyo Bay, Japan, and its neighboring coastal regions.

ECOP-8:
Heterogeneity in temporally fluctuating environments

  1. AlexanderBrowning University of Melbourne
    "Heterogeneity in temporally fluctuating environments"
    2. Many biological systems regulate phenotypic heterogeneity as a fitness-maximising strategy in uncertain and dynamic environments. Analysis of such strategies is typically confined both to a discrete set of environmental conditions, and to a discrete (often binary) set of phenotypes specialised to each condition. In this talk, we extend on both fronts to encapsulate both a discrete and continuous spectrum of phenotypes arising in response to two broad classes of environmental fluctuations that drive changes in the phenotype-dependent growth rates. We present a series of analytical and semi-analytical results that reveal regimes in which both discrete and continuous phenotypic heterogeneity is evolutionary advantageous.

ECOP-9:
Animal movement models with spatiotemporal memory

  1. KyunghanChoi Postdoctoral Research Fellow/ University of Alberta
    "Animal movement models with spatiotemporal memory"
    2. In this study, we examine how explicit spatial memory influences different mathematical models in various ecological dispersal contexts. Specifically, we analyze three memory-based dispersal strategies: (1) gradient-based movement, where individuals respond to environmental gradients; (2) environment matching, which promotes a uniform population distribution; and (3) location-based movement, where decisions are based solely on local suitability. These strategies correspond to diffusion-advection, Fickian diffusion, and Fokker-Planck diffusion models, respectively. Additionally, we explore steady-state problems for each strategy to highlight the differences between models incorporating temporal memory and those incorporating spatiotemporal memory.

IMMU-1:
Uncovering the impact of infection routes on within-host MPXV dynamics: insights from a mathematical modeling study

  1. QiDeng York University
    "Uncovering the impact of infection routes on within-host MPXV dynamics: insights from a mathematical modeling study"
    2. The unprecedented mpox outbreak in non-endemic regions during 2022-2023, which has seen a recent resurgence in late 2023-2024, poses a significant public health threat. Despite its global spread, the viral dynamics of mpox infection and the specific characteristics driving these outbreaks remain insufficiently explored. We develop mathematical models to examine the interactions between host immune responses and the virus across three distinct infection routes (intravenous, intradermal, and intrarectal). The models are calibrated using viral load data from macaques infected through each of these three infection routes. Subsequently, we calculate the infectiousness of each infected macaque, finding that the proportion of presymptomatic infectiousness is highest in those infected via sexual contact, followed by skin-to-skin contact. These observations demonstrate that close contact during sexual activity is a significant route of viral transmission, with presymptomatic spread playing a crucial role in the 2022-2023 multi-country outbreak and potentially also in the 2023-2024 multi-source outbreak. Leveraging model predictions and infectiousness data, we assess the impact of antiviral drugs on interventions against mpox infection. Model simulations suggest that early administration of antiviral drugs can reduce peak viral loads, even in individuals with compromised immunity, particularly in cases of infection through skin-to-skin and sexual contact. These results underscore the importance of initiating antiviral treatment as early as possible for mpox-infected patients with compromised immune systems, such as those who are HIV-positive.

IMMU-10:
The secrets of “OP7”, an influenza DIP: mathematical model, impact of mutations and antiviral mechanisms

  1. DanielRüdiger Max Planck Institute Magdeburg
    "The secrets of “OP7”, an influenza DIP: mathematical model, impact of mutations and antiviral mechanisms"
    2. Defective interfering particles (DIPs) are mutated, replication-incompetent virions that can inhibit their corresponding standard virus (STV). Previous studies have shown the effectiveness of DIPs against various virus species, highlighting them as promising broad-spectrum antivirals. OP7, an influenza DIP with 37 nucleotide substitutions in its segment 7 (S7) vRNA, has been found to suppress STV replication more effectively than conventional DIPs. However, the effects of these mutations on the replication of OP7 and its mechanism of interference with the STV remained unclear. In this study, we investigated the infection dynamics during a coinfection of influenza STV and OP7 in cell culture. We monitored the dynamics of viral RNAs, assessed viral protein levels, and determined virus titers. With these experimental results, we developed a mathematical model to simulate the coinfection of STV and OP7. Subsequently, we used this model to explore various hypotheses about the impact of mutations on virus replication and to predict the suppression of STV by OP7 in passaging experiments. In vitro experiments show that S7-OP7 surpasses the levels of all STV genome segments. Model simulations suggest this is induced by a significantly increased rate of replication, attributed to mutations in S7-OP7 inducing a “superpromoter”. Additionally, simulations predicted a notable reduction in viral mRNA transcription for S7-OP7, which was later validated experimentally. Moreover, we deduce that the M1 protein derived from S7-OP7 mRNA is likely defective. Lastly, the model accurately predicts the spread of OP7 and the suppression of STV in infected cell cultures over multiple passages under various initial conditions. In summary, we developed a mathematical model that enables a thorough examination of STV and OP7 coinfection, improves our understanding of DIP interference mechanisms, and supports the development of antiviral therapies.

IMMU-11:
Missed an antibiotic dose - what to do?

  1. Hwai-RayTung University of Utah
    "Missed an antibiotic dose - what to do?"
    2. What should you do if you miss a dose of antibiotics? Despite the prevalence of missed antibiotic doses, there is vague or little guidance on what to do when a dose is forgotten. In this paper, we consider the effects of different patient responses after missing a dose using a mathematical model that links antibiotic concentration with bacteria dynamics. We show using simulations that, in some circumstances, (a) missing just a few doses can cause treatment failure, and (b) this failure can be remedied by simply taking a double dose after a missed dose. We then develop an approximate model that is analytically tractable and use it to understand when it might be advisable to take a double dose after a missed dose.

IMMU-12:
Antihistamine Efficacy in Relation to the Morphology of Skin Eruptions in Chronic Spontaneous Urticaria

  1. YingXie Kyoto University
    "Antihistamine Efficacy in Relation to the Morphology of Skin Eruptions in Chronic Spontaneous Urticaria"
    2. Chronic spontaneous urticaria (CSU) is a persistent skin disorder characterized by red, itchy eruptions of various shapes, known as wheals. These wheals appear and disappear daily, persisting for months or even decades, and severely impact patients' quality of life. The standard treatment for CSU primarily consists of second-generation H1 antihistamines, often administered at higher-than-usual doses. However, approximately 30% of patients remain symptomatic despite these conventional therapies. On the other hand, our previous mathematical modeling and clinical studies have identified five distinct types of wheal shapes through the development of clinical criteria for eruption geometry (EGe Criteria) and have shown how the characteristics of each wheal type are involved in the pathophysiology of CSU. These findings suggest that CSU may be classified into five medical subtypes based on wheal morphology. Thus, in this study, we explore the effectiveness of antihistamines based on wheal shape. We first evaluate the efficacy of antihistamines in silico using three key measures: wheal area, itching severity, and wheal expansion dynamics, across the five identified wheal types. Additionally, we validate some of our theoretical observations using clinical data from patients. By elucidating the relationships among the key networks involved in CSU pathophysiology, wheal morphology, and drug efficacy, we can enhance the development of more accurate diagnostic tools and treatment strategies in clinical settings.

IMMU-13:
Analysing CD8+ T cell dynamics in cancer using distribution modelling

  1. NissrinAlachkar University Hospital Bonn, Institute of Experimental Oncology (IEO)
    "Analysing CD8+ T cell dynamics in cancer using distribution modelling"
    2. CD8+ T cells, also known as cytotoxic T cells, play a crucial role in fighting cancer by directly targeting and eliminating tumour cells. However, prolonged exposure to tumour antigens drives these cells into exhaustion, leading to the loss of their cytotoxic functions and subsequent tumour progression. The differentiation pathway undertaken by CD8+ T cells significantly influences the efficacy and persistence of the anti-tumour response. This pathway is shaped by collective inter- and intracellular decision-making processes within a complex dynamic network, involving interactions among various immune cell populations through direct cell-cell contact or signalling molecules such as cytokines. A mechanistic understanding of CD8+ T cell differentiation into specific phenotypic subsets, as well as the complex network governing this process, is essential. To address this, we develop a quantitative, data-driven mathematical model of CD8+ T cell population dynamics in response to cancer cells, capturing cell-cell interactions, cell proliferation, and T cell differentiation into effector or exhausted subsets. We analyse multiple possible network motifs governing CD8+ T cell differentiation and proliferation. In addition, we incorporate a response-time modelling approach, where the waiting-time distribution between cell states is described by a gamma rather than an exponential distribution. This approach accounts for the system’s intracellular networks in an input-to-output formulation while keeping the model’s complexity relatively manageable for analysis.

IMMU-14:
Modelling the evolution of B cell responses to vaccination

  1. RituparnaBanerjee University of British Columbia
    "Modelling the evolution of B cell responses to vaccination"
    2. Vaccinations have historically proven to be an effective means of conferring immunity in case of various diseases by enhancing the body’s preparedness for future infection events. The success of a vaccination program depends on various factors like dose composition and time gap between vaccinations. To produce an effective response, the immune system relies heavily on B cells, among other immune cells, as these cells mature to produce antibodies. In this presentation I will present a simplified mechanistic model of B cell evolution (mutation and selection) during the immune response to vaccination, which explicitly includes the germinal centre and extrafollicular pathways. We apply our model to build an understanding of how these pathways might work together to generate a signature in the evolutionary history of B cell clonal families within a single person, considering different possible vaccination systems (homologous and heterologous). We also plan on comparing phylogenetic trees generated by our model with real trees obtained from longitudinal studies.

IMMU-15:
Flare Dynamics and Disease Progression in Palindromic Rheumatism

  1. SomashreeChakraborty PhD Student/IISER Pune (India)
    "Flare Dynamics and Disease Progression in Palindromic Rheumatism"
    2. Synovial flares in palindromic rheumatism (PR) are aperiodic inflammatory episodes occurring in the joints, that are thought to follow a relapsing-remitting pattern. The transient and unpredictable nature of such flares is consistent with asymptomatic and non-periodic intervals. We examine the cytokine dynamics in a two-dimensional model of rheumatoid arthritis (RA) and characterise such flares as an excitable trajectory, arising out of stochastic triggers. We address questions pertaining to the frequency, decay, and persistence of synovial flares in individuals with palindromic disease. Our findings demonstrate how adaptive regulations can rescue flares that become “locked” in a 'metastable' state. However, if repetitive locking events occur over a longer timescale, they can activate a secondary adaptation toward a healthy state, which may eventually become maladaptive. Therefore, we argue that the primary mechanism underlying the progression to chronicity lies in the conflict between adaptation and maladaptation, which drives the system toward the fully developed state of rheumatoid arthritis.

IMMU-16:
Estimating the efficacy of BCG vaccination on Mycobacterium tuberculosis dynamics and dissemination in ultra-low dose infected mice: A mathematical modelling framework

  1. DipanjanChakraborty Texas Biomedical Research Institute
    "Estimating the efficacy of BCG vaccination on Mycobacterium tuberculosis dynamics and dissemination in ultra-low dose infected mice: A mathematical modelling framework"
    2. BCG vaccine is the only licensed vaccine against tuberculosis (TB), a disease caused by Mycobacterium tuberculosis (Mtb). Even though billions of individuals have been vaccinated with BCG, efficacy of BCG vaccine and mechanisms by which it provides protection remain poorly understood. In a recent study, Plumlee et al. (Plos Pathogens, 19(11), e1011825, 2023) infected over a thousand mice, about half of which were vaccinated with BCG, with an ultra-low dose of Mtb (about 1 bacterium/mouse). Motivated from their study, we developed several alternative mathematical models describing Mtb dynamics in the initially infected lung (named Lung 1) and Mtb dissemination to the collateral lung (Lung 2) and fitted these models to the data from Plumlee et al. Experiments. Interestingly, proposed alternative models assuming direct or indirect Mtb dissemination describe the data well on Mtb dynamics in unvaccinated mice with similar quality. Further, we predict that Mtb replicates rapidly early during the infection, is controlled 1-2 months post-infection, and resumes replication in the chronic phase. By fitting the models to Mtb dissemination data in BCG-vaccinated mice we found that the data are best explained if BCG reduces both the rate of Mtb replication in the lungs (by 9%) and the rate of Mtb dissemination between the lungs (by 89%). Moreover, we implemented stochastic simulations of Mtb dissemination in unvaccinated and BCG-vaccinated mice, but these simulations did not fully account for the observed variability. However, stochastically simulating Mtb infection of right and left lung and dissemination between the lungs over time could successfully explain large CFU variability. Further, power analysis predicts the number of mice required in each mice group to obtain 80% power with different vaccine efficiencies. So, our mathematical modelling approach can be used to rigorously quantify efficacy of other TB vaccines in settings of ultra-low dose Mtb infection.

IMMU-17:
Mathematical modeling suggests that Mycobacterium tuberculosis CFU/CEQ ratio is not a robust indicator of cumulative bacteria killing

  1. AllanFriesen Texas Biomedical Research Institute
    "Mathematical modeling suggests that Mycobacterium tuberculosis CFU/CEQ ratio is not a robust indicator of cumulative bacteria killing"
    2. Correlates of protection against infection with Mycobacterium tuberculosis (Mtb) or against tuberculosis (TB) remain poorly defined. The ratio of colony forming units (CFUs) to chromosomal equivalents (CEQs), Z = CFU/CEQ, has been used as a metric for how effectively Mtb is killed in vivo. However, the contribution of bacterial killing to changes in CFU/CEQ ratio during an infection has not been rigorously investigated. We developed alternative mathe- matical models to study the dynamics of CFUs, CEQs, and Z during an Mtb infection. We find that the ratio Z alone cannot be used to infer the death rate of bacteria, unless the dynamics of CEQs and CFUs are entirely uncoupled, which is biologically unreasonable and inconsistent with the view that CEQs reflect an accumulation of both viable and non-viable bacteria. We estimate a half life of about 20 days of Mtb H37Rv CEQs in mice, similar to that found for Mtb Erdman in cynomolgus macaques. Although this seems slow, we found that estimated rates of Mtb replication and death are extremely sensitive even to slow decay of detectable Mtb genomes. We provide evidence of substantial killing of Mtb bacteria prior to arrival of adaptive immunity to the site of infection. We also propose experiments that will allow to more accurately measure the rate of Mtb DNA loss helping more rigorously to quantify impact of immunity on within-host Mtb dynamics.

IMMU-18:
In Silico Investigation of the Role of Local and Global Inflammation-Driven Feedback in Myelopoiesis and Clonal Expansion

  1. YusufJamilu Umar Khalifa University, Abu Dhabi
    "In Silico Investigation of the Role of Local and Global Inflammation-Driven Feedback in Myelopoiesis and Clonal Expansion"
    2. Chronic inflammation disrupts hematopoietic homeostasis, causing pathological myelopoiesis and malignant clones that grow. The study uses a mathematical model with local (bone marrow) and global (peripheral inflammation) negative feedback mechanisms to examine how inflammation-driven regulations affect HSC self-renewal, progenitor dynamics, and differentiation. Healthy and malignant populations compete in the model, which examines system stability through feedback mechanisms. The results show that chronic inflammation can cause myelopoietic disorders by overproducing progenitor cells and disrupting lineage balance without global feedback regulation. Self-renewal feedback regulates stem cell proliferation to strengthen hematopoietic cells and mitigate chronic inflammation damages. Because excessive suppression can destabilize hematopoiesis, the model suggests tightly controlling negative feedback on progenitor cells. Mutations affecting global feedback can cause malignant clones, revealing how inflammation causes hematological malignancies like MDS and AML.

IMMU-19:
Simulating affinity maturation under sequential SARS-CoV-2 infections

  1. JasmineKreig Los Alamos National Laboratory
    "Simulating affinity maturation under sequential SARS-CoV-2 infections"
    2. Part of the immune response upon infection involves B cells and a process known as affinity maturation. During affinity maturation, produced antibodies increase in affinity to presented antigen. Additionally, plasma B cells and memory B cells are created. This is to allow the system to remember and quickly mount a response to the presented antigen in the case of a repeat infection. Repeated exposures to the same antigen will produce antibodies of successively greater affinities. However, as antigen move away in antigenic distance from the initial strain (antigenic drift), the ability of the body to cross-reactively neutralize the antigen decreases. This issue has been well documented in cases of influenza and there is a concern it is occurring in SARS-CoV-2 given successive variants of concern (VOC). Such VOCs would be less susceptible to any immune protection gained from vaccination and prior infection. We modeled these processes using an agent-based model (ABM) that considers B cells (naïve, plasma, memory), antibodies, and antigens. We represent receptor (B cells, antibodies) and epitope (antigens) proteins in Euclidean shape space, simulating binding between these agents based on Hamming distance. We also consider the formation of immune complexes—free antibodies bound to antigen which limits the antigen’s ability to infect more cells. We simulated SARS-CoV-2 infections using our ABM. We present results that examine immune responses when presented with various VOCs and differing immune imprinting.

IMMU-2:
Surfing the Actin Wave: Mathematical Modeling of Natural Killer Cell Synapse Formation

  1. MontanaFerita University of Utah
    "Surfing the Actin Wave: Mathematical Modeling of Natural Killer Cell Synapse Formation"
    2. Natural killer (NK) cells are members of the innate immune system and are proving to be a lethal weapon against cancer. To unlock the full power of NK cells, we must first address the central question: How does an NK cell recognize a malignant cell? To assess a target cell, an NK cell forms an immunological synapse, which is the interaction zone between the two cells. Ligand-receptor binding within the synapse triggers downstream activating and inhibitory signaling pathways that integrate to control the actin cytoskeleton network. Dominating activating signals causes the NK cell’s actin network to reorganize which transports more receptors to the synapse, thereby generating a positive feedback loop. Mechanistically, activating signals lead to the activation of the Arp2/3 complex which creates a branched actin network. In return, the flow of this network drives the centripetal transport of receptors to the synapse. We propose an advection-diffusion model to capture this phenomenon. Furthermore, we test what ligand-receptor densities permit synapse formation.

IMMU-20:
Viral rebound occurrence immediately after drug discontinuation involving neither drug resistance nor latent reservoir

  1. HayashiRena Kyushu University
    "Viral rebound occurrence immediately after drug discontinuation involving neither drug resistance nor latent reservoir"
    2. Some viruses exhibit “rebound” when the administration of antiviral drugs is discontinued. Viral rebound caused by resistance mutations or latent reservoirs has been studied mathematically. In this study, we investigated the viral rebound due to other causes. Since immunity is weaker during antiviral treatment than without the treatment, drug discontinuation may lead to an increase in the viral load. We analyzed the dynamics of the number of virus-infected cells, cytotoxic T lymphocytes, and memory cells and identified the conditions under which the viral load increased upon drug discontinuation. If drug is administered for an extended period, a viral rebound occurs when the ratio of viral growth rate in the absence to that in the presence of the antiviral drug exceeds the “rebound threshold.” We analyzed how the rebound threshold depended on the patient’s conditions and the type of treatment. Mathematical and numerical analyses revealed that rebound after discontinuation was more likely to occur when the drug effectively reduced viral proliferation, drug discontinuation was delayed, and the processes activating immune responses directly were stronger than those occurring indirectly through immune memory formation. We discussed additional reasons for drugs to cause viral rebound more likely.

IMMU-21:
Multi-Scale Hybrid Agent-Based Model Investigating mTORC1’s Influence on COVID-19.

  1. Sandra AnnieTsiorintsoa University of Florida
    "Multi-Scale Hybrid Agent-Based Model Investigating mTORC1’s Influence on COVID-19."
    2. COVID-19 outcomes vary widely among individuals, with most having mild illness, while a small percentage experience severe symptoms and a minor fraction death. Several treatments for COVID-19 have been proposed. One of the most promising is the inhibition of mTORC1 by Sirolimus. However, not all patients are sensitive to this treatment. To uncover the complex relations behind the heterogeneity and sensitivity of some individuals to treatments, we developed a hybrid agent-based model of the innate immune response to study the infection in the whole lung. The model includes key cells involved in the disease and critical intracellular factors such as NF-kB, IRF3, STAT1, and mTORC1. We calibrated and validated our model using literature and our own experimental data. We used it to explore different scenarios and explain our experimental results showing a positive correlation between mTORC1 activity and viral replication but a negative correlation between mTORC1 and IFN-a expression. Our initial simulations showed that mTORC1 is a master regulator of intracellular viral response and suggested novel intervention targets upstream of mTORC1. Our aim is to personalize the model and quantify the role of mTORC1 in the COVID-19 heterogeneity.

IMMU-3:
Quantifying the dynamics of Kaposi’s sarcoma-associated herpesvirus persistence

  1. MadeleineGastonguay Johns Hopkins University
    "Quantifying the dynamics of Kaposi’s sarcoma-associated herpesvirus persistence"
    2. Kaposi’s sarcoma-associated herpesvirus (KSHV) is a causative agent of several lymphoproliferative diseases, particularly in immunocompromised individuals. These malignancies originate from latently infected B cells, where KSHV persists as extrachromosomal episomes. While the viral protein LANA is essential for viral maintenance during latency, the mechanisms enabling lifelong persistence remain unclear. To quantify episome dynamics, we developed a mathematical model of latent KSHV replication and segregation during cell division, and a statistical framework to infer viral dynamics from fluorescent microscopy images. We built a Gibbs sampler to extract episome counts from imperfectly resolved images of pre- and post-division cells. Using these counts, we estimate the efficiency of replication and segregation, propagating imaging uncertainty into our parameter estimates. Our framework, validated on synthetic data, provided similar estimates of replication efficiency (78%, 95% CI [53%, 90%]) and segregation efficiency (91% [78%, 100%]) when applied to fixed and live images of cells transfected with either full-length KSHV or a minimal plasmid capable of episome maintenance. Simulations of a dividing cell population showed that imperfect replication and segregation preclude decades-long persistence without the assistance of additional mechanisms such as cell-survival benefits to infection or occasional lytic replication. We also modeled KSHV-dependent malignancies to evaluate episome replication and segregation as targets to control tumor growth. Simulations revealed that reducing replication effectively disrupts tumor growth, with the required reduction dependent on cell division kinetics. Our results suggest that KSHV employs a partitioning mechanism, as opposed to random segregation, though replication and segregation are imperfect. Furthermore, targeting episome replication may offer a viable strategy to reduce tumor burden in KSHV-associated malignancies.

IMMU-4:
Viral rebound kinetics following single and combination immunotherapy for HIV/SIV

  1. MadeleineGastonguay Institute for Computational Medicine, Johns Hopkins University
    "Viral rebound kinetics following single and combination immunotherapy for HIV/SIV"
    2. Combination antiretroviral therapy (ART) can treat but not cure HIV, motivating the development of therapies that stimulate the immune system to control or eliminate infection. Two such immunotherapies- a TLR7 agonist and a therapeutic vaccine - were previously tested in SIV-infected rhesus macaques. Animals received ART alone or with concurrent single or combination immunotherapy, and viral rebound was monitored after treatment interruption. Many treated animals exhibited altered rebound kinetics, and a subset achieved either complete viral suppression or immune control after an initial rebound. However, the mechanisms driving these effects are unknown: do these therapies deplete the latent reservoir or enhance antiviral immunity, and do they act synergistically? To investigate the effects of immunotherapy, we built a mathematical model of viral dynamics incorporating latent cell reactivation and a generalized immune response. We confirmed the model could reproduce the range of rebound trajectories seen in the data, and examined whether parameters could be reliably estimated from the available data. Using nonlinear mixed-effects modeling, we quantified interindividual variability and identified significant differences in model parameters between treatment groups. Our results indicate that the vaccine alone reduces latent virus reactivation and enhances immune response avidity. The TLR7 agonist, when administered after late ART initiation, increases target cell availability and reduces the latent reservoir. We found that regardless of ART initiation, the two therapies act synergistically to further enhance immune response avidity. Immune avidity appeared to increase with later ART initiation, although whether this effect is specific to TLR7 treatment is unclear. Our model provides mechanistic insight into immunotherapeutic control of viral rebound and can be adapted to predict their impact in controlling HIV, guiding future therapeutic design and clinical trials.

IMMU-5:
Optimization of Pertussis Immunization Using Mathematical Models

  1. JonahHall UBC
    "Optimization of Pertussis Immunization Using Mathematical Models"
    2. Pertussis (whooping cough), caused by Bordetella pertussis, is most severe in infants, with most deaths occurring in unvaccinated infants under three months of age. Vaccination with the DTaP (priming) and TdaP (booster) immunizations is effective, with TdaP given during pregnancy and DTaP in infancy. However, immunomodulation can dampen the IgG response in infants born to vaccinated mothers. We hypothesize that adjusting the vaccination schedule could reduce immunomodulation and enhance vaccine efficacy. Since empirically testing multiple schedules is impractical, we propose using mathematical modeling alongside two experimental mouse models to determine an optimal schedule. Pregnant and infant mice will be immunized following a murine analog of standard vaccination. These data will inform our model, allowing us to estimate key immune parameters. Once parametrized, our model will propose schedules that maximize infant antibody response. A second mouse experiment will test these schedules, comparing immune responses to assess their efficacy. This approach will help evaluate immunomodulation mechanisms and refine vaccination strategies. The mechanistic evaluation of immunomodulation is of significance due to its lack of effective investigations to date.

IMMU-6:
Antibiotic Resistance and Dosing in Bacterial Biofilms

  1. AdnanKhan Lahore University of Management Sciences
    "Antibiotic Resistance and Dosing in Bacterial Biofilms"
    2. In this talk, we will present effective antibiotic regimens in the presence of drug-resistant bacteria in biofilms. We begin by discussing models of in-vivo antimicrobial resistance transfer within bacterial biofilms, focusing on various one-dimensional biofilm models. Our approach includes modeling resistance acquisition through horizontal gene transfer between resistant and susceptible strains while also accounting for the role of persistor cells. We examine the effects of periodic antibiotic dosing at a constant level, showing that it may not always lead to bacterial eradication. To address this challenge, we utilize a numerical optimization algorithm to determine the optimal antibiotic dosing strategy. Additionally, we analyze how changes in different model parameters impact the qualitative behavior of the optimal dosing regimen.

IMMU-7:
A Mathematical Model for Pseudo-Progression in CAR-T therapy of B-cell Lymphomas

  1. GustavLindwall Max Planck Institute for Evolutionary Biology
    "A Mathematical Model for Pseudo-Progression in CAR-T therapy of B-cell Lymphomas"
    2. CAR-T cell therapy, where patient T cells are genetically modified to target CD19-presenting B cells, has transformed the treatment landscape for several types of B-cell lymphomas. However, this therapy often triggers a strong inflammatory response, which can cause the tumor to temporarily swell in the days following CAR-T infusion — a phenomenon known as pseudo-progression. In this work, we present a dynamical model of CAR-T cell therapy that explicitly incorporates the role of pro-inflammatory cytokines in shaping treatment outcomes. Our model reproduces a wide spectrum of clinical trajectories, including complete remission, treatment failure, and transient pseudo-progression. Importantly, the model’s parameters correspond to measurable patient-specific factors, allowing us to explore how individual patient characteristics influence long-term treatment success. The parametrization of the model maps on to measurable patient characteristics, and we discuss how these parameters impact the long term behavior of the model.

IMMU-8:
Genetic regulation of vibrio vulnificus hemolysin drives population heterogeneity

  1. KathrynLynch University of Utah
    "Genetic regulation of vibrio vulnificus hemolysin drives population heterogeneity"
    2. Individual bacterium make decisions at a genetic level as a result of various types of gene regulation; this process plays out on a population level to inform colony growth. Vibrio vulnificus is an opportunistic Gramnegative marine pathogen with a limiting growth factor of iron. Compared to other foodborne pathogens, Vibrio vulnificus has a high mortality rate and relatively poorly understood virulence mechanisms. When inside a human host, this bacteria utilizes heme as a source of iron, necessitating the ability to turn pieces of the heme acquisition system off and on in response to various environmental signals. As establishment of infection depends on Vibrio vulnificus’s ability to change from a marine to human environment, the ability to switch on the heme-intake system is an important part of establishment of initial infection. One such part of this system is the hemolysin VvhA. This toxin is excreted by the bacterium to lyse erythrocytes, thereby releasing heme into the extracellular environment where the bacteria can use it as a source of iron. This toxin is regulated by a complex set of factors including nutrient availability and quorum sensing. Exploring this gene regulatory network via bifurcation analysis reveals a complex bifurcation structure. These dynamics allow an individual bacterium to integrate a variety of signals in response to a changing environment. In particular, bistability in the system points to the likelihood of a heterogenous bacterial colony, where many bacteria benefit from a smaller number of hemolysin producers. This allows for modeling both a heterogeneous population and incorporation of the physiological mechanism by which cells make the decision to switch states. The interdependence between toxin production, nutrient availability, and colony growth result in interplay between the bacteria and their environment, allowing for insights into the overall course of infection.

IMMU-9:
Towards a mathematical model of the methotrexate effect on immunogenicity to adalimumab in axial spondyloarthritis

  1. PeterRashkov Institute of Mathematics and Informatics, Bulgarian Academy of Science, Sofia, Bulgaria
    "Towards a mathematical model of the methotrexate effect on immunogenicity to adalimumab in axial spondyloarthritis"
    2. Axial spondyloarthritis (SpA) is a chronic inflammatory disease impacting the joints in the axial skeleton (e.g. chest, spine, pelvis). Tumor necrosis factor inhibitors (TNFi), such as the monoclonal antibody adalimumab, are used to treat severe cases, but therapy is often discontinued due to loss of efficacy, not least resulting from immunogeniocity and development of anti-drug antibodies (ADA). The disease-modifying drug methotrexate (MTX) has shown potential in reducing the formation of ADA to various TNFi in rheumatoid arthritis (Krickaert et al, 2012), but little is known about its mode of action. We adapt a mechanistic mathematical model for immunogencity towards adalimumab based on Chen et al. 2014 to describe the impact of MTX in reducing immunogenicity, and parametrise it based on patient data from a multicentric randomised trial (Ducoureau et al, 2020). ODEs describe the pharmacokinetics and pharmacodynamics of the therapeutic compounds (adalimumab only or adalimumab and MTX, depending on patient cohort), the dynamics of T and B lymphocytes, antigen presenting cells, and some relevant cytokines for the disease. Due to the large size of this model, we employ several reduced models to estimate some of the parameter values. The model is used to simulate several scenarios in order to elucidate the most likely modes of action of MTX to reduce immunogenicity by comparing the simulated and measured ADA titres along 5 hospital visits. This is joint work with Sara Sottile (Bologna, Italy) and Denis Mulleman (Tours, France). This work is based upon work from COST Action ENOTTA (CA21147), supported by COST (European Cooperation in Science and Technology), and Contract KP-06-DKOST-13 of the Bulgarian Fund for Scientific Research.

MEPI-1:
Studying Disease Reinfection Rates, Vaccine Efficacy and the Timing of Vaccine Rollout in the context of Infectious Diseases

  1. ElizabethAmona Virginia Commonwealth University
    "Studying Disease Reinfection Rates, Vaccine Efficacy and the Timing of Vaccine Rollout in the context of Infectious Diseases"
    2. The global landscape has undergone distinct waves of COVID-19 infections, compounded by the emergence of variants, thereby introducing additional complexities to the ongoing pandemic. This research uniquely explores the varied efficacy of existing vaccines and the pivotal role of vaccination timing in the context of COVID-19. Departing from conventional modeling, we introduce two models that account for the impact of vaccines on infections, reinfections, and deaths. We estimate model parameters under the Bayesian framework, specifically utilizing the Metropolis-Hastings Sampler. The study conducts data-driven scenario analyses for the State of Qatar, quantifying the potential duration during which the healthcare system could have been overwhelmed by an influx of new COVID-19 cases surpassing available hospital beds. Additionally, the research explores similarities in predictive probability distributions of cumulative infections, reinfections, and deaths, employing the Hellinger distance metric. Comparative analysis, utilizing the Bayes factor, underscores the plausibility of a model assuming a different susceptibility rate to reinfection, as opposed to assuming the same susceptibility rate for both infections and reinfections. Results highlight the adverse outcomes associated with delayed vaccination, emphasizing the efficacy of early vaccination in reducing infections, reinfections, and deaths. Our research advocates prioritizing early vaccination as a key strategy in effectively combating future pandemics. This study contributes vital insights for evidence-based public health interventions, providing clarity on vaccination strategies and reinforcing preparedness for challenges posed by infectious diseases.

MEPI-10:
Leveraging deep learning and social heterogeneity to detect early warning signals of disease outbreaks

  1. ZitaoHe University of Waterloo
    "Leveraging deep learning and social heterogeneity to detect early warning signals of disease outbreaks"
    2. Identifying early warning signals (EWS) of shifts in vaccinating behaviors can be helpful in predicting disease outbreaks. Evolutionary game theory has been used to model individual vaccination decisions, while bifurcation theory has identified statistical EWS, such as increasing variance and lag-1 autocorrelation, near critical transitions. However, these conventional methods often struggle with noisy data. In this study, we improve coupled behavior-disease models by incorporating population heterogeneity, distinguishing between social media users and non-users, and examining the role of homophily in shaping disease dynamics. We develop deep learning classifiers, including Long Short-Term Memory (LSTM) and Residual Neural Networks (ResNet), trained on simulated data from a stochastic coupled model with Lévy noise that captures the heavy-tailed fluctuations characteristic of real-world systems. Our results show that these models outperform traditional statistical indicators in both sensitivity and specificity while offering clearer interpretability on empirical data. This approach provides a robust framework for detecting EWS and improving outbreak prediction, highlighting the power of deep learning in real-time public health monitoring.

MEPI-11:
A theoretical framework to quantify the tradeoff between individual and population benefits of expanded antibiotic use

  1. LindsayKeegan University of Utah
    "A theoretical framework to quantify the tradeoff between individual and population benefits of expanded antibiotic use"
    2. The use of antibiotics during a disease outbreak presents a critical tradeoff between immediate treatment benefits to the individual and the long-term risk to the population. Typically, the extensive use of antibiotics has been thought to increase selective pressures, leading to resistance. This study explores scenarios where expanded antibiotic treatment can be advantageous for both individual and population health. We develop a mathematical framework to assess the impacts on outbreak dynamics of choosing to treat moderate infections not treated under current guidelines, focusing on cholera as a case study. We derive conditions under which treating moderate infections can sufficiently decrease transmission and reduce the total number of antibiotic doses administered. We identify two critical thresholds: the Outbreak Prevention Threshold (OPT), where expanded treatment reduces the reproductive number below one and halts transmission, and the Dose Utilization Threshold (DUT), where expanded treatment results in fewer total antibiotic doses used than under current guidelines. For cholera, we find that treating moderate infections can feasibly stop an outbreak when the untreated reproductive number is less than 1.42 and will result in fewer does used compared to current guidelines when the untreated reproductive number is less than 1.53. These findings demonstrate that conditions exist under which expanding treatment to include moderate infections can reduce disease spread and the selective pressure for antibiotic resistance. These findings extend to other pathogens and outbreak scenarios, suggesting potential targets for optimized treatment strategies that balance public health benefits and antibiotic stewardship.

MEPI-12:
Spatial spread of epidemic in a system of weakly connected networks

  1. EvgeniyKhain Oakland University
    "Spatial spread of epidemic in a system of weakly connected networks"
    2. A metapopulation consists of a group of spatially distanced subpopulations, each occupying a separate patch. It is usually assumed that each localized patch is well-mixed. In this talk, we will discuss the spread of an epidemic in a system of weakly connected patches, where the disease dynamics of each patch occurs on a network. The SIR dynamics in a single patch is governed by the rate of disease transmission, the disease duration, and the node degree distribution of a network. Monte-Carlo simulations of the model reveal the phenomenon of spatial disease propagation. The speed of front propagation and its dependence on the single patch parameters and on the strength of interaction between the patches was determined analytically, and a good agreement with simulation results was observed [1]. Next, we will discuss front propagation in case of an Allee effect, where the effective transmission rate depends on the fraction of infected, and the state of no epidemic is linearly stable. We discovered [2] a novel phenomenon of front stoppage: in some regime of parameters, the front solution ceases to exist, and the propagating pulse of infection decays despite the initial outbreak. [1]. E. Khain and M. Iyengar, Phys. Rev. E 107, 034309 (2023). [2]. E. Khain, Phys. Rev. E 107, 064303 (2023).

MEPI-13:
Optimizing Vaccine Efficacy Trials for Emerging Respiratory Epidemics: A Mathematical Modeling Approach

  1. SoyoungKim National Institute for Mathematical Sciences (NIMS)
    "Optimizing Vaccine Efficacy Trials for Emerging Respiratory Epidemics: A Mathematical Modeling Approach"
    2. Evaluating vaccine efficacy (VE) during emerging epidemics is challenging due to unpredictable transmission dynamics. An age-structured SEIAR compartmental model was developed using South Korea’s 2022 population and parameters from COVID-19 and the 2009 H1N1 pandemic to optimize RCT timing and sample size. Simulations varied trial initiation (±10%, ±20%, ±30% of the epidemic peak), follow-up (4–12 weeks), recruitment (2–12 weeks), and non-pharmaceutical interventions (10–20%). Results showed that VE remained stable, but sample size requirements fluctuated, decreasing post-peak before rising sharply. Starting trials 30% before the peak with extended recruitment minimized sample sizes without compromising power. NPIs expanded trial feasibility, and sample size estimates from simulated placebo cases maintained >85% power, avoiding under- or over-powering. This model provides a framework for designing adaptive and efficient vaccine trials in future respiratory epidemics.

MEPI-14:
Effective Vaccination Strategies Against Dengue in Brazil: A Mathematical Modeling Approach Incorporating Spatial and Demographic Heterogeneities

  1. YoungsukKo Yale University
    "Effective Vaccination Strategies Against Dengue in Brazil: A Mathematical Modeling Approach Incorporating Spatial and Demographic Heterogeneities"
    2. Brazil has experienced recurrent dengue outbreaks, with over 18 million reported cases since 2000 and a record-breaking surge in 2024. Notably, there has been a demographic shift in disease burden, with an increasing proportion of severe cases and fatalities among the elderly. Current vaccination strategies, including the WHO-recommended Qdenga® rollout for children, may not effectively address this emerging risk. This study employs a mathematical modeling approach to evaluate age-specific and geographically targeted vaccination strategies. A Susceptible-Infected-Recovered (SIR)-based model, calibrated using historical dengue data from Brazil's Notifiable Diseases Information System (SINAN), incorporates spatial heterogeneity across 27 states and demographic factors such as prior exposure and birth rates. We assess the impact of different vaccination strategies by estimating averted infections, hospitalizations, fatalities, and years of life lost. Preliminary findings indicate significant variation in the force of infection across states and suggest that prioritizing vaccination for elderly populations may substantially reduce severe disease burden. This modeling framework provides a quantitative basis for optimizing vaccination policies, with potential applications to other arboviral diseases and endemic settings worldwide.

MEPI-15:
Quantifying Shifts in Social Contact Patterns: A Post-Covid Analysis in South Korea

  1. JonggulLee National Institute for Mathematical Sciences
    "Quantifying Shifts in Social Contact Patterns: A Post-Covid Analysis in South Korea"
    2. Social contact patterns are crucial for understanding infectious disease transmission, but detailed data has been scarce in South Korea. We conducted a two-week survey covering various periods, including school vacations and holidays. Participants provided information on their contacts, including location, duration, frequency, and characteristics of the contact person. Analysis of the data from 1,987 participants revealed 133,776 contacts, averaging 4.81 contacts per person daily. Contact numbers varied by age, household size, and time period. The highest number of contacts was observed in the 5-19 age group, lowest in the 20-29 group, and gradually increased up to the 70+ group. Larger households tended to have more contacts. Contact patterns differed significantly across time periods. Weekdays during the school semester showed the highest number of contacts, followed by weekdays during vacations, the Lunar New Year holidays, and weekends. During the Lunar New Year, there was an increase in contacts with extended family members, enhancing subnational social mixing. These findings provide valuable insights into social contact patterns in South Korea, which can improve our understanding of disease transmission and aid in developing region-specific epidemiological models.

MEPI-16:
How to Deal with the Health-economy Dilemma during a Pandemic: Research Framework and User-interactive Dashboard

  1. JongminLee Department of Mathematics, Konkuk University
    "How to Deal with the Health-economy Dilemma during a Pandemic: Research Framework and User-interactive Dashboard"
    2. During the early stages of the COVID-19 pandemic, it was important to minimize both medical and economic costs. In this study, we introduce a machine learning-based multi-objective optimization framework that can propose cost-effective social distancing strategies. Our approach finds Pareto solutions that balance different goals, like reducing infections and minimizing social distancing costs. Then, the cost-benefit analysis can adjust each cost factor—for example, value of statistical life (VSL), fatality rate, or GDP. We also provide an interactive web dashboard so that policymakers and the public can test various scenarios easily. We tested this framework on the COVID-19 pandemic in Korea. The results show that the difference between the cost-optimal strategy and implemented strategy is 10% in cost. Notably, our results reveal two distinct patterns in cost-optimal solutions. When social distancing cost is proportional to intervention intensity, an on-off lockdown strategy proves most economical. In contrast, when the cost increases sharply with intensifying social distancing, maintaining a consistently moderate level of intervention minimizes overall expenses. By letting people explore different cost settings and intervention strategies, this tool can support more balanced decisions during emerging infectious disease crises in the future.

MEPI-17:
Estimating pathogen introduction rates from serological data to characterize past and future patterns of transmission

  1. AlexanderMeyer University of Notre Dame
    "Estimating pathogen introduction rates from serological data to characterize past and future patterns of transmission"
    2. The unpredictable timing of infectious disease outbreaks poses significant challenges for public health preparedness. For many pathogens, this unpredictability is due to uncertainty regarding introduction rates—the frequency with which the pathogen is introduced into at-risk populations. We present three model-driven advances toward quantifying pathogen introduction rates and their effects on outbreak timing and size. Our method relies on the assumption that pathogen introductions can only cause large outbreaks when population immunity is sufficiently low (i.e., the reproduction number R(t) > 1). First, we demonstrate that, for pathogens that cause lifelong immunity, introduction rates can be estimated from age-structured serological data. Second, we estimate annual rates of chikungunya virus (CHIKV, a mosquito-borne pathogen) introductions into 17 populations in Africa and Asia using serological data collected between 1973 and 2015. Our median estimates ranged from 1 to 70 CHIKV introductions per 10 million people per year. Finally, we used simulations to show how the introduction rate of a pathogen can shape its transmission patterns over time in affected populations. A lower introduction rate allows population immunity to wane between introductions, leading to large but infrequent outbreaks. In contrast, a higher introduction rate causes frequent low-level transmission, resulting in elevated population immunity that precludes large outbreaks. Together, these results illustrate how age-structured serology, a common type of epidemiological data, can be leveraged to better understand both historical and future transmission patterns in different populations.

MEPI-18:
From process to structure of EWSs

  1. FranciscaOlajide University of Ottawa
    "From process to structure of EWSs"
    2. The emergence of infectious diseases remains a huge challenge to public health. Early detection of outbreaks using early warning signals (EWSs) offers an invaluable opportunity for effective preparedness and disease management. In this study, we seek to understand the structure of these signals using a mechanistic model that captures epidemic and social processes. We analyzed the simulated time series for change points and EWSs (autocorrelation and variance). All time series showed the expected delay in that the detected change point occurred significantly after the parameter passed the bifurcation point. These early warning signals exhibited a stronger response after the threshold for disease emergence had been exceeded. Assessing different disease progression and intervention models will help determine the most effective signals for use in public-health settings.

MEPI-19:
Pre-exposure vaccination in the high-risk population is crucial in controlling mpox resurgence in Canada

  1. AndrewOmame York University Toronto, Canada
    "Pre-exposure vaccination in the high-risk population is crucial in controlling mpox resurgence in Canada"
    2. As mpox spread continues across several endemic and non-endemic countries around the world, vaccination has become an integral part of the global response to control the epidemic. Some vaccines have been recommended for use against mpox by the World Health organization (WHO). As the roll-out of mpox vaccines continue across the globe, it is imperative to develop mathematical models to support public health officials and governments agencies in optimizing vaccination strategies to curtail the resurgence of mpox. In this article, we develop a compartmental mathematical model to investigate the impact of vaccination in controlling a potential mpox resurgence in Canada. The model categorizes individuals into high- and low-risk groups and incorporates pre-exposure vaccination in the high-risk group and post-exposure vaccination in the high- and low-risk groups. The vaccine-free version of the model was calibrated to the daily reported cases of mpox in Canada from April to October 2022, from which we estimated key model parameters, including the sexual and non-sexual transmission rates. Furthermore, we calibrated the full model to the daily reported cases of mpox in Canada in 2024, to estimate the current mpox vaccination rates in Canada. Our results highlight the importance of pre-exposure vaccination in the high-risk group on controlling a potential resurgence of mpox in Canada, and the minimal effects of post-exposure vaccination in the high- and low-risk groups on the outbreak. In addition, our model predicts the possibility of mpox becoming endemic in Canada, in the absence of pre-exposure vaccination in the high-risk group. Overall, our modeling result suggests that pre-exposure vaccination in the high-risk group is crucial in controlling mpox outbreak in Canada. Stepping up this vaccination is sufficient to avert a potential mpox resurgence in Canada.

MEPI-2:
An update estimation method for the stochastic epidemic models and their statistics analysis

  1. viswanathanarunachalam UNIVERSIDAD NACIONAL DE COLOMBIA
    "An update estimation method for the stochastic epidemic models and their statistics analysis"
    2. Stochasticity is introduced to bring new insight into the modelling of population dynamics of diseases. Many systems, in nature, are subject to stochastic perturbations. In this talk, we present differential equations with stochastic perturbations and the updated data estimation method for estimating the transmission rate changes over time. The models for the population dynamics under SEIR epidemic models with stochastic perturbations are presented for the dynamics of the COVID-19 pandemic in Bogota, Colombia. We performed computational experiments to interpret COVID-19 dynamics using actual data for the proposed models. We estimated the model parameters and updated their reported infected and recovered data estimates. (joint work with Andres Rios-Gutierrez )

MEPI-20:
Mathematical Model For Dengue and its Co-Endemicity with Chikungunya virus

  1. RosemaryOmoregie University of Benin, Nigeria
    "Mathematical Model For Dengue and its Co-Endemicity with Chikungunya virus"
    2. A deterministic nonlinear mathematical model describing the population dynamics for Dengue and Chikungunya virus taken into consideration the effect of misdiagnosis due to the co-endemicity of the two viruses in the human population. It is necessary to understand the most important parameters involved in their dynamics that may help in developing strategies for prevention, control and joint treatments. The model is rigorously analyzed qualitatively and thresholds for eradication are established.

MEPI-21:
Could malaria mosquitoes be controlled by periodic release of transgenic mosquitocidal Metarhizium pingshaense? A mathematical modeling approach

  1. BinodPant Northeastern University
    "Could malaria mosquitoes be controlled by periodic release of transgenic mosquitocidal Metarhizium pingshaense? A mathematical modeling approach"
    2. Mosquito-borne diseases, such as malaria, remain a major global health challenge, necessitating the exploration of innovative vector control strategies. Naturally occurring entomopathogenic fungi have been shown to reduce mosquito lifespan, but their slow-acting nature has limited their practical application. Advances in biotechnology have led to the development of transgenic fungus strains (this study will focus on Metarhizium pingshaense strain) engineered to express insecticidal toxins, significantly increasing their efficacy against malaria vector mosquitoes. To our knowledge, this is the first deterministic model designed to assess the impact of fungal-based mosquito control. The proposed model accounts for multiple transmission pathways of the fungal infection, including mating-based transmission from infected males to females and indirect transmission via contact with infectious mosquito carcasses. The model is analyzed to determine equilibrium states, local stability conditions, and the reproduction number. Numerical simulations explore various release scenarios, evaluating the effectiveness of periodic versus continuous fungal release in different ecological settings. The results indicate that transgenic Metarhizium pingshaense has the potential to significantly reduce mosquito populations, particularly when release strategies are optimized.

MEPI-22:
Mathematical assessment of the roles of vaccination and Pap screening on the incidence of HPV and related cancers in South Korea

  1. SoyoungPark University of Maryland
    "Mathematical assessment of the roles of vaccination and Pap screening on the incidence of HPV and related cancers in South Korea"
    2. Human Papillomavirus (HPV) is a major sexually-transmitted infection that causes various cancers and genital warts in humans. In addition to accounting for about 99% of cervical cancer cases, it significantly contributes to anal, penile, vaginal, and head and neck cancers. Although HPV is vaccine-preventable (and highly efficacious vaccines exist for preventing infection with some of the most oncogenic HPV subtypes in the targeted population), the disease continues to cause major public health burden globally (largely due to inequity in access to the main control resources (i.e., access to Pap smear and vaccination) and low vaccination coverage in most communities that implement routine HPV vaccination). This lecture is based on the use of a new mathematical model (for the natural history of HPV, together with the associated neoplasia) for assessing the combined population-level impacts of Pap cytology screening and vaccination against the spread of HPV in a heterogeneous (heterosexual and homosexual) population. The model, which takes the form of a deterministic system of nonlinear differential equations, will be calibrated and validated using HPV-related cancer data from South Korea. Theoretical and numerical simulation results will be presented. Conditions for achieving vaccine-derived herd-immunity threshold (for achieving HPV elimination in Korea) will be derived.

MEPI-23:
Mind over matter: balancing the benefits of COVID lockdowns with their cost on mental health

  1. AsaRishel University of Maryland, College Park
    "Mind over matter: balancing the benefits of COVID lockdowns with their cost on mental health"
    2. The COVID-19 pandemic took its toll not only on the physical health of those who lived through it, but also on their mental health. I will present a model of the direct and indirect effects of COVID-19 and the associated public policies on mental health. This is an SIRS model of COVID-19, with compartments for mild, acute, and chronic COVID-19 infections and additional compartments for populations with mental health symptoms. Parameters are determined based on fitting from the first wave of COVID-19 in the New York state population, which includes several changes in local government policy, e.g, lockdown orders, which have an effect on the rate at which mental health systems develop. Finally, an additional “delay” term is included in the model to account for the delay between lockdowns going into effect and individuals developing mental health symptoms. The goal of our analysis is to understand how government policy in response to a pandemic can seek to maximize the population's quality-adjusted life years (QALY), which is a measure not only of lifespan, but also the quality of the years lived. I will present some preliminary results suggesting the optimal timing and strength of government lockdown mandates.

MEPI-24:
Compositional Complexity in Genomic Patterns and Classification

  1. somdatasina IISER Kolkata, India
    "Compositional Complexity in Genomic Patterns and Classification"
    2. A genome consists of a long string of four letters (bases A, T, C, G). How the information of biochemical processes stored in this string of bases is an open question. Are their higher order structures, such as, words, sentences, semantics, and a grammar in the DNA language (compositional complexity)? DNA from different species exhibit differences in global sequence composition, and this is used as markers to align larger sequences - grouping of genomes based on homology. Classification of genomes through similarity and dissimilarity is at the heart of Phylogenetics/Genomic Epidemiology. It uses several statistical-mathematical methods to align and compare the base sequences of multiple genomes, which are both computational resource intensive and time consuming for similar sequences. We develop and use an “alignment-free” method based on the Chaos-Game-Representation (CGR) of Statistical Physics, to successfully classify very closely related genomes of sub and sub-sub-species of HIV1 and mutants of Covid19. This useful approach requires less computational resources and time for analysis.

MEPI-25:
Counterfactual COVID-19: Modeling Alternative Mitigation and Vaccination Policies for Canada

  1. MarwaTuffaha York University
    "Counterfactual COVID-19: Modeling Alternative Mitigation and Vaccination Policies for Canada"
    2. COVID-19, a global pandemic with severe health and economic repercussions, has prompted various approaches to mitigate its impact. We adapt an age-structured SEIVS model—incorporating waning immunity and partial protection—to explore counterfactual scenarios of non-pharmaceutical interventions (e.g., school/workplace closures, distancing) and selected vaccine policy changes in Canada. By altering contact patterns and compliance levels, we assess potential outcomes under stricter, earlier, or more relaxed mitigation measures, with a lesser emphasis on shifting vaccination rollouts. Findings indicate that timely, robust mitigation can substantially reduce severe disease and delay epidemic peaks, whereas delayed or minimal interventions lead to higher case burden. Integrating vaccine strategies into these scenarios further highlights the interplay between pharmaceutical and non-pharmaceutical measures, showcasing how modeling can inform dynamic policy-making for ongoing and future public health crises.

MEPI-26:
Singular Perturbation Analysis of a Two-Time Scale Model of Vector-Borne Disease

  1. Woldegebriel AssefaWoldegerima York University
    "Singular Perturbation Analysis of a Two-Time Scale Model of Vector-Borne Disease"
    2. Biological systems evolve across different spatial and temporal scales. Modeling such complex systems gives rise to multi-scale differential equations that may be written as ODEs, PDEs, DDEs, SDEs, or Difference Equations. Particularly, vector-borne disease models are often described using ordinary differential equations with multiple time scales, which can involve singular perturbations—situations where rapid transitions or significant changes in system behavior occur due to small parameter variations or the interaction between fast and slow dynamics. To analyze these multi- time scale problems, we employ tools such as Geometric Singular Perturbation Theory (GSPT), Tikhonov’s Theorem, and Fenichel’s Theory. These methods provide insights into complex phenomena, including the loss of normal hyperbolicity and other intricate behaviors. Particularly, applying singular perturbation theory to vector-borne diseases allows us to reduce the dynamics to a one-time scale and understand their dynamics. To illustrate this, we present a Zika virus model and apply Tikhonov’s theorem and GSPT to investigate the model’s asymptotic behavior. Additionally, we conduct a bifurcation analysis to explore how the system’s behavior changes with variations in the parameter . We illustrate the various qualitative scenarios of the reduced system under singular perturbation. We will show that the fast–slow models, even though in nonstandard form, can be studied by means of GSPT.

MEPI-27:
Stochastic epidemic model with memory on the previous infection and with varying infectivity and waning immunity

  1. Arsene Bricezotsa ngoufack Université du Québec à Montréal
    "Stochastic epidemic model with memory on the previous infection and with varying infectivity and waning immunity"
    2. After an individual has been infected by a pathogen, T lymphocytes store information about the pathogen. Consequently, upon reinfection by the same pathogen, an immune response memory is triggered. This immune memory allows the body to react very quickly against the pathogen. Indeed, when an individual recovers from a virus, sometimes the individual acquires full immunity. In some cases, the individual's immunity persists for some period, after which it decreases progressively and can even disappear. I will then present a stochastic epidemic model with memory on the previous infection, incorporating varying infectivity and waning immunity. More precisely, we will present a functional law of large numbers when the size of the population tend to infinity. We will also present results on the behaviour of the epidemic, more precisely the threshold for the existence of an endemic equilibrium, and study the stability of the endemic equilibrium.

MEPI-28:
Confinement Tonicity Determines Long-Term Epidemics

  1. Alexis ErichAlmocera Department of Mathematics, Physics and Computer Science, College of Science and Mathematics, University of the Philippines Mindanao
    "Confinement Tonicity Determines Long-Term Epidemics"
    2. Self-isolation and stay-at-home measures are crucial for curbing the spread of contagious pathogens while vaccines are being developed. Furthermore, research during the 2019-22 coronavirus pandemic (COVID-19) emphasizes that proper enforcement and timely lifting of these measures are vital for effective disease management. In this context, we analyzed a simple dynamical system to understand how an epidemic progresses by isolating susceptible individuals (confinement) and reintroducing them to infection (deconfinement). This model captures the overall magnitude and direction of flows between confined and deconfined groups—akin to osmosis—leading to a dimensionless quantity defined as confinement tonicity. Our mathematical analysis suggests that confinement tonicity influences the final epidemic size, providing insights into careful quarantine management for effective disease control.

MEPI-29:
Detecting pathogen transmission from genetic sequence data

  1. AlexanderBeams Simon Fraser University
    "Detecting pathogen transmission from genetic sequence data"
    2. The accrual of nucleotide substitutions in pathogen genomes accompanies their transmission through host populations. Because lineages with higher fitness tend to transmit rapidly to new hosts before incurring very many substitutions, large numbers of related sequences are usually interpreted as evidence of transmission success. Quantities like the local branching index (LBI) aim to identify successful lineages in this way by scoring sequences according to the number of close relatives captured in the dataset. While statistics like LBI are easily calculated from a given phylogenetic tree (or a distribution of trees), observation errors related to sampling bias and censoring may introduce spurious signals of transmission success. To disentangle these effects, we use stochastic compartmental models to simulate outbreaks and generate distributions of phylogenies under a variety of testing programs (such as surveillance of symptomatic cases, or cross-sectional prevalence studies). By characterizing the types of phylogenies expected under these situations, we can work towards a clearer understanding of the types of signals that are likely to be detected with sequence data.

MEPI-3:
Estimating the strength of symptom propagation from synthetic data

  1. PhoebeAsplin University of Warwick
    "Estimating the strength of symptom propagation from synthetic data"
    2. Symptom propagation occurs when an individual’s symptom severity is correlated with the symptom severity of the individual who infected them. Determining whether - and to what extent - these correlations exist requires data-driven methods. In this study, we use synthetic data to determine the types of data required to estimate the strength of symptom propagation and investigate the effect of reporting bias on these estimates. We found that even a relatively small number of contact tracing data points was sufficient to gain a reasonable estimate for the strength of symptom propagation. Increasing the number of contact tracing data points further improved our estimates. In contrast, population incidence alone was insufficient to accurately estimate the symptom propagation parameters, even with a large number of data points. Nonetheless, concurrently using population incidence data with contact tracing data led to increased accuracy when estimating the overall disease severity. We then considered the effect of severe cases being more likely to be reported in the contact tracing data. When contact tracing data alone was used, we found that our estimates for the strength of symptom propagation were robust to all reporting bias scenarios considered. However, the reporting bias led us to overestimate the overall disease severity. Using population incidence data in addition to contact tracing data reduced the error in disease severity but at the cost of increasing the error in the strength of symptom propagation when reporting bias was in both primary and secondary cases. Consequently, these errors led to us sometimes finding support for symptom propagation, even when the synthetic data was generated without.

MEPI-30:
Unraveling the Complex Role of Climate in Dengue Dynamics

  1. OliveCawiding Korea Advanced Institute of Science and Technology (KAIST)
    "Unraveling the Complex Role of Climate in Dengue Dynamics"
    2. Dengue fever has emerged as an increasingly alarming public health challenge, further complicated by the impacts of climate change on control efforts. Yet, the full extent of climate's impact on dengue incidence remains poorly understood. To investigate this, we employed an advanced causal inference method to 16 regions in the Philippines, selected for their diverse climatic conditions. Unlike previous methods for detecting regulatory relationships, this method is capable of detecting nonlinear and joint effects of temperature and rainfall to dengue incidence. We found that temperature consistently increased dengue incidence throughout all the regions, while rainfall effects differed depending on location. Further analysis showed that this pattern is due to the variation in dry season length, a factor previously overlooked. Specifically, our results showed that regions with low variation in dry season length experience a negative impact of rainfall on dengue incidence likely due to strong flushing effect on mosquito habitats, while regions with high variation in dry season length experience a positive impact, likely due to increased mosquito breeding sites. This study offers a fresh perspective on the relationship between climate and dengue incidence, emphasizing the need for tailored prevention strategies based on local climate conditions.

MEPI-31:
Spatial-temporal heterogeneity in the associations of COVID-19 transmission and human mobility

  1. SunhwaChoi National Institute for Mathematical Sciences
    "Spatial-temporal heterogeneity in the associations of COVID-19 transmission and human mobility"
    2. This study investigates the spatial-temporal heterogeneity in the relationship between human mobility and COVID-19 transmission across 229 regions in South Korea during six epidemic waves from January 2020 to September 2022. While previous research primarily focused on the early stages of the pandemic and the impacts of mobility restrictions, our study utilizes mobility data from SK Telecom and COVID-19 case data from the Korea Disease Control and Prevention Agency to provide a more comprehensive analysis. We applied empirical mode decomposition (EMD) and clustering analysis to classify regional mobility patterns and conducted cross-correlation analysis to assess the relationship between mobility and confirmed cases. The findings indicate that incoming mobility significantly influenced the number of confirmed cases in urban and densely populated areas, whereas rural regions exhibited contrasting patterns. Moreover, these relationships evolved across different epidemic waves, highlighting the influence of regional characteristics and public health interventions. This study underscores the need to consider spatial-temporal heterogeneity in mobility-transmission dynamics to develop tailored public health strategies and enhance preparedness for future pandemics.

MEPI-32:
Early detection of disease outbreaks and non-outbreaks using incidence data: A framework using feature-based time series classification and machine learning

  1. ShanGao University of Alberta
    "Early detection of disease outbreaks and non-outbreaks using incidence data: A framework using feature-based time series classification and machine learning"
    2. Forecasting the occurrence and absence of novel disease outbreaks is essential for disease management, yet existing methods are often context-specific, require a long preparation time, and non-outbreak prediction remains understudied. To address this gap, we propose a novel framework using a feature-based time series classification (TSC) method to forecast outbreaks and non-outbreaks. We tested our methods on synthetic data from a Susceptible–Infected–Recovered (SIR) model for slowly changing, noisy disease dynamics. Outbreak sequences give a transcritical bifurcation within a specified future time window, whereas non-outbreak (null bifurcation) sequences do not. We identified incipient differences, reflected in 22 statistical features and 5 early warning signal indicators, in time series of infectives leading to future outbreaks and non-outbreaks. Classifier performance, given by the area under the receiver-operating curve (AUC), ranged from 0.99 for large expanding windows of training data to 0.7 for small rolling windows. The framework is further evaluated on four empirical datasets: COVID-19 incidence data from Singapore, 18 other countries, and Edmonton, Canada, as well as SARS data from Hong Kong, with two classifiers exhibiting consistently high accuracy. Our results highlight detectable statistical features distinguishing outbreak and non-outbreak sequences well before potential occurrence, in both synthetic and real-world datasets presented in this study.

MEPI-33:
Optimal Interventions for Plasmodium vivax Malaria Control in Seoul: A Cost-Benefit Analysis of Tafenoquine and Non-Pharmaceutical Strategies

  1. JiwonHan Konkuk University
    "Optimal Interventions for Plasmodium vivax Malaria Control in Seoul: A Cost-Benefit Analysis of Tafenoquine and Non-Pharmaceutical Strategies"
    2. The increase in Plasmodium vivax malaria cases in Korea highlights the necessity to reevaluate intervention strategies as climate patterns change. In 2024, confirmed cases rose by 37% compared to the previous three years' average, along with an increase in vector mosquito populations. In response, the Korea Disease Control and Prevention Agency (KCDA) expanded designated malaria risk areas in Seoul. Effective control depends on optimizing non-pharmaceutical interventions with primaquine-based treatment. As tafenoquine emerges as a potential alternative treatment, evaluating its impact on malaria transmission, relapse rate and cost-effectiveness within public health systems is essential. To address these issues, we developed a mathematical model incorporating climate variability to assess the effectiveness of non-pharmaceutical interventions under different climate scenarios. Using the Improved Multi-Objective Differential Evolution (IMODE) algorithm, we analyzed the optimal interventions based on observed malaria control measures. Our results suggest that optimal intervention strategies can significantly reduce malaria transmission and relapse rate, highlighting the cost-effectiveness of tafenoquine and optimal intervention approaches in Korea’s malaria control measures.

MEPI-34:
Mathematical Modeling of Regional Healthcare Accessibility and Excess Mortality during COVID-19: A Cluster-Based Study in South Korea

  1. daeiljang National Institute for Mathematical Sciences
    "Mathematical Modeling of Regional Healthcare Accessibility and Excess Mortality during COVID-19: A Cluster-Based Study in South Korea"
    2. Abstract Background: Healthcare accessibility is a key determinant of health outcomes during pandemics. Disparities in access may contribute to indirect excess mortality beyond reported COVID-19 deaths. This study quantitatively examines the impact of regional healthcare accessibility on non-COVID excess mortality in South Korea using a mathematical modeling approach. Methods: We first performed hierarchical clustering based on the average travel time to various healthcare facilities, classifying regions into two groups: Cluster 0 (high accessibility) and Cluster 1 (low accessibility). A CatBoost model trained on 2014–2019 data predicted expected deaths for 2020–2022, and excess mortality was calculated as the difference between observed and predicted deaths. Finally, multiple linear regression was then used to evaluate the association between accessibility time and non-COVID excess mortality. Results: Our analysis revealed that regions with high healthcare accessibility (Cluster 0) exhibited excess mortality patterns that closely aligned with reported COVID-19 deaths. In contrast, regions with lower accessibility (Cluster 1) experienced a significant increase in non-COVID excess mortality, particularly during the Omicron surge (fifth and sixth pandemic waves). The regression analysis demonstrated that longer healthcare accessibility times were significantly associated with higher non-COVID excess mortality in later pandemic stages. Conclusion: This study demonstrates that regional disparities in healthcare accessibility contribute to indirect excess mortality during pandemics. The findings highlight the importance of targeted policy interventions, such as strengthening healthcare infrastructure and expanding telemedicine, to reduce health inequalities and enhance public health resilience in future crises.

MEPI-35:
MPUGAT : A Novel Framework for Inferring Dynamic Infectious Disease Transmission with Graph Attention

  1. MinjiLee UNIST (Ulsan National Institute of Science and Technology)
    "MPUGAT : A Novel Framework for Inferring Dynamic Infectious Disease Transmission with Graph Attention"
    2. Epidemic modeling is essential for understanding and managing the spread of infectious diseases. However, it often faces challenges related to unidentifiability due to high-dimensional parameters. Therefore, integrating various data sources to infer epidemic parameters is crucial for reliable modeling. We propose MPUGAT, a hybrid framework that combines a multi-patch compartmental model with a spatiotemporal deep learning approach. By leveraging a Graph Attention Network (GAT), MPUGAT effectively captures spatiotemporal infection patterns from diverse time series data to infer a dynamic transmission matrix. Applied to COVID-19 data from South Korea, MPUGAT demonstrates superior performance in estimating the time-varying transmission matrix, aligning well with real-world dynamics. This framework offers a novel approach to integrating easily accessible, low-dimensional, non-epidemic-related data into epidemic modeling, enhancing both inference and interpretability.

MEPI-36:
Comparison of the Effectiveness and Costs of Hepatitis A Vaccination Strategies by Age in the Republic of Korea

  1. YunaLim Konkuk University
    "Comparison of the Effectiveness and Costs of Hepatitis A Vaccination Strategies by Age in the Republic of Korea"
    2. Improved hygiene conditions by economic growth and the introduction of the national immunization program for infants have led to variations in hepatitis A antibody prevalence across age groups in Korea. Specifically, individuals in their 20s to 40s have the lowest antibody prevalence. Given that the fatality rate of hepatitis A increases with age, the low immunity level among young adults suggests that, without additional preventive interventions, there is a risk of increased deaths in older age groups in the future. We developed an age-structured transmission model that accounts for age-specific antibody prevalence and fatality rates to assess the impact of adult vaccination, assuming it starts in 2025. We compared vaccination strategies targeting individuals in their 20s to 30s and those in their 40s to 50s, considering that antibody testing costs are incurred for the latter group in Korea. Our study shows that when total costs for vaccination are fixed, vaccinating individuals in their 40s to 50s covers 0.2 times fewer individuals than vaccinating those in their 20s to 30s but reduces deaths by 1.3 to 1.5 times more. When the total vaccine supply is fixed, the total and annual costs of vaccinating individuals in their 40s to 50s are 1.2 times higher than those for the 20s to 30s group, while the reduction in deaths is 1.7 to 1.8 times greater. From the perspective of reducing deaths, vaccinating individuals in their 40s to 50s is more effective than vaccinating those in their 20s to 30s. Furthermore, our research suggests that if an additional vaccination intervention is introduced for individuals in their 20s to 30s, military personnel may continue to receive only a single-dose vaccination, as is currently practiced.

MEPI-37:
Dynamics and selection of many-strain pathogens in Dengue virus

  1. RafaelLopes Yale University
    "Dynamics and selection of many-strain pathogens in Dengue virus"
    2. Dengue virus (DENV) has been causing outbreaks and epidemics over the course of the whole XX century. Recently, the size of the seasonal epidemics has been sequentially reaching record-breaking numbers, in 2023 WHO reported globally a record of over 6.8 million infections, and last year the WHO reported again a record-breaking number of confirmed cases, with over 10.5 million confirmed cases. Mainly those confirmed cases have happened in the Americas which has reportedly been affected by different serotypes of the virus. The region of Central America and the Caribbean is mainly affected by DENV3, while the South America region is affected in major number by the DENV1 and DENV2 serotypes. This situation raises the concern of how immunity and the ecological niche of the serotypes works and how this can be better understood to help design plans of contingency. To do so, we have adapted a well-known model to many-strain pathogen dynamics from the point of view of the strains. The model keeps the dynamics simple while being robust in incorporate as many as needed different strains. We modified the model to first reproduce the dengue dynamics in human and mosquitoes population, from that we change the demographics of each population to study how different relative time of infection to life span can give rises to different effects in the strain space and infection niches. All four serotypes have almost total homotypic immunity and, in the short term, heterotypic immunity. The goal here is to have a simple formulation for all the four serotypes and understand how this different dynamical regimes on different hosts affect i) the emergence of niche to the strains, and ii) how it determines endemicity of the disease in humans.

MEPI-38:
Modelling the human immune response dynamics during progression from Mycobacterium latent infection to disease

  1. NicholasOpoku African Institute for Mathematical Sciences
    "Modelling the human immune response dynamics during progression from Mycobacterium latent infection to disease"
    2. In this paper, we study the immune system’s response to infection with the bacteria Mycobacterium tuberculosis (the causative agent of tuberculosis). The response by the immune system is either global (lymph node, thymus, and blood) or local (at the site of infection). The response by the immune system against tuberculosis (TB) at the site of infection leads to the formation of spherical structures which comprised of cells, bacteria, and effector molecules known as granuloma. We developed a deterministic model capturing the dynamics of the immune system, macrophages, cytokines and bacteria. The hallmark of Mycobacterium tuberculosis (MTB) infection in the early stages requires a strong protective cell-mediated naive T cells differentiation which is characterised by antigen-specific interferon gamma (IFN-γ). The host immune response is believed to be regulated by the interleukin-10 cytokine by playing the critical role of orchestrating the T helper 1 and T helper 2 dominance during disease progression. The basic reproduction number is computed and a stability analysis of the equilibrium points is also performed. Through the computation of the reproduction number, we predict disease progression scenario including the latency state. The occurrence of latent infection is shown to depend on a number of effector function and the bacterial load for R0 < 1. The model predicts that endemically there is no steady state behaviour; rather it depicts the existence of the MTB to be a continuous process progressing over a differing time period. Simulations of the model predict the time at which the activated macrophages overcome the infected macrophages (switching time) and observed that the activation rate (ω) correlates negatively with it. The efficacy of potential host-directed therapies was determined by the use of the model.

MEPI-39:
Impact of Waning Immunity on Measles Outbreaks and Vaccination Strategies in Nosocomial Infection

  1. JunyoungPark Konkuk University
    "Impact of Waning Immunity on Measles Outbreaks and Vaccination Strategies in Nosocomial Infection"
    2. Secondary vaccine failure(SVF) following the second dose of measles-mumps-rubella(MMR) vaccine has resulted in low seroprevalence among healthcare workers(HCWs) in their 20s in the Republic of Korea. During the 2019 measles outbreak, 73% of confirmed cases in a hospital were seropositive yet still infected, highlighting that the presence of antibodies does not guarantee full protection. This study evaluates the impact of waning immunity on future measles outbreaks and develops vaccination strategies to control the nosocomial transmission. We developed a SEIR model incorporating three immunity states; Protected, Partially protected, and Seronegative, and integrated hospital seroprevalence and age structure. Using the stochastic Gillespie algorithm, we simulated the 2019 outbreak and predicted future scenarios. Our analysis revealed that the transmission rate among seronegative individuals was approximately 2.66 times higher than that of partially protected individuals. In long-term projections, vaccination only for new HCWs reduced confirmed cases by 41–51% compared to no vaccination. In contrast, vaccination for all HCWs suppressed outbreaks for approximately one year by reducing the effective reproduction number below 1. However, infections among partially protected individuals caused the overall outbreak size to increase over time. While current guidelines for third dose of MMR focus on seronegative individuals, our study provides mathematical evidence that booster shots for all HCWs are a more effective strategy than targeting only seronegative individuals in nosocomial environments.

MEPI-4:
Mathematical Modelling of Vaccination Strategy for Seasonal Influenza in South Korea

  1. Vijay PalBajiya Konkuk University, Seoul, South Korea
    "Mathematical Modelling of Vaccination Strategy for Seasonal Influenza in South Korea"
    2. Seasonal influenza remains a significant public health concern in South Korea, with seasonal outbreaks contributing to a high burden of disease and healthcare costs. To inform effective vaccination strategies, mathematical models can provide insights into the dynamics of influenza transmission and the impact of various vaccination strategies. In this talk, I will discuss a compartmental model to simulate the spread of seasonal influenza in South Korea, incorporating factors such as vaccination coverage, demographic structure, and virus transmission characteristics. The model is calibrated using historical data on influenza incidence, vaccination rates, and population demographics. I will discuss different vaccination strategies, including age-targeted vaccination, the impact of varying vaccine efficacy levels. The findings of considered work suggest that a targeted vaccination strategy, focusing on high-risk groups, combined with public health measures to increase vaccine uptake, could significantly reduce the incidence of influenza and mitigate its economic impact. This mathematical framework provides valuable guidance for optimizing influenza vaccination policies in South Korea and can be adapted to other countries facing similar challenges with seasonal influenza control.

MEPI-5:
Modeling the Impact of Social Behavior, Under-Reporting, and Resources on Tuberculosis During COVID-19

  1. SaritaBugalia The University of Arizona
    "Modeling the Impact of Social Behavior, Under-Reporting, and Resources on Tuberculosis During COVID-19"
    2. Despite being curable and preventable, tuberculosis (TB) still causes the highest mortality rates in the human population. However, the number of TB cases significantly reduced globally in 2020, according to the Global Tuberculosis Report by the World Health Organization, coinciding with the COVID-19 pandemic. These reductions in TB cases are likely due to a complex interplay between disruptions in TB health services and the case counts resulting from COVID-19. We developed a compartmental model for the co-infection of tuberculosis and COVID-19 in the human population to assess the impact of medical resources, mobility, under-reporting, and the social behavior (follow social distancing and face mask) of infected individuals with either disease. We computed the basic reproduction numbers for TB alone, COVID-19 alone, and the co-infection scenario. Additionally, key parameters and basic reproduction numbers were estimated by utilizing case studies from low-income, middle-income, and high-income countries in a multi-patch scenario. Our results indicate that increased social behavior among infected individuals significantly reduces the number of co-infected individuals. The impact of mobility was assessed using a two-patch model with emigration and immigration rates. It shows that the mobility of unreported infectious individuals significantly increases both active cases of TB and COVID-19. This study provides significant recommendations for medical providers and public health officials regarding TB elimination in high-income countries and TB reduction in lower-income countries with a high disease burden. The findings are also relevant for studying TB in the context of future pandemic scenarios.

MEPI-6:
The Mathematical model for tick population and tick-borne disease transmission dynamics in Korea

  1. HeejinChoi Ulsan National Institute of Science and Technology (UNIST)
    "The Mathematical model for tick population and tick-borne disease transmission dynamics in Korea"
    2. Ticks are known as the important vectors that can carry and spread various tick-borne diseases by biting humans. Representative tick-borne diseases include Lyme disease, which is prevalent worldwide, tick-borne encephalitis, which is mainly prevalent in Europe and Africa, and Severe Fever Thrombocytopenia Syndrome (SFTS), one of the emerging infectious diseases spreading in East Asia. Among these tick-borne diseases, severe fever thrombocytopenia syndrome (SFTS) is an infectious disease that was first recognized in the 2010s and has recently threatened human health. Since the disease was discovered less than 20 years ago, research on the ecology of vectors and transmission dynamics of SFTS is still insufficient. Therefore, in this study, we developed the stage-structured mathematical model for the population dynamics of Haemaphysalis longicornis (Asian Longhorned Ticks), the main vector transmitting SFTS, and extended the model to include the transmission dynamics of SFTS in Korea. Based on the model, we analyzed the impact of climate change on tick population with climate-dependent parameters in the model. Additionally, the effects of control measures have been investigated following the changes in the tick population, SFTS patients, and the costs associated with SFTS.

MEPI-7:
Exploring the potential impact of a chlamydia vaccine in the US population using an agent-based model

  1. QiDeng York University
    "Exploring the potential impact of a chlamydia vaccine in the US population using an agent-based model"
    2. Chlamydia trachomatis (CT) infection is the most reported bacterial sexually transmitted infection (STI) in the United States (US). Despite many cases being asymptomatic, infection can lead to complications such as pelvic inflammatory disease (PID) in females, and infertility in both females and males. We developed an agent-based transmission model to evaluate the impact of a potential CT vaccine on the prevalence of CT infections and associated PID in the US population. The model simulates an evolving sexual network of 10,000 sexually active agents aged 15–54, including heterosexuals, female sex workers, and men who have sex with men, following Susceptible–Exposed–Infected–Recovered–Susceptible (SEIRS) transmission dynamics. A key strength of the model is its rigorous two-step calibration procedure, which first matches real CT prevalence by age and sex, followed by real PID prevalence by age in the US. This ensures realistic alignment with epidemiological patterns. The model incorporates both vaccination and test-and-treat strategies, enabling direct comparisons between interventions. We then evaluated the impact of different scenarios of vaccination coverage and targeting, assuming a vaccine with 80% efficacy against infection and a 5-year duration of protection. The results demonstrate a gender-neutral vaccine recommendation is projected to achieve the highest impact in reducing CT prevalence and PID burden, even with a moderate vaccination coverage. Beyond CT, this is flexible, computationally efficient framework is adaptable to study other STIs and assess the effectiveness of various intervention strategies, given appropriate epidemiological and behavioral data. By providing actionable insights, this framework serves as a decision-support tool for policymakers, public health officials, and vaccine developers.

MEPI-8:
Efficient simulation of stochastic household transmission models

  1. LouiseDyson University of Warwick
    "Efficient simulation of stochastic household transmission models"
    2. Many infectious diseases transmit more effectively within a household than in other settings. This presents the opportunity to exploit the inherent household structure when gathering data, fitting models and enacting interventions. A drawback of these models, however, is their computational cost when simulating and fitting, presenting a technical challenge. We present recent progress in this area, and discuss the potential for the use of modelling in considering potential household-based interventions.

MEPI-9:
Semi-field versus experimental hut trials: Comparing methods for novel insecticide-treated net evaluation for malaria control

  1. EmmaFairbanks University of warwick
    "Semi-field versus experimental hut trials: Comparing methods for novel insecticide-treated net evaluation for malaria control"
    2. We aim to compare results for the predicted reduction in vectorial capacity caused by pyrethroid and pyrethroid-piperonyl butoxide insecticide treated nets (ITNs) between semi-field Ifakara Ambiant Chamber tests (I-ACT) and experimental hut experiments. Mathematical modelling and Bayesian inference frameworks estimated ITN effects on mosquito behavioural endpoints (repelled, killed before/after feeding) to predict reductions in Anopheles gambiae’s vectorial capacity for Plasmodium falciparum transmission. The reduction in biting estimates are generally greater for I-ACT, possibly due to lower mosquito aggression: Although I-ACT vectors are probing before release, experimental hut vectors are actively seeking a blood meal. I-ACT estimates higher probability of killing vectors which have fed, while experimental huts show greater killing before feeding, possibly due to their open-system design, where vectors can contact the net, then attempt to exit and get trapped. This is supported by most of the mosquitoes being caught before feeding being in the exit trap. While the I-ACT is a closed system, were vectors cannot exit or be trapped, increasing the likelihood of returning to host-seeking and feeding. Despite these differences, both methods yielded similar predictions for the overall reduction in vectorial capacity. Results suggest that I-ACT provides a good initial assessment of the impact of adulticide modes of action of these nets. Challenges of semi-field experiments include how to model the change in efficacy from practical use over time. However, important advantages include the ability to easily trial different strains of vector (including different resistance levels) and allowing rapid data collection. Parameterising models with location-specific bionomic parameters allows for setting -specific predictions of the impact of different nets, with the potential to include additional modes of action for other active ingredients.

MFBM-1:
In-silico modeling of simple enzyme kinetics: from Michaelis-Menten to microscopic rate constants

  1. SilviaBerra IRCCS Ospedale Policlinico San Martino, Genova, Italy
    "In-silico modeling of simple enzyme kinetics: from Michaelis-Menten to microscopic rate constants"
    2. Chemical Reaction Networks (CRNs) provide a powerful framework for modeling interactions between multiple chemical species within complex biological pathways. Their dynamics can be described by the mass-action law, representing variations over time in species concentrations through large systems of ODEs. The study of CRNs modeling signaling mechanisms within single cells has been successfully applied to provide insight into oncogenic pathways, enabling more predictive cancer models and improved therapeutic strategies [1,2,3]. Developing a CRN requires selecting the involved species, defining their interactions, and identifying key parameters such as microscopic reaction rates. This talk presents a method to retrieve these rates, particularly in the context of enzyme kinetics. We consider a simple model, where an enzyme E binds to a substrate S, forming a complex C that dissociates into a product P while regenerating E. This process is governed by four ODEs with three reaction rates: forward k_f, reverse k_r, and catalytic k_cat, typically hard to determine experimentally. A common simplification leads to the Michaelis-Menten (MM) model, where enzyme kinetics is characterized by a single ODE depending on two measurable parameters: the Michaelis constant K_M, and the maximum reaction rate V_max. These parameters may be expressed as functions of microscopic rates and are more accessible experimentally. This talk addresses the inverse problem of estimating k_f and k_r from K_M and V_max. A computational algorithm for solving this problem is presented and analyzed, along with an estimate of the reconstruction accuracy, and numerical simulations demonstrating its potential for refining kinetic models in biological and biomedical research. [1] Sommariva et al., J. Math. Biol., 82 (6): 55, 2021. [2] Berra et al., J. Optim. Theory Appl., 200 (1): 404-427, 2024. [3] Sommariva et al., Front. Syst. Biol., 3: 1207898, 2023.

MFBM-10:
Cell fate control in space and time

  1. LucyHam The University of Melbourne
    "Cell fate control in space and time"
    2. Genetically identical cells can adopt distinct, stable states, playing a crucial role in development and tissue organisation. This talk explores the mechanisms driving cell fate decisions, focusing on the interplay between gene regulatory networks and cell-to-cell communication. Using spatial stochastic models that capture fine-scale regulatory dynamics, we demonstrate how feedback loops and paracrine signalling function as switch-like controllers of cell fate, enabling transitions from transient to stable states. We derive mathematical expressions predicting the threshold signalling strength required to trigger phase transitions and establish a fundamental limit on the spatial spread of phenotypic regions. Specifically, we show that the mean region size scales proportionally to the cubic root of the signalling strength, implying that large, stable domains are prohibitively costly to maintain. This trade-off between robustness and signalling precision highlights the constraints organisms must navigate during development to maintain spatial organisation. Our findings provide key insights into the principles governing multicellular patterning and the regulation of tissue structure.

MFBM-11:
The Origins of Transient Bimodality

  1. JamesHolehouse The Santa Fe Institute
    "The Origins of Transient Bimodality"
    2. Deterministic and stochastic models, though often used to describe the same biological systems, can yield qualitatively different predictions. In particular, deterministic bistability does not necessarily imply stochastic bimodality, and vice versa. Multistability and multimodality are typically seen as indicators of distinct system behaviors, often inferred from stochastic simulation trajectories. In this talk, I explore the disconnect between probability modes and behavioral modes in the context of transient bimodality—also known as “adiabatic explosions”—which refers to metastable probability modes that do not correspond to distinct dynamical behaviors at the trajectory level. I present recent findings that link the emergence of these transient modes to a breakdown of the central limit theorem, specifically in the context of first-passage time distributions to absorbing states. I conclude by discussing how transient bimodality challenges conventional interpretations of system behavior in biological contexts and highlight conditions under which this phenomenon becomes particularly relevant.

MFBM-12:
Multiscale Probabilistic Modeling - A Bayesian Approach to Augment Mechanistic Models of Cell Signaling with Machine-Learning Predictions of Binding Affinity

  1. HollyHuber University of Southern California
    "Multiscale Probabilistic Modeling - A Bayesian Approach to Augment Mechanistic Models of Cell Signaling with Machine-Learning Predictions of Binding Affinity"
    2. Recently, the Nobel Prize winning machine learning (ML) model, AlphaFold, expanded its protein structure prediction capabilities from monomers to multimers with AlphaFold3. Here, we investigate this expanded utility in the novel context of mechanistic models of cell signaling. These models describe cell signaling events, such as binding, amongst a network of molecules, mostly proteins, and have been applied to answer both clinical and fundamental biology questions. For example, cell signaling models have been used to propose improvements to CAR-T cell therapies and to elucidate cellular ‘decision making’. Use of these models is oftentimes limited by a sparsity of data for parameterization. Thus, in this work, we introduce a Bayesian framework that incorporates information about protein structure to guide parameter inference for mechanistic models. Rather than searching all plausible parameter values, we can refine our search by considering information that is specific to the proteins involved in the signaling event. Excitingly, we find augmenting mechanistic models of signaling to be uniquely compatible with established ML models. We test our approach on two signaling models. In both cases, our approach improves parameter estimates—however, these improvements do not significantly change prediction performance on test data for either model. We find that this is due to a lack of sensitivity between the informed parameters and the test outputs. In contrast, when we examine an output that is sensitive to the changed parameters, we see a clear change in the predicted dynamics. We note that our proposed approach is limited to parameters of reversible, bimolecular binding reactions. Yet, excitingly, mechanistic models of cell signaling are often comprised of such reactions, ensuring the relatively wide applicability of our inference approach in this context.

MFBM-13:
Well-Posedness and Stability Analysis of a PDE-ODE Model for the Evolution of Bacterial Persisters

  1. ChongmingLi Queen's University Department of Mathematics and Statistics
    "Well-Posedness and Stability Analysis of a PDE-ODE Model for the Evolution of Bacterial Persisters"
    2. Most antibiotics kill bacteria by disrupting cell wall formation during mitosis. Bacterial persisters are individuals within a population that avoid this fate by not replicating. We use a parabolic PDE to model the phenotypic switch between normal, active bacteria and persisters along with a nonlocal birth-jump process that captures epigenetic inheritance. In addition, we relate bacterial population development to resource dynamics in order to depict a more realistic bacterial growth limit. Mathematically, the model consists of a non-local PDE coupled to an ODE. We prove the well-posedness of the model using semi-group theory and the Banach fixed point theorem. We then examine the evolutionarily stable strategies of persister cells by conducting a global invasion analysis with an appropriately chosen Lyapunov functional.

MFBM-14:
A Voronoi Cell-Based model for Corneal epithelial cells

  1. neda khodabakhsh jonianiMrs She
    "A Voronoi Cell-Based model for Corneal epithelial cells"
    2. The cornea represents the outermost transparent layer of the eye and is structured in multiple layers. The corneal epithelium, which forms the exterior surface of the cornea, is distinct from many other epithelial tissues in that it consists of 5 to 7 layers, rather than a single layer. This stratification process involves the upward movement of cells from the basal layer to the upper layers, a mechanism known as cell delamination. Additionally, the integrity of the corneal epithelium is maintained through the migration of new basal cells from the periphery toward the centre of the cornea. Despite its crucial role in maintaining corneal function, the regulatory mechanisms governing this process, as well as how it adapts to cell loss during wound healing, remain poorly understood. Our research aims to explore the regulation of corneal cell behavior through the use of a Voronoi cell-based model, which links local cellular interactions to the emergent dynamics observed in the stratified epithelium.

MFBM-15:
Data-driven Construction of Reduced Size Models Using Computational Singular Perturbation Method.

  1. IsmailaMuhammed Khalifa University
    "Data-driven Construction of Reduced Size Models Using Computational Singular Perturbation Method."
    2. Most biological systems have underlying multiple spatial or temporal scales that require reduced-order models to capture their essential dynamics and analyze them. However, traditional model reduction techniques, such as Computational Singular Perturbation (CSP), rely on the availability of the governing or dynamical equations, which are often unknown from data in biomedical applications. To address this limitation, we propose a data-driven CSP framework that integrates Sparse Identification of Nonlinear Dynamics (SINDy) and Neural Networks to extract time-scale separated models directly from data. Our approach is validated on the Michaelis-Menten enzyme kinetics model, a well-established multiscale system, by identifying reduced models for the standard Quasi-Steady-State Approximation (sQSSA) and reverse Quasi-Steady-State Approximation (rQSSA). When the full model cannot be identified by SINDy due to noise, we use Neural Networks to estimate the Jacobian matrix, allowing CSP to determine the regions where reduced models are valid. We further analyze Partial Equilibrium Approximation (PEA) case, where the dynamics span both sQSSA and rQSSA regimes, requiring dataset splitting to accurately identify region-specific models. The results demonstrate that SINDy struggles in the presence of noise to identify full model from data that have underlying timescale evolution, but remains effective for identifying reduced models when dataset are partitioned correctly.

MFBM-16:
Estimating Rate Parameters in Super-Resolution Imaging via Hidden Continuous Markov Chains with Discretized Emissions

  1. ShikunNie UBC
    "Estimating Rate Parameters in Super-Resolution Imaging via Hidden Continuous Markov Chains with Discretized Emissions"
    2. In this talk, I will illustrate how to model the dynamics of the fluorophores used in single-molecule localization microscopy (SMLM) as a hidden Markov chain with discretized emissions. I will generalize the proposed models in literature into a simple framework model. With the 3-state model as a particular example of our general formulation, I will show the process to obtain the transmission matrix by constructing a system of linear inhomogeneous transport partial differential equations (PDEs), which is solved by repeated Laplace-Inverse Laplace transforms. To demonstrate the usefulness of the transmission matrix, we designed two simple algorithms to solve the inference problem of the transition rates. In conclusion, the general formation is widely applicable to various techniques in SMLM, representative of the SMLM camera and adaptable to solve other active research problems such as molecule counting problems.

MFBM-17:
Long-Lasting and Slowly Varying Transient Dynamics in Discrete-Time Systems

  1. AnthonyPasion Queen's University
    "Long-Lasting and Slowly Varying Transient Dynamics in Discrete-Time Systems"
    2. Mathematical models of ecological and epidemiological systems often focus on asymptotic dynamics, such as equilibria and periodic orbits. However, many systems exhibit long transient behaviors where certain variables of interest remain in a slowly evolving state for an extended period before undergoing rapid change. These transient dynamics can have significant implications for population persistence, disease outbreaks, and ecosystem stability. In this work, we analyze long-lasting and slowly varying transient dynamics in discrete-time systems. We extend previous theoretical frameworks by identifying conditions under which an observable of the system can exhibit prolonged transients and derive criteria for characterizing these dynamics. Our results show that specific points in the state space, analogous to transient centers in continuous-time systems, can generate and sustain long transients in discrete-time models. We further demonstrate how these properties manifest in predator-prey models and epidemiological systems, particularly in contexts where populations or disease prevalence remain low for an extended period before experiencing a sudden shift. These findings provide a foundation for understanding and predicting long transients in discrete-time ecological and epidemiological models. (Joint Work with FMG Magpantay)

MFBM-18:
using networks for modelling three-dimensional structures of proteins

  1. somdatasina INDIAN INSTITUTE OF SCIENCE EDUCATION RESEARCH (IISER) KOLKATA, INDIA
    "using networks for modelling three-dimensional structures of proteins"
    2. Proteins are macromolecules in the cell performing most of the metabolic processes. The protein is made up of a linear chain of amino-acids (primary structure) synthesized, through transcription and translation of the corresponding gene/DNA sequence inside the cell. The functional protein is a three-dimensional structure that is formed due to spontaneous or assisted folding of the linear chain decided by the physicochemical forces exerted due to the size, charge and chemical nature of the amino acids. The 3D structure essentially determines the function of the protein - known as the 'Structure-Function paradigm' in molecular biophysics. We have modelled the 3-dimensional structure of proteins using the network/graph theory, where the amino acids are the nodes, and links are the physicochemical forces that hold any two amino acids together. I will show how the network approach can clearly explain the large functional differences in proteins and their mutants, having insignificant structural variations, not easily identifiable using standard structural biology methods, and thereby questioning the universality of the 'Structure-Function paradigm'.

MFBM-19:
From Data to Dynamics: Uncovering Cell Signaling Networks with Physics-Informed Machine Learning

  1. NathanSmyers University of North Carolina at Chapel Hill
    "From Data to Dynamics: Uncovering Cell Signaling Networks with Physics-Informed Machine Learning"
    2. Cell signaling is governed by complex networks of biochemical interactions. These networks are critical for a wide range of cellular functions, including detecting environmental changes and cellular motility. Modeling these processes with reaction-diffusion equations (RDEs) requires prior knowledge of protein-protein interactions for constructing the underlying network. The complex nature of signaling pathways means many relevant interactions may be unknown. To address this challenge, we developed a deep learning-based method to infer reaction networks from data. By integrating a physics-informed neural network (PINN) with a neural network for symbolic regression, this method learns interpretable RDE models from spatiotemporal data, effectively learning the biochemical reactions driving dynamics. To develop and validate our approach, we applied it to data generated from a model of cell polarity establishment. This approach has the potential to overcome limitations from incomplete knowledge of protein-protein interactions, serving as a powerful tool for uncovering how cells regulate complex behaviors.

MFBM-2:
An asymptotic upscaling of transport across bacterial membranes

  1. MollyBrennan University College London
    "An asymptotic upscaling of transport across bacterial membranes"
    2. Multiscale problems are prevalent in many real world scenarios, especially in biology, where the behaviour of a single microorganism can have considerable impact over lengthscales much larger than its own. In this work we consider the effect of the membrane microstructure of a bacterial cell on the behaviour of concentration profiles of relevant molecules on bacterial and bacterial colony lengthscales. Transport through the outer membrane of gram-negative bacteria is restricted to specific channels and non-specific porins. These provide a size-restricted passageway for small molecules through an otherwise impermeable membrane. The effects of these channels are important, for example, antibiotics must cross the outer membrane in order to effectively target gram-negative bacteria, and quorum sensing molecules must cross the membrane to allow bacterial colonies to coordinate mass phenotypic changes such as the production of virulence factors. In mathematical models this limiting transport mechanism across the membrane is often represented via phenomenological constitutive boundary conditions. In this work, we systematically derive the correct effective boundary conditions to impose across a bacterial membrane in terms of physical channel and porin properties. We use a hybrid mathematical approach, combining multiscale methodology such as asymptotic homogenisation and boundary layer theory with numerical simulations. More broadly, because we consider a generic membrane geometry and do not impose a specific outer problem, the results that we derive have a wide scope of potential applications beyond bacterial membranes, for example, to model water vapour or heat loss through fabrics, or mass transfer through surface coatings in chemical engineering.

MFBM-20:
Cell-cycle dependence of bursty gene expression: insights from fitting mechanistic models to single-cell RNA-seq data

  1. AugustinasSukys The University of Melbourne
    "Cell-cycle dependence of bursty gene expression: insights from fitting mechanistic models to single-cell RNA-seq data"
    2. Many genes are expressed in bursts of transcription, associated with alternating active and inactive promoter states. Such transcriptional bursting is characterised by the burst frequency and burst size, which describe how often a burst occurs and how many transcripts are produced per burst. These two burst parameters offer a simple, intuitive and practical quantitative description of bursty gene expression dynamics. However, a transcriptome-wide picture of how the burst frequency and size are modulated due to gene replication and other cell-cycle dependent factors remains missing. To address this, we fit mechanistic models of gene expression to mRNA count data for thousands of mouse genes, obtained by sequencing of single cells whose cell-cycle position has been inferred previously. Although we observe substantial heterogeneity in transcriptional regulation, we find that upon DNA replication, the genome-wide median burst frequency approximately halves, while the median burst size remains mostly unchanged, thus shedding light on the effect of gene dosage compensation. We show that to accurately estimate the bursting kinetics from sequencing data, mechanistic models must explicitly account for gene copy number variation and extrinsic noise due to factors varying across the cell cycle, whereas correcting for technical noise due to imperfect mRNA capture is less critical.

MFBM-21:
Mathematical modeling of transcription-independent splicing events in human gene expression

  1. ElizabethTrofimenkoff University of Lethbridge
    "Mathematical modeling of transcription-independent splicing events in human gene expression"
    2. Pre-mRNA often contains introns, which are non-coding sequences that need to be cut out or spliced before translation occurs. The spliceosome, an essential catalyst composed of several proteins with specific sequence affinities, is required for this process. Very long introns must be removed in pieces, a process known as recursive splicing. The experimental literature on the time it takes for the splicing process to occur is inconsistent. Splicing was traditionally believed to be a slow process that could take anywhere from one to tens of minutes per splicing event. However, recent reports suggest that some splicing events occur within a few tens of seconds. We developed the chemical master equation corresponding to the biochemical mechanism of splicing, allowing us to derive the system’s probability distribution, and perform a stability analysis on two conditions based on an unknown association constant parameter associated with the binding step of the scaffolding complex. We also concluded that the distribution of splice times for a single event ranges from a few tens of seconds to a few tens of minutes. Through sensitivity analyses, we have found that the mean splicing time and distribution are almost entirely dependent on the rate at which the spliceosome is activated in the assembly process—i.e. when the U1 and U4 splicing factors dissociate—which confirms that this is the rate limiting step in the catalytic process. Finally, we have examined the distributions of recursive splicing up to six events, and derived analytic solutions for these recursive splicing events in the case where the scaffolding complex strongly binds to the pre-mRNA complex (the condition thought to favor recursive binding), thus providing a model that can be fit to experimental data to in order to evaluate the number of recursive splicing events occurring.

MFBM-22:
Estimating the Growth Rate of Tumor Cells from Biopsy Samples Using an Extended Mean Field Approximation

  1. Siti MaghfirotulUlyah Khalifa University, Abu Dhabi, United Arab Emirates
    "Estimating the Growth Rate of Tumor Cells from Biopsy Samples Using an Extended Mean Field Approximation"
    2. A biopsy is a common procedure used to diagnose diseases like cancer, infections, or inflammatory conditions. In cell population studies, biopsy samples provide valuable data to analyze cellular growth, proliferation rates, and structural abnormalities, which are essential for understanding disease progression. Estimating the growth (proliferation) rate of human cells is a challenging task. To address this, we have developed a method based on the birth-death Markov process to simulate the logistic growth model. We applied an extended Mean Field Approximation (MFA) for birth-death Markov processes, which accounts for fluctuations in the evolution of observables, such as moments. By calculating the theoretical moments from the birth-death process, we solved the inverse problem and estimated the growth rate. Additionally, we performed Markov Chain Monte Carlo (MCMC) simulations for both logistic growth and logistic growth with the Allee effect. The moments of the simulated population were used to predict the growth rate through regression analysis, achieving a high R-squared value. Finally, by applying this approach to biopsy data, one can estimate the proliferation rate of human cells with greater accuracy.

MFBM-23:
Bayesian inference of chemical reaction network parameters given reaction degeneracy: an approximate analytic solution

  1. JohnVastola Harvard University
    "Bayesian inference of chemical reaction network parameters given reaction degeneracy: an approximate analytic solution"
    2. Although chemical reaction networks (CRN) provide performant and biophysically plausible models for explaining single-cell genomic data, inference of reaction network parameters in this setting usually assumes available data points can be viewed as independent samples from a steady state distribution. Less is known about how to perform efficient parameter inference in the case that there is a continuous-time data stream, which adds complexity like nontrivial correlations between samples from different times. In the continuous-time setting, one has two natural questions: (i) given a set of reactions that could plausibly explain the observed data stream, what are reasonable estimates of the associated reaction rate parameters? and (ii) what is the minimal set of reactions necessary to explain the data? Both questions can be formalized as Bayesian inference problems, with the former concerning the inference of a model-dependent parameter posterior, and the latter concerning ‘structure’ inference. If one can assume each possible reaction has a different stoichiometry vector, there is a well-known analytic solution to both problems; if reactions can have the same stoichiometry vector (i.e., there is reaction degeneracy), both problems become substantially more difficult, and no analytic solution is known. We present the first approximate analytic solution to both problems, which is valid when the number of observations becomes sufficiently large. In its regime of validity, this solution allows its user to avoid expensive likelihood computations that can involve summing over an exponentially large number of terms. We discuss interesting consequences of this solution, like the fact that ‘simpler’ models with fewer reactions are preferred over more complex ones, and the fact that the parameter posteriors of non-identifiable models are strongly prior-dependent.

MFBM-24:
The Mathematics of Deep Neural Networks with Application in Predicting the Spread of Avian Influenza Through Disease-Informed Neural Networks (DINNs)

  1. Woldegebriel AssefaWoldegerima York University
    "The Mathematics of Deep Neural Networks with Application in Predicting the Spread of Avian Influenza Through Disease-Informed Neural Networks (DINNs)"
    2. Deep learning has emerged in many fields in recent times where neural networks are used to learn and understand data. This study combines deep learning frameworks with epidemiological models and is aimed specifically at the creation and testing of DINNs with a view to modeling the infection dynamics of epidemics. Our research thus trains the DINN on synthetic data derived from an SI-SIR model designed for Avian influenza and shows the model’s accuracy in predicting extinction and persistence conditions. In the method, a twelve hidden layer model was constructed with sixty-four neurons per layer and ReLU activation function was used. The network is trained to predict the time evolution of five state variables for birds and humans over 50,000 epochs. The overall loss minimized to 0.000006, characterized by a combination of data and physics losses, enabling the DINN to follow the differential equations describing the disease progression.

MFBM-25:
Studying Retinal Detachment Progression Using an Immersed Boundary Method

  1. WilliamAnnan Clarkson University
    "Studying Retinal Detachment Progression Using an Immersed Boundary Method"
    2. Retinal detachment occurs when the neurosensory retina separates from the retinal pigment epithelium (RPE), disrupting the nutrient supply to photoreceptor cells. There are three types of retinal detachment: exudative (ERD), tractional (TRD), and rhegmatogenous (RRD), with RRD being the most common. RRD develops when a retinal tear or hole allows vitreous humor to enter the subretinal space, causing the neurosensory retina to detach from the RPE. If left untreated, this condition can lead to irreversible vision loss. Although ophthalmological tools can detect RRD, its rate of progression—particularly due to continuous eye movement—remains poorly understood. This study develops a fluid-structure interaction model to examine how various factors, including retinal thickness, elasticity, adhesion strength between the retina and RPE, vitreous humor density and viscosity, and eye rotation speed, influence detachment progression. By quantifying detachment rates under different conditions, this research aims to enhance our understanding of RRD dynamics and refine estimates of effective treatment timelines to prevent permanent visual impairment. Student: William Ebo Annan Advisors: Prof. Diana White & Prof. Emmanuel O.A. Asamani

MFBM-26:
From homogeneity to heterogeneity: Refining stochastic simulations of gene regulation

  1. Seok JooChae Rice University
    "From homogeneity to heterogeneity: Refining stochastic simulations of gene regulation"
    2. Gene regulation, affected by random molecular fluctuations, is often modeled assuming DNA is evenly distributed in the nucleus—an unrealistic simplification. We found that when key molecules move slowly, these models fail unless uneven spatial distribution is included, which slows simulations. We explored simplification techniques to speed up the process while keeping accuracy. This study stresses the need for tools balancing efficiency and precision in modeling gene regulation with spatial differences.

MFBM-27:
Benchmarking Causal Discovery Methods for Partially Observed Biochemical Kinetics

  1. HollyChambers Imperial College London
    "Benchmarking Causal Discovery Methods for Partially Observed Biochemical Kinetics"
    2. Systems of intracellular biochemical reactions are complex, often involving components that cannot be directly measured. Representing these systems as networks, with nodes representing biochemical species and edges their reactions, helps quantitatively characterize their function and effects of dysregulation. Causal discovery methods can uncover functional interactions within these networks from purely observational data, detecting hidden effects from partial observations. These effects appear as common causes of observed variables, or through time-lagged effects from intermediate causes. We benchmark the causal discovery method temporal Multivariate Information-based Inductive Causation (tMIIC) alongside other state-of-the-art tools, for time series data from biochemical kinetic models. Our results demonstrate tMIIC’s high recall in identifying interactions within toy reaction networks. By selectively omitting data, we consider both latent confounders (the standard choice for benchmarking these methods) and unobserved species participating in reactions. tMIIC detects latent confounders using bidirected edges, and unobserved species through time-delayed edges, locating hidden effects and estimating their typical timescales. Finally, we extend these benchmarks to reconstruct an experimentally calibrated model of the epidermal growth factor receptor signalling network – a system frequently dysregulated in cancer. Altogether, our work showcases the feasibility and usefulness of causal discovery methods like tMIIC for data-driven mathematical modelling of biochemical reactions.

MFBM-28:
The Role of Positive Affect in Predicting the Onset of Pain in Pediatric Sickle Cell Patients

  1. EuniceClark Virginia Commonwealth University
    "The Role of Positive Affect in Predicting the Onset of Pain in Pediatric Sickle Cell Patients"
    2. Sickle cell disease (SCD) is a group of inherited health conditions that affect the red blood cells. Millions across the globe are affected by SCD. More than 100,000 Americans and nine out of ten people in the United States who have SCD are of African descent. Individuals that carry SCD produce these abnormally shaped red blood cells (RBC) which can adversely affect the body. These red blood cells shaped like sickles do not live as long as healthy RBCs and can cause blockages in blood vessels that can lead to pain. Managing pain episodes is the focus of our current research. Valrie et al. (2021) showed that there were correlations between sleep quality and pain the next day and also positive affect and pain. Positive affect (PA) is measured using self-reported scales to evaluate the level of positive emotions a person is feeling at a certain time. Therefore, we have developed different mathematical models to study sickle cell disease pain, one of which shows the relationship between sleep and pain and the other that focuses on positive affect. Previous studies have considered PA as a mediator between sleep and pain, however for our research, we will treat positive affect as a potential driver to predict pain. This work utilizes diary data from pediatric patients and considers differences in the adolescent and children subpopulations.

MFBM-29:
Learning Interactions in Collective Dynamics

  1. Nipunide Silva Clarkson University
    "Learning Interactions in Collective Dynamics"
    2. Interacting particle systems, also known as agent-based models (ABMs), represent one category of dynamical systems that are used to study a wide range of physical phenomena across multiple scales. Examples from science and engineering include cell migration, swarm robotics, social psychology, and animal migration patterns and interactions. A ubiquitous feature of such systems is that they exhibit a form of emergence: local interactions leading to large-scale coordination. A fundamental scientific question is thus to understand the local interactions that give rise to the observed emergent dynamics. We are interested in methods for learning interactions generally, which can describe any ABM defined by an interaction kernel without making any additional assumptions about the analytical form of this kernel (i.e. it is non-parametric). The advantage of this kernel-based approach is that it incorporates the underlying physics of the model (i.e. collective dynamics), which more general approaches may ignore, potentially limiting their effectiveness. We propose to extend a non-parametric statistical learning approach for learning the interaction kernel for systems with both self-propulsion and collective dynamics, given an observed set of trajectories. First, we parametrically learn the intra-agent force while simultaneously inferring the interaction kernel non-parametrically. The method is validated on two well-known models. We extended this approach to learn the intra-agent force non-parametrically. Also, we explored how to identify the best-fit model among all possible variations for learning interacting particle collective motion based on observations.. Also, we will introduce an alternative neural network framework to the existing non-parametric statistical learning approach.

MFBM-3:
PhysiGym : bridging the gap between the Gymnasium reinforcement learning application interface and the PhysiCell agent-based modeling framework

  1. ElmarBucher Indiana University / Intelligent Systems Engineering
    "PhysiGym : bridging the gap between the Gymnasium reinforcement learning application interface and the PhysiCell agent-based modeling framework"
    2. Reinforcement learning (RL) is a powerful machine learning paradigm in which an RL agent learns to discover optimal strategies in uncertain environments. The RL control strategy has achieved remarkable success in complex tasks such as playing Chess, Go, and StarCraft. For RL, the prevailing application interface (API) standard is Gymnasium, a Python library [1]. Agent-based (AB) modeling is a mathematical, dynamical system modeling approach where the parts of the system, the so-called agents, autonomously act according to agent-type specific rules. PhysiCell is an AB modeling framework written in C++ and was implemented to model multicellular systems based on Newtonian physics. Cells are the agents. The cell type specifies the rule set the agents apply. Tissue structure emerges from the cell interactions. Substrates like oxygen can be modeled with the integrated BioFVM diffusive transport solver. Additionally, intracellular models can be integrated into cell agents [2]. The resulting AB models are 2 or 3-dimensional, off-lattice, center-based, and multiscale in space and time. In this talk, we will introduce PhysiGym, a well-documented and on all major operating systems tested open-source framework written in C++ and Python that allows to control PhysiCell models over the Gymnasium API. After a brief introduction to AB models and RL, we will discuss the implementation and obtained results from our tumor microenvironment model and the RL algorithms we applied to the model. In the future, PhysiGym can be used to learn from simulations possible mechanisms that might explain how biology systems react to similar real-world control. Furthermore, if cancer patient digital twins are written as PhysiCell models, PhysiGym could ultimately be used by oncologists to explore RL reward functions to improve treatment efficacy, reduce side effects, and slow or prevent resistance. References: [1] https://gymnasium.farama.org/ , [2] https://PhysiCell.org

MFBM-30:
Wildfire Forecasting from Sparse Observational Measurements

  1. LouisaEbby North Carolina State University
    "Wildfire Forecasting from Sparse Observational Measurements"
    2. As wildfires increase in frequency and intensity due to climate change, so does the need to create better forecasts. The wildfire perimeter is seldom fully observable, but the geospatial locations of first responder and 911 civilian cellphone calls provide a sparse representation of the wildfire in real time. We use these calls to estimate the complete fire perimeter at specific times. We present a state estimation method that yields smaller reconstruction errors than existing methods. Using the reconstruction as an initial condition, we run a cellular automaton to predict the future state of the fire. As a case study, we use calls from Maui during the devastating wildfires in August 2023 to predict the final fire perimeter.

MFBM-31:
Inferring the cause of recurrent Plasmodium vivax malaria with statistical genetics

  1. Yong SeeFoo University of Melbourne
    "Inferring the cause of recurrent Plasmodium vivax malaria with statistical genetics"
    2. One of the difficulties in eliminating Plasmodium vivax malaria lies in its ability to cause recurrent infections following the activation of dormant parasites (relapse). However, this can be confused with recurrent infections due to treatment failure (recrudescence), or a new infectious mosquito bite (reinfection). Distinguishing the cause of recurrent Plasmodium vivax malaria in each patient is critical for malaria control efforts, such as efficacy studies of drug treatments. We address this need by developing a statistical tool to infer the cause of Plasmodium vivax recurrent malaria from genetic data, implemented through the R package Pv3Rs. Each mode of malaria recurrence – relapse, recrudescence, and relapse – feature different levels of genetic relatedness between parasites. We use Bayesian hierarchical modelling to translate genetic relatedness in observed data to interpretable probabilities for each mode of malaria recurrence. We illustrate the utility of our model by applying it to Plasmodium vivax microsatellite marker data of acute malaria patients treated with high-dose primaquine. The ability to probabilistically resolve the cause of recurrent malaria helps provide more accurate failure rates of drug treatments.

MFBM-32:
Evaluating Genetic Engineering Trade-offs Through Whole-cell Modeling of Escherichia coli

  1. RileyJuenemann Stanford University
    "Evaluating Genetic Engineering Trade-offs Through Whole-cell Modeling of Escherichia coli"
    2. Genetically engineered bacteria are increasingly utilized to manufacture products that are difficult, expensive, or impractical to synthesize chemically. These products have potential applications ranging from medicine to sustainability. However, metabolic pathway introduction, extensive feedback mechanisms in the cell, and evolutionary forces complicate the engineering of bacterial strains that are well-suited for the task. We need tools that will enable us to anticipate these challenges, as well as increase efficiency and enable novel design. A recently published large-scale model of Escherichia coli has enabled us to simulate many distinct cellular processes and capture their complex interactions on a system-wide level. This model incorporates decades of heterogeneous data collection from E. coli literature to fit over 19,000 parameters for the mechanistic ordinary differential equations describing processes in the cell. We now introduce components related to genetic engineering, with an initial focus on chromosome modification. In this poster, we describe preliminary work analyzing the trade-offs between maximizing exogenous protein production and preserving cell health. Our numerical experiments varying the expression level of a single gfp gene reveal how exogenous gene products sequester resources in key cellular processes. We anticipate that these methods will set the stage for large-scale computational genetic engineering design tools as they develop and expand.

MFBM-33:
How rebellious are reindeer teens?

  1. AkinaKuperus University of Victoria
    "How rebellious are reindeer teens?"
    2. The Svalbard reindeer is a subspecies of Rangifer tarandus that is endemic to the arctic island, making them vulnerable to the impacts of climate change. Among the semi-isolated coastal populations, juvenile dispersal is crucial to maintaining viability, particularly with more rain-on-snow events making it challenging to access food. However, the absence of sea ice is a potential barrier to dispersal. Using step selection functions, we can understand how parental mimicry and individual exploration conditioned on habitat covariates drive dispersal among juveniles. Incorporating learning into step selection functions is an emerging area of research, allowing for a deeper understanding of animal movement behaviour. This project will develop new techniques for step selection functions, as well as providing key insights regarding learning and dispersal of juvenile Svalbard reindeer.

MFBM-34:
Kolmogorov Arnold Networks and Symbolic Regression can recover dynamics from time series data

  1. LucasMacQuarrie Korea Advanced Institute of Science and Technology
    "Kolmogorov Arnold Networks and Symbolic Regression can recover dynamics from time series data"
    2. Modeling with systems of differential equations requires prior knowledge to create a fully specified model reflecting our understanding of biological systems, but sometimes we don’t have a complete understanding of the systems we are interested in. If we have time series data of our variables of interest, multilayer perceptron models can take the place of unknown terms in our equations to produce solutions that fit the data well but due to the nature of multilayer perceptron models are not very interpretable. Interpretability can be improved by combining the simpler compositionality of Kolmogorov Arnold Networks with symbolic regression, allowing for the discovery of unknown terms from time series data. In this poster, we leverage the interpretability of Kolmogorov Arnold Networks with symbolic regression to recover a logistic growth term from time series data generated by a predator-prey model.

MFBM-35:
Forecasting Mountain Pine Beetle Infestations

  1. KévanRastello University of Victoria
    "Forecasting Mountain Pine Beetle Infestations"
    2. Accurate ecological forecasts provide useful insights to inform policy and management, but building models to produce these forecasts is challenging. Modelling approaches can vary from mechanistic models that attempt to capture the underlying ecological processes to purely phenomenological or statistical models that rely on inferences from data. These different approaches are likely to have different strengths depending on the metric being predicted, the amount of data for training, and the time horizon of the prediction. In particular, too strong reliance on past data may lead to incorrect inferences about the future of ecosystems under novel conditions, such as those induced by climate change and anthropogenic disturbances. We here study several models of different paradigms, including neutral models, to predict Mountain pine beetle (MPB) infestations in Alberta, Canada. MPB life history makes mathematical modeling challenging, as they use complicated chemical signalling processes and occasionally disperse over very long distances above the tree canopy. During a recent hyperepidemic in neighbouring British Columbia, MPB were able to overcome the natural border of the Rocky Mountains and spread into Alberta. Alberta dedicated extensive resources to monitor and control this spread, including helicopter surveys of infested trees. We use this data to study the predictive accuracy of several models that range in complexity and mechanistic basis. We discuss general trends in model performance with the aim of providing practice advice about the types of models that may achieve the greatest predictive accuracy given different data availability, target year, and forecast horizons.

MFBM-36:
Spatiotemporal modelling the spread of invasive pests across Great Britain

  1. KaitlynRies Newcastle University
    "Spatiotemporal modelling the spread of invasive pests across Great Britain"
    2. Invasive species pose a significant threat to biodiversity, the environment, and the economy. They are expensive to manage and monitor, in the UK alone the estimated annual cost to the economy is £4 billion. The spread of invasive species is increasing at unprecedented rates, as a result of expanding human trade networks and climate change. One invasive species of note is the oak processionary moth (OPM), which became established in the UK in 2006 through accidental importation. OPMs are harmful defoliators of oak trees, leaving them vulnerable to other stressors and diseases. They are also harmful to humans; the caterpillars have urticating hairs which can cause breathing difficulties. The eradication of OPM in the UK has been deemed unfeasible with the current management strategy focused on containing their spread. In partnership with Fera Science, we are combining mathematical and statistical models to describe and predict the spread of OPM across the UK and to inform future management strategies. This poster will showcase our work on an agent-based (individual-based) modelling approach for capturing OPM spread across the South-East of England. This model uses a lattice-based grid where a cell is either infested or susceptible (analogous to an SI model) to OPM, with cells becoming infested based on their distance to infested cells under the assumption of different dispersal kernels. We can then use the model to guide new management strategies and scenario test which may allow better containment.

MFBM-37:
Modeling Immune Cell Trajectories to Uncover Underlying Motility Drivers

  1. FatemehSaghafifar University of British Columbia
    "Modeling Immune Cell Trajectories to Uncover Underlying Motility Drivers"
    2. Immune cells observed under a microscope often exhibit motion that deviates from simple random (Brownian) walks, yet the precise factors driving these deviations remain poorly understood. Statistical approaches, such as hidden Markov models, segment cell trajectories into multiple pure Brownian motion regimes and estimate a diffusion coefficient for each. While these methods provide insight into short-term movement changes and can be used to predict future positions, they offer limited explanation of the underlying biological motivation. Here, we propose a novel framework based on a transport equation to probe the fundamental causes of anomalous diffusion in immune cell trajectories. By fitting a generalized model to data, we can distinguish whether deviations from pure diffusion arise from a correlation in the cell’s motion—implying a “memory” of previous steps—or from a bias driven by an external factor, such as a cytokine gradient. In the latter scenario, immune cells may adjust their paths when approaching target cells (e.g., cancer cells), giving rise to a biased random walk. Moreover, the goal is to come up with a framework that accomodates the possibility of both memory effects and bias (a biased correlated random walk), enabling a more comprehensive description of immune cell motility. From our initial tests, it looks like this transport-based approach can spot unique signs of correlation or bias in immune cell movement just by analyzing time-lapse data. This could help us better understand how immune cells navigate their environments and may even open new avenues for guiding their behavior in therapeutic settings.

MFBM-38:
Optimizing Enzyme Inhibition Analysis: Precise Estimation of Inhibition Constants Using a Single Inhibitor Concentration

  1. Yun MinSong Biomedical Mathematics Group, Institute for Basic Science
    "Optimizing Enzyme Inhibition Analysis: Precise Estimation of Inhibition Constants Using a Single Inhibitor Concentration"
    2. Enzyme inhibition analysis is essential in drug development and food processing, necessitating precise estimation of inhibition constants. Traditionally, these constants are estimated through experiments using multiple substrate and inhibitor concentrations, but inconsistencies across studies highlight a need for a more systematic approach to set experimental designs across all types of enzyme inhibition. Here, we addressed this by analyzing the error landscape of estimations in various experimental designs. We found that nearly half of the conventional data is dispensable and even introduces bias. Instead, by incorporating the relationship between IC50 and inhibition constants into the fitting process, we found that using a single inhibitor concentration greater than IC50 suffices for precise estimation. This novel IC50-based optimal approach, which we name 50-BOA, substantially reduces (>75%) the number of experiments required while ensuring precision and accuracy. Additionally, we provide a user-friendly package that implements the 50-BOA.

MFBM-39:
Mathematical modelling of actin polymerization in biological condensates

  1. FynnWolf University of Bergen
    "Mathematical modelling of actin polymerization in biological condensates"
    2. Biological condensates are membraneless organelles within the cell or the nucleus which perform an array of different tasks and typically consist of DNA/RNA and protein. Actin is a protein that exists in most eukaryotic cells and transitions between monomeric and filamentous states. In its filamentous state, actin forms networks, which perform vital tasks inside the cell. Recent research has shown interactions between biological condensates and cytoskeletal filaments, such as actin. The focus of these works was on morphological changes of condensates, transportation of condensates along cytoskeletal structures and on bundling of actin filaments inside condensates. While works have shown that condensates facilitate actin polymerization, a theoretical mathematical description of the cooperativity between condensates and actin polymerization is still missing. In this work we use a master equation to capture the polymerization kinetics of actin in a multicompartment system of condensates and dilute phase containing monomeric and filamentous actin. We believe that the model will allow us to make predictions about the number and length of fibers polymerized inside condensates. The findings of this study will hopefully further our understanding of the cooperative behavior between actin and biological condensates and help us understand how biological condensates are involved in the creation and maintenance of the actin networks inside the cell.

MFBM-4:
A Bayesian inference framework to calibrate one-dimensional velocity-jump models for single-agent motion using discrete-time noisy data

  1. AriannaCeccarelli University of Oxford
    "A Bayesian inference framework to calibrate one-dimensional velocity-jump models for single-agent motion using discrete-time noisy data"
    2. Advances in experimental techniques allow the collection of high-resolution spatio-temporal data that track individual motile entities over time and could be used to calibrate mathematical models of individual motility. However, experimental data is intrinsically discrete and noisy, and these characteristics complicate the effective calibration of models for individual motion. We consider individuals whose movement can be described by velocity-jump models in one spatial dimension, characterised by successive Markovian transitions between a network of n states, each with a specified velocity and a fixed rate of switching to every other state. We develop a Bayesian framework to calibrate these models to discrete and noisy data, which uses a likelihood consisting of approximations to the model solutions which we previously obtained. We apply the framework to recover the model parameters of simulated data, including the probabilities of switching to every other state. Moreover, we test the ability of the framework to select the most appropriate model to fit the data, including comparisons varying the number of states n.

MFBM-40:
Computational modelling of dynamics of viral attachment to mucus and epithelial cell surfaces

  1. LiamYih UBC, Institute of Applied Mathematics
    "Computational modelling of dynamics of viral attachment to mucus and epithelial cell surfaces"
    2. Inspired by the dynamics of Influenza A attachment to the epithelial cells of the upper respiratory tract, we are developing a dynamic biophysical model of virus attachment to cell surface receptors. Major challenges to modelling include modelling multiple ligands distributed across the viral surface, and developing a dynamic binding and unbinding model which incorporates forces applied to the ligand-receptor pair. In this presentation, I will describe some of these challenges and explain how our computational approach based on the principles of Steven Andrews SMOLDYN is being used to overcome them. I will also describe how we envisage using our framework to study practical questions about how viruses penetrate mucosal and ciliary layers in the upper respiratory tract, and how this framework can be extended to virus-like systems such as engineered nanoparticles for drug delivery.

MFBM-41:
Hybrid discrete/continuum forward and backward operators, with applications to large-population extinction time problems

  1. PeterThomas Case Western Reserve University
    "Hybrid discrete/continuum forward and backward operators, with applications to large-population extinction time problems"
    2. Safta et al (J.~Comp.~Phys. 2015) introduced a hybrid discrete/continuous representation of Kolmogorov's forward operator, $mathcal{L}$, for numerically simulating the evolution of probability distributions on state spaces spanning both large and small numbers of molecules. Motivated by first-passage-time (e.g. extinction time) and exit-distribution problems, we extend Safta et al's approach to establish a hybrid discrete/continuum representation of Kolmogorov's backward operator $mathcal{L}^dagger$, the formal adjoint of $mathcal{L}$ also known as the Markov process generator, or the stochastic Koopman operator. We apply our coarsened backward operators to several birth-death processes of increasing complexity, leveraging exact results where available to evaluate their speed and accuracy.

MFBM-5:
Bifurcations in a Patch-forming Plankton Model with Toxin Liberation Delay

  1. JuanchoCollera University of the Philippines Baguio
    "Bifurcations in a Patch-forming Plankton Model with Toxin Liberation Delay"
    2. Harmful algal blooms (HABs) are characterized by rapid growth of algae, and can be caused by toxin-producing phytoplankton (TPP). When HABs occur, oxygen in the water depletes and thus can kill fish and other marine creatures causing both environmental and economic damages. In this talk, we consider a zooplankton-phytoplankton model under the assumption that the TPP exhibits group defense so that zooplankton predation decreases at high TPP density. Furthermore, we assume that toxin liberation by the TPP is not instantaneous but is rather mediated by a time lag, which is also known as the toxin liberation delay (TLD). Our results show that the model system undergoes a Hopf bifurcation around a coexistence equilibrium when the value of the TLD reaches a certain threshold. For values of the TLD just above the threshold, the stable limit cycle that is created depicts the manageable periodic fluctuation of the populations. However, when the value of the TLD is increased further, recurring blooms of various periodicity were observed which can be attributed to the occurrence of period-doubling bifurcations.

MFBM-6:
Cellular protein transport: Queuing models and parameter estimation in stochastic systems

  1. AdelleCoster School of Mathematics & Statistics, UNSW, Sydney Australia
    "Cellular protein transport: Queuing models and parameter estimation in stochastic systems"
    2. Real-world systems, especially in biology, exhibit significant complexity and inherent limitations in observability. What methods can enhance our understanding of the mechanisms underlying their functionality? Additionally, how can we develop and test explanatory models within a stochastic environment? Evaluating the effectiveness of these models requires quantitative measurements of the disparity between model outputs and observed data. While mean-field, deterministic models have well-established approaches for such assessments, stochastic systems—particularly those constrained by multiple data types—need carefully designed quantitative comparison methods. Methods for inferring the parameters of stochastic models generally require analytical forms of the model solutions, large data sets, summary statistics, or assumptions on the distribution of model outputs. These approaches can be limiting if you wish to preserve the information in the variability of the data but you do not have sufficient data to reliably fit distributions or determine robust statistics. We present a hierarchical approach to develop a distance measure for the direct comparison of model output distributions to experimentally observed distributions, avoiding any assumptions about distributions and the need to choose summary statistics. Our distance measure allows for constraining the model with multiple experiments, not necessarily of the same type, such that each experiment constrains some, or all, of the model parameters. We use this distance for parameter estimation with our queuing model of intracellular GLUT4 translocation. We will explore some practical considerations when using the distance for parameter inference, such as the effects of model output sampling and experimental error. Fitting the queuing model to data allowed us to uncover a possible mechanism of GLUT4 sequestration and release in response to insulin. Authors: Brock D. Sherlock and Adelle C.F. Coster

MFBM-7:
A Multi-Cellular Network Model Predicts Changes in Glomerular Endothelial Structure in Diabetic Kidney Disease

  1. AshleeFord Versypt University at Buffalo, The State University of New York
    "A Multi-Cellular Network Model Predicts Changes in Glomerular Endothelial Structure in Diabetic Kidney Disease"
    2. Diabetic kidney disease (DKD) progression is often marked by early glomerular endothelial cell (GEC) dysfunction, including alterations in the fenestration size and number linked to impaired glomerular filtration. However, the cellular mechanisms regulating GEC fenestrations remain poorly understood due to limitations in existing in vitro models, challenges in imaging small fenestrations in vivo, and inconsistencies between in vitro and in vivo findings. This study used a logic-based protein-protein interaction network model with normalized Hill functions for dynamics to explore how glucose-mediated signaling dysregulation impacts fenestration dynamics in GECs. We identified key drivers of fenestration loss and size changes by incorporating signaling pathways related to actin remodeling, myosin light chain kinase, Rho-associated kinase, calcium, and VEGF and its receptor. The model predicted how hyperglycemia in diabetic mice leads to significant fenestration loss and increased size of fenestrations. We found that glycemic control in the pre-DKD stage mitigated signaling dysregulation but was less effective as DKD developed and progressed. The model suggested alternative disease intervention strategies to maintain fenestration structure integrity, such as targeting Rho-associated kinase, VEGF-A, NFκB, and actin stress fibers.

MFBM-8:
An agent-based model with ECM to study the mechanics of DCIS microinvasions

  1. AnaForero Pinto Moffitt Cancer Center/ University of South Florida
    "An agent-based model with ECM to study the mechanics of DCIS microinvasions"
    2. Microinvasions in ductal carcinoma in situ (DCIS) are malignant cells that have broken through the basement membrane (BM) and extend into the stroma with no focus larger than 1 mm. Since microinvasions constitute the first step in the metastatic cascade, identifying the causes of microinvasions will help distinguish between progressors or non-progressors among the DCIS patients, thus improving treatment. The mechanical tumor-stroma interactions play an important role in this process. Studies have shown that elevated collagen stiffening, deposition, and fibril crosslinking are correlated with tumor aggressiveness and invasion in breast cancer. Therefore, here we present SilicoDCIS, a 2D off-lattice center-based agent-based model (ABM) of ductal carcinoma in situ (DCIS) growth and its interaction with the extracellular matrix (ECM) to investigate the mechanical conditions that may lead to tumor microinvasions. SilicoDCIS simulates the division, growth, and migration of tumor cells in DCIS while interacting with other cell types and the ECM. This includes the BM, the myoepithelial and epithelial cell layers, and the collagen in the ECM. The ECM was modeled as a vector field, where the direction of each vector gives the orientation of a collagen bundle, and the vector magnitude is related to the bundle density. The growing DCIS can remodel the ECM (density and orientation), and in turn, the ECM applies a reciprocal force (proportional to the local collagen density) opposite to the tumor growth. With SilicoDCIS, we studied the mechanical effects of cancer cell proliferation and migration on the BM and the ECM. We found that higher cell migration force leads to increased BM stress and ECM density (on the tumor edges where cells migrate) and that the escape of the migrating cells from the duct vs. their intraductal confinement depends on cell speed. SilicoDCIS may provide insights into the mechanics of DCIS microinvasions to guide the design of future experiments.

MFBM-9:
Exploring Mathematical Techniques in Collective Behaviour and Decision Making in Animal Groups

  1. Clark KendrickGo Collaborative Analytics Group, Department of Mathematics, Ateneo de Manila University
    "Exploring Mathematical Techniques in Collective Behaviour and Decision Making in Animal Groups"
    2. 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.

NEUR-1:
A predictive propensity measure to enter REM sleep

  1. AlexanderGinsberg The University of Utah, Department of Mathematics
    "A predictive propensity measure to enter REM sleep"
    2. During sleep periods, most mammals alternate multiple times between rapid-eye-movement (REM) sleep and non-REM (NREM) sleep. A common theory proposes that these transitions are governed by an ``hourglass-like'' homeostatic need to enter REM sleep that accumulates during the inter-REM interval and partially discharges during REM sleep. However, markers or mechanisms for REM homeostatic pressure remain undetermined. Recently, an analysis of sleep in mice demonstrated that the cumulative distribution function (CDF) of the amount of NREM sleep between REM bouts correlates with REM bout duration, suggesting that time in NREM sleep influences REM sleep need. Here, we build on those results and construct a predictive measure for the propensity to enter REM sleep as a function of time in NREM sleep since the previous REM episode. The REM propensity measure is precisely defined as the probability to enter REM sleep before the accumulation of an additional pre-specified amount of NREM sleep. Analyzing spontaneous sleep in mice, we find that, as NREM sleep accumulates between REM bouts, the REM propensity exhibits a peak value and then decays to zero with further NREM accumulation. We show that the REM propensity at REM onset predicts features of the subsequent REM bout under certain conditions. Specifically, during the light phase and for REM propensities occurring before the peak in propensity, the REM propensity at REM onset is correlated with REM bout duration, and with the probability of the occurrence of a short REM cycle called a sequential REM cycle. Further, we also find that proportionally more REM sleep occurs during sequential REM cycles, supporting a correlation between high values of our REM propensity measure and high REM sleep need. These results support the theory that a homeostatic need to enter REM sleep accrues during NREM sleep, but only for a limited range of NREM sleep accumulation. Time permitting, we will discuss current research directions.

NEUR-2:
Accelerating Solutions of Nonlinear PDEs Using Machine Learning: A Case Study with the Network Transport Model

  1. BrandonImstepf University of California, Merced
    "Accelerating Solutions of Nonlinear PDEs Using Machine Learning: A Case Study with the Network Transport Model"
    2. Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder affecting approximately 10% of Americans over age 65, leading to memory loss, cognitive decline, and impaired daily function. Disease progression correlates with the spread of tau and amyloid-beta proteins, which aggregate into neurofibrillary tangles. While macroscopic whole-brain network models predict large-scale protein deposition patterns, they lack the specificity to capture individual disease progression. Conversely, microscale neuron-neuron models offer highly detailed biochemical aggregation and transport simulations but are computationally prohibitive for whole-brain parameter inference. In this work, we explore using machine learning to accelerate whole-brain simulations by approximating explicit solutions to the microscopic Two-Neuron Transport Model (TNTM), a partial differential equation describing tau flux along a neuron, incorporating biochemical aggregation, fragmentation, and transport. We simulate a single-edge model across physiological ranges of biochemical parameters and boundary conditions, then compare regression methods with varying levels of interpretability, from neural networks (low) to symbolic regression via PySR (high). Neural networks achieve the lowest error but lack biological insight. Linear and polynomial regression compute rapidly but yield high errors with limited interpretability. Symbolic regression achieves a balance between accuracy and transparency. This work demonstrates the potential of machine learning for computationally scalable AD modeling, opening avenues for patient-specific parameterization using AD data repositories.

NEUR-3:
Phase Reduction of Heterogeneous Coupled Oscillators

  1. YoungminPark University of Florida
    "Phase Reduction of Heterogeneous Coupled Oscillators"
    2. We introduce a method to identify phase equations for heterogeneous oscillators beyond the weak coupling regime. This strategy is an extension of the theory from [Y. Park and D. Wilson, SIAM J. Appl. Dyn. Syst., 20 (2021), pp. 1464--1484] and yields coupling functions for N general limit-cycle oscillators with arbitrary types of coupling, with similar benefits as the classic theory of weakly coupled oscillators. These coupling functions enable the study of oscillator networks in terms of phase-locked states, whose stability can be determined using straightforward linear stability arguments. We demonstrate the utility of this approach by reducing and analyzing conductance-based thalamic neuron model. The reduction correctly predicts the emergence of new phase-locked states as a function of coupling strength and heterogeneity. We conclude with a brief remark on recent extensions to n:m phase-locking and N-body interactions.

NEUR-4:
Finite-time synchronization of fractional order neural networks via LMI approach

  1. MadinaOtkel Nazarbayev University
    "Finite-time synchronization of fractional order neural networks via LMI approach"
    2. This study investigates the finite-time synchronization of complex-valued fractional-order memristive neural networks (CVFOMNNs) with time-varying delays. By separating the model into real and imaginary parts, we design a unified sliding-mode surface and construct a suitable sliding-mode controller to synchronize the drive system state trajectories to those of the response system. We prove that the novel super-twisting sliding mode controller forces the error system to reach a sliding surface in a finite time and decreases the chattering effect. Moreover, the LMI conditions are formulated to guarantee the finite-time synchronization of the CVFOMNNs with time-varying delays by using a less conservative Lyapunov-Krasovskii function. Finally, numerical simulations are presented to validate the effectiveness of the theoretical results.

ONCO-1:
Testing the feasibility of estimating the migration to proliferation rate ratio in glioblastoma from single time-point MRI data

  1. RafaelBravo University of Texas at Austin
    "Testing the feasibility of estimating the migration to proliferation rate ratio in glioblastoma from single time-point MRI data"
    2. Introduction: Glioblastoma tumors with a high migration to proliferation ratio (D/k ratio) are more drug resistant, suggesting 1) that estimating D/k ratios can help predict patient responses, and 2) investigating the cellular mechanisms behind D/k ratios can help understand the mechanisms of drug resistance. Here we quantify the identifiability of D/k ratios using synthetic tumor data. Materials and Methods: We used the standard reaction-diffusion model (i.e. logistic growth and diffusion) initialized with a single cell. Using this model, we grew synthetic tumors, saving the density field for calibration once the tumor had filled 10% of the domain. We fixed proliferation (k, 1/day) and used grid search followed by Levenberg-Marquardt optimization to calibrate the diffusion rate (D, mm2/day) and growth time (t, days) that produced density distributions matching the synthetic data as closely as possible. We quantified the ability of the algorithm to accurately and precisely identify D/k ratios in both the presence and absence of Gaussian noise. Results: Our algorithm finds D/k ratios with very high accuracy: 0.95 +/- 0.72% difference from correct D/k with k fixed at 0.01/day. We found that with 5% noise added the ability to accurately recover D/k ratios improved as its magnitude increased: 9.6 +/- 13.38% when D/k = 10-2 mm2 versus 2.7 +/- 3.65% when D/k = 1 mm2 Future Directions: Ongoing work will establish the minimal data requirements to accurately estimate D/k ratios within 10% of the correct values, and then apply the technique to the brain tumor image segmentation (BRATS) dataset. All BRATS patients have a single segmented pre-treatment MRI (N = 103) which we will use to estimate their D/k ratios. A subset of the BRATS patients also has RNA microarray data available (N = 91). We plan to correlate the patients’ D/k ratios with gene set scores derived from their microarray data to identify cellular mechanisms that potentially underly the D/k ratios.

ONCO-10:
Using a pharmacokinetic ctDNA shedding model to develop a biomarker of tumor response to targeted therapy

  1. AaronLi University of Minnesota
    "Using a pharmacokinetic ctDNA shedding model to develop a biomarker of tumor response to targeted therapy"
    2. Early prediction of response to therapy or lack thereof is essential for efficient treatment planning. Next-gen sequencing (NGS) has made it possible to non-invasively collect and sequence circulating tumor DNA (ctDNA) from longitudinal plasma samples. While ctDNA data continues to be collected on a wide variety of cancers and treatment types, it is still unclear what ctDNA biomarkers are most indicative of treatment success or failure. We present a pharmacokinetic model of ctDNA shedding under targeted therapy. Using this model to simulate a cohort of virtual patients, we demonstrate the predictive potential of a biomarker based on early ctDNA sampling. We compare the performance of the biomarker to that of a neural network classifier as well as existing ctDNA biomarkers. We show that our biomarker is able to match and exceed the performance of alternatives, both in terms of accuracy and earliness of prediction.

ONCO-11:
Patient-specific forecasting of prostate cancer progression to higher-risk disease during active surveillance

  1. GuillermoLorenzo Group of Numerical Methods, Department of Mathematics, University of A Coruna, Spain
    "Patient-specific forecasting of prostate cancer progression to higher-risk disease during active surveillance"
    2. Prostate cancer (PCa) usually exhibits low or intermediate risk at diagnosis, for which active surveillance (AS) is an established clinical option. Patients in AS are monitored via serum Prostate Specific Antigen (PSA), multiparametric magnetic resonance imaging (mpMRI), and biopsies. If these exams indicate tumor progression to higher-risk disease, curative treatment is typically recommended (e.g., surgery, radiotherapy). Hence, AS combats overtreatment of indolent PCa, thereby avoiding unnecessary treatment that can induce side effects reducing quality of life (e.g., incontinence, impotence) but without prolonging longevity. However, monitoring protocols for AS rely on an observational and population-based approach that does not account for the heterogeneous nature of PCa dynamics and cannot provide an early identification of progressing patients. To address these two critical limitations, we propose using personalized predictions of tumor progression based on biomechanistic features that describe the heterogeneous disease dynamics for each patient (e.g., tumor cell density, proliferation activity). To this end, we first calculate these patient-specific features from MRI-informed, organ-scale predictions of a biomechanistic model of prostate cancer growth. Then, a generalized logistic classifier is leveraged to map these features to risk groups. Since our PCa growth predictions are spatiotemporally-resolved, we can calculate the biomechanistic features describing PCa dynamics and the tumor risk over time, thus enabling the calculation of time to progression for each patient. Here, we present a preliminary study of our approach in which we demonstrate the accuracy of our personalized growth and progression predictions in a small patient cohort. Although further improvement and testing in larger cohorts are required, we believe that our predictive technology could be leveraged to inform clinical decision-making and personalize AS protocols for PCa patients.

ONCO-12:
A structured-PDE approach to targeting a quiescent sub-population under hypoxia and anti-tumour therapies in paediatric glioma.

  1. RubyNixson Mathematical Institute, University of Oxford
    "A structured-PDE approach to targeting a quiescent sub-population under hypoxia and anti-tumour therapies in paediatric glioma."
    2. Paediatric diffuse midline gliomas are highly aggressive, incurable, childhood tumours. Their location in the brainstem limits treatment to radiotherapy, which allows an average survival time of 9-11 months. A sub-population of quiescent tumour cells are thought to be responsible for the poor outcomes of these patient. Quiescence is often viewed as a reversible resting state in which cells temporarily exit the cell cycle, the process controlling DNA replication and cell division. Radiosensitivity varies during the cell cycle, and quiescent cells exhibit a higher relative level of radio-resistance. Hypoxia (physiologically low levels of oxygen) also impacts cell cycle progression and quiescence, as well as response to radiotherapy, contributing to poor patient outcomes. We build on existing mathematical models of cell cycle progression under treatment which account for the radio-resistance of quiescent cells and their ability to re-enter the cell cycle and proliferate. We derive a system of partial differential equations (PDEs), which structures cells by the time spent in each cell cycle phase and allows transitions to and from a quiescent phase. By considering oxygen-dependent cell cycle progression, we use the model to investigate how the proportion of quiescent cells changes when we impose fluctuating oxygen dynamics and treat with radiotherapy. We extend existing studies that optimise treatment schedules using a balance of treatment outcome and toxicity/cost by incorporating a fixed radiotherapy schedule to investigate the impact of a hypothetical drug that alters the transition dynamics to and/or from quiescence. By considering different mechanisms of action for this hypothetical drug, we use our PDE model to identify candidate drugs with the potential to slow tumour progression and improve patient outcomes. This work will inform our clinical collaborators if such an improvement is possible, and what the design of a suitable drug should be.

ONCO-13:
MRI-based mathematical modeling to predict the response of cervical cancer patients to chemoradiation

  1. ReshmiPatel The University of Texas at Austin
    "MRI-based mathematical modeling to predict the response of cervical cancer patients to chemoradiation"
    2. Concurrent chemoradiation followed by brachytherapy is the standard-of-care treatment for locally advanced cervical cancer (LACC), but 30% of treated patients experience local recurrence [1], indicating a need for patient-specific, optimized therapeutic regimens to improve outcomes. We aim to predict patient-specific response to chemoradiation by applying our established MRI-based mathematical modeling framework [2]. The study cohort consisted of 10 LACC patients who underwent imaging with T2-weighted MRI, dynamic contrast-enhanced MRI, and diffusion-weighted MRI (DWI) before (V1), after two weeks (V2), and after five weeks (V3) of chemoradiation [3]. We registered all patient-specific MRI data within and between visits, and maps of the number of tumor cells (NTC) were calculated from the DWI-derived apparent diffusion coefficients. Our biology-based reaction-diffusion models characterize the spatiotemporal change in NTC as a function of cell diffusion, proliferation, and therapy-induced death. We consider two model options for cell death: (A) distinct chemotherapy (exponential decay) and radiotherapy (instantaneous decrease according to the linear-quadratic model) terms and (B) a single exponential decay term describing chemoradiation. Proliferation and chemoradiation efficacy rates were calibrated to the V1 and V2 NTC maps, and the calibrated model was run forward to predict the NTC at V3. Using Model (A), the concordance correlation coefficient (CCC) between the observed and predicted V1 to V3 change in total tumor cellularity was 0.87, and the CCC between the observed and predicted change in tumor volume was 0.90; using Model (B), the CCC values were 0.97 and 0.90, respectively. These preliminary findings show the promise of our mathematical modeling framework in predicting LACC response to chemoradiation. References: [1]. CCCMAC. Cochrane Database Syst Rev. 2010. [2]. Jarrett et al. Nat Protoc. 2021. [3]. Bowen et al. J Magn Reson Imaging. 2018.

ONCO-14:
Wave-like behaviour in cancer evolution

  1. ChayPaterson University of Manchester
    "Wave-like behaviour in cancer evolution"
    2. Compound birth-death processes are widely used to model the age-incidence curves of many cancers [1]. There are efficient schemes for directly computing the relevant probability distributions in the context of linear multi-stage clonal expansion (MSCE) models [2]. However, these schemes have not been generalised to models on arbitrary graphs, forcing the use of either full stochastic simulations or mean-field approximations, which can become inaccurate at late times or old ages [3, 4]. Here, we present a numerical integration scheme for directly computing survival probabilities of a first-order birth-death process on an arbitrary directed graph, without the use of stochastic simulations. As a concrete application, we show that this new numerical method can be used to infer the parameters of an example graphical model from simulated data.

ONCO-15:
Patient-Specific MRI-Integrated Computational Modeling of Tumor Fluid Dynamics and Drug Transport

  1. HoomanSalavati Ghent University
    "Patient-Specific MRI-Integrated Computational Modeling of Tumor Fluid Dynamics and Drug Transport"
    2. Introduction: Mathematical modeling is a key tool for understanding solid tumor biophysics, progression, and treatment resistance. Biophysical changes, such as elevated interstitial fluid pressure (IFP), are identified as major barriers to effective drug delivery. Incorporating patient-specific data into mathematical models offers the potential for personalized prognosis and treatment strategies for cancer patients. In this study, we explored the integration of patient-specific data from dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted MRI (DW-MRI) into a computational fluid dynamics (CFD) model of solid tumors to estimate the IFP and drug penetration profiles. Methods: As part of a translational study (EC/2019/1330, approved by Ghent University Hospital, Belgium), a patient with peritoneal metastasis underwent multi-sequential MRI, including T1-weighted (T1w) anatomical imaging, DCE-MRI, and DW-MRI. Tumor interstitial fluid pressure (IFP) was directly measured using a pressure transducer-tipped catheter for model validation. The CFD tumor model described interstitial fluid flow using Darcy’s law, the continuity equation, and Starling’s law, while drug penetration was modeled via the convection-diffusion-reaction equation. The 3D tumor geometry was derived from T1w images, vascular permeability from DCE-MRI using the extended-Tofts model, and hydraulic conductivity from DW-MRI. Results: An elevated IFP zone was observed in the central region of the tumor (up to 14 mmHg), while a lower IFP zone appeared at the tumor's edge. The clinically recorded IFP values (12.0 ± 2.5 mmHg) corresponded well with the simulation results. Drug penetration varied across the tumor, with deeper penetration in low-IFP regions. Conclusion: An image-based CFD model captured IFP and drug distribution variability, aligning with clinical data. This approach advances personalized oncology, potentially improving treatment strategies through noninvasive, patient-specific modeling.

ONCO-16:
Mechanistic modelling of cluster formation in metastatic melanoma

  1. NathanSchofield University of Oxford
    "Mechanistic modelling of cluster formation in metastatic melanoma"
    2. Melanoma is the most aggressive type of skin cancer, yet survival rates are excellent if it is diagnosed early. However, if metastasis occurs, five-year survival rates drop significantly. During the early stages of tumour initiation, melanoma cells form clusters within the primary tumour which promote metastasis. In the absence of biological tools to visualise cluster formation at primary tumour sites, we develop mathematical models to generate mechanistic insight into their formation. For this work we utilise in vitro data for two distinct melanoma cell phenotypes, one more proliferative and the other more invasive. This data consists of experiments for each phenotype individually, resulting in homogeneous clusters, as well as mixtures of the two phenotypes, resulting in heterogeneous clusters. We develop a series of differential-equation-based models using a coagulation-fragmentation-proliferation framework to describe the growth dynamics of homogeneous clusters, incorporating different functional forms for cell proliferation and cluster splitting. We then extend these models to describe the formation of heterogeneous cell clusters by considering both cluster size and phenotypic composition. We fit the models to experimental data, using a Bayesian framework to perform parameter inference and information criteria to perform model selection. In this way, we characterise and quantify differences in the clustering behaviour of two melanoma phenotypes in homogeneous and heterogeneous clusters, particularly the cluster coagulation, proliferation, and splitting rates. We find that the coagulation rate for the invasive phenotype is much larger than that for the proliferative phenotype, and evaluate how well different modelling assumptions fit the data in order to increase our understanding of the mechanisms driving metastasis. In future work, the models will be used to inform further experiments and, in particular, to suggest and test strategies for inhibiting metastasis.

ONCO-17:
Topological quantification of colorectal cancer tissue structure

  1. SergioSerrano de Haro Ivanez University of Oxford
    "Topological quantification of colorectal cancer tissue structure"
    2. A hallmark of colorectal cancer is the structural disruption of the colonic tissue, a process correlated with disease progression. Intestinal crypts, glands essential for homeostasis, lose their tubular morphology - and function - due to uncontrolled cell proliferation and tissue invasion. Evaluating this deterioration in biopsied samples is critical for both patient diagnosis and prognosis. Histopathological methods are essential for assessing colorectal cancer status, but their precision and reproducibility can be improved. Spatial biology provides a mathematical framework to analyse the structural properties of biological data; in this work, we apply techniques from topological data analysis and network science to quantify architectural changes in colorectal cancer progression. Using cell point clouds derived from immunohistochemistry imaging, we construct cell networks that encode topological tissue features. We employ these networks to segment large, imaged samples into smaller, biologically meaningful regions of interest that preserve tissue architecture. We compare the performance of our approach to conventional segmentation methods such as quadrat division. Within these segmented regions, we further employ methods from persistent homology to quantify tissue structure, with the long-term goal of identifying novel biomarkers of disease progression.

ONCO-18:
An ODE-SDE Model for Ct-DNA dynamics

  1. PujanShrestha Texas A&M University
    "An ODE-SDE Model for Ct-DNA dynamics"
    2. Effective cancer therapies, while continuously improving, are often constrained by lower detection limits of disease. Tumor-immune dynamics in this limit present one of the most pressing knowledge gaps as cancer ultimate escape or elimination are often determined following an intervening period of population equilibrium sustained at low population size. Population dynamics in this small-population limit are affected by intrinsic noise in the tumor-immune interaction, as are estimates of population disease burden by extrinsic noise in acquiring such estimates through associated biomarkers. We present a modeling framework that investigates the interactions between tumor cells and the immune system in the small population regime, focusing on how these interactions influence biomarker levels. The framework combines deterministic elements, which describe tumor growth and immune responses, with stochastic components that capture the inherent variability in biomarker release. We use a system of ordinary differential equations (ODEs) to represent the tumor-immune dynamics between an adaptive immune compartment, immunogenic tumor cells, and evasive tumor cells. The immune system’s role in controlling tumor growth is reflected in the tumor-immune interaction terms. Apoptotic death via tumor-immune interactions and necrotic death via the tumor competition under a shared carrying capacity both contribute to the release of a tumor biomarker. We focus on applying our model to ct-DNA, wherein we frame ct-DNA dynamics using a stochastic differential equation (SDE). This SDE framework accounts for the variability in ct-DNA release due to the dynamic tumor-immune interactions, as well as inherent biological noise, such as DNA degradation and clearance. By coupling the ODE system of equations for tumor-immune dynamics with the SDE for ct-DNA release, we can use the model to study the fluctuations in ct-DNA levels driven by tumor-immune dynamics and exogenous sampling noise.

ONCO-19:
Cell-Cell Fusion in Cancer: Key In Silico Tumor Evolutionary Behaviors

  1. PaulameenaShultes Case Western Reserve University
    "Cell-Cell Fusion in Cancer: Key In Silico Tumor Evolutionary Behaviors"
    2. Cell-cell fusion is a known phenomenon throughout the human body. It characterizes a wide range of physiological and pathological processes, ranging from placentation and embryogenesis to cancer stem cell (CSC) formation. There is increasing evidence that cell-cell fusion can play key roles in the development and progression of cancer, particularly by increasing intratumor heterogeneity and potentiating somatic evolution. There are many unanswered questions surrounding the characteristics that define cancer cell-cell fusion events, their frequency in in vivo tumor conditions, and whether or not cell-cell fusion is a universal phenomenon across cancer. Using a combination of in vitro and in silico approaches, we can begin to answer some of these questions. We have developed a preliminary cellular automata model using HAL to evaluate the effect of variable cell-cell fusion rates and behaviors under a range of tumor microenvironmental conditions. By comparing our spatial model to a suite of ordinary differential equations, we can begin to estimate the effects of cell-cell fusion on the genomic heterogeneity and malignancy potential of cancers in vivo. I demonstrate the importance of improving fusion rate estimates using the simplest iteration of an in silico cellular automata model (coined SimpleFusion). The preliminary SimpleFusion model results illustrate how much the impact of cell fusion, as measured by the percentage of cells that have had a fusion event in their lineage, changes between orders of magnitude of fusion rates. Corresponding ODE models demonstrate similar results despite the lack of encoded spatial information. By studying these two types of models (ABM, ODEs) in combination, we can begin to understand what parameters most directly define the cell-cell fusion population dynamics in our in vitro fusion experiments and, in turn, in vivo conditions as well.

ONCO-2:
Adipocyte-derived lipids promote phenotypic bistability in a structured population model for melanoma growth

  1. KeithChambers University of Oxford
    "Adipocyte-derived lipids promote phenotypic bistability in a structured population model for melanoma growth"
    2. Melanoma cells exhibit a continuum of proliferative to invasive phenotypes. While single-cell and spatial transcriptomics have enabled biologists to quantify the distribution of phenotype amongst melanoma cells, a complete mechanistic understanding is currently lacking. A key issue is the impact of adipocyte-derived lipids, whose uptake by melanoma cells drives an invasive response that may lead to metastasis. To address this, we have developed a phenotype-structured model for melanoma cell populations that couples the phenotype dynamics to the essential aspects of intracellular lipid metabolism and the extracellular microenvironment. In this talk, I will first introduce a single-cell ODE model that illustrates how lipid uptake gives rise to phenotypic bistability in melanoma cells. I will then show how a phenotype-structured population model, whose advection term is informed by the single-cell model, exhibits a range of qualitative behaviours, including cyclic solutions and bimodal phenotypic distributions. Together, these results increase understanding of the role played by adipocyte-derived lipids and other microenvironment factors in shaping the distribution of phenotype in melanoma cell populations. We speculate that our modelling framework may also be applicable to other lipid-rich tumours (e.g. breast and ovarian cancers) that are commonly associated with increased metastasis.

ONCO-20:
Identifying Critical Immunological Features of Tumor Control and Escape Using Mathematical Modeling

  1. RachelSousa University of California, Irvine
    "Identifying Critical Immunological Features of Tumor Control and Escape Using Mathematical Modeling"
    2. The immune system can eradicate cancer, but various immunosuppressive mechanisms active within a tumor curb this beneficial response. Cytotoxic T cells (CD8s), regulatory T cells (Tregs), and antigen-presenting dendritic cells (DCs) play an important role in the immune response; however, it is very cumbersome to unravel the effects of multimodal interactions between tumor and immune cells and their contributions to tumor control using an experimental approach alone. Thus, to better understand the mechanisms that govern the interactions between immune cells and tumor cells and to identify the critical immunological features associated with tumor control and tumor escape, we built a mechanistic mathematical model of CD8s, Tregs, DCs, and tumor cells. The model accounts for tumor immunogenicity, the effects of IL-2 prolonging T cell lifespan, Treg suppression of antitumor immune response through CTLA-4, recruitment of immune cells into the tumor environment, and interferon-gamma upregulation of PD-L1 on DCs and tumor cells to deactivate T cells. We successfully fit the model to experimental data of tumor and immune cell dynamics. Employing Latin Hypercube Sampling, we generated over 1000 parameter sets that capture the sensitivity of αPD-1 immunotherapy. By comparing the parameter sets, we gain an insight into which mechanisms impinge the success of immunotherapy. We are now utilizing the model to explore combination immunotherapies that enhance the immune response in partial- and non-responders of αPD-1 immunotherapy. In particular, we are investigating what combinations of αPD-1, αCTLA-4, αICOSL, and αLAG-3 will stimulate CD8 activation without promoting Treg activation. After identifying the top combination therapies, we will validate our predictions experimentally. This integrated approach of modeling and experimental validation aims to advance our understanding of tumor-immune interactions and guide the development of more effective immunotherapeutic strategies.

ONCO-21:
Regulation of Intra- and Intercellular Metabolite Transport in Cancer Metabolism

  1. FabianSpill University of Birmingham
    "Regulation of Intra- and Intercellular Metabolite Transport in Cancer Metabolism"
    2. Metabolite transport is essential for cellular homeostasis, energy production, and metabolic adaptation. In cancer, dysregulated transport sustains tumor growth and alters redox balance. The mitochondrial solute carrier SLC25A10 facilitates succinate, malate, and phosphate exchange, influencing central carbon metabolism. However, its transport kinetics and physiological directionality remain poorly understood. We present a mathematical model of SLC25A10 based on a ping-pong kinetic mechanism, capturing competitive dynamics between malate and succinate. Our simulations reveal that under normal conditions, malate flux dominates due to its higher binding affinity. However, in succinate dehydrogenase (SDH) dysfunction, excess succinate induces a transient efflux shift and phosphate flux reversal. If experimentally validated, this metabolic shift could serve as a biomarker for tumors with SDH mutations. Integrating our kinetic model with genome-scale metabolic networks, we highlight the role of mitochondrial transport in cancer metabolism. Specifically, in multiple myeloma, metabolic crosstalk between plasma cells and bone marrow stromal cells is key to tumor progression. Our findings demonstrate the power of mathematical modeling in uncovering transport-mediated metabolic vulnerabilities, offering potential therapeutic targets for cancer and metabolic diseases.

ONCO-22:
Computational Modeling of the Aging Human Bone Marrow and Its Role in Blood Cancer Development

  1. ThomasStiehl Institute for Computational Biomedicine and Disease Modeling, University Hospital RWTH Aachen, Aachen, Germany & Department of Science and Environment, Roskilde University, Roskilde, Denmark
    "Computational Modeling of the Aging Human Bone Marrow and Its Role in Blood Cancer Development"
    2. Blood cancers pose a growing medical and economic challenge in aging societies. Every day, the human bone marrow (BM) generates more than 100 billion blood cells. This process is driven by hematopoietic stem cells (HSCs), which retain their ability to proliferate and self-renew throughout life. However, over time, HSCs accumulate mutations that may lead to malignant transformation, as seen in acute myeloid leukemia (AML), one of the most aggressive cancers. Even in healthy individuals, the BM undergoes age-related changes, including a decline in cell numbers, remodeling of the BM micro-environment, and a bias in HSC differentiation. Emerging evidence suggests that these alterations create a favorable environment for the expansion of mutated cells, thereby promoting blood cancer development and progression. Mathematical and computational models facilitate our understanding of how BM aging contributes to malignant cell growth. We propose nonlinear ordinary differential equation models to describe blood cell formation and clonal competition in the human BM. The models incorporate micro-environmental and systemic feedback loops and are informed by data from both healthy individuals and cancer patients. Our findings suggest that the age-related decline in HSC self-renewal, combined with increased chronic inflammation (inflammaging), makes the BM more susceptible to the expansion of mutated cells and at the same time impairs treatment response. Through mathematical analysis, quantitative simulations, and patient data fitting, we study the following questions: 1. How do HSC proliferation & self-renewal change during physiological aging? 2. How do age-related alterations in healthy BM contribute to blood cancer development? 3. What is the impact of chronic inflammation on HSC function and blood cancer progression? 4. How do age-related BM changes affect treatment responses, e.g., in AML patients? 5. How could treatment protocols be adapted to elderly patients?

ONCO-23:
Evolution of phenotypic plasticity leads to tumor heterogeneity with implications for therapy

  1. SimonSyga TUD Dresden University of Technology
    "Evolution of phenotypic plasticity leads to tumor heterogeneity with implications for therapy"
    2. Cancer is a significant global health issue, with treatment challenges arising from intratumor heterogeneity. This study examines the complex relationship between somatic evolution and phenotypic plasticity, explicitly focusing on the interplay between cell migration and proliferation [1]. We propose that evolution does not act directly on phenotypic traits, like the proliferation rate, but on the phenotypic plasticity in response to the microenvironment [2]. We study this hypothesis using a novel, spatially explicit model that tracks individual cells' phenotypic and genetic states. We assume cells change between mobile and growing states controlled by inherited and mutation-driven genotypes and the cells' microenvironment. We observe that cells at the tumor edge evolve to favor migration over proliferation and vice versa in the tumor bulk. However, this phenotypic heterogeneity can be realized by distinct regulations of the phenotypic switch, which depend on the apoptosis rate and the cells' ability to sense their environment. Emerging synthetic tumors display varying levels of heterogeneity, which we show are predictors of the cancer's recurrence time after treatment. Interestingly, higher phenotypic heterogeneity predicts poor treatment outcomes, unlike genetic heterogeneity. [1] Hatzikirou, H. et al. (2010). 'Go or Grow': the key to the emergence of invasion in tumour progression? Math. Med. Biol., 29(1), 49-65. [2] Syga S. et al. (2024) Evolution of phenotypic plasticity leads to tumor heterogeneity with implications for therapy. PLOS Comput. Biol. 20(8): e1012003.

ONCO-24:
Traveling wave speed and profile of a “go or grow” glioblastoma multiforme model

  1. AishaTurysnkozha Nazarbayev University
    "Traveling wave speed and profile of a “go or grow” glioblastoma multiforme model"
    2. Glioblastoma multiforme (GBM) is a fast-growing and deadly brain tumor due to its ability to aggressively invade the nearby brain tissue. A host of mathematical models in the form of reaction–diffusion equations have been formulated and studied in order to assist clinical assessment of GBM growth and its treatment prediction. To better understand the speed of GBM growth and form, we propose a two population reaction–diffusion GBM model based on the ‘go or grow’ hypothesis. Our model is validated by in vitro data and assumes that tumor cells are more likely to leave and search for better locations when resources are more limited at their current positions. Our findings indicate that the tumor progresses slower than the simpler Fisher model, which is known to overestimate GBM progression. Moreover, we obtain accurate estimations of the traveling wave solution profiles under several plausible GBM cell switching scenarios by applying the approximation method introduced by Canosa.

ONCO-25:
Machine learning-assisted mechanistic modeling to predict disease progression in acute myeloid leukemia patients

  1. ChenxuZhu Institute for Computational Biomedicine - Disease Modeling
    "Machine learning-assisted mechanistic modeling to predict disease progression in acute myeloid leukemia patients"
    2. Blood cell formation is a complex process which is driven by hematopoietic stem cells (HSCs). HSCs give rise to progenitors and precursors which eventually produce mature blood cells, such as white blood cells, red blood cells, and platelets. Acute myeloid leukemia (AML) is an aggressive blood cancer which originates from leukemic stem cells (LSCs) and is characterized by the accumulation of aberrant immature cells, referred to as leukemic blasts. Due to the impairment of healthy blood cell formation, many AML patients suffer from life-threatening complications, such as bleeding or infection. Although treated with high-dose chemotherapy, many patients relapse and need salvage therapy. To reveal the mechanisms of disease progression and relapse, we proposed a mathematical model that accounts for competition of HSCs and LSCs in the stem cell niche and physiological feedback regulations before, during, and after chemotherapy. We fit the model to data of 7 individual patients and simulate variations of the treatment protocol. Our simulation results can recapitulate the non-monotonic recovery of HSCs observed in relapsing patients. The model suggests using the decline of HSC counts during remission as an indication for salvage therapy in patients lacking minimal residual disease markers. To bring our model closer to clinical applications, we propose a machine learning assisted mechanistic model that ensuring adherence to biological principles while learning from a larger clinical AML dataset. By embedding mechanistic constraints into machine learning, we aim to identify patient-specific predictors of relapse while preserving biological interpretability.

ONCO-26:
A Mathematical Model of the Synthetic Notch Receptor

  1. AlexanderDiefes Duke University
    "A Mathematical Model of the Synthetic Notch Receptor"
    2. Synthetic receptors are engineered proteins with potential applications to studying cell-cell interactions and cancer cell therapy. One promising research direction is engineering the Notch receptor, a transmembrane protein that can detect extracellular signals such as antigens or other ligands, and convert them to intracellular signals to activate expression of certain genes. Both the intracellular and extracellular domains can be engineered and replaced with alternative domains, creating the family of modified Notch receptors known as synthetic Notch (synNotch). SynNotch has the potential to improve chimeric antigen receptor (CAR) T-cell therapy by tuning binding affinity to a specific cancer antigen and minimizing off-target effects. We propose an ordinary differential equation model of synNotch receptor activity that has predictive value of how custom cell response behaviors can be programmed. The mathematical model couples activation dynamics on a fast timescale, characteristic of receptor-ligand interactions, and of slower downstream gene expression dynamics. Local and global sensitivity analyses indicate model parameters that yield the greatest variability in downstream results, indicating their potential to be engineered for different functions. Specifically, we find that ligand association and ligand-dependent activation have the greatest potential for modulating transcription factor release.

ONCO-27:
Circulating tumor DNA Dynamics as a Leading Indicating Biomarker for Time to Progression in HPV-associated Anal Squamous Cell Carcinoma

  1. Phebe MHavor Moffitt Cancer Center/University of South Florida
    "Circulating tumor DNA Dynamics as a Leading Indicating Biomarker for Time to Progression in HPV-associated Anal Squamous Cell Carcinoma"
    2. Circulating tumor DNA (ctDNA) has emerged as a promising biomarker for monitoring cancer progression and treatment response in real time. In anal squamous cell carcinoma (ASCC), where 80-90% of cases are linked to human papillomavirus (HPV), ctDNA demonstrates high sensitivity in tracking disease dynamics, often detecting progression earlier than imaging while enabling frequent assessment and correlating with tumor burden. Our study examined how patient-specific modeling of ctDNA dynamics can predict time to progression in HPV-associated ASCC. We analyzed longitudinal data from 32 ASCC patients receiving immunotherapy every 3 weeks for up to 2 years, exploring correlations between tumor volume and ctDNA levels. We developed a mathematical model calibrated to patient-specific tumor volume and ctDNA dynamics during immunotherapy. Results show that relative changes in ctDNA positively correlate with tumor volume changes, with lower baseline ctDNA associated with better clinical responses. In some complete responders, ctDNA became undetectable before radiological confirmation, demonstrating both tumor reduction and ctDNA clearance. However, all patients eventually progressed. Parameter analysis revealed that treatment efficacy significantly impacts ctDNA shedding patterns, often causing characteristic peaks in ctDNA levels. These dynamics could serve as an early warning system for progression, potentially enabling more timely intervention. The model effectively characterizes patient-specific tumor and ctDNA dynamics. Results suggest alternative strategies, including chemotherapy, could optimize dosing regimens based on ctDNA patterns to improve responses and extend time to progression. This work establishes a foundation for integrating ctDNA surveillance into treatment monitoring for ASCC patients.

ONCO-28:
Integrating topological data analysis and biology-based modeling to characterize murine tumor growth and angiogenesis

  1. David A.Hormuth, II The University of Texas at Austin
    "Integrating topological data analysis and biology-based modeling to characterize murine tumor growth and angiogenesis"
    2. Biology-based modeling and topological data analysis (TDA) are powerful techniques for characterizing properties of tumors and vascular networks, but there has been limited effort to integrate these approaches to characterize in vivo tumor growth. Persistent homology offers a systematic approach to identify features such as connected components, loops, and voids across different scales in high-dimensional datasets. Likewise, biology-based models can be calibrated to longitudinal data to yield tumor-specific parameters describing tumor and vascular growth. In this study, we applied TDA and biology-based modeling to longitudinal MRI collected in nine animals with C6 glioma tumors. Animals were imaged up to seven times over a two week period to measure the cell density and blood volume fraction. We computed persistent homology of cubical complexes filtered by the ratio of blood volume fraction to normalized tumor cell density to characterise connected components, loops, and voids in the 3D data. We summarised the output in 15 topological features which quantify known biological properties of the data. For the biology-based modeling approach, a two-species reaction-diffusion model describing tumor growth and angiogenesis was calibrated to longitudinal data to estimate parameters describing tumor growth, invasion, angiogenesis, and vessel death. We then performed k-means clustering on a combined set of topological and biology-based modeling features yielding three clusters. Clusters 1 and 2 consisted of tumors that exhibited voids (necrosis), while Cluster 3 consisted of tumors without well-defined voids. Notably animals in Clusters 1 and 2 had a lower ratio of vascular proliferation to tumor proliferation than Cluster 3. This preliminary study indicates that there may be relationships between topological features and biology-based parameters. Further development of these methods could yield a framework to assign improved model parameters of tumor growth and response.

ONCO-29:
Modeling Metastatic Cancer Treatment with Neoantigen Peptide Vaccine

  1. JessicaKingsley University of Tennessee-Knoxville
    "Modeling Metastatic Cancer Treatment with Neoantigen Peptide Vaccine"
    2. We begin with a system of ordinary differential equations for an immunological treatment of a primary tumor by neoantigen peptide vaccines. This system is coupled with a partial differential equation of metastasis that tracks the number of metastases per time and size. Vaccine dose is taken as a control in the primary tumor ordinary differential equation to slow tumor growth and the spread of metastatic tumors. An optimal control problem is formulated to design vaccine treatment.

ONCO-3:
Using Single-Cell Data-driven Boolean Network Models to Analyze Prostate Cancer Dynamics

  1. MoriahEchlin Tampere University
    "Using Single-Cell Data-driven Boolean Network Models to Analyze Prostate Cancer Dynamics"
    2. 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 Boolean network models 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.

ONCO-30:
Estimating Treatment Sensitivity in Synthetic and In Vitro Tumors Using a Random Differential Equation Model

  1. NatalieMeacham University of California, Merced
    "Estimating Treatment Sensitivity in Synthetic and In Vitro Tumors Using a Random Differential Equation Model"
    2. Resistance to treatment, which comes from the heterogeneity of cell types within tumors, is a leading cause of poor treatment outcomes in cancer patients. Previous mathematical work modeling cancer over time has neither emphasized the relationship between cell heterogeneity and treatment resistance nor depicted heterogeneity with sufficient nuance. To respond to the need to depict a wide range of resistance levels, we develop a random differential equation model of tumor growth. Random differential equations are differential equations in which the parameters are random variables. In the inverse problem, we aim to recover the sensitivity to treatment as a probability mass function. This allows us to observe what proportions of cells exist at different sensitivity levels. After validating the method with synthetic data, we apply it to monoclonal and mixture cell population data of isogenic Ba/F3 murine cell lines to uncover each tumor's levels of sensitivity to treatment as a probability mass function. We emphasize the applications of this project by fitting the model to patient prostate cancer data to recover changes in treatment sensitivity over multiple treatment cycles.

ONCO-31:
The role of EMT in Ovarian Cancer: Insights from a Mathematical Model

  1. SamuelOliver Swansea University
    "The role of EMT in Ovarian Cancer: Insights from a Mathematical Model"
    2. Epithelial-to-mesenchymal transition (EMT) is a critical process in cancer progression that can significantly reduce the effectiveness of treatments. EMT occurs when cells undergo phenotypic changes, resulting in altered behaviours compared to their original state. This transition may lead to increased drug resistance, greater cell plasticity, and enhanced metastatic potential. As a result, understanding and studying the role of EMT in tumour progression and treatment response is essential. In this study, we utilise a 3D agent-based multiscale modelling framework with PhysiCell to examine the role of EMT over time in two ovarian cancer cell lines, OVCAR-3 and SKOV-3. This approach enables us to investigate the spatiotemporal dynamics of ovarian cancer and provide insights into the development of the tumours. The model incorporates microenvironmental conditions, adjusting cellular behaviours based on factors such as substrate concentrations and the proximity of neighbouring cells. The OVCAR-3 and SKOV-3 cell lines exhibit significantly different tumour architectures, allowing for the exploration of various tumour dynamics and morphologies. The model successfully captures biological patterns observed in tumour growth and progression, offering valuable insights into the dynamics of these cell lines. Additionally, sensitivity analysis is conducted to evaluate the impact of parameter variations on model outcomes.

ONCO-32:
Uncovering Cancer's Fitness Landscape

  1. MeaghanParks Case Western Reserve University
    "Uncovering Cancer's Fitness Landscape"
    2. CRISPR-based genome editing technologies have enabled massively-parallel genomic screens, such as DepMap – a Broad Consortium effort to catalog gene knockouts in cancer cell lines. These projects find that the growth effects of a mutation depend heavily on the background genotype of a cell. Evolutionary theory has studied the effects of background genotype on mutations for generations and has uncovered general patterns across the tree of life These patterns found in evolving populations have culminated in a ‘Geometric Model’ of adaptation that has successfully predicted the effects of novel combinations of mutations in yeast and E. coli. This model could in principle be applied to DepMap and other massively-parallel genomic screens to learn genotype to phenotype to fitness mappings and potentially predict the evolution of a population. Fitting this model to large-scale real data, however, is challenging because the model infers a latent (hidden) space of phenotypes with mathematical symmetries which confuse regression methods. Here, we present a methodology for fitting a Geometric Model of adaptation to large-scale genomic screens that eliminates rotational, translational, and permutation symmetries in the inferred phenotype space and successfully reconstructs genotype to phenotype to fitness mappings of Liver cancer cell line knockout data. Thus, making comprehensive quantitative models of genotype to phenotype to fitness mappings possible in a multitude of diseases, which in turn will allow us to infer phenotypic complexity and predict treatment response.

ONCO-33:
A bibliometric study of past and present trends in mathematical oncology

  1. KiraPugh Uppsala University
    "A bibliometric study of past and present trends in mathematical oncology"
    2. Mathematical oncology is an interdisciplinary research field in which mathematical modelling, analysis, and simulation are used to study cancer. In this work, we perform a bibliometric analysis to describe how mathematical oncology has changed over time. We quantitatively interrogate temporal trends in the field by analysing article metadata such as authors, publication dates, titles, article keywords, and abstracts. We specifically investigate if and how these trends have been shaped by paradigm-shifting research advances and world events. The data are collected from bibliographic databases such as Web of Science and Scopus, as well as the world's most prominent mathematical biology journals including: the Bulletin of Mathematical Biology, the Journal of Mathematical Biology, the Journal of Theoretical Biology, and Mathematical Biosciences. We show that, since the 1960's, mathematical oncology has become increasingly data-driven, international, and interdisciplinary.

ONCO-34:
Mathematical Modeling of Persistent Treatment Responses After Cancer Radiotherapy

  1. LaraSchmalenstroer Group of Bioinformatics and Computational Biophysics, University of Duisburg-Essen
    "Mathematical Modeling of Persistent Treatment Responses After Cancer Radiotherapy"
    2. Solid tumors such as pancreatic cancer are major causes of cancer-related deaths worldwide. Despite the availability of multiple treatment options such as radiotherapy or chemotherapy, long-term survival rates of patients with solid tumors remain low due to the development of treatment resistance and tumor recurrence. It has been experimentally observed that irradiation induces shifts in tumor growth kinetics, highlighting the need to unravel both short- and long-term cellular responses to irradiation. Computational models have been used to complement experimental studies by quantifying complex interactions between radiation, tumor biology, and treatment variables. While the common approach of employing the linear-quadratic model and its derivatives by computing the survival fraction is successful in describing short-term effects of radiation on a tumor, it is not suitable for capturing dynamic, persistent, long-term treatment effects. In this study, we developed a phenomenological differential equation-based model that integrates both immediate and delayed radiotherapy effects. A key feature of our model is the inclusion of probabilistic proliferation dynamics. We incorporate cancer cell proliferation rates as the determinant of radiosensitivity, aligning with the well-established hypothesis that highly proliferative cells are more radiosensitive than slower proliferating cells. By using these proliferation rates to determine the rate of cell death after irradiation, the model predicts a heterogeneous cancer cell killing rate, resulting in a variable fraction of surviving cells and a subsequent shift in the composition of the tumor. Thus, the model provides mechanistic insights into relapse dynamics and heterogeneous treatment responses. In the future, we want to extend our model by including immune cell dynamics to investigate the impact of radiation on the tumor microenvironment and the reciprocal interactions between cancer cells and the immune system.

ONCO-35:
Determining the impact of tumor heterogeneity on radiation dose planning via MRI-based mathematical modeling. Tarini Thiagarajan 1, Thomas E. Yankeelov 1-6, Bikash Panthi 7, Caroline Chung 7, David A. Hormuth II 1-2. 1 Oden Institute for Computational Engineering and Sciences,  2 Livestrong Cancer Institutes,  3 Biomedical Engineering,  4 Diagnostic Medicine, and  5 Oncology, The University of Texas at Austin.  6 Department of Imaging Physics,  7 Radiation Oncology, MD Anderson Cancer Center.

  1. TariniThiagarajan Oden Institute for Computational Engineering and Sciences
    "Determining the impact of tumor heterogeneity on radiation dose planning via MRI-based mathematical modeling. Tarini Thiagarajan 1, Thomas E. Yankeelov 1-6, Bikash Panthi 7, Caroline Chung 7, David A. Hormuth II 1-2. 1 Oden Institute for Computational Engineering and Sciences,  2 Livestrong Cancer Institutes,  3 Biomedical Engineering,  4 Diagnostic Medicine, and  5 Oncology, The University of Texas at Austin.  6 Department of Imaging Physics,  7 Radiation Oncology, MD Anderson Cancer Center."
    2. Radiotherapy planning typically assumes homogeneous efficacy across the tumor, which can lead to an overestimation of tumor cell death and control. We seek to quantify these errors by identifying the radiation dose boost required by non-homogeneous treatment efficacy models to yield the tumor response predicted by homogeneous response. We collected data from ten glioblastoma patients who received a total of 60 Gray (Gy) of radiation delivered in 30 fractions, concurrent chemotherapy, and were imaged prior to, and then at one-, three-, and five-months post-therapy. These data were used to inform a patient-specific, mechanically coupled reaction-diffusion model describing the spatiotemporal progression of tumor growth and response to therapy. The radiotherapy effect was modeled as an instantaneous decrease in the tumor volume fraction (ϕ) after each dose, with the surviving fraction (SF) defined as the ratio between the post- and pre-treatment ϕs. For each patient, the proliferation rates, diffusion coefficients, and SFs were calibrated to data up to five months post-therapy. We then simulated tumor growth using the calibrated model with (a) homogeneous SF, or heterogeneous SF, based on (b) vascularity or (c) cell density. We determined the patient-specific global dose boost required for models (b) and (c) to match the response predicted by model (a) one-month post-radiotherapy for SFs of 0.2-1.0 in increments of 0.05. For 0.55-0.95 SFs, model (b)’s predicted response required an additional 0.67 +/- 0.30 (mean +/- standard deviation) Gy per day, while model (c) only needed an additional 0.27 +/- 0.18 Gy per day across all patients. There was a statistically significant (P < 0.05, Wilcoxon rank sum test) difference between dose predictions for models (b) and (c) across all SFs. We developed an approach to calculate the radiation dose increases needed by non-homogeneous treatment efficacy models to match the tumor response predicted by a homogeneous model.

ONCO-36:
Personalized prediction and risk assessment of post-radiotherapy biochemical relapse of prostate cancer using mechanistic forecasts of prostate-specific antigen dynamics under uncertainty

  1. Miguel AnxoVicente Pardal Universidade da Coruña
    "Personalized prediction and risk assessment of post-radiotherapy biochemical relapse of prostate cancer using mechanistic forecasts of prostate-specific antigen dynamics under uncertainty"
    2. The analysis of prostate-specific antigen (PSA) dynamics after external beam radiotherapy is crucial for detecting prostate cancer recurrence. A significant increase in PSA post-radiotherapy often indicates biochemical relapse, although this evolution can be gradual and may take many years to manifest. Current clinical criteria for defining biochemical relapse rely on observation of population-based markers, using fixed thresholds to assess patient progression after a minimum value of PSA is reached. However, this approach does not account for individual tumor dynamics, which may delay recurrence detection and subsequent treatment. To overcome this limitation, we propose anticipating PSA increases using patient-specific forecasts obtained with a mechanistic model that describes post-radiotherapy tumor dynamics. This model utilizes longitudinal PSA measurements, which are routinely collected as part of standard-of-care management for prostate cancer before and after radiotherapy. By applying Bayesian calibration to the model using these data series, we can thus predict patient-specific PSA dynamics, accounting for the uncertainties in the model and data. Additionally, we can obtain the probabilistic distribution of key model-based biomarkers of biochemical relapse (e.g., surviving tumor cell proliferation rate, PSA nadir, and time to biochemical relapse), allowing for early identification of biochemically-relapsing patients. By leveraging our probabilistic formulation, we also introduce risk measures based on the distributions of these biomarkers to allow for a more accurate assessment of an individual’s risk of relapse (e.g., superquantiles). Finally, although validation in larger, more diverse cohorts is needed and extensions of the model could be implemented, this approach has the potential to improve clinical decision-making by personalizing the monitoring of prostate cancer patients after radiotherapy and anticipating disease progression to advanced stages.

ONCO-37:
Modeling the Immune System: The Case of MMC Chemotherapy Treatment for Non-Invasive Bladder Cancer

  1. MaromYosef Ariel University, Israel
    "Modeling the Immune System: The Case of MMC Chemotherapy Treatment for Non-Invasive Bladder Cancer"
    2. Non-muscle-invasive bladder cancer (NMIBC) is one of the most prevalent oncological diseases worldwide, originating in the bladder epithelium, known as the urothelium. Mitomycin C (MMC) chemotherapy is a widely used treatment that reduces recurrence rates and prolongs progression-free survival. However, its full mechanism of action in BC and its immune-related effects, which are crucial for the formulation of an ideal regimen of MMC, remains to be elucidated. This work integrates systems immunology principles with temporal ordinary differential equations (ODEs) to provide a test bed for the theoretical investigation of immune system dynamics during disease progression and chemotherapy administration. We first identify distinct tumor and immune populations and formulate their specific interactions based on biological research. After, we simulate hypothetical BC cases to illustrate the complex dynamics of specialized cell types that bridge the innate and adaptive immune responses. [1] Yosef, M., & Bunimovich-Mendrazitsky, S. (2024). Mathematical model of MMC chemotherapy for non-invasive bladder cancer treatment. Frontiers in Oncology, 14. https://doi.org/10.3389/fonc.2024.1352065 [2] Bunimovich-Mendrazitsky, S., Pisarev, V., & Kashdan, E. (2015). Modeling and simulation of a low-grade urinary bladder carcinoma. Computers in Biology and Medicine, 58, 118–129. https://doi.org/10.1016/j.compbiomed.2014.12.022

ONCO-4:
Enhancing immunotherapies: Insights from the mathematical modelling of a microfluidic device

  1. AlexisFarman UCL (University College London)
    "Enhancing immunotherapies: Insights from the mathematical modelling of a microfluidic device"
    2. A pivotal aspect of developing effective immunotherapies for solid tumors is the robust testing of product efficacy inside in vitro platforms.Collaborating with an experimental team that developed a novel microfluidic device at Children’s National Hospital (CNH), we developed a mathematical model to investigate immune cell migration and cytotoxicity within the device. Specifically, we study Chimeric Antigen Receptor (CAR) T-cell migration inside the channels, treating the cell as a moving boundary driven by a chemoattractant concentration gradient. The chemoattractant concentration is governed by two partial differential equations (PDEs) that incorporate key geometric elements of the device. We examine the motion of the cell as a function of its occlusion of the channel and find that certain cell shapes allow for multiple cells to travel inside the channel simultaneously. Additionally, we identify parameter regimes under which cells clog the channel, impairing their movement. All our findings are validated against experimental data provided by CNH. We integrate our model results into a broader model of the device, which also examines the cytotoxicity of CAR T-cells. This provides a tool for distinguishing experimental artifacts from genuine CAR T-cell behavior. This collaboration enabled the team at Children’s National Hospital to refine experimental conditions and uncover mechanisms enhancing CAR T-cell efficacy. [1] D Irimia, G Charras, N Agrawal, T Mitchison, M Toner, Polar stimulation and constrained cell migration in microfluidic channels,, Lab on a Chip 7 (12), 1783-1790

ONCO-5:
Extrachromosomal DNA driven oncogene spatial heterogeneity and evolution in glioblastoma

  1. MagnusHaughey Barts Cancer Institute
    "Extrachromosomal DNA driven oncogene spatial heterogeneity and evolution in glioblastoma"
    2. Extrachromosomal DNA (ecDNA) oncogene amplification is associated with treatment resistance and shorter survival in cancer. Currently, the spatial dynamics of ecDNA, and their evolutionary impact, are poorly understood. Here, we investigate ecDNA spatial-temporal evolution by integrating computational modeling with samples from 94 treatment-naive human IDH-wildtype glioblastoma patients. Random ecDNA segregation combined with ecDNA-conferred fitness advantages induce predictable spatial ecDNA copy-number patterns which depend on ecDNA oncogenic makeup. EGFR-ecDNAs often reach high copy-number, confer strong fitness advantages and do not co-amplify other oncogenes on the same ecDNA. In contrast, PDGFRA-ecDNAs reach lower copy-number, confer weaker fitness advantages and co-amplify other oncogenes. EGFR structural variants occur exclusively on ecDNA, arise from and are intermixed with wild-type EGFR-ecDNAs. Modeling suggests wild-type and variant EGFR-ecDNAs often accumulate before clonal expansion, even in patients co-amplifying multiple ecDNA species. Early emergence of oncogenic ecDNA under strong positive selection is confirmed in vivo and in vitro in mouse neural stem cells. Our results implicate ecDNA as a driver of gliomagenesis, and suggest a potential time window in which early ecDNA detection may facilitate more effective intervention.

ONCO-6:
Data Driven Mathematical Modelling Highlights the Impact of Bivalency on the Optimum Affinity for Monoclonal Antibody Therapies

  1. LukeHeirene University of Oxford
    "Data Driven Mathematical Modelling Highlights the Impact of Bivalency on the Optimum Affinity for Monoclonal Antibody Therapies"
    2. Monoclonal antibody (mAb)-based therapeutics are pivotal in treating a wide range of diseases, including cancer. One key mechanism by which these antibodies exert anti-tumour effects is through antibody-dependent cellular cytotoxicity (ADCC). In ADCC, mAbs bind to specific antigens on tumour cells and Fc receptors on immune effector cells. This trimeric complex triggers these effector cells to kill the tumour. ADCC is influenced by multiple factors, notably the properties of the mAb and its interactions with Fc receptors and target antigens. However, the optimum conditions for ADCC remain unclear. In this study, we investigate how variations in target antigen and mAb properties, particularly antibody valency, modulate ADCC response to identify parameters that maximize its potency. We developed an ordinary differential equation (ODE) model to simulate mAb binding within the immune synapse and quantify trimeric complex formation. To link the number of trimeric complexes to ADCC response, we validated the model using Bayesian inference on ADCC assay data. The results suggest that lower-affinity mAbs enhance ADCC by increasing the number of target cell-bound antibodies. Our validated model indicates that a “steric penalty” is necessary for bivalently target-bound versus monovalently target-bound antibodies. Due to constraints from dual antigen binding, these antibodies experience limited mobility, reducing Fc receptor engagement. After model validation, we explored variations in target expression, binding affinity, and antibody valency on ADCC potency, quantified by EC50. Our key finding is that the optimal binding affinity for maximizing ADCC potency depends on antibody valency. Monovalent antibodies are most potent at high affinity, while bivalent antibodies peak at lower affinities. Furthermore, the magnitude of this effect varies with target expression levels.

ONCO-7:
Spectral Spatial Analysis of Cancer Biopsies: Validation through in-silico data and extension to logistic growth models

  1. VeronikaHofmann Technical University of Munich
    "Spectral Spatial Analysis of Cancer Biopsies: Validation through in-silico data and extension to logistic growth models"
    2. MD Anderson's Enderling lab recently invented a spectral spatial analysis method for estimating tumor cell diffusivity and proliferation rate from single-point-in-time biopsies of breast cancer. In combination with clinical data from the patients these parameters could help identify a new biomarker for radiotherapy. In their first study, they investigate the relationship between the power spectral density (PSD) of the three-dimensional reaction-diffusion (RD) equation with exponential growth (as model of spreading cancer cells) and the two-point correlation function of the cell distribution in the biopsy (a spatial statistic). Their results make the approach seem promising, and this work aims to validate and extend their findings. Firstly, we develop a model to generate in-silico data to validate the parameter estimation method. This is done by solving the RD equation for different growth terms (exponential and logistic), adding Gaussian noise and 'translating' its continuous results into spatial point patterns which are interpreted as cell nuclei in the 'biopsy', and then applying the method to see if the original parameters can be retrieved. This model contains several features: dimensionality can be switched between 2D and 3D, cell size can be adjusted, cuts can be added to the point pattern, and in the 3D case, biopsy thickness is variable and the plane where the slice through the 'tumor' is made can be freely chosen. And secondly, the spectral analysis method is altered by proposing a numerical solution to the PSD of the RD equation with logistic growth (valid for arbitrary dimensions). Logistic growth is assumed to be the more realistic model, however, it is harder to handle as no analytical solution is available for the equation, and hence neither for the PSD. The validation results from the in-silico data are assessed and their meaning for the application to real patient data is discussed under consideration of the different types of cell growth.

ONCO-8:
Integrating clinical data in mechanistic modeling of colorectal cancer evolution in inflammatory bowel disease

  1. BrianJohnson UC San Diego
    "Integrating clinical data in mechanistic modeling of colorectal cancer evolution in inflammatory bowel disease"
    2. Patients with inflammatory bowel disease (IBD) face an elevated risk of colorectal cancer (CRC), necessitating lifelong surveillance to find and remove precancers before they become malignant. Current one-size-fits-all approaches are inadequate and tailored strategies that consider cancer evolution are needed. To address this, we developed a mechanistic framework of IBD-CRC progression. Our multi-type branching process model accounts for IBD onset, mutational processes, and both precancerous (adenoma/dysplasia) and malignant clonal expansion. Initial parameter estimation for mutation and growth rates when fitting the multi-stage clonal expansion model to epidemiological IBD-CRC data yielded similar estimates to those found previously in sporadic CRC but suggest higher mutation rates and slightly lower growth rates in IBD. However, this data may not perfectly represent the natural history, as surveillance colonoscopy with lesion removal and colectomy alter the observable progression. Further, fitting to cancer incidence data alone presents parameter identifiability issues, restricting our initial fit to four parameters. To address these limitations, our study draws upon extensive clinical data from the U.S. Veterans Health Administration, employing validated methods using large language models to construct high-quality datasets with detailed information on surveillance colonoscopy timing, colectomies, and intermediate lesions extracted from pathology reports. To integrate these data, we developed a complementary fast simulation model, which will be released as an R package. This simulation model incorporates clinical interventions, such as colonoscopy with size-dependent lesion removal. Our combined analytical and simulation approach captures the complex precancerous evolution in IBD, providing a quantitative foundation for more effective, personalized surveillance guidelines. Further, this approach can be adapted to improve surveillance in the general population.

ONCO-9:
Mathematical Modelling of Tertiary Lymphoid Structures in Cancer

  1. NicholasLai University of Oxford
    "Mathematical Modelling of Tertiary Lymphoid Structures in Cancer"
    2. Tertiary lymphoid structures (TLSs) are organised aggregates of immune cells that form at sites of inflammation in chronic diseases, such as cancer. It is hypothesised that, in cancer, TLSs act as local hubs for the generation and regulation of a tumour-specific immune response from inside the tumour microenvironment (TME). TLSs initially form as well-mixed aggregates of T- and B-cells and mature into organised structures consisting of an inner B-cell zone surrounded by an outer T-cell zone. The presence of TLSs correlates with positive patient outcomes in several cancer types, but the mechanisms governing their formation, maturation, and role in the antitumour response remain poorly understood. Motivated by analysis of spatial transcriptomics images of TLSs in colorectal cancer, we develop an agent-based model to investigate TLS formation, maturation, and function in cancer. We model T-cells and B-cells as discrete agents which are attracted to diffusible chemokines (CXCL13 and CCL19) produced by resident stromal cells in the TME. These interactions lead to the formation of a well-mixed lymphoid aggregate that later matures into distinct T- and B-cell zones due to the segregated expression of these chemokines. Our results identify key parameters governing TLS development and suggest conditions under which TLSs are able to control tumour growth. This framework provides a qualitative basis for understanding TLS dynamics and their potential role in cancer immunotherapy.

OTHE-1:
A Continuous-Time Stochastic Model for Mutation Effects in Microbial Population

  1. MojganEzadian Lindi Wahl, Western University
    "A Continuous-Time Stochastic Model for Mutation Effects in Microbial Population"
    2. Mutation accumulation (MA) experiments are crucial for understanding evolution. In microbial populations, these experiments typically involve periods of population growth, where a single individual forms a visible colony, followed by severe bottlenecks. Studies on the effects of positive and negative selection in MA experiments have shown that, for example, with 20 generations of growth between bottlenecks, beneficial mutations will be substantially over-represented cite{}; this effect is known as ``selection bias''. In previous work, we developed a fully stochastic discrete-time model that includes realistic offspring distributions, accounting for genetic drift and allowing for the loss of rare lineages. We demonstrated that when drift is included, selection bias is even stronger than previously predicted cite{}. Since bacterial division is unlikely to remain synchronized over 20 or more generations, this study extends the discrete-time model to a continuous-time framework. Since lineages that start reproducing early accrue a compounded advantage, a continuous-time model offers an even more accurate correction for selection in MA experiments.

OTHE-2:
Practical parameter identifiability of respiratory mechanics in the extremely preterm infant

  1. RichardFoster Virginia Commonwealth University
    "Practical parameter identifiability of respiratory mechanics in the extremely preterm infant"
    2. The complexity of mathematical models describing respiratory mechanics has grown in recent years, however, parameter identifiability of such models has only been studied in the last decade in the context of observable data. This study investigates parameter identifiability of a nonlinear respiratory mechanics model tuned to the physiology of an extremely preterm infant, using global Morris screening, local deterministic sensitivity analysis, and singular value decomposition-based subset selection. The model predicts airflow and dynamic pulmonary volumes and pressures under varying levels of continuous positive airway pressure, and a range of parameters characterizing both surfactant-treated and surfactant-deficient lung. The model was adapted to data from a spontaneously breathing 1 kg infant using gradient-based optimization to estimate the parameter subset characterizing the patient's state of health.

OTHE-3:
Characterizing Regulatory Interactions and Dimensionality in Gene Networks Driving Cell-Fate Choices

  1. PradyumnaHarlapur Indian Institute of Science
    "Characterizing Regulatory Interactions and Dimensionality in Gene Networks Driving Cell-Fate Choices"
    2. Cell-fate decisions involve coordinated genome-wide expression changes, typically leading to a limited number of phenotypes. Although often modeled as simple toggle switches, these rather simplistic representations often disregard the complexity of regulatory networks governing these changes. Here, we unravel design principles underlying complex cell decision-making networks in multiple contexts. We show that the emergent dynamics of these networks are consistently low-dimensional, as quantified by the variance explained by principal component 1 (PC1). This low dimensionality in phenotypic space arises from extensive feedback loops in these networks arranged to effectively enable the formation of two teams of mutually inhibiting nodes (Hari*, Harlapur* et al. iScience 2025). We use team strength as a metric to quantify these feedback interactions and show its strong correlation with PC1 variance. We then examined how biological networks are organized with specific topologies that allow them to remain sparse while effectively coordinating decision-making under various levels of coherent interactions (i.e., structural balance). We found that networks with low coherence needed higher densities to show coordinated expression profiles. The balance between sparsity and coordinated control highlights the role of network architecture in ensuring stable and robust phenotypic outcomes, providing new insights into how GRNs guide cellular behavior precisely yet adaptable. These results shed light on how, despite being very sparse, the networks that govern various cellular decisions follow certain basic design principles to ensure the expression between the nodes involved is well coordinated.

OTHE-4:
Mean first passage time and its application in ocular drug development

  1. PatriciaLamirande University of Oxford
    "Mean first passage time and its application in ocular drug development"
    2. Standard of care for various retinal diseases involves recurrent intravitreal injections. This motivates math- ematical modelling efforts to identify influential factors for drug residence time, aiming to minimise the administration frequency. To this end, we developed a mean first passage time (MFPT) modelling frame- work, to investigate the scaling relationships of ocular pharmacokinetics in humans and animal species and to inform drug development. The MFPT describes how long it takes, on average, for a random walker to reach a given target, and is a valuable method to quantify the efficacy of diffusion transport. In this work, we derived a partial differen- tial equation system for the MFPT to quantify drug residence time, and solved it in detailed anatomical 3D geometries of eyes of animal species used in drug development assessments. For human eyes, we investigated the impact of variability in vitreous cavity size and eccentricity, and in injection location, on drug elimination. Model simulations revealed a dependence of residence time on ocular size and injection location. Inter- individual variability in human eyes had a significant influence on residence time (half-life range of 5-7 days), showing a strong correlation to axial length and vitreal volume. The modelling results suggest that experimental variability in ocular half-life is partially attributed to anatomical differences and injection site location.

OTHE-5:
Mathematical Modeling of Circadian Rhythms: Applications to Phase Prediction and Fatigue Reduction

  1. CalebMayer Stanford University
    "Mathematical Modeling of Circadian Rhythms: Applications to Phase Prediction and Fatigue Reduction"
    2. As consumer-grade wearable technology has become more prevalent in recent years, large-scale collections of data have been made available for researchers. We analyze significant amounts of wearable data to determine the circadian features that differ across groups and time frames. Using wearable activity, steps, and heart rate data, we adapt mathematical models to accurately estimate circadian phase across populations. This has a number of applications, including chronotherapeutic drug delivery, reducing fatigue, and shift work scheduling. We demonstrate applications to estimating circadian phase (dim light melatonin onset, or DLMO) in a home-based cohort of later-life adults, showing that activity-based models perform similarly or better than light-based models in DLMO estimation. We further use these models to provide wearable-based lighting interventions for reducing cancer-related fatigue. In particular, we test whether these lighting interventions, delivered via a mobile app, reduce cancer-related fatigue in a randomized controlled trial with 138 breast cancer, prostate cancer, and hematopoietic stem cell transplant patients. These interventions, based on real-time assessment of circadian rhythm through wearable devices, improve certain measures of fatigue (e.g., daily measurements of fatigue) in cancer patients. Further studies are needed to tune these models and assess the effect of lighting interventions in broader and more diverse cancer care settings.

OTHE-6:
Mathematical modelling of subchondral bone adaptation, microdamage, and repair in Thoroughbred racehorses

  1. MichaelPan The University of Melbourne
    "Mathematical modelling of subchondral bone adaptation, microdamage, and repair in Thoroughbred racehorses"
    2. Musculoskeletal injuries can significantly impact the careers of racehorses, and are a common cause of lost training days and fatality. Most bone injuries arise from the gradual accumulation of microcracks through repeated training rather than spontaneous events. If severe enough, cracks may propagate through the bone and lead to fractures, often necessitating euthanasia. While training promotes bone adaptation to higher mechanical loads, overtraining can cause excessive damage. To better understand the biological processes underlying bone injury, we developed a lumped parameter model that combines the processes of bone adaptation, microdamage accumulation and bone repair. The model parameters were calibrated to experimental observations of bone volume fraction and time to fracture in racehorses. A sensitivity analysis identified joint loads (arising from training speed) and strides per day (arising from training distance) as key factors contributing to bone damage. Simulations of a typical training program showed that the majority of damage is incurred from training at racing speeds. While some microdamage is repaired during training, our model estimates that this process is insufficient to offset the damage accumulated. These findings emphasise the critical role of regular rest in preventing bone injury.

OTHE-7:
Unveiling the Drivers of Fetal Weight Estimation: Which Ultrasound Measurements Matter Most?

  1. VasilisTsilidis Department of Mathematics, University of Patras
    "Unveiling the Drivers of Fetal Weight Estimation: Which Ultrasound Measurements Matter Most?"
    2. Fetal weight estimation via ultrasound is performed by measuring biometric parameters such as the biparietal diameter (BPD), head circumference (HC), abdominal circumference (AC), and femur length (FL), which are then used in various mathematical formulas to calculate the estimated weight. But do all parameters matter equally? To assess their contribution on fetal weight estimation, we analyzed 29 published formulas across 26 diverse global datasets. Results show that AC is consistently the parameter of greatest importance, while head measurements (BPD, HC) often add little value, particularly in the later stages of pregnancy. Additionally, nearly half of the formulas include redundant parameters, and two-thirds exhibit a crossover in parameter importance—some transition from low to high significance, while others decline from high to low—over the course of gestation. These findings highlight opportunities to simplify fetal weight estimation for clinicians, prioritizing AC reliability and trimming unnecessary inputs. Our work bridges mathematics and prenatal care, offering clearer guidelines to improve ultrasound-based predictions and support healthier pregnancy outcomes.

OTHE-8:
Bee Determined: A Mathematical Analysis of Trapline Formation

  1. FerdinandGruenenwald University of Victoria
    "Bee Determined: A Mathematical Analysis of Trapline Formation"
    2. Many foraging animals, including bees, develop near-optimal movement patterns based on memory. While models have simulated how bees establish deterministic traplines, formal mathematical proofs of their behavior remain scarce. We address this gap by adapting and simplifying the Dubois et al. (2024) model to enable mathematical analysis. We prove that simulated bees will always eventually converge to a single deterministic route. Additionally, we propose conjectures about the distribution of routes to which simulated bees may converge. Future work could explore inference methods for learned behavior based on this model. These findings have implications beyond biology, providing insights into reinforced random walks and reinforcement learning.

OTHE-9:
On the study of Complete Electrode Model for Electroencephalography

  1. Arrianne CrystalVelasco University of the Philippines Diliman
    "On the study of Complete Electrode Model for Electroencephalography"
    2. In this work, we study the applicability of the Complete Electrode Model (CEM) to Electroencephalography (EEG). EEG is a non-invasive imaging technique where it aims to localize cerebral sources generating the measured EEG signals. An existence and uniqueness result of this model is proved. Preliminary numerical implementation are also done.





Organizers
  • Jay Newby, University of Alberta
  • Hao Wang, University of Alberta



Organizing committee
  • Thomas Hillen, University of Alberta
  • Dan Coombs, University of British Columbia
  • Mark Lewis, University of Victoria
  • Wylie Stroberg, University of Alberta
  • Gerda de Vries, University of Alberta
  • Ruth Baker, University of Oxford
  • Amber Smith, University of Tennessee Health Science Center
Website
  • Jeffrey West
Scientific committee
  • Ruth Baker, University of Oxford
  • Mark Lewis, University of Victoria
  • Frederick R Adler, University of Utah
  • Jennifer Flegg, University of Melbourne
  • Jana Gevertz, The College of New Jersey
  • Jude Kong, University of Toronto
  • Kathleen Wilkie, Toronto Metropolitan University
  • Wylie Stroberg, University of Alberta
  • Jay Newby, University of Alberta




We wish to acknowledge that we are located within Treaty 6 territory and Metis Nation of Alberta Region 4. We acknowledge this land as the traditional home for many Indigenous Peoples including the Cree, Blackfoot, Metis, Nakota Sioux, Dene, Saulteaux, Anishinaabe, Inuit and many others whose histories, languages, and cultures continue to influence our vibrant community.








Organizers
  • Jay Newby, University of Alberta
  • Hao Wang, University of Alberta
Organizing committee
  • Thomas Hillen, University of Alberta
  • Dan Coombs, University of British Columbia
  • Mark Lewis, University of Victoria
  • Wylie Stroberg, University of Alberta
  • Gerda de Vries, University of Alberta
  • Ruth Baker, University of Oxford
  • Amber Smith, University of Tennessee Health Science Center
Scientific committee
  • Ruth Baker, University of Oxford
  • Mark Lewis, University of Victoria
  • Frederick R Adler, University of Utah
  • Jennifer Flegg, University of Melbourne
  • Jana Gevertz, The College of New Jersey
  • Jude Kong, University of Toronto
  • Kathleen Wilkie, Toronto Metropolitan University
  • Wylie Stroberg, University of Alberta
  • Jay Newby, University of Alberta
Website
  • Jeffrey West



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