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DTSTART;TZID=America/New_York:20261001T104000
DTEND;TZID=America/New_York:20261001T113000
DTSTAMP:20260919T145149Z
CREATED:20260824T221925Z
LAST-MODIFIED:20260919T145149Z
UID:2332-1790851200-1790854200@biomath.math.ufl.edu
SUMMARY:Wenrui Hao (Pennsylvania State University)
DESCRIPTION:AI-Driven Mathematical Modeling for Personalized Predictions in Alzheimer’s Disease\n\nAlzheimer’s disease (AD) is a highly heterogeneous neurodegenerative disorder\, with patients exhibiting diverse biomarker trajectories\, progression rates\, and treatment responses. Capturing this variability requires models that can integrate mechanistic understanding with large-scale clinical data.\nIn this talk\, we present recent advances in AI-driven mathematical modeling for constructing patient-specific digital twins of Alzheimer’s disease. Our framework combines mechanistic differential equation models with machine learning techniques to learn individualized parameters and latent disease states from multimodal clinical and biomarker data. These digital twins enable simulation of disease progression\, identification of patient subtypes\, and in silico evaluation of therapeutic strategies.\nThis work demonstrates how integrating AI with mathematical modeling can provide a principled and interpretable approach to personalized prediction in Alzheimer’s disease\, advancing both precision medicine and computational methodologies for complex biomedical systems.
URL:https://biomath.math.ufl.edu/event/dr-wenrui-hao-pennsylvania-state-university/
LOCATION:Zoom\, To obtain the Zoom link\, please contact Hemaho Taboe at hemahobeaugtaboe@ufl.edu or Calistus Ngonghala at calistusnn@ufl.edu.
CATEGORIES:Fall 2026
ATTACH;FMTTYPE=image/jpeg:https://biomath.math.ufl.edu/wp-content/uploads/sites/607/Wenrui-Hao-e1787606820997.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260924T104000
DTEND;TZID=America/New_York:20260924T113000
DTSTAMP:20260918T172826Z
CREATED:20260824T222944Z
LAST-MODIFIED:20260918T172826Z
UID:2329-1790246400-1790249400@biomath.math.ufl.edu
SUMMARY:Toni Gui (UF\, Biostatistics)
DESCRIPTION:Incorporating Genomic Sequences into Stochastic Transmission Modeling to Improve the Analysis of SARS-CoV-2 Transmission Dynamics \nThe SARS-CoV-2 pandemic has highlighted the growing importance of infectious disease analysis. An accurate and robust model can empower public health leaders to make timely decisions on social distancing and vaccination policies\, thereby reducing the number of cases\, hospitalizations and deaths. However\, the emergence of new variants and subvariants can significantly alter the transmissibility\, immune escape capacity and virulence of the pathogen in a short time\, making the number of cases\, hospitalizations and deaths difficult to predict. To enhance the timeliness and accuracy of forecasting\, SARS-CoV-2 sequencing data can be utilized. These data constitute a vast and continuously growing resource\, with millions of sequences collected and reported over the past few years. By incorporating the evolution of SARS-CoV-2 virus into classic transmission models\, we conclude that genomic data is crucial for capturing trends in epidemiological data when new variants and subvariants emerge\, leading to the development of a more reliable model and enhancing our knowledge of transmission dynamics and control.
URL:https://biomath.math.ufl.edu/event/dr-toni-gui-university-of-florida-biostatistics-department/
LOCATION:423 Little Hall
CATEGORIES:Fall 2026
ATTACH;FMTTYPE=image/jpeg:https://biomath.math.ufl.edu/wp-content/uploads/sites/607/Toni-e1787606205641.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260917T103000
DTEND;TZID=America/New_York:20260917T113000
DTSTAMP:20260915T100758Z
CREATED:20260915T100758Z
LAST-MODIFIED:20260915T100758Z
UID:2385-1789641000-1789644600@biomath.math.ufl.edu
SUMMARY:No Seminar
DESCRIPTION:
URL:https://biomath.math.ufl.edu/event/no-seminar-3/
CATEGORIES:Fall 2026
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260910T104000
DTEND;TZID=America/New_York:20260910T113000
DTSTAMP:20260903T152422Z
CREATED:20260824T222836Z
LAST-MODIFIED:20260903T152422Z
UID:2320-1789036800-1789039800@biomath.math.ufl.edu
SUMMARY:Hannah G. Anderson (Moffitt)
DESCRIPTION:Robust schedules of adoptive cell therapy in a virtual murine cohort of bladder cancer with experimental validation \nEven under the same treatment\, responses can vary. Virtual cohorts build on an available\, often limited\, dataset to capture these differences and enable the discovery of treatment protocols that work well for a wide variety of individuals. In this presentation\, we refine current virtual cohort pipelines by improving data handling\, ensuring the virtual cohort can be used to stratify individuals into treatment subgroups based on their data\, and validating that it matches the observed variability in the data. We use techniques such as structural and practical identifiability\, sensitivity analysis\, the “accept-or-reject” method expanded to multiple cohorts\, and the Kolmogorov-Smirnov test to achieve these objectives. To illustrate\, we apply this pipeline to a murine dataset of orthotopic bladder cancer treated with gemcitabine (Gem) and immunotherapy using OT-1 cells. We generate over 10\,000 virtual mice that replicate the dynamics of three cell subpopulations in the tumor (cancer cells\, T cells\, and myeloid-derived suppressor cells) and variability of data from four experimental cohorts (control\, Gem\, OT-1\, and Gem+OT-1). Then\, using this virtual murine cohort\, we predict robust Gem+OT-1 schedules suitable for a wide range of mice. After testing these schedules experimentally\, the results are compared to the virtual cohort predictions. \nFor a preprint of the work: https://pmc.ncbi.nlm.nih.gov/articles/PMC13060980/
URL:https://biomath.math.ufl.edu/event/dr-hannah-g-anderson-moffitt/
CATEGORIES:Fall 2026
ATTACH;FMTTYPE=image/jpeg:https://biomath.math.ufl.edu/wp-content/uploads/sites/607/DrANA-e1787604617878.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260903T104000
DTEND;TZID=America/New_York:20260903T113000
DTSTAMP:20260903T144130Z
CREATED:20260824T222902Z
LAST-MODIFIED:20260903T144130Z
UID:2318-1788432000-1788435000@biomath.math.ufl.edu
SUMMARY:B. Sagar (University of Central Florida\, Mathematics)
DESCRIPTION:Dynamics of a two-stage epidemiological model with post-infection mortality and transmission heterogeneity\n  \nMany infectious diseases exhibit stage-structured progression (e.g.\, Influenza and COVID-19). This motivates us to formulate a two-stage epidemiological model that incorporates key post infection features\, including reinfection and post-infection mortality (PIM). The model emphasizes the role of transmission heterogeneity in shaping disease dynamics\, influencing both endemic levels and oscillatory behaviors. Numerical simulations show that late-stage hyper-infectivity leads to sustained periodic oscillations\, while early-stage hyper-infectivity tends to suppress such oscillations\, yielding a stable endemic equilibrium.
URL:https://biomath.math.ufl.edu/event/dr-b-sagar-university-of-central-florida/
LOCATION:Zoom\, To obtain the Zoom link\, please contact Hemaho Taboe at hemahobeaugtaboe@ufl.edu or Calistus Ngonghala at calistusnn@ufl.edu.
CATEGORIES:Fall 2026
ATTACH;FMTTYPE=image/jpeg:https://biomath.math.ufl.edu/wp-content/uploads/sites/607/DrSagar-e1787605028652.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260827T104000
DTEND;TZID=America/New_York:20260827T113000
DTSTAMP:20260824T223506Z
CREATED:20260824T223506Z
LAST-MODIFIED:20260824T223506Z
UID:2358-1787827200-1787830200@biomath.math.ufl.edu
SUMMARY:No Seminar
DESCRIPTION:
URL:https://biomath.math.ufl.edu/event/no-seminar-2/
CATEGORIES:Fall 2026
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260409T104000
DTEND;TZID=America/New_York:20260409T113000
DTSTAMP:20260817T155548Z
CREATED:20260203T161222Z
LAST-MODIFIED:20260817T155548Z
UID:2251-1775731200-1775734200@biomath.math.ufl.edu
SUMMARY:Qing Han (Augusta University\, Mathematics)
DESCRIPTION:Pertussis transmission models and demographic shifts under evolving vaccination programs\nPertussis\, a highly contagious infection of the respiratory tract\, was one of the main causes of child morbidity and mortality in developed countries\, in the pre-vaccine era. Following the scale-up and roll-out of childhood vaccination programs in the 1940s–1960s\, the incidence and severity of pertussis decreased drastically. However\, despite high vaccination coverage rates for more than 50 years\, pertussis is still a re-emerging disease requiring novel vaccination strategies taking full consideration of ongoing demographic shifts. Here\, we developed an age-structured transmission dynamics model which allows progressive waning of natural and vaccine-induced immunity\, distinguishes between diagnosed and undiagnosed infections\, and incorporates imported cases and seasonal infections from out-of-province travel. We also developed techniques to calculate key demographic characteristics required in our transmission dynamics model with age stratification guided by the surveillance data\, as well as existing and potential immunization programs. The model was fitted to and validated by the age-stratified pertussis incidence data in the province of Ontario\, Canada\, and the simulation based on the validated model shows a case distribution shift from adolescents to infants until 2050\, and calls for additional boosting immunizations in the province. The fundamental platforms and modeling techniques in this study are suitable for use to assess any other immunization programs for childhood diseases elsewhere.
URL:https://biomath.math.ufl.edu/event/qing-han-augusta-university-mathematics/
LOCATION:Zoom
CATEGORIES:Spring 2026
ATTACH;FMTTYPE=image/jpeg:https://biomath.math.ufl.edu/wp-content/uploads/sites/607/han_qing.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260402T104000
DTEND;TZID=America/New_York:20260402T113000
DTSTAMP:20260817T155541Z
CREATED:20260203T161320Z
LAST-MODIFIED:20260817T155541Z
UID:2253-1775126400-1775129400@biomath.math.ufl.edu
SUMMARY:Erica J. Graham (Bryn Mawr College\, Mathematics)
DESCRIPTION:Modeling GLP-1 Treatments in Ovulatory Dysfunction\nGLP-1 receptor agonists (GLP-1 RAs) have gained widespread attention for their effectiveness in treating metabolic abnormalities such as type 2 diabetes and obesity. Central to their success is the regulation of glucose-insulin metabolism and improvement of insulin resistance. In reproductive endocrinology\, GLP-1 RAs have also shown promise as off-label treatment of polycystic ovary syndrome (PCOS)\, which results from reproductive hormone dysregulation and is often accompanied by insulin resistance. The use of insulin-sensitizing drugs has been a common approach to treating some forms of PCOS\, even though its precise pathogenesis remains unclear. Here we describe a mathematical framework to examine the specific effects of GLP-1 RAs on ovulatory function/defects\, and we discuss the underlying mechanisms that may determine treatment efficacy.
URL:https://biomath.math.ufl.edu/event/erica-j-graham-bryn-mawr-college-mathematics/
LOCATION:Zoom
CATEGORIES:Spring 2026
ATTACH;FMTTYPE=image/jpeg:https://biomath.math.ufl.edu/wp-content/uploads/sites/607/graham_erica.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260326T104000
DTEND;TZID=America/New_York:20260326T113000
DTSTAMP:20260817T155534Z
CREATED:20260203T164931Z
LAST-MODIFIED:20260817T155534Z
UID:2269-1774521600-1774524600@biomath.math.ufl.edu
SUMMARY:David Basanta Gutierrez (Integrated Mathematical Oncology\, H. Lee Moffitt Cancer Center & Research Institute)
DESCRIPTION:Integrated Mathematical Modeling of Cancer – Modeling the ecology and evolution of cancer and treatment resistance\nWe present a suite of hybrid agent-based models (ABMs) that integrate discrete cell-level dynamics with continuous microenvironmental signaling to study eco-evolutionary processes in cancer. Our 2D on-lattice framework couples agent behaviors—proliferation\, mutation\, and selection—with reaction-diffusion equations governing cytokine fields (e.g.\, RANKL\, BDF)\, enabling spatially explicit simulations of tumor-stroma interactions. We apply this approach to two systems: myeloma-driven bone remodeling\, where environment-mediated drug resistance (EMDR) emerges from stromal protection rather than intrinsic mutation; and metastatic prostate cancer\, where stroma-mediated resistance alters treatment landscape topology. The models serve as computational stress tests of conceptual biological hypotheses\, exploring how parameter regimes—mutation probability\, microenvironmental protection strength\, and adaptive dosing schedules—shape clonal diversity and resistance dynamics. Our results demonstrate that eco-evolutionary modeling can generate non-intuitive predictions about treatment sequencing and adaptive therapy design.
URL:https://biomath.math.ufl.edu/event/david-basanta-gutierrez-integrated-mathematical-oncology-h-lee-moffitt-cancer-center-research-institute/
LOCATION:Zoom
CATEGORIES:Spring 2026
ATTACH;FMTTYPE=image/jpeg:https://biomath.math.ufl.edu/wp-content/uploads/sites/607/basanta-gutierrez_david.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260312T104000
DTEND;TZID=America/New_York:20260312T113000
DTSTAMP:20260817T155523Z
CREATED:20260203T165016Z
LAST-MODIFIED:20260817T155523Z
UID:2271-1773312000-1773315000@biomath.math.ufl.edu
SUMMARY:Mark Lewis (University of Victoria\, Mathematics and Statistics)
DESCRIPTION:One equation helps solve three paradoxes in the spatial ecology of predators and prey\nIn this talk I will introduce three paradoxes in the spatial ecology of predators and prey (1) Buffer Zone Paradox: Why do wolves maintain stable buffer zones for prey\, even though they may be only saving prey for the neighboring packs? (2) Road Use Paradox: Why are wolves attracted to roads and related linear features\, even though that can mean higher chances of dying? (3) Path Less Travelled Paradox: Why do wolves preferentially travel to places they haven’t been recently\, even if it means fewer prey? To help solve these paradoxes\, I will start with the Fokker-Planck equation\, which describes the probability density function for an individual undergoing a random walk. I will then employ a mixture of mathematical approaches including nonlinear advection-diffusion\, differential games\, first passage time theory and stochastic processes. All of the resulting models will be fit to data before drawing scientific conclusions.
URL:https://biomath.math.ufl.edu/event/mark-lewis-university-of-victoria-mathematics-and-statistics/
LOCATION:Zoom
CATEGORIES:Spring 2026
ATTACH;FMTTYPE=image/jpeg:https://biomath.math.ufl.edu/wp-content/uploads/sites/607/lewis_mark.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260305T104000
DTEND;TZID=America/New_York:20260305T113000
DTSTAMP:20260817T155515Z
CREATED:20260226T191752Z
LAST-MODIFIED:20260817T155515Z
UID:2285-1772707200-1772710200@biomath.math.ufl.edu
SUMMARY:Wenrui Hao (Pennsylvania State University\, Mathematics)
DESCRIPTION:A Data-Driven Computational Framework for Identifiability and Nonlinear Dynamics Discovery in Complex Systems\nData-driven modeling is essential for deciphering complex biological systems\, yet its utility is often constrained by two fundamental hurdles: the inability to guarantee parameter identifiability and the high computational cost of learning nonlinear dynamics. This talk introduces a unified computational framework designed to overcome these challenges\, bridging theoretical rigor with scalable machine learning. \nThe first component of the framework establishes a computational foundation for practical identifiability. By leveraging the Fisher Information Matrix and its theoretical links to coordinate identifiability\, we propose an efficient method for identifiability assessment. We further introduce regularization-based strategies to manage non-identifiable parameters\, thereby enhancing model reliability and facilitating robust uncertainty quantification. \nTo address the discovery of nonlinear dynamics\, we present the Laplacian Eigenfunction-Based Neural Operator (LE-NO). This operator learning framework is specifically engineered for modeling reaction–diffusion equations. By projecting nonlinear operators onto Laplacian eigenfunctions\, LE-NO achieves superior computational efficiency and generalization across varying boundary conditions\, effectively bypassing the limitations of large-scale architectures and data scarcity. \nFinally\, we demonstrate the framework’s utility in the context of Alzheimer’s disease modeling. We show that this integrated approach ensures reliable parameter inference while capturing the intricate nonlinear dynamics of disease progression\, providing a critical step toward the development of high-fidelity digital twins for neurodegenerative pathology.
URL:https://biomath.math.ufl.edu/event/wenrui-hao-pennsylvania-state-university-mathematics/
LOCATION:Zoom
CATEGORIES:Spring 2026
ATTACH;FMTTYPE=image/jpeg:https://biomath.math.ufl.edu/wp-content/uploads/sites/607/hao_wenrui.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260226T104000
DTEND;TZID=America/New_York:20260226T113000
DTSTAMP:20260223T161822Z
CREATED:20260203T161108Z
LAST-MODIFIED:20260223T161822Z
UID:2249-1772102400-1772105400@biomath.math.ufl.edu
SUMMARY:Quindel Jones (UF Laboratory for Systems Medicine)
DESCRIPTION:Mechanistic Models as Hypotheses: Integrating Structure and Data from Sickle Cell Disease to Bacterial Infection\nMechanistic models gain scientific value when treated as testable hypotheses rather than fixed descriptions of biology. I illustrate this idea through two modeling studies that differ sharply in how biology and data shape mechanistic structure. I begin with my work on sterile inflammation in sickle cell disease (SCD)\, where sparse\, cross‑sectional clinical data meant the modeling process had to start with biology. I constructed an ODE framework directly from mechanistic understanding of endothelial activation\, platelet dynamics\, neutrophil behavior\, and inflammatory signaling. Because the available clinical data were sparse and cross‑sectional\, they served primarily as a validation and parameter‑estimation tool rather than a driver of model structure. This allowed me to identify which parameters most strongly influenced the observed clinical shifts and to characterize the inflammatory pathways most responsible for transitions into vaso‑occlusive crisis. This project reflects a structure‑first approach: biology defines the model\, and data provide limited constraints. \nIn contrast\, my current infection work begins with data. Longitudinal murine measurements of bacterial burden\, immune activity\, and tissue damage allow me to construct and compare multiple mechanistic hypotheses for Klebsiella pneumoniae pneumonia\, according to Ganusov’s 2016 framework. By calibrating each model variant and applying AICc‑based model rejection\, I use data not simply to validate structure but to actively eliminate unsupported mechanisms through falsification. This strong‑inference approach transforms mechanistic models into testable scientific hypotheses. \nTogether\, these projects illustrate a unified modeling philosophy: mechanistic structure provides interpretability\, data provide constraint\, and strong inference links the two. The talk will emphasize ODE modeling\, nonlinear immune dynamics\, and the role of hypothesis‑driven model comparison in understanding complex biological systems.
URL:https://biomath.math.ufl.edu/event/quindel-jones-uf-laboratory-for-systems-medicine/
LOCATION:423 Little Hall
CATEGORIES:Spring 2026
ATTACH;FMTTYPE=image/jpeg:https://biomath.math.ufl.edu/wp-content/uploads/sites/607/jones_quindel.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260219T104000
DTEND;TZID=America/New_York:20260219T113000
DTSTAMP:20260817T155509Z
CREATED:20260203T160915Z
LAST-MODIFIED:20260817T155509Z
UID:2247-1771497600-1771500600@biomath.math.ufl.edu
SUMMARY:Jonathan Rubin (University of Pittsburgh\, Mathematics)
DESCRIPTION:Geometric Mechanisms of Temporal Variability and Precision in Transient Biological Dynamics\nUnderstanding transient temporal effects—when events occur and how variable their timing is—represents a central challenge across biological systems. While traditional analyses often focus on steady-state behaviors and average frequencies\, the response variability and timing precision observed in real biological systems demand a deeper understanding of transient dynamics and the geometric structures that shape timing. \nThis talk presents dynamical systems frameworks for understanding temporal variability and precision and input specificity across two very different biological contexts. First\, in predator-prey population cycles\, I show how stochasticity interacts with proximity to saddle points to generate substantial cycle period variability\, as associated with population outbreaks. Second\, in the context of neuronal responses to time-varying inputs\, I demonstrate the dynamical features underlying post-inhibitory facilitation and slope detection\, two selective response phenomena characteristic of brainstem auditory neurons. Finally\, in an analysis that combines both periodic forcing and noise\, I introduce the dynamic threshold curve (DTC)\, which captures how the geometry of excitable systems determines their phase-locking precision\, which may contribute to the remarkable temporal accuracy of auditory neurons. \nAcross these systems\, geometric structures associated with nonlinear dynamics emerge as the key determinants of when events occur and how variable their timing becomes. This perspective provides both mechanistic explanations for observed biological phenomena and tools for analyzing and predicting temporal dynamics in complex system.
URL:https://biomath.math.ufl.edu/event/jonathan-rubin-university-of-pittsburgh-mathematics/
LOCATION:Zoom
CATEGORIES:Spring 2026
ATTACH;FMTTYPE=image/jpeg:https://biomath.math.ufl.edu/wp-content/uploads/sites/607/rubin_jonathan.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260212T104000
DTEND;TZID=America/New_York:20260212T113000
DTSTAMP:20260817T155502Z
CREATED:20260203T160811Z
LAST-MODIFIED:20260817T155502Z
UID:2245-1770892800-1770895800@biomath.math.ufl.edu
SUMMARY:Natalia Komarova (UC San Diego\, Mathematics)
DESCRIPTION:Mathematical modeling of spatial evolution with applications to cancer\nEvolutionary dynamics permeates life and life-like systems. Mathematical methods can be used to study evolutionary processes\, such as selection\, mutation\, and drift\, and to make sense of many phenomena in life sciences. In this talk I will discuss how spatial interactions may change the laws of evolution\, giving rise to a system of scaling laws that describe the growth of disadvantageous\, neutral\, and advantageous mutants in growing populations. Applications of these laws to bacterial growth and carcinogenesis will be discussed.
URL:https://biomath.math.ufl.edu/event/natalia-komarova-uc-san-diego-mathematics/
LOCATION:Zoom
CATEGORIES:Spring 2026
ATTACH;FMTTYPE=image/jpeg:https://biomath.math.ufl.edu/wp-content/uploads/sites/607/komarova_natalia.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260202T150000
DTEND;TZID=America/New_York:20260202T155000
DTSTAMP:20260131T032233Z
CREATED:20260131T032136Z
LAST-MODIFIED:20260131T032233Z
UID:2233-1770044400-1770047400@biomath.math.ufl.edu
SUMMARY:Math Dept Colloquium: Peter Thomas (Case Western Reserve University\, Applied Mathematics\, and Statistics)
DESCRIPTION:A Universal Description of Stochastic Oscillators\nMany systems in physics\, chemistry and biology exhibit oscillations with a pronounced random component. Such stochastic oscillations can emerge via different mechanisms\, for example linear dynamics of a stable focus with fluctuations\, limit-cycle systems perturbed by noise\, or excitable systems in which random inputs lead to a train of pulses. Despite their diverse origins\, the phenomenology of random oscillations can be strikingly similar. In joint work with Alberto Perez\, Benjamin Lindner\, and Boris Gutkin\, we have introduced a nonlinear transformation of stochastic oscillators via a complex-valued function\, Q\, that greatly simplifies and unifies the mathematical description of the oscillator’s spontaneous activity\, its response to external time-dependent perturbation\, and the correlation statistics of different oscillators that are weakly coupled. The Q function is the eigenfunction of Kolmogorov’s backward operator (also called the stochastic Koopman operator) with the least negative (but non-vanishing) eigenvalue λ1=μ1+iΩ1. The resulting power spectrum of the complex-valued function is exactly given by a Lorentz spectrum with peak frequency Ω1 and half-width μ1; its susceptibility with respect to a weak external forcing is given by a simple one-pole filter\, centered around Ω1; and the cross-spectrum between two coupled oscillators can be easily expressed by a combination of the spontaneous power spectra of the uncoupled systems and their susceptibilities. Our approach makes qualitatively different stochastic oscillators comparable\, provides simple characteristics for the coherence of the random oscillation\, and gives a framework for the description of both weakly and strongly coupled stochastic oscillators. \nJoint work with:\nAlberto Perez-Cervera (Universitat Politècnica de Catalunya\, Barcelona)\nBoris Gutkin (Ecole Normale Supérieure\, Paris)\nBenjamin Lindner (Humboldt University\, Berlin)\nMax Kreider (Penn State University)
URL:https://biomath.math.ufl.edu/event/math-dept-colloquium-peter-thomas-case-western-reserve-university-applied-mathematics-and-statistics/
LOCATION:339 Little Hall (The Atrium)
CATEGORIES:Spring 2026
ATTACH;FMTTYPE=image/jpeg:https://biomath.math.ufl.edu/wp-content/uploads/sites/607/thomas_peter.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260129T104000
DTEND;TZID=America/New_York:20260129T113000
DTSTAMP:20260127T034628Z
CREATED:20260127T034628Z
LAST-MODIFIED:20260127T034628Z
UID:2230-1769683200-1769686200@biomath.math.ufl.edu
SUMMARY:Adriana Del Pino (UF Biomedical Engineering)
DESCRIPTION:Systems-Level Mapping of the Tumor Microenvironment in Platinum-Resistant Ovarian Cancer\nOvarian cancer remains the most lethal gynecologic cancer\, with limited improvements in patient survival despite advances in personalized medicine and a high rate of recurrence (~80%). Current standard-of-care involves platinum-based chemotherapy\, but the emergence of resistant clones limits long-term efficacy. Existing models often overlook critical interactions between cancer cells and their microenvironment. Therefore\, we investigated the ovarian cancer microenvironment to identify stromal and immune cell populations driving treatment resistance. We employed a multi-modal systems biology approach\, integrating multiplex immunohistochemistry (mIF)\, bulk\, and single-cell RNA sequencing to characterize the ovarian cancer microenvironment. Cell type composition was quantified using ImageJ (mIF) and computational deconvolution tools (CIBERSORTx\, singleR) for normal (n=6-13) and cancer (n=7-20) samples. Platinum-sensitivity was determined by mapping single-cell data to a clinically annotated reference. Differential expression analysis and pathway enrichment were performed to identify key biological processes between normal vs cancer and sensitive vs resistant phenotypes. We found increased macrophage and T-cell marker expression\, alongside decreased fibroblast abundance\, in cancer samples compared to normal tissues. Pathway analysis revealed upregulation of the inflammatory and TNF alpha signaling pathway in cancer samples compared to normal\, promoting inflammation\, cell survival and proliferation. Importantly\, resistant samples exhibited higher expression of macrophage markers and enrichment of the epithelial-to-mesenchymal transition (EMT)\, suggesting an aggressive phenotype characterized by invasion and metastasis. These results demonstrate that platinum-resistant ovarian cancer is characterized by dynamic alterations in immune cell recruitment and stromal support\, revealing a disrupted tumor microenvironment ecosystem.
URL:https://biomath.math.ufl.edu/event/adriana-del-pino-uf-biomedical-engineering/
LOCATION:423 Little Hall
CATEGORIES:Spring 2026
ATTACH;FMTTYPE=image/jpeg:https://biomath.math.ufl.edu/wp-content/uploads/sites/607/del-pino-herrera_adriana.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260122T104000
DTEND;TZID=America/New_York:20260122T113000
DTSTAMP:20260120T165850Z
CREATED:20260120T165850Z
LAST-MODIFIED:20260120T165850Z
UID:2227-1769078400-1769081400@biomath.math.ufl.edu
SUMMARY:Abhiram Hegade (UF Mathematics)
DESCRIPTION:Network Architecture Dictates Dynamics: Operating Principles of Interconnected Feedback Loops in Cell Fate Transitions\nInterconnected feedback loops are prevalent in cell fate transitions\, yet their operating principles remain largely unexplored. In this talk\, we will examine high-dimensional feedback loops (HDFLs) organized into serial\, hub\, and cyclic network architectures. We will see that network architecture dictates the accessible phenotype space: high-centrality hub networks robustly restrict dynamics to bistability\, whereas lower-centrality serial and cyclic networks support higher-dimensional state spaces.
URL:https://biomath.math.ufl.edu/event/abhiram-hegade-uf-mathematics-2/
LOCATION:423 Little Hall
CATEGORIES:Spring 2026
ATTACH;FMTTYPE=image/jpeg:https://biomath.math.ufl.edu/wp-content/uploads/sites/607/hegade_abhiram.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260115T104000
DTEND;TZID=America/New_York:20260115T113000
DTSTAMP:20260817T155451Z
CREATED:20260115T170359Z
LAST-MODIFIED:20260817T155451Z
UID:2223-1768473600-1768476600@biomath.math.ufl.edu
SUMMARY:Theo Gibbs (New York University\, Center for Genomics and Systems Biology)
DESCRIPTION:Do higher-order interactions promote coexistence in diverse ecological communities?\nFrom the human microbiome to the Amazon rainforest\, diverse ecological communities are widespread in the natural world\, but we do not know how this diversity is maintained by the interactions between species. A central assumption in most ecological models is that the interactions in a community operate only between pairs of species. However\, two species may interactively affect the growth of a third species. Although interactions among three or more species\, called higher-order interactions\, have the potential to modify our theoretical understanding of coexistence\, ecologists lack clear expectations for how these interactions shape community structure. In this talk\, I will show that randomly-sampled higher-order interactions are unlikely to generate widespread coexistence. By contrast\, higher-order interactions that have specific relationships with the underlying pairwise interactions can stabilize coexistence in diverse communities. Last\, I will present experimental evidence that higher-order interactions structure the dynamics of annual plant communities. I will conclude with a brief discussion of ongoing work that further integrates theoretical and experimental approaches.
URL:https://biomath.math.ufl.edu/event/theo-gibbs-new-york-university-center-for-genomics-and-systems-biology/
LOCATION:Zoom
CATEGORIES:Spring 2026
ATTACH;FMTTYPE=image/jpeg:https://biomath.math.ufl.edu/wp-content/uploads/sites/607/gibbs_theo.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251120T104000
DTEND;TZID=America/New_York:20251120T113000
DTSTAMP:20260115T170928Z
CREATED:20250910T181622Z
LAST-MODIFIED:20260115T170928Z
UID:2188-1763635200-1763638200@biomath.math.ufl.edu
SUMMARY:Linh Huynh (Dartmouth\, Mathematics)
DESCRIPTION:Adaptive Dynamics on High-Dimensional Optimization Random Landscapes\nWhat do spin glasses (a subfield of high-dimensional probability and statistical physics)\, evolutionary biology\, and artificial intelligence (AI) have in common? All involve optimization on rugged landscapes where metastable states pose significant challenges to the search for optima. In this talk\, I will discuss an example of such landscapes. In particular\, I will discuss adaptive dynamics of Large Language Model landscapes subject to environmental noise from human-AI interactions. I will highlight how techniques from spin glasses can help studying AI and evolutionary biology.
URL:https://biomath.math.ufl.edu/event/linh-huynh-dartmouth-mathematics/
LOCATION:Zoom
CATEGORIES:Fall 2025
ATTACH;FMTTYPE=image/jpeg:https://biomath.math.ufl.edu/wp-content/uploads/sites/607/linh-e1755880627382.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251113T104000
DTEND;TZID=America/New_York:20251113T113000
DTSTAMP:20251114T154554Z
CREATED:20250910T171251Z
LAST-MODIFIED:20251114T154554Z
UID:2186-1763030400-1763033400@biomath.math.ufl.edu
SUMMARY:Meghan Ferrall-Fairbanks (UF\, Department of Biomedical Engineering)
DESCRIPTION:Engineering ovarian cancer solutions using evolutionary medicine\, math modeling\, and big data\nOvarian cancer is the most lethal gynecologic cancer despite the progress we’ve made in personalized medicine; there has only been modest survival gains for ovarian cancer patients with over 80% of patients experiencing recurrent disease. Furthermore\, many current clinical trials often fail to account for complex patient environments\, resulting in findings that do not translate well to broader populations. Our approaches aim to understand how the host conditioning affects that individual’s cellular interactions to leverage the extensive information available in patients’ electronic health records and disease status to tailor therapeutic options. We use multi-modal data\, machine learning\, mathematical modeling\, and in vitro culture systems to explore the impact of host conditioning on ovarian cancer progression and response to therapy. Our long-term goal is to employ multidisciplinary approaches to elucidate the plasticity response of cellular phenotypes as they adapt to various environments and exposures\, ultimately personalizing treatments and improving human health. Our recent findings highlight the adaptability of cellular phenotypes in response to different environments\, and these interactions underscore the potential of our approaches.
URL:https://biomath.math.ufl.edu/event/meghan-ferrall-fairbanks-uf-department-of-biomedical-engineering/
LOCATION:423 Little Hall
CATEGORIES:Fall 2025
ATTACH;FMTTYPE=image/jpeg:https://biomath.math.ufl.edu/wp-content/uploads/sites/607/ferrall-fairbanks_meghan.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251106T104000
DTEND;TZID=America/New_York:20251106T113000
DTSTAMP:20260115T170919Z
CREATED:20250910T171145Z
LAST-MODIFIED:20260115T170919Z
UID:2184-1762425600-1762428600@biomath.math.ufl.edu
SUMMARY:Allison Cruikshank (Duke\, Mathematics)
DESCRIPTION:Mechanistic Insights Into Parkinson’s Disease and Sex Differences in Liver Oxidative Stress\nIn volume transmission\, or neuromodulation\, neurons communicate not through direct\, one-to-one synaptic connections\, but by releasing neurotransmitters broadly into the extracellular space from numerous varicosities. This type of signaling is particularly relevant for serotonin and dopamine neurons\, which project from the dorsal raphe and substantia nigra\, respectively\, to the hippocampus. In the first part of my talk\, I will discuss recent findings on their interplay in the brain and how this makes serotonin a potential biomarker for Parkinson’s disease. The second part will focus on sex differences in oxidative stress management in peripheral tissues such as the liver. Women generally have lower oxidative stress and higher glutathione levels than men\, reflecting the influence of sex hormones. Using mathematical modeling\, I explore the mechanisms underlying these differences and why estrogen supplementation has differential effects on a key cardiovascular biomarker in pre- versus post-menopausal women. By linking clinical data with modeling\, this work provides insights into the underlying biology and informs sex- and menopausal status-specific approaches in medicine.
URL:https://biomath.math.ufl.edu/event/allison-cruikshank-duke-mathematics/
LOCATION:Zoom
CATEGORIES:Fall 2025
ATTACH;FMTTYPE=image/jpeg:https://biomath.math.ufl.edu/wp-content/uploads/sites/607/IMG_6090-e1762186562155.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251030T104000
DTEND;TZID=America/New_York:20251030T113000
DTSTAMP:20260115T170912Z
CREATED:20250910T171009Z
LAST-MODIFIED:20260115T170912Z
UID:2181-1761820800-1761823800@biomath.math.ufl.edu
SUMMARY:Denis Patterson (Durham University\, Department of Mathematical Sciences)
DESCRIPTION:Mathematical modelling of malaria: Population-scale dynamics\, vaccination\, and evolution\nMalaria transmission and persistence depend critically on the interaction between parasite dynamics\, human immunity\, and epidemiological feedbacks. I will review recent work with collaborators developing and analysing structured PDE models spanning both population and within-host scales. At the population level\, we couple vector–host epidemiology with the acquisition and loss of anti-disease immunity. Bifurcation analysis reveals the changing structure of endemic equilibria as transmission intensity rises\, and our exploration of vaccination strategies\, motivated by the RTS\,S vaccine\, highlights how seasonal transmission profiles may impact interventions. We also investigate multiple potential causes of backward bifurcation in this model. At the within-host level\, we examine how parasites allocate resources between proliferation and transmission stages under immune pressure. Using an age‑of‑infection–structured model\, we characterize immune-driven clearance thresholds and analyse parasite investment strategies\, contrasting constant versus time-varying allocation rules. Our results indicate that adaptive immunity can impose a survival-reproduction tradeoff that explains why malaria parasites cannot evolve ever faster within-host multiplication. Together\, these studies illustrate how immunity and feedbacks across scales shape malaria outcomes and inform intervention strategies.
URL:https://biomath.math.ufl.edu/event/denis-patterson-durham-university-department-of-mathematical-sciences/
LOCATION:Zoom
CATEGORIES:Fall 2025
ATTACH;FMTTYPE=image/jpeg:https://biomath.math.ufl.edu/wp-content/uploads/sites/607/patterson_denis.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251023T104000
DTEND;TZID=America/New_York:20251023T113000
DTSTAMP:20260115T170845Z
CREATED:20250910T170826Z
LAST-MODIFIED:20260115T170845Z
UID:2179-1761216000-1761219000@biomath.math.ufl.edu
SUMMARY:Calina Copos (Northeastern University\, Biology and Mathematics)
DESCRIPTION:Modeling insights into subcellular cytoskeleton organization with external size changes\nActin is one of the most abundant proteins in eukaryotic cells and a fundamental component of the cytoskeleton\, playing a critical role in maintaining cell structure and enabling motility. A compelling preliminary experimental observation underpins our work: in micropatterned epithelial cells of increasing sizes\, the mechanical energy does not scale linearly with size. Instead\, an optimal force is generated at a critical cell size\, suggesting a force response that combines both passive and active mechanical components. To explore this phenomenon\, we present a mechanical model of the actin cytoskeleton in an adherent cell that captures the observed biphasic response in force production\, arising from an underlying scaling law in cytoskeletal mechanical properties. Complementing this\, we develop an agent-based model that simulates the microscopic dynamics of actin filament formation\, incorporating crosslinkers and myosin motors. Within this framework\, we test various hypotheses — such as the impact of limited resources — that could give rise to the scaling law identified in the macroscopic model. Together\, these efforts constitute a multiscale approach aimed at uncovering the mechanisms by which cell size regulates cytoskeletal force generation.
URL:https://biomath.math.ufl.edu/event/calina-copos-northeastern-university-biology-and-mathematics/
LOCATION:Zoom
CATEGORIES:Fall 2025
ATTACH;FMTTYPE=image/png:https://biomath.math.ufl.edu/wp-content/uploads/sites/607/copos_calina.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251009T104000
DTEND;TZID=America/New_York:20251009T113000
DTSTAMP:20260115T170834Z
CREATED:20250910T170241Z
LAST-MODIFIED:20260115T170834Z
UID:2177-1760006400-1760009400@biomath.math.ufl.edu
SUMMARY:John Rinzel (NYU\, Neural Science and Mathematics)
DESCRIPTION:Bistable Dynamics of Perceiving Ambiguous Stimuli\nWhen experiencing an ambiguous sensory stimulus (e.g.\, the vase-faces image)\, subjects may report random alternations (time scale\, seconds) between the possible interpretations. I will describe dynamical models with multiple time scales for neuronal populations that compete (fast time scale) through mutual inhibition for dominance – showing alternations (slow time scale). The models behave as noisy oscillators or as multistable systems subject to noise-driven switching. In highly idealized formulations networks are percept specific without direct representation of stimulus features. Our recent work involves perception of ambiguous auditory stimuli (e.g.\, http://auditoryneuroscience.com/scene-analysis/streaming-galloping-rhythm slider settings: rate=8\, df=3 or 6 or 10). The models explicitly incorporate sound features — perceptual selectivity is emergent rather than built-in. I will compare modeling results with behavioral (human) data from our psychophysical experiments.
URL:https://biomath.math.ufl.edu/event/john-rinzel-nyu-neural-science-and-mathematics/
LOCATION:Zoom
CATEGORIES:Fall 2025
ATTACH;FMTTYPE=image/jpeg:https://biomath.math.ufl.edu/wp-content/uploads/sites/607/Rinzel-John-e1762186605622.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251002T104000
DTEND;TZID=America/New_York:20251002T113000
DTSTAMP:20260115T170852Z
CREATED:20250910T170201Z
LAST-MODIFIED:20260115T170852Z
UID:2175-1759401600-1759404600@biomath.math.ufl.edu
SUMMARY:Cheng Ly (VCU\, Department of Mathematics and Applied Mathematics)
DESCRIPTION:Are differences between Parkinson’s patients and healthy subjects like other brain diseases?\nThe dynamical state of cortical neural activity constrains the complexity of functions it can perform. A marginally stable dynamical state – called criticality – is thought to be beneficial for brain functions that require multiple time scales\, broad dynamic range\, and large information storage and transmission. A growing body of evidence suggests that criticality is a feature of healthy brain dynamics\, but breaks down in certain brain disorders.  Here we ask whether Parkinson’s disease incurs deviation from criticality compared to healthy controls.  We analyze human resting state EEG activity in primary motor cortex. Parkinson’s patients exhibit prominent oscillatory brain activity in multiple frequency bands (low delta and high theta) that is not present in controls.  Surprisingly\, we find that these emergent oscillations are close to criticality\, i.e.\, amplitude fluctuations with approximate temporal scale invariance.  We compare traditional signatures of criticality and more principled measurements of proximity to criticality using our recently developed approach based on temporal renormalization group theory and information theory. Our new approach and traditional methods agree\, suggesting that critical dynamics are not always healthy; Parkinson’s disease is associated with the emergence of near critical oscillations in motor cortex.
URL:https://biomath.math.ufl.edu/event/cheng-ly-vcu-department-of-mathematics-and-applied-mathematics/
LOCATION:Zoom
CATEGORIES:Fall 2025
ATTACH;FMTTYPE=image/jpeg:https://biomath.math.ufl.edu/wp-content/uploads/sites/607/hdsht_officeL-scaled-e1762186626969.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250925T104000
DTEND;TZID=America/New_York:20250925T113000
DTSTAMP:20260115T170817Z
CREATED:20250910T170116Z
LAST-MODIFIED:20260115T170817Z
UID:2172-1758796800-1758799800@biomath.math.ufl.edu
SUMMARY:Alessandro Selvitella (Purdue University Fort Wayne\, Data Science and Applied Statistics)
DESCRIPTION:A connubio of Machine Learning and PDEs for Scientific Discovery in the Biological Sciences\nIn this talk\, I will describe some problems at the intersection of data science and applied mathematics with particular focus on questions emerging in the study of the neuro-musculo-skeletal-environmental system. Many aspects of this system can be studied with (potentially different) combinations of machine learning and differential equations methods. An underlying theme of these problems is the partial understanding in mathematical terms of the phenomenon under study and the availability of some limited data\, together with the need of combining information from different experimental sources. Another theme is that of the complexity of the models\, which might be intrinsically geometric and include both continuous and discrete components\, but that can sometimes be accurate even if expressed in terms of biologically motivated latent variables of reduced dimensions. If time permits\, applications of some of these tools to problems in infectious diseases dynamics will also be discussed.
URL:https://biomath.math.ufl.edu/event/alessandro-selvitella-purdue-university-fort-wayne-data-science-and-applied-statistics/
LOCATION:Zoom
CATEGORIES:Fall 2025
ATTACH;FMTTYPE=image/jpeg:https://biomath.math.ufl.edu/wp-content/uploads/sites/607/selvitella_alessandro_maria.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250904T104000
DTEND;TZID=America/New_York:20250904T113000
DTSTAMP:20260115T170809Z
CREATED:20250203T164304Z
LAST-MODIFIED:20260115T170809Z
UID:2096-1756982400-1756985400@biomath.math.ufl.edu
SUMMARY:Jay Newby (University of Alberta\, Mathematical and Statistical Sciences)
DESCRIPTION:Extreme first passage times for populations of identical rare events\nA collection of identical and independent rare event first passage times is considered. The problem of finding the fastest out of N such events to occur is called an extreme first passage time. The rare event times are singular and limit to infinity as a positive parameter scaling the noise magnitude is reduced to zero. In contrast\, previous work has shown that the mean of the fastest event time goes to zero in the limit of an infinite number of walkers. The combined limit is studied. In particular\, the mean time and the most likely path taken by the fastest random walker are investigated. Using techniques from large deviation theory\, it is shown that there is a distinguished limit where the mean time for the fastest walker can take any positive value\, depending on a single proportionality constant. Furthermore\, it is shown that the mean time and most likely path can be approximated using the solution to a variational problem related to the single-walker rare event.
URL:https://biomath.math.ufl.edu/event/jay-newby-university-of-alberta-mathematical-and-statistical-sciences/
LOCATION:Zoom
CATEGORIES:Fall 2025
ATTACH;FMTTYPE=image/jpeg:https://biomath.math.ufl.edu/wp-content/uploads/sites/607/jay-newby-e1755880713573.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250417T104000
DTEND;TZID=America/New_York:20250417T113000
DTSTAMP:20250822T163211Z
CREATED:20250203T164737Z
LAST-MODIFIED:20250822T163211Z
UID:2105-1744886400-1744889400@biomath.math.ufl.edu
SUMMARY:Binod Pant (Northeastern University\, Network Science Institute)
DESCRIPTION:Could malaria mosquitoes be controlled by periodic release of transgenic mosquitocidal Metarhizium pingshaense? A mathematical modeling approach\nMalaria remains one of the world’s deadliest vector-borne diseases\, with WHO reporting 249 million cases and 608\,000 deaths across 85 countries in 2022 alone. Widespread insecticide-based interventions have significantly reduced malaria burden\, but these gains are now threatened by increasing Anopheles resistance to all chemical compounds in current vector control tools. Entomopathogenic fungi show promise as alternative biological control agents against adult malaria mosquitoes by infecting them through contact and reducing their lifespan. Transgenic Metarhizium pingshaense strain engineered to express insecticidal toxins significantly outperform natural strains\, achieving greater than 80% mosquito mortality within one week compared to 1-2 weeks for non-transgenic variants. This work represents the first attempt to mathematically analyze the population-level impact of periodically releasing transgenic fungus-exposed male mosquitoes\, providing critical insights into optimal release strategies for achieving significant wild mosquito population suppression in malaria-endemic settings.
URL:https://biomath.math.ufl.edu/event/binod-pant-northeastern-university-network-science-institute/
LOCATION:Zoom
CATEGORIES:Spring 2025
ATTACH;FMTTYPE=image/jpeg:https://biomath.math.ufl.edu/wp-content/uploads/sites/607/66e8a873a27c47b2d5e9740f_Binod_web-scaled-e1755880513498.jpeg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250313T104000
DTEND;TZID=America/New_York:20250313T113000
DTSTAMP:20250822T163004Z
CREATED:20250203T164152Z
LAST-MODIFIED:20250822T163004Z
UID:2092-1741862400-1741865400@biomath.math.ufl.edu
SUMMARY:Hayriye Gulbudak (University of Louisiana at Lafayette\, Mathematics)
DESCRIPTION:Bistability between acute and chronic states in a Model of Hepatitis B Virus Dynamics\nUnderstanding the mechanisms responsible for different clinical outcomes following hepatitis B infection requires a systems investigation of dynamical interactions between the virus and the immune system. To help elucidate mechanisms of protection and those responsible from transition from acute to chronic disease\, we developed a deterministic mathematical model of hepatitis B infection that accounts for cytotoxic immune responses resulting in infected cell death\, non-cytotoxic immune responses resulting in infected cell cure and protective immunity from reinfection\, and cell proliferation. We analyzed the model and presented outcomes based on three important disease markers: the basic reproduction number R0\, the infected cells death rate δ (describing the effect of cytotoxic immune responses)\, and the liver carrying capacity K (describing the liver susceptibility to infection). Using asymptotic and bifurcation analysis techniques\, we determined regions where virus is cleared\, virus persists\, and where clearance-persistence is determined by the size of viral inoculum. These results can guide the development of personalized intervention.
URL:https://biomath.math.ufl.edu/event/hayriye-gulbudak-university-of-louisiana-at-lafayette-department-of-mathematics/
LOCATION:423 Little Hall
CATEGORIES:Spring 2025
ATTACH;FMTTYPE=image/jpeg:https://biomath.math.ufl.edu/wp-content/uploads/sites/607/HayriyeGulbudak_2024_2x3-e1755880823851.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250306T104000
DTEND;TZID=America/New_York:20250306T113000
DTSTAMP:20250822T162809Z
CREATED:20250203T164009Z
LAST-MODIFIED:20250822T162809Z
UID:2088-1741257600-1741260600@biomath.math.ufl.edu
SUMMARY:Rachel Nicks (University of Nottingham\, School of Mathematical Sciences)
DESCRIPTION:Insights into oscillatory neural dynamics using a phase-amplitude framework\nModel reduction techniques can provide useful insight into the dynamics behaviour of high dimensional oscillatory systems such as networks of neurons or neural field models. However\, the utility of the classical technique of phase reduction is limited by the assumption that the local dynamics for each node in a network remain on the stable limit cycle of the uncoupled system. Here we will discuss the use of a framework where each oscillator is described by an amplitude given by its slowest decaying isostable coordinate in addition to its phase on limit cycle. This allows for representation of trajectories away from (but near) the limit cycle. \n  \nWe first discuss the application of this framework to discrete networks where the resulting phase-isostable network equations can be used compute existence and stability conditions for phase-locked states in networks of identical nodes\, extending known results for phase-reduced equations beyond the weak coupling limit. We demonstrate the power of the general framework by considering small and large networks of Morris-Lecar neurons\, capturing stability changes and quasiperiodic behaviour that are beyond the descriptive scope of classical first-order phase reduction\, and are shown to be in good qualitative agreement with the dynamics of the original network through numerical simulations and bifurcation analysis. Delays in both the node dynamics and network interactions can strongly influence patterns of phase-locked states and their bifurcations. We will briefly discuss ongoing work incorporating delayed interactions as well as phase and amplitude response of delay induced node oscillations within a phase-amplitude network setting. Finally\, we will show how phase-isostable reduction can provide useful information about the stability breakdown of phase waves in continuum neural field models\, revealing parameter regimes where more exotic behaviour such as weak turbulence or chimera states may exist. \nReferences: \n[1] R Nicks\, R Allen and S Coombes 2024 Insights into oscillator network dynamics using a phase-isostable framework\, Chaos\, Vol 34\, 013141 \n[2]  R Nicks\, R Allen and S Coombes 2024 Phase and amplitude responses for delay equations using harmonic balance\, Physical Review E\, Vol 110\, L012202
URL:https://biomath.math.ufl.edu/event/rachel-nicks-university-of-nottingham-school-of-mathematical-sciences/
LOCATION:Zoom
CATEGORIES:Spring 2025
ATTACH;FMTTYPE=image/jpeg:https://biomath.math.ufl.edu/wp-content/uploads/sites/607/profile-e1755881058238.jpg
END:VEVENT
END:VCALENDAR