University of Florida Homepage

Monica Hurdal (Florida State University, Mathematics)

423 Little Hall

Turing Patterns as a Model for Brain Folding Development Neuroscientists are interested in correlating brain function with anatomy. However, the highly complex folding patterns of the brain and the high variability in folding patterns across individuals contribute to the difficulty in understanding healthy brain function and disease. Interestingly, there is no consensus in neurobiology as

Colloquium: Hrvoje Šikić (University of Zagreb, Mathematics)

339 Little Hall (The Atrium)

The Eye-Lens Growth Model Biological lens in the eye of a mammal focuses light on the retina. Its shape and size is crucial for that purpose. We provide the first ever mathematical growth model of the mouse eye-lens and succeed in capturing a variety of behavior about the size of the lens, number of cells

Jonathan Touboul (Brandeis University, Mathematics)

Zoom

Chaos and homeostasis in multiple timescales dynamics Complex nonlinear and network dynamics, as observed in many biological systems, are prone to generate complex chaotic activity. Fortunately, in nature, these systems are still able to maintain essential biological function, and may be robust to changes in the environment despite their chaotic nature, until they break down

Paul Bressloff (Imperial College London, Mathematics)

Zoom

Cytoneme-mediated morphogenesis Morphogen protein gradients play an essential role in the spatial regulation of patterning during embryonic development.  The most commonly accepted mechanism of protein gradient formation involves the diffusion and degradation of morphogens from a localized source. Recently, an alternative mechanism has been proposed, which is based on cell-to-cell transport via thin, actin-rich cellular

Hemaho Taboe (UF Mathematics)

423 Little Hall

Unraveling Lassa Fever Persistence: A Compartmental Model with Environmental Virus-Host-Vector Interaction Lassa fever (LF), a severe viral hemorrhagic disease transmitted by rodents, particularly Mastomys natalensis, is endemic in West Africa, notably Nigeria, with substantial morbidity and mortality rates. Existing mathematical models on LF lack integration of the crucial environmental component, overlooking transmission through contaminated surfaces. Moreover,

Wenjing Zhang (Texas Tech University, Mathematics)

368 Little Hall

Sensitivity, Bifurcation, and Stochastic Analysis of Tuberculosis Progression Mycobacterium tuberculosis infection features various disease outcomes: clearance, latency, active disease, and latent tuberculosis infection (LTBI) reactivation. Identifying the decisive factors for disease outcomes and progression is crucial to elucidate the macrophages-tuberculosis interaction and provide insights into therapeutic strategies. To achieve this goal, we first model the

Hannah Anderson (UF Mathematics)

423 Little Hall

Optimal control of combination immunotherapy in a glioblastoma-immune dynamics model Glioblastoma (GBM) is the most common type of primary brain tumor. It is very aggressive with minimally effective treatment options. Current therapies yield a median survival of 15 months. Treatment failure may be caused by the highly immune-suppressed glioma microenvironment, leading to a rise in

Daniel A. Cruz (UF Laboratory for Systems Medicine)

423 Little Hall

Data Assimilation in Medical Models and Digital Twins One of the growing areas of interest in the context of biological and medical models is the question of how to personalize a model to a target organism or patient in order to improve model predictions. This question is inherently tied to data assimilation (DA): the process

Bryce Morsky (Florida State University, Mathematics)

Zoom

NPIs, vaccination, and collective action under social norms Social dynamics are an integral part of the spread of disease affecting contact rates as well as the adoption of pharmaceutical and non-pharmaceutical interventions (NPIs). This talk will present behavioural-epidemiological models that feature tipping-point dynamics for the uptake of vaccines or adoption of NPIs that combine the risk

Rodica Curtu (University of Iowa, Mathematics)

Zoom

Discovering dynamical patterns of activity from single-trial intracranial EEG recordings In this talk I will discuss a data-driven method that leverages time-delayed coordinates, diffusion maps, and dynamic mode decomposition, to identify neural features in large scale brain recordings that correlate with subject-reported perception. The method captures the dynamics of perception at multiple timescales and distinguishes