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T.J. Sego (UF Laboratory for Systems Medicine)

423 Little Hall

Reproducible Stochastic Simulation Stochastic simulations are commonly used to quantitatively or semi-quantitatively describe the dynamics of biological systems. At various scales and in multiple applications, stochastic simulation better reflects observed biological processes and robustness. Various methods are widely used to incorporate stochasticity into biological simulation, such as the Gillespie stochastic simulation algorithm for systems biology

Harsh Jain (University of Minnesota Duluth, Mathematics & Statistics)

Zoom

Uncertainty Quantification in Complex Models of Complicated Biology Validated mathematical models of complex biological phenomena are increasingly recognized as invaluable for elucidating mechanisms that underlie real-world (experimental or clinical) observations. Agent-based models (ABMs) have emerged as a natural formulation of choice in such models, providing a logical structure for capturing the multiple time and spatial

Math Dept Colloquium: Claus Kadelka (Iowa State University, Mathematics)

339 Little Hall (The Atrium)

Including human behavior in infectious disease models The COVID-19 pandemic has revealed the good and the bad of infectious disease models. While a well-developed model provides invaluable insights needed to understand and combat the pandemic, many models suffer from imperfect or simplistic assumptions that result in inaccurate or even completely wrong predictions. In this talk,

Martina Conte (University of Parma, Mathematical, Physical, and Computer Sciences)

Zoom

A multi-scale approach for modeling cell dynamics The characterization of biological phenomena related to cell evolution and their interactions with the microenvironment often involves processes occurring across a range of spatial and temporal scales. As a result, mathematical models designed to describe cell dynamics must capture this inherent multi-scale complexity. In this seminar, we introduce

Andreas Buttenshön (University of Massachusetts Amherst, Mathematics and Statistics)

Zoom

How cells stay together; a mechanism for maintenance of a robust cluster explored by local and nonlocal continuum models Formation of organs and specialized tissues in embryonic development requires migration of cells to specific targets. In some instances, such cells migrate as a robust cluster. We here explore a recent local approximation of nonlocal continuum

Mingtao Xia (New York University, Courant Institute of Mathematical Sciences)

423 Little Hall

Reconstruction of stochastic differential equations characterizing noisy single-cell molecular dynamics In the talk, I shall introduce our recent work on developing machine-learning-based methods for reconstructing noisy single-cell molecular dynamics as stochastic differential equations, which aims to quantify intrinsic fluctuations in the stochastic process characterizing cellular dynamics. I will then discuss how to apply my methods

Math Dept Colloquium: Zhaosheng Feng (University of Texas Rio Grande Valley, Mathematical and Statistical Sciences)

339 Little Hall (The Atrium)

Parabolic System of Aggregation Formation in Bacterial Colonies The goal of this talk is to study a fourth-order nonlinear parabolic system with dispersion for describing bacterial aggregation. Analytical solution of traveling wave is found by taking into account the dispersion coefficient. Numerically, we demonstrate that the initial concentration of bacteria in the form of a

Ugur Abdulla (OIST, Analysis and Partial Differential Equations Unit)

LIT 368

Cancer Detection via Electrical Impedance Tomography and Optimal Control of Elliptic PDEs A new mathematical framework utilizing the theory of Partial Differential Equations(PDE), inverse problems and optimal control of systems with distributed parameters for the detection of the cancerous tumor growth in the human body is developed. The Inverse Electrical Impedance Tomography (EIT) problem on

Sergei Pilyugin (University of Florida Department of Mathematics)

423 Little Hall

Continuous culture models of microbial kinetics In this talk, I will start with a bit of history of mathematical models of microbial kinetics in both batch and continuous cultures, briefly discuss the principle of competitive exclusion, and then concentrate on the models that incorporate the bacterial aggregation in various forms such as the flocculation and