Mathematical Biology

The speaker will be rescheduled at a later date.

Image analysis and agent-based modeling of tumor-immune interactions in the glioblastoma microenvironment Glioblastoma is a highly aggressive and deadly brain cancer with no current treatment options available that can achieve remission. One potential explanation for minimally effective treatments is the ability of gliomas to take advantage of processes within the body's immune system to infiltrate …

My trajectory towards mathematical modeling of pulmonary infections The immune response to respiratory infections is highly complex and multiscale, making it amenable for mathematical modeling. Fungal respiratory infections are becoming increasingly prevalent and pose the threat of antimicrobial resistance. The immune response to respiratory pathogens is highly complex and multiscale, making it difficult to predict …

Novel Statistical Analysis Methods in Neuroimaging Using Diffusion MRI In this talk, I will discuss the fundamentals of diffusion MRI and explain how various features are extracted and their importance in neuroimaging analysis. I will then discuss some of the scientific applications and end with future possibilities.

Unveiling the Hidden Threat: The Impact of Sub-Optimum Treatment on Acquired Immunity, Asymptomatic Cases, Malaria Dynamics Malaria remains a persistent global health issue, despite ongoing control efforts such as anti-malarial drugs and insecticide-treated bed-nets, indoor residual spraying, etc.. The greatest impact of malaria, a mosquito-borne illness, is felt in Africa. This study develops a compartmental …

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 …

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 …

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, …

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 …