Mathematical Biology

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 …

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 …

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 …

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 …

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 …