Speaker
Description
Mean first passage time is a fundamental quantity for characterizing search and encounter processes, since it measures the expected time required for a moving agent to first reach a target. In the context of immune cell dynamics, this provides a natural way to connect cell motility to functional outcomes such as target finding and cytotoxic efficiency. For natural killer cells and other immune cells, experimental trajectory data suggest that motion is not purely diffusive, but often exhibits persistence and directional bias, making classical diffusion-based first passage results insufficient for mechanistic interpretation.
This work develops a mathematical framework for studying mean first passage times in persistent random walk models motivated by immune cell migration. The goal is to understand how features such as directional persistence, environmental bias, and spatial geometry shape encounter statistics and influence search efficiency. By linking microscopic movement rules to macroscopic first passage behaviour, the framework aims to provide interpretable quantities that can help distinguish different search strategies and clarify which motility features are most relevant for effective immune surveillance. More broadly, the work highlights mean first passage time as a useful bridge between cell trajectory data, stochastic transport models, and biologically meaningful measures of function.
