Modeling and Stochastic Simulation of Close-Contact Dynamics in Immune Recognition

University Of Notre Dame, Notre Dame IN

Investigators

Abstract

The project supported under this award will use mathematics to understand how the immune system is able to locate and fend off threats. White blood cells known as T cells are a key component of the immune system. Their responsibility is to search for relatively rare molecular signatures of pathogens. Autoimmune, immunodeficient diseases and cancer arise from a failure of this mechanism. The basic principles of how T cells efficiently discriminate between rare foreign cells and abundant host cells is not completely understood. This project will develop a new generation of theoretical and computational tools to massively accelerate the random sampling of T cell activation times. Working with experimental collaborators, the project will explore (through mathematical modeling, stochastic analysis and large scale computational simulations) the role that the cell-to-cell contact topography plays in immune recognition. This award will support undergraduate and graduate student researchers to work on this project. This project will reach the broader mathematical community through mini-symposia at upcoming conferences and thematic workshops hosted by the American Mathematical Society. When T cells encounter a cell of interest, they scan its surface for rare diffusing signaling molecules. This search is conducted in a bustling and noisy environment and so T cell activation is a stochastic process described mathematically as a first passage problem. Previous mathematical models of this process assumed the interface between the cells was large and contiguous. Recent experimental work has revealed that T cells extend numerous finger-like projections called microvilli to sculpt a highly transient and distributed interface. This project will build new models of T cell activation that incorporate the recently discovered role of stochastic topography arising from microvilli sampling. Much existing first passage theory has focused on determination of the mean of the distribution. Owing to the sensitivity of T cell activation, this project will require the development of new probabilistic tools to obtain the full distribution of passage times in a variety of narrow escape problems. To complement these theoretical results, the proposal will develop a new suite of computational (deterministic and Monte-Carlo) tools for studying stochastic processes in dynamic and heterogeneous environments. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.