A variational deep-learning approach to modeling memory T cell dynamics.

ABSTRACT: Mechanistic models of dynamic, interacting cell populations have yielded many insights into the growth and resolution of immune responses. Historically these models have described the behavior of pre-defined cell types based on small numbers of phenotypic markers. The ubiquity of deep phenotyping therefore presents a new challenge; how do we confront tractable and interpretable mathematical models with high-dimensional data? To tackle this problem, we studied the development and persistence of lung-resident memory CD4 and CD8 T cells (T_RM) in mice infected with influenza virus. We developed an approach in which dynamical model parameters and the population structure are inferred simultaneously. This method uses deep learning and stochastic variational inference and is trained on

SUBMITTER: van Dorp CH 

PROVIDER: S-EPMC11888226 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature