Inference of germinal center evolutionary dynamics via simulation-based deep learning

TL;DR

This paper employs simulation-based deep learning to infer the unknown relationship between B cell affinity and reproductive success within germinal centers, providing insights into the evolutionary dynamics of immune responses.

Contribution

It introduces a novel deep learning framework to infer the affinity-fitness response function from germinal center simulations, advancing understanding of B cell evolution.

Findings

Learned the affinity-fitness response function from experimental data.

Demonstrated the effectiveness of simulation-based deep learning in immunological inference.

Provided open-source code and datasets for reproducibility.

Abstract

B cells and the antibodies they produce are vital to health and survival, motivating research on the details of the mutational and evolutionary processes in the germinal centers (GC) from which mature B cells arise. It is known that B cells with higher affinity for their cognate antigen (Ag) will, on average, tend to have more offspring. However the exact form of this relationship between affinity and fecundity, which we call the ``affinity-fitness response function'', is not known. Here we use deep learning and simulation-based inference to learn this function from a unique experiment that replays a particular combination of GC conditions many times. All code is freely available at https://github.com/matsengrp/gcdyn, while datasets and inference results can be found at https://doi.org/10.5281/zenodo.15022130.