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

DK Ralph, AG Bakis, J Galloway, AA Vora… - arXiv preprint arXiv …, 2025 - arxiv.org
Environmental Science paper q-bio.PE Suggest

… 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 …

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@article{2508.09871v1,
Author = {Duncan K Ralph and Athanasios G Bakis and Jared Galloway and Ashni A Vora and Tatsuya Araki and Gabriel D Victora and Yun S Song and William S DeWitt and Frederick A Matsen IV},
Title = {Inference of germinal center evolutionary dynamics via simulation-based
deep learning},
Eprint = {2508.09871v1},
ArchivePrefix = {arXiv},
PrimaryClass = {q-bio.PE},
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.},
Year = {2025},
Month = {Aug},
Url = {http://arxiv.org/abs/2508.09871v1},
File = {2508.09871v1.pdf}
}

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