Ralph Duncan K, Bakis Athanasios G, Galloway Jared, Vora Ashni A, Araki Tatsuya, Victora Gabriel D, Song Yun S, DeWitt William S, Matsen Frederick A
Fred Hutchinson Cancer Research Center, Seattle, Washington, USA.
Department of Statistics, University of California, Irvine, CA.
ArXiv. 2025 Aug 13:arXiv:2508.09871v1.
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 (GCs) 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.
B细胞及其产生的抗体对健康和生存至关重要，这推动了对生发中心（GCs）中成熟B细胞产生的突变和进化过程细节的研究。众所周知，对其同源抗原（Ag）具有更高亲和力的B细胞，平均而言，往往会有更多的后代。然而，亲和力与繁殖力之间这种关系的确切形式，即我们所称的“亲和力-适应性反应函数”，尚不清楚。在这里，我们使用深度学习和基于模拟的推理，从一个独特的实验中学习这个函数，该实验多次重现了特定的GC条件组合。所有代码可在https://github.com/matsengrp/gcdyn上免费获取，而数据集和推理结果可在https://doi.org/10.5281/zenodo.15022130上找到。
