Adaptive discrimination of antigen risk by predictive coding in immune system
Adaptive discrimination of antigen risk by predictive coding in immune system
复制标题
通过免疫系统中的预测编码自适应区分抗原风险
DOI：
10.1101/2021.12.12.472285
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Naoki Honda
中科院分区：
文献类型：
--
作者：
Yoshido Kana;Naoki Honda
免疫系统根据过去的经验区分有害和无害的抗原;然而，其潜在机制在很大程度上是未知的。从机器学习的角度来看，学习系统预测观察结果，并根据预测误差更新预测结果，这一过程被称为“预测编码”。在这里，我们通过采用预测编码的概念来模拟T细胞的群体动力学;辅助性和调节性T细胞分别预测抗原量和过度免疫应答。它们的预测错误信号，可能通过细胞因子，诱导它们分化为记忆T细胞。通过数值模拟，我们发现免疫系统识别抗原风险取决于抗原的浓度和输入速度。此外，我们的模型再现了历史依赖的歧视，如在过敏发作和随后的治疗。总之，这项研究提供了一个新的框架，以提高我们对免疫系统如何自适应地学习不同抗原的风险的理解。
The immune system discriminates between harmful and harmless antigens based on past experiences; however, the underlying mechanism is largely unknown. From the viewpoint of machine learning, the learning system predicts the observation and updates the prediction based on prediction error, a process known as ‘predictive coding’. Here, we modeled the population dynamics of T cells by adopting the concept of predictive coding; helper and regulatory T cells predict the antigen amount and excessive immune response, respectively. Their prediction error signals, possibly via cytokines, induce their differentiation to memory T cells. Through numerical simulations, we found that the immune system identifies antigen risks depending on the concentration and input rapidness of the antigen. Further, our model reproduced history-dependent discrimination, as in allergy onset and subsequent therapy. Together, this study provided a novel framework to improve our understanding of how the immune system adaptively learns the risks of diverse antigens.