How a well-adapting immune system remembers
How a well-adapting immune system remembers
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DOI：
10.1073/pnas.1812810116
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发表时间：
2019-04-30
影响因子：
11.1
通讯作者：
Mora, Thierry
中科院分区：
文献类型：
--
作者：
Mayer, Andreas;Balasubramanian, Vijay;Mora, Thierry
预测环境未来状态的自适应智能体必须权衡对新观察结果的信任与对先前经验的信任。鉴于此，我们提出适应性免疫系统作为一个动态贝叶斯机制的观点，通过平衡新病原体遭遇的证据和过去的感染经验来更新其记忆库，以预测和准备未来的威胁。这一框架将观察到的早期记忆池的快速增长与中年平稳期与学习稀疏环境显著特征的便利性联系起来。我们还推导了一个与当前疫苗反应实验一致的调制内存池更新规则。我们的研究结果表明，病原环境是稀疏的，记忆库显著降低了感染成本，即使是适度的采样。预测的最优更新方案映射到通常认为的抗原受体竞争动态。
An adaptive agent predicting the future state of an environment must weigh trust in new observations against prior experiences. In this light, we propose a view of the adaptive immune system as a dynamic Bayesian machinery that updates its memory repertoire by balancing evidence from new pathogen encounters against past experience of infection to predict and prepare for future threats. This framework links the observed initial rapid increase of the memory pool early in life followed by a midlife plateau to the ease of learning salient features of sparse environments. We also derive a modulated memory pool update rule in agreement with current vaccine-response experiments. Our results suggest that pathogenic environments are sparse and that memory repertoires significantly decrease infection costs, even with moderate sampling. The predicted optimal update scheme maps onto commonly considered competitive dynamics for antigen receptors.