The Immune System Computes the State of the Body: Crowd Wisdom, Machine Learning, and Immune Cell Reference Repertoires Help Manage Inflammation
The Immune System Computes the State of the Body: Crowd Wisdom, Machine Learning, and Immune Cell Reference Repertoires Help Manage Inflammation
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DOI：
10.3389/fimmu.2019.00010
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发表时间：
2019-01-22
影响因子：
7.3
通讯作者：
Efroni, Sol
中科院分区：
文献类型：
--
作者：
Cohen, Irun R.;Efroni, Sol
在这里，我们概述了哺乳动物免疫系统的更新和扩展的经典克隆选择范例。而不是专注于严格的自我非自我歧视，我们提出，该系统协调可变的炎症反应，维持身体及其与微生物组的共生关系，同时消除致病性感染因子和肿瘤的威胁。本文提出了四点：1）免疫系统通过部署单个淋巴细胞作为细胞计算机器来分类身体的健康和病理状态-包括自身和外来元素;免疫细胞将来自身体的输入信号转化为特异性免疫反应的输出。2）不是独立的克隆反应，一群单独激活的免疫系统细胞在淋巴器官中共同反应，通过一种自我调节的方式做出集体决定。组织群体智慧或群体智慧。3）免疫细胞群的集体选择，如鱼群，通过相对少量的个体调节器对变化的条件做出反应来改变-这种集体炎症反应是动态响应的。4）自身反应性自身抗体和T细胞受体（TCR）由健康个体共享的剧目在基于经验的监督机器学习的生物版本中起作用。免疫系统的决策是通过淋巴细胞发育期间遇到的自身抗原训练集的形成经验来启动的;这些最初训练的T细胞和B细胞库形成了健康概况，然后指导对稍后遇到的抗原测试集的免疫反应。这种基于经验的机器学习策略类似于监督机器学习算法。我们提出实验来测试这些想法。免疫系统的这一概述对监测健康和治疗自身免疫性疾病、癌症和同种异体移植反应具有临床意义。
Here, we outline an overview of the mammalian immune system that updates and extends the classical clonal selection paradigm. Rather than focusing on strict self-not-self discrimination, we propose that the system orchestrates variable inflammatory responses that maintain the body and its symbiosis with the microbiome while eliminating the threat from pathogenic infectious agents and from tumors. The paper makes four points:1) The immune system classifies healthy and pathologic states of the body-including both self and foreign elements-by deploying individual lymphocytes as cellular computing machines; immune cells transform input signals from the body into an output of specific immune reactions.2) Rather than independent clonal responses, groups of individually activated immune-system cells co-react in lymphoid organs to make collective decisions through a type of self-organizing swarm intelligence or crowd wisdom.3) Collective choices by swarms of immune cells, like those of schools of fish, are modified by relatively small numbers of individual regulators responding to shifting conditions-such collective inflammatory responses are dynamically responsive.4) Self-reactive autoantibody and T-cell receptor (TCR) repertoires shared by healthy individuals function in a biological version of experience-based supervised machine learning. Immune system decisions are primed by formative experience with training sets of self-antigens encountered during lymphocyte development; these initially trained T cell and B cell repertoires form a Wellness Profile that then guides immune responses to test sets of antigens encountered later. This experience-based machine learning strategy is analogous to that deployed by supervised machine-learning algorithms.We propose experiments to test these ideas. This overview of the immune system bears clinical implications for monitoring wellness and for treating autoimmune disease, cancer, and allograft reactions.