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Immune Responses: A Stochastic Model

机译:免疫反应:随机模型

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摘要

Immune phenomena are explained from the reductionist view of the immune system as a collection of cells, molecules, and their interactions. Although this approach has produced abundant valuable information, it has added increased complexity. Artificial Immune Systems (AIS) have relied on this theoretical framework to emulate the desired characteristics of immunity. However, the complexity of the theoretical base has lead to an impasse in AIS research, suggesting that a new theoretical framework is needed. A theoretical model is presented here that explains immune responses as a "swarm function". The model proposes a system based on two stochastic networks: a central recursive network, wherein the proportion of agents is determined and maintained, and a peripheral network, wherein the random interactions of these agents determine if an inflammatory response will emerge from the system.
机译:免疫现象是从免疫系统的还原剂视图中解释为细胞,分子和它们的相互作用的集合。虽然这种方法产生了丰富的宝贵信息,但它增加了复杂性的增加。人工免疫系统(AIS)依赖于这种理论框架来模拟免疫的所需特征。然而,理论基础的复杂性导致AIS研究的僵局,这表明需要一种新的理论框架。这里介绍了理论模型,其解释了免疫应答作为“群体功能”。该模型提出了一种基于两个随机网络的系统:中央递归网络,其中确定和维持的药物比例,以及外围网络,其中这些试剂的随机相互作用确定了炎症反应是否将从系统中涌现。

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