BACKGROUND There is an increasing need for complex computational models to perform in silico experiments as an adjunct to in vitro and in vivo experiments in immunology. We introduce Microscopic Stochastic Immune System Simulator (MiStImm), an agent-based simulation tool, that is designed to study the self-nonself discrimination of the adaptive immune system. MiStImm can simulate some components of the humoral adaptive immune response, including T cells, B cells, antibodies, danger signals, interleukins, self cells, foreign antigens, and the interactions among them. The simulation starts after conception and progresses step by step (in time) driven by random simulation events. We also have provided tools to visualize and analyze the output of the simulation program. RESULTS As the first application of MiStImm, we simulated two different immune models, and then we compared performances of them in the mean of self-nonself discrimination. The first model is a so-called conventional immune model, and the second model is based on our earlier T-cell model, called "one-signal model", which is developed to resolve three important paradoxes of immunology. Our new T-cell model postulates that a dynamic steady state coupled system is formed through low-affinity complementary TCR-MHC interactions between T cells and host cells. The new model implies that a significant fraction of the naive polyclonal T cells is recruited into the first line of defense against an infection. Simulation experiments using MiStImm have shown that the computational realization of the new model shows real patterns. For example, the new model develops immune memory and it does not develop autoimmune reaction despite the hypothesized, enhanced TCR-MHC interaction between T cells and self cells. Simulations also demonstrated that our new model gives better results to overcome a critical primary infection answering the paradox "how can a tiny fraction of human genome effectively compete with a vastly larger pool of mutating pathogen DNA?" CONCLUSION The outcomes of our in silico experiments, presented here, are supported by numerous clinical trial observations from the field of immunotherapy. We hope that our results will encourage investigations to make in vitro and in vivo experiments clarifying questions about self-nonself discrimination of the adaptive immune system. We also hope that MiStImm or some concept in it will be useful to other researchers who want to implement or compare other immune models.

中文翻译：

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MiStImm：一种基于代理的模拟工具，用于研究适应性免疫反应的自我-非自我歧视。


背景技术越来越需要复杂的计算模型来进行计算机模拟实验，作为免疫学中体外和体内实验的辅助手段。我们介绍了微观随机免疫系统模拟器（MiStImm），这是一种基于代理的模拟工具，旨在研究适应性免疫系统的自体-非自体辨别。 MiStImm可以模拟体液适应性免疫反应的一些组成部分，包括T细胞、B细胞、抗体、危险信号、白细胞介素、自身细胞、外来抗原以及它们之间的相互作用。模拟在受孕后开始，并在随机模拟事件的驱动下逐步（及时）进行。我们还提供了可视化和分析模拟程序输出的工具。结果作为MiStImm的首次应用，我们模拟了两种不同的免疫模型，然后比较了它们在自体-非自体辨别均值方面的表现。第一个模型是所谓的常规免疫模型，第二个模型是基于我们早期的T细胞模型，称为“单信号模型”，它是为了解决免疫学的三个重要悖论而开发的。我们的新 T 细胞模型假设，动态稳态耦合系统是通过 T 细胞和宿主细胞之间的低亲和力互补 TCR-MHC 相互作用形成的。新模型意味着很大一部分初始多克隆 T 细胞被招募到抵御感染的第一道防线。使用 MiStImm 进行的模拟实验表明，新模型的计算实现显示了真实的模式。例如，尽管假设 T 细胞和自身细胞之间的 TCR-MHC 相互作用增强，但新模型会产生免疫记忆，并且不会产生自身免疫反应。 模拟还表明，我们的新模型可以提供更好的结果来克服关键的原发感染，回答了“人类基因组的一小部分如何有效地与更大的突变病原体 DNA 库竞争？”的悖论。结论 这里介绍的我们的计算机实验结果得到了免疫治疗领域大量临床试验观察结果的支持。我们希望我们的结果将鼓励进行体外和体内实验的研究，以澄清有关适应性免疫系统的自我-非自我歧视的问题。我们还希望 MiStImm 或其中的某些概念对想要实施或比较其他免疫模型的其他研究人员有用。