Singapore Immunology Network, BMSI, Singapore.
Immunol Res. 2012 Sep;53(1-3):251-65. doi: 10.1007/s12026-012-8305-7.
Immunological studies frequently analyze individual components (e.g., signaling pathways) of immune systems in a reductionist manner. In contrast, systems immunology aims to give a synthetic understanding of how these components function together as a whole. While immunological research involves in vivo and in vitro experiments, systems immunology research can also be conducted in silico. With an increasing interest in systems-level studies spawned by high-throughput technologies, many immunologists are looking forward to insights provided by computational modeling and simulation. However, modeling and simulation research has mainly been conducted in computational fields, and therefore, little material is available or accessible to immunologists today. This survey is an attempt at bridging the gap between immunologists and systems immunology modeling and simulation. Modeling and simulation refer to building and executing an in silico replica of an immune system. Models are specified within a mathematical or algorithmic framework called formalism and then implemented using software tools. A plethora of modeling formalisms and software tools are reported in the literature for systems immunology. However, it is difficult for a new entrant to the field to know which of these would be suitable for modeling an immunological application at hand. This paper covers three aspects. First, it introduces the field of system immunology emphasizing on the modeling and simulation components. Second, it gives an overview of the principal modeling formalisms, each of which is illustrated with salient applications in immunological research. This overview of formalisms and applications is conducted not only to illustrate their power but also to serve as a reference to assist immunologists in choosing the best formalism for the problem at hand. Third, it lists major software tools, which can be used to practically implement models in these formalisms. Combined, these aspects can help immunologists to start experimenting with in silico models. Finally, future research directions are discussed. Particularly, we identify integrative frameworks to facilitate the coupling of different modeling formalisms and modeling the adaptation properties through evolution of immune systems as the next key research efforts necessary to further develop the multidisciplinary field of systems immunology.
免疫学研究经常以还原论的方式分析免疫系统的各个组成部分（例如信号通路）。相比之下，系统免疫学旨在综合理解这些组件作为一个整体是如何协同工作的。虽然免疫学研究涉及体内和体外实验，但系统免疫学研究也可以在计算机上进行。随着高通量技术引发对系统水平研究的兴趣日益浓厚，许多免疫学家期待计算建模和模拟提供的见解。然而，建模和模拟研究主要在计算领域进行，因此，目前免疫学家可用或可访问的材料很少。本调查旨在弥合免疫学家与系统免疫学建模和模拟之间的差距。建模和模拟是指构建和执行免疫系统的计算机副本。模型在称为形式主义的数学或算法框架内指定，然后使用软件工具来实现。文献中报道了大量用于系统免疫学的建模形式主义和软件工具。然而，对于该领域的新进入者来说，很难知道其中哪些适用于对当前正在进行的免疫学应用进行建模。本文涵盖三个方面。首先，它介绍了系统免疫学领域，重点介绍了建模和模拟组件。其次，它概述了主要的建模形式主义，其中每个形式主义都通过在免疫学研究中的突出应用进行了说明。这种对形式主义和应用程序的概述不仅旨在说明它们的功能，还旨在作为参考，帮助免疫学家选择最适合手头问题的形式主义。第三，它列出了主要的软件工具，可用于在这些形式主义中实际实现模型。这些方面结合起来可以帮助免疫学家开始尝试计算机模型。最后，讨论了未来的研究方向。特别是，我们确定了集成框架，以促进不同建模形式主义的耦合，并通过免疫系统的进化来模拟适应特性，这是进一步发展系统免疫学这一多学科领域所需的下一个关键研究工作。
