Chavali Arvind K, Gianchandani Erwin P, Tung Kenneth S, Lawrence Michael B, Peirce Shayn M, Papin Jason A
Department of Biomedical Engineering, University of Virginia, Health System, Charlottesville, VA 22908, USA.
Trends Immunol. 2008 Dec;29(12):589-99. doi: 10.1016/j.it.2008.08.006. Epub 2008 Oct 27.
The immune system is comprised of numerous components that interact with one another to give rise to phenotypic behaviors that are sometimes unexpected. Agent-based modeling (ABM) and cellular automata (CA) belong to a class of discrete mathematical approaches in which autonomous entities detect local information and act over time according to logical rules. The power of this approach lies in the emergence of behavior that arises from interactions between agents, which would otherwise be impossible to know a priori. Recent work exploring the immune system with ABM and CA has revealed novel insights into immunological processes. Here, we summarize these applications to immunology and, particularly, how ABM can help formulate hypotheses that might drive further experimental investigations of disease mechanisms.
免疫系统由众多相互作用的成分组成，这些成分会产生一些有时出人意料的表型行为。基于主体的建模（ABM）和细胞自动机（CA）属于一类离散数学方法，其中自主实体检测局部信息，并根据逻辑规则随时间推移采取行动。这种方法的强大之处在于主体之间相互作用所产生的行为，否则这些行为是无法先验得知的。最近利用ABM和CA对免疫系统进行的研究揭示了免疫过程的新见解。在这里，我们总结这些在免疫学中的应用，特别是ABM如何有助于提出假设，从而推动对疾病机制的进一步实验研究。
