Characterizing emergent properties of immunological systems with multi-cellular rule-based computational modeling

被引：66
作者：
Chavali, Arvind K. [1 ]
Gianchandani, Erwin P. [1 ]
Tung, Kenneth S. [2 ]
Lawrence, Michael B. [1 ]
Peirce, Shayn M. [1 ]
Papin, Jason A. [1 ]
机构：
[1] Univ Virginia, Dept Biomed Engn, Charlottesville, VA 22908 USA
[2] Univ Virginia, Dept Pathol, Charlottesville, VA 22908 USA
来源：
TRENDS IN IMMUNOLOGY | 2008年 / 29卷 / 12期
基金：
美国国家科学基金会;
关键词：
D O I：
10.1016/j.it.2008.08.006
中图分类号：
R392 [医学免疫学]; Q939.91 [免疫学];
学科分类号：
100102 ;
摘要：
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 overtime 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.
引用
收藏
页码：589 / 599
页数：11
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