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Transcript
An Agent-Based Model Demonstrates that the Immune System Behaves Like a Complex System and a Scale-Free Network Virginia A. Folcika and Charles G. Orosza,† a Department of Surgery/Transplant, The Ohio State University College of Medicine, 350 Means Hall, 1654 Upham Dr., Columbus, Ohio, 43210. † Charles G. Orosz was an exceptional mentor and an enthusiastic proponent of the study of the immune system as a complex system. He conceived of the Basic Immune Simulator and contributed significantly to this work before his passing on 8-7-05. Abstract We have used Repast to create an agent-based computer simulation to study the complex network behavior of the immune system in a way that is not possible using a living system. It includes agent and signal representations of all of the basic cells and cytokines/chemokines (respectively) of the immune system. The agents interact in three cellular automatons representing a functional bodily (parenchymal) tissue, secondary lymphoid tissue and the lymphatic and/or humoral circulation. The behavior of the agents in the simulation emulates both normal and pathological immune system behavior in a generic viral infection scenario. By designating the agents as nodes and interactions between the agents as links, the simulation was used to examine the network properties of the simulated immune response to a virus. The agents representing cells were programmed to count their meaningful interactions with other agents, to characterize the connectivity of the immune system network. The connectivity data generated during the simulated immune response demonstrated behavior like that of a scale-free network for the system as a whole. Comparison of the connectivity of the agent types identified agents representing dendritic cells as hubs in the network. The connectivity of each agent type also correlated with their contribution to the success of the immune response in eliminating virally infected agents representing parenchymal cells. There are many complex pathological conditions involving the immune system that lack effective treatment strategies, such as immune-mediated organ transplant rejection, hypersensitivity reactions and autoimmunity. The information that our agent-based model generates can be used to devise more effective strategies for specifically controlling the immune response in a variety of pathological situations.