Download An Agent-Based Model Demonstrates that the

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.