Immune Responses: A Stochastic Model

  • Anastasio Salazar-Bañuelos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5132)


Immune phenomena are explained from the reductionist view of the immune system as a collection of cells, molecules, and their interactions. Although this approach has produced abundant valuable information, it has added increased complexity. Artificial Immune Systems (AIS) have relied on this theoretical framework to emulate the desired characteristics of immunity. However, the complexity of the theoretical base has lead to an impasse in AIS research, suggesting that a new theoretical framework is needed. A theoretical model is presented here that explains immune responses as a ”swarm function”. The model proposes a system based on two stochastic networks: a central recursive network, wherein the proportion of agents is determined and maintained, and a peripheral network, wherein the random interactions of these agents determine if an inflammatory response will emerge from the system.


Lymphatic System Clonal Selection Transplant Rejection Complex Adaptive System Immune Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Anastasio Salazar-Bañuelos
    • 1
    • 2
  1. 1.Hotchkiss Brain Institute 
  2. 2.Department of Surgery, Division of TransplantationUniversity of CalgaryCanada

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