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bio-molecular and chemical interaction models [21]. We see the most promising
potential in agent models that incorporate swarm intelligence techniques [2,22],
as this results in more accurate and realistic models, in particular when spacial
aspects play a key role in defining patterns of interaction, in understanding their
emergent properties, and in helping to shed some light on the inner workings
of complexity as, for example, displayed by the immune system. Biological sys-
tems inherently operate in a 3-dimensional world. Therefore, we have focused
our efforts on building swarm-based, 3-D simulations of biological systems which
exhibit a high degree of self-organization, triggered by relatively simple interac-
tions of a large number of agents of different types. The immune system is just
one example that allows for this bottom-up modeling approach. Other models
include the study of chemotaxis within a colony of evolving bacteria [23,24], the
simulation of transcription, translation, and specific gene regulatory processes
within the lactose operon [25,26], as well as studies of anity and cooperation
among gene regulatory agents for the λ switch in E. coli [27].
3
The Decentralized Defenses of Immunity
One aspect that makes the human immune system particularly interesting—
but more challenging from a modeling perspective—is its vastly decentralized
arrangement. Tissue and organs of the lymphatic system are widely spread
throughout the body, which provides good coverage against any infectious agents
that might enter the body at almost any location. Even the two key play-
ers responsible for specific immunity originate from different locations within
the body: T cells come from the thymus, whereas B cells are made in the
bone marrow. The lymphocytes then travel through the blood stream to sec-
ondary lymphoid organs: the lymph nodes, spleen, and tonsils. Within these
organs, B and T cells are rather tightly packed, but can still move around freely,
which makes them easier to model as agents interacting in a 3-D simulation
space.
Lymph nodes can then be considered the primary locations of interactions
among T cells, activated by antigens. T cells, in turn, activate B cells, which
evolve into memory B cells and antibody-producing plasma B cells. Both types of
activated lymphocytes will subsequently enter the lymphatic system, from where
they eventually return to the blood stream. This enables the immune system to
spread its activated agents widely through the body. Finally, the lymphocytes
return to other lymph nodes, where they can recruit further agents or trigger
subsequent responses. Hence, B and T cells as well as other immune system
agents (antibodies, cytokines, dendritic cells, antigen presenting cells, etc.) are
in a constant flow between different locations in the human body [28].
4
Simulating Decentralized Immune Responses
Our overall goal is to build a whole body simulation of the immune system
(Fig. 1). This, of course, does not only require a large amount of computing
 
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