Information Technology Reference
In-Depth Information
life sciences community, who usually do not have any programming background
or any aspiration nor time to learn how to use a modeling environment. Making
simulation tools (almost) seamless to use for researchers and introducing such
tools into classrooms in biology and medicine greatly increases the understanding
of how useful computer-based simulations can be in order to explore and facili-
tate answers to research questions and, as a side effect, gain an appreciation of
emergent effects resulting from orchestrated interactions of 'bio-agents'.
In this paper we present our latest version of a swarm-based simulation envi-
ronment, which, we think, fulfills these criteria, and implements an interactive
virtual laboratory for the exploration of the interplay of human immune system
agents and their resulting overall response patterns. The rest of the paper is
organized as follows. In Section 2 we give an overview of related simulation and
modeling approaches regarding immune system processes. A biological perspec-
tive of the decentralized immune defenses is presented in Section 3. The key
design aspects and main results of our
IMMS:VIGO::3D
simulation system are
described in Section 4, where we also discuss simulation experiments for clonal
selection, primary and secondary responses to viral infection, as well as reac-
tions to bacterial infection. Finally, in Section 5, we conclude the paper with a
summary of our work and suggestions for the necessary next steps towards an
encompassing immune system simulation environment.
2
Related and Previous Work
The immune system (IS) has been studied from a modeling perspective for a long
time. Early, more general approaches looked at the immune system in the context
of adaptive and learning systems [3,4], with some connections to early artificial
intelligence approaches [5]. Purely mathematical models, mainly based on dif-
ferential equations, try to capture the overall behaviour patterns and changes of
concentrations during immune system responses [6,7,8,9,10]. A more recent alge-
braic model of B and T cell interactions provides a formal basis to describe bind-
ing and mutual recognition, and can serve as a mathematical basis for further
computational models, similar to formalisms for artificial neural networks [11].
Agent-based computational approaches, in the form of cellular automata, in-
troduced spacial aspects to immune system simulations [12]. In the context
of clonal selection, the influence of different anities among interacting func-
tional units, which leads to self-organizing properties, was recognized and studied
through computational models [13,14]. These models have been expanded into
larger and more general simulation environments for various aspects of the hu-
man immune system [15,16]. There is also a large number of modeling approaches
within specific areas in the context of immune system-related processes, such as
for HIV/AIDS [17]. An excellent overview of these modeling strategies can be
found in [18].
Most current methods consider immune response processes as emergent phe-
nomena in complex adaptive system [10], where agent-based models play a more
and more dominant role [19,20], even in the broader application domain of
