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Design Patterns, many recipes, well tested and validated by many programmers be-
fore, can easily be transposed and adapted to the simulation at hand. Additionally,
large space of freedom is provided for effortlessly testing different hypothesis without
compromising the rest of the code. As a matter of fact, OO technologies have invaded
the software world since programmers are more and more engaged in the develop-
ment of complex software, their complexity being due to the presence of many actors
interacting in subtle ways. Think of “Amazon”, “traffic regulation”, “meteorology”.
Without any doubt, the immune system exhibits this kind of complexity.
Although programmers will certainly benefit from OO technologies when conceiv-
ing and writing the code of immune system actors and interactions, biologists, even
those, still in majority, reluctant to software simulation, might also see some interest
in the formalism underlying UML diagrams. The use of the diagrams goes not with-
out a deep clarification and disambiguation of the reality to model. To draw a class
diagram, a biologist will need to clarify whether a “subclass” link between a type of
cell and another type is really a subclass in the OO sense. For that he will need to
clearly state what is definitely common between these two types and why does he
really perceive the second as a subclass of the first. The “prototype” and “flyweight”
patterns will force him to a deeper understanding of the cloning process. The “State”
patterns will force him to a better explicitation of what is distinct between cells when
they find themselves in distinct states. The “Bridge” patterns will help him to relate or
not the many taxonomies which fulfill immunology topics and how to relate them.
The leaders of the software world (I am referring here to the “Object Management
Group”) insist more and more in assimilating programming with modeling i.e. in
relaxing the coding part to concentrate more on the modeling part. In doing so, they
warmly advocate the increased use of UML diagrams and Design Patterns. On ac-
count of the extraordinary software developments that the adoption of these new
strategies has allowed, I don't see any reason why immunologists interesting in com-
puter simulation should remain immune to this software propaganda and campaign.
ICARIS conferences might be ideal opportunities for these immunologist hackers to
meet and to confront their diagrams and patterns once a year.
References
1.
Antia, R. and M. Lipsitch. 1998 Mathematical models of parasite responses to host im-
mune defenses. Parasitology 115 :pp. 155-167
2.
Bersini (1999) Design Patterns for an OO Chemistry - In proceedings of the 1999 Euro-
pean Conference on Artificial Life - MIT Press.
3.
Bersini, H. 2002. Self-Assertion vs Self-Recognition: A Tribute to Francisco Varela. Proc.
of the first ICARIS Conference - pp. 107-112.
4.
Brent, Roger and Jehoshua Bruck,. 2006. Can Computers Help to Explain Biology? Na-
ture (03/23/06) Vol. 440, No. 7083, P. 416
5.
Celada, F. and P.E. Seiden. 1992. A computer model of cellular interactions in the immune
system. Immunol. Today 13 - pp.56-62
6.
Chao, D.L., Davenport, M.P., Forrest, S. and A.S. Perelson. 2003. Stochastic stage-
structured modelling of the adaptive immune system. In Proceedings of the IEEE Com-
puter Society Bioinformatics Conference (CSB 2003) - pp. 124-131
