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engineer must be resigned to partly losing control if he wishes to obtain something
useful. The computer finds the solutions by brute force trial and error, while the engi-
neer concentrates on observing and indicating the most promising directions for re-
search. Due to a limited but sharp understanding of the immune system as, first of all,
a pattern recognition and classifier system, able to separate and to distinguish the bad
from the good stimuli just on the basis of exogenous criteria, the main derived appli-
cations have been “classification”, “clustering” and “optimisation”. In previous
ICARIS I already had the opportunity to regret this state of affairs since I can not
succeed to see any new useful ideas that “engineers” did not have, even in the absence
of the least concern for immunology. As a consequence, I would rather attempt in this
paper to make a plea for following the “Alife” re-centring and for a shift from the
engineering to the “modelling” perspective, by which the “theoretical immunologist”
would turn back to be the more precious partner of the discussion.
But what theoretical immunologists, who obviously did not wait for us in their
modelling enterprise ([1, 5, 9, 10, 11, 23, 25, 27]), can expect from us and from this
advocated rebalancing. If, so far, we failed to convince the engineers of any possible
insight, how else could we convince the immunologist even more knowledgeable of
this common topic of interest? What can they expect from these new “merlin hack-
ers”, whose ambitions seem, above all, disproportionably naïve? Before answering
that key question, I would like to review how computer models of theoretical biology,
whoever develops them, can be useful in various ways. These ways will be presented
in terms of their increasing importance or by force of impact. First of all, software
models can open the door to a new style of training of some major biological princi-
ples. This is the case, for example, for Richard Dawkins who, bearing the Darwinian
good news, does so with the help of a computer simulation where creatures known as
“biomorphs” evolve on a computer screen by means of genetic algorithms. There is
nothing here that biologists are not aware of, no new biological fact apart from an
unsurpassable illustration of Darwinian principles. However, the fact that ever more
surprising and complex biomorphs appear in a deliberately simplified succession of
selection, reproduction and mutation, while based on well-known mechanisms, just
illustrates how this process works and works well. If a picture is worth a thousand
words, this is all the more true of a computer simulation, especially when it is highly
coloured and have a “sexy” appearance on the screen. Only informatics can reproduce
a near infinity of elementary mechanisms in a confined space and time and reach the
surprising although “well-known” outcome in a decent time. I would guess that the
cellular automata IMMSIM (immune simulation) model developed these last 15 years
by Celada, Seiden and Kleinstein [5, 21] among other roles, fulfils this very important
pedagogical one, to explain and illustrate the processes of “immunization” and
“memory of previous antigenic exposure”. Biologists are not really stunned by what
they see, but simply happy to “verify” it and to exploit this software support for peda-
gogical purposes.
Additionally, computer platforms and simulations can, insofar as they are suffi-
ciently comprehensible, flexible, quantifiable and universal, be used more “experi-
mentally” by the biologist, who will find in them a simplified means of simulating
and validating their qualitative understanding of biology. Cellular automata, Boolean
networks, genetic algorithms and algorithmic chemistry are excellent examples of
software that have been parameterised and used to produce and test different natural
