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be of different sub-classes, indifferently part the APC cell. The difference this DP
points out with the previous solution presented in fig.1 should be clarified and re-
solved by immunologists. The “decorator” DP allows separating a fundamental char-
acteristic of a cell from a set of added functionalities (the decorators) which can vary
from a cell to another. It is a much flexible alternative to the use of subclassing. Func-
tionalities can be added or removed simply by adding or deleting wrappers around an
object. For instance, one cellular object could present a certain form of antigenic re-
ceptor but with no capacity to present antigens. Another cellular object could just be
effective in a specific way while a third instance of cell could simultaneously present
antigen in a given way and be effective in another. Finally, the “State” pattern is a
direct result of the state-transition diagram such as the one presented in figure 3. Each
state gives rise to one subclass and all aspects and functions specific to this state
(what the cell is doing while in this state, what are the possible transitions from this
state) are installed in this subclass. We will turn to that last DP in the next section.
Although I had many times the opportunity to defend the ideas of applying OO
principles in biological simulations [2], I had an excellent surprise in discovering a set
of recent publications by Irun Cohen (one of our immunologist guests at ICARIS this
year), David Harel (the instigator behind the integration of the state-transition dia-
gram in UML) and Sol Efroni (who is developing the software solutions) in which the
need of applying OO technologies for immune modeling was advocated with great
confidence and impressive software realizations [13, 14]. Extracted from one of this
paper [13]: “
Interestingly, most of the experimental data in biology accumulates in an
object oriented manner… Concerning the immune system, a significant amount of
data exists about its cellular components but very little is known concerning the way
these cells collaborate to function as a system… Object orientation fits the way we
think, it fits the way the experimental data are collected and it seems suitable for
coping with the challenge of understanding how biological objects collaborate to
establish a system
”. I can't agree more.
3 An Elementary Clonal Selection Model
The simplistic clonal selection and memorization model to be presented in this section
is entirely derived from the Seiden, Cellada and Kleinstein IMMSIM software [5, 21].
It is even further simplified to only concentrate on three actors: B cells, antigens and
antibodies. As such, it must not to be intended as any relevant attempt in a pure im-
munological perspective but rather as an illustration of how well UML diagrams and
Design Patterns generally apply to this type of simulation. As illustrated in the three
plots at the bottom of the next figure (above: showing the evolution of the B cell and
antibody populations responding to the pathogenic stimulation and, below: the evolu-
tion of the pathogenic population), this simulation is able to reproduce the basic
immune response to a pathogenic intrusion (the pathogen is destroyed by the com-
plementary antibodies) and the memorization of this response (the second time the
same pathogen is introduced in the system, it is eliminated much faster).
