Biomedical Engineering Reference
In-Depth Information
present parallel information processing for the
coordination of tissue functioning in each and
every cell that composes this tissue. All these
characteristics are very interesting from a com-
putational viewpoint.
Another point of view is to study the biologi-
cal model as a design model. At present human
designs use a top-down view, this methodology has
served well. However thinking on the construction
of software and hardware systems with a high
number of elements, the design crisis is served.
Verify formally the systems when interactions and
possible states grows, becomes near impossible
due the combinatorial explosion of configuration
using a traditional way. Living systems suggest
interesting solutions for these problems, such
as that the information defining the organism is
contained within each part. Consequently, if the
designers want to increase the complexity of the
systems, one way is to study the biological model
trying to mimic its solutions.
This paper presents the development of a model
that tries to emulate the biological cells and to
take advantage of some of their characteristics by
trying to adapt them to artificial cells. The model
is based on a set of techniques known as
Artiicial
Embryogeny
(Stanley & Miikkulainen, 2003) or
Computational Embryology
(Kumar, 2004).
do a top-down approach to the problem. On the
other hand could be found the chemical models
based on the Turing's ideas (Turing, 1952) which
do a down-top approach.
On the last one, the starting point of this field
could be found in the modelling of gene regula-
tory networks, performed by Kauffmann in 1969
(Kauffman, 1969). After that work, several devel-
ops were carried out on subjects such as the gen-
eration of complex behaviour by the differential
expression of certain genes. This behaviour causes
a cascade influence on the expressions of others
genes (Mjolsness, Sharp & Reinitz, 1995).
The work performed by the scientific com-
munity can be divided into two main branches.
The more theoretical branch uses the emulation
of cell capabilities such as cellular differentia-
tion and metabolism (Kaneko 2006; Kitano et
al., 2005) to create a model that functions as a
natural cell. The purpose of this work is to do an
in-depth study of the biological model.
The more practical branch mainly focuses
on the development of a cell inspired-model that
might be applicable to other problems (Bentley,
2002; Kumar & Bentley (eds), 2003; Stanley &
Miikkulainen, 2003). According to this model,
every cell would not only have genetic informa-
tion that encodes the general performance of
the system, it would also act as a processor that
communicates with the other cells. This model is
mainly applied to the solution of simple 3D spatial
problems, robot control, generative encoding for
the construction of artificial organisms in simu-
lated physical environments and real robots, or
to the development of the evolutionary design of
hardware and circuits (Endo, Maeno & Kitano,
2003; Tufte & Haddow, 2005).
The most relevant models are the following:
the Kumar and Bentley model (Kumar & Bent-
ley (eds), 2003), which uses the theory of fractal
proteins (Bentley, 2002) for the calculation of
protein concentration; the Eggenberger model
(Eggenberger, 1996), which uses the concepts of
cellular differentiation and cellular movement
bACkGROUND
The Evoluationary Computation (EC) field has
given rise to a set of models that are grouped
under the name of Artifial Embryogeny (AE), first
introduced by Stanley and Mikkulainnen (Stanley
& Miikkulainen, 2003). This group refers to all the
models that try to apply certain characteristics of
biological embryonic cells to computer problem
solving, i.c. self-organisation, failure tolerance,
and parallel information processing.
The work on AE has two points of view. On the
one hand could be found the grammatical models
based on L-systems (Lindenmayer, 1968) which
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