Biomedical Engineering Reference
In-Depth Information
Irradiation
facility
Experiments
DNA damages
Systems biology
simulation tool
Geant4/
DNA
Organism
model
DNA damages
probabilities
Organisms
models
database
Modeling/simulation
Figure 15.3
Proposed radiobiology framework.
as possible morphogenesis principles, we will introduce bioinspired meta-
heuristics (such as genetic algorithm, swarm intelligence, simulated annealing)
to mimic the biological selection of active genes. They are used to constrain/
drive cellular automata to follow a particular development cycle (which can
be the normal one or a pathological one). One interesting point with this
approach is that it will enable us to build patient dedicated organs using data
from imaging devices as some of the quantitative values (such as volume,
surface, number of cells) needed by the model are patient dedicated. The great
challenge will be to include the good modeling scales with the right sharpness
to learn about the impact of radiation exposure. Scales such as genotype and
phenotype seem to be necessary to understand and to study the impact of
radiation from a quantitative point of view (which is rather common) and from
a qualitative point of view (which is less common).
Thus, an iteration of the framework we propose (Fig. 15.3) is a sequential
process with three steps. First, the organism model is built from cells to the
full organism. Next the organism model is irradiated in Geant4-DNA to
produce probabilities for DNA damages. Finally, some simulation scenarios
are designed according to those probabilities and the organism is simulated
with those input parameters.
15.7
CONCLUSION
Through this chapter, we have shared our experience with using grids as an
infrastructure to investigate life sciences in an innovative way. After eight
years of working together with biologists, chemists, computer scientists, physi-
cians, physicists, and many others, we are deeply convinced that grids provide
a unique framework to build multidisciplinary collaborations in the fi eld of
simulation and modeling because they are about sharing resources and there-
fore ideas, We have provided a few examples of successful scientifi c initiatives
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