| dc.description.abstract | There has always been a great interest in cellular behaviour. From the
molecular level, studying the chemistry of the reactions that occur in
cell, and the physical interactions between those molecules, to the
scale of the cell itself and its behaviour in response to various
phenomena. Suffice it to say, that cellular behaviour is highly
complex and, therefore, it is difficult to predict how cells will
behave or to even describe their behaviour in detail. Traditionally
cell biology has been done solely in the laboratory. That has always
yielded interesting results and science. There are some aspects of
phenomena that, due to cost, time, or other factors, need to be
studied computationally. Especially if these stimuli occur on very
short or long time scales. Therefore, a number of models have been
proposed in order to study cell behaviour.
Unfortunately, these methods can only be used in certain situations
and circumstances. These methods can, and do, produce interesting and
valid results. Yet there is not really any model available that can be
used to model more than one or two kinds of cell behaviour. For
example, methods that can show cell sorting do not necessarily show
packing. Furthermore, many of the models in the literature represent
cells as collections of points, or polygons, so cellular interactions
at interfaces cannot be studied efficiently. The goal of the work
presented here was to develop a three dimensional model of cells using
Molecular Dynamics. Cells are represented as spherical meshes of mass
points. And these mass points are placed in a force field that
emulates cellular interactions such as adhesion, repulsion, and
friction. The results of this work indicates that the model developed
can reproduce qualitatively valid cellular behaviour. And the model
can be extended to include other effects.
It must also be recognized that \gls{md} is very expensive
computationally. Especially in the case of this model as many mass
points are needed in the cellular mesh to ensure adequate spatial
resolution. Higher performance is always needed either to study larger
systems or to iterate on smaller systems more quickly. The most
obvious way to alleviate this problem is too use high performance
hardware. It will be shown that this performance is most accessible,
after some effort, with \gls{gpu} acceleration. The model developed in
this work will be implemented with \gls{gpu} acceleration. The
code generated in this way is quite fast. | en |
