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Paul Brinkmeier 2023-07-12 13:58:06 +02:00
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\section{Introduction}
Computational models of cell behavior can be useful to simulate and reproduce experiments.
In addition, they can show us how well our understanding models reality.
In addition, they show us how well our understanding models reality.
A popular approach is the \acrfull{cpm}, where each cell is modeled as a set of connected pixels or voxels on a two- or three-dimensional lattice.
To simulate biological processes involving thousands of cells, large lattices are needed.
Due to the local nature of the computations involved, the \acrshort{cpm} lends itself well to distributed programming.
We will base our work on \acrfull{cis}, which is an implementation of the \acrshort{cpm} based on the \acrfull{nastja} framework.
I will base my work on \acrfull{cis}, which is a distributed implementation of the \acrshort{cpm} based on the \acrfull{nastja} framework.
In order to be true to \emph{in vivo}/\emph{in vitro} findings, such \emph{in silico} models must take into account a multitude of factors influencing cell behavior.
One such factor is the interaction with the \acrfull{ecm}, the structural scaffold which cells are embedded in.
In this work we will focus on the viscoelasticity of the collagen networks in the \acrshort{ecm}.
We will explore models of viscoelasticity that, similar to the \acrshort{cpm} itself, employ local interactions to model global effects.
In this work I will focus on the viscoelasticity of the collagen networks in the \acrshort{ecm}.
I will explore models of viscoelasticity that, similar to the \acrshort{cpm} itself, employ local interactions to model global effects.
This is required to fit the implementation into the \acrshort{nastja} framework so that it can be seamlessly integrated with \acrshort{cis}.
Additionally, we will investigate the performance of our model using different implementations on both \acrshort{cpu}s and \acrshort{gpu}s.
Additionally, I will investigate the performance of my model using different implementations on both \acrshort{cpu}s and \acrshort{gpu}s.
\section{Research}
@ -76,7 +76,7 @@ After the stencil is computed for each block, the \emph{halo}, i.e.\ the boundar
The \acrshort{ecm} is the part of a tissue that surrounds the cells.
It provides their physical and biochemical environment, thereby influencing cell behavior~\cite{frantz2010}.
While the \acrshort{ecm} consists of a variety of components, we focus on a single essential component:
While the \acrshort{ecm} consists of a variety of components, I focus on a single essential component:
Fibrous collagen networks and their viscoelasticity.
\acrshort{ecm} viscoelasticity has been established as an important factor in cell behavior~\cite{chaudhuri2020}.
For example, the \acrshort{ecm} confines cells and restricts processes such as migration, spreading, growth and mitosis.
@ -85,8 +85,8 @@ In turn, this deformation can have an influence on cell behavior, resulting in a
\subsection{Models of the \acrshort{ecm} in the \acrshort{cpm}}
In this section we list current approaches to modeling the \acrshort{ecm} in \acrshort{cpm} simulations.
We focus on approaches that explicitly model the plasticity of \acrshort{ecm} collagens.
In this section I list current approaches to modeling the \acrshort{ecm} in \acrshort{cpm} simulations.
I present approaches that explicitly model the plasticity of \acrshort{ecm} collagens.
\paragraph{Static Cell}
@ -125,7 +125,7 @@ The most common configurations for describing viscoelastic solids are
\end{itemize}
Depending on the specific viscoelastic characteristics that are to be predicted, a particular model can be chosen.
In order to align the viscoelastic \acrshort{ecm} model with the \acrshort{cpm}, we consider approaches that model viscoelastic materials on square lattices.
In order to align the viscoelastic \acrshort{ecm} model with the \acrshort{cpm}, I consider approaches that model viscoelastic materials on square lattices.
In particular, the following approaches might be relevant.
\paragraph{Discrete Particle Method}
@ -146,44 +146,44 @@ Perhaps for this particular use case, a \acrshort{lbm} could be configured to mo
\section{Contribution}
In this work we will explore lattice-based viscoelastic simulations of the \acrshort{ecm} in the \acrshort{cpm}.
In this work I will explore lattice-based viscoelastic simulations of the \acrshort{ecm} in the \acrshort{cpm}.
\subsection{Method}
In order to model cell-matrix interactions, we will develop a method that allows cells to influence the \acrshort{ecm} simulation.
To model matrix-cell interactions, we will expand the Hamiltonian of the \acrshort{cpm} to include a term dependent on the local configuration of the \acrshort{ecm}.
This should make it possible for our model to simulate the strong coupling of cells and \acrshort{ecm}.
In order to model cell-matrix interactions, I will develop a method that allows cells to influence the \acrshort{ecm} simulation.
To model matrix-cell interactions, I will expand the Hamiltonian of the \acrshort{cpm} to include a term dependent on the local configuration of the \acrshort{ecm}.
This should make it possible for my model to simulate the strong coupling of cells and \acrshort{ecm}.
I will explore which of the models listed above is the most promising and compare to them to existing approaches.
For the \acrshort{cpm} we use the distributed implementation \acrshort{cis}.
\acrshort{cis} is based on the \acrshort{nastja} framework implemented using \acrshort{mpi}, which we will use to develop our model of the \acrshort{ecm}.
For the \acrshort{cpm} I will use the distributed implementation \acrshort{cis}.
\acrshort{cis} is based on the \acrshort{nastja} framework implemented using \acrshort{mpi}, which I will use to implement my model of the \acrshort{ecm}.
In order to reduce simulation times we will employ implementation techniques such as \acrshort{gpu} programming.
As the implementation performance of our model will depend on several interconnected factors such as cache efficiency, network latency and \acrshort{gpu} communication cost we will need to benchmark it on a common test setup.
In order to reduce simulation times I will employ implementation techniques such as \acrshort{gpu} programming.
As the implementation performance of my model will depend on several interconnected factors such as cache efficiency, network characteristics and \acrshort{gpu} communication cost I will need to benchmark multiple implementations on a common test setup.
\subsection{Challenges}
Our preliminary experiments have produced some questions and likely challenges that our work will need to address.
My preliminary experiments have produced some questions and likely challenges that my work will need to address.
\paragraph{Spatial Scale}
While we could simply use the same lattice for the \acrshort{ecm} model as for the \acrshort{cpm}, it is not clear that this will deliver the best results.
While I could simply use the same lattice for the \acrshort{ecm} model as for the \acrshort{cpm}, it is not clear that this will deliver the best results.
It could be useful to use a scaled lattice, e.g.\ where the lattice spacing of the \acrshort{ecm} model is twice as long.
\paragraph{Temporal Scale}
Compared to cells, the waves in a viscoelastic material move quickly.
It is likely that our model of the \acrshort{ecm} will have to go through multiple time steps between the \acrshortpl{mcs} of the \acrshort{cpm}.
It is likely that my model of the \acrshort{ecm} will have to go through multiple time steps between the \acrshortpl{mcs} of the \acrshort{cpm}.
In the context of \acrshort{nastja}, this means an increased number of halo exchanges between ranks per \acrshort{mcs}.
In order to reduce the number of halo exchanges, we could increase the width of the halo which allows the \acrshort{ecm} simulation to run for multiple time steps between halo exchanges.
In order to reduce the number of halo exchanges, one could increase the width of the halo which allows the \acrshort{ecm} simulation to run for multiple time steps between halo exchanges.
As this approach necessarily leads to diminishing returns as the halo data gets bigger, an efficient configuration needs to be investigated.
\paragraph{Implementation Performance}
As \acrshort{cis} is designed to large and therefore compute-heavy simulations, it is worthwhile to measure the and optimize the compute needed by our implementation.
As \acrshort{cis} is designed to large and therefore compute-heavy simulations, it is worthwhile to measure the and optimize the computational resources needed by my implementation.
Since the discrete particle method is a dense approach, it should be possible to leverage common parallelization techniques such as vectorization and \acrshort{gpu} programming to improve performance.
In particular, it might prove useful to run the \acrshort{cpm} on \acrshortpl{cpu} and the \acrshort{ecm} model of \acrshortpl{gpu}.
We will experiment with these techniques and evaluate the possible improvements.
I will experiment with these techniques and evaluate the possible improvements.
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