110 lines
4.7 KiB
TeX
110 lines
4.7 KiB
TeX
\documentclass[a4paper]{article}
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\usepackage[
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style=numeric,
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]{biblatex}
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\usepackage[acronym]{glossaries}
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\usepackage[utf8]{inputenc}
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\usepackage{hyperref}
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\usepackage{todonotes}
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\addbibresource{references.bib}
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\makeglossaries{}
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\newacronym{cis}{CiS}{Cells in Silico}
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\newacronym{cpu}{CPU}{Central Processing Unit}
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\newacronym{cpm}{CPM}{Cellular Potts Model}
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\newacronym{ecm}{ECM}{Extracellular Matrix}
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\newacronym{fem}{FEM}{Finite Element Method}
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\newacronym{gpu}{GPU}{Graphics Processing Unit}
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\newacronym{lbm}{LBM}{Lattice Boltzmann Model}
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\newacronym{mcs}{MCS}{Monte-Carlo Step}
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\newacronym{mpi}{MPI}{Message Passing Interface}
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\newacronym{nastja}{NAStJA}{Neoteric Autonomous Stencil code for Jolly Algorithms}
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\begin{document}
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\title{Exposé}
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\author{Paul Brinkmeier}
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\date{July 2023}
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\maketitle
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\section{Introduction}
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Computational models of cell behavior can be useful to simulate and reiterate experiments.
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In addition, they can show us how well our understanding models reality.
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One popular model is the \acrfull{cpm}, where each cell is modeled as a set of connected pixels or voxels on a two- or three-dimensional lattice.
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To simulate biological processes involving thousands of cells, large lattices are needed.
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Due to the local nature of the computations involved, the \acrshort{cpm} lends itself well to distributed programming.
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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.
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One such factor is the \acrfull{ecm} that cells exist in.
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In this work we will focus on the viscoelasticity of the collagen networks in the \acrshort{ecm}.
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We explore models of viscoelasticity that, similar to the \acrshort{cpm} itself, employ local interactions to model global effects.
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In this manner the simulations remain parallelizable.
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Additionally, we investigate the performance of our model using different implementations on both \acrshort{cpu}s and \acrshort{gpu}s.
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\section{Research}
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\subsection{The \acrfull{cpm}}
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The \acrshort{cpm}~\cite{graner1992} models cells as sets of sites on a square lattice which are usually connected.
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Each lattice site is assigned the integer cell ID of the cell it belongs to.
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The behavior of the cells is regulated by the Hamiltonian $H$, which represents the energy of a particular lattice.
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The Hamiltonian consists of at least a term for the adhesion between cells on the lattice, but can be extended by other terms such as cell volume or alignment.
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To advance the \acrshort{cpm}, a \acrfull{mcs} is performed:
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The cell ID of a random lattice site is changed to the cell ID of one of its neighbors and the difference in energy $\Delta H$ is calculated.
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The update is always accepted if the energy decreases.
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If the energy does not decrease, the update is accepted probabilistically, where greater increases are less probable.
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Repeated \acrshortpl{mcs} minimize $H$.
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From an implementor's perspective, the \acrshort{cpm} has a great advantage over other approaches to cell simulation such as agent-based modeling:
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Since updates happen on a square lattice and changes in energy can be calculated locally, it lends itself well to distributed programming.
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\acrfull{cis}~\cite{berghoff2020} is a parallel implementation of the \acrshort{cpm} based on the \acrfull{nastja} framework~\cite{berghoff2018}.
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\acrshort{nastja} offers an abstraction layer for implementing stencil codes on the \acrfull{mpi}, making it possible to leverage large-scale parallelism for \acrshort{cis}.
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\subsection{The \acrfull{ecm}}
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\todo{Sketch the ECM (2 sentences)}
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While the \acrshort{ecm} consists of a variety of components~\cite{frantz2010}, we focus on a single essential component:
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Fibrous collagen networks and their viscoelasticity.
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\acrshort{ecm} viscoelasticity has been established as an important factor in cell behavior~\cite{chaudhuri2020}.
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For example, the \acrshort{ecm} confines cells and restricts processes such as migration, spreading, growth and mitosis.
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These processes also affect the \acrshort{ecm} and can lead to permanent deformation.
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In turn, this deformation can have an influence on cell behavior, resulting in self-reinforcing effects (see for example~\cite{mierke2021}).
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In the next section we list a few current approaches for modeling the \acrshort{ecm} in the \acrshort{cpm}.
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\subsection{Models of the \acrshort{ecm} in the \acrshort{cpm}}
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% static, constant cellid
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% static with degradation
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% FEM approach
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% network approach (includes viscoelasticity)
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\subsection{Models of Viscoelastic Materials}
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% maxwell, KV, SLS, etc.
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% ELM
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\section{Contribution}
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\subsection{Method}
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\subsection{Challenges}
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\newpage
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\printglossary[type=\acronymtype]
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\printbibliography{}
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\end{document}
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