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$pdf_mode = 1;
$pdf_previewer = 'start evince';
@default_files = ('expose.tex');
# Make latexmk -c remove .bbl file
$bibtex_use = 2;
# Make the glossaries package happy
add_cus_dep('glo', 'gls', 0, 'run_makeglossaries');
add_cus_dep('acn', 'acr', 0, 'run_makeglossaries');
sub run_makeglossaries {
my ($base_name, $path) = fileparse($_[0]);
system("makeglossaries '$base_name'");
}
push @generated_exts, 'glo', 'gls', 'glg';
push @generated_exts, 'acn', 'acr', 'alg';
$clean_ext .= ' %R.ist %R.xdy';

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## Building `expose.pdf`
```
latexmk
```
## Cleaning up intermediate files
```
latexmk -c
```
## With preview
Uses `evince` by default (you can change this in `.latexmkrc`).
```
latexmk -pvc
```

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

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@article{berghoff2020,
abstract = {Background: Discoveries in cellular dynamics and tissue development constantly reshape our understanding of fundamental biological processes such as embryogenesis, wound-healing, and tumorigenesis. High-quality microscopy data and ever-improving understanding of single-cell effects rapidly accelerate new discoveries. Still, many computational models either describe few cells highly detailed or larger cell ensembles and tissues more coarsely. Here, we connect these two scales in a joint theoretical model. Results: We developed a highly parallel version of the cellular Potts model that can be flexibly applied and provides an agent-based model driving cellular events. The model can be modular extended to a multi-model simulation on both scales. Based on the NAStJA framework, a scaling implementation running efficiently on high-performance computing systems was realized. We demonstrate independence of bias in our approach as well as excellent scaling behavior. Conclusions: Our model scales approximately linear beyond 10,000 cores and thus enables the simulation of large-scale three-dimensional tissues only confined by available computational resources. The strict modular design allows arbitrary models to be configured flexibly and enables applications in a wide range of research questions. Cells in Silico (CiS) can be easily molded to different model assumptions and help push computational scientists to expand their simulations to a new area in tissue simulations. As an example we highlight a 10003 voxel-sized cancerous tissue simulation at sub-cellular resolution.},
author = {Marco Berghoff and Jakob Rosenbauer and Felix Hoffmann and Alexander Schug},
doi = {10.1186/s12859-020-03728-7},
issn = {14712105},
issue = {1},
journal = {BMC Bioinformatics},
keywords = {Cellular Potts model,Massively parallel,Tissue growth},
month = {10},
pmid = {33023471},
publisher = {BioMed Central Ltd},
title = {Cells in Silico-introducing a high-performance framework for large-scale tissue modeling},
volume = {21},
year = {2020},
}
@generic{frantz2010,
author = {Christian Frantz and Kathleen M. Stewart and Valerie M. Weaver},
doi = {10.1242/jcs.023820},
issn = {00219533},
issue = {24},
journal = {Journal of Cell Science},
month = {12},
pages = {4195-4200},
pmid = {21123617},
title = {The extracellular matrix at a glance},
volume = {123},
year = {2010},
}
@article{berghoff2018,
abstract = {In the last decades, simulations have been established in several fields of science and industry to study various phenomena by solving, inter alia, partial differential equations. For an efficient use of current and future high performance computing systems, with many thousands of computation ranks, high node-level performance, scalable communication, and the omission of unnecessary calculations are of high priority in the development of new solvers. The challenge of contemporary simulation applications is to bridge the gap between the scales of the various physical processes. We introduce the NAStJA framework, a block-based MPI parallel solver for arbitrary algorithms, based on stencil code or other regular grid methods. NAStJA decomposes the domain of spatially complex structures into small cuboid blocks. A special feature of NAStJA is the dynamic block adaption which modifies the calculation domain around the region where the computation currently takes place, and hence avoids unnecessary calculations. This often occurs, inter alia, in phase-field simulations. Block creation and deletion is managed autonomously within local neighborhoods. A basic load balancing mechanism allows a re-distribution of newly created blocks to the involved computing ranks. The use of a multi-hop network, to distribute information to the entire domain, avoids collective all-gather communications. Thus, we can demonstrate excellent scaling. The present scaling tests substantiate the enormous advantage of this adaptive method. For certain simulation scenarios, we can show that the calculation effort and memory consumption can be reduced to only 3.5 percent, compared to the classical full-domain reference simulation. The overhead of 70-100 percent for the dynamic adapting block creation is significantly lower than the gain. The approach is not restricted to phase-field simulations, and can be employed in other domains of computational science to exploit sparsity of computing regions.},
author = {Marco Berghoff and Ivan Kondov and Johannes Hotzer},
doi = {10.1109/TPDS.2018.2819672},
issn = {15582183},
issue = {10},
journal = {IEEE Transactions on Parallel and Distributed Systems},
keywords = {Stencil code,distributed memory,load balancing,massively parallel performance,multi-hop network,partial differential equation,phase-field method,scalable parallel algorithms},
month = {10},
pages = {2282-2296},
publisher = {IEEE Computer Society},
title = {Massively Parallel Stencil Code Solver with Autonomous Adaptive Block Distribution},
volume = {29},
year = {2018},
}
@article{mierke2021,
abstract = {Biological materials such as extracellular matrix scaffolds, cancer cells, and tissues are often assumed to respond elastically for simplicity; the viscoelastic response is quite commonly ignored. Extracellular matrix mechanics including the viscoelasticity has turned out to be a key feature of cellular behavior and the entire shape and function of healthy and diseased tissues, such as cancer. The interference of cells with their local microenvironment and the interaction among different cell types relies both on the mechanical phenotype of each involved element. However, there is still not yet clearly understood how viscoelasticity alters the functional phenotype of the tumor extracellular matrix environment. Especially the biophysical technologies are still under ongoing improvement and further development. In addition, the effect of matrix mechanics in the progression of cancer is the subject of discussion. Hence, the topic of this review is especially attractive to collect the existing endeavors to characterize the viscoelastic features of tumor extracellular matrices and to briefly highlight the present frontiers in cancer progression and escape of cancers from therapy. Finally, this review article illustrates the importance of the tumor extracellular matrix mechano-phenotype, including the phenomenon viscoelasticity in identifying, characterizing, and treating specific cancer types.},
author = {Claudia Tanja Mierke},
doi = {10.3389/FCELL.2021.785138/BIBTEX},
issn = {2296634X},
journal = {Frontiers in Cell and Developmental Biology},
keywords = {cancer,collagen,confinement,extracellular matrix,homogeneities,hydrogels,matrix mechanics,viscoelasticity},
month = {12},
pages = {3536},
publisher = {Frontiers Media S.A.},
title = {Viscoelasticity Acts as a Marker for Tumor Extracellular Matrix Characteristics},
volume = {9},
year = {2021},
}
@article{chaudhuri2020,
abstract = {Substantial research over the past two decades has established that extracellular matrix (ECM) elasticity, or stiffness, affects fundamental cellular processes, including spreading, growth, proliferation, migration, differentiation and organoid formation. Linearly elastic polyacrylamide hydrogels and polydimethylsiloxane (PDMS) elastomers coated with ECM proteins are widely used to assess the role of stiffness, and results from such experiments are often assumed to reproduce the effect of the mechanical environment experienced by cells in vivo. However, tissues and ECMs are not linearly elastic materials—they exhibit far more complex mechanical behaviours, including viscoelasticity (a time-dependent response to loading or deformation), as well as mechanical plasticity and nonlinear elasticity. Here we review the complex mechanical behaviours of tissues and ECMs, discuss the effect of ECM viscoelasticity on cells, and describe the potential use of viscoelastic biomaterials in regenerative medicine. Recent work has revealed that matrix viscoelasticity regulates these same fundamental cell processes, and can promote behaviours that are not observed with elastic hydrogels in both two- and three-dimensional culture microenvironments. These findings have provided insights into cell–matrix interactions and how these interactions differentially modulate mechano-sensitive molecular pathways in cells. Moreover, these results suggest design guidelines for the next generation of biomaterials, with the goal of matching tissue and ECM mechanics for in vitro tissue models and applications in regenerative medicine. This Review explores the role of viscoelasticity of tissues and extracellular matrices in cell–matrix interactions and mechanotransduction and the potential utility of viscoelastic biomaterials in regenerative medicine.},
author = {Ovijit Chaudhuri and Justin Cooper-White and Paul A. Janmey and David J. Mooney and Vivek B. Shenoy},
doi = {10.1038/s41586-020-2612-2},
issn = {1476-4687},
issue = {7822},
journal = {Nature 2020 584:7822},
keywords = {Humanities and Social Sciences,Science,multidisciplinary},
month = {8},
pages = {535-546},
pmid = {32848221},
publisher = {Nature Publishing Group},
title = {Effects of extracellular matrix viscoelasticity on cellular behaviour},
volume = {584},
url = {https://www.nature.com/articles/s41586-020-2612-2},
year = {2020},
}
@article{graner1992,
abstract = {We simulate the sorting of a mixture of two types of biological cells using a modified version of the large-Q Potts model with differential adhesivity. We find long-distance cell movement leading to sorting with a logarithmic increase in the length scale of homogeneous clusters. Sorted clusters then round. We find two successive phases: A rapid boundary-driven creation of a low-cohesivity cell monolayer around the aggregate, and a slower boundary-independent internal rearrangement. © 1992 The American Physical Society.},
author = {François Graner and James A. Glazier},
doi = {10.1103/PhysRevLett.69.2013},
issn = {00319007},
issue = {13},
journal = {Physical Review Letters},
month = {9},
pages = {2013},
publisher = {American Physical Society},
title = {Simulation of biological cell sorting using a two-dimensional extended Potts model},
volume = {69},
url = {https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.69.2013},
year = {1992},
}