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Cells in Silico – introducing a high-performance framework for large-scale tissue modeling

Berghoff, Marco 1; Rosenbauer, Jakob; Hoffmann, Felix 1; Schug, Alexander
1 Scientific Computing Center (SCC), Karlsruher Institut für Technologie (KIT)


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.

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.

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. ... mehr

Verlagsausgabe §
DOI: 10.5445/IR/1000126174
Veröffentlicht am 16.11.2020
DOI: 10.1186/s12859-020-03728-7
Zitationen: 9
Web of Science
Zitationen: 7
Zitationen: 12
Cover der Publikation
Zugehörige Institution(en) am KIT Scientific Computing Center (SCC)
Universität Karlsruhe (TH) – Zentrale Einrichtungen (Zentrale Einrichtungen)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 10.2020
Sprache Englisch
Identifikator ISSN: 1471-2105
KITopen-ID: 1000126174
HGF-Programm 46.11.01 (POF III, LK 01) Computational Science and Mathematical Methods
Erschienen in BMC bioinformatics
Verlag Springer Fachmedien Wiesbaden
Band 21
Heft 1
Seiten 436
Vorab online veröffentlicht am 06.10.2020
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Web of Science
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