This title appears in the Scientific Report :
2020
Please use the identifier:
http://dx.doi.org/10.1186/s12859-020-03728-7 in citations.
Please use the identifier: http://hdl.handle.net/2128/25965 in citations.
Cells in Silico – introducing a high-performance framework for large-scale tissue modeling
Cells in Silico – introducing a high-performance framework for large-scale tissue modeling
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 accelerat...
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Personal Name(s): | Berghoff, Marco |
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Rosenbauer, Jakob / Hoffmann, Felix / Schug, Alexander (Corresponding author) | |
Contributing Institute: |
Jülich Supercomputing Center; JSC John von Neumann - Institut für Computing; NIC |
Published in: | BMC bioinformatics, 21 (2020) 1, S. 436 |
Imprint: |
Heidelberg
Springer
2020
|
PubMed ID: |
33023471 |
DOI: |
10.1186/s12859-020-03728-7 |
Document Type: |
Journal Article |
Research Program: |
Doktorand ohne besondere Förderung Forschergruppe Schug Computational Science and Mathematical Methods |
Link: |
Get full text OpenAccess OpenAccess |
Publikationsportal JuSER |
Please use the identifier: http://hdl.handle.net/2128/25965 in citations.
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. |