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 accelerate...

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Vydáno v:BMC bioinformatics Ročník 21; číslo 1; s. 1 - 21
Hlavní autoři: Berghoff, Marco, Rosenbauer, Jakob, Hoffmann, Felix, Schug, Alexander
Médium: Journal Article
Jazyk:angličtina
Vydáno: London BioMed Central 06.10.2020
BioMed Central Ltd
Springer Nature B.V
BMC
Témata:
Algorithms
Analysis
Analysis and modelling of complex systems
Bioinformatics
Biological activity
Biomedical and Life Sciences
Cell adhesion & migration
Cell culture
Cellular Potts model
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Computer applications
Embryogenesis
Embryonic growth stage
Energy
High performance computing
Life Sciences
Massively parallel
Mathematical models
Microarrays
Modular design
Multiprocessing
Partial differential equations
Propagation
Simulation
Software
Tissue growth
Tissues
Tumorigenesis
Wound healing
Massively parallel
Tissue growth
Cellular Potts model
ISSN:1471-2105, 1471-2105
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Popis
Shrnutí: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 1000 3 voxel-sized cancerous tissue simulation at sub-cellular resolution.
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ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-020-03728-7