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

BMC Bioinformatics

Jakob Rosenbauer¹  Alexander Schug²  Felix Hoffmann³  Marco Berghoff³ 

[1] John von Neumann Institute for Computing, Jülich Supercomputer Centre, Forschungszentrum Jülich, 52428, Jülich, Germany;John von Neumann Institute for Computing, Jülich Supercomputer Centre, Forschungszentrum Jülich, 52428, Jülich, Germany;Faculty of Biology, University of Duisburg-Essen, 45141, Essen, Germany;Steinbuch Centre for Computing, Karlsruhe Institute of Technology, 76344, Eggenstein-Leopoldshafen, Germany;

关键词: Tissue growth;    Massively parallel;    Cellular Potts model;   

DOI  :  10.1186/s12859-020-03728-7

来源: Springer

【 摘 要 】

BackgroundDiscoveries 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.ResultsWe 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.ConclusionsOur 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.

【 授权许可】

CC BY