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Related Experiment Video

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

Marco Berghoff1, Jakob Rosenbauer2, Felix Hoffmann1

  • 1Steinbuch Centre for Computing, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, 76344, Germany.

BMC Bioinformatics
|October 7, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a scalable computational model connecting single-cell details with large tissue simulations. The Cells in Silico (CiS) model efficiently simulates complex biological processes like tissue development and disease at unprecedented scales.

Keywords:
Cellular Potts modelMassively parallelTissue growth

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Area of Science:

  • Computational Biology
  • Biophysics
  • Systems Biology

Background:

  • Cellular dynamics and tissue development are crucial for understanding embryogenesis, wound healing, and tumorigenesis.
  • Current computational models often focus on either detailed single-cell behavior or coarse-grained tissue ensembles, lacking a unified approach.
  • Bridging the gap between single-cell resolution and large-scale tissue dynamics is essential for advancing biological insights.

Purpose of the Study:

  • To develop a joint theoretical model that integrates single-cell dynamics with large-scale tissue simulations.
  • To create a flexible and scalable computational framework for simulating complex biological systems.
  • To enable high-resolution simulations of tissue development and disease processes.

Main Methods:

  • Developed a highly parallelized version of the cellular Potts model (CPM) as an agent-based framework.
  • Implemented a modular, multi-model simulation capability for versatile application.
  • Utilized the NAStJA framework for efficient scaling on high-performance computing (HPC) systems.

Main Results:

  • Demonstrated a bias-independent approach with excellent scaling behavior.
  • Achieved approximately linear scaling beyond 10,000 cores, enabling large-scale 3D tissue simulations.
  • Successfully simulated a 1000^3 voxel cancerous tissue at sub-cellular resolution.

Conclusions:

  • The Cells in Silico (CiS) model facilitates simulations of large-scale 3D tissues limited only by computational resources.
  • Its modular design allows flexible configuration for diverse research questions in computational biology.
  • The model empowers computational scientists to explore new frontiers in tissue simulation, including disease modeling.