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A software architecture for multi-cellular system simulations on graphics processing units.

Anne Jeannin-Girardon1, Pascal Ballet, Vincent Rodin

  • 1UMR 6285, Lab-STICC, Université de Bretagne Occidentale, 29200, Brest, France, anne.jeannin@univ-brest.fr.

Acta Biotheoretica
|August 1, 2013
PubMed
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Simulating complex biological systems is challenging. This study introduces a modular software architecture optimized for graphics processing units (GPUs) to enable efficient multi-cellular system simulations.

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Systems Biology

Background:

  • Virtual environment simulations offer cost-effective alternatives to in vivo studies for biological understanding.
  • Simulating complex biological systems is difficult due to multi-scale models, numerous entities, and non-parallel architectures.
  • General-purpose computing on graphics processing units (GPUs) using frameworks like CUDA and OpenCL presents opportunities for parallelization.

Purpose of the Study:

  • To present a novel software architecture for simulating multi-cellular systems.
  • To address the challenges of simulating complex biological systems on parallel architectures.
  • To facilitate efficient biological mechanism simulations using GPU acceleration.

Main Methods:

  • Developed a modular software architecture.

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  • Implemented data structures optimized for graphics processing unit (GPU) architectures.
  • Designed the architecture to handle complex biological models and large numbers of entities.

    • Main Results:

    • The architecture allows for the implementation of various models for multi-cellular system simulation.
    • It enables efficient simulation of biological mechanisms by leveraging GPU parallelization.
    • The modular design and adapted data structures facilitate easier parallelization of biological models.

    Conclusions:

    • The presented software architecture effectively supports the simulation of complex multi-cellular systems.
    • This approach enhances the efficiency of biological mechanism simulations through GPU utilization.
    • The architecture provides a scalable and adaptable solution for computational biology research.