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Parallelisation strategies for agent based simulation of immune systems.

Mozhgan Kabiri Chimeh1, Peter Heywood2, Marzio Pennisi3

  • 1Department of Computer Science, University of Sheffield, Sheffield, UK. m.kabiri-chimeh@sheffield.ac.uk.

BMC Bioinformatics
|December 12, 2019
PubMed
Summary
This summary is machine-generated.

Agent Based Modeling (ABM) simulates immune responses. This study optimizes parallel computing for cell interactions in ABM, enhancing biological system analysis on parallel hardware.

Keywords:
Agent based modelingCellular modellingComputational modellingFLAME GPUGPGPUHigh-performance computingParallel simulation

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Area of Science:

  • Computational Biology
  • Immunology
  • Systems Biology

Background:

  • Agent Based Modeling (ABM) is increasingly used to study complex biological systems, particularly immune response behavior.
  • Simulating large-scale multi-agent systems in biology demands significant computational resources, necessitating parallel computing approaches.
  • ABM provides a bottom-up approach for analyzing and interpreting biological data to address complex problems.

Purpose of the Study:

  • To explore and evaluate different parallelization strategies for simulating pairwise cell interactions within Agent Based Models.
  • To analyze the performance and algorithmic design choices for cell interactions in both continuous and discrete spatial environments within a parallel computing framework.

Main Methods:

  • Investigated parallel computing approaches for optimizing Agent Based Models (ABM).
  • Focused on parallelizing the simulation of pairwise cell interactions, a critical component of immune system models.
  • Evaluated performance and design choices for cell interactions in continuous and discrete spaces within a parallel environment.

Main Results:

  • Demonstrated the effectiveness of parallelization techniques for simulating cell-to-cell interactions in biological systems.
  • Provided a comparative analysis of different parallel implementation strategies for agent-based simulations.
  • Quantified the performance gains achievable through parallel computing for complex biological models.

Conclusions:

  • The presented parallelization methods are applicable to a wide range of biological systems featuring cell-to-cell interactions.
  • Each implementation offers distinct advantages and disadvantages, informing the development of complete immune system models on parallel hardware.
  • These optimized approaches facilitate the creation of more comprehensive and computationally efficient simulations of biological processes.