Parallelisation strategies for agent based simulation of immune systems

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.