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BioDynaMo: a modular platform for high-performance agent-based simulation

Authors: Alberto Di Meglio; Marcus Kaiser; Marcus Kaiser; Marcus Kaiser; Vasileios Vavourakis; Vasileios Vavourakis; Marco Manca; +11 Authors

BioDynaMo: a modular platform for high-performance agent-based simulation

Abstract

Abstract Motivation Agent-based modeling is an indispensable tool for studying complex biological systems. However, existing simulation platforms do not always take full advantage of modern hardware and often have a field-specific software design. Results We present a novel simulation platform called BioDynaMo that alleviates both of these problems. BioDynaMo features a modular and high-performance simulation engine. We demonstrate that BioDynaMo can be used to simulate use cases in: neuroscience, oncology and epidemiology. For each use case, we validate our findings with experimental data or an analytical solution. Our performance results show that BioDynaMo performs up to three orders of magnitude faster than the state-of-the-art baselines. This improvement makes it feasible to simulate each use case with one billion agents on a single server, showcasing the potential BioDynaMo has for computational biology research. Availability and implementation BioDynaMo is an open-source project under the Apache 2.0 license and is available at www.biodynamo.org. Instructions to reproduce the results are available in the supplementary information. Supplementary information Available at https://doi.org/10.5281/zenodo.5121618.

Keywords

Original Paper, Software Design, Computational Biology, 006, Computer Simulation, Software, Algorithms

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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