BioDynaMo: an agent-based simulation platform for scalable computational biology research
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Computer simulation is an indispensable tool for studying complex biological systems. In particular, agent-based modeling is an attractive method to describe biophysical dynamics. However, two barriers limit faster progress. First, simulators do not always take full advantage of parallel and heterogeneous hardware. Second, many agent-based simulators are written with a specific research problem in mind and lack a flexible software design. Consequently, researchers have to spend an unnecessarily long time implementing their simulation and have to compromise either on model resolution or system size. We present a novel simulation platform called BioDynaMo that alleviates both of these problems researchers face in computer simulation of complex biological systems. BioDynaMo features a general-purpose and high-performance simulation engine. The engine simulates cellular elements, their interactions within a 3D physical environment, and their cell-internal genetic dynamics. We demonstrate BioDynaMo's wide range of application with three example use cases: soma clustering, neural development, and tumor spheroid growth. We validate our results with experimental data, and evaluate the performance of the simulation engine. We compare BioDynaMo's performance with a state-of-the-art baseline, and analyze its scalability. We observe a speedup of 20–124× over the state-of-the-art baseline using one CPU core and a parallel speedup between 67× and 76× using 72 physical CPU cores with hyperthreading enabled. Combining these two results, we conclude that, on our test system, BioDynaMo is at least three orders of magnitude faster than the state-of-the-art serial baseline. These improvements make it feasible to simulate neural development with 1.24 billion agents on a single server with 1TB memory, and 12 million agents on a laptop with 16GB memory.
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