Gell: A GPU-powered 3D hybrid simulator for large-scale multicellular system

Abstract

As a powerful but computationally intensive method, hybrid computational models study the dynamics of multicellular systems by evolving discrete cells in reacting and diffusing extracellular microenvironments. As the scale and complexity of studied biological systems continuously increase, the exploding computational cost starts to limit large-scale cell-based simulations. To facilitate the large-scale hybrid computational simulation and make it feasible on easily accessible computational devices, we develop a fast and memory-efficient open-source GPU-based hybrid computational modeling platform Gell (GPU Cell), for large-scale system modeling. We fully parallelize the simulations on GPU for high computational efficiency and propose a novel voxel sorting method to further accelerate the modeling of massive cell-cell mechanical interaction with negligible additional memory footprint. As a result, Gell efficiently handles simulations involving tens of millions of cells on a personal computer. We compare the performance of Gell with a state-of-the-art paralleled CPU-based simulator on a hanging droplet spheroid growth task and further demonstrate Gell with a ductal carcinoma in situ (DCIS) simulation. Gell affords ~150X acceleration over the paralleled CPU method with one-tenth of the memory requirement. Author SummaryNumerical cell simulations provide indispensable insight into the cell-to-tumor tissue transition and help reduce biological experimental variables. However, the availability and practicality of large-scale cell simulation tools have been limited by high computational cost, slow performance, or proprietary. Recent developments in open-source simulation codes and GPU implementation have partially addressed the challenge. We further optimized the cell simulation platform for GPU implementation in this work. As a result, benchmark cell simulation experiments can be performed efficiently on a personal computer with a modern GPU. We made the platform open source to encourage community adoption and collective development.