Abstract
Cellular signalling is a vital process in living organisms for coordinating highly diverse responses to various stimuli. Particle-based modelling excels in its ability to model complex features of cellular signalling pathways including stochasticity, spatial effects, and heterogeneity, thus improving our understanding of critical decision processes in biology. Yet, particle-based modelling is computationally prohibitive to implement. We recently developed FaST (FLAME-accelerated signalling tool), a software tool that harnesses the power of high-performance computation to reduce the computational burden of particle-based modelling. In particular, employing the unique massively parallel architecture of graphic processing units (GPUs) provided extreme speed ups of simulations by >650-fold. In this chapter, we provide a step-by-step walkthrough of how to use FaST to create GPU-accelerated simulations of a simple cellular signalling network. We further explore how the flexibility of FaST can be used to implement entirely customized simulations while still including the intrinsic speed up advantages of GPU-based parallelization.
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Fullstone, G. (2023). Rapid Particle-Based Simulations of Cellular Signalling with the FLAME-Accelerated Signalling Tool (FaST) and GPUs. In: Nguyen, L.K. (eds) Computational Modeling of Signaling Networks. Methods in Molecular Biology, vol 2634. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3008-2_9
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DOI: https://doi.org/10.1007/978-1-0716-3008-2_9
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