A major barrier to the practical utilization of large, complex models of biochemical systems is the lack of open-source computational tools to evaluate model behaviors over high-dimensional parameter spaces. This is due to the high computational expense of performing thousands to millions of model simulations required for statistical analysis. To address this need, we have implemented a user-friendly interface between cupSODA, a GPU-powered kinetic simulator, and PySB, a Python-based modeling and simulation framework. For three example models of varying size, we show that for large numbers of simulations PySB/cupSODA achieves order-of-magnitude speedups relative to a CPU-based ordinary differential equation integrator

Harris, L., Nobile, M., Pino, J., Lubbock, A., Besozzi, D., Mauri, G., et al. (2017). GPU-powered model analysis with PySB/cupSODA. BIOINFORMATICS, 33(21), 3492-3494 [10.1093/bioinformatics/btx420].

GPU-powered model analysis with PySB/cupSODA

Nobile, MS;Besozzi, D;Mauri, G;Cazzaniga, P
;
2017

Abstract

A major barrier to the practical utilization of large, complex models of biochemical systems is the lack of open-source computational tools to evaluate model behaviors over high-dimensional parameter spaces. This is due to the high computational expense of performing thousands to millions of model simulations required for statistical analysis. To address this need, we have implemented a user-friendly interface between cupSODA, a GPU-powered kinetic simulator, and PySB, a Python-based modeling and simulation framework. For three example models of varying size, we show that for large numbers of simulations PySB/cupSODA achieves order-of-magnitude speedups relative to a CPU-based ordinary differential equation integrator
Articolo in rivista - Articolo scientifico
GPU; simulation; SYSTEMS BIOLOGY; BIOCHEMICAL SYSTEMS; BIONETGEN; PYTHON
English
28-giu-2017
2017
33
21
3492
3494
btx420
none
Harris, L., Nobile, M., Pino, J., Lubbock, A., Besozzi, D., Mauri, G., et al. (2017). GPU-powered model analysis with PySB/cupSODA. BIOINFORMATICS, 33(21), 3492-3494 [10.1093/bioinformatics/btx420].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/169868
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