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BMC Bioinformatics. 2017 May 10;18(1):246. doi: 10.1186/s12859-017-1666-0.

LASSIE: simulating large-scale models of biochemical systems on GPUs.

Author information

1
Department of Informatics, Systems and Communication, University of Milano-Bicocca, Viale Sarca 336, Milano, 20126, Italy.
2
SYSBIO.IT Centre of Systems Biology, Piazza della Scienza 2, Milano, 20126, Italy.
3
Department of Human and Social Sciences, University of Bergamo, Piazzale Sant'Agostino 2, Bergamo, 24129, Italy. paolo.cazzaniga@unibg.it.
4
SYSBIO.IT Centre of Systems Biology, Piazza della Scienza 2, Milano, 20126, Italy. paolo.cazzaniga@unibg.it.

Abstract

BACKGROUND:

Mathematical modeling and in silico analysis are widely acknowledged as complementary tools to biological laboratory methods, to achieve a thorough understanding of emergent behaviors of cellular processes in both physiological and perturbed conditions. Though, the simulation of large-scale models-consisting in hundreds or thousands of reactions and molecular species-can rapidly overtake the capabilities of Central Processing Units (CPUs). The purpose of this work is to exploit alternative high-performance computing solutions, such as Graphics Processing Units (GPUs), to allow the investigation of these models at reduced computational costs.

RESULTS:

LASSIE is a "black-box" GPU-accelerated deterministic simulator, specifically designed for large-scale models and not requiring any expertise in mathematical modeling, simulation algorithms or GPU programming. Given a reaction-based model of a cellular process, LASSIE automatically generates the corresponding system of Ordinary Differential Equations (ODEs), assuming mass-action kinetics. The numerical solution of the ODEs is obtained by automatically switching between the Runge-Kutta-Fehlberg method in the absence of stiffness, and the Backward Differentiation Formulae of first order in presence of stiffness. The computational performance of LASSIE are assessed using a set of randomly generated synthetic reaction-based models of increasing size, ranging from 64 to 8192 reactions and species, and compared to a CPU-implementation of the LSODA numerical integration algorithm.

CONCLUSIONS:

LASSIE adopts a novel fine-grained parallelization strategy to distribute on the GPU cores all the calculations required to solve the system of ODEs. By virtue of this implementation, LASSIE achieves up to 92× speed-up with respect to LSODA, therefore reducing the running time from approximately 1 month down to 8 h to simulate models consisting in, for instance, four thousands of reactions and species. Notably, thanks to its smaller memory footprint, LASSIE is able to perform fast simulations of even larger models, whereby the tested CPU-implementation of LSODA failed to reach termination. LASSIE is therefore expected to make an important breakthrough in Systems Biology applications, for the execution of faster and in-depth computational analyses of large-scale models of complex biological systems.

KEYWORDS:

Deterministic simulation; Fine-grained parallelization; GPU computing; Graphics Processing Unit; LSODA; Numerical integration method; Nvidia CUDA; Reaction-based model; Rule-based model; Systems biology

PMID:
28486952
PMCID:
PMC5424297
DOI:
10.1186/s12859-017-1666-0
[Indexed for MEDLINE]
Free PMC Article

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