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

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      Abstract

      BackgroundMathematical 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.ResultsLASSIE 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.ConclusionsLASSIE 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-017-1666-0) contains supplementary material, which is available to authorized users.

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      Exact stochastic simulation of coupled chemical reactions

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        A general method for numerically simulating the stochastic time evolution of coupled chemical reactions

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          COPASI--a COmplex PAthway SImulator.

          Simulation and modeling is becoming a standard approach to understand complex biochemical processes. Therefore, there is a big need for software tools that allow access to diverse simulation and modeling methods as well as support for the usage of these methods. Here, we present COPASI, a platform-independent and user-friendly biochemical simulator that offers several unique features. We discuss numerical issues with these features; in particular, the criteria to switch between stochastic and deterministic simulation methods, hybrid deterministic-stochastic methods, and the importance of random number generator numerical resolution in stochastic simulation. The complete software is available in binary (executable) for MS Windows, OS X, Linux (Intel) and Sun Solaris (SPARC), as well as the full source code under an open source license from http://www.copasi.org.
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            Author and article information

            Affiliations
            [1 ]ISNI 0000 0001 2174 1754, GRID grid.7563.7, Department of Informatics, Systems and Communication, , University of Milano-Bicocca, ; Viale Sarca 336, Milano, 20126 Italy
            [2 ]ISNI 0000000106929556, GRID grid.33236.37, Department of Human and Social Sciences, , University of Bergamo, ; Piazzale Sant’Agostino 2, Bergamo, 24129 Italy
            [3 ]SYSBIO.IT Centre of Systems Biology, Piazza della Scienza 2, Milano, 20126 Italy
            Contributors
            andrea.tangherloni@disco.unimib.it
            nobile@disco.unimib.it
            daniela.besozzi@unimib.it
            mauri@disco.unimib.it
            ORCID: http://orcid.org/0000-0001-7780-0434, paolo.cazzaniga@unibg.it
            Journal
            BMC Bioinformatics
            BMC Bioinformatics
            BMC Bioinformatics
            BioMed Central (London )
            1471-2105
            10 May 2017
            10 May 2017
            2017
            : 18
            5424297
            1666
            10.1186/s12859-017-1666-0
            © The Author(s) 2017

            Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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            © The Author(s) 2017

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