Abstract
The computational investigation of a biological system often requires the execution of a large number of simulations to analyze its dynamics, and to derive useful knowledge on its behavior under physiological and perturbed conditions. This analysis usually turns out into very high computational costs when simulations are run on central processing units (CPUs), therefore demanding a shift to the use of high-performance processors. In this work we present a simulator of biological systems, called cupSODA, which exploits the higher memory bandwidth and computational capability of graphics processing units (GPUs). This software allows to execute parallel simulations of the dynamics of biological systems, by first deriving a set of ordinary differential equations from reaction-based mechanistic models defined according to the mass-action kinetics, and then exploiting the numerical integration algorithm LSODA. We show that cupSODA can achieve a 112 × speedup on GPUs with respect to equivalent executions of LSODA on CPUs.
This work is partially supported by Regione Lombardia, project “Network Enabled Drug Design (NEDD)”, and by the Research Infrastructure “SYSBIO Centre of Systems Biology”.
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References
Aldridge, B., Burke, J., Lauffenburger, D., Sorger, P.: Physicochemical modelling of cell signalling pathways. Nature Cell Biology 8, 1195–1203 (2006)
Chou, I., Voit, E.: Recent developments in parameter estimation and structure identification of biochemical and genomic systems. Mathematical Biosciences 219(2), 57–83 (2009)
Besozzi, D., Cazzaniga, P., Pescini, D., Mauri, G., Colombo, S., Martegani, E.: The role of feedback control mechanisms on the establishment of oscillatory regimes in the Ras/cAMP/PKA pathway in S. cerevisiae. EURASIP Journal on Bioinformatics and Systems Biology 2012(10) (2012)
Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., Tarantola, S.: Global Sensitivity Analysis: The Primer. Wiley-Interscience (2008)
Raue, A., Kreutz, C., Maiwald, T., Bachmann, J., Schilling, M., Klingmüller, U., Timmer, J.: Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood. Bioinformatics 25(15), 1923–1929 (2009)
Dräger, A., Kronfeld, M., Ziller, M.J., Supper, J., Planatscher, H., Magnus, J.B., Oldiges, M., Kohlbacher, O., Zell, A.: Modeling metabolic networks in C. glutamicum: a comparison of rate laws in combination with various parameter optimization strategies. BMC Systems Biology 3(5) (2009)
Besozzi, D., Cazzaniga, P., Mauri, G., Pescini, D., Vanneschi, L.: A comparison of genetic algorithms and particle swarm optimization for parameter estimation in stochastic biochemical systems. In: Pizzuti, C., Ritchie, M.D., Giacobini, M. (eds.) EvoBIO 2009. LNCS, vol. 5483, pp. 116–127. Springer, Heidelberg (2009)
Nobile, M.S., Besozzi, D., Cazzaniga, P., Mauri, G., Pescini, D.: A GPU-based multi-swarm PSO method for parameter estimation in stochastic biological systems exploiting discrete-time target series. In: Giacobini, M., Vanneschi, L., Bush, W.S. (eds.) EvoBIO 2012. LNCS, vol. 7246, pp. 74–85. Springer, Heidelberg (2012)
Nobile, M.S., Cazzaniga, P., Besozzi, D., Pescini, D., Mauri, G.: Reverse engineering of kinetic reaction networks by means of cartesian genetic programming and particle swarm optimization. In: Proceedings of IEEE Congress on Evolutionary Computation (CEC 2013) (In press, 2013)
Kentzoglanakis, K., Poole, M.: A swarm intelligence framework for reconstructing gene networks: Searching for biologically plausible architectures. IEEE/ACM Transactions on Computational Biology and Bioinformatics 9(2), 358–371 (2012)
Koza, J.R., Mydlowec, W., Lanza, G., Yu, J., Keane, M.A.: Automatic computational discovery of chemical reaction networks using genetic programming. In: Džeroski, S., Todorovski, L. (eds.) Computational Discovery 2007. LNCS (LNAI), vol. 4660, pp. 205–227. Springer, Heidelberg (2007)
Ando, S., Sakamoto, E., Iba, H.: Evolutionary modeling and inference of gene network. Information Sciences 145(3-4), 237–259 (2002)
Butcher, J.C.: Numerical Methods for Ordinary Differential Equations. John Wiley & Sons (2003)
Gillespie, D.T.: Stochastic simulation of chemical kinetics. Annual Review of Physical Chemistry 58, 35–55 (2007)
Petzold, L.: Automatic selection of methods for solving stiff and nonstiff systems of ordinary differential equations. SIAM Journal of Scientific and Statistical Computing 4(1), 136–148 (1983)
Demattè, L., Prandi, D.: GPU computing for systems biology. Briefings in Bioinformatics 11(3), 323–333 (2010)
Payne, J., Sinnott-Armstrong, N., Moore, J.: Exploiting graphics processing units for computational biology and bioinformatics. Interdisciplinary Sciences, Computational Life Sciences 2(3), 213–220 (2010)
Harvey, M.J., De Fabritiis, G.: A survey of computational molecular science using graphics processing units. Wiley Interdisciplinary Reviews: Computational Molecular Science 2(5), 734–742 (2012)
Zhou, Y., Liepe, J., Sheng, X., Stumpf, M.P.H., Barnes, C.: GPU accelerated biochemical network simulation. Bioinformatics 27(6), 874–876 (2011)
Vigelius, M., Lane, A., Meyer, B.: Accelerating reaction-diffusion simulations with general-purpose graphics processing units. Bioinformatics 27(2), 288–290 (2011)
Farber, R.: Topical perspective on massive threading and parallelism. Journal of Molecular Graphics and Modelling 30, 82–89 (2011)
Nvidia: CUDA C Programming Guide v5.0 (2012)
Wolkenhauer, O., Ullah, M., Kolch, W., Kwang-Hyun, C.: Modeling and simulation of intracellular dynamics: choosing an appropriate framework. IEEE Transactions on Nanobiosciences 3(3), 200–207 (2004)
Nvidia: CUDA C Best Practices Guide (2012)
Hoops, S., Sahle, S., Gauges, R., Lee, C., Pahle, J., Simus, N., Singhal, M., Xu, L., Mendes, P., Kummer, U.: COPASI - a COmplex PAthway SImulator. Bioinformatics 22(24), 3067–3074 (2006)
Nelson, D., Cox, M.: Lehninger Principles of Biochemistry. W. H. Freeman Company (2004)
Wang, Y., Christley, S., Mjolsness, E., Xie, X.: Parameter inference for discretely observed stochastic kinetic models using stochastic gradient descent. BMC Systems Biology 4(1) (2010)
Cazzaniga, P., Pescini, D., Besozzi, D., Mauri, G., Colombo, S., Martegani, E.: Modeling and stochastic simulation of the Ras/cAMP/PKA pathway in the yeast Saccharomyces cerevisiae evidences a key regulatory function for intracellular guanine nucleotides pools. Journal of Biotechnology 133(3), 377–385 (2008)
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Nobile, M.S., Besozzi, D., Cazzaniga, P., Mauri, G., Pescini, D. (2013). cupSODA: A CUDA-Powered Simulator of Mass-Action Kinetics. In: Malyshkin, V. (eds) Parallel Computing Technologies. PaCT 2013. Lecture Notes in Computer Science, vol 7979. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39958-9_32
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DOI: https://doi.org/10.1007/978-3-642-39958-9_32
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