Advertisement

Accelerated Analysis of Biological Parameters Space Using GPUs

  • Marco S. NobileEmail author
  • Giancarlo Mauri
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10421)

Abstract

Mathematical modeling and computer simulation represent a valuable mean to integrate experimental research for the study of biological systems. However, many computational methods—e.g., sensitivity analysis—require the execution of a massive number of simulations to investigate the model behavior in physiological or perturbed conditions, which can be a computationally challenging task. This huge amount of simulations is necessary to collect data in the vast space of kinetic parameters. This paper provides the state-of-the-art of biochemical simulators relying on Graphics Processing Units (GPUs) in the context of Systems Biology. Moreover, we discuss two examples of integration of such simulators into computational methods for parameter sweep and sensitivity analysis, both implemented using the Python language.

References

  1. 1.
    Cazzaniga, P., Damiani, C., Besozzi, D., Colombo, R., Nobile, M.S., Gaglio, D., Pescini, D., Molinari, S., Mauri, G., Alberghina, L., Vanoni, M.: Computational strategies for a system-level understanding of metabolism. Metabolites 4, 1034–1087 (2014)CrossRefGoogle Scholar
  2. 2.
    Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., Tarantola, S.: Analysis, Global Sensitivity Analysis: The Primer. Wiley-Interscience, Hoboken (2008)zbMATHGoogle Scholar
  3. 3.
    Moles, C.G., Mendes, P., Banga, J.R.: Parameter estimation in biochemical pathways: a comparison of global optimization methods. Genome Res. 13(11), 2467–2474 (2003)CrossRefGoogle Scholar
  4. 4.
    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). doi: 10.1007/978-3-642-29066-4_7 CrossRefGoogle Scholar
  5. 5.
    Nvidia: Nvidia CUDA C Programming Guide 8.0 (2016)Google Scholar
  6. 6.
    Bland, A.S., Wells, J.C., Messer, O.E., et al.: Titan: early experience with the Cray XK6 at oak ridge national laboratory. In: Proceedings of Cray User Group Conference (CUG 2012) (2012)Google Scholar
  7. 7.
    Nobile, M.S., Cazzaniga, P., Tangherloni, A., Besozzi, D.: Graphics processing units in bioinformatics, computational biology and systems biology. Brief. Bioinform. (2016)Google Scholar
  8. 8.
    Gillespie, D.T.: Exact stochastic simulation of coupled chemical reactions. J. Comput. Phys. 81, 2340–2361 (1977)Google Scholar
  9. 9.
    Wolkenhauer, O., Ullah, M., Kolch, W., Kwang-Hyun, C.: Modeling and simulation of intracellular dynamics: choosing an appropriate framework. IEEE Trans. Nanobiosci. 3(3), 200–207 (2004)CrossRefGoogle Scholar
  10. 10.
    Butcher, J.C.: Numerical Methods for Ordinary Differential Equations. Wiley, Chichester (2003)CrossRefzbMATHGoogle Scholar
  11. 11.
    Petzold, L.: Automatic selection of methods for solving stiff and nonstiff systems of ordinary differential equations. SIAM J. Sci. Stat. Comput. 4, 136–148 (1983)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Wilkinson, D.: Stochastic modelling for quantitative description of heterogeneous biological systems. Nat. Rev. Genet. 10(2), 122–133 (2009)CrossRefGoogle Scholar
  13. 13.
    Gibson, M.A., Bruck, J.: Efficient exact stochastic simulation of chemical systems with many species and many channels. J. Phys. Chem. A 104(9), 1876–1889 (2000)CrossRefGoogle Scholar
  14. 14.
    Rathinam, M., Petzold, L.R., Cao, Y., Gillespie, D.T.: Stiffness in stochastic chemically reacting systems: the implicit tau-leaping method. J. Chem. Phys. 119, 12784–12794 (2003)CrossRefGoogle Scholar
  15. 15.
    Cao, Y., Gillespie, D.T., Petzold, L.R.: Efficient step size selection for the tau-leaping simulation method. J. Chem. Phys. 124(4), 044109 (2006)CrossRefGoogle Scholar
  16. 16.
    Re, A., Caravagna, G., Pescini, D., Nobile, M.S., Cazzaniga, P.: Approximate simulation of chemical reaction systems with micro, meso and macro-scales. In: Proceedings of the 13th International Conference on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB2016) (2016)Google Scholar
  17. 17.
    Harris, L.A., Clancy, P.: A “partitioned leaping” approach for multiscale modeling of chemical reaction dynamics. J. Chem. Phys. 125(14), 144107 (2006)CrossRefGoogle Scholar
  18. 18.
    Salis, H., Kaznessis, Y.: Accurate hybrid stochastic simulation of a system of coupled chemical or biochemical reactions. J. Chem. Phys. 122(5), 054103 (2005)CrossRefGoogle Scholar
  19. 19.
    Ackermann, J., Baecher, P., Franzel, T., Goesele, M., Hamacher, K.: Massively-parallel simulation of biochemical systems. In: Proceedings of Massively Parallel Computational Biology on GPUs, Jahrestagung der Gesellschaft für Informatik e.V, pp. 739–750 (2009)Google Scholar
  20. 20.
    Zhou, Y., Liepe, J., Sheng, X., Stumpf, M.P.H., Barnes, C.: GPU accelerated biochemical network simulation. Bioinformatics 27(6), 874–876 (2011)CrossRefGoogle Scholar
  21. 21.
    Hoops, S., Sahle, S., Gauges, R., et al.: COPASI - a COmplex PAthway SImulator. Bioinformatics 22, 3067–3074 (2006)CrossRefGoogle Scholar
  22. 22.
    Nobile, M.S., Besozzi, D., Cazzaniga, P., Mauri, G.: GPU-accelerated simulations of mass-action kinetics models with cupSODA. J. Supercomputing 69(1), 17–24 (2014)CrossRefGoogle Scholar
  23. 23.
    Tangherloni, A., Nobile, M.S., Besozzi, D., Mauri, G., Cazzaniga, P.: LASSIE: simulating large-scale models of biochemical systems on GPUs. BMC Bioinform. 18(1), 246 (2017)CrossRefGoogle Scholar
  24. 24.
    Li, H., Petzold, L.R.: Efficient parallelization of the stochastic simulation algorithm for chemically reacting systems on the graphics processing unit. Int. J. High Perform. Comput. Appl. 24(2), 107–116 (2010)CrossRefGoogle Scholar
  25. 25.
    Sumiyoshi, K., Hirata, K., Hiroi, N., et al.: Acceleration of discrete stochastic biochemical simulation using GPGPU. Front. Physiol. 6(42) (2015)Google Scholar
  26. 26.
    Komarov, I., D’Souza, R.M.: Accelerating the gillespie exact stochastic simulation algorithm using hybrid parallel execution on graphics processing units. PLoS ONE 7(11), e46693 (2012)CrossRefGoogle Scholar
  27. 27.
    Gillespie, D.T., Petzold, L.R.: Improved leap-size selection for accelerated stochastic simulation. J. Chem. Phys. 119, 8229–8234 (2003)CrossRefGoogle Scholar
  28. 28.
    Komarov, I., D’Souza, R.M., Tapia, J.: Accelerating the gillespie \(\tau \)-leaping method using graphics processing units. PLoS ONE 7(6), e37370 (2012)CrossRefGoogle Scholar
  29. 29.
    Nobile, M.S., Cazzaniga, P., Besozzi, D., et al.: cuTauLeaping: a GPU-powered tau-leaping stochastic simulator for massive parallel analyses of biological systems. PLoS ONE 9(3), e91963 (2014)CrossRefGoogle Scholar
  30. 30.
    Wilhelm, T.: The smallest chemical reaction system with bistability. BMC Syst. Biol. 3(1), 90 (2009)CrossRefGoogle Scholar
  31. 31.
    Saltelli, A., Ratto, M., Tarantola, S., Campolongo, F.: Sensitivity analysis for chemical models. Chem. Rev. 105, 2811–2827 (2005)CrossRefzbMATHGoogle Scholar
  32. 32.
    Morris, M.D.: Factorial sampling plans for preliminary computational experiments. Technometrics 33(2), 161–174 (1991)CrossRefGoogle Scholar
  33. 33.
    Campolongo, F., Cariboni, J., Saltelli, A.: An effective screening design for sensitivity analysis of large models. Environ. Model. Softw. 22(10), 1509–1518 (2007). Modelling, computer-assisted simulations, and mapping of dangerous phenomena for hazard assessmentCrossRefGoogle Scholar
  34. 34.
    Saltelli, A., Annoni, P., Azzini, I., Campolongo, F., Ratto, M., Tarantola, S.: Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index. Comput. Phys. Commun. 181(2), 259–270 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  35. 35.
    Sobol, I.M., Kucherenko, S.: Derivative based global sensitivity measures and their link with global sensitivity indices. Math. Comput. Simul. 79(10), 3009–3017 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  36. 36.
    Usher, W., Herman, J., Whealton, C., Hadka, D.: Salib/salib: Launch!, October 2016Google Scholar
  37. 37.
    Degasperi, A., Gilmore, S.: Sensitivity analysis of stochastic models of bistable biochemical reactions. In: Bernardo, M., Degano, P., Zavattaro, G. (eds.) SFM 2008. LNCS, vol. 5016, pp. 1–20. Berlin, Heidelberg (2008). doi: 10.1007/978-3-540-68894-5_1 CrossRefGoogle Scholar
  38. 38.
    Nobile, M.S., Besozzi, D., Cazzaniga, P., Pescini, D., Mauri, G.: Reverse engineering of kinetic reaction networks by means of cartesian genetic programming and particle swarm optimization. In: 2013 IEEE Congress on Evolutionary Computation, vol. 1, pp. 1594–1601. IEEE (2013)Google Scholar
  39. 39.
    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 of Scientific Knowledge. LNCS, vol. 4660, pp. 205–227. Springer, Heidelberg (2007). doi: 10.1007/978-3-540-73920-3_10 CrossRefGoogle Scholar
  40. 40.
    Cumbo, F., Nobile, M.S., Damiani, C., Colombo, R., Mauri, G., Cazzaniga, P.: COSYS: computational systems biology infrastructure. In: Proceedings of the 13th International Conference on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB2016) (2016)Google Scholar
  41. 41.
    Nvidia: nvGRAPH v8.0 (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Informatics, Systems and CommunicationUniversity of Milano-BicoccaMilanoItaly
  2. 2.SYSBIO.IT Centre of Systems BiologyMilanoItaly

Personalised recommendations