Skip to main content
SpringerLink
Log in

GPU-accelerated simulations of mass-action kinetics models with cupSODA

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

In the last years, graphics processing units (GPUs) witnessed ever growing applications for a wide range of computational analyses in the field of life sciences. Despite its large potentiality, GPU computing risks remaining a niche for specialists, due to the programming and optimization skills it requires. In this work we present cupSODA, a simulator of biological systems that exploits the remarkable memory bandwidth and computational capability of GPUs. cupSODA allows to efficiently execute in parallel large numbers of simulations, which are usually required to investigate the emergent dynamics of a given biological system under different conditions. cupSODA works by automatically deriving the system of ordinary differential equations from a reaction-based mechanistic model, defined according to the mass-action kinetics, and then exploiting the numerical integration algorithm, LSODA. We show that cupSODA can achieve a \(86 \times \) speedup on GPUs with respect to equivalent executions of LSODA on the CPU.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Notes

  1. cupSODA automatically executes the conversion from the stochastic to the deterministic formulation of both reaction constants and initial molecular amounts, given that the volume of the modeled biological system is specified.

References

  1. Aldridge B, Burke J, Lauffenburger D et al (2006) Physicochemical modelling of cell signalling pathways. Nat Cell Biol 8:1195–1203

    Article  Google Scholar 

  2. Besozzi D, Cazzaniga P, Pescini D et al (2012) The role of feedback control mechanisms on the establishment of oscillatory regimes in the Ras/cAMP/PKA pathway in S. cerevisiae. EURASIP J Bioinf Syst Biol 2012:10

    Article  Google Scholar 

  3. Chou I, Voit E (2009) Recent developments in parameter estimation and structure identification of biochemical and genomic systems. Math Biosci 219(2):57–83

    Article  MATH  MathSciNet  Google Scholar 

  4. Demattè L, Prandi D (2010) GPU computing for systems biology. Brief Bioinform 11(3):323–333

    Article  Google Scholar 

  5. Farber R (2011) Topical perspective on massive threading and parallelism. J Mol Graphics Modell 30:82–89

    Article  Google Scholar 

  6. Harvey MJ, De Fabritiis G (2012) A survey of computational molecular science using graphics processing units. WIREs Comput Mol Sci 2(5):734–742

    Article  Google Scholar 

  7. Hoops S, Sahle S, Gauges R et al (2006) COPASI: a COmplex PAthway SImulator. Bioinformatics 22(24):3067–3074

    Article  Google Scholar 

  8. Koza J, Mydlowec W, Lanza G et al (2007) 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

  9. Nelson D, Cox M (2004) Lehninger principles of biochemistry. W. H. Freeman Co, New York

    Google Scholar 

  10. Nobile MS, Besozzi D, Cazzaniga P et al (2013) cupSODA: a CUDA-powered simulator of mass-action kinetics. In: Malyshkin V (ed) Proceedings of 12th international conference on parallel computing technologies (PaCT 2013), vol LNCS 7979, pp 344–357

  11. Nobile MS, Cazzaniga P, Besozzi D et al (2013) Reverse engineering of kinetic reaction networks by means of Cartesian genetic programming and particle swarm optimization. In: IEEE congress evolutionary computation (CEC 2013), pp 1594–1601

  12. Petzold L (1983) Automatic selection of methods for solving stiff and nonstiff systems of ordinary differential equations. SIAM J Sci Stat Comp 4(1):136–148

    Article  MATH  MathSciNet  Google Scholar 

  13. Vigelius M, Lane A, Meyer B (2011) Accelerating reaction-diffusion simulations with general-purpose graphics processing units. Bioinformatics 27(2):288–290

    Article  Google Scholar 

  14. Wang Y, Christley S, Mjolsness E et al (2010) Parameter inference for discretely observed stochastic kinetic models using stochastic gradient descent. BMC Syst Biol 4:99

  15. Wilkinson D (2009) Stochastic modelling for quantitative description of heterogeneous biological systems. Nat Rev Genet 10:122–133

    Article  Google Scholar 

  16. Wolkenhauer O, Ullah M, Kolch W et al (2004) Modeling and simulation of intracellular dynamics: choosing an appropriate framework. IEEE Trans Nanobiosci 3(3):200–207

    Article  Google Scholar 

  17. Zhou Y, Liepe J, Sheng X et al (2011) GPU accelerated biochemical network simulation. Bioinformatics 27(6):874–876

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca, Viale Sarca 336, 20126 , Milano, Italy

    Marco S. Nobile & Giancarlo Mauri

  2. Dipartimento di Scienze Umane e Sociali, Università degli Studi di Bergamo, Piazzale S. Agostino 2, 24129 , Bergamo, Italy

    Paolo Cazzaniga

  3. Dipartimento di Informatica, Università degli Studi di Milano, Via Comelico 39, 20135 , Milano, Italy

    Daniela Besozzi

  4. SYSBIO Centre for Systems Biology, Milano, Italy

    Marco S. Nobile, Paolo Cazzaniga, Daniela Besozzi & Giancarlo Mauri

Authors
  1. Marco S. Nobile
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Paolo Cazzaniga
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Daniela Besozzi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Giancarlo Mauri
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marco S. Nobile.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nobile, M.S., Cazzaniga, P., Besozzi, D. et al. GPU-accelerated simulations of mass-action kinetics models with cupSODA. J Supercomput 69, 17–24 (2014). https://doi.org/10.1007/s11227-014-1208-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-014-1208-8

Keywords

Search

Navigation