Advertisement

Estimation of Kinetic Reaction Constants: Exploiting Reboot Strategies to Improve PSO’s Performance

  • Simone SpolaorEmail author
  • Andrea Tangherloni
  • Leonardo Rundo
  • Paolo Cazzaniga
  • Marco S. Nobile
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10834)

Abstract

The simulation and analysis of mathematical models of biological systems require a complete knowledge of the reaction kinetic constants. Unfortunately, these values are often difficult to measure, but they can be inferred from experimental data in a process known as Parameter Estimation (PE). In this work, we tackle the PE problem using Particle Swarm Optimization (PSO) coupled with three different reboot strategies, which aim to reinitialize particle positions to avoid local optima. In particular, we highlight the better performance of PSO coupled with the reboot strategies with respect to standard PSO. Finally, since the PE requires a huge number of simulations at each iteration of PSO, we exploit cupSODA, a GPU-powered deterministic simulator, which performs all simulations and fitness evaluations in parallel.

Keywords

Particle Swarm Optimization Parameter Estimation GPGPU computing cupSODA Systems Biology 

Notes

Acknowledgement

We gratefully acknowledge the support of NVIDIA Corporation with the donation of the GeForce GTX Titan X GPU used for this research.

This work was conducted in part using the resources of the Advanced Computing Center for Research and Education at Vanderbilt University, Nashville, TN, USA.

References

  1. 1.
    Aldridge, B.B., Burke, J.M., Lauffenburger, D.A., Sorger, P.K.: Physicochemical modelling of cell signalling pathways. Nat. Cell Biol. 8, 1195–1203 (2006)CrossRefGoogle Scholar
  2. 2.
    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).  https://doi.org/10.1007/978-3-642-01184-9_11CrossRefGoogle Scholar
  3. 3.
    Cazzaniga, P., Nobile, M.S., Besozzi, D.: The impact of particles initialization in PSO: parameter estimation as a case in point. In: Proceedings of IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), pp. 1–8 (2015)Google Scholar
  4. 4.
    Chou, I.C., Voit, E.O.: Recent developments in parameter estimation and structure identification of biochemical and genomic systems. Math. Biosci. 219(2), 57–83 (2009)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Da Ros, S., et al.: A comparison among stochastic optimization algorithms for parameter estimation of biochemical kinetic models. Appl. Soft Comput. 13(5), 2205–2214 (2013)CrossRefGoogle Scholar
  6. 6.
    De Oca, M.A.M., Stutzle, T., Birattari, M., Dorigo, M.: Frankenstein’s PSO: a composite particle swarm optimization algorithm. IEEE Trans. Evol. Comput. 13(5), 1120–1132 (2009)CrossRefGoogle Scholar
  7. 7.
    Dräger, A., Kronfeld, M., Ziller, M.J., Supper, J., Planatscher, H., Magnus, J.B.: Modeling metabolic networks in C. glutamicum: a comparison of rate laws in combination with various parameter optimization strategies. BMC Syst. Biol. 3(5) (2009)Google Scholar
  8. 8.
    García-Nieto, J., Alba, E.: Restart particle swarm optimization with velocity modulation: a scalability test. Soft Comput. 15(11), 2221–2232 (2011)CrossRefGoogle Scholar
  9. 9.
    Harris, L.A., et al.: GPU-powered model analysis with PySB/cupSODA. Bioinformatics 33(21), 3492–3494 (2017). (btx420)CrossRefGoogle Scholar
  10. 10.
    Limpert, E., Stahel, W.A., Abbt, M.: Log-normal distributions across the sciences: keys and clues. BioScience 51(5), 341–352 (2001)CrossRefGoogle Scholar
  11. 11.
    Mendes, P., Kell, D.: Non-linear optimization of biochemical pathways: applications to metabolic engineering and parameter estimation. Bioinformatics (Oxford, England) 14(10), 869–883 (1998)CrossRefGoogle Scholar
  12. 12.
    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
  13. 13.
    Nobile, M.S., Besozzi, D., Cazzaniga, P., Mauri, G.: GPU-accelerated simulations of mass-action kinetics models with cupSODA. J. Supercomput. 69(1), 17–24 (2014)CrossRefGoogle Scholar
  14. 14.
    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).  https://doi.org/10.1007/978-3-642-29066-4_7CrossRefGoogle Scholar
  15. 15.
    Nobile, M.S., Cazzaniga, P., Besozzi, D., Pescini, D., Mauri, G.: cuTauLeaping: a GPU-powered tau-leaping stochastic simulator for massive parallel analyses of biological systems. PLoS ONE 9(3), e91963 (2014)CrossRefGoogle Scholar
  16. 16.
    Nvidia: CUDA C Best Practices Guide (2012)Google Scholar
  17. 17.
    Nvidia: Nvidia CUDA C Programming Guide 7.5 (2015)Google Scholar
  18. 18.
    Orellana, A., Minetti, G.F.: A modified binary-PSO for continuous optimization. In: XV Congreso Argentino de Ciencias de la Computación (2009)Google Scholar
  19. 19.
    Petre, I., et al.: A simple mass-action model for the eukaryotic heat shock response and its mathematical validation. Nat. Comput. 10(1), 595–612 (2011)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Petzold, L.R.: Automatic selection of methods for solving stiff and nonstiff systems of ordinary differential equations. SIAM J. Sci. Stat. Comput. 4, 136–148 (1983)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Szallasi, Z., Stelling, J., Periwal, V.: System Modeling in Cellular Biology: From Concepts to Nuts and Bolts. The MIT Press, Boston (2006)CrossRefGoogle Scholar
  22. 22.
    Trelea, I.C.: The particle swarm optimization algorithm: convergence analysis and parameter selection. Inf. Process. Lett. 85(6), 317–325 (2003)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Vitorino, L., Ribeiro, S., Bastos-Filho, C.J.: A mechanism based on artificial bee colony to generate diversity in particle swarm optimization. Neurocomputing 148, 39–45 (2015)CrossRefGoogle Scholar
  24. 24.
    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

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Informatics, Systems and CommunicationUniversity of Milano-BicoccaMilanoItaly
  2. 2.Institute of Molecular Bioimaging and PhysiologyItalian National Research CouncilCefalúItaly
  3. 3.SYSBIO.IT Centre of Systems BiologyMilanoItaly
  4. 4.Department of Human and Social SciencesUniversity of BergamoBergamoItaly

Personalised recommendations