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

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Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB 2017)


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.

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  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)

    Article  Google Scholar 

  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).

    Chapter  Google Scholar 

  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. 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)

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. García-Nieto, J., Alba, E.: Restart particle swarm optimization with velocity modulation: a scalability test. Soft Comput. 15(11), 2221–2232 (2011)

    Article  Google Scholar 

  9. Harris, L.A., et al.: GPU-powered model analysis with PySB/cupSODA. Bioinformatics 33(21), 3492–3494 (2017). (btx420)

    Article  Google Scholar 

  10. Limpert, E., Stahel, W.A., Abbt, M.: Log-normal distributions across the sciences: keys and clues. BioScience 51(5), 341–352 (2001)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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).

    Chapter  Google Scholar 

  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)

    Article  Google Scholar 

  16. Nvidia: CUDA C Best Practices Guide (2012)

    Google Scholar 

  17. Nvidia: Nvidia CUDA C Programming Guide 7.5 (2015)

    Google Scholar 

  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. 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)

    Article  MathSciNet  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  21. Szallasi, Z., Stelling, J., Periwal, V.: System Modeling in Cellular Biology: From Concepts to Nuts and Bolts. The MIT Press, Boston (2006)

    Book  Google Scholar 

  22. Trelea, I.C.: The particle swarm optimization algorithm: convergence analysis and parameter selection. Inf. Process. Lett. 85(6), 317–325 (2003)

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

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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.

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Correspondence to Simone Spolaor .

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Spolaor, S., Tangherloni, A., Rundo, L., Cazzaniga, P., Nobile, M.S. (2019). Estimation of Kinetic Reaction Constants: Exploiting Reboot Strategies to Improve PSO’s Performance. In: Bartoletti, M., et al. Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2017. Lecture Notes in Computer Science(), vol 10834. Springer, Cham.

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