Advertisement

A Parallel Differential Evolution Algorithm for Parameter Estimation in Dynamic Models of Biological Systems

  • D. R. PenasEmail author
  • Julio R. Banga
  • P. González
  • R. Doallo
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 294)

Abstract

Metaheuristics are gaining increased attention as efficient solvers for hard global optimization problems arising in bioinformatics and computational systems biology. Differential Evolution (DE) is one of the most popular algorithms in that class. However, the original algorithm requires many evaluations of the objective function, so its application to realistic computational systems biology problems, like those considering parameter estimation in dynamic models, results in excessive computation times. In this work we present a modified DE method which has been extended to exploit the structure of parameter estimation problems and which is able to run efficiently in parallel machines. In particular, we describe an asynchronous parallel implementation of DE which also incorporates three new search heuristics which exploit the structure of parameter estimation problems. The efficiency and robustness of the resulting method is illustrated with two types of benchmarks problems (i) black-box global optimization problems and (ii) calibration of systems biology dynamic models. The results show that the proposed algorithm achieves excellent results, not only in terms of quality of the solution, but also regarding speedup and scalability.

Keywords

Computational Systems Biology Parallel Metaheuristics Differential Evolution 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Greenberg, H.J., Hart, W.E., Lancia, G.: Opportunities for combinatorial optimization in computational biology. INFORMS Journal on Computing 16(3), 211–231 (2004)CrossRefzbMATHMathSciNetGoogle Scholar
  2. 2.
    Larrañaga, P., Calvo, B., Santana, R., Bielza, C., Galdiano, J., Inza, I., Lozano, J.A., Armañanzas, R., Santafé, G., Pérez, A., et al.: Machine learning in bioinformatics. Briefings in Bioinformatics 7(1), 86–112 (2006)CrossRefGoogle Scholar
  3. 3.
    Banga, J.R.: Optimization in computational systems biology. BMC Systems Biology 2(1), 47 (2008)CrossRefMathSciNetGoogle Scholar
  4. 4.
    Villaverde, A.F., Banga, J.R.: Reverse engineering and identification in systems biology: strategies, perspectives and challenges. Journal of The Royal Society Interface 11(91), 20130505 (2014)CrossRefGoogle Scholar
  5. 5.
    Crainic, T.G., Toulouse, M.: Parallel strategies for meta-heuristics. Springer (2003)Google Scholar
  6. 6.
    Alba, E.: Parallel metaheuristics: a new class of algorithms, vol. 47. Wiley-Interscience (2005)Google Scholar
  7. 7.
    Perkins, T.J., Jaeger, J., Reinitz, J., Glass, L.: Reverse engineering the gap gene network of drosophila melanogaster. PLOS Computational Biology 2(5), e51 (2006)Google Scholar
  8. 8.
    Jostins, L., Jaeger, J.: Reverse engineering a gene network using an asynchronous parallel evolution strategy. BMC Systems Biology 4(1) (2010)Google Scholar
  9. 9.
    Storn, R., Price, K.: Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11(4), 341–359 (1997)CrossRefzbMATHMathSciNetGoogle Scholar
  10. 10.
    Price, K., Storn, R.M., Lampinen, J.A.: Differential Evolution: A Practical Approach to Global Optimization. Natural Computing Series. Springer-Verlag New York, Inc., Secaucus (2005)Google Scholar
  11. 11.
    Chakraborty, U.K.: Advances in Differential Evolution. 1 edn. Springer Publishing Company, Incorporated (2008)Google Scholar
  12. 12.
    Das, S., Suganthan, P.N.: Differential evolution: A survey of the state-of-the-art. IEEE Transactions on Evolutionary Computation 15(1), 4–31 (2011)CrossRefGoogle Scholar
  13. 13.
    Egea, J.A., Rodríguez-Fernández, M., Banga, J.R., Martí, R.: Scatter search for chemical and bio-process optimization. Journal of Global Optimization 37(3), 481–503 (2007)CrossRefzbMATHMathSciNetGoogle Scholar
  14. 14.
    Dennis Jr., J.E., Gay, D.M., Welsch, R.E.: Algorithm 573: Nl2sol an nonlinear least-squares algorithm. ACM Transactions on Mathematical Software (TOMS) 7(3), 369–383 (1981)CrossRefGoogle Scholar
  15. 15.
    Alba, E., Luque, G., Nesmachnow, S.: Parallel metaheuristics: recent advances and new trends. International Transactions in Operational Research 20(1), 1–48 (2013)CrossRefzbMATHGoogle Scholar
  16. 16.
    Hansen, N., Auger, A., Finck, S., Ros, R.: Real-parameter black-box optimization benchmarking 2009: Experimental setup. Technical Report RR-6828, INRIA (2009)Google Scholar
  17. 17.
  18. 18.
    Moles, C.G., Mendes, P., Banga, J.R.: Parameter estimation in biochemical pathways: a comparison of global optimization methods. Genome Research 13(11), 2467–2474 (2003)CrossRefGoogle Scholar
  19. 19.
    Locke, J., Millar, A., Turner, M.: Modelling genetic networks with noisy and varied experimental data: the circadian clock in Arabidopsis thaliana. Journal of Theoretical Biology 234(3), 383–393 (2005)CrossRefMathSciNetGoogle Scholar
  20. 20.
    Lipniacki, T., Paszek, P., Brasier, A.R., Luxon, B., Kimmel, M.: Mathematical model of nf-κb regulatory module. Journal of Theoretical Biology 228(2), 195–215 (2004)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • D. R. Penas
    • 1
    Email author
  • Julio R. Banga
    • 1
  • P. González
    • 2
  • R. Doallo
    • 2
  1. 1.BioProcess Engineering Group, IIM-CSICVigoSpain
  2. 2.Computer Architecture GroupUniversity of A CoruñaCoruñaSpain

Personalised recommendations