Skip to main content

Validation of a Morphogenesis Model of Drosophila Early Development by a Multi-objective Evolutionary Optimization Algorithm

  • Conference paper
Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (EvoBIO 2009)

Abstract

We apply evolutionary computation to calibrate the parameters of a morphogenesis model of Drosophila early development. The model aims to describe the establishment of the steady gradients of Bicoid and Caudal proteins along the antero-posterior axis of the embryo of Drosophila. The model equations consist of a system of non-linear parabolic partial differential equations with initial and zero flux boundary conditions. We compare the results of single- and multi-objective variants of the CMA-ES algorithm for the model the calibration with the experimental data. Whereas the multi-objective algorithm computes a full approximation of the Pareto front, repeated runs of the single-objective algorithm give solutions that dominate (in the Pareto sense) the results of the multi-objective approach. We retain as best solutions those found by the latter technique. From the biological point of view, all such solutions are all equally acceptable, and for our test cases, the relative error between the experimental data and validated model solutions on the Pareto front are in the range 3% − 6%. This technique is general and can be used as a generic tool for parameter calibration problems.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Alves, F., Dilão, R.: Modelling segmental patterning in Drosophila: Maternal and gap genes. Journal of Theoretical Biology 241, 342–359 (2006)

    Article  MathSciNet  Google Scholar 

  2. Coello Coello, C.A., Van Veldhuizen, D.A., Lamont, G.B.: Evolutionary algorithms for solving multi-objective problems. Kluwer Academic Publishers, Dordrecht

    Google Scholar 

  3. Das, I., Dennis, J.E.: A closer look at drawbacks of minimizing weighted sums of objectives for pareto set generation in multicriteria optimization problems. Structural optimization 14(1), 63–69 (1997)

    Article  Google Scholar 

  4. Deb, K.: Multi-objective optimization using evolutionary algorithms. Wiley, Chichester (2001)

    MATH  Google Scholar 

  5. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2) (2002)

    Google Scholar 

  6. Dilão, R., Muraro, D.: Calibration and validation of mathematical models describing the gradient of Bicoid in the embryo of Drosophila (2009) (preprint)

    Google Scholar 

  7. Dilão, R., Sainhas, J.: Validation and Calibration of Models for Reaction-Diffusion Systems. Int. J. of Bifurcation and Chaos 8, 1163–1182 (1998)

    Article  MATH  Google Scholar 

  8. Emmerich, M., Beume, N., Naujoks, B.: An EMO Algorithm Using the Hypervolume Measure as Selection Criterion. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 62–76. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  9. Fleischer, M.: The Measure of Pareto Optima. Applications to Multi-objective Metaheuristics. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L. (eds.) EMO 2003. LNCS, vol. 2632, pp. 519–533. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  10. Handl, J., Lovell, S.C., Knowles, J.: Investigations into the Effect of Multiobjectivisation in Protein Structure Prediction. In: Rudolph, G., Jansen, T., Lucas, S., Poloni, C., Beume, N., et al. (eds.) PPSN 2008. LNCS, vol. 5199, pp. 702–711. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  11. Hansen, N., Ostermeier, A., Gawelczyk, A.: On the Adaptation of Arbitrary Normal Mutation Distributions in Evolution Strategies: The Generating Set Adaptation. In: ICGA 1995, pp. 57–64 (1995)

    Google Scholar 

  12. Hansen, N., Ostermeier, A.: Adapting arbitrary normal mutation distributions in evolution strategies: The covariance matrix adaptation. In: ICEC 1996, pp. 312–317. IEEE Press, Los Alamitos (1996)

    Google Scholar 

  13. Hansen, N., Ostermeier, A.: Completely Derandomized Self-Adaptation in Evolution Strategies. Evolutionary Computation 9(2), 159–195 (2001)

    Article  Google Scholar 

  14. Hansen, N., Müller, S., Koumoutsakos, P.: Reducing the Time Complexity of the Derandomized Evolution Strategy with Covariance Matrix Adaptation (CMA-ES). Evolutionary Computation 11(1) (2003)

    Google Scholar 

  15. Auger, A., Hansen, N.: A Restart CMA Evolution Strategy With Increasing Population Size. In: CEC 2005, pp. 1769–1776. IEEE Press, Los Alamitos (2005)

    Google Scholar 

  16. Hansen, N., et al.: Comparison of Evolutionary Algorithms on a Benchmark Function Set. In: CEC 2005 Special Session (2005)

    Google Scholar 

  17. Hansen, N., Ros, R., Mauny, N., Schoenauer, M., Auger, A.: PSO Facing Non-Separable and Ill-Conditioned Problems. INRIA Research Report number RR-6447 (2008)

    Google Scholar 

  18. Igel, C., Hansen, N., Roth, S.: Covariance Matrix Adaptation for Multi-objective Optimization. Evolutionary Computation 15(1), 1–28 (2007)

    Article  Google Scholar 

  19. Knowles, J.D., Corne, D.W., Fleisher, M.: Bounded Archiving using the Lebesgue Measure. In: Proceedings of CEC 2003, vol. 4, pp. 2490–2497. IEEE Press, Los Alamitos (2003)

    Google Scholar 

  20. Myasnikova, E., Kosman, D., Reinitz, J., Samsonova, M.: Spatio-temporal registration of the expression patterns of Drosophila segmentation genes. In: Seventh International Conference on Intelligent Systems for Molecular Biology, pp. 195–201. AAAI Press, Menlo Park (1999)

    Google Scholar 

  21. Myasnikova, E., Samsonova, A., Kozlov, K., Samsonova, M., Reinitz, J.: Registration of the expression patterns of Drosophila segmentation genes by two independent methods. Bioinformatics 17(1), 3–12 (2001)

    Article  Google Scholar 

  22. Nusslein-Volhard, C.: Gradients that organize embryo development. Scientific American, 54–61 (1996)

    Google Scholar 

  23. Rechenberg, I.: Evolutionsstrategie: Optimierung Technischer Systeme nach Prinzipien der Biologischen Evolution, Werkstatt Bionik und Evolutionstechnik. Frommann-Holzboog, Stuttgart (1973)

    Google Scholar 

  24. Rivera-Pomar, R., Jäckle, H.: From gradients to stripes in Drosophila embryogenesis: Filling in the gaps. Trends Genet. 12, 478–483 (1996)

    Article  Google Scholar 

  25. Schwefel, H.-P.: Numerical Optimization of Computer Models. J. Wiley & Sons, New York (1981–1995)

    Google Scholar 

  26. Sebag, M., Schoenauer, M., Maitournam, H.: Parametric and non-parametric identification of macro-mechanical models. In: Quadraglia, D., et al. (eds.) Genetic Algorithms and Evolution Strategies in Engineering and Computer Sciences, pp. 327–340. John Wiley, Chichester (1997)

    Google Scholar 

  27. Zitzler, E., Thiele, L.: Multiobjective optimization using evolutionary algorithms - a comparative case study. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 292–301. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Dilão, R., Muraro, D., Nicolau, M., Schoenauer, M. (2009). Validation of a Morphogenesis Model of Drosophila Early Development by a Multi-objective Evolutionary Optimization Algorithm. In: Pizzuti, C., Ritchie, M.D., Giacobini, M. (eds) Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. EvoBIO 2009. Lecture Notes in Computer Science, vol 5483. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01184-9_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-01184-9_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01183-2

  • Online ISBN: 978-3-642-01184-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics