Optimal Experimental Design in the Modelling of Pattern Formation

  • Adrián López García de Lomana
  • Àlex Gómez-Garrido
  • David Sportouch
  • Jordi Villà-Freixa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5101)

Abstract

Gene regulation plays a major role in the control of developmental processes. Pattern formation, for example, is thought to be regulated by a limited number genes translated into transcription factors that control the differential expression of other genes in different cells in a given tissue. We focused on the Notch pathway during the formation of chess-like patterns along development. Simplified models exist of the patterning by lateral inhibition due to the Notch-Delta signalling cascade. We show here how parameters from the literature are able to explain the steady-state behavior of model tissues of several sizes, although they are not able to reproduce time series of experiments. In order to refine the parameters set for data from real experiments we propose a practical implementation of an optimal experimental design protocol that combines parameter estimation tools with sensitivity analysis, in order to minimize the number of additional experiments to perform.

Keywords

lateral inhibition GRN optimal experimental design multicellular system 

References

  1. 1.
    Tomline, C.J., Axelrod, J.D.: Biology by numbers: mathematical modelling in developmental biology. Nature Reviews 8, 331–340 (2007)Google Scholar
  2. 2.
    Jaeger, J., Surkova, S., Blagov, M., Janssens, H., Kosman, D., Kozlov, K.N., Manu, M.E., Vanario-Alonso, C., Samsonova, M., Sharp, D.H., Reinitz, J.: Dynamic control of positional information in the early Drosophila embryo. Nature 430, 368–371 (2004)CrossRefGoogle Scholar
  3. 3.
    de Jong, H.: Modeling and simulation of genetic regulatory systems: a literature review. J Comput. Biol. 9(1), 67–103 (2002)CrossRefGoogle Scholar
  4. 4.
    Meinhardt, H.: Computational modelling of epithelial patterning. Curr. Opin. Genet. Dev. 17(4), 272–280 (2007)CrossRefGoogle Scholar
  5. 5.
    von Dassow, G., Meir, E., Munro, E.M., Odell, G.M.: The segment polarity network is a robust developmental module. Nature 406, 188–192 (2000)CrossRefGoogle Scholar
  6. 6.
    Faller, D., Klingmüller, U.T.J.: Simulation methods for optimal experimental design in systems biology. Simulation 79, 717–725 (2003)CrossRefGoogle Scholar
  7. 7.
    Coti, C., Herault, T., Peyronnet, S., Rezmerita, A., Cappello, F.: Grid services for MPI. In: ACM/IEEE (ed.) Proceedings of the 8th IEEE International Symposium on Cluster Computing and the Grid (CCGrid 2008), Lyon, France (May 2008)Google Scholar
  8. 8.
    Alsina, B., Abello, G., Ulloa, E., Henriqw, D., Pujades, C., Giraldez, F.: FGF signaling is required for determination of otic neuroblasts in the chick embryo. Dev. Biol. 267(1), 119–134 (2004)CrossRefGoogle Scholar
  9. 9.
    Rodriguez-Fernandez, M., Egea, J.A., Banga, J.R.: Novel metaheuristic for parameter estimation in nonlinear dynamic biological systems. BMC Bioinformatics 7, 483 (2006)CrossRefGoogle Scholar
  10. 10.
    Alsina, B., Garcia de Lomana, A., Villà-Freixa, F., Giraldez, F.: (submitted, 2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Adrián López García de Lomana
    • 1
  • Àlex Gómez-Garrido
    • 1
  • David Sportouch
    • 1
  • Jordi Villà-Freixa
    • 1
  1. 1.Grup de Recerca en Informàtica BiomèdicaIMIM-Universitat Pompeu FabraBarcelonaSpain

Personalised recommendations