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Experimental Determination of Intrinsic Drosophila Embryo Coordinates by Evolutionary Computation

  • Alexander V. Spirov
  • Carlos E. Vanario-Alonso
  • Ekaterina N. Spirova
  • David M. Holloway
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7986)

Abstract

Early fruit fly embryo development begins with the formation of a chemical blueprint that guides cellular movements and the development of organs and tissues. This blueprint sets the intrinsic spatial coordinates of the embryo. The coordinates are curvilinear from the start, becoming more curvilinear as cells start coherent movements several hours into development. This dynamic aspect of the curvature is an important characteristic of early embryogenesis: characterizing it is crucial for quantitative analysis and dynamic modeling of development. This presents a number of methodological problems for the elastic deformation of 3D and 4D data from confocal microscopy, to standardize images and follow temporal changes. The parameter searches for these deformations present hard optimization problems. Here we describe our evolutionary computation approaches to these problems. We outline some of the immediate applications of these techniques to crucial problems in Drosophila developmental biology.

Keywords

Coordinate Transformation Drosophila Embryo Segmentation Gene Intact Embryo Cylindrical Projection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    De Boer, B.A., Ruijter, J.M., Voorbraak, F.P.J.M., Moorman, A.F.: More than a decade of developmental gene expression atlases: where are we now? Nucleic Acids Res. 37, 7349–7359 (2009)CrossRefGoogle Scholar
  2. 2.
    Fowlkes, C.C., LuengoHendriks, C.L., Keränen, S.V.E., Weber, G.H., et al.: A Quantitative Spatio-temporal Atlas of Gene Expression in the Drosophila Blastoderm. Cell 133, 364–374 (2008)CrossRefGoogle Scholar
  3. 3.
    Pereanu, W., Hartenstein, V.: Digital three-dimensional models of Drosophila development. Curr. Opin. Genet. Dev. 14, 382–391 (2004)CrossRefGoogle Scholar
  4. 4.
    Pawley, J.B. (ed.): Handbook of Biological Confocal Microscopy, 3rd edn. Springer, Berlin (2006)Google Scholar
  5. 5.
    Spirov, A.V., Kazansky, A.B., Timakin, D.L., Reinitz, J., Kosman, D.: Reconstruction of the dynamics of the Drosophila genes from sets of images sharing a common pattern. Special Issue on Imaging In Bioinformatics. Journal of Real-Time Imaging 8, 507–518 (2002)zbMATHCrossRefGoogle Scholar
  6. 6.
    Spirov, A.V., Holloway, D.: Evolutionary techniques for image processing to construct integrated dataset of early Drosophila embryo genes activity. EURASIP Journal on Applied Signal Processing 8, 824–833 (2003)Google Scholar
  7. 7.
    Surkova, S., Kosman, D., Kozlov, K., Manu, M.E., Samsonova, A.A., Spirov, A., Vanario-Alonso, C.E., Samsonova, M., Reinitz, J.: Characterization of the Drosophila segment determination morphome. Dev. Biol. 313, 844–862 (2008)CrossRefGoogle Scholar
  8. 8.
    Kozlov, K., Myasnikova, E., Pisarev, A., Samsonova, M., Reinitz, J.: A method for two-dimensional registration and construction of the two-dimensional atlas of gene expression patterns in situ. Silico Biol. 2, 125–141 (2002)Google Scholar
  9. 9.
    Sorzano, C.O.S., Blagov, M., Thevenaz, P., Myasnikova, E., Samsonova, M., Unser, M.: Algorithm for Spline-Based Elastic Registration in Application to Confocal Images of Gene Expression. Pattern Recognition and Image Analysis 16, 93–96 (2006)CrossRefGoogle Scholar
  10. 10.
    Preibisch, S.W., Saalfeld, S., Schindelin, J., Tomancak, P.: Software for bead-based reg-istration of selective plane illumination microscopy data. Nat. Methods 7, 418–419 (2010)CrossRefGoogle Scholar
  11. 11.
    Spirov, A.V., Timakin, D.L., Reinitz, J., Kosman, D.: Using of Evolutionary Computa-tions in Image Processing for Quantitative Atlas of Drosophila Genes Expression. In: Proceedings of Third European Workshop on Evolutionary Computation In Image Analysis and Signal Processing, Milan, pp. 374–383 (2001)Google Scholar
  12. 12.
    Spirov, A.V., Timakin, D.L., Reinitz, J., Kosman, D.: Experimental Determination of Drosophila Embryonic Coordinates by Genetic Algorithms, the Simplex Method, and Their Hybrid. In: Oates, M.J., Lanzi, P.L., Li, Y., Cagnoni, S., Corne, D.W., Fogarty, T.C., Poli, R., Smith, G.D. (eds.) EvoIASP 2000, EvoWorkshops 2000, EvoFlight 2000, EvoSCONDI 2000, EvoSTIM 2000, EvoTEL 2000, and EvoROB/EvoRobot 2000. LNCS, vol. 1803, pp. 97–106. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  13. 13.
    Thompson, D.W.: On Growth and Form. Dover reprint of 2nd edn., 1942, 1st edn., 1917) (1992)Google Scholar
  14. 14.
    Holloway, D.M., Harrison, L.G., Kosman, D., Vanario-Alonso, C.E., Spirov, A.V.: Analysis of pattern precision shows that Drosophila segmentation develops substantial independence from gradients of maternal gene products. Dev. Dyn. 235, 2949–2960 (2006)CrossRefGoogle Scholar
  15. 15.
    Zamparo, L., Perkins, T.J.: Statistical lower bounds on protein copy number from fluorescence expression images. Bioinformatics 25, 2670–2676 (2009)CrossRefGoogle Scholar
  16. 16.
    Myasnikova, E., Samsonova, M., Kosman, D., Reinitz, J.: Removal of background signal from in situ data on the expression of segmentation genes in Drosophila. Dev. Genes Evol. 215, 320–326 (2005)CrossRefGoogle Scholar
  17. 17.
    Surkova, S., Myasnikova, E., Janssens, H., et al.: Pipeline for acquisition of quantitative data on segmentation gene expression from confocal images. Fly 2, 58–66 (2008)Google Scholar
  18. 18.
    Poustelnikova, E., Pisarev, A., Blagov, M., Samsonova, M., Reinitz, J.: A database for management of gene expression data in situ. Bioinformatics 20, 2212–2221 (2004)CrossRefGoogle Scholar
  19. 19.
    [ ]Lecuyer, E., Yoshida, H., Parthasarathy, N., Alm, C.et al: Global analysis of mRNA lo-calization reveals a prominent role in organizing cellular architecture and function. Cell 131 (2007) 174-187 Google Scholar
  20. 20.
    Zinzen, R.P., Senger, K., Levine, M., Papatsenko, D.: Computational models for neuro-genic gene expression in the Drosophila embryo. Current Biology 16, 1358–1365 (2006)CrossRefGoogle Scholar
  21. 21.
    Gregor, T., Bialek, W., de Ruyter van Steveninck, R.R., Tank, D.W., Wieschaus, E.F.: Diffusion and scaling during early embryonic pattern formation. Proc. Natl. Acad. Sci (USA) 102, 18403–18407 (2005)CrossRefGoogle Scholar
  22. 22.
    He, F., Wen, Y., Deng, J., Lin, X., Lu, L.J., Jiao, R., Ma, J.: Probing intrinsic properties of a robust morphogen gradient in Drosophila. Dev. Cell 15, 558–567 (2008)CrossRefGoogle Scholar
  23. 23.
    Crauk, O., Dostatni, N.: Bicoid determines sharp and precise target gene expression in the Drosophila embryo. Current Biol. 15, 1888–1898 (2005)CrossRefGoogle Scholar
  24. 24.
    Arvey, A., Hermann, A., Hsia, C.C., Ie, E., Freund, Y., McGinnis, W.: Minimizing off-target signals in RNA fluorescent in situ hybridization. Nucleic Acids Res. (2010), doi:10.1093/nar/gkq042Google Scholar
  25. 25.
    Akam, M.: The molecular basis for metameric pattern in the Drosophila embryo. Development 101, 1–22 (1987)Google Scholar
  26. 26.
    Lawrence, P.A.: The making of a fly. London, 228 p. Blackwell Scientific (1992)Google Scholar
  27. 27.
    Panzer, S., Weigel, D., Beckendorf, S.K.: Organogenesis in Drosophila melanogaster: embryonic salivary gland determination is controlled by homeotic and dorsoventral patterning genes. Development 114, 49–57 (1992)Google Scholar
  28. 28.
    Skeath, J.B., Panganiban, G.F., Carroll, S.B.: The ventral nervous system defective gene controls proneural gene expression at two distinct steps during neuroblast formation in Drosophila. Development 120, 1517–1524 (1994)Google Scholar
  29. 29.
  30. 30.
  31. 31.
  32. 32.
    Preibisch, S.W., Saalfeld, S., Rohlfing, T., Tomancák, P.: Bead-based mosaicing of single plane illumination microscopy images using geometric local descriptor matching. In: SPIE Medical Imaging Conference, Lake Buena Vista, Fla, pp. 1–10 (2009)Google Scholar
  33. 33.
    LuengoHendriks, C.L.: KeränenS.V.E., Fowlkes, C.C., Simirenko, L.et al.: Three-dimensional morphology and gene expression in the Drosophilablastoderm at cellular resolution I: data acquisition pipeline. Genome Biology 7, R123 (2006), doi:10.1186/gb-2006-7-12-r123Google Scholar
  34. 34.
    Bookstein, F.L.: Principal Warps: Thin-Plate Splines and Decomposition of Deformations. IEEE Trans. Pattern Analysis and Machine Intelligence 11, 567–585 (1989)zbMATHCrossRefGoogle Scholar
  35. 35.
    Siegel, A.F.: Geometric data analysis: An interactive graphics program for shape comparison. In: Launer, R.L., Siegel, A.F. (eds.) Modern Data Analysis, pp. 103–122. Academic Press (1981)Google Scholar
  36. 36.
    Siegel, A.F., Benson, R.H.: A robust comparison of biological shapes. Biometrics 38, 341–350 (1982)zbMATHCrossRefGoogle Scholar
  37. 37.
    Bookstein, F.L.: Morphometric Tools for Landmark Data: Geometry and Biology, p. 435. Cambridge U. Press, Cambridge (1991)zbMATHGoogle Scholar
  38. 38.
    Abbasi, R., Mashhadihan, M., Abbasi, M., Kiabi, B.: Geometric morphometric study of populations of the social wasp, Polistesdominulus (Christ, 1791) from Zanjan province, north-west Iran. New Zealand Journal of Zoology 36, 41–46 (2009)CrossRefGoogle Scholar
  39. 39.
    Keränen, S.V.E., Fowlkes, C.C., LuengoHendriks, C.L., Sudar, D., Knowles, D.W., Malik, J., Biggin, M.D.: Three-dimensional morphology and gene expression in the Drosophilablastoderm at cellular resolution II: dynamics. Genome Biology 7, R124 (2006), doi:10.1186/gb-2006-7-12-r124Google Scholar
  40. 40.
    Zallen, J.A., Zallen, R.: Cell-pattern disordering during convergent extension in Drosophila. Journal of Physics: Condensed Matter 16, S5073–S5080 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Alexander V. Spirov
    • 1
  • Carlos E. Vanario-Alonso
    • 3
  • Ekaterina N. Spirova
    • 2
    • 3
  • David M. Holloway
    • 4
  1. 1.Computer Science and Center of Excellence in Wireless and Information TechnologyStony Brook UniversityUSA
  2. 2.The I.M.Sechenov Institute of Evolutionary Physiology & BiochemistrySt.-PetersburgRussia
  3. 3.Applied Mathematics and Statistics and Center for Developmental GeneticsStony Brook UniversityUSA
  4. 4.Mathematics Department, British Columbia Institute of TechnologyBurnabyCanada

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