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Using of Evolutionary Computations in Image Processing for Quantitative Atlas of Drosophila Genes Expression

  • Alexander V. Spirov
  • Dmitry L. Timakin
  • John Reinitz
  • David Kosman
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2037)

Abstract

It is well known, that organism of animal, consisting of many billions cells, is formed by consequent divisions of the only cell - zygote. In so doing, embryo cells are permanently communicating by means of biochemical signals. As a result, proper genes were being activated at proper time in proper cells of the embryo.

Modern confocal microscopes being equipped by lasers and computers give possibility to trace-through the cell fate of early embryo for such a classical model object, as fruit fly Drosophila melanogaster. By this approach, it is possible to retrace the detailed dynamics of activity of genes-controllers of development with the resolution on the level of individual nuclei for each of 4-6 thousand cells, composing early fly embryo. The final result of this analysis will be the quantitative atlas of Drosophila genes action (expression): http://www.iephb.nw.ru/ spirov/atlas. To achieve this aim we need to receive statistically authentic summary picture of detailed pattern dynamics proceeding from a large number of scanned embryos. This presupposes the elaboration of the methods of preprocessing, elastic deformation, registration and interpolation of the confocal-microscopy images of embryos.

For this purpose we apply modern heuristic methods of optimization to the processing of our images. Namely classic GA approach is used for finding a suitable elastic deformation, for registering the images and for finding a Fourier interpolation of concentration (gene-expression) surfaces. All GA programs considered are the developments of “evolution strategies program” from EO-0.8.5 C++ library (Merelo).

Keywords

Elastic Deformation Evolutionary Computation Segmentation Gene Irregular Mesh Early Drosophila Embryo 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Alexander V. Spirov
    • 1
  • Dmitry L. Timakin
    • 2
  • John Reinitz
    • 3
  • David Kosman
    • 3
  1. 1.The Sechenov Institute of Evolutionary Physiology and BiochemistrySt. PetersburgRussia
  2. 2.Dept. of Automation and Control Systems, Computer Science and Engineering FacultySt.-Petersburg State Technical UniversitySt.-PetersburgRussia
  3. 3.Dept. of Biochemistry and Molecular BiologyMt. Sinai Medical SchoolNew YorkUSA

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