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Using of Evolutionary Computations in Image Processing for Quantitative Atlas of Drosophila Genes Expression
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Using of Evolutionary Computations in Image Processing for Quantitative Atlas of Drosophila Genes Expression
Alexander V. Spirov5, Dmitry L. Timakin6, John Reinitz7 and David Kosman7
| (5) |
The Sechenov Institute of Evolutionary Physiology and Biochemistry, 44 Thorez Ave., St. Petersburg, 194223, Russia |
| (6) |
Dept. of Automation and Control Systems, Computer Science and Engineering Faculty, St.-Petersburg State Technical University, 29 Polytechnic St, St.-Petersburg, 194064, Russia |
| (7) |
Dept. of Biochemistry and Molecular Biology, Mt. Sinai Medical School, 1020, One Gustave L. Levy Place, New York, NY 10029, USA |
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).
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