Evolutionary techniques for image processing a large dataset of early Drosophila gene expression

Authors:
Alexander Spirov;David M. Holloway
Affiliations:
Department of Applied Mathematics and Statistics and The Center for Developmental Genetics, Stony Brook University, Stony Brook, NY and The Sechenov Institute of Evolutionary Physiology and Bioche ...;Mathematics Department, British Columbia Institute of Technology, Burnaby, British Columbia, Canada and Chemistry Department, University of British Columbia, Vancouver, British Columbia, Canada
Venue:
EURASIP Journal on Applied Signal Processing
Year:
2003

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Abstract

Understanding how genetic networks act in embryonic development requires a detailed and statistically significant dataset integrating diverse observational results. The fruit fly (Drosophila melanogaster) is used as a model organism for studying developmental genetics. In recent years, several laboratories have systematically gathered confocal microscopy images of patterns of activity (expression) for genes governing early Drosophila development. Due to both the high variability between fruit fly embryos and diverse sources of observational errors, some new nontrivial procedures for processing and integrating the raw observations are required. Here we describe processing techniques based on genetic algorithms and discuss their efficacy in decreasing observational errors and illuminating the natural variability in gene expression patterns. The specific developmental problem studied is anteroposterior specification of the body plan.