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Bioinformatics 2006 22(14):e497-e506; doi:10.1093/bioinformatics/btl224
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© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@oxfordjournals.org

Computational inference of the molecular logic for synaptic connectivity in C. elegans

Vinay Varadan 1, David M. Miller, III 2 and Dimitris Anastassiou 1,*

1 Center for Computational Biology and Bioinformatics (C2B2) and Department of Electrical Engineering, Columbia University New York, NY 10027, USA
2 Department of Cell and Developmental Biology, Vanderbilt University Nashville, TN 37232, USA

*To whom correspondence should be addressed.

Motivation: The nematode C. elegans is an ideal model organism in which to investigate the biomolecular mechanisms underlying the connectivity of neurons, because synaptic connections are described in a comprehensive wiring diagram and methods for defining gene expression profiles of individual neurons are now available.

Results: Here we present computational techniques linking these two types of information. A systems-based approach (EMBP: Entropy Minimization and Boolean Parsimony) identifies sets of synergistically interacting genes whose joint expression predicts neural connectivity. We introduce an information theoretic measure of the multivariate synergy, a fundamental concept in systems biology, connecting the members of these gene sets. We present and validate our preliminary results based on publicly available information, and demonstrate that their synergy is exceptionally high indicating joint involvement in pathways. Our strategy provides a robust methodology that will yield increasingly more accurate results as more neuron-specific gene expression data emerge. Ultimately, we expect our approach to provide important clues for universal mechanisms of neural interconnectivity.

Contact: anastas{at}ee.columbia.edu

Supplementary Information: Expression and connectivity data will be available and maintained in the future as new results become available, together with software and additional clarifying descriptions of our techniques, on www.ee.columbia.edu/~anastas/ismb2006


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