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Ann. N.Y. Acad. Sci., Annals PrePrint, published online ahead of print October 9, 2007 doi: 10.1196/annals.1407.019 Copyright © 2007 by the New York Academy of Sciences description
1 Biomedical Informatics, Columbia University, 1130 St. Nicholas Ave, New York, New York, 10032, United States 2 Biomedical Informatics, Columbia University, 1130 St Nicholas Ave, New York, New York, 10032, United States
* To whom correspondence should be addressed. E-mail: adam{at}dbmi.columbia.edu. PrePrint Abstract
Since the advent of gene expression microarray technology over ten years ago, many computational approaches have been developed with the goal of using statistical associations between mRNA abundance profiles to predict transcriptional regulatory interactions. The ultimate goal is to develop causal network models describing the transcriptional influences that genes exert on each other (via their protein products), which can be used to predict network disruptions (e.g. mutations) leading to a disease phenotype, as well as the appropriate therapeutic intervention. However, microarray data measures only a small component of the interacting variables in a genetic regulatory network, as cells are known to regulate gene expression via many diverse mechanisms. While many researchers have acknowledged the questionable interpretation of statistical dependencies between mRNA profiles, there has been very little work on theoretically characterizing the nature of inferred dependencies using models that account for unobserved interacting variables. In this work, we review the theory behind reverse engineering algorithms derived from three separate disciplines—system control theory, graphical models, and information theory—and highlight several mathematical relationships between the various methods. We then apply recent theoretical work on constructing graphical models with latent variables to the context of reverse engineering genetic networks. We demonstrate that even the addition of simple latent variables induces statistical dependencies between non-directly interacting (e.g. co-regulated) genes that cannot be eliminated by conditioning on any observed variables. Key Words:
Reverse Engineering, Gene Expression, Latent Variables
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