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Bioinformatics 2007 23(13):i359-i366; doi:10.1093/bioinformatics/btm170
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© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

SPINE: a framework for signaling-regulatory pathway inference from cause-effect experiments

Oved Ourfali 1, Tomer Shlomi 1, Trey Ideker 3, Eytan Ruppin 1,2 and Roded Sharan 1,*

1School of Computer Science, 2School of Medicine, Tel-Aviv University, Tel-Aviv, Israel and 3Department of Bioengineering, University of California, San Diego, CA 92093, USA

*To whom correspondence should be addressed.


  Abstract

Motivation: The complex program of gene expression allows the cell to cope with changing genetic, developmental and environmental conditions. The accumulating large-scale measurements of gene knockout effects and molecular interactions allow us to begin to uncover regulatory and signaling pathways within the cell that connect causal to affected genes on a network of physical interactions.

Results: We present a novel framework, SPINE, for Signaling-regulatory Pathway INferencE. The framework aims at explaining gene expression experiments in which a gene is knocked out and as a result multiple genes change their expression levels. To this end, an integrated network of protein–protein and protein-DNA interactions is constructed, and signaling pathways connecting the causal gene to the affected genes are searched for in this network. The reconstruction problem is translated into that of assigning an activation/repression attribute with each protein so as to explain (in expectation) a maximum number of the knockout effects observed. We provide an integer programming formulation for the latter problem and solve it using a commercial solver.

We validate the method by applying it to a yeast subnetwork that is involved in mating. In cross-validation tests, SPINE obtains very high accuracy in predicting knockout effects (99%). Next, we apply SPINE to the entire yeast network to predict protein effects and reconstruct signaling and regulatory pathways. Overall, we are able to infer 861 paths with confidence and assign effects to 183 genes. The predicted effects are found to be in high agreement with current biological knowledge.

Availability: The algorithm and data are available at http://cs.tau.ac.il/~roded/SPINE.html

Contact: roded{at}post.tau.ac.il


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