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Bioinformatics Advance Access originally published online on August 30, 2007
Bioinformatics 2007 23(20):2775-2783; doi:10.1093/bioinformatics/btm409
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© The Author 2007. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Mining biological networks for unknown pathways

Ali Cakmak * and Gultekin Ozsoyoglu

Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA

*To whom correspondence should be addressed.


  Abstract

Motivation: Biological pathways provide significant insights on the interaction mechanisms of molecules. Presently, many essential pathways still remain unknown or incomplete for newly sequenced organisms. Moreover, experimental validation of enormous numbers of possible pathway candidates in a wet-lab environment is time- and effort-extensive. Thus, there is a need for comparative genomics tools that help scientists predict pathways in an organism's biological network.

Results: In this article, we propose a technique to discover unknown pathways in organisms. Our approach makes in-depth use of Gene Ontology (GO)-based functionalities of enzymes involved in metabolic pathways as follows:

  1. Model each pathway as a biological functionality graph of enzyme GO functions, which we call pathway functionality template.
  2. Locate frequent pathway functionality patterns so as to infer previously unknown pathways through pattern matching in metabolic networks of organisms.

We have experimentally evaluated the accuracy of the presented technique for 30 bacterial organisms to predict around 1500 organism-specific versions of 50 reference pathways. Using cross-validation strategy on known pathways, we have been able to infer pathways with 86% precision and 72% recall for enzymes (i.e. nodes). The accuracy of the predicted enzyme relationships has been measured at 85% precision with 64% recall.

Availability: Code upon request.

Contact: ali.cakmak{at}case.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: John Quackenbush

Received on May 2, 2007; revised on July 20, 2007; accepted on August 8, 2007

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