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Bioinformatics Advance Access published online on July 19, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn378
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© The Author (2008). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Data-Driven Extraction of Relative Reasoning Rules to Limit Combinatorial Explosion in Biodegradation Pathway Prediction

Kathrin Fenner 1,2,3,*, Junfeng Gao 4, Stefan Kramer 5, Lynda Ellis 4 and Larry Wackett 3

1Eawag, Swiss Federal Institute of Aquatic Science and Technology, CH-8600 Dübendorf, Switzerland
2Institute of Biogeochemistry and Pollutant Dynamics (IBP), ETH Zurich, CH-8092 Zürich, Switzerland
3Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, St. Paul, MN 55108, USA
4Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN 55455, USA
5Institute for Informatics/I12, Technical University of Munich, Boltzmannstr. 3, D-85748 Garching bei München, Germany

*To whom correspondence should be addressed. Kathrin Fenner, E-mail: kathrin.fenner{at}eawag.ch


  Abstract

Motivation: The University of Minnesota Pathway Prediction System (UM-PPS) is a rule-based expert system to predict plausible biodegradation pathways for organic compounds. However, iterative application of these rules to generate biodegradation pathways leads to combinatorial explosion. We use data from known biotransformation pathways to rationally determine biotransformation priorities (relative reasoning rules) to limit this explosion.

Results: 112 relative reasoning rules were identified and implemented. In one prediction step, i.e., per one generation predicted, the use of relative reasoning decreases predicted biotransformations by over 25% for 50 compounds used to generate the rules and by about 15% for an external validation set of 47 xenobiotics, including pesticides, biocides, and pharmaceuticals. The percentage of correctly predicted, experimentally known products remains at 75% when relative reasoning is used. The set of relative reasoning rules identified therefore effectively reduces the number of predicted transformation products without compromising the quality of the predictions.

Availability: The UM-PPS server is freely available on the web to all users at the time of submission of this manuscript and will be available following publication at: http://umbbd.msi.umn.edu/predict/.

Contact: kathrin.fenner{at}eawag.ch

Supplementary Information: available at Bioinformatics online.

Associate Editor: Thomas Lengauer

Received on October 16, 2007; revised on June 17, 2008; accepted on July 17, 2008

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