Symbolic inference of xenobiotic metabolism.
We present a new symbolic computational approach to elucidate the biochemical networks of living systems de novo and we apply it to an important biomedical problem: xenobiotic metabolism. A crucial issue in analyzing and modeling a living organism is understanding its biochemical network beyond what is already known. Our objective is to use the available metabolic information in a representational framework that enables the inference of novel biochemical knowledge and whose results can be validated experimentally. We describe a symbolic computational approach consisting of two parts. First, biotransformation rules are inferred from the molecular graphs of compounds in enzyme-catalyzed reactions. Second, these rules are recursively applied to different compounds to generate novel metabolic networks, containing new biotransformations and new metabolites. Using data for 456 generic reactions and 825 generic compounds from KEGG we were able to extract 110 biotransformation rules, which generalize a subset of known biocatalytic functions. We tested our approach by applying these rules to ethanol, a common substance of abuse and to furfuryl alcohol, a xenobiotic organic solvent, which is absent in metabolic databases. In both cases our predictions on the fate of ethanol and furfuryl alcohol are consistent with the literature on the metabolism of these compounds.
School of Medicine, University of Colorado, 4200 East 9th Avenue, B-119, Denver, CO 80262, USA. daniel.mcshan@uchsc.edu
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McShan
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Updadhayaya
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Shah
D000465:Algorithms; D001711:Biotransformation; D019295:Computational Biology; D016208:Databases, Factual; D000431:Ethanol / Q000378:metabolism; D005663:Furans / Q000378:metabolism; D008954:Models, Biological; D015262:Xenobiotics / Q000378:metabolism*
D005663:Furans; D015262:Xenobiotics; D000431:Ethanol; C012986:furfuryl alcohol