PathMiner: predicting metabolic pathways by heuristic search.
BACKGROUND
Automated methods for biochemical pathway inference are becoming increasingly important for understanding biological processes in living and synthetic systems. With the availability of data on complete genomes and increasing information about enzyme-catalyzed biochemistry it is becoming feasible to approach this problem computationally. In this paper we present PathMiner, a system for automatic metabolic pathway inference. PathMiner predicts metabolic routes by reasoning over transformations using chemical and biological information.


RESULTS
We build a biochemical state-space using data from known enzyme-catalyzed transformations in Ligand, including, 2917 unique transformations between 3890 different compounds. To predict metabolic pathways we explore this state-space by developing an informed search algorithm. For this purpose we develop a chemically motivated heuristic to guide the search. Since the algorithm does not depend on predefined pathways, it can efficiently identify plausible routes using known biochemical transformations.
McShan
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School of Medicine, University of Colorado, 4200 East Ninth Avenue, C-245 Denver, Colorado 80262, USA.
Rao
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Shah
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D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D008660:Metabolism / Q000502:physiology*; D008954:Models, Biological; D008956:Models, Chemical; D009097:Multienzyme Complexes / Q000378:metabolism*; D012984:Software
D009097:Multienzyme Complexes