Metabolism is the set of chemical reactions that occur in living organisms in order to maintain life, and a genome-scale metabolic network can be reconstructed by identifying, categorizing and interconnecting all the genes, proteins, reactions and metabolites that participate in the metabolic activity of a biological system to form a metabolic network. Enzymes play a very important part in metabolism, and the identification of all genes encoding metabolic enzymes and to assign correct Enzyme Commission classification (EC) numbers to them is pivotal in the reconstruction of metabolic networks. In this paper, we represent an automated and efficient gene-enzyme identification method in the reconstruction of metabolic networks: Hybrid Participle Algorithm (HPA). In order to prove the usefulness of HPA, we reconstructed the metabolic networks of Escherichia_coli_K12 using both PathoLogic and HPA. The results indicate that by using HPA, we can identify more metabolic genes and their corresponding enzymes than by using other methods based on the whole name match method, such as PathoLogic. And the F-measures of our results are higher than the F-measures of the results obtained by using PathoLogic. HPA provides an automated and efficient way to identify the metabolic genes and their corresponding enzymes.
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