title: Aligning Metabolic Pathways Exploiting Binary Relation of Reactions.
abstract: Metabolic pathway alignment has been widely used to find one-to-one and/or one-to-many reaction mappings to identify the alternative pathways that have similar functions through different sets of reactions, which has important applications in reconstructing phylogeny and understanding metabolic functions. The existing alignment methods exhaustively search reaction sets, which may become infeasible for large pathways. To address this problem, we present an effective alignment method for accurately extracting reaction mappings between two metabolic pathways. We show that connected relation between reactions can be formalized as binary relation of reactions in metabolic pathways, and the multiplications of zero-one matrices for binary relations of reactions can be accomplished in finite steps. By utilizing the multiplications of zero-one matrices for binary relation of reactions, we efficiently obtain reaction sets in a small number of steps without exhaustive search, and accurately uncover biologically relevant reaction mappings. Furthermore, we introduce a measure of topological similarity of nodes (reactions) by comparing the structural similarity of the k-neighborhood subgraphs of the nodes in aligning metabolic pathways. We employ this similarity metric to improve the accuracy of the alignments. The experimental results on the KEGG database show that when compared with other state-of-the-art methods, in most cases, our method obtains better performance in the node correctness and edge correctness, and the number of the edges of the largest common connected subgraph for one-to-one reaction mappings, and the number of correct one-to-many reaction mappings. Our method is scalable in finding more reaction mappings with better biological relevance in large metabolic pathways.
authors.affiliation: School of Computer Science and Engineering, South China University of Technology, Guangzhou, China.|School of Computer, Electronics and Information, Guangxi University, Nanning, China.
authors.forename: Yiran
authors.identifier: http://orcid.org/0000-0001-6289-8766
authors.initials: Y
authors.lastname: Huang
authors.affiliation: School of Computer, Electronics and Information, Guangxi University, Nanning, China.
authors.forename: Cheng
authors.initials: C
authors.lastname: Zhong
authors.affiliation: Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands.
authors.forename: Hai Xiang
authors.initials: HX
authors.lastname: Lin
authors.affiliation: State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, Guangxi University, Nanning, China.
authors.forename: Jing
authors.initials: J
authors.lastname: Huang
mesh_terms: D053858:Metabolic Networks and Pathways; D008954:Models, Biological