SubMAP: Aligning metabolic pathways with subnetwork mappings

Ferhat Ay*, Manolis Kellis, Tamer Kahveci

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    65 Scopus citations

    Abstract

    We consider the problem of aligning two metabolic pathways. Unlike traditional approaches, we do not restrict the alignment to one-to-one mappings between the molecules (nodes) of the input pathways (graphs). We follow the observation that, in nature, different organisms can perform the same or similar functions through different sets of reactions and molecules. The number and the topology of the molecules in these alternative sets often vary from one organism to another. With the motivation that an accurate biological alignment should be able to reveal these functionally similar molecule sets across different species, we develop an algorithm that first measures the similarities between different nodes using a mixture of homology and topological similarity. We combine the two metrics by employing an eigenvalue formulation. We then search for an alignment between the two input pathways that maximizes a similarity score, evaluated as the sum of the similarities of the mapped subnetworks of size at most a given integer k, and also does not contain any conflicting mappings. Here we prove that this maximization is NP-hard by a reduction from the maximum weight independent set (MWIS) problem. We then convert our problem to an instance of MWIS and use an efficient vertex-selection strategy to extract the mappings that constitute our alignment. We name our algorithm SubMAP (Subnetwork Mappings in Alignment of Pathways). We evaluate its accuracy and performance on real datasets. Our empirical results demonstrate that SubMAP can identify biologically relevant mappings that are missed by traditional alignment methods. Furthermore, we observe that SubMAP is scalable for metabolic pathways of arbitrary topology, including searching for a query pathway of size 70 against the complete KEGG database of 1,842 pathways. Implementation in C++ is available at http://bioinformatics.cise.ufl.edu/SubMAP. html.

    Original languageEnglish (US)
    Pages (from-to)219-235
    Number of pages17
    JournalJournal of Computational Biology
    Volume18
    Issue number3
    DOIs
    StatePublished - Mar 1 2011

    Funding

    Keywords

    • Alternative reaction sets
    • maximum weight independent set
    • metabolic pathway alignment
    • one-to-many mappings
    • subnetwork mappings

    ASJC Scopus subject areas

    • Computational Mathematics
    • Genetics
    • Molecular Biology
    • Computational Theory and Mathematics
    • Modeling and Simulation

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