Abstract

BackgroundMolecular pathways represent an ensemble of interactions occurring among molecules within the cell and between cells. The identification of similarities between molecular pathways across organisms and functions has a critical role in understanding complex biological processes. For the inference of such novel information, the comparison of molecular pathways requires to account for imperfect matches (flexibility) and to efficiently handle complex network topologies. To date, these characteristics are only partially available in tools designed to compare molecular interaction maps.ResultsOur approach MIMO (Molecular Interaction Maps Overlap) addresses the first problem by allowing the introduction of gaps and mismatches between query and template pathways and permits -when necessary- supervised queries incorporating a priori biological information. It then addresses the second issue by relying directly on the rich graph topology described in the Systems Biology Markup Language (SBML) standard, and uses multidigraphs to efficiently handle multiple queries on biological graph databases. The algorithm has been here successfully used to highlight the contact point between various human pathways in the Reactome database.ConclusionsMIMO offers a flexible and efficient graph-matching tool for comparing complex biological pathways.

Highlights

  • Molecular pathways represent an ensemble of interactions occurring among molecules within the cell and between cells

  • We compared the performances of MIMO with those of SAGA [20], which has been explicitly designed for efficient graph-database querying and which is, to date, the only publicly available tool closely related to our work

  • The main features of MIMO are: (i) Easy-to-use: MIMO takes as input biological networks encoded with the Systems Biology Markup Language (SBML) standard

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Summary

Results

Our approach MIMO (Molecular Interaction Maps Overlap) addresses the first problem by allowing the introduction of gaps and mismatches between query and template pathways and permits -when necessarysupervised queries incorporating a priori biological information. It addresses the second issue by relying directly on the rich graph topology described in the Systems Biology Markup Language (SBML) standard, and uses multidigraphs to efficiently handle multiple queries on biological graph databases. The algorithm has been here successfully used to highlight the contact point between various human pathways in the Reactome database

Background
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