ecmtool: fast and memory-efficient enumeration of elementary conversion modes

MOTIVATION: Characterizing all steady-state flux distributions in metabolic models remains limited to small models due to the explosion of possibilities. Often it is sufficient to look only at all possible overall conversions a cell can catalyze ignoring the details of intracellular metabolism. Such a characterization is achieved by elementary conversion modes (ECMs), which can be conveniently computed with ecmtool. However, currently, ecmtool is memory intensive, and it cannot be aided appreciably by parallelization. RESULTS: We integrate mplrs—a scalable parallel vertex enumeration method—into ecmtool. This speeds up computation, drastically reduces memory requirements and enables ecmtool’s use in standard and high-performance computing environments. We show the new capabilities by enumerating all feasible ECMs of the near-complete metabolic model of the minimal cell JCVI-syn3.0. Despite the cell’s minimal character, the model gives rise to [Formula: see text] ECMs and still contains several redundant sub-networks. AVAILABILITY AND IMPLEMENTATION: ecmtool is available at https://github.com/SystemsBioinformatics/ecmtool. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.