Ecmtool: fast and memory-efficient enumeration of elementary conversion modes.

Abstract

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. 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 4.2×109 ECMs and still contains several redundant sub-networks. ecmtool is available at https://github.com/SystemsBioinformatics/ecmtool. Supplementary data are available at Bioinformatics online.