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 4.2x10(9) ECMs and still contains several redundant sub-networks.

Automated summary

Characterizing all steady-state flux distributions in metabolic models is challenging due to the explosion of possibilities. We integrate a scalable parallel vertex enumeration method into an existing tool, ecmtool, to speed up computation and reduce memory requirements. By applying this enhanced tool to a minimal cell metabolic model, we discover a large number of elementary conversion modes (ECMs) and identify redundant sub-networks.