ecmtool: fast and memory-efficient enumeration of elementary conversion modes.
| Autor: | Buchner B; acib GmbH, Austrian Centre of Industrial Biotechnology, 1190 Vienna, Austria., Clement TJ; Systems Biology Lab, Vrije Universiteit, 1081HV Amsterdam, The Netherlands., de Groot DH; Biozentrum, Swiss Institute of Bioinformatics, University of Basel, 4056 Basel, Switzerland., Zanghellini J; Department of Analytical Chemistry, University of Vienna, 1090 Vienna, Austria. |
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| Jazyk: | angličtina |
| Zdroj: | Bioinformatics (Oxford, England) [Bioinformatics] 2023 Mar 01; Vol. 39 (3). |
| DOI: | 10.1093/bioinformatics/btad095 |
| Abstrakt: | 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 4.2×109 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. (© The Author(s) 2023. Published by Oxford University Press.) |
| Databáze: | MEDLINE |
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