ecmtool: fast and memory-efficient enumeration of elementary conversion modes
- Author(s)
- Bianca Buchner, Tom J. Clement, Daan H. de Groot, Jürgen Zanghellini
- Abstract
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.
- Organisation(s)
- Department of Analytical Chemistry
- External organisation(s)
- acib – Austrian Centre of Industrial Biotechnology, Vrije Universiteit Amsterdam, Universität Basel
- Journal
- Bioinformatics (Oxford, England)
- Volume
- 39
- ISSN
- 1367-4803
- DOI
- https://doi.org/10.1093/bioinformatics/btad095
- Publication date
- 03-2023
- Peer reviewed
- Yes
- Austrian Fields of Science 2012
- 106005 Bioinformatics
- ASJC Scopus subject areas
- Statistics and Probability, Biochemistry, Molecular Biology, Computer Science Applications, Computational Theory and Mathematics, Computational Mathematics
- Portal url
- https://ucris.univie.ac.at/portal/en/publications/ecmtool-fast-and-memoryefficient-enumeration-of-elementary-conversion-modes(d2b3ba14-9f6b-4aa5-bcff-e0c774b3349d).html