Abstract
Background
Elementary flux mode (EFM) analysis is a well-established, yet computationally challenging approach to characterize metabolic networks. Standard algorithms require huge amounts of memory and lack scalability which limits their application to single servers and consequently limits a comprehensive analysis to medium-scale networks. Recently, Avis et al. developed —a parallel version of the lexicographic reverse search (lrs) algorithm, which, in principle, enables an EFM analysis on high-performance computing environments (Avis and Jordan. mplrs: a scalable parallel vertex/facet enumeration code. arXiv:1511.06487, 2017). Here we test its applicability for EFM enumeration.
Results
We developed , a Python package that gives users access to the enumeration capabilities of . uses COBRApy to process metabolic models from sbml files, performs loss-free compressions of the stoichiometric matrix, and generates suitable inputs for as well as , providing support not only for our proposed new method for EFM enumeration but also for already established tools. By leveraging COBRApy, also allows the application of additional reaction boundaries and seamlessly integrates into existing workflows.
Conclusion
We show that due to ’s properties, the algorithm is perfectly suited for high-performance computing (HPC) and thus offers new possibilities for the unbiased analysis of substantially larger metabolic models via EFM analyses. is an open-source program that comes together with a designated workflow and can be easily installed via pip.
Metabolomics integrated elementary flux mode analysis in large metabolic networks
