Flux balance analysis (FBA) is one of the most often applied methods on genome-scale metabolic networks. Although FBA uniquely determines the optimal yield, the pathway that achieves this is usually not unique. The analysis of the optimal-yield flux space has been an open challenge. Flux variability analysis is only capturing some properties of the flux space, while elementary mode analysis is intractable due to the enormous number of elementary modes. However, it has been found by Kelk et al. 2012, that the space of optimal-yield fluxes decomposes into flux modules. These decompositions allow a much easier but still comprehensive analysis of the optimal-yield flux space. Using the mathematical definition of module introduced by M¨uller and Bockmayr 2013, we discovered that flux modularity is rather a local than a global property which opened connections to matroid theory. Specifically, we show that our modules correspond one-to-one to so-called separators of an appropriate matroid. Employing efficient algorithms developed in matroid theory we are now able to compute the decomposition into modules in a few seconds for genome-scale networks. Using that every module can be represented by one reaction that represents its function, in this paper, we also present a method that uses this decomposition to visualize the interplay of modules. We expect the new method to replace flux variability analysis in the pipelines for metabolic networks.
Additional Metadata
THEME Life Sciences (theme 5)
Publisher Springer
Persistent URL dx.doi.org/10.1007/978-3-319-05269-4_16
Series Lecture Notes in Computer Science
Conference Annual International Conference on Computational Molecular Biology
Citation
Müller, A, Bruggeman, F.J, Olivier, B.G, & Stougie, L. (2014). Fast Flux Module Detection using Matroid Theory. In Proceedings of Annual International Conference on Computational Molecular Biology 2014 (RECOMB 18) (pp. 192–206). Springer. doi:10.1007/978-3-319-05269-4_16