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. 2020 Dec 1;21(6):1875-1885.
doi: 10.1093/bib/bbz094.

Towards scaling elementary flux mode computation

Ehsan Ullah  1 , Mona Yosafshahi  1 , Soha Hassoun  2
Affiliations

Towards scaling elementary flux mode computation

Ehsan Ullah et al. Brief Bioinform. .

Abstract

While elementary flux mode (EFM) analysis is now recognized as a cornerstone computational technique for cellular pathway analysis and engineering, EFM application to genome-scale models remains computationally prohibitive. This article provides a review of aspects of EFM computation that elucidates bottlenecks in scaling EFM computation. First, algorithms for computing EFMs are reviewed. Next, the impact of redundant constraints, sensitivity to constraint ordering and network compression are evaluated. Then, the advantages and limitations of recent parallelization and GPU-based efforts are highlighted. The article then reviews alternative pathway analysis approaches that aim to reduce the EFM solution space. Despite advances in EFM computation, our review concludes that continued scaling of EFM computation is necessary to apply EFM to genome-scale models. Further, our review concludes that pathway analysis methods that target specific pathway properties can provide powerful alternatives to EFM analysis.

Keywords: elementary flux mode analysis; elementary flux modes; parallel computing; pathway analysis; scalability.

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Figures

Figure 1
Figure 1
A graphical representation of pathways in 3-dimensional ux space. Each axis corresponds to a reaction ux. A pathway is geometrically represented as a ray. The dark lines are the extreme rays (correspond to EFMs) of a convex cone.
Figure 2
Figure 2
Cumulative combinations and comparisons performed in different algorithmic iterations of EFM-Tool and gEFM. Each iteration corresponds to a constraint. Cumulative combinations and comparisons for EFMTool are less than that of gEFM.
See this image and copyright information in PMC

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