On the representativeness and stability of a set of EFMs

Motivation: Elementary flux modes are a well-known tool for analyzing metabolic networks. The whole set of elementary flux modes (EFMs) cannot be computed in most genome-scale networks due to their large cardinality. Therefore, different methods have been proposed to compute a smaller subset of EFMs that can be used for studying the structure of the network. These latter methods pose the problem of studying the representativeness of the calculated subset. In this article, we present a methodology to tackle this problem.Results: We have introduced the concept of stability for a particular network parameter and its relation to the representativeness of the EFM extraction method studied. We have also defined several metrics to study and compare the EFM biases. We have applied these techniques to compare the relative behavior of previously proposed methods in two case studies. Furthermore, we have presented a new method for the EFM computation (PiEFM), which is more stable (less biased) than previous ones, has suitable representativeness measures, and exhibits better variability in the extracted EFMs.Availability and implementationSoftware and additional material are freely available at .

Automated summary

This article presents a methodology to improve the representativeness of the subset of elementary flux modes (EFMs) computed in metabolic networks. The authors introduce the concept of stability for a network parameter and define several metrics to study and compare EFM biases. They compare previously proposed methods and present a new method (PiEFM) that is more stable, has better representativeness measures, and exhibits better variability in the extracted EFMs.