Abstract
BackgroundNon-negative linear combinations of elementary flux modes (EMs) describe all feasible reaction flux distributions for a given metabolic network under the quasi steady state assumption. However, only a small subset of EMs contribute to the physiological state of a given cell.ResultsIn this paper, a method is proposed that identifies the subset of EMs that best explain the physiological state captured in reaction flux data, referred to as principal EMs (PEMs), given a pre-specified universe of EM candidates. The method avoids the evaluation of all possible combinations of EMs by using a branch and bound approach which is computationally very efficient. The performance of the method is assessed using simulated and experimental data of Pichia pastoris and experimental fluxome data of Saccharomyces cerevisiae. The proposed method is benchmarked against principal component analysis (PCA), commonly used to study the structure of metabolic flux data sets.ConclusionsThe overall results show that the proposed method is computationally very effective in identifying the subset of PEMs within a large set of EM candidates (cases with ~100 and ~1000 EMs were studied). In contrast to the principal components in PCA, the identified PEMs have a biological meaning enabling identification of the key active pathways in a cell as well as the conditions under which the pathways are activated. This method clearly outperforms PCA in the interpretability of flux data providing additional insights into the underlying regulatory mechanisms.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1063-0) contains supplementary material, which is available to authorized users.
Highlights
Non-negative linear combinations of elementary flux modes (EMs) describe all feasible reaction flux distributions for a given metabolic network under the quasi steady state assumption
For more details see the Additional file 1. This allows comparing the set of EMs identified with Principal Elementary Mode Analysis (PEMA) to the active EMs that were used for data generation, hereupon termed “active EMs”
It was shown that PEMA identifies the principal elementary modes (PEMs), which are those combinations of EMs that account for most of the variance in the flux data, and that principal elementary flux mode (PEM) are a faithful representation of active pathways
Summary
Non-negative linear combinations of elementary flux modes (EMs) describe all feasible reaction flux distributions for a given metabolic network under the quasi steady state assumption. Elementary flux mode analysis has proven to be a powerful method to understand the structural properties of metabolic networks [1,2,3,4,5]. This approach can be employed to assess which reactions and educts are involved in producing a certain compound, to determine optimal yields or to analyze the consequences of certain reactions taking a zero value as invoked by metabolic engineering or changes in the cellular environment [6]. Investigating which of the p’s have nonzero contributions for a given phenotype is useful for two reasons [8]: 1) The biological interpretability of EM-based pathway analysis is improved, which can help to focus on studying physiologically active processes; and 2) Changes in the physiological state of the cell can be quantified, enabling the causes of change to be elucidated
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