TY - JOUR
T1 - A principal components method constrained by elementary flux modes
T2 - Analysis of flux data sets
AU - von Stosch, Moritz
AU - de Azevedo, Cristiana Rodrigues
AU - Luís, Mauro
AU - de Azevedo, Sebastiao Feyo
AU - Oliveira, Rui
N1 - info:eu-repo/grantAgreement/FCT/3599-PPCDT/PTDC%2FBBB-BSS%2F2800%2F2012/PT#
info:eu-repo/grantAgreement/FCT/OE/SFRH%2FBPD%2F84573%2F2012/PT#
Deutscher Akademischer Austausch Dienst, Reference number: 6818.
PY - 2016
Y1 - 2016
N2 - Background: 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. However, only a small subset of EMs contribute to the physiological state of a given cell. Results: In 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. Conclusions: The 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.
AB - Background: 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. However, only a small subset of EMs contribute to the physiological state of a given cell. Results: In 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. Conclusions: The 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.
KW - Elementary flux modes
KW - Flux data analysis
KW - Fluxome data analysis
KW - Principal component analysis
KW - Principle elementary modes
UR - http://www.scopus.com/inward/record.url?scp=85007473710&partnerID=8YFLogxK
U2 - 10.1186/s12859-016-1063-0
DO - 10.1186/s12859-016-1063-0
M3 - Article
C2 - 27146133
AN - SCOPUS:85007473710
SN - 1471-2105
VL - 17
JO - BMC Bioinformatics
JF - BMC Bioinformatics
IS - 1
M1 - 200
ER -