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2018 ; 34
(14
): 2409-2417
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Principal metabolic flux mode analysis
#MMPMID29420676
Bhadra S
; Blomberg P
; Castillo S
; Rousu J
Bioinformatics
2018[Jul]; 34
(14
): 2409-2417
PMID29420676
show ga
MOTIVATION: In the analysis of metabolism, two distinct and complementary
approaches are frequently used: Principal component analysis (PCA) and
stoichiometric flux analysis. PCA is able to capture the main modes of
variability in a set of experiments and does not make many prior assumptions
about the data, but does not inherently take into account the flux mode structure
of metabolism. Stoichiometric flux analysis methods, such as Flux Balance
Analysis (FBA) and Elementary Mode Analysis, on the other hand, are able to
capture the metabolic flux modes, however, they are primarily designed for the
analysis of single samples at a time, and not best suited for exploratory
analysis on a large sets of samples. RESULTS: We propose a new methodology for
the analysis of metabolism, called Principal Metabolic Flux Mode Analysis (PMFA),
which marries the PCA and stoichiometric flux analysis approaches in an elegant
regularized optimization framework. In short, the method incorporates a variance
maximization objective form PCA coupled with a stoichiometric regularizer, which
penalizes projections that are far from any flux modes of the network. For
interpretability, we also introduce a sparse variant of PMFA that favours flux
modes that contain a small number of reactions. Our experiments demonstrate the
versatility and capabilities of our methodology. The proposed method can be
applied to genome-scale metabolic network in efficient way as PMFA does not
enumerate elementary modes. In addition, the method is more robust on
out-of-steady steady-state experimental data than competing flux mode analysis
approaches. AVAILABILITY AND IMPLEMENTATION: Matlab software for PMFA and SPMFA
and dataset used for experiments are available in
https://github.com/aalto-ics-kepaco/PMFA. SUPPLEMENTARY INFORMATION:
Supplementary data are available at Bioinformatics online.
free
free
free
