Limits...
Projection to latent pathways (PLP): a constrained projection to latent variables (PLS) method for elementary flux modes discrimination.

Ferreira AR, Dias JM, Teixeira AP, Carinhas N, Portela RM, Isidro IA, von Stosch M, Oliveira R - BMC Syst Biol (2011)

Bottom Line: PLP has two concomitant goals: (1) maximisation of correlation between EFM weighting factors and measured envirome data and (2) minimisation of redundancy by eliminating EFM with low correlation with the envirome.Overall, our results demonstrate that PLP slightly outperforms PLS in terms of predictive power.This offers a significant advantage over PLS since its abstract structure cannot be associated with the underlying biological structure.

View Article: PubMed Central - HTML - PubMed

Affiliation: REQUIMTE, Systems Biology & Engineering Group, DQ/FCT, Universidade Nova de Lisboa, Campus Caparica, Portugal.

ABSTRACT

Background: Elementary flux modes (EFM) are unique and non-decomposable sets of metabolic reactions able to operate coherently in steady-state. A metabolic network has in general a very high number of EFM reflecting the typical functional redundancy of biological systems. However, most of these EFM are either thermodynamically unfeasible or inactive at pre-set environmental conditions.

Results: Here we present a new algorithm that discriminates the "active" set of EFM on the basis of dynamic envirome data. The algorithm merges together two well-known methods: projection to latent structures (PLS) and EFM analysis, and is therefore termed projection to latent pathways (PLP). PLP has two concomitant goals: (1) maximisation of correlation between EFM weighting factors and measured envirome data and (2) minimisation of redundancy by eliminating EFM with low correlation with the envirome.

Conclusions: Overall, our results demonstrate that PLP slightly outperforms PLS in terms of predictive power. But more importantly, PLP is able to discriminate the subset of EFM with highest correlation with the envirome, thus providing in-depth knowledge of how the environment controls core cellular functions. This offers a significant advantage over PLS since its abstract structure cannot be associated with the underlying biological structure.

Show MeSH

Related in: MedlinePlus

Schematic representation of decomposition operations performed by PLS and PLP algorithms. The main differences between PLS and PLP are related to the computation of Y-loadings. In PLS Q are abstract variables calculated to maximise correlation between X and Y, while in PLP Q comprises a subset of active EFM.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3750108&req=5

Figure 1: Schematic representation of decomposition operations performed by PLS and PLP algorithms. The main differences between PLS and PLP are related to the computation of Y-loadings. In PLS Q are abstract variables calculated to maximise correlation between X and Y, while in PLP Q comprises a subset of active EFM.

Mentions: Unconstrained maximisation of covariance can be performed by the widely used method projection to latent structures (PLS), also known as partial least squares. Figure 1 shows the structural differences between PLS and PLP. Since PLP is derived from PLS, in the lines below we first review PLS decomposition and then show how it can be extended to PLP.


Projection to latent pathways (PLP): a constrained projection to latent variables (PLS) method for elementary flux modes discrimination.

Ferreira AR, Dias JM, Teixeira AP, Carinhas N, Portela RM, Isidro IA, von Stosch M, Oliveira R - BMC Syst Biol (2011)

Schematic representation of decomposition operations performed by PLS and PLP algorithms. The main differences between PLS and PLP are related to the computation of Y-loadings. In PLS Q are abstract variables calculated to maximise correlation between X and Y, while in PLP Q comprises a subset of active EFM.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3750108&req=5

Figure 1: Schematic representation of decomposition operations performed by PLS and PLP algorithms. The main differences between PLS and PLP are related to the computation of Y-loadings. In PLS Q are abstract variables calculated to maximise correlation between X and Y, while in PLP Q comprises a subset of active EFM.
Mentions: Unconstrained maximisation of covariance can be performed by the widely used method projection to latent structures (PLS), also known as partial least squares. Figure 1 shows the structural differences between PLS and PLP. Since PLP is derived from PLS, in the lines below we first review PLS decomposition and then show how it can be extended to PLP.

Bottom Line: PLP has two concomitant goals: (1) maximisation of correlation between EFM weighting factors and measured envirome data and (2) minimisation of redundancy by eliminating EFM with low correlation with the envirome.Overall, our results demonstrate that PLP slightly outperforms PLS in terms of predictive power.This offers a significant advantage over PLS since its abstract structure cannot be associated with the underlying biological structure.

View Article: PubMed Central - HTML - PubMed

Affiliation: REQUIMTE, Systems Biology & Engineering Group, DQ/FCT, Universidade Nova de Lisboa, Campus Caparica, Portugal.

ABSTRACT

Background: Elementary flux modes (EFM) are unique and non-decomposable sets of metabolic reactions able to operate coherently in steady-state. A metabolic network has in general a very high number of EFM reflecting the typical functional redundancy of biological systems. However, most of these EFM are either thermodynamically unfeasible or inactive at pre-set environmental conditions.

Results: Here we present a new algorithm that discriminates the "active" set of EFM on the basis of dynamic envirome data. The algorithm merges together two well-known methods: projection to latent structures (PLS) and EFM analysis, and is therefore termed projection to latent pathways (PLP). PLP has two concomitant goals: (1) maximisation of correlation between EFM weighting factors and measured envirome data and (2) minimisation of redundancy by eliminating EFM with low correlation with the envirome.

Conclusions: Overall, our results demonstrate that PLP slightly outperforms PLS in terms of predictive power. But more importantly, PLP is able to discriminate the subset of EFM with highest correlation with the envirome, thus providing in-depth knowledge of how the environment controls core cellular functions. This offers a significant advantage over PLS since its abstract structure cannot be associated with the underlying biological structure.

Show MeSH
Related in: MedlinePlus