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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.

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Frequency of selection of EFM. A bootstrapping technique was implemented in which 200 PLP runs are performed for randomly selected calibration and validation data sets with 67 points each. Frequency is calculated as the EFM selection count divided by the total number of runs.
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Figure 3: Frequency of selection of EFM. A bootstrapping technique was implemented in which 200 PLP runs are performed for randomly selected calibration and validation data sets with 67 points each. Frequency is calculated as the EFM selection count divided by the total number of runs.

Mentions: PLS belongs to a class of multivariate regression techniques that can be used to model high dimensional data sets with low number of sampling points [25]. However, when the number of samples is too low, the partitioning into calibration and validation sets may have a high impact on the final model structure. Since stemming from PLS, the same problem does in principle apply to PLP. In order to assess EFM discrimination variability due to data partitioning, a bootstrapping technique was implemented, in which PLP and PLS were repeated 200 times with randomly selected calibration and validation partitions with 67 points each. Figure 3 shows the frequency of selection of EFM resulting from the bootstrapping analysis. The complete set of results is provided as Additional File 3. These results evidence a subset of frequently selected EFM, which include EFM1, EFM2, EFM4, EFM6, EFM11, EFM179 and EFM210 with frequency of selection higher than 75% and EFM69, EFM72, EFM173 and EFM206 with frequency of selection higher than 50%. Less frequently selected EFMs are very sensitive to the data partitioning and to experimental noise and thus less reliable to interpret.


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)

Frequency of selection of EFM. A bootstrapping technique was implemented in which 200 PLP runs are performed for randomly selected calibration and validation data sets with 67 points each. Frequency is calculated as the EFM selection count divided by the total number of runs.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Frequency of selection of EFM. A bootstrapping technique was implemented in which 200 PLP runs are performed for randomly selected calibration and validation data sets with 67 points each. Frequency is calculated as the EFM selection count divided by the total number of runs.
Mentions: PLS belongs to a class of multivariate regression techniques that can be used to model high dimensional data sets with low number of sampling points [25]. However, when the number of samples is too low, the partitioning into calibration and validation sets may have a high impact on the final model structure. Since stemming from PLS, the same problem does in principle apply to PLP. In order to assess EFM discrimination variability due to data partitioning, a bootstrapping technique was implemented, in which PLP and PLS were repeated 200 times with randomly selected calibration and validation partitions with 67 points each. Figure 3 shows the frequency of selection of EFM resulting from the bootstrapping analysis. The complete set of results is provided as Additional File 3. These results evidence a subset of frequently selected EFM, which include EFM1, EFM2, EFM4, EFM6, EFM11, EFM179 and EFM210 with frequency of selection higher than 75% and EFM69, EFM72, EFM173 and EFM206 with frequency of selection higher than 50%. Less frequently selected EFMs are very sensitive to the data partitioning and to experimental noise and thus less reliable to interpret.

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