Email updates

Keep up to date with the latest news and content from BMC Systems Biology and BioMed Central.

Open Access Methodology article

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

Ana R Ferreira12, João ML Dias1, Ana P Teixeira23, Nuno Carinhas23, Rui MC Portela1, Inês A Isidro1, Moritz von Stosch4 and Rui Oliveira12*

Author Affiliations

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

2 Instituto de Biologia Experimental e Tecnológica (IBET), Apartado 12, 2781-901 Oeiras, Portugal

3 Instituto de Tecnologia Química e Biológica - Universidade Nova de Lisboa (ITQB-UNL), Apartado 127, 2781-901 Oeiras, Portugal

4 LEPAE, Departamento de Engenharia Química, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias s/n, 4200-465 Porto, Portugal

For all author emails, please log on.

BMC Systems Biology 2011, 5:181  doi:10.1186/1752-0509-5-181

Published: 1 November 2011

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.