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

Abstract

BackgroundElementary 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.ResultsHere 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.ConclusionsOverall, 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.

Towards a widespread adoption of metabolic modeling tools in biopharmaceutical industry: a process systems biology engineering perspective

“…Despite their benefits and their first applications stemming from 1992 46 , the first application of hybrid modeling in systems biology only arose in 2010 47 and few have been reported ever since [48][49][50][51][52][53] . One reason might be the absence of dedicated software that would allow for a straightforward development of these type of models.…”

Fusion of genomic, proteomic and phenotypic data: the case of potyviruses

“…The purpose of this approach is to gain insight into the molecular interactions that occur during the virus infection more than to construct a robust predictive model. Similar grey models have been proposed during the last years, using exploratory 18--19 and predictive methods 20 , dealing with metabolic networks. To improve the predictive power of the model we would need more genetic and proteomic information, such as the analysis of codon usage and, specially, the characterization of protein structure.…”

Network Analysis and Modeling in Systems Biology

“…Another branch of research that deals with community detection, in this case restricted to metabolic network, is known as elementary modes, extreme pathways and minimal generators (Ferreira et al 2011;Llaneras and Picó 2010). They are not used in this thesis but it is worth it to mention them because they have gathered some attention these last few years.…”

“…These have been extensively used in systems biology before in a wide range of applications: from data fusion (FolchFortuny et al 2016), pathway determination (Ferreira et al 2011;Folch-Fortuny et al 2015), network decomposition (Barrett, Herrgard, and Palsson 2009) and 45 metabolic flux analysis (González-Martínez et al 2014). PCA will be used in Chapter 4 for constraint-based metabolic model characterization and PLS will also be used in Chapter 4 for analysing the robustness of the conclusions and in Chapter 3 for finding functional modules in a viral PPIN.…”

“…The purpose of this approach is to gain insight into the molecular interactions that occur during the virus infection more than to construct a robust predictive model. Similar grey models have been proposed during the last few years, using exploratory (Folch-Fortuny et al 2015;González-Martínez et al 2014) and predictive methods (Ferreira et al 2011), dealing with metabolic networks. To improve the predictive power of the model more genetic and proteomic information would be needed, such as the analysis of codon usage and, specially, the characterization of the protein structure.…”