ArticleBMC systems biology2011
Projection to latent pathways (PLP): a constrained projection to latent variables (PLS) method for elementary flux modes discrimination.
Article in BMC systems biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 15 citations in OpenAlex.
- SBML2HYB: a Python interface for SBML compatible hybrid modeling.Bioinformatics (Oxford, England) · 2023Article
- Genome-scale modeling of Chinese hamster ovary cells by hybrid semi-parametric flux balance analysis.Bioprocess and biosystems engineering · 2022Article
- Towards a widespread adoption of metabolic modeling tools in biopharmaceutical industry: a process systems biology engineering perspective.NPJ systems biology and applications · 2020Review
- The shortcomings of accurate rate estimations in cultivation processes and a solution for precise and robust process modeling.Bioprocess and biosystems engineering · 2020Article
- A principal components method constrained by elementary flux modes: analysis of flux data sets.BMC bioinformatics · 2016Article
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Authors and funding
8 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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