Principal Metabolic Flux Mode Analysis

In recent years, much progress has been achieved in the computational analysis of the metabolic networks, as a consequence of the rapid growth of the omics database. However, current literature analysis algorithms still lack good biological interpretability of the analysis results. Moreover, they can not be applied on a whole-genome level. This thesis assesses the potential of the Principal Metabolic Flux Mode Analysis (PMFA). The PMFA is a novel algorithm that was recently developed, which aims to improve the interpretability of Principal Component Analysis (PCA), through including a stoichiometric regularization to the PCA objective function. The PMFA can determine the flux modes that explain the highest variability in the network and it can also scale-up to a whole-genome level using the sparse version of PMFA. Furthermore, this thesis compares the PMFA to the recent approach Principal Elementary Mode Analysis (PEMA), which also tries to enhance the PCA interpretability. However, this approach is computationally heavy and thus fails to handle the large-scale networks (e.g., whole-genome). In order to further determine the feasibility of the PMFA approach for the analysis of metabolism, a Graph-regularized Matrix Factorization (GMF) was developed analogous to PMFA framework, similarly by adding the network stoichiometric matrix to a graph-structured matrix factorization framework. The results illustrate the potential of PMFA as a metabolic network analysis for identifying fluxes that explain maximum variation in the network and it can be used to analyze whole-genome level. In addition, the results showed that GMF method performed well in predicting active Elementary Modes (EMs) on simulated data but failed to work on large networks, while PEMA had the lowest performance among all methods. Based on the results, future work can be conducted to improve the GMF approach in terms of genome-scale analysis through including sparsity

Principal elementary mode analysis (PEMA)

Principal component analysis (PCA) has been widely applied in fluxomics to compress data into a few latent structures in order to simplify the identification of metabolic patterns. These latent structures lack a direct biological interpretation due to the intrinsic constraints associated with a PCA model. Here we introduce a new method that significantly improves the interpretability of the principal components with a direct link to metabolic pathways. This method, called principal elementary mode analysis (PEMA), establishes a bridge between a PCA-like model, aimed at explaining the maximum variance in flux data, and the set of elementary modes (EMs) of a metabolic network. It provides an easy way to identify metabolic patterns in large fluxomics datasets in terms of the simplest pathways of the organism metabolism. The results using a real metabolic model of Escherichia coli show the ability of PEMA to identify the EMs that generated the different simulated flux distributions. Actual flux data of E. coli and Pichia pastoris cultures confirm the results observed in the simulated study, providing a biologically meaningful model to explain flux data of both organisms in terms of the EM activation. The PEMA toolbox is freely available for non-commercial purposes on http://mseg.webs.upv.es.Research in this study was partially supported by the Spanish Ministry of Economy and Competitiveness and FEDER funds from the European Union through grants DPI2011-28112-C04-02 and DPI2014-55276-C5-1R. We would also acknowledge Fundacao para a Ciencia e Tecnologia for PhD fellowships with references SFRH/BD/67033/2009, SFRH/BD/70768/2010 and PTDC/BBB-BSS/2800/2012.Folch Fortuny, A.; Marques, R.; Isidro, IA.; Oliveira, R.; Ferrer, A. (2016). Principal elementary mode analysis (PEMA). Molecular BioSystems. 12(3):737-746. doi:10.1039/c5mb00828jS73774612

Dispensability of Escherichia coli's latent pathways

Gene-knockout experiments on single-cell organisms have established that expression of a substantial fraction of genes is not needed for optimal growth. This problem acquired a new dimension with the recent discovery that environmental and genetic perturbations of the bacterium Escherichia coli are followed by the temporary activation of a large number of latent metabolic pathways, which suggests the hypothesis that temporarily activated reactions impact growth and hence facilitate adaptation in the presence of perturbations. Here we test this hypothesis computationally and find, surprisingly, that the availability of latent pathways consistently offers no growth advantage, and tends in fact to inhibit growth after genetic perturbations. This is shown to be true even for latent pathways with a known function in alternate conditions, thus extending the significance of this adverse effect beyond apparently nonessential genes. These findings raise the possibility that latent pathway activation is in fact derivative of another, potentially suboptimal, adaptive response

The solution space of metabolic networks: producibility, robustness and fluctuations

Flux analysis is a class of constraint-based approaches to the study of biochemical reaction networks: they are based on determining the reaction flux configurations compatible with given stoichiometric and thermodynamic constraints. One of its main areas of application is the study of cellular metabolic networks. We briefly and selectively review the main approaches to this problem and then, building on recent work, we provide a characterization of the productive capabilities of the metabolic network of the bacterium E.coli in a specified growth medium in terms of the producible biochemical species. While a robust and physiologically meaningful production profile clearly emerges (including biomass components, biomass products, waste etc.), the underlying constraints still allow for significant fluctuations even in key metabolites like ATP and, as a consequence, apparently lay the ground for very different growth scenarios.Comment: 10 pages, prepared for the Proceedings of the International Workshop on Statistical-Mechanical Informatics, March 7-10, 2010, Kyoto, Japa