MCR-ALS on metabolic networks: Obtaining more meaningful pathways

Abstract

With the aim of understanding the flux distributions across a metabolic network, i.e. within living cells, Principal Component Analysis (PCA) has been proposed to obtain a set of orthogonal components (pathways) capturing most of the variance in the flux data. The problems with this method are (i) that no additional information can be included in the model, and (ii) that orthogonality imposes a hard constraint, not always reasonably. To overcome these drawbacks, here we propose to use a more flexible approach such as Multivariate Curve Resolution -Alternating Least Squares (MCR-ALS) to obtain this set of biological pathways through the network. By using this method, different constraints can be included in the model, and the same source of variability can be present in different pathways, which is reasonable from a biological standpoint. This work follows a methodology developed for Pichia pastoris cultures grown on different carbon sources, lately presented in [González Martínez et al. 2014].In this paper a different grey modelling approach, which aims to incorporate a priori knowledge through constraints on the modelling algorithms, is applied to the same case of study. The results of both models are compared to show their strengths and weaknesses.

Developments and Challenges in Online NIR Spectroscopy for Meat Processing

“…Multivariate curve resolution‐Alternating least squares (MCR‐ALS) is the most popular chemometric method used for the resolution of multiple component responses in unknown unresolved mixtures (Jaumot and others ). It is an iterative method that performs a bilinear decomposition of the built data matrix by means of an alternating least squares optimization (Felten and others ; Folch‐Fortuny and others ). MCR‐ALS is not a popular technique for meat analysis using NIR spectroscopy.…”

Flux-dependent graphs for metabolic networks

“…This model ( Fig. 2A) contains 72 metabolites and 95 reactions, grouped into 11 pathways, which describe the main biochemical routes in central carbon metabolism [40][41][42]. We provide a Supplemental Spreadsheet with full details of the reactions and metabolites in this model, as well as all the results presented below.…”

Network Analysis and Modeling in Systems Biology

“…This model is relatively small, with 72 metabolites (20 extracellular and 52 intracellular) and 95 reactions (20 exchange reactions, 25 transport reactions, 49 metabolic reactions and one biomass reaction). The canonical way to describe metabolic reactions is in terms of subsystems or pathways that consist of reactions that serve a specific function (Folch-Fortuny et al 2015;Schilling, Letscher, and Palsson 2000;Schuster, Fell, and Dandekar 2000). For example, the reactions that form glycolysis convert D-glucose into pyruvate and produce adenosine triphosphate (ATP) and nicotinamide adenine dinucleotide (NADH).…”

“…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.…”