Towards improved genome-scale metabolic network reconstructions: unification, transcript specificity and beyond

Abstract

Genome-scale metabolic network reconstructions provide a basis for the investigation of the metabolic properties of an organism. There are reconstructions available for multiple organisms, from prokaryotes to higher organisms and methods for the analysis of a reconstruction. One example is the use of flux balance analysis to improve the yields of a target chemical, which has been applied successfully. However, comparison of results between existing reconstructions and models presents a challenge because of the heterogeneity of the available reconstructions, for example, of standards for presenting gene-protein-reaction associations, nomenclature of metabolites and reactions or selection of protonation states. The lack of comparability for gene identifiers or model-specific reactions without annotated evidence often leads to the creation of a new model from scratch, as data cannot be properly matched otherwise. In this contribution, we propose to improve the predictive power of metabolic models by switching from gene-protein-reaction associations to transcript-isoform-reaction associations, thus taking advantage of the improvement of precision in gene expression measurements. To achieve this precision, we discuss available databases that can be used to retrieve this type of information and point at issues that can arise from their neglect. Further, we stress issues that arise from non-standardized building pipelines, like inconsistencies in protonation states. In addition, problems arising from the use of non-specific cofactors, e.g. artificial futile cycles, are discussed, and finally efforts of the metabolic modelling community to unify model reconstructions are highlighted.

EColiCore2: a reference network model of the central metabolism of Escherichia coli and relationships to its genome-scale parent model

“…An essential prerequisite for a successful application of this modeling framework is the careful reconstruction of the metabolic network model to be investigated. Whereas smaller metabolic models, for instance, of the central metabolism, are often compiled from established biochemical “text book” knowledge, the reconstruction of genome-scale models (covering the entire metabolism of small metabolites) was greatly facilitated due to the availability of sequenced genomes for many organisms4567. Several tools have been developed to support the reconstruction and gap filling process to eventually obtain high-quality genome-scale models89101112.…”

Pan-genome-scale network reconstruction: a framework to increase the quantity and quality of metabolic network reconstructions throughout the tree of life

“…To resolve these issues of non-conserved function in ortholog groups, we created new "functional ortholog groups" to assign these exceptions. Future methods can address these issues by assigning biological function to internal nodes in phylogenetic trees, or annotating reactions with transcripts instead of genes (Pfau et al, 2015).…”

Analysis of metabolomic data: tools, current strategies and future challenges for omics data integration

“…To make graph layouts informative and reproducible, several visualization strategies have been proposed and adopted. A frequently used visualization method is the ball-and-stick diagram, where pathway data are presented as networks, with compounds (e.g., metabolites, proteins) as nodes and reactions as edges [23,37,52]. Nodes can be placed hierarchically (a father node with one or more child nodes) or radially as in radial networks or hive plots, which more closely represent the complexity of biological systems [53].…”

“…Although several literature about metabolomic data production and analysis techniques exist [3][4][5][15][16][17][18][19][20][21][22][23], to the best of our knowledge, a comprehensive review illustrating the available tools for the analysis and integration of metabolomic data with other 'omics' data has not been reported yet. Here, we describe and compare four tools (MetaCore TM , MetaboAnalyst, InCroMAP and 3Omics [24][25][26][27]), which we selected among the several ones currently available for metabolomic data analysis and integration (Table 1 have not yet overcome the previous ones.…”