Metabolic Modelling as a Framework for Metabolomics Data Integration and Analysis

Abstract

Metabolic networks are regulated to ensure the dynamic adaptation of biochemical reaction fluxes to maintain cell homeostasis and optimal metabolic fitness in response to endogenous and exogenous perturbations. To this end, metabolism is tightly controlled by dynamic and intricate regulatory mechanisms involving allostery, enzyme abundance and post-translational modifications. The study of the molecular entities involved in these complex mechanisms has been boosted by the advent of high-throughput technologies. The so-called omics enable the quantification of the different molecular entities at different system layers, connecting the genotype with the phenotype. Therefore, the study of the overall behavior of a metabolic network and the omics data integration and analysis must be approached from a holistic perspective. Due to the close relationship between metabolism and cellular phenotype, metabolic modelling has emerged as a valuable tool to decipher the underlying mechanisms governing cell phenotype. Constraint-based modelling and kinetic modelling are among the most widely used methods to study cell metabolism at different scales, ranging from cells to tissues and organisms. These approaches enable integrating metabolomic data, among others, to enhance model predictive capabilities. In this review, we describe the current state of the art in metabolic modelling and discuss future perspectives and current challenges in the field.

Seeing the forest for the trees: Retrieving plant secondary biochemical pathways from metabolome networks

“…However, retrosynthesis, performed on a multitude of precursor–product metabolite pairs, might boost the metabolic network complexity. In this case, a similar strategy could be applied as currently performed in the curation of GSMM, i.e., applying CBM to remove non-essential edges on the one hand and to point pathway gaps on the other hand [25] . Further support for particular biotransformation paths can also be provided via another GSMM-applied strategy: pathway mapping using multi-omics data, especially when derived from time-course or comparative studies.…”

“…When attempting to complete the metabolic network of a particular species, gaps representing unknown reactions have to be filled in, which demands an exhaustive search for all gene–protein–reaction associations by concatenating gene–protein and protein–reaction information from different databases. Subsequently, a final curation via constraint-based modeling (CBM, see Glossary) [23] , [24] , [25] yields a so-called genome-scale metabolic model (GSMM) [26] , [27] , [28] . Metabolic networks are mainly displayed either as a homogenous network (nodes and edges reflecting metabolites and reactions) or as a bipartite graph characterized by two types of nodes (representing metabolites and enzymes), in which the edges represent links between substrates/products and their respective enzymes.…”

Systematic Review of Multi-Omics Approaches to Investigate Toxicological Effects in Macrophages

“…For example, increased metabolite abundances may result either from an elevated metabolism or an accumulation due to the inhibition of individual steps. This issue can be overcome by the use of metabolic flux analyses [ 167 ] or the investigation of additional omics layers [ 168 ].…”

Constraint-based modeling and machine learning applications for analysis and optimization of fermentation parameters

“…Constraint-based modeling (CBM) can overcome the limitations of the kinetic models by reducing the need for complex kinetic parameters. Therefore, this approach has been extensively used for understanding the behavior of genome-wide systems [31]. The main goal of CBM is to build models with high prediction accuracy to analyze the genome-scale networks and shed light on relationships between genotype, phenotype, and environmental conditions [32,33].…”