Comparison of metabolic states using genome-scale metabolic models

• Published in: PLoS computational biology Vol. 17; no. 11; p. e1009522
• Main Authors: Sarathy, Chaitra, Breuer, Marian, Kutmon, Martina, Adriaens, Michiel E, Evelo, Chris T, Arts, Ilja C W
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.11.2021; Public Library of Science (PLoS)
• Subjects: Adipocytes; Adipocytes - metabolism; Algorithms; Amino acids; Amino Acids, Branched-Chain - metabolism; Analysis; Approximation; Biology and Life Sciences; Biomarkers; Cell metabolism; Chain branching; Citric Acid Cycle; Computational Biology; Computer and Information Sciences; Computer Simulation; Constraint modelling; Decomposition; Diabetes; Fatty acids; Fatty Acids - metabolism; Genome, Human; Genomes; Genomics; Humans; Knowledge bases (artificial intelligence); Mathematical models; Medicine and Health Sciences; Metabolic Diseases - genetics; Metabolic Diseases - metabolism; Metabolic flux; Metabolic Flux Analysis - statistics & numerical data; Metabolic networks; Metabolic Networks and Pathways - genetics; Metabolism; Metabolites; Methods; Models, Biological; Models, Genetic; Network analysis; Obesity; Obesity - genetics; Obesity - metabolism; Phenotypes; Physiology; Principal Component Analysis; Principal components analysis; Probability distribution; Tricarboxylic acid cycle
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1009522

Genome-scale metabolic models (GEMs) are comprehensive knowledge bases of cellular metabolism and serve as mathematical tools for studying biological phenotypes and metabolic states or conditions in various organisms and cell types. Given the sheer size and complexity of human metabolism, selecting parameters for existing analysis methods such as metabolic objective functions and model constraints is not straightforward in human GEMs. In particular, comparing several conditions in large GEMs to identify condition- or disease-specific metabolic features is challenging. In this study, we showcase a scalable, model-driven approach for an in-depth investigation and comparison of metabolic states in large GEMs which enables identifying the underlying functional differences. Using a combination of flux space sampling and network analysis, our approach enables extraction and visualisation of metabolically distinct network modules. Importantly, it does not rely on known or assumed objective functions. We apply this novel approach to extract the biochemical differences in adipocytes arising due to unlimited vs blocked uptake of branched-chain amino acids (BCAAs, considered as biomarkers in obesity) using a human adipocyte GEM (iAdipocytes1809). The biological significance of our approach is corroborated by literature reports confirming our identified metabolic processes (TCA cycle and Fatty acid metabolism) to be functionally related to BCAA metabolism. Additionally, our analysis predicts a specific altered uptake and secretion profile indicating a compensation for the unavailability of BCAAs. Taken together, our approach facilitates determining functional differences between any metabolic conditions of interest by offering a versatile platform for analysing and comparing flux spaces of large metabolic networks.