Comparison of metabolic states using genome-scale metabolic models

Autor: Chris T. Evelo, Michiel E. Adriaens, Martina Kutmon, Ilja C. W. Arts, Chaitra Sarathy, Marian Breuer
Přispěvatelé: Maastricht Centre for Systems Biology, RS: FPN MaCSBio, RS: FSE MaCSBio, RS: NUTRIM - R1 - Obesity, diabetes and cardiovascular health, RS: FHML MaCSBio, Bioinformatica
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Metabolic Processes
Genome scale
Human metabolism
Biochemistry
Medical Conditions
Animal Cells
Adipocytes
Medicine and Health Sciences
Metabolites
Biology (General)
OBESE
Energy-Producing Organelles
Analysis method
Connective Tissue Cells
Principal Component Analysis
Ecology
Fatty Acids
Mitochondria
Cell metabolism
Adipose Tissue
Computational Theory and Mathematics
Connective Tissue
Modeling and Simulation
Anatomy
Cellular Types
Cellular Structures and Organelles
Algorithms
Metabolic Networks and Pathways
Network Analysis
Research Article
Computer and Information Sciences
Cell Physiology
QH301-705.5
Citric Acid Cycle
LEUCINE
Computational biology
Bioenergetics
Biology
Models
Biological
ALANINE
Metabolic Networks
Cellular and Molecular Neuroscience
Metabolic Diseases
Genetics
Humans
Computer Simulation
Obesity
Molecular Biology
Ecology
Evolution
Behavior and Systematics
RELEASE
Cellular metabolism
Models
Genetic
Genome
Human
Inborn Errors of Metabolism
Computational Biology
Biology and Life Sciences
Cell Biology
Metabolic Flux Analysis
Cell Metabolism
Amino Acid Metabolism
Biological Tissue
Metabolism
Biological significance
Metabolic Disorders
METABOLIC FEATURES
Flux (metabolism)
Amino Acids
Branched-Chain
Zdroj: PLoS Computational Biology, Vol 17, Iss 11 (2021)
PLoS Computational Biology, 17(11):e1009522. Public Library of Science
PLoS Computational Biology
PLoS Computational Biology, Vol 17, Iss 11, p e1009522 (2021)
ISSN: 1553-7358
Popis: 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.
Author summary Cellular metabolism is a highly complex and interconnected system. As many lifestyle diseases in humans have a strong metabolic component, it is important to understand metabolic differences between healthy and diseased states. In systems biology, metabolic behaviours are investigated using genome-scale metabolic models. In addition to the sheer size and complexity of the genome-scale metabolic models of human systems, using existing analysis methods is challenging and the parameter selection is not straightforward. Therefore, novel methodological frameworks are necessary for analysing metabolic conditions despite the challenges posed by human models. Particularly, an ongoing challenge has been that of comparing several phenotypes for identifying condition- or disease-specific metabolic signatures. We address this significant challenge by developing a scalable and model-driven approach, ComMet (Comparison of Metabolic states). ComMet enables an in-depth investigation and comparison of metabolic phenotypes in large models while also identifying the underlying functional differences. Novel hypotheses can be generated using ComMet for not only understanding known metabolic phenotypes better but also for guiding the design of new experiments to validate the processes predicted by ComMet.
Databáze: OpenAIRE
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