A systematic evaluation of Mycobacterium tuberculosis Genome-Scale Metabolic Networks
| Autor: | Wallqvist, Anders, López-Agudelo, Víctor A., Mendum, Tom A., Laing, Emma, Wu, Huihai, Baena, Andres, Barrera, Luis F., Beste, Dany J. V., Rios-Estepa, Rigoberto |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Glycerol Genome scale Metabolic network Nitrogen Metabolism Biochemistry 0302 clinical medicine Drug Metabolism Genotype Metabolites Medicine and Health Sciences Biomass Biology (General) Ecology biology Simulation and Modeling Systems Biology Phenotype Lipids Actinobacteria Cholesterol Computational Theory and Mathematics Modeling and Simulation Thermodynamics Metabolic Pathways Network Analysis Metabolic Networks and Pathways Research Article Computer and Information Sciences QH301-705.5 Systems biology Gene prediction Computational biology Research and Analysis Methods Models Biological Mycobacterium tuberculosis 03 medical and health sciences Cellular and Molecular Neuroscience Metabolic Networks Predictive Value of Tests Genetics Pharmacokinetics False Positive Reactions Molecular Biology Ecology Evolution Behavior and Systematics Pharmacology Bacteria Organisms Biology and Life Sciences Bayes Theorem biology.organism_classification Carbon Culture Media Metabolic pathway 030104 developmental biology Metabolism 030217 neurology & neurosurgery Genome Bacterial Software |
| Zdroj: | PLoS Computational Biology, Vol 16, Iss 6, p e1007533 (2020) PLoS Computational Biology |
| ISSN: | 1553-7358 |
| Popis: | Metabolism underpins the pathogenic strategy of the causative agent of TB, Mycobacterium tuberculosis (Mtb), and therefore metabolic pathways have recently re-emerged as attractive drug targets. A powerful approach to study Mtb metabolism as a whole, rather than just individual enzymatic components, is to use a systems biology framework, such as a Genome-Scale Metabolic Network (GSMN) that allows the dynamic interactions of all the components of metabolism to be interrogated together. Several GSMNs networks have been constructed for Mtb and used to study the complex relationship between the Mtb genotype and its phenotype. However, the utility of this approach is hampered by the existence of multiple models, each with varying properties and performances. Here we systematically evaluate eight recently published metabolic models of Mtb-H37Rv to facilitate model choice. The best performing models, sMtb2018 and iEK1011, were refined and improved for use in future studies by the TB research community. Author summary The tuberculosis bacillus, Mycobacterium tuberculosis (Mtb), is a global killer causing millions of deaths every year and is therefore a major burden to human health. Treatment of tuberculosis requires a cocktail of antibiotics for a minimum of 6 months. Treatment failure is common and is a major driver in the upward trend of antibiotic resistance, recognized by the World Health Organization as one of top ten threats to global health. A key to the success of Mtb as a human pathogen is ascribed to its extraordinary metabolic flexibility. Understanding the metabolism of Mtb is therefore an important goal of TB researchers as metabolic pathways present attractive drug targets. A powerful approach to study metabolism is through the use of genome-scale metabolic networks which enable metabolism to be studied at the whole system level rather than one enzyme at a time. Here, we comprehensively compare available genome scale metabolic networks. Our results identify the best performing networks for a variety of modelling approaches. This work allowed us to refine these models for the TB community to use in future studies to probe the metabolism of this formidable human pathogen. |
| Databáze: | OpenAIRE |
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