Por favor, use este identificador para citar o enlazar este ítem: https://hdl.handle.net/10495/42028
Título : A systematic evaluation of Mycobacterium tuberculosis Genome-Scale Metabolic Networks
Autor : Baena García, Andrés
López Agudelo, Víctor Alonso
Barrera Robledo, Luis Fernando
Ríos Estepa, Rigoberto
Wu, HuiHai
Laing, Emma
Beste, Dany
Mendum, Tom
metadata.dc.subject.*: Teorema de Bayes
Bayes Theorem
Biomasa
Biomass
Carbono
Carbon
Colesterol
Cholesterol
Medios de Cultivo
Culture Media
Reacciones Falso Positivas
False Positive Reactions
Mycobacterium tuberculosis
Fenotipo
Phenotype
Valor Predictivo de las Pruebas
Predictive Value of Tests
Redes y Vías Metabólicas
Metabolic Networks and Pathways
Glicerol
Glycerol
Genoma Bacteriano
Genome, Bacterial
Biología de Sistemas
Systems Biology
https://id.nlm.nih.gov/mesh/D001499
https://id.nlm.nih.gov/mesh/D018533
https://id.nlm.nih.gov/mesh/D002244
https://id.nlm.nih.gov/mesh/D002784
https://id.nlm.nih.gov/mesh/D003470
https://id.nlm.nih.gov/mesh/D005189
https://id.nlm.nih.gov/mesh/D009169
https://id.nlm.nih.gov/mesh/D010641
https://id.nlm.nih.gov/mesh/D011237
https://id.nlm.nih.gov/mesh/D053858
https://id.nlm.nih.gov/mesh/D005990
https://id.nlm.nih.gov/mesh/D016680
https://id.nlm.nih.gov/mesh/D049490
Fecha de publicación : 2020
Editorial : Public Library of Science
Citación : López-Agudelo VA, Mendum TA, Laing E, Wu H, Baena A, Barrera LF, Beste DJV, Rios-Estepa R. A systematic evaluation of Mycobacterium tuberculosis Genome-Scale Metabolic Networks. PLoS Comput Biol. 2020 Jun 15;16(6):e1007533. doi: 10.1371/journal.pcbi.1007533.
Resumen : ABSTRACT: 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.
metadata.dc.identifier.eissn: 1553-7358
ISSN : 1553-734X
metadata.dc.identifier.doi: 10.1371/journal.pcbi.1007533
Aparece en las colecciones: Artículos de Revista en Ciencias Médicas

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