Grupo de Bioprocesos, Departamento de Ingeniería Química, Universidad de Antioquia UdeA, Medellín, Colombia.
Grupo de Inmunología Celular e Inmunogenética (GICIG), Facultad de Medicina, Universidad de Antioquia UdeA, Medellín, Colombia.
PLoS Comput Biol. 2020 Jun 15;16(6):e1007533. doi: 10.1371/journal.pcbi.1007533. eCollection 2020 Jun.
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.
代谢是导致结核病的病原体结核分枝杆菌(Mtb)致病策略的基础,因此代谢途径最近重新成为有吸引力的药物靶点。研究 Mtb 代谢的一种有力方法是采用系统生物学框架,例如基因组规模代谢网络(GSMN),它可以一起研究代谢所有组成部分的动态相互作用。已经构建了几个用于 Mtb 的 GSMN 网络,并用于研究 Mtb 基因型与其表型之间的复杂关系。然而,由于存在多种模型,每个模型的特性和性能都不同,因此这种方法的实用性受到了阻碍。在这里,我们系统地评估了八种最近发表的 Mtb-H37Rv 代谢模型,以促进模型选择。表现最好的模型 sMtb2018 和 iEK1011 经过改进和完善,可供结核病研究界在未来的研究中使用。