rMTA: robust metabolic transformation analysis.
Bioinformatics
; 35(21): 4350-4355, 2019 11 01.
Article
en En
| MEDLINE
| ID: mdl-30923806
ABSTRACT
MOTIVATION The development of computational tools exploiting -omics data and high-quality genome-scale metabolic networks for the identification of novel drug targets is a relevant topic in Systems Medicine. Metabolic Transformation Algorithm (MTA) is one of these tools, which aims to identify targets that transform a disease metabolic state back into a healthy state, with potential application in any disease where a clear metabolic alteration is observed. RESULTS:
Here, we present a robust extension to MTA (rMTA), which additionally incorporates a worst-case scenario analysis and minimization of metabolic adjustment to evaluate the beneficial effect of gene knockouts. We show that rMTA complements MTA in the different datasets analyzed (gene knockout perturbations in different organisms, Alzheimer's disease and prostate cancer), bringing a more accurate tool for predicting therapeutic targets. AVAILABILITY AND IMPLEMENTATION rMTA is freely available on The Cobra Toolbox https//opencobra.github.io/cobratoolbox/latest/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Programas Informáticos
/
Redes y Vías Metabólicas
Tipo de estudio:
Prognostic_studies
Idioma:
En
Año:
2019
Tipo del documento:
Article