Detalle Publicación

rMTA: robust metabolic transformation analysis

Autores: Valcarcel, L. V. ; Torrano, V. ; Tobalina, L. ; Carracedo, A.; Planes Pedreño, Francisco Javier (Autor de correspondencia)
Título de la revista: BIOINFORMATICS
ISSN: 1367-4803
Volumen: 35
Número: 21
Páginas: 4350 - 4355
Fecha de publicación: 2019
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.