Luis V Valcárcel1,2,3, Verónica Torrano3,4, Luis Tobalina5, Arkaitz Carracedo3,4,6,7, Francisco J Planes1. 1. Tecnun, University of Navarra, San Sebastián 20018, Spain. 2. Area de Hemato-Oncología, IDISNA, Centro de Investigación Médica Aplicada (CIMA), University of Navarra, Pamplona, Spain. 3. Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Faculty of Medicine, Joint Research Centre for Computational Biomedicine, RWTH Aachen University, Aachen D-52074, Germany. 4. CIC bioGUNE, Bizkaia Technology Park, Derio, Spain. 5. Faculty of Medicine, Joint Research Centre for Computational Biomedicine, RWTH Aachen University, Aachen D-52074, Germany. 6. Ikerbasque, Basque foundation for science, Bilbao, Spain. 7. Biochemistry and Molecular Biology Department, University of the Basque Country (UPV/EHU), Bilbao E-48080, Spain.
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.
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.