@article {422,
	title = {Differential metabolic activity and discovery of therapeutic targets using summarized metabolic pathway models.},
	journal = {NPJ Syst Biol Appl},
	volume = {5},
	year = {2019},
	month = {2019},
	pages = {7},
	abstract = {<p>In spite of the increasing availability of genomic and transcriptomic data, there is still a gap between the detection of perturbations in gene expression and the understanding of their contribution to the molecular mechanisms that ultimately account for the phenotype studied. Alterations in the metabolism are behind the initiation and progression of many diseases, including cancer. The wealth of available knowledge on metabolic processes can therefore be used to derive mechanistic models that link gene expression perturbations to changes in metabolic activity that provide relevant clues on molecular mechanisms of disease and drug modes of action (MoA). In particular, pathway modules, which recapitulate the main aspects of metabolism, are especially suitable for this type of modeling. We present Metabolizer, a web-based application that offers an intuitive, easy-to-use interactive interface to analyze differences in pathway metabolic module activities that can also be used for class prediction and in silico prediction of knock-out (KO) effects. Moreover, Metabolizer can automatically predict the optimal KO intervention for restoring a diseased phenotype. We provide different types of validations of some of the predictions made by Metabolizer. Metabolizer is a web tool that allows understanding molecular mechanisms of disease or the MoA of drugs within the context of the metabolism by using gene expression measurements. In addition, this tool automatically suggests potential therapeutic targets for individualized therapeutic interventions.</p>},
	keywords = {Computational Biology, Computer Simulation, Drug discovery, Gene Regulatory Networks, Humans, Internet, Metabolic Networks and Pathways, Models, Biological, Neoplasms, Phenotype, Software, Transcriptome},
	issn = {2056-7189},
	doi = {10.1038/s41540-019-0087-2},
	author = {Cubuk, Cankut and Hidalgo, Marta R and Amadoz, Alicia and Rian, Kinza and Salavert, Francisco and Pujana, Miguel A and Mateo, Francesca and Herranz, Carmen and Carbonell-Caballero, Jos{\'e} and Dopazo, Joaquin}
}
@article {1203,
	title = {Actionable pathways: interactive discovery of therapeutic targets using signaling pathway models.},
	journal = {Nucleic acids research},
	year = {2016},
	month = {2016 May 2},
	abstract = {The discovery of actionable targets is crucial for targeted therapies and is also a constituent part of the drug discovery process. The success of an intervention over a target depends critically on its contribution, within the complex network of gene interactions, to the cellular processes responsible for disease progression or therapeutic response. Here we present PathAct, a web server that predicts the effect that interventions over genes (inhibitions or activations that simulate knock-outs, drug treatments or over-expressions) can have over signal transmission within signaling pathways and, ultimately, over the cell functionalities triggered by them. PathAct implements an advanced graphical interface that provides a unique interactive working environment in which the suitability of potentially actionable genes, that could eventually become drug targets for personalized or individualized therapies, can be easily tested. The PathAct tool can be found at: http://pathact.babelomics.org.},
	keywords = {actionable genes, Disease mechanism, drug action mechanism, Drug discovery, pathway analysis, personalized medicine, signalling, therapeutic targets},
	issn = {1362-4962},
	doi = {10.1093/nar/gkw369},
	url = {http://nar.oxfordjournals.org/content/early/2016/05/02/nar.gkw369.full},
	author = {Salavert, Francisco and Hidago, Marta R and Amadoz, Alicia and Cubuk, Cankut and Medina, Ignacio and Crespo, Daniel and Carbonell-Caballero, Jos{\'e} and Joaqu{\'\i}n Dopazo}
}
