@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 {1005,
	title = {Genomics and transcriptomics in drug discovery.},
	journal = {Drug discovery today},
	volume = {19},
	number = {2},
	year = {2014},
	month = {2013 Jun 14},
	pages = {126-32},
	abstract = {The popularization of genomic high-throughput technologies is causing a revolution in biomedical research and, particularly, is transforming the field of drug discovery. Systems biology offers a framework to understand the extensive human genetic heterogeneity revealed by genomic sequencing in the context of the network of functional, regulatory and physical protein-drug interactions. Thus, approaches to find biomarkers and therapeutic targets will have to take into account the complex system nature of the relationships of the proteins with the disease. Pharmaceutical companies will have to reorient their drug discovery strategies considering the human genetic heterogeneity. Consequently, modeling and computational data analysis will have an increasingly important role in drug discovery.},
	keywords = {adverse effects, Drug discovery, drug repositioning, metagenomics, modeling, network analysis, pathway analysis},
	issn = {1878-5832},
	doi = {10.1016/j.drudis.2013.06.003},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/23773860},
	author = {Dopazo, Joaquin}
}
