@article {49724,
	title = {Phenotype-based cell-specific metabolic modeling reveals metabolic liabilities of cancer.},
	journal = {Elife},
	volume = {3},
	year = {2014},
	month = {2014},
	abstract = {<p>Utilizing molecular data to derive functional physiological models tailored for specific cancer cells can facilitate the use of individually tailored therapies. To this end we present an approach termed PRIME for generating cell-specific genome-scale metabolic models (GSMMs) based on molecular and phenotypic data. We build >280 models of normal and cancer cell-lines that successfully predict metabolic phenotypes in an individual manner. We utilize this set of cell-specific models to predict drug targets that selectively inhibit cancerous but not normal cell proliferation. The top predicted target, MLYCD, is experimentally validated and the metabolic effects of MLYCD depletion investigated. Furthermore, we tested cell-specific predicted responses to the inhibition of metabolic enzymes, and successfully inferred the prognosis of cancer patients based on their PRIME-derived individual GSMMs. These results lay a computational basis and a counterpart experimental proof of concept for future personalized metabolic modeling applications, enhancing the search for novel selective anticancer therapies.</p>},
	keywords = {algorithms, Antineoplastic Agents, Biomarkers, Tumor, Carboxy-Lyases, Cell Line, Tumor, Cell Proliferation, Citric Acid Cycle, Fatty Acids, Gene Knockdown Techniques, Genome, Human, HUMANS, Lymphocytes, Models, Biological, Neoplasms, Oxidation-Reduction, PHENOTYPE, Precision Medicine},
	issn = {2050-084X},
	doi = {10.7554/eLife.03641},
	author = {Yizhak, Keren and Gaude, Edoardo and Le D{\'e}v{\'e}dec, Sylvia and Waldman, Yedael Y and Stein, Gideon Y and van de Water, Bob and Frezza, Christian and Ruppin, Eytan}
}
@article {49728,
	title = {Predicting selective drug targets in cancer through metabolic networks.},
	journal = {Mol Syst Biol},
	volume = {7},
	year = {2011},
	month = {2011},
	pages = {501},
	abstract = {<p>The interest in studying metabolic alterations in cancer and their potential role as novel targets for therapy has been rejuvenated in recent years. Here, we report the development of the first genome-scale network model of cancer metabolism, validated by correctly identifying genes essential for cellular proliferation in cancer cell lines. The model predicts 52 cytostatic drug targets, of which 40\% are targeted by known, approved or experimental anticancer drugs, and the rest are new. It further predicts combinations of synthetic lethal drug targets, whose synergy is validated using available drug efficacy and gene expression measurements across the NCI-60 cancer cell line collection. Finally, potential selective treatments for specific cancers that depend on cancer type-specific downregulation of gene expression and somatic mutations are compiled.</p>},
	keywords = {Cell Line, Tumor, Cell Proliferation, Computational Biology, Cytostatic Agents, Down-Regulation, Drug Delivery Systems, Gene Expression Regulation, Neoplastic, HUMANS, Metabolic Networks and Pathways, Models, Biological, Neoplasms, RNA, Small Interfering},
	issn = {1744-4292},
	doi = {10.1038/msb.2011.35},
	author = {Folger, Ori and Jerby, Livnat and Frezza, Christian and Gottlieb, Eyal and Ruppin, Eytan and Shlomi, Tomer}
}
