CBCB faculty Eytan Ruppin’s group publish a paper in eLife on phenotype-based cell-specific metabolic modeling
Utilizing molecular data to derive functional physiological models tailored for specific cancer cells can facilitate the use of individually tailored therapies. To this end, they present an approach termed PRIME for generating cell-specific genome-scale metabolic models (GSMMs) based on molecular and phenotypic data. They built >280 models of normal and cancer cell-lines that successfully predict metabolic phenotypes in an individual manner. They 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, was experimentally validated and the metabolic effects of MLYCD depletion were investigated. Furthermore, they 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.
“Phenotype-based cell-specific metabolic modeling reveals metabolic liabilities of cancer” article: http://www.cs.tau.ac.il/~ruppin/prime.pdf