Predicting drug targets and biomarkers of cancer via genome-scale metabolic modeling

Livnat Jerby, Eytan Ruppin

Research output: Contribution to journalReview articlepeer-review

Abstract

The metabolism of cancer cells is reprogrammed in various ways to support their growth and survival. Studying these phenomena to develop noninvasive diagnostic tools and selective treatments is a promising avenue. Metabolic modeling has recently emerged as a new way to study human metabolism in a systematic, genome-scale manner by using pertinent high-throughput omics data. This method has been shown in various studies to provide fairly accurate estimates of the metabolic phenotype and its modifications following genetic and environmental perturbations. Here, we provide an overview of genome-scale metabolic modeling and its current use to model human metabolism in health and disease. We then describe the initial steps made using it to study cancer metabolism and how it may be harnessed to enhance ongoing experimental efforts to identify drug targets and biomarkers for cancer in a rationale-based manner.

Original languageEnglish
Pages (from-to)5572-5584
Number of pages13
JournalClinical Cancer Research
Volume18
Issue number20
DOIs
StatePublished - 15 Oct 2012

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