Predicting Drug Targets and Biomarkers of Cancer via Genome-Scale Metabolic Modeling
Predicting Drug Targets and Biomarkers of Cancer via Genome-Scale Metabolic Modeling
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DOI：
10.1158/1078-0432.ccr-12-1856
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发表时间：
2012-10-15
影响因子：
11.5
通讯作者：
Ruppin, Eytan
中科院分区：
文献类型：
--
作者：
Jerby, Livnat;Ruppin, Eytan
癌细胞的代谢以各种方式重新编程，以支持其生长和存活。研究这些现象以开发非侵入性诊断工具和选择性治疗是一条有前途的途径。代谢建模是近年来兴起的一种利用相关的高通量组学数据以系统的、基因组规模的方式研究人类代谢的新方法。该方法已在各种研究中显示，可提供相当准确的代谢表型及其在遗传和环境扰动后的变化估计。在这里，我们提供了一个基因组规模的代谢建模及其目前使用的健康和疾病的人体代谢模型的概述。然后，我们描述了使用它来研究癌症代谢的初步步骤，以及如何利用它来加强正在进行的实验工作，以合理的方式确定癌症的药物靶点和生物标志物。临床癌症研究; 18（20）; 5572-84。(C)2012年AACR。
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. Clin Cancer Res; 18(20); 5572-84. (C) 2012 AACR.