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Studying metabolic flux adaptations in cancer through integrated experimental-computational approaches
BMC Biology ( IF 5.4 ) Pub Date : 2019-07-04 , DOI: 10.1186/s12915-019-0669-x
Shoval Lagziel , Won Dong Lee , Tomer Shlomi

The study of tumorigenic rewiring of metabolic flux is at the heart of cancer metabolic research. Here, we review two widely used computational flux inference approaches: isotope tracing coupled with Metabolic Flux Analysis (13C-MFA) and COnstraint-Based Reconstruction and Analysis (COBRA). We describe the applications of these complementary modeling techniques for studying metabolic adaptations in cancer cells due to genetic mutations and the tumor microenvironment, as well as for identifying novel enzymatic targets for anti-cancer drugs. We further highlight the advantages and limitations of COBRA and 13C-MFA and the main challenges ahead.

中文翻译:

通过综合实验-计算方法研究癌症中的代谢通量适应性

代谢通量的致瘤性重新连接研究是癌症代谢研究的核心。在这里,我们回顾了两种广泛使用的计算通量推论方法:同位素示踪与代谢通量分析(13C-MFA)和基于约束的重构与分析(COBRA)。我们描述了这些互补的建模技术的应用,用于研究由于遗传突变和肿瘤微环境而在癌细胞中的代谢适应性,以及用于识别抗癌药物的新型酶标。我们进一步强调了COBRA和13C-MFA的优势和局限性以及未来的主要挑战。
更新日期:2019-07-04
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