Anticancer drug discovery through genome-scale metabolic modeling
Review article, 2017

Altered metabolism has long been recognized as a defining property of cancer physiology, but is experiencing renewed interest as the importance of such alterations are becoming fully realized. Once regarded merely as a side effect of a damaging mutation or a general increase in proliferation rate, metabolic network rewiring is now viewed as an intentional process to optimize tumor growth and maintenance, and can even drive cancer transformation. This has motivated the search for anticancer targets among enzymes in the metabolic network of cancer cells. Genome-scale metabolic models (GEMs) provide the necessary framework to systematically interrogate this network, and many recent studies have successfully employed GEMs to predict anticancer drug targets in the metabolic networks of various cancer types.

Antimetabolites

Genome-scale metabolic model

Integrative omics analysis

Cancer metabolism

Flux balance analysis

Personalized medicine

Author

Jonathan Robinson

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Jens B Nielsen

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Royal Institute of Technology (KTH)

Current Opinion in Systems Biology

2452-3100 (ISSN)

Vol. 4 1-8

Subject Categories

Bioinformatics (Computational Biology)

Bioinformatics and Systems Biology

Cancer and Oncology

DOI

10.1016/j.coisb.2017.05.007

More information

Latest update

5/2/2018 1