PT  - JOURNAL ARTICLE
AU  - Ziwei Dai
AU  - Liyan Xu
AU  - Hongrong Hu
AU  - Kun Liao
AU  - Shuye Deng
AU  - Qiyi Chen
AU  - Shiyu Yang
AU  - Qian Wang
AU  - Shuaishi Gao
AU  - Bo Li
AU  - Luhua Lai
TI  - Identification of Cancer–associated Metabolic Vulnerabilities by Modeling Multi-objective Optimality in Metabolism
AID  - 10.1101/198333
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 198333
4099  - http://biorxiv.org/content/early/2017/10/04/198333.short
4100  - http://biorxiv.org/content/early/2017/10/04/198333.full
AB  - Computational modeling of the genome-wide metabolic network is essential for designing new therapeutics targeting cancer-associated metabolic disorder, which is a hallmark of human malignancies. However, previous studies generally assumed that metabolic fluxes of cancer cells are subjected to the maximization of biomass production, despite the wide existence of trade-offs among multiple metabolic objectives. To address this issue, we developed a multi-objective model of cancer metabolism with algorithms depicting approximate Pareto surfaces and incorporating multiple omics datasets. To validate this approach, we built individualized models for NCI-60 cancer cell lines, and accurately predicted cell growth rates and other biological consequences of metabolic perturbations in these cells. By analyzing the landscape of approximate Pareto surface, we identified a list of metabolic targets essential for cancer cell proliferation and the Warburg effect, and further demonstrated their close association with cancer patient survival. Finally, metabolic targets predicted to be essential for tumor progression were validated by cell-based experiments, confirming this multi-objective modelling method as a novel and effective strategy to identify cancer-associated metabolic vulnerabilities.