BACKGROUND Cancer cells undergo global reprogramming of cellular metabolism to satisfy demands of energy and biomass during proliferation and metastasis. Computational modeling of genome-scale metabolic models is an effective approach for designing new therapeutics targeting dysregulated cancer metabolism by identifying metabolic enzymes crucial for satisfying metabolic goals of cancer cells, but nearly all previous studies neglect the existence of metabolic demands other than biomass synthesis and trade-offs between these contradicting metabolic demands. It is thus necessary to develop computational models covering multiple metabolic objectives to study cancer metabolism and identify novel metabolic targets. METHODS We developed a multi-objective optimization model for cancer cell metabolism at genome-scale and an integrated, data-driven workflow for analyzing the Pareto optimality of this model in achieving multiple metabolic goals and identifying metabolic enzymes crucial for maintaining cancer-associated metabolic phenotypes. Using this workflow, we constructed cell line-specific models for a panel of cancer cell lines and identified lists of metabolic targets promoting or suppressing cancer cell proliferation or the Warburg Effect. The targets were then validated using knockdown and over-expression experiments in cultured cancer cell lines. RESULTS We found that the multi-objective optimization model correctly predicted phenotypes including cell growth rates, essentiality of metabolic genes and cell line specific sensitivities to metabolic perturbations. To our surprise, metabolic enzymes promoting proliferation substantially overlapped with those suppressing the Warburg Effect, suggesting that simply targeting the overlapping enzymes may lead to complicated outcomes. We also identified lists of metabolic enzymes important for maintaining rapid proliferation or high Warburg Effect while having little effect on the other. The importance of these enzymes in cancer metabolism predicted by the model was validated by their association with cancer patient survival and knockdown and overexpression experiments in a variety of cancer cell lines. CONCLUSIONS These results confirm this multi-objective optimization model as a novel and effective approach for studying trade-off between metabolic demands of cancer cells and identifying cancer-associated metabolic vulnerabilities, and suggest novel metabolic targets for cancer treatment.

中文翻译：

---

通过建模代谢中的多目标最优性，确定与癌症相关的代谢脆弱性。

背景技术癌细胞经历细胞代谢的整体重编程以满足增殖和转移过程中能量和生物量的需求。基因组规模代谢模型的计算建模是一种有效的方法，可通过识别对满足癌细胞代谢目标至关重要的代谢酶来设计针对癌症代谢失调的新疗法，但几乎所有以前的研究都忽略了生物质合成和贸易以外的代谢需求的存在这些矛盾的代谢需求之间的差异。因此，有必要开发涵盖多个代谢目标的计算模型，以研究癌症代谢并确定新的代谢目标。方法我们开发了一个多目标优化模型，用于基因组范围内的癌细胞代谢以及集成的，数据驱动的工作流程，用于分析该模型在实现多个代谢目标和确定对于维持与癌症相关的代谢表型至关重要的代谢酶方面的帕累托最优性。使用此工作流程，我们为一组癌细胞系构建了细胞系特异性模型，并确定了促进或抑制癌细胞增殖或Warburg效应的代谢靶标列表。然后使用敲除和过表达实验在培养的癌细胞系中验证靶标。结果我们发现，多目标优化模型可以正确预测表型，包括细胞生长速率，代谢基因的必要性以及细胞系对代谢扰动的特异性敏感性。令我们惊讶的是 促进增殖的代谢酶与抑制Warburg效应的酶基本重叠，这表明仅针对重叠的酶可能会导致复杂的结果。我们还确定了对于维持快速增殖或高Warburg效应而对其他物质几乎没有影响的重要代谢酶。这些酶在模型中预测的癌症代谢中的重要性已通过与多种癌症细胞系中癌症患者的生存以及基因敲除和过表达实验的关联而得到验证。结论这些结果证实了这种多目标优化模型是研究癌细胞代谢需求与确定癌症相关代谢脆弱性之间权衡的一种新颖有效的方法，