Ensemble modeling of cancer metabolism.
Ensemble modeling of cancer metabolism.
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DOI：
10.3389/fphys.2012.00135
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发表时间：
2012
影响因子：
4
通讯作者：
Mahadevan R
中科院分区：
文献类型：
--
作者：
Khazaei T;McGuigan A;Mahadevan R
癌细胞的代谢行为适应其增殖的需要，有显著的变化，如乳酸分泌和葡萄糖摄取率增加。在这项工作中，我们使用集成建模（EM）框架来深入了解和预测肿瘤细胞的潜在药物靶点。EM生成了一组模型，这些模型跨越了受热力学约束的动力学参数空间。基于已知目标的扰动数据用于筛选整个模型集合以获得子集，其预测性越来越强。EM允许纳入调控信息，并通过以基本反应形式表示反应，在分子水平上捕获酶促反应的行为。本研究考虑了一个由58个反应组成的代谢网络，包括糖酵解、戊糖磷酸途径、脂质代谢、氨基酸代谢，并包括关键酶的变构调节。实验测量的细胞内和细胞外代谢物浓度用于开发模型集合以及已建立的药物靶点信息。由此产生的模型预测，相对于目前已知的药物靶点，当被抑制时，转醛缩酶（TALA）和琥珀酰辅酶a连接酶（SUCOAS1m）会导致生长速度显著降低。此外，结果表明，与单一酶靶点的抑制相比，转醛缩酶和甘氨酸羟甲基转移酶（GHMT2r）的协同抑制将导致生长速度降低三倍。
The metabolic behavior of cancer cells is adapted to meet their proliferative needs, with notable changes such as enhanced lactate secretion and glucose uptake rates. In this work, we use the Ensemble Modeling (EM) framework to gain insight and predict potential drug targets for tumor cells. EM generates a set of models which span the space of kinetic parameters that are constrained by thermodynamics. Perturbation data based on known targets are used to screen the entire ensemble of models to obtain a sub-set, which is increasingly predictive. EM allows for incorporation of regulatory information and captures the behavior of enzymatic reactions at the molecular level by representing reactions in the elementary reaction form. In this study, a metabolic network consisting of 58 reactions is considered and accounts for glycolysis, the pentose phosphate pathway, lipid metabolism, amino acid metabolism, and includes allosteric regulation of key enzymes. Experimentally measured intracellular and extracellular metabolite concentrations are used for developing the ensemble of models along with information on established drug targets. The resulting models predicted transaldolase (TALA) and succinyl-CoA ligase (SUCOAS1m) to cause a significant reduction in growth rate when repressed, relative to currently known drug targets. Furthermore, the results suggest that the synergistic repression of transaldolase and glycine hydroxymethyltransferase (GHMT2r) will lead to a threefold decrease in growth rate compared to the repression of single enzyme targets.
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影响因子：
64.8
作者：
Frezza, Christian;Zheng, Liang;Gottlieb, Eyal
通讯作者：
Gottlieb, Eyal
影响因子：
3.4
作者：
Jankowski, Matthew D.;Henry, Christopher S.;Hatzimanikatis, Vassily
通讯作者：
Hatzimanikatis, Vassily
DOI：
10.1073/pnas.0610772104
发表时间：
2007-02-06
影响因子：
11.1
作者：
Duarte, Natalie C.;Becker, Scott A.;Palsson, Bernhard O.
通讯作者：
Palsson, Bernhard O.
DOI：
10.1073/pnas.1011365107
发表时间：
2010-11-30
影响因子：
11.1
作者：
Park, Kidong;Millet, Larry J.;Bashir, Rashid
通讯作者：
Bashir, Rashid
影响因子：
--
作者：
Vazquez A;Liu J;Zhou Y;Oltvai ZN
通讯作者：
Oltvai ZN