A model of yeast glycolysis based on a consistent kinetic characterisation of all its enzymes.
A model of yeast glycolysis based on a consistent kinetic characterisation of all its enzymes.
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基于其所有酶的一致动力学表征的酵母糖酵解模型。
DOI：
10.1016/j.febslet.2013.06.043
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发表时间：
2013-09-02
期刊：
影响因子：
3.5
通讯作者：
Mendes P
中科院分区：
文献类型：
--
作者：
Smallbone K;Messiha HL;Carroll KM;Winder CL;Malys N;Dunn WB;Murabito E;Swainston N;Dada JO;Khan F;Pir P;Simeonidis E;Spasić I;Wishart J;Weichart D;Hayes NW;Jameson D;Broomhead DS;Oliver SG;Gaskell SJ;McCarthy JE;Paton NW;Westerhoff HV;Kell DB;Mendes P
我们提出了一种生成代谢动力学模型的实验和计算流水线，并展示了它在酿酒酵母糖酵解中的应用。从一个近似的数学模型出发，我们采用了“知识循环”策略，确定了对流量控制最多的步骤。这些步骤中各个同工酶的动力学参数是在标准化条件下进行实验测量的。应用实验策略来建立同工酶和代谢物的体内浓度。这些数据被整合到一个数学模型中，该模型用于预测一组新的代谢物浓度，并重新评估系统的控制性能。这项自下而上的模拟研究显示，对最直接涉及酵母糖酵解的代谢网络的控制比之前认为的更广泛。
We present an experimental and computational pipeline for the generation of kinetic models of metabolism, and demonstrate its application to glycolysis in Saccharomyces cerevisiae. Starting from an approximate mathematical model, we employ a “cycle of knowledge” strategy, identifying the steps with most control over flux. Kinetic parameters of the individual isoenzymes within these steps are measured experimentally under a standardised set of conditions. Experimental strategies are applied to establish a set of in vivo concentrations for isoenzymes and metabolites. The data are integrated into a mathematical model that is used to predict a new set of metabolite concentrations and reevaluate the control properties of the system. This bottom-up modelling study reveals that control over the metabolic network most directly involved in yeast glycolysis is more widely distributed than previously thought.
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影响因子：
--
作者：
Dikicioglu D;Karabekmez E;Rash B;Pir P;Kirdar B;Oliver SG
通讯作者：
Oliver SG
影响因子：
4.4
作者：
Cronwright, GR;Rohwer, JM;Prior, BA
通讯作者：
Prior, BA
影响因子：
--
作者：
Heavner BD;Smallbone K;Barker B;Mendes P;Walker LP
通讯作者：
Walker LP
影响因子：
--
作者：
Dobson PD;Smallbone K;Jameson D;Simeonidis E;Lanthaler K;Pir P;Lu C;Swainston N;Dunn WB;Fisher P;Hull D;Brown M;Oshota O;Stanford NJ;Kell DB;King RD;Oliver SG;Stevens RD;Mendes P
通讯作者：
Mendes P
影响因子：
4.8
作者：
Albert, MA;Haanstra, JR;Michels, PAM
通讯作者：
Michels, PAM