A new framework for the estimation of control parameters in metabolic pathways using lin-log kinetics.
A new framework for the estimation of control parameters in metabolic pathways using lin-log kinetics.
复制标题
使用线性对数动力学估计代谢途径中控制参数的新框架。
DOI：
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发表时间：
2004
期刊：
影响因子：
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通讯作者：
J. Heijnen
中科院分区：
文献类型：
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作者：
Liang Wu;Weiming Wang;W. V. van Winden;W. V. van Gulik;J. Heijnen
生化途径的控制特性可以通过代谢控制分析框架中定义的控制系数和弹性来描述。然而，使用传统的代谢控制分析关系来确定这些参数受到实验困难（例如，实现和测量生物系统中的小变化）和缺乏适当的数学程序（例如，当进行更实际的大变化时）的限制。在本文中，最近开发的线性对数方法，提出了避免上述问题，并适用于估计控制参数从稳态实验中获得的测量。线性对数的方法采用近似的线性对数动力学参数化的弹性，并提供了分析解决方案通量和代谢物浓度时，大的变化。使用已发表的通量和代谢物浓度数据，这些数据得自糖酵解将3-磷酸甘油酸转化为丙酮酸的重建部分[Giersch，C.（1995）Eur. J. Biochem.227，194-201]。使用线性对数方法，可以将来自不同实验的所有数据结合起来，通过线性回归给出现实的弹性和通量控制系数估计值。尽管变化很大，根据线性对数模型的测量值和计算值之间的通量和代谢物浓度得到了很好的协议。此外，它表明，线性对数方法允许严格的统计评估，以确定最佳的参考状态和最佳的模型结构假设。总之，线性对数的方法解决了传统的代谢控制分析为基础的方法中遇到的实际问题，通过引入合适的非线性动力学，从而提供了一个新的框架与改进的程序估计的弹性和控制参数从大扰动实验。
The control properties of biochemical pathways can be described by control coefficients and elasticities, as defined in the framework of metabolic control analysis. The determination of these parameters using the traditional metabolic control analysis relationships is, however, limited by experimental difficulties (e.g. realizing and measuring small changes in biological systems) and lack of appropriate mathematical procedures (e.g. when the more practical large changes are made). In this paper, the recently developed lin-log approach is proposed to avoid the above-mentioned problems and is applied to estimate control parameters from measurements obtained in steady state experiments. The lin-log approach employs approximative linear-logarithmic kinetics parameterized by elasticities and provides analytical solutions for fluxes and metabolite concentrations when large changes are made. Published flux and metabolite concentration data are used, obtained from a reconstructed section of glycolysis converting 3-phosphoglycerate to pyruvate [Giersch, C. (1995) Eur. J. Biochem. 227, 194-201]. With the lin-log approach, all data from different experiments can be combined to give realistic elasticity and flux control coefficient estimates by linear regression. Despite the large changes, a good agreement of fluxes and metabolite concentrations is obtained between the measured and calculated values according to the lin-log model. Furthermore, it is shown that the lin-log approach allows a rigorous statistical evaluation to identify the optimal reference state and the optimal model structure assumption. In conclusion, the lin-log approach addresses practical problems encountered in the traditional metabolic control analysis-based methods by introducing suitable nonlinear kinetics, thus providing a novel framework with improved procedures for the estimation of elasticities and control parameters from large perturbation experiments.
影响因子：
3.8
作者：
Shaoning Yu;Lucy L. Y. Lee;J. Lee
通讯作者：
Shaoning Yu;Lucy L. Y. Lee;J. Lee