Liang Wu, Weiming Wang, Wouter A van Winden, Walter M van Gulik, Joseph J Heijnen
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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.</p>","PeriodicalId":11817,"journal":{"name":"European journal of biochemistry","volume":"271 16","pages":"3348-59"},"PeriodicalIF":0.0000,"publicationDate":"2004-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/j.0014-2956.2004.04269.x","citationCount":"81","resultStr":"{\"title\":\"A new framework for the estimation of control parameters in metabolic pathways using lin-log kinetics.\",\"authors\":\"Liang Wu, Weiming Wang, Wouter A van Winden, Walter M van Gulik, Joseph J Heijnen\",\"doi\":\"10.1111/j.0014-2956.2004.04269.x\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The control properties of biochemical pathways can be described by control coefficients and elasticities, as defined in the framework of metabolic control analysis. 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引用次数: 81
摘要
生化途径的控制特性可以用控制系数和弹性来描述,在代谢控制分析的框架中定义。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微小变化)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。为了避免上述问题,本文提出了最近发展起来的林对数方法,并将其应用于从稳态实验中得到的测量值估计控制参数。林-对数方法采用近似的线性-对数动力学参数化的弹性,并提供分析解决方案,当通量和代谢物浓度的大变化。已发表的通量和代谢物浓度数据,从糖酵解将3-磷酸甘油酸转化为丙酮酸的重构部分获得[Giersch, C. (1995) Eur。[j].生物化学学报,2016,33(2):444 - 444。使用林对数方法,可以将不同实验的所有数据结合起来,通过线性回归给出真实的弹性和通量控制系数估计。尽管变化很大,但根据林-对数模型,在通量和代谢物浓度的测量值和计算值之间获得了很好的一致性。此外,lin-log方法可以通过严格的统计评估来确定最优参考状态和最优模型结构假设。总之,林对数方法通过引入适当的非线性动力学,解决了传统基于代谢控制分析方法中遇到的实际问题,从而为大扰动实验中弹性和控制参数的估计提供了一个新的框架和改进的程序。
A new framework for the estimation of control parameters in metabolic pathways using lin-log kinetics.
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.