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整合的米氏方程:似曾相识还是似曾相识?

The integrated Michaelis-Menten rate equation: déjà vu or vu jàdé?

机构信息

Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Vrazov trg 2, Ljubljana, Slovenia.

出版信息

J Enzyme Inhib Med Chem. 2013 Aug;28(4):879-93. doi: 10.3109/14756366.2012.688039. Epub 2012 May 28.

Abstract

A recent article of Johnson and Goody (Biochemistry, 2011;50:8264-8269) described the almost-100-years-old paper of Michaelis and Menten. Johnson and Goody translated this classic article and presented the historical perspective to one of incipient enzyme-reaction data analysis, including a pioneering global fit of the integrated rate equation in its implicit form to the experimental time-course data. They reanalyzed these data, although only numerical techniques were used to solve the model equations. However, there is also the still little known algebraic rate-integration equation in a closed form that enables direct fitting of the data. Therefore, in this commentary, I briefly present the integral solution of the Michaelis-Menten rate equation, which has been largely overlooked for three decades. This solution is expressed in terms of the Lambert W function, and I demonstrate here its use for global nonlinear regression curve fitting, as carried out with the original time-course dataset of Michaelis and Menten.

摘要

最近,约翰逊和古迪(Johnson and Goody)在《生物化学》(Biochemistry)杂志 2011 年第 50 卷第 8264-8269 期上发表了一篇文章,介绍了迈克尔利斯和门滕(Michaelis and Menten)近 100 年前的论文。约翰逊和古迪对这篇经典文章进行了翻译,并从历史角度介绍了早期酶反应数据分析之一,包括对积分速率方程的开创性整体拟合,这种拟合以其隐式形式应用于实验时间过程数据。他们重新分析了这些数据,尽管仅使用数值技术来求解模型方程。然而,还有一个鲜为人知的封闭形式的代数速率积分方程,可以直接拟合数据。因此,在这篇评论中,我简要介绍了被忽视了三十年的米氏方程的积分解。该解用兰伯特 W 函数表示,我在这里展示了它在全局非线性回归曲线拟合中的应用,该拟合是用米氏和门滕的原始时间过程数据集进行的。

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