在不预先了解权重的情况下对酶动力学数据进行拟合。
Fitting of enzyme kinetic data without prior knowledge of weights.
作者信息
Cornish-Bowden A, Endrenyi L
出版信息
Biochem J. 1981 Mar 1;193(3):1005-8. doi: 10.1042/bj1931005.
Abstract
A method is described for fitting equations to enzyme kinetic data that requires minimal assumptions about the error structure of the data. The dependence of the variances on the velocities is not assumed, but is deduced from internal evidence in the data. The effect of very bad observations ('outliers') is mitigated by decreasing the weight of observations that give large deviations from the fitted equation. The method works well in a wide range of circumstances when applied to the Michaelis-Menten equation, but it is not limited to this equation. It can be applied to most of the equations in common use for the analysis of steady-state enzyme kinetics. It has been implemented as a computer program that can fit a wide variety of equations with two, three or four parameters and two or three variables.
摘要
本文描述了一种将方程拟合到酶动力学数据的方法，该方法对数据的误差结构所需假设最少。不假定方差对速度的依赖性，而是从数据中的内部证据推导得出。通过降低与拟合方程偏差较大的观测值的权重，可减轻极差观测值（“异常值”）的影响。当应用于米氏方程时，该方法在广泛的情况下都能很好地发挥作用，但它并不局限于该方程。它可应用于稳态酶动力学分析中常用的大多数方程。它已被实现为一个计算机程序，该程序可以拟合具有两个、三个或四个参数以及两个或三个变量的各种方程。
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