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Fitting of enzyme kinetic data without prior knowledge of weights

机译:酶动力学数据的拟合,无需事先了解重量

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pA 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./p
机译:>描述了一种用于将方程式拟合到酶动力学数据的方法,该方法需要关于数据的误差结构的最小假设。不假定方差对速度的依赖性,而是根据数据中的内部证据推导得出。通过减少观察值的权重可以减轻非常差的观察值(“离群值”)的影响,这些权值会与拟合方程式产生较大偏差。当将该方法应用于Michaelis-Menten方程时,该方法在各种情况下均能很好地工作,但并不限于此方程。它可以应用于分析稳态酶动力学的大多数常用方程式。它已被实现为计算机程序,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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