Abstract When using discontinuous assay of reactions, initial rates are often estimated from a limited number of time points. There has been no detailed study of how best to do this. In this work, time courses were simulated by different theoretical equations (including strong product inhibition, first order, Michaelis–Menten and truly linear), but with random error addition to each data point. Various methods were tested to fit an initial rate to the data, and the result compared with the known “true” value. Fitting a simple quadratic generally gives initial rates as accurate as any other curve, and is better than a linear fit if there are about 8 or more time points. For fewer points a linear fit gives less variable and often more accurate rates. The absolute contribution to data point error has a major impact on rate accuracy, and often dominates that due to curvature, so that sampling to at least 10% conversion is preferred. The accuracy of a linear fit can be improved by methods that reject some later points based on curvature tests. Awareness of these effects can help avoid rate inaccuracies of 10% or more due to poor methods of data analysis.

中文翻译：

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从不连续的进度数据估计初始速率

摘要 当使用反应的不连续分析时，初始速率通常是从有限数量的时间点估计的。没有详细研究如何最好地做到这一点。在这项工作中，时间过程由不同的理论方程（包括强乘积抑制、一阶、Michaelis-Menten 和真正线性）模拟，但每个数据点都有随机误差。测试了各种方法以将初始速率拟合到数据，并将结果与​​已知的“真实”值进行比较。拟合简单的二次曲线通常会给出与任何其他曲线一样准确的初始速率，如果有大约 8 个或更多的时间点，则比线性拟合更好。对于更少的点，线性拟合给出的变量更少，而且通常更准确。对数据点误差的绝对贡献对速率精度有重大影响，并且通常由于曲率而占主导地位，因此首选采样到至少 10% 的转换。可以通过基于曲率测试拒绝某些稍后点的方法来提高线性拟合的准确性。意识到这些影响可以帮助避免由于数据分析方法不佳而导致的 10% 或更多的比率不准确。