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RECFIT:一种基于微型计算机的非线性回归曲线拟合程序包,用于β-肾上腺素能受体亚型的定量解析和非特异性结合的估算。

RECFIT: a microcomputer-based nonlinear regression curve-fitting package for the quantitative resolution of beta-adrenoceptor subtypes and estimation of nonspecific binding.

作者信息

Clark B, Kelman A W, Trope G E, Titinchi S J

出版信息

Comput Biomed Res. 1984 Jun;17(3):248-57. doi: 10.1016/s0010-4809(84)80016-6.

Abstract

A dedicated nonlinear regression based curve-fitting packages has been developed for quantitative analysis of beta-adrenoceptor subtypes. A feature of this package is the provision to obtain initial parameter estimates using a conversational graphical technique where the user is prompted for parameter estimates, and the resulting curve is displayed over the data. Data are then fitted to a one- or two-binding site model with user-selected weighting and constraints on the parameters. A novel feature is the provision to estimate the nonspecific binding component in the assays as a parameter in the model. The printout for each model consists of the parameters and their standard deviations, estimates of the goodness of fit, an analysis of residuals, and a graph of the data points overlayed with the fitted curve. The nonlinear regression algorithm is based on that of Gauss-Newton. When unweighted, the values determined by RECFIT are essentially the same as those found using the BMDPAR programs on an ICL 2976 mainframe computer. The implementation is reasonably efficient; a typical run for a two-site fit, using nine data points and estimating nonspecific binding, took a total time of about 8 min, excluding data entry and derivation of initial estimates, 4 min for the fitting, 30 sec for graph generation, and 2-3 min for printing of the graph and data.

摘要

已开发出一个专门用于β-肾上腺素能受体亚型定量分析的基于非线性回归的曲线拟合程序包。该程序包的一个特点是提供了一种交互式图形技术来获得初始参数估计值,在这种技术中,会提示用户输入参数估计值,并将生成的曲线显示在数据之上。然后,使用用户选择的加权和参数约束,将数据拟合到单结合位点或双结合位点模型。一个新颖的特点是在分析中能够将非特异性结合成分作为模型中的一个参数进行估计。每个模型的打印输出包括参数及其标准差、拟合优度估计、残差分析以及覆盖有拟合曲线的数据点图。非线性回归算法基于高斯-牛顿算法。在未加权的情况下,RECFIT确定的值与在ICL 2976大型计算机上使用BMDPAR程序得到的值基本相同。该程序的实现效率相当高;对于一个双位点拟合的典型运行,使用9个数据点并估计非特异性结合,总耗时约8分钟,这其中不包括数据输入和初始估计值的推导,拟合耗时4分钟,生成图形耗时30秒,打印图形和数据耗时2 - 3分钟。

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