In those situations, the 5P model may work better as it allows asymmetrical data fitting by adding another parameter, G (Figure 2). However, some immuno- and bio-assay data are not symmetrical and need additional flexibility. The 4P curve fit is a symmetrical function: one half of the curve is exactly symmetrical to the other half with the EC 50/IC 50 in the middle. The response increases with concentration if A D. Where y is the response, D is the response at infinite analyte concentration, A is the response at zero analyte concentration, x is the analyte concentration, C is the inflection point (EC 50/IC 50), and B is the slope factor. Therefore, the 5P model gives a better fit. Although the 4P model gives a smooth symmetrical curve, data are clearly asymmetrical. Concentration-response curve fitted with the 4P and the 5P curve fit models for comparison. The 4P curve fit is described by the following equation:įigure 2. They require at least four data points and five data points for the 4P and 5P curve fit model, respectively, but a more accurate fit is obtained by using at least six points for these regression types1. The two most common nonlinear curve fits are the 4P and 5P, which are sigmoid functions that produce an S shaped curve (Figure 2).
#GRAPHPAD PRISM 5 HYPERBOLIC FIT PRO#
SoftMax Pro has been implemented with the most widely used iterative procedure for nonlinear curve fitting, the Levenberg-Marquardt algorithm, in order to achieve the best possible curve-fitting. SoftMax Pro provides 17 non-linear regression curve-fitting methods these include quadratic, cubic, quartic, log-logit, cubic spline, exponential, rectangular hyperbola (with and without a linear term), two-parameter exponential, bi-exponential, bi-rectangular hyperbola, two site competition, Gaussian, Brain-Cousens, 4P, 5P, and 5P alternate. In order to choose the correct fit, it is important to understand the general shape of the model curves and compare them with the shape of the data points 2. The goal is also to find those parameter values that minimize the deviations between the measured and the expected values. In this case, the relationship between the measured values and the measurement variable is nonlinear. Nonlinear data are commonly modeled using logistic regression. However, in most cases, the relationship between measured values and measurement variables is nonlinear.įigure 1. The primary advantage of this method is that it is simple. The linear range of an assay can be determined using a minimum of three data points on the x-axis however, additional standard concentrations within the specified range should be added to improve the accuracy of the fit1. SoftMax Pro will find the best straight line through the data (Figure 1). These are linear (y = A + Bx), semi-log (y = A + B * log10(x)) and log-log (log 10(y) = A + B * log 10(x)). SoftMax Pro provides three linear regression curve-fitting methods. The slope of the line is B and A is the y intercept when x=0. It is represented by the equation y = A + Bx, where x (generally the concentration) is an independent variable and y (the response) is the dependent variable. The simplest method to analyze data is to use a linear regression curve fit. In addition, a protocol has been implemented with the sum of squared errors and the Akaike’s Information Criterion methods in order to evaluate different curve fit models to best represent the data. This technical note discusses the different linear and non-linear regression models available in SoftMax Pro 7. These ensure that the plotted curve is as close as possible to the curve that expresses the concentration versus response relationship by adjusting the curve fit parameters of the chosen model to best fit the data.
#GRAPHPAD PRISM 5 HYPERBOLIC FIT SOFTWARE#
SoftMax® Pro 7 Software offers 21 different curve fit options, including the four parameter logistic (4P) and five parameter logistic (5P) nonlinear regression models. Therefore, the goal of curve fitting is to find the parameter values that most closely match the data, or in other words, the best mathematical equation that represents the empirical data. The curve fit of choice should represent the most accurate relationship between two known variables: x and y. High-Content Screening with AgileOptix TechnologyĬhoosing the correct curve fit model is crucial when determining important characteristics of data such as the rate of change, upper and lower asymptotes of the curve, or the EC 50/IC 50 values.PROTEIN DETECTION, QUANTITATION, ANALYSIS.NUCLEIC ACID (DNA/RNA) DETECTION & ANALYSIS.SpectraTest Validation Plates and Recertification.GXP SOFTWARE INSTALLATION AND VALIDATION.HIGH-THROUGHPUT, HIGH CONTENT SCREENING.
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