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Table of contents
Intro to regression
Nonlinear regression
Curve fitting with Prism
Interpreting the results
Comparing two curves
Distributions of best-fit values
Radioligand binding
Saturation binding
Competitive binding

Kinetics of binding

Dose-response curves
Enzyme kinetics
Standard curves


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Introduction
How to fit
Interpolating
Replicate unknowns
Troubleshooting
More information
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In April 2003, GraphPad released Prism 4 and published Fitting Models to Biological Data using Linear and Nonlinear Regression. This book includes all the information that comprises curvefit.com, and much more. You can read this book as a pdf file.

Introduction to standard curves

Standard curves are used to determine the concentration of substances. First you perform an assay with various known concentrations of a substance you are trying to measure. The response might be optical density, luminescence, fluorescence, radioactivity or something else. Graph these data to make a standard curve - concentration on the X axis, and assay measurement on the Y axis.

Also perform the same assay with your unknown samples. You want to know the concentration of the substance in each of these unknown samples.

To analyze the data, fit a line or curve through the standards. For each unknown, read across the graph from the spot on the Y-axis that corresponds to the assay measurement of the unknown until you intersect the standard curve. Read down the graph until you intersect the X-axis. The concentration of substance in the unknown sample is the value on the X-axis.

In the example below, the unknown sample had 1208 counts per minute, so the concentration of the hormone is 0.236 micromolar.

Prism makes it very easy to fit your standard curve, and to read (interpolate) the concentration of unknown samples.

How to fit standard curves


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