Similarly, there are three ways to deal with the bottom plateau: Fit the data only, set Bottom to be a constant equal to the average of the NS controls, and put the NS controls into the fit as if they were a very high concentration of inhibitor. I prefer the third method, as it analyzes all the data, but that is not a strong preference. The results will be very similar with any of these methods, because the data form a complete dose-response curve with a clear top plateau that is indistinguishable from the blank. You can't enter zero, because zero is not defined on a log scale. Simply enter a low dose, perhaps 10 -10 or 10 -11.
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When fitting this curve, you need to decide how to fit the top plateau of the curve. The data of the experimental dose-response curve (red dots) extend all the way between the two control values. The ones on the right are in the presence of a maximal concentration of a standard inhibitor, so define "0%". The ones on the left (Blank) have no inhibitor, so define "100%". The green symbols show measurements made with controls. Just stand on your head when you view the figures. All the ideas can be applied to stimulatory curves and EC50 (E for effective) as well. The rest of this article is about IC50 (I for inhibition, for downward sloping dose-response curves). But when you actually go to fit data to determine these values, there are several complexities and ambiguities.
The IC50 is the concentration of an inhibitor where the response (or binding) is reduced by half. The EC50 is the concentration of a drug that gives half-maximal response. The concepts of IC50 and EC50 are fundamental to pharmacology.
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