Choosing a dose-response equation
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Choosing a dose-response equation |
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To choose a dose-response model in Prism, you need to answer three questions: Stimulation or inhibition? Prism offers one set of dose-response equations for stimulation and another set for inhibition. The inhibitory equations are set up to run downhill. The only difference is that the inhibitory equations fit the IC50 ("I" for inhibition) while the stimulation equations fit the EC50 ("E" for effective). If the curve goes up hill, choose from the set of stimulation equations. If the curve goes down hill, choose from the set of inhibition equations. Standard slope or variable slope? A huge variety of dose-response curves follow the same steepness as receptor binding, so have a Hill slope of 1.00 (for stimulation) or -1.00 (for inhibition). Because this standard slope is so common, Prism comes with equations with the standard slope built in. The equations that don't have 'variable slope' in their name assume the standard slope. If you have lots of data points (more than a dozen, perhaps lots more), then you can fit the slope by picking a variable slope equation. If you have fewer data points, and a standard system, it makes sense to choose an equation with a standard slope. Normalized or not? The dose-response model has four parameters: the bottom plateau, the top plateau, the EC50, and the slope factor (which is often constrained to a standard value). The main goal of fitting the dose-response curve in many situations is to determine the best-fit value of the EC50, which is the concentration that provokes a response halfway between the top and bottom plateaus. If those plateaus are not well defined, the EC50 will be very uncertain. Think of it this way: If you have not defined "100" and "0" very precisely, you also have not defined "50" precisely, and therefore cannot determine the EC50 precisely. One way to solve the problem is to constrain the Top or Bottom, or both, to control values. An alternative is to normalize your data so responses run from 0 to 100, and then choose a "normalized response" model. These models don't fit the bottom and top plateaus, but rather assume that the bottom plateau is 0 and the top plateau is 100. Only choose a 'normalized response' equation when you have determined the values that define 0 and 100 very precisely. Just because you have normalized your data to run from 0 to 100, you don't have to pick a 'normalized response' model. Special dose-response models Prism has a set of special models used for special dose-response situations: |