A nonlinear quantitative response assay (parallel-curve model, full curve fits) is a full curve fit method which takes the whole dose-response relationship into consideration, including asymptotes.

When you do a biostatistical analysis with our PLA 3.0 bioassay software, you can chose between different types of nonlinear full curve fits:

  • 3-parameter logistic curve fit
  • 4-parameter logistic curve fit
  • 5-parameter logistic curve fit
3-parameter logistic fit with control line to fix the upper asymptote on quantitative responses
3-parameter logistic fit with control line to fix the upper asymptote on quantitative responses
4-parameter logistic fit on quantitative responses
4-parameter logistic fit on quantitative responses
5-parameter logistic fit on quantitative responses
5-parameter logistic fit on quantitative response

The 4-parameter logistic curve fit is the most common approach. It models a symmetric sigmoidal dose-response correlationship. The 5-parameter logistic fit function adds an asymmetry parameter, which can be useful to account for ceiling or floor-effects. The 3-parameter model is a reduced 4-parameter model, where one of the asymptotes has to be set to a fixed value or to the mean of a control line, which allows the system to deal with truncated data.

PLA 3.0 allows you to carry out the regression for each standard/sample combination in your bioassay separately or at once.

Use this approach for all typical bioassays, such as

  • binding assays (for example ELISA),
  • enzyme activity assays (for example spectrophotometric, fluorometric or radiometric assay),
  • cell based assays (for example cell proliferation, cytotoxicity or cell death assay) or
  • cell and gene therapy assays.

Example report of a nonlinear quantitative response assay analyzed in PLA 3.0