Three models will be fitted to two data sets. One model can be ruled out directly after fitting one of the two data sets. One model is able to fit both data sets, but not at the same time. Only the last model is able to describe both setting at once.


  1. Use the model search (magnifying glass icon) and add library models M1, M2, M3

    Model search

    Searching for M1

  2. Duplicate each model

    Duplicate model

    Duplicated models

  3. Optional: Use the < and > keys to shift the position of a selected model

    Ordered models

  4. Select the first M1 model and add a data set from the data library:

    Add data from library

  5. Map data file column names to model observables and driving input variables in the mapping dialog which opens automatically.

  6. Press the assembly icon to combine the first model-data-couple into the fitting assembly, i.e. into the lower list:


  7. Arrange the figures and start fitting:


    Start fitting

  8. Observe that M1 fits the data very well. The driving input is shown in green - the ligand concentration. The measurements are shown in blue including their standard deviations. The observable is given as the red line.

  9. Add library data from the continuous stimulation to the second model M1 and fit again. This time, the model is not able to explain the measurements. The chi-square value is much larger than the number of data points. Therefore, the model is wrong.

  10. Apply the same steps with model M2. Both data sets can be explained by the model if fitted separately. In order to apply a multi-experiment fit, where both data sets are fitted simultaneously, i.e. with the same parameter values, select both M2-data couples and press the combine button:

  11. Fitting reveals that the model is not able to explain both data sets at the same time:

  12. Only M3 is capable to explain both data sets at the same time: