How to Perform Enzyme Kinetics Analysis

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The Enzyme Kinetics App is designed for performing enzyme kinetics analysis in two steps:

  1. Properly organize the data.
  2. Fit the data with proper models.

Prior to performing the fitting process using the Enzyme Kinetics App, the data should be well-organized. When you click on the Enzyme Kinetics icon in Apps Gallery, a dialog opens with settings for your input data.

The Enzyme Kinetics Sample.opj shipped along with this app is located in %LocalAppData%\OriginLab\Apps\Enzyme Kinetics\Samples folder. You can find it by right-clicking on the App icon in the Apps Gallery and selecting the Show Samples Folder from the context menu. The data in [Book1]Sheet1 is for a reversible inhibition study:


The data include 2 measurements of substrate and 3 measurements of inhibitor, with 2 replicates for each inhibitor measurement. It should be organized as in the following:


There are two independent variables in the above example. The values of the first independent variable should be stored in the X column, while the second are in the corresponding row labels named Indep2 Value.


In the above example, for the 1st measurement of substrate, the independent variables are:

substrate = {5, 10, 20, 30, 50, 100, 200}

inhibitor = {10, 50, 100}

for the 2nd measure of substrate, the independent variables are:

substrate = {10, 15, 25, 35, 55, 105, 205}

inhibitor = {10, 50, 100}

Once the input data are ready, you can click the Fit Model button on the Input Sheet, bringing up the dialog for fitting. The following picture shows the settings used in this example:


The fitting process created 2 sheets: EK Fitted Report, and EK Fitted Curves. The sheet EK Fitted Curves stores the data for graphs in EK Fitted Report.

In the reportsheet EK Fitted Report:


  1. Model Information: this branch reveals the fitting functions used in particular models. You can find them in the Enzyme Kinetics category when fitting using the built-in tool Nonlinear Curve Fit tool (Analysis: Fitting: Nonlinear Curve Fit).
  2. Parameters: this branch shows the fitted parameters of each model, including the Value, Standard Error, 95% LCL, and 95% UCL of each parameter.
  3. Statistics: this branch stores the Number of Points, Degrees of Freedom, Reduced Chi-Sqr, R-Square(COD), Adj. R-Square, and Fit Status of each model.
  4. Model Comparison: this branch sorts the AICc values of models, by default . The smaller the AICc, the better the model. R-Square(COD) and Sy.x are also listed in this branch.
  5. Fitted Curves Plot: this branch displays the scatter plot of the original data and the line plot of the fitted curve for each model. One graph for each model.
  6. Residual Plots: this branch compares the residuals of each model. For each measurement of a model, a scatter plot with a line at y = 0 will be created.

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