Data files

Batch sample publishing

Batch sample publishing

Batch sample publishing

Batch sample publishing

Batch sample publishing

Batch sample publishing

Samples underwent size exclusion chromoatography

Linear fits for replicas of initial rate experiments for different substrate concentrations. The linear fits are only performed for data within the selected region (shaded in grey), and are shown in the inset for clarity and quality control. The legend gives the coefficient of determination for the linear fits (r2).

Averaged initial rates of replica measurements (N=4). Standard deviations of the measurements are plotted as shades to the average value-

Michaelis-Menten fit of initial rates at different substrate concentrations. The 1-sigma confidence interval gives the range in which the "true" enzyme reaction parameters are expected. It is based on the standard deviation of the averaged initial rates. The standard deviation of the fit provides a measure of how well the Michaelis-Menten equation represents the data.

This is the raw data that contains the input data needed by the unedited version of the "MichaelisMentenNotebook.ipynb" Jupyter notebook. Download it and store it in a folder "data" in the same place as the notebook.

You can use the notebook to analyze your own data, but make sure the organization is the same as in this file, and also change the "filename" variable in the Jupyter notebook (on line [3])

Badly chosen substrate concentrations for an intital rate experiment give data that makes it difficult to fit with a kinetic model and judge the quality of the fit.

Well chosen substrate concentrations for an intital rate experiment give data that makes it easy to fit with a kinetic model and judge the quality of the fit and use it as a basis for the final gold-round experiment.

Very well chosen substrate concentrations for an intital rate experiment give data that makes it very easy to fit with a kinetic model and judge the quality of the fit.

An initial choice of 5 widely spaced substrates enables a first, rough estimate of Km to inform the design of first-round experiments.