Use a Jupyter Notebook to understand how a progress curve experiment can look like

Work of the Gygli group...

Investigation 1: An experimental workflow to provide detailed information of the molecular mechanisms of enzymes is described. This workflow will help in the application of enzymes in technical processes by providing crucial parameters needed to plan, model and implement biocatalytic processes more efficiently. These parameters are homogeneity of the enzyme sample (HES), kinetic and thermodynamic parameters of enzyme kinetics and binding of reactants to enzymes. The techniques used to measure ...

Because enzyme activity depends very much on the reaction conditions, it is crucial to report all these metadata (see for example the STRENDA Guidelines:https://www.beilstein-strenda-db.org/strenda/public/guidelines.xhtml).

Another challenge in experiments to determine enzyme reaction parameters is the choice of suitable substrate concentrations to enable optimal kinetic fits and the informed choice of a kinetic model.

A Jupyter notebook is given to assist in the choice of substrate concentrations ...

This study briefly shows how a Progress Curve (Time-Course) Analysis can look like.

This is the Jupyter Notebook to allow editing and working with the code. It is a simple example of how a progress curve experiment can look like.

This is the pdf of the Jupyter Notebook to allow looking at the notebook without installing anything. It is a simple example of how a progress curve experiment can look like.