PyEnzymeKinetics

Software-package for easy enzyme kinetic parameter estimation. Supports EnzymeML format (not yet) Allows comprehensive data analysis from experimental raw data to kinetic parameters

Calibration

• linear and non-linear calibration for calculating concentrations from analytic signal (spectroscopy / HPLC)
• Selection of best fit model based on akaike-criterion

Michaelis Menten kinetics

• Fitting of enzyme assay data Michaelis Menten equations for:
  • irreversible MM
  • enzyme inactivation
  • inhibition

Dependencies

• standard stuff

Installation

pip install PyEnzymeKinetics

⚙️ Example code

This example will demonstrate, how to analyze data from an enzyme kinetics experiment. Call an EnzymeKinetics object and provide provide data in the form of numpy arrays. Note that all data has to be provided in the same unit!

• time has to be an one-dimentional array, resresenting the individual time points of measurements.
• initial_substrate concentrations need to be provided. Multiple measurement_series at different initial substrate concentrations can be provided.
• Eighter substrate or product concentrations need to be provided. The array can be two-dimentional in order to provide multiple measurement series e.g. for different initial substrate concentrations or varying inhibitor concentrations. Hence, an array for multiple concentrations should have the shape (len(init_substrates), len(measurements_series)). Note measurement_series need to be in the same order as in initial_substrate.
• Eighter on or multiple concentrations can be provided for enzyme. For multiple (varying) concentrations, the array needs to be as long as the initial_substrate array.
• One or multiple inhibitor concentrations can be provided. For the estimation of the inhibitor constant Ki data with and without inhibitor need to be provided. Therefore, the array should look like: [0, 0, 0, 0.69, 0.69, 0.69], resembling 3 reactions without and 3 reactions with inhibitor present.

from pyenzymekinetics import EnzymeKinetics

# Provide experimental data as numpy arrays
kinetics = EnzymeKinetics(
    time=time,
    init_substrate=init_substrate,
    substrate=substrate #or product = product,
    enzyme=enzyme,
    inhibitor=inhibitor # optional
)

# fit experimental data to kinetic models
kinetics.fit_models()

The fit-quality of the model is evaluated by comparing the Akaike (AIC) information criterion between all models. By calling EnzymeKinetics.result_dict an overview of all models with the respective AIC score is returned.

EnzymeKinetics.visualize_fit() plots the experimental data and the kinetic model with the respective estimated parameters of the best model according to the AIC score. Additionaly, fit-statistics of the fitted kinetic model are printed.

Optionaly, alternative models can be assessed by providing the name of the respective model: EnzymeKinetics.visualize_fit("substrate inhibition")

# plot experimental data to kinetic models
kinetics.visualize_fit()

# plot the result of the substrate inhibition model
kinetics.visualize_fit("substrate inhibition")


## Authors

Contributors names and contact info

Max Haeussler
max.haeussler@t-online.de


## License

This project is licensed under the [NAME HERE] License - see the LICENSE.md file for details

## Acknowledgments

ex