EnzymePynetics is a Python-based tool designed for fitting time-course data of enzyme-catalyzed reactions to various kinetic models.

• Estimator Initialization: Utilizes an Estimator object for each dataset, derived from an EnzymeMLDocument. This document is created by MTPHandler and includes vital information on measurement data and reaction components.
• Reaction Equation Definition: Allows for the specification of educts, products, and enzymes in the reaction equation. Users can define different substrate and enzyme rate laws.
• Kinetic Parameter Initialization: Includes parameters like turnover number ($k_{cat}$), Michaelis constant ($K_M$), competitive inhibition constant ($K_{ic}$), uncompetitive inhibition constant ($K_{iu}$), and time-dependent enzyme inactivation rate ($k_{ie}$). These parameters are initialized with specific values and bounds based on the dataset.
• Parameter Estimation: Features a robust fitting process using substrate concentration data, integrating the rate laws of a ReactionSystem. Utilizes the Lmfit implementation of the Levenberg-Marquardt algorithm for fitting.
• Comprehensive Analysis and Visualization: After fitting, it provides an overview of parameters, their standard errors, and the Akaike Information Criterion (AIC) for each system. Additionally, a correlation matrix and interactive visualizations of fitted systems are available for evaluating the fit quality.
• Integration with EnzymeMLDocument: Selected kinetic models, along with estimated parameters and uncertainties, are added to the EnzymeMLDocument. This document is then serialized as an SBML-compliant .omex archive for each experimental condition.

To begin using EnzymePynetics, clone the repository:

git clone https://github.com/FAIRChemistry/EnzymePynetics/