This organization is home to various useful tools, libraries, and data models revolving around the data exchange format EnzymeML.
EnzymeML is a free, open XML-based format that adheres to the FAIR principles and serves as a standard for the systematic monitoring and exchange of data pertaining to enzyme-catalyzed reactions. Its primary objective is to facilitate the storage and transfer of enzyme kinetics data across electronic laboratory notebooks, software tools, and databases.
An EnzymeML document encompasses a comprehensive collection of data, including both the results of experimental measurements and the modeling outcomes. This may include time course data derived from a series of experiments with varying initial substrate concentrations, analyzed using the Michaelis-Menten model.
EnzymeML is compatible with the Systems Biology Markup Language (SBML) and continues to be developed and extended by an international community, with support from the STRENDA Commission.
In the following, you can find a list of repositories that are part of the EnzymeML project and its toolbox. We provide a brief description for each repository, but you can find more detailed information in the respective README files.
- pyEnzyme - The Python library for handling EnzymeML.
- EnzymeML Schema - The XML Schema Definition for EnzymeML.
- Lauterbach 2022 - Workflows and example files from Nature Methods (2023).
The following publications provide more information about EnzymeML and its use cases.
- EnzymeML—a data exchange format for biocatalysis and enzymology (The FEBS Journal, 2022) - The original publication introducing EnzymeML.
- EnzymeML: seamless data flow and modeling of enzymatic data (Nature Methods, 2023) - Application scenarios and use cases for EnzymeML.
The EnzymeML Training course is a collaborative project with multiple experimental biocatalysis groups, jointly developing novel tools and standards for FAIR and reproducible data analysis of biocatalytic data.
