The adaption of the API to an application is enabled by an additional thin layer, which maps the objects of the API to the equivalent objects defined within the respective application. Thus, by editing a template, the functionality of reading and writing of EnzymeML can be easily incorporated into an application without the need to modify the API. For five applications (COPASI import/export, STRENDA-DB export, BioCatNet export, SABIO-RK import, simulation of time course data), application-specific thin API layers are provided (TL_COPASI, TL_STRENDAML and TL_BioCatNet, respectively). Because the API enables batch processing, management of enzymatic data is scalable, and high throughput strategies of experimentation and data analysis become feasible. By data export in formats such as Pandas DataFrame, large datasets could be analyzed by novel analysis methods based on machine learning.