EnzymeML follows the Standards of Reporting Enzymology Data (STRENDA) Guidelines, a comprehensive set of metadata describing reaction conditions and kinetic models. EnzymeML is written in eXtensible Markup Language (XML). It builds on the well-established Systems Biology Markup Language (SBML) and includes information about the enzyme, the substrate(s) and product(s), the reaction conditions, the selected kinetic model, and estimated kinetic parameters (find the XML schema definition here). The measured time course of substrate or product concentrations is stored in a comma-separated value (CSV) formatted file. The XML- and the CSV file are combined into a single EnzymeML document using the widely-used OMEX format
You can implement EnzymeML read and write functionalities into your software application (electronic lab notebooks, laboratory information management systems, modeling platforms, or database) by using the EnzymeML Application Programming Interface (API), either as a python library to be implemented locally with your software, as a RESTful API to be implemented locally or via a RESTful API installed on our web server.
We start with an EnzymeML document which contains the experimental conditions (protein sequence of the enzyme, enzyme concentration, temperature, initial substrate concentration, and time course of the substrate). The goal is to read the time course data in a modeling tool, estimate kinetic parameters, and add the kinetic parameters and the selected kinetic model to the EnzymeML document
As a result, the EnzymeML document does not only contain the experimentally measured data, but also the model and the estimated kinetic parameters. One experimental dataset might be modeled by many different kinetic models, therefore the EnzymeML document might contain different kinetic models and the respective parameters together with a single experimental dataset.