How can EnzymeML help you to manage your experimental data?
Managing experimental data with EnzymeML
Measured time course data on substrate and/or product concentrations is saved in the same document with metadata about the enzyme and the reaction conditions. This faciliates replicability and reproducibility of experimental assays.
EnzymeML documents are standardized, so their format is independent of the experimental scientist. Documents are machine- and human-readable. Markup languanges (ML) are specifically designed for machines, and therefore they are not optimally for user reading. EnzymeML follows internationally recognized standards (SBML, MathML). An application programming interface (API) allows for the easy integration of applications for data visualization and modeling.
Modelling data (kinetic rate law, kinetic parameters) can be added into the same EnzymeML document. Multiple models can be applied to the same dataset.
EnzymeML documents can be considered as a micropublication, because it contains data and metadata. By assigning a digital object identifier (DOI) to EnzymeML documents, they can be easily referenced.
Currently, we provide a tool to convert spreadsheets into EnzymeML documents. We are developing a simple graphical user interface, the BioCatHub platform, to collect experimental data and to generate EnzymeML documents.
- Applications which read and write EnzymeML
In order to make full use of a data exchange format, applications are required to use it. We are presently collaborating with software developers to provide EnzymeML read and write functionalities to the modelling platforms COPASI and PySCeS. We are also developing the BioCatHub platform as simple tool for experimentalists to collect experimental data and metadata using a graphical user interface, and then to generate EnzymeML documents. Additionally, we are working with the databases STRENDA DB and SABIO-RK to upload EnzymeML documents as entries into their systems.
- Extension of the data model
Currently, EnzymeML documents contain information about reaction conditions (temperature, buffer, additives) and specify the enzyme, the substrates, and the products. We are planning to extend EnzymeML to include more details about the enzyme, scuh as How was enzyme concentration measured? Which protein batch was used? How was a recombinant protein expressed and purified?). Also we are extending the dcoument to include information about the reaction vessel and the process (Which microtiter plate or cuvette was used? Include flow reactor as reaction vessel), and about the measurement protocols for substrate and product concentrations.