EnzymeML is an XML-based data exchange format that supports the comprehensive documentation of enzymatic data by describing reaction conditions, time courses of substrate and product concentrations, the kinetic model, and the estimated kinetic constants. EnzymeML is based on the Systems Biology Markup Language, which was extended by implementing the STRENDA Guidelines. An EnzymeML document serves as a container to transfer data between experimental platforms, modeling tools, and databases. EnzymeML supports the scientific community by introducing a standardized data exchange format to make enzymatic data findable, accessible, interoperable, and reusable according to the FAIR data principles. An application programming interface in Python supports the integration of software tools for data acquisition, data analysis, and publication. The feasibility of a seamless data flow using EnzymeML is demonstrated by creating an EnzymeML document from a structured spreadsheet or from a STRENDA DB database entry, by kinetic modeling using the modeling platform COPASI, and by uploading to the enzymatic reaction kinetics database SABIO-RK.
%0 Journal Article
%1 range2022enzymemla
%A Range, Jan
%A Halupczok, Colin
%A Lohmann, Jens
%A Swainston, Neil
%A Kettner, Carsten
%A Bergmann, Frank T.
%A Weidemann, Andreas
%A Wittig, Ulrike
%A Schnell, Santiago
%A Pleiss, Jürgen
%B The FEBS Journal
%D 2022
%I John Wiley & Sons, Ltd
%J The FEBS Journal
%K EXC2075 PN2 PN2-6(II) selected
%N 19
%P 5864--5874
%R https://doi.org/10.1111/febs.16318
%T EnzymeML—a data exchange format for biocatalysis and enzymology
%U https://doi.org/10.1111/febs.16318
%V 289
%X EnzymeML is an XML-based data exchange format that supports the comprehensive documentation of enzymatic data by describing reaction conditions, time courses of substrate and product concentrations, the kinetic model, and the estimated kinetic constants. EnzymeML is based on the Systems Biology Markup Language, which was extended by implementing the STRENDA Guidelines. An EnzymeML document serves as a container to transfer data between experimental platforms, modeling tools, and databases. EnzymeML supports the scientific community by introducing a standardized data exchange format to make enzymatic data findable, accessible, interoperable, and reusable according to the FAIR data principles. An application programming interface in Python supports the integration of software tools for data acquisition, data analysis, and publication. The feasibility of a seamless data flow using EnzymeML is demonstrated by creating an EnzymeML document from a structured spreadsheet or from a STRENDA DB database entry, by kinetic modeling using the modeling platform COPASI, and by uploading to the enzymatic reaction kinetics database SABIO-RK.
@article{range2022enzymemla,
abstract = {EnzymeML is an XML-based data exchange format that supports the comprehensive documentation of enzymatic data by describing reaction conditions, time courses of substrate and product concentrations, the kinetic model, and the estimated kinetic constants. EnzymeML is based on the Systems Biology Markup Language, which was extended by implementing the STRENDA Guidelines. An EnzymeML document serves as a container to transfer data between experimental platforms, modeling tools, and databases. EnzymeML supports the scientific community by introducing a standardized data exchange format to make enzymatic data findable, accessible, interoperable, and reusable according to the FAIR data principles. An application programming interface in Python supports the integration of software tools for data acquisition, data analysis, and publication. The feasibility of a seamless data flow using EnzymeML is demonstrated by creating an EnzymeML document from a structured spreadsheet or from a STRENDA DB database entry, by kinetic modeling using the modeling platform COPASI, and by uploading to the enzymatic reaction kinetics database SABIO-RK.},
added-at = {2024-03-26T11:56:11.000+0100},
author = {Range, Jan and Halupczok, Colin and Lohmann, Jens and Swainston, Neil and Kettner, Carsten and Bergmann, Frank T. and Weidemann, Andreas and Wittig, Ulrike and Schnell, Santiago and Pleiss, Jürgen},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/27374fa4e37d70d3bacba3811529fb13a/testusersimtech},
booktitle = {The FEBS Journal},
comment = {https://doi.org/10.1111/febs.16318},
doi = {https://doi.org/10.1111/febs.16318},
interhash = {20f102763eeeba30f81a505953861422},
intrahash = {7374fa4e37d70d3bacba3811529fb13a},
issn = {1742464X},
journal = {The FEBS Journal},
keywords = {EXC2075 PN2 PN2-6(II) selected},
month = {10},
number = 19,
orcid = {0000-0003-1045-8202},
pages = {5864--5874},
publisher = {John Wiley & Sons, Ltd},
timestamp = {2024-03-26T11:56:11.000+0100},
title = {EnzymeML—a data exchange format for biocatalysis and enzymology},
url = {https://doi.org/10.1111/febs.16318},
volume = 289,
year = 2022
}