The design of biocatalytic reaction systems is highly complex owing to the dependency of the estimated kinetic parameters on the enzyme, the reaction conditions, and the modeling method. Consequently, reproducibility of enzymatic experiments and reusability of enzymatic data are challenging. We developed the XML-based markup language EnzymeML to enable storage and exchange of enzymatic data such as reaction conditions, the time course of the substrate and the product, kinetic parameters and the kinetic model, thus making enzymatic data findable, accessible, interoperable and reusable (FAIR). The feasibility and usefulness of the EnzymeML toolbox is demonstrated in six scenarios, for which data and metadata of different enzymatic reactions are collected and analyzed. EnzymeML serves as a seamless communication channel between experimental platforms, electronic lab notebooks, tools for modeling of enzyme kinetics, publication platforms and enzymatic reaction databases. EnzymeML is open and transparent, and invites the community to contribute. All documents and codes are freely available at https://enzymeml.org.
Beschreibung
EnzymeML: seamless data flow and modeling of enzymatic data | Nature Methods
%0 Journal Article
%1 Lauterbach2023
%A Lauterbach, Simone
%A Dienhart, Hannah
%A Range, Jan
%A Malzacher, Stephan
%A Spöring, Jan-Dirk
%A Rother, Dörte
%A Pinto, Maria Filipa
%A Martins, Pedro
%A Lagerman, Colton E.
%A Bommarius, Andreas S.
%A Høst, Amalie Vang
%A Woodley, John M.
%A Ngubane, Sandile
%A Kudanga, Tukayi
%A Bergmann, Frank T.
%A Rohwer, Johann M.
%A Iglezakis, Dorothea
%A Weidemann, Andreas
%A Wittig, Ulrike
%A Kettner, Carsten
%A Swainston, Neil
%A Schnell, Santiago
%A Pleiss, Jürgen
%D 2023
%J Nature Methods
%K forschungsdaten myown metadata from:diglezakis
%R 10.1038/s41592-022-01763-1
%T EnzymeML: seamless data flow and modeling of enzymatic data
%U https://doi.org/10.1038/s41592-022-01763-1
%X The design of biocatalytic reaction systems is highly complex owing to the dependency of the estimated kinetic parameters on the enzyme, the reaction conditions, and the modeling method. Consequently, reproducibility of enzymatic experiments and reusability of enzymatic data are challenging. We developed the XML-based markup language EnzymeML to enable storage and exchange of enzymatic data such as reaction conditions, the time course of the substrate and the product, kinetic parameters and the kinetic model, thus making enzymatic data findable, accessible, interoperable and reusable (FAIR). The feasibility and usefulness of the EnzymeML toolbox is demonstrated in six scenarios, for which data and metadata of different enzymatic reactions are collected and analyzed. EnzymeML serves as a seamless communication channel between experimental platforms, electronic lab notebooks, tools for modeling of enzyme kinetics, publication platforms and enzymatic reaction databases. EnzymeML is open and transparent, and invites the community to contribute. All documents and codes are freely available at https://enzymeml.org.
@article{Lauterbach2023,
abstract = {The design of biocatalytic reaction systems is highly complex owing to the dependency of the estimated kinetic parameters on the enzyme, the reaction conditions, and the modeling method. Consequently, reproducibility of enzymatic experiments and reusability of enzymatic data are challenging. We developed the XML-based markup language EnzymeML to enable storage and exchange of enzymatic data such as reaction conditions, the time course of the substrate and the product, kinetic parameters and the kinetic model, thus making enzymatic data findable, accessible, interoperable and reusable (FAIR). The feasibility and usefulness of the EnzymeML toolbox is demonstrated in six scenarios, for which data and metadata of different enzymatic reactions are collected and analyzed. EnzymeML serves as a seamless communication channel between experimental platforms, electronic lab notebooks, tools for modeling of enzyme kinetics, publication platforms and enzymatic reaction databases. EnzymeML is open and transparent, and invites the community to contribute. All documents and codes are freely available at https://enzymeml.org.},
added-at = {2023-02-11T11:41:35.000+0100},
author = {Lauterbach, Simone and Dienhart, Hannah and Range, Jan and Malzacher, Stephan and Sp{\"o}ring, Jan-Dirk and Rother, D{\"o}rte and Pinto, Maria Filipa and Martins, Pedro and Lagerman, Colton E. and Bommarius, Andreas S. and H{\o}st, Amalie Vang and Woodley, John M. and Ngubane, Sandile and Kudanga, Tukayi and Bergmann, Frank T. and Rohwer, Johann M. and Iglezakis, Dorothea and Weidemann, Andreas and Wittig, Ulrike and Kettner, Carsten and Swainston, Neil and Schnell, Santiago and Pleiss, J{\"u}rgen},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2d24bb9a4642a30a5ae43320ea5ee1019/forschungsdaten},
day = 09,
description = {EnzymeML: seamless data flow and modeling of enzymatic data | Nature Methods},
doi = {10.1038/s41592-022-01763-1},
interhash = {60c60ee207cf7cede90dbd3c170db9c2},
intrahash = {d24bb9a4642a30a5ae43320ea5ee1019},
issn = {1548-7105},
journal = {Nature Methods},
keywords = {forschungsdaten myown metadata from:diglezakis},
month = feb,
timestamp = {2023-02-11T10:41:35.000+0100},
title = {EnzymeML: seamless data flow and modeling of enzymatic data},
url = {https://doi.org/10.1038/s41592-022-01763-1},
year = 2023
}