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@ARTICLE{Lauterbach:1002265,
      author       = {Lauterbach, Simone and Dienhart, Hannah and Range, Jan and
                      Malzacher, Stephan and Spöring, Jan-Dirk and Rother, Dörte
                      and Pinto, Maria Filipa and Martins, Pedro and Lagerman,
                      Colton E. and Bommarius, Andreas S. and Høst, 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, Jürgen},
      title        = {{E}nzyme{ML}: seamless data flow and modeling of enzymatic
                      data},
      journal      = {Nature methods},
      volume       = {20},
      issn         = {1548-7091},
      address      = {London [u.a.]},
      publisher    = {Nature Publishing Group},
      reportid     = {FZJ-2023-01247},
      pages        = {400-402},
      year         = {2023},
      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.},
      cin          = {IBG-1},
      ddc          = {610},
      cid          = {I:(DE-Juel1)IBG-1-20101118},
      pnm          = {2171 - Biological and environmental resources for
                      sustainable use (POF4-217)},
      pid          = {G:(DE-HGF)POF4-2171},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {36759590},
      UT           = {WOS:000931967700001},
      doi          = {10.1038/s41592-022-01763-1},
      url          = {https://juser.fz-juelich.de/record/1002265},
}