%0 Journal Article %1 pr12030597 %A Behr, Alexander S. %A Surkamp, Julia %A Abbaspour, Elnaz %A Häußler, Max %A Lütz, Stephan %A Pleiss, Jürgen %A Kockmann, Norbert %A Rosenthal, Katrin %D 2024 %J Processes %K CRC1333 EnzymeML imported myown updated %N 3 %R 10.3390/pr12030597 %T Fluent Integration of Laboratory Data into Biocatalytic Process Simulation Using EnzymeML, DWSIM, and Ontologies %U https://www.mdpi.com/2227-9717/12/3/597 %V 12 %X The importance of biocatalysis for ecologically sustainable syntheses in the chemical industry and for applications in everyday life is increasing. To design efficient applications, it is important to know the related enzyme kinetics; however, the measurement is laborious and error-prone. Flow reactors are suitable for rapid reaction parameter screening; here, a novel workflow is proposed including digital image processing (DIP) for the quantification of product concentrations, and the use of structured data acquisition with EnzymeML spreadsheets combined with ontology-based semantic information, leading to rapid and smooth data integration into a simulation tool for kinetics evaluation. One of the major findings is that a flexibly adaptive ontology is essential for FAIR (findability, accessibility, interoperability, reusability) data handling. Further, Python interfaces enable consistent data transfer.

@article{pr12030597, abstract = {The importance of biocatalysis for ecologically sustainable syntheses in the chemical industry and for applications in everyday life is increasing. To design efficient applications, it is important to know the related enzyme kinetics; however, the measurement is laborious and error-prone. Flow reactors are suitable for rapid reaction parameter screening; here, a novel workflow is proposed including digital image processing (DIP) for the quantification of product concentrations, and the use of structured data acquisition with EnzymeML spreadsheets combined with ontology-based semantic information, leading to rapid and smooth data integration into a simulation tool for kinetics evaluation. One of the major findings is that a flexibly adaptive ontology is essential for FAIR (findability, accessibility, interoperability, reusability) data handling. Further, Python interfaces enable consistent data transfer.}, added-at = {2024-03-17T18:26:42.000+0100}, article-number = {597}, author = {Behr, Alexander S. and Surkamp, Julia and Abbaspour, Elnaz and Häußler, Max and Lütz, Stephan and Pleiss, Jürgen and Kockmann, Norbert and Rosenthal, Katrin}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2bb7617f5145362f61515d2fa15665a15/pumaibtb_tb}, doi = {10.3390/pr12030597}, interhash = {be23b371cf24275eba01814e83c0a081}, intrahash = {bb7617f5145362f61515d2fa15665a15}, issn = {2227-9717}, journal = {Processes}, keywords = {CRC1333 EnzymeML imported myown updated}, number = 3, orcid = {0000-0003-1045-8202}, timestamp = {2024-04-02T10:22:54.000+0200}, title = {Fluent Integration of Laboratory Data into Biocatalytic Process Simulation Using EnzymeML, DWSIM, and Ontologies}, url = {https://www.mdpi.com/2227-9717/12/3/597}, volume = 12, year = 2024 }