Ontology-Based Laboratory Data Acquisition With EnzymeML for Process Simulation of Biocatalytic Reactors





Electronic Lab Notebooks, Enzymatic Catalysis, Knowledge Graph, Process Simulation


The presented work explores the use of ontologies and standardized enzymatic data to set up enzymatic reactions in process simulators, such as DWSIM. Setting up an automated workflow to start a process simulation based on enzymatic data obtained from the laboratory can help save costs and time during the development phase. Standardized conditions are crucial for accurate comparison and analysis of enzymatic data, where ontologies provide a standardized vocabulary and semantic relations between relevant concepts. To ensure standardized data, an electronic lab notebook (ELN) is used based on EnzymeML, an open standard XML-based format for enzyme kinetics data. Furthermore, two ontologies are merged and the result is extended for the use in the Python-based workflow. The resulting data is stored in a knowledge graph for research data in a machine-accessible and human-readable format. Thus, the study demonstrates a workflow that allows for the direct translation of ELN data into a process simulation via ontologies.


Download data is not yet available.


R. Siedentop et al., “Getting the Most Out of Enzyme Cascades: Strategies to Optimize In Vitro Multi-Enzymatic Reactions,” Catalysts 2021, 11, 1183., doi: https://doi.org/10.3390/catal11101183

P. De Santis et al., “The rise of continuous flow biocatalysis – fundamentals, very re-cent developments and future perspectives,” In React. Chem. Eng. 5 (12), pp. 2155–2184., doi: https://doi.org/10.1039/D0RE00335B

D. Medeiros, “DWSIM - Open Source Process Simulator,” URL: https://dwsim.org/

M.J. Menke et al., “Development of an Ontology for Biocatalysis,” Chemie Ingenieur Technik, 2022, 94: 1827-1835, doi: https://doi.org/10.1002/cite.202200066

J. Grühn et al., ”From Coiled Flow Inverter to Stirred Tank Reactor – Bioprocess De-velopment and Ontology Design,” Chemie Ingenieur Technik, 2022, 94: 852-863., doi: https://doi.org/10.1002/cite.202100177

J. Range et al., “EnzymeML—a data exchange format for biocatalysis and enzymolo-gy,” FEBS J, 2022, 289: 5864-5874., doi: https://doi.org/10.1111/febs.16318

S. Lauterbach et al., “EnzymeML: seamless data flow and modeling of enzymatic data,” Nat. Methods, 2023, 20, 400–402., doi: https://doi.org/10.1038/s41592-022-01763-1

Jan Range, Frank Bergmann, Johann Rohwer, AnnaReisch, Hannah Dienhart, & SL-2204. (2022). EnzymeML/PyEnzyme: PyEnzyme 1.1.3 (v1.1.3). Zenodo. https://doi.org/10.5281/zenodo.6457299

S. Arndt et al., “Metadata4Ing: An ontology for describing the generation of research data within a scientific activity”. (1.1.0). Zenodo. DOI: https://doi.org/10.5281/zenodo.770601

J. B. Lamy, “Owlready: Ontology-oriented programming in Python with automatic clas-sification and high level constructs for biomedical ontologies,” Artificial Intelligence in Medicine. 80., doi: https://doi.org/10.1016/j.artmed.2017.07.002




Conference Proceedings Volume


Poster presentations II (Call for Papers)
Received 2023-04-25
Accepted 2023-06-30
Published 2023-09-07

Funding data