Development of an Ontology for Biocatalysis

Menke, Marian J.; Behr, Alexander S.; Rosenthal, Katrin; Linke, David; Kockmann, Norbert; Bornscheuer, Uwe T.; Dörr, Mark

doi:10.1002/cite.202200066

Cited by 4 publications

(4 citation statements)

References 31 publications

(29 reference statements)

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“…The inertization with pure nitrogen of the storage tank takes place through a sintered filter (VitraPor ® , Robu Glasfilter, Hattert, Germany). For slug flow generation, a peristaltic pump (Ismatec ISM597, Ismatec Industry Solutions GmbH, Grevenbroich, Germany) pumps the reaction solution through a T-mixer (7), where it is contacted with the gas phase in a co-current flow. In order to control the concentration of ABTS ox (Merck, Darmstadt, Germany) in the reaction solution before it enters the straight capillary reactor, a photosensor (6) is positioned between the T-mixer and the peristaltic pump.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Fluent Integration of Laboratory Data into Biocatalytic Process Simulation Using EnzymeML, DWSIM, and Ontologies

Behr,

Surkamp,

Abbaspour

et al. 2024

Processes

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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.

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Section: Methodsmentioning

confidence: 99%

“…To tackle these challenges, ontologies offer a solution by providing standardized vocabularies and semantic relationships among Processes 2024, 12, 597 2 of 13 pertinent concepts in the research domains. This facilitates precise comparison and analysis of enzymatic data [6,7].…”

Section: Introductionmentioning

confidence: 99%

Fluent Integration of Laboratory Data into Biocatalytic Process Simulation Using EnzymeML, DWSIM, and Ontologies

Behr,

Surkamp,

Abbaspour

et al. 2024

Processes

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“…However, to extract useful information from the retrieved dataset, the data must be adequately pre-processed or cleaned, e.g., managing errors and missing data, detecting and removing duplicates, outliers and irrelevant information, as the quality of data heavily influences the precision of the final outcome [82]. Within this scope, and with the aim to facilitate the use of information throughout multiple scientific areas, steps towards the standardization of data and of semantics have been recently achieved [83]. In stage 2, algorithms process the data that is to be fed to the selected model.…”

Section: An Overview and Basic Conceptsmentioning

confidence: 99%

Machine Learning: A Suitable Method for Biocatalysis

Sampaio

Fernandes

2023

Catalysts

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Biocatalysis is currently a workhorse used to produce a wide array of compounds, from bulk to fine chemicals, in a green and sustainable manner. The success of biocatalysis is largely thanks to an enlargement of the feasible chemical reaction toolbox. This materialized due to major advances in enzyme screening tools and methods, together with high-throughput laboratory techniques for biocatalyst optimization through enzyme engineering. Therefore, enzyme-related knowledge has significantly increased. To handle the large number of data now available, computational approaches have been gaining relevance in biocatalysis, among them machine learning methods (MLMs). MLMs use data and algorithms to learn and improve from experience automatically. This review intends to briefly highlight the contribution of biocatalysis within biochemical engineering and bioprocesses and to present the key aspects of MLMs currently used within the scope of biocatalysis and related fields, mostly with readers non-skilled in MLMs in mind. Accordingly, a brief overview and the basic concepts underlying MLMs are presented. This is complemented with the basic steps to build a machine learning model and followed by insights into the types of algorithms used to intelligently analyse data, identify patterns and develop realistic applications in biochemical engineering and bioprocesses. Notwithstanding, and given the scope of this review, some recent illustrative examples of MLMs in protein engineering, enzyme production, biocatalyst formulation and enzyme screening are provided, and future developments are suggested. Overall, it is envisaged that the present review will provide insights into MLMs and how these are major assets for more efficient biocatalysis.

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“…Furthermore, laboratory technicians who record enzyme-specific data may not be the same individuals who execute process simulations. To address these challenges, ontologies can be used as they provide a standardized vocabulary and semantic relations between concepts relevant to the research domains, enabling accurate comparison and analysis of enzymatic data [4,5]. Furthermore, electronic laboratory notebooks (ELNs) help laboratory experimenters to record laboratory data and generating a machine-readable data collection while mitigating data loss.…”

Section: Introductionmentioning

confidence: 99%

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

Behr,

Abbaspour,

Rosenthal

et al. 2023

Proc Conf Res Data Infrastr

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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.

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Development of an Ontology for Biocatalysis

Cited by 4 publications

References 31 publications

Fluent Integration of Laboratory Data into Biocatalytic Process Simulation Using EnzymeML, DWSIM, and Ontologies

Fluent Integration of Laboratory Data into Biocatalytic Process Simulation Using EnzymeML, DWSIM, and Ontologies

Machine Learning: A Suitable Method for Biocatalysis

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

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