TUHH Open Research
Help
  • Log In
    New user? Click here to register.Have you forgotten your password?
  • English
  • Deutsch
  • Communities & Collections
  • Publications
  • Research Data
  • People
  • Institutions
  • Projects
  • Statistics
  1. Home
  2. TUHH
  3. Publication References
  4. Closed-loop identification of enzyme kinetics applying model-based design of experiments
 
Options

Closed-loop identification of enzyme kinetics applying model-based design of experiments

Publikationstyp
Journal Article
Date Issued
2024-08-27
Sprache
English
Author(s)
Hennecke, Leon  
Technische Biokatalyse V-6  
Schaare, Lucas  orcid-logo
Systemverfahrenstechnik V-4  
Skiborowski, Mirko 
Systemverfahrenstechnik V-4  
Liese, Andreas  orcid-logo
Technische Biokatalyse V-6  
TORE-URI
https://hdl.handle.net/11420/48982
Journal
Reaction chemistry and engineering  
Volume
9
Start Page
2984
End Page
2993
Article Number
d4re00127c
Citation
Reaction Chemistry and Engineering 9: 2984-2993 (2024)
Publisher DOI
10.1039/d4re00127c
Scopus ID
2-s2.0-85202159648
Publisher
Royal Society of Chemistry
Accurate kinetic models for enzyme catalysed reactions are integral to process development and optimisation. However, the collection of useful kinetic data is heavily dependent on the experimental design and execution. In order to reduce the limitations associated with traditional statistical design and manual experiments, this study introduces an integrated, automated approach to identifying kinetic models based on model-based optimal experimental design. The immobilised formate dehydrogenase of Candida boidinii catalyses the enzymatic reduction of NAD+ to NADH and is used as a model system. Continuous collection of UV/Vis data under steady-state conditions is employed to determine the kinetic parameters in a packed bed reactor. Automation of the experimental work was utilised in Python to compensate for the need for more time-consuming data collection. A completely automated closed-loop system was created and appropriate kinetic models for anticipating process dynamics were identified. The automated platform was able to identify the correct kinetic model out of eight candidate models with only 15 experiments. Further extension of the design space improved model discrimination and led to a properly parameterized kinetic model with sufficeintly high parameter precision for the conditions under examination.
DDC Class
660.6: Biotechnology
TUHH
Weiterführende Links
  • Contact
  • Send Feedback
  • Cookie settings
  • Privacy policy
  • Impress
DSpace Software

Built with DSpace-CRIS software - Extension maintained and optimized by 4Science
Design by effective webwork GmbH

  • Deutsche NationalbibliothekDeutsche Nationalbibliothek
  • ORCiD Member OrganizationORCiD Member Organization
  • DataCiteDataCite
  • Re3DataRe3Data
  • OpenDOAROpenDOAR
  • OpenAireOpenAire
  • BASE Bielefeld Academic Search EngineBASE Bielefeld Academic Search Engine
Feedback