English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Talk

Machine learning-supported cybergenetic modeling, optimization and control for synthetic microbial communities

MPS-Authors
/persons/resource/persons270597

Espinel-Rios,  Sebastian
Analysis and Redesign of Biological Networks, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;
International Max Planck Research School (IMPRS), Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;

/persons/resource/persons86151

Bettenbrock,  Katja       
Analysis and Redesign of Biological Networks, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;

/persons/resource/persons86189

Klamt,  Steffen
Analysis and Redesign of Biological Networks, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

Espinel-Rios, S., Bettenbrock, K., Klamt, S., Avalos, J., & Findeisen, R. (2023). Machine learning-supported cybergenetic modeling, optimization and control for synthetic microbial communities. Talk presented at 33rd European Symposium on Computer-Aided Process Engineering (ESCAPE-33). Athens, Greece. 2023-06-18 - 2023-06-21.


Cite as: https://hdl.handle.net/21.11116/0000-000D-9F5A-C
Abstract
There is no abstract available