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  Model-based approach for predicting the impact of genetic modifications on product yield in biopharmaceutical manufacturing—Application to influenza vaccine production

Duvigneau, S., Dürr, R., Laske, T., Bachmann, M., Dostert, M., & Kienle, A. (2020). Model-based approach for predicting the impact of genetic modifications on product yield in biopharmaceutical manufacturing—Application to influenza vaccine production. PLoS Computational Biology, 16(6): e1007810. doi:10.1371/journal.pcbi.1007810.

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Item Permalink: https://hdl.handle.net/21.11116/0000-0007-1BF0-C Version Permalink: https://hdl.handle.net/21.11116/0000-0008-FEB4-F
Genre: Journal Article

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Copyright:©2020Duvigneauet al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium,provided the original author and source are credited.
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https://creativecommons.org/licenses/by/4.0/

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 Creators:
Duvigneau, S.1, 2, Author           
Dürr, Robert3, Author           
Laske, Tanja4, Author           
Bachmann, Mandy4, Author           
Dostert, Melanie4, Author           
Kienle, Achim1, 3, Author           
Affiliations:
1Otto-von-Guericke-Universität Magdeburg, External Organizations, ou_1738156              
2International Max Planck Research School (IMPRS), Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society, ou_1738143              
3Process Synthesis and Process Dynamics, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society, ou_1738153              
4Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society, ou_1738140              

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Language(s): eng - English
 Dates: 2020
 Publication Status: Issued
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1371/journal.pcbi.1007810
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Title: PLoS Computational Biology
Source Genre: Journal
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Publ. Info: San Francisco, CA : Public Library of Science
Pages: - Volume / Issue: 16 (6) Sequence Number: e1007810 Start / End Page: - Identifier: ISSN: 1553-734X
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000017180_1