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  RedCom: A strategy for reduced metabolic modeling of complex microbial communities and its application for analyzing experimental datasets from anaerobic digestion

Koch, S., Kohrs, F., Lahmann, P., Bissinger, T., Wendschuh, S., Benndorf, D., et al. (2019). RedCom: A strategy for reduced metabolic modeling of complex microbial communities and its application for analyzing experimental datasets from anaerobic digestion. PLoS Computational Biology, 15(2): e1006759. doi:10.1371/journal.pcbi.1006759.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0003-291F-E Version Permalink: http://hdl.handle.net/21.11116/0000-0003-2920-B
Genre: Journal Article

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kluge_3028677.pdf (Publisher version), 4MB
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© 2019 Koch et 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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 Creators:
Koch, Sabine1, 2, Author              
Kohrs, Fabian3, Author              
Lahmann, Patrick3, Author              
Bissinger, Thomas4, Author              
Wendschuh, Stefan3, Author
Benndorf, Dirk3, 4, Author              
Reichl, Udo3, 4, Author              
Klamt, Steffen2, Author              
Affiliations:
1International Max Planck Research School (IMPRS), Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society, ou_1738143              
2Analysis and Redesign of Biological Networks, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society, ou_1738139              
3Otto-von-Guericke University Magdeburg, Faculty for Process and Systems Engineering, Magdeburg, Germany, ou_persistent22              
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: 2019
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1371/journal.pcbi.1006759
Other: data_escidoc:3028677
 Degree: -

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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: 15 (2) Sequence Number: e1006759 Start / End Page: - Identifier: ISSN: 1553-734X
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000017180_1