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  Predicting compositions of microbial communities from stoichiometric models with applications for the biogas process

Koch, S., Benndorf, D., Fronk, K., Reichl, U., & Klamt, S. (2016). Predicting compositions of microbial communities from stoichiometric models with applications for the biogas process. Biotechnology for Biofuels, 9: 17. doi:10.1186/s13068-016-0429-x.

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This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://​creativecommons.​org/​licenses/​by/​4.​0/​), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://​creativecommons.​org/​publicdomain/​zero/​1.​0/​) applies to the data made available in this article, unless otherwise stated.

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 Creators:
Koch, Sabine1, 2, Author              
Benndorf, Dirk3, Author
Fronk, Karen4, Author
Reichl, Udo3, 5, Author              
Klamt, Steffen1, Author              
Affiliations:
1Analysis and Redesign of Biological Networks, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society, ou_1738139              
2International Max Planck Research School (IMPRS), Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society, DE, ou_1738143              
3Otto-von-Guericke-Universität Magdeburg, External Organizations, ou_1738156              
4Harz University of Applied Sciences, ou_persistent22              
5Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society, ou_1738140              

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Language(s): eng - English
 Dates: 2016
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1186/s13068-016-0429-x
Other: pubdata_escidoc:2246236
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Title: Biotechnology for Biofuels
Source Genre: Journal
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Publ. Info: London
Pages: - Volume / Issue: 9 Sequence Number: 17 Start / End Page: - Identifier: ISSN: 1754-6834