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  From elementary flux modes to elementary flux vectors: Metabolic pathway analysis with arbitrary linear flux constraints

Klamt, S., Regensburger, G., Gerstl, P., Jungreuthmayer, C., Schuster, S., Mahadevan, R., et al. (2017). From elementary flux modes to elementary flux vectors: Metabolic pathway analysis with arbitrary linear flux constraints. PLoS Computational Biology, 13(4): e1005409. doi: 10.1371/journal.pcbi.1005409.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-002D-4A75-D Version Permalink: https://hdl.handle.net/11858/00-001M-0000-002D-7878-5
Genre: Journal Article

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© 2017 Klamt et al. This is an open access article distributed under the terms of the Creative Commons Attribution License CC BY 4.0
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 Creators:
Klamt, Steffen1, Author           
Regensburger, G.2, Author
Gerstl, P.M.3, Author
Jungreuthmayer, C.4, Author
Schuster, S.5, Author
Mahadevan, R.6, Author
Zanghellini, J3, Author
Müller, S.7, Author
Affiliations:
1Analysis and Redesign of Biological Networks, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society, ou_1738139              
2Institute for Algebra, Johannes Kepler University Linz (JKU), Linz, Austria , ou_persistent22              
3Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria, Austrian Centre of Biotechnology, Vienna, Austria , ou_persistent22              
4Austrian Centre of Biotechnology, Vienna, Austria, TGM - Technologisches Gewerbemuseum, Vienna, Austria , ou_persistent22              
5Department of Bioinformatics, Faculty of Biology and Pharmacy, Friedrich Schiller University Jena, Jena, Germany , ou_persistent22              
6 Department of Chemical Engineering & Applied Chemistry, Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada , ou_persistent22              
7 Radon Institute for Computational and Applied Mathematics (RICAM), Austrian Academy of Sciences, Linz, Austria , ou_persistent22              

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Language(s): eng - English
 Dates: 2017
 Publication Status: Issued
 Pages: -
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 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1371/journal.pcbi.1005409
Other: pubdata_escidoc:2444732
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Title: PLoS Computational Biology
Source Genre: Journal
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Pages: - Volume / Issue: 13 (4) Sequence Number: e1005409 Start / End Page: - Identifier: ISSN: 1553-734X