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| 001 | 188342 | ||
| 005 | 20210129215157.0 | ||
| 024 | 7 | _ | |a 10.1016/j.copbio.2015.02.003 |2 doi |
| 024 | 7 | _ | |a 0958-1669 |2 ISSN |
| 024 | 7 | _ | |a 1879-0429 |2 ISSN |
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| 037 | _ | _ | |a FZJ-2015-01750 |
| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Buescher, Joerg M |0 P:(DE-HGF)0 |b 0 |e Corresponding Author |
| 245 | _ | _ | |a A roadmap for interpreting 13C metabolite labeling patterns from cells |
| 260 | _ | _ | |a Amsterdam [u.a.] |c 2015 |b Elsevier Science |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1457697885_312 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a article |2 DRIVER |
| 520 | _ | _ | |a Measuring intracellular metabolism has increasingly led to important insights in biomedical research. 13C tracer analysis, although less information-rich than quantitative 13C flux analysis that requires computational data integration, has been established as a time-efficient method to unravel relative pathway activities, qualitative changes in pathway contributions, and nutrient contributions. Here, we review selected key issues in interpreting 13C metabolite labeling patterns, with the goal of drawing accurate conclusions from steady state and dynamic stable isotopic tracer experiments. |
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| 700 | 1 | _ | |a Antoniewicz, Maciek R |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Boros, Laszlo G |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Burgess, Shawn C |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Brunengraber, Henri |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Clish, Clary B |0 P:(DE-HGF)0 |b 5 |
| 700 | 1 | _ | |a DeBerardinis, Ralph J |0 P:(DE-HGF)0 |b 6 |
| 700 | 1 | _ | |a Feron, Olivier |0 P:(DE-HGF)0 |b 7 |
| 700 | 1 | _ | |a Frezza, Christian |0 P:(DE-HGF)0 |b 8 |
| 700 | 1 | _ | |a Ghesquiere, Bart |0 P:(DE-HGF)0 |b 9 |
| 700 | 1 | _ | |a Gottlieb, Eyal |0 P:(DE-HGF)0 |b 10 |
| 700 | 1 | _ | |a Hiller, Karsten |0 P:(DE-HGF)0 |b 11 |
| 700 | 1 | _ | |a Jones, Russell G |0 P:(DE-HGF)0 |b 12 |
| 700 | 1 | _ | |a Kamphorst, Jurre J |0 P:(DE-HGF)0 |b 13 |
| 700 | 1 | _ | |a Kibbey, Richard G |0 P:(DE-HGF)0 |b 14 |
| 700 | 1 | _ | |a Kimmelman, Alec C |0 P:(DE-HGF)0 |b 15 |
| 700 | 1 | _ | |a Locasale, Jason W |0 P:(DE-HGF)0 |b 16 |
| 700 | 1 | _ | |a Lunt, Sophia Y |0 P:(DE-HGF)0 |b 17 |
| 700 | 1 | _ | |a Maddocks, Oliver DK |0 P:(DE-HGF)0 |b 18 |
| 700 | 1 | _ | |a Malloy, Craig |0 P:(DE-HGF)0 |b 19 |
| 700 | 1 | _ | |a Metallo, Christian M |0 P:(DE-HGF)0 |b 20 |
| 700 | 1 | _ | |a Meuillet, Emmanuelle J |0 P:(DE-HGF)0 |b 21 |
| 700 | 1 | _ | |a Munger, Joshua |0 P:(DE-HGF)0 |b 22 |
| 700 | 1 | _ | |a Nöh, Katharina |0 P:(DE-Juel1)129051 |b 23 |u fzj |
| 700 | 1 | _ | |a Rabinowitz, Joshua D |0 P:(DE-HGF)0 |b 24 |
| 700 | 1 | _ | |a Ralser, Markus |0 P:(DE-HGF)0 |b 25 |
| 700 | 1 | _ | |a Sauer, Uwe |0 P:(DE-HGF)0 |b 26 |
| 700 | 1 | _ | |a Stephanopoulos, Gregory |0 P:(DE-HGF)0 |b 27 |
| 700 | 1 | _ | |a St-Pierre, Julie |0 P:(DE-HGF)0 |b 28 |
| 700 | 1 | _ | |a Tennant, Daniel A |0 P:(DE-HGF)0 |b 29 |
| 700 | 1 | _ | |a Wittmann, Christoph |0 P:(DE-HGF)0 |b 30 |
| 700 | 1 | _ | |a Vander Heiden, Matthew G |0 P:(DE-HGF)0 |b 31 |
| 700 | 1 | _ | |a Vazquez, Alexei |0 P:(DE-HGF)0 |b 32 |
| 700 | 1 | _ | |a Vousden, Karen |0 P:(DE-HGF)0 |b 33 |
| 700 | 1 | _ | |a Young, Jamey D |0 P:(DE-HGF)0 |b 34 |
| 700 | 1 | _ | |a Zamboni, Nicola |0 P:(DE-HGF)0 |b 35 |
| 700 | 1 | _ | |a Fendt, Sarah-Maria |0 P:(DE-HGF)0 |b 36 |
| 773 | _ | _ | |a 10.1016/j.copbio.2015.02.003 |g Vol. 34, p. 189 - 201 |0 PERI:(DE-600)2019676-3 |p 189 - 201 |t Current opinion in biotechnology |v 34 |y 2015 |x 0958-1669 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/188342/files/FZJ-2015-01750.pdf |y Restricted |
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