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Items: 1 to 20 of 80

1.

Adaption to glucose limitation is modulated by the pleotropic regulator CcpA, independent of selection pressure strength.

Price CE, Branco Dos Santos F, Hesseling A, Uusitalo JJ, Bachmann H, Benavente V, Goel A, Berkhout J, Bruggeman FJ, Marrink SJ, Montalban-Lopez M, de Jong A, Kok J, Molenaar D, Poolman B, Teusink B, Kuipers OP.

BMC Evol Biol. 2019 Jan 10;19(1):15. doi: 10.1186/s12862-018-1331-x.

2.

Maintaining maximal metabolic flux by gene expression control.

Planqué R, Hulshof J, Teusink B, Hendriks JC, Bruggeman FJ.

PLoS Comput Biol. 2018 Sep 20;14(9):e1006412. doi: 10.1371/journal.pcbi.1006412. eCollection 2018 Sep.

3.

Understanding start-up problems in yeast glycolysis.

Overal GB, Teusink B, Bruggeman FJ, Hulshof J, Planqué R.

Math Biosci. 2018 May;299:117-126. doi: 10.1016/j.mbs.2018.03.007. Epub 2018 Mar 15.

PMID:
29550298
4.

Metabolic enzyme cost explains variable trade-offs between microbial growth rate and yield.

Wortel MT, Noor E, Ferris M, Bruggeman FJ, Liebermeister W.

PLoS Comput Biol. 2018 Feb 16;14(2):e1006010. doi: 10.1371/journal.pcbi.1006010. eCollection 2018 Feb.

5.

Statistics and simulation of growth of single bacterial cells: illustrations with B. subtilis and E. coli.

van Heerden JH, Kempe H, Doerr A, Maarleveld T, Nordholt N, Bruggeman FJ.

Sci Rep. 2017 Nov 23;7(1):16094. doi: 10.1038/s41598-017-15895-4.

6.

Effects of growth rate and promoter activity on single-cell protein expression.

Nordholt N, van Heerden J, Kort R, Bruggeman FJ.

Sci Rep. 2017 Jul 24;7(1):6299. doi: 10.1038/s41598-017-05871-3.

7.

Taking chances and making mistakes: non-genetic phenotypic heterogeneity and its consequences for surviving in dynamic environments.

van Boxtel C, van Heerden JH, Nordholt N, Schmidt P, Bruggeman FJ.

J R Soc Interface. 2017 Jul;14(132). pii: 20170141. doi: 10.1098/rsif.2017.0141. Review.

8.

Model-based quantification of metabolic interactions from dynamic microbial-community data.

Hanemaaijer M, Olivier BG, Röling WF, Bruggeman FJ, Teusink B.

PLoS One. 2017 Mar 9;12(3):e0173183. doi: 10.1371/journal.pone.0173183. eCollection 2017.

9.

Evolutionary pressures on microbial metabolic strategies in the chemostat.

Wortel MT, Bosdriesz E, Teusink B, Bruggeman FJ.

Sci Rep. 2016 Jul 6;6:29503. doi: 10.1038/srep29503.

10.

Public goods and metabolic strategies.

Bachmann H, Bruggeman FJ, Molenaar D, Branco Dos Santos F, Teusink B.

Curr Opin Microbiol. 2016 Jun;31:109-115. doi: 10.1016/j.mib.2016.03.007. Epub 2016 Apr 4. Review.

11.

Constraint-based stoichiometric modelling from single organisms to microbial communities.

Gottstein W, Olivier BG, Bruggeman FJ, Teusink B.

J R Soc Interface. 2016 Nov;13(124). pii: 20160627. doi: 10.1098/rsif.2016.0627. Review.

12.

G Protein-Coupled Receptor Signaling Networks from a Systems Perspective.

Roth S, Kholodenko BN, Smit MJ, Bruggeman FJ.

Mol Pharmacol. 2015 Sep;88(3):604-16. doi: 10.1124/mol.115.100057. Epub 2015 Jul 10. Review.

PMID:
26162865
13.

Metabolism at evolutionary optimal States.

Rabbers I, van Heerden JH, Nordholt N, Bachmann H, Teusink B, Bruggeman FJ.

Metabolites. 2015 Jun 2;5(2):311-43. doi: 10.3390/metabo5020311. Review.

14.

Multiplex Eukaryotic Transcription (In)activation: Timing, Bursting and Cycling of a Ratchet Clock Mechanism.

Rybakova KN, Bruggeman FJ, Tomaszewska A, Moné MJ, Carlberg C, Westerhoff HV.

PLoS Comput Biol. 2015 Apr 24;11(4):e1004236. doi: 10.1371/journal.pcbi.1004236. eCollection 2015 Apr.

15.

Systems modeling approaches for microbial community studies: from metagenomics to inference of the community structure.

Hanemaaijer M, Röling WF, Olivier BG, Khandelwal RA, Teusink B, Bruggeman FJ.

Front Microbiol. 2015 Mar 19;6:213. doi: 10.3389/fmicb.2015.00213. eCollection 2015.

16.

Silence on the relevant literature and errors in implementation.

Bastiaens P, Birtwistle MR, Blüthgen N, Bruggeman FJ, Cho KH, Cosentino C, de la Fuente A, Hoek JB, Kiyatkin A, Klamt S, Kolch W, Legewie S, Mendes P, Naka T, Santra T, Sontag E, Westerhoff HV, Kholodenko BN.

Nat Biotechnol. 2015 Apr;33(4):336-9. doi: 10.1038/nbt.3185. No abstract available.

PMID:
25850052
17.

Interplay between constraints, objectives, and optimality for genome-scale stoichiometric models.

Maarleveld TR, Wortel MT, Olivier BG, Teusink B, Bruggeman FJ.

PLoS Comput Biol. 2015 Apr 7;11(4):e1004166. doi: 10.1371/journal.pcbi.1004166. eCollection 2015 Apr.

18.

Binding proteins enhance specific uptake rate by increasing the substrate-transporter encounter rate.

Bosdriesz E, Magnúsdóttir S, Bruggeman FJ, Teusink B, Molenaar D.

FEBS J. 2015 Jun;282(12):2394-407. doi: 10.1111/febs.13289. Epub 2015 Apr 24.

19.

How fast-growing bacteria robustly tune their ribosome concentration to approximate growth-rate maximization.

Bosdriesz E, Molenaar D, Teusink B, Bruggeman FJ.

FEBS J. 2015 May;282(10):2029-44. doi: 10.1111/febs.13258. Epub 2015 Mar 26.

20.

Fast flux module detection using matroid theory.

Reimers AC, Bruggeman FJ, Olivier BG, Stougie L.

J Comput Biol. 2015 May;22(5):414-24. doi: 10.1089/cmb.2014.0141. Epub 2015 Jan 7.

PMID:
25565150

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