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Items: 25

1.

Finding MEMo: minimum sets of elementary flux modes.

Röhl A, Bockmayr A.

J Math Biol. 2019 Oct;79(5):1749-1777. doi: 10.1007/s00285-019-01409-5. Epub 2019 Aug 6.

PMID:
31388689
2.

Formalizing Metabolic-Regulatory Networks by Hybrid Automata.

Liu L, Bockmayr A.

Acta Biotheor. 2019 Jul 24. doi: 10.1007/s10441-019-09354-y. [Epub ahead of print]

PMID:
31342219
3.

Computing irreversible minimal cut sets in genome-scale metabolic networks via flux cone projection.

Röhl A, Riou T, Bockmayr A.

Bioinformatics. 2019 Aug 1;35(15):2618-2625. doi: 10.1093/bioinformatics/bty1027.

PMID:
30590390
4.

Cellular trade-offs and optimal resource allocation during cyanobacterial diurnal growth.

Reimers AM, Knoop H, Bockmayr A, Steuer R.

Proc Natl Acad Sci U S A. 2017 Aug 1;114(31):E6457-E6465. doi: 10.1073/pnas.1617508114. Epub 2017 Jul 18.

5.

A mixed-integer linear programming approach to the reduction of genome-scale metabolic networks.

Röhl A, Bockmayr A.

BMC Bioinformatics. 2017 Jan 3;18(1):2. doi: 10.1186/s12859-016-1412-z.

6.

Elucidating temporal resource allocation and diurnal dynamics in phototrophic metabolism using conditional FBA.

Rügen M, Bockmayr A, Steuer R.

Sci Rep. 2015 Oct 26;5:15247. doi: 10.1038/srep15247.

7.

Computing Elementary Flux Modes Involving a Set of Target Reactions.

David L, Bockmayr A.

IEEE/ACM Trans Comput Biol Bioinform. 2014 Nov-Dec;11(6):1099-107. doi: 10.1109/TCBB.2014.2343964.

PMID:
26357047
8.

Sequential metabolic phases as a means to optimize cellular output in a constant environment.

Palinkas A, Bulik S, Bockmayr A, Holzhütter HG.

PLoS One. 2015 Mar 18;10(3):e0118347. doi: 10.1371/journal.pone.0118347. eCollection 2015.

9.

Double and multiple knockout simulations for genome-scale metabolic network reconstructions.

Goldstein YA, Bockmayr A.

Algorithms Mol Biol. 2015 Jan 9;10(1):1. doi: 10.1186/s13015-014-0028-y. eCollection 2015.

10.

Generic flux coupling analysis.

Reimers AC, Goldstein Y, Bockmayr A.

Math Biosci. 2015 Apr;262:28-35. doi: 10.1016/j.mbs.2015.01.003. Epub 2015 Jan 22.

PMID:
25619608
11.

Dynamic optimization of metabolic networks coupled with gene expression.

Waldherr S, Oyarzún DA, Bockmayr A.

J Theor Biol. 2015 Jan 21;365:469-85. doi: 10.1016/j.jtbi.2014.10.035. Epub 2014 Nov 6.

12.

Flux modules in metabolic networks.

Müller AC, Bockmayr A.

J Math Biol. 2014 Nov;69(5):1151-79. doi: 10.1007/s00285-013-0731-1. Epub 2013 Oct 19.

PMID:
24141488
13.

Preservation of dynamic properties in qualitative modeling frameworks for gene regulatory networks.

Jamshidi S, Siebert H, Bockmayr A.

Biosystems. 2013 May;112(2):171-9. doi: 10.1016/j.biosystems.2013.03.001. Epub 2013 Mar 14.

PMID:
23499821
14.

Fast thermodynamically constrained flux variability analysis.

Müller AC, Bockmayr A.

Bioinformatics. 2013 Apr 1;29(7):903-9. doi: 10.1093/bioinformatics/btt059. Epub 2013 Feb 6.

PMID:
23390138
15.

Analysis and characterization of asynchronous state transition graphs using extremal states.

Lorenz T, Siebert H, Bockmayr A.

Bull Math Biol. 2013 Jun;75(6):920-38. doi: 10.1007/s11538-012-9782-5. Epub 2012 Oct 19.

PMID:
23081730
16.

Analysis of metabolic subnetworks by flux cone projection.

Marashi SA, David L, Bockmayr A.

Algorithms Mol Biol. 2012 May 29;7(1):17. doi: 10.1186/1748-7188-7-17.

17.

Time series dependent analysis of unparametrized Thomas networks.

Klarner H, Siebert H, Bockmayr A.

IEEE/ACM Trans Comput Biol Bioinform. 2012 Sep-Oct;9(5):1338-51.

PMID:
22529333
18.

F2C2: a fast tool for the computation of flux coupling in genome-scale metabolic networks.

Larhlimi A, David L, Selbig J, Bockmayr A.

BMC Bioinformatics. 2012 Apr 23;13:57. doi: 10.1186/1471-2105-13-57.

19.

On flux coupling analysis of metabolic subsystems.

Marashi SA, David L, Bockmayr A.

J Theor Biol. 2012 Jun 7;302:62-9. doi: 10.1016/j.jtbi.2012.02.023. Epub 2012 Mar 3.

PMID:
22406036
20.

Network reduction in metabolic pathway analysis: elucidation of the key pathways involved in the photoautotrophic growth of the green alga Chlamydomonas reinhardtii.

Rügen M, Bockmayr A, Legrand J, Cogne G.

Metab Eng. 2012 Jul;14(4):458-67. doi: 10.1016/j.ymben.2012.01.009. Epub 2012 Feb 10.

PMID:
22342232
21.

FFCA: a feasibility-based method for flux coupling analysis of metabolic networks.

David L, Marashi SA, Larhlimi A, Mieth B, Bockmayr A.

BMC Bioinformatics. 2011 Jun 15;12:236. doi: 10.1186/1471-2105-12-236.

22.

A model-based method for investigating bioenergetic processes in autotrophically growing eukaryotic microalgae: application to the green algae Chlamydomonas reinhardtii.

Cogne G, Rügen M, Bockmayr A, Titica M, Dussap CG, Cornet JF, Legrand J.

Biotechnol Prog. 2011 May-Jun;27(3):631-40. doi: 10.1002/btpr.596. Epub 2011 May 12.

PMID:
21567987
23.

Exploring metabolic pathways in genome-scale networks via generating flux modes.

Rezola A, de Figueiredo LF, Brock M, Pey J, Podhorski A, Wittmann C, Schuster S, Bockmayr A, Planes FJ.

Bioinformatics. 2011 Feb 15;27(4):534-40. doi: 10.1093/bioinformatics/btq681. Epub 2010 Dec 10.

PMID:
21149278
24.

Flux coupling analysis of metabolic networks is sensitive to missing reactions.

Marashi SA, Bockmayr A.

Biosystems. 2011 Jan;103(1):57-66. doi: 10.1016/j.biosystems.2010.09.011. Epub 2010 Oct 1.

PMID:
20888889
25.

Direct phasing by binary integer programming.

Lunin VY, Urzhumtsev A, Bockmayr A.

Acta Crystallogr A. 2002 May;58(Pt 3):283-91. Epub 2002 Apr 18.

PMID:
11961290

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