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Items: 1 to 20 of 111

1.

Decomposing complex reaction networks using random sampling, principal component analysis and basis rotation.

Barrett CL, Herrgard MJ, Palsson B.

BMC Syst Biol. 2009 Mar 6;3:30. doi: 10.1186/1752-0509-3-30.

2.

Monte Carlo sampling and principal component analysis of flux distributions yield topological and modular information on metabolic networks.

Sariyar B, Perk S, Akman U, Hortaçsu A.

J Theor Biol. 2006 Sep 21;242(2):389-400. Epub 2006 Jul 24.

PMID:
16860341
4.

Estimating the size of the solution space of metabolic networks.

Braunstein A, Mulet R, Pagnani A.

BMC Bioinformatics. 2008 May 19;9:240. doi: 10.1186/1471-2105-9-240.

5.

FlexFlux: combining metabolic flux and regulatory network analyses.

Marmiesse L, Peyraud R, Cottret L.

BMC Syst Biol. 2015 Dec 15;9:93. doi: 10.1186/s12918-015-0238-z.

6.

Predicting outcomes of steady-state ¹³C isotope tracing experiments using Monte Carlo sampling.

Schellenberger J, Zielinski DC, Choi W, Madireddi S, Portnoy V, Scott DA, Reed JL, Osterman AL, Palsson B.

BMC Syst Biol. 2012 Jan 30;6:9. doi: 10.1186/1752-0509-6-9.

7.

Monte Carlo sampling can be used to determine the size and shape of the steady-state flux space.

Wiback SJ, Famili I, Greenberg HJ, Palsson BØ.

J Theor Biol. 2004 Jun 21;228(4):437-47.

PMID:
15178193
8.
9.

Elimination of thermodynamically infeasible loops in steady-state metabolic models.

Schellenberger J, Lewis NE, Palsson BØ.

Biophys J. 2011 Feb 2;100(3):544-53. doi: 10.1016/j.bpj.2010.12.3707. Erratum in: Biophys J. 2011 Mar 2;100(5):1381.

10.

Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis.

Chandrasekaran S, Price ND.

Proc Natl Acad Sci U S A. 2010 Oct 12;107(41):17845-50. doi: 10.1073/pnas.1005139107. Epub 2010 Sep 27.

12.

Phenotype prediction in regulated metabolic networks.

Kaleta C, Centler F, di Fenizio PS, Dittrich P.

BMC Syst Biol. 2008 Apr 25;2:37. doi: 10.1186/1752-0509-2-37.

13.

On correlated reaction sets and coupled reaction sets in metabolic networks.

Marashi SA, Hosseini Z.

J Bioinform Comput Biol. 2015 Aug;13(4):1571003. doi: 10.1142/S0219720015710031. Epub 2015 Jan 30.

PMID:
25747383
14.

Flux imbalance analysis and the sensitivity of cellular growth to changes in metabolite pools.

Reznik E, Mehta P, Segrè D.

PLoS Comput Biol. 2013;9(8):e1003195. doi: 10.1371/journal.pcbi.1003195. Epub 2013 Aug 29.

15.

Environmental versatility promotes modularity in genome-scale metabolic networks.

Samal A, Wagner A, Martin OC.

BMC Syst Biol. 2011 Aug 24;5:135. doi: 10.1186/1752-0509-5-135.

16.

Evolutionary plasticity and innovations in complex metabolic reaction networks.

Matias Rodrigues JF, Wagner A.

PLoS Comput Biol. 2009 Dec;5(12):e1000613. doi: 10.1371/journal.pcbi.1000613. Epub 2009 Dec 18.

17.

Can the whole be less than the sum of its parts? Pathway analysis in genome-scale metabolic networks using elementary flux patterns.

Kaleta C, de Figueiredo LF, Schuster S.

Genome Res. 2009 Oct;19(10):1872-83. doi: 10.1101/gr.090639.108. Epub 2009 Jun 18.

18.

Symbolic flux analysis for genome-scale metabolic networks.

Schryer DW, Vendelin M, Peterson P.

BMC Syst Biol. 2011 May 23;5:81. doi: 10.1186/1752-0509-5-81.

19.

Characterizing the optimal flux space of genome-scale metabolic reconstructions through modified latin-hypercube sampling.

Chaudhary N, Tøndel K, Bhatnagar R, dos Santos VA, Puchałka J.

Mol Biosyst. 2016 Mar;12(3):994-1005. doi: 10.1039/c5mb00457h.

PMID:
26818782
20.

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

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