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Items: 1 to 20 of 31

1.

A principal components method constrained by elementary flux modes: analysis of flux data sets.

von Stosch M, Rodrigues de Azevedo C, Luis M, Feyo de Azevedo S, Oliveira R.

BMC Bioinformatics. 2016 May 4;17(1):200. doi: 10.1186/s12859-016-1063-0.

2.

In Silico Constraint-Based Strain Optimization Methods: the Quest for Optimal Cell Factories.

Maia P, Rocha M, Rocha I.

Microbiol Mol Biol Rev. 2015 Nov 25;80(1):45-67. doi: 10.1128/MMBR.00014-15. Review.

3.

Avoiding the Enumeration of Infeasible Elementary Flux Modes by Including Transcriptional Regulatory Rules in the Enumeration Process Saves Computational Costs.

Jungreuthmayer C, Ruckerbauer DE, Gerstl MP, Hanscho M, Zanghellini J.

PLoS One. 2015 Jun 19;10(6):e0129840. doi: 10.1371/journal.pone.0129840.

4.

Discovery of substrate cycles in large scale metabolic networks using hierarchical modularity.

Sridharan GV, Ullah E, Hassoun S, Lee K.

BMC Syst Biol. 2015 Feb 13;9:5. doi: 10.1186/s12918-015-0146-2.

5.

Exploiting stoichiometric redundancies for computational efficiency and network reduction.

Ingalls BP, Bembenek E.

In Silico Biol. 2015;12(1-2):55-67. doi: 10.3233/ISB-140464. Review.

6.

Spatial localization of the first and last enzymes effectively connects active metabolic pathways in bacteria.

Meyer P, Cecchi G, Stolovitzky G.

BMC Syst Biol. 2014 Dec 14;8:131. doi: 10.1186/s12918-014-0131-1.

7.

In-silico prediction of key metabolic differences between two non-small cell lung cancer subtypes.

Rezola A, Pey J, Rubio Á, Planes FJ.

PLoS One. 2014 Aug 5;9(8):e103998. doi: 10.1371/journal.pone.0103998.

8.

Minimal metabolic pathway structure is consistent with associated biomolecular interactions.

Bordbar A, Nagarajan H, Lewis NE, Latif H, Ebrahim A, Federowicz S, Schellenberger J, Palsson BO.

Mol Syst Biol. 2014 Jul 1;10:737. doi: 10.15252/msb.20145243.

9.

On functional module detection in metabolic networks.

Koch I, Ackermann J.

Metabolites. 2013 Aug 12;3(3):673-700. doi: 10.3390/metabo3030673.

10.

Metabolic adaptation and protein complexes in prokaryotes.

Krüger B, Liang C, Prell F, Fieselmann A, Moya A, Schuster S, Völker U, Dandekar T.

Metabolites. 2012 Nov 16;2(4):940-58. doi: 10.3390/metabo2040940.

11.

Promise and reality in the expanding field of network interaction analysis: metabolic networks.

Bazzani S.

Bioinform Biol Insights. 2014 Apr 16;8:83-91. doi: 10.4137/BBI.S12466.

12.

Complete enumeration of elementary flux modes through scalable demand-based subnetwork definition.

Hunt KA, Folsom JP, Taffs RL, Carlson RP.

Bioinformatics. 2014 Jun 1;30(11):1569-78. doi: 10.1093/bioinformatics/btu021.

13.

Structural control of metabolic flux.

Sajitz-Hermstein M, Nikoloski Z.

PLoS Comput Biol. 2013;9(12):e1003368. doi: 10.1371/journal.pcbi.1003368.

14.

Integrating gene and protein expression data with genome-scale metabolic networks to infer functional pathways.

Pey J, Valgepea K, Rubio A, Beasley JE, Planes FJ.

BMC Syst Biol. 2013 Dec 8;7:134. doi: 10.1186/1752-0509-7-134.

15.

Recent advances in 2D and 3D in vitro systems using primary hepatocytes, alternative hepatocyte sources and non-parenchymal liver cells and their use in investigating mechanisms of hepatotoxicity, cell signaling and ADME.

Godoy P, Hewitt NJ, Albrecht U, Andersen ME, Ansari N, Bhattacharya S, Bode JG, Bolleyn J, Borner C, Böttger J, Braeuning A, Budinsky RA, Burkhardt B, Cameron NR, Camussi G, Cho CS, Choi YJ, Craig Rowlands J, Dahmen U, Damm G, Dirsch O, Donato MT, Dong J, Dooley S, Drasdo D, Eakins R, Ferreira KS, Fonsato V, Fraczek J, Gebhardt R, Gibson A, Glanemann M, Goldring CE, Gómez-Lechón MJ, Groothuis GM, Gustavsson L, Guyot C, Hallifax D, Hammad S, Hayward A, Häussinger D, Hellerbrand C, Hewitt P, Hoehme S, Holzhütter HG, Houston JB, Hrach J, Ito K, Jaeschke H, Keitel V, Kelm JM, Kevin Park B, Kordes C, Kullak-Ublick GA, LeCluyse EL, Lu P, Luebke-Wheeler J, Lutz A, Maltman DJ, Matz-Soja M, McMullen P, Merfort I, Messner S, Meyer C, Mwinyi J, Naisbitt DJ, Nussler AK, Olinga P, Pampaloni F, Pi J, Pluta L, Przyborski SA, Ramachandran A, Rogiers V, Rowe C, Schelcher C, Schmich K, Schwarz M, Singh B, Stelzer EH, Stieger B, Stöber R, Sugiyama Y, Tetta C, Thasler WE, Vanhaecke T, Vinken M, Weiss TS, Widera A, Woods CG, Xu JJ, Yarborough KM, Hengstler JG.

Arch Toxicol. 2013 Aug;87(8):1315-530. doi: 10.1007/s00204-013-1078-5. Review.

16.

Integration of time-resolved transcriptomics data with flux-based methods reveals stress-induced metabolic adaptation in Escherichia coli.

Töpfer N, Jozefczuk S, Nikoloski Z.

BMC Syst Biol. 2012 Nov 30;6:148. doi: 10.1186/1752-0509-6-148.

17.

Bioinformatic approaches for functional annotation and pathway inference in metagenomics data.

De Filippo C, Ramazzotti M, Fontana P, Cavalieri D.

Brief Bioinform. 2012 Nov;13(6):696-710. doi: 10.1093/bib/bbs070. Review.

18.

Random sampling of elementary flux modes in large-scale metabolic networks.

Machado D, Soons Z, Patil KR, Ferreira EC, Rocha I.

Bioinformatics. 2012 Sep 15;28(18):i515-i521. doi: 10.1093/bioinformatics/bts401.

19.

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.

20.

Salmonella enterica: a surprisingly well-adapted intracellular lifestyle.

Dandekar T, Astrid F, Jasmin P, Hensel M.

Front Microbiol. 2012 May 3;3:164. doi: 10.3389/fmicb.2012.00164.

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