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Items: 1 to 20 of 160

1.

Predicting metabolic engineering knockout strategies for chemical production: accounting for competing pathways.

Tepper N, Shlomi T.

Bioinformatics. 2010 Feb 15;26(4):536-43. doi: 10.1093/bioinformatics/btp704. Epub 2009 Dec 23.

2.

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.

3.

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.

4.

Identification of genome-scale metabolic network models using experimentally measured flux profiles.

Herrgård MJ, Fong SS, Palsson BØ.

PLoS Comput Biol. 2006 Jul 7;2(7):e72. Epub 2006 May 10.

5.

Adaptive bi-level programming for optimal gene knockouts for targeted overproduction under phenotypic constraints.

Ren S, Zeng B, Qian X.

BMC Bioinformatics. 2013;14 Suppl 2:S17. doi: 10.1186/1471-2105-14-S2-S17. Epub 2013 Jan 21.

6.

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.

7.

Multiobjective flux balancing using the NISE method for metabolic network analysis.

Oh YG, Lee DY, Lee SY, Park S.

Biotechnol Prog. 2009 Jul-Aug;25(4):999-1008. doi: 10.1002/btpr.193.

PMID:
19572405
8.

Prediction of microbial growth rate versus biomass yield by a metabolic network with kinetic parameters.

Adadi R, Volkmer B, Milo R, Heinemann M, Shlomi T.

PLoS Comput Biol. 2012;8(7):e1002575. doi: 10.1371/journal.pcbi.1002575. Epub 2012 Jul 5.

9.

Exploiting the pathway structure of metabolism to reveal high-order epistasis.

Imielinski M, Belta C.

BMC Syst Biol. 2008 Apr 30;2:40. doi: 10.1186/1752-0509-2-40.

10.

Flux variability scanning based on enforced objective flux for identifying gene amplification targets.

Park JM, Park HM, Kim WJ, Kim HU, Kim TY, Lee SY.

BMC Syst Biol. 2012 Aug 21;6:106. doi: 10.1186/1752-0509-6-106.

11.

Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production.

Colijn C, Brandes A, Zucker J, Lun DS, Weiner B, Farhat MR, Cheng TY, Moody DB, Murray M, Galagan JE.

PLoS Comput Biol. 2009 Aug;5(8):e1000489. doi: 10.1371/journal.pcbi.1000489. Epub 2009 Aug 28.

12.

In silico strategy to rationally engineer metabolite production: A case study for threonine in Escherichia coli.

Rodríguez-Prados JC, de Atauri P, Maury J, Ortega F, Portais JC, Chassagnole C, Acerenza L, Lindley ND, Cascante M.

Biotechnol Bioeng. 2009 Jun 15;103(3):609-20. doi: 10.1002/bit.22271.

PMID:
19219914
13.

Inferring branching pathways in genome-scale metabolic networks.

Pitkänen E, Jouhten P, Rousu J.

BMC Syst Biol. 2009 Oct 29;3:103. doi: 10.1186/1752-0509-3-103.

14.
15.

Improved computational performance of MFA using elementary metabolite units and flux coupling.

Suthers PF, Chang YJ, Maranas CD.

Metab Eng. 2010 Mar;12(2):123-8. doi: 10.1016/j.ymben.2009.10.002. Epub 2009 Oct 27.

PMID:
19837183
16.

Computing the shortest elementary flux modes in genome-scale metabolic networks.

de Figueiredo LF, Podhorski A, Rubio A, Kaleta C, Beasley JE, Schuster S, Planes FJ.

Bioinformatics. 2009 Dec 1;25(23):3158-65. doi: 10.1093/bioinformatics/btp564. Epub 2009 Sep 30.

17.

Identification of functional differences in metabolic networks using comparative genomics and constraint-based models.

Hamilton JJ, Reed JL.

PLoS One. 2012;7(4):e34670. doi: 10.1371/journal.pone.0034670. Epub 2012 Apr 16.

18.

An optimization model for metabolic pathways.

Planes FJ, Beasley JE.

Bioinformatics. 2009 Oct 15;25(20):2723-9. doi: 10.1093/bioinformatics/btp441. Epub 2009 Jul 20.

19.

Genome-scale in silico aided metabolic analysis and flux comparisons of Escherichia coli to improve succinate production.

Wang Q, Chen X, Yang Y, Zhao X.

Appl Microbiol Biotechnol. 2006 Dec;73(4):887-94. Epub 2006 Aug 23.

PMID:
16927085
20.

Using flux balance analysis to guide microbial metabolic engineering.

Curran KA, Crook NC, Alper HS.

Methods Mol Biol. 2012;834:197-216. doi: 10.1007/978-1-61779-483-4_13.

PMID:
22144361
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