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Items: 1 to 20 of 116

1.

Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth.

Ibarra RU, Edwards JS, Palsson BO.

Nature. 2002 Nov 14;420(6912):186-9.

PMID:
12432395
3.

In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data.

Edwards JS, Ibarra RU, Palsson BO.

Nat Biotechnol. 2001 Feb;19(2):125-30.

PMID:
11175725
4.

Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models.

Lewis NE, Hixson KK, Conrad TM, Lerman JA, Charusanti P, Polpitiya AD, Adkins JN, Schramm G, Purvine SO, Lopez-Ferrer D, Weitz KK, Eils R, König R, Smith RD, Palsson BØ.

Mol Syst Biol. 2010 Jul;6:390. doi: 10.1038/msb.2010.47.

5.

In silico design and adaptive evolution of Escherichia coli for production of lactic acid.

Fong SS, Burgard AP, Herring CD, Knight EM, Blattner FR, Maranas CD, Palsson BO.

Biotechnol Bioeng. 2005 Sep 5;91(5):643-8.

PMID:
15962337
6.

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.

PMID:
16927085
7.

Model-assisted formate dehydrogenase-O (fdoH) gene knockout for enhanced succinate production in Escherichia coli from glucose and glycerol carbon sources.

Mienda BS, Shamsir MS, Md Illias R.

J Biomol Struct Dyn. 2016 Nov;34(11):2305-16. doi: 10.1080/07391102.2015.1113387.

PMID:
26510527
8.

In silico deletion of PtsG gene in Escherichia coli genome-scale model predicts increased succinate production from glycerol.

Mienda BS, Shamsir MS.

J Biomol Struct Dyn. 2015;33(11):2380-9. doi: 10.1080/07391102.2015.1036461.

PMID:
25921851
9.

Evolutionary programming as a platform for in silico metabolic engineering.

Patil KR, Rocha I, Förster J, Nielsen J.

BMC Bioinformatics. 2005 Dec 23;6:308.

10.
12.

Genome-derived minimal metabolic models for Escherichia coli MG1655 with estimated in vivo respiratory ATP stoichiometry.

Taymaz-Nikerel H, Borujeni AE, Verheijen PJ, Heijnen JJ, van Gulik WM.

Biotechnol Bioeng. 2010 Oct 1;107(2):369-81. doi: 10.1002/bit.22802.

PMID:
20506321
13.

Global transcriptional programs reveal a carbon source foraging strategy by Escherichia coli.

Liu M, Durfee T, Cabrera JE, Zhao K, Jin DJ, Blattner FR.

J Biol Chem. 2005 Apr 22;280(16):15921-7.

15.
16.

Flux analysis and control of the central metabolic pathways in Escherichia coli.

Holms H.

FEMS Microbiol Rev. 1996 Dec;19(2):85-116. Review.

PMID:
8988566
17.

Scalable method to determine mutations that occur during adaptive evolution of Escherichia coli.

Raghunathan A, Palsson BO.

Biotechnol Lett. 2003 Mar;25(5):435-41.

PMID:
12882568
18.

Natural computation meta-heuristics for the in silico optimization of microbial strains.

Rocha M, Maia P, Mendes R, Pinto JP, Ferreira EC, Nielsen J, Patil KR, Rocha I.

BMC Bioinformatics. 2008 Nov 27;9:499. doi: 10.1186/1471-2105-9-499.

19.

Understanding the adaptive growth strategy of Lactobacillus plantarum by in silico optimisation.

Teusink B, Wiersma A, Jacobs L, Notebaart RA, Smid EJ.

PLoS Comput Biol. 2009 Jun;5(6):e1000410. doi: 10.1371/journal.pcbi.1000410.

20.

Systems approach to refining genome annotation.

Reed JL, Patel TR, Chen KH, Joyce AR, Applebee MK, Herring CD, Bui OT, Knight EM, Fong SS, Palsson BO.

Proc Natl Acad Sci U S A. 2006 Nov 14;103(46):17480-4.

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