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Gene transfer and elimination in bacterial crosses with strain K-12 of Escherichia coli.

Journal of bacteriology

WEINBERG R.
PMID: 13843554
J Bacteriol. 1960 Apr;79:558-63. doi: 10.1128/jb.79.4.558-563.1960.

No abstract available.

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WEINBERG R. Gene transfer and elimination in bacterial crosses with strain K-12 of Escherichia coli. J Bacteriol. 1960;79:558-63doi: 10.1128/jb.79.4.558-563.1960.
WEINBERG, R. (1960). Gene transfer and elimination in bacterial crosses with strain K-12 of Escherichia coli. Journal of bacteriology, 79558-63. https://doi.org/10.1128/jb.79.4.558-563.1960
WEINBERG, R. "Gene transfer and elimination in bacterial crosses with strain K-12 of Escherichia coli." Journal of bacteriology vol. 79 (1960): 558-63. doi: https://doi.org/10.1128/jb.79.4.558-563.1960
WEINBERG R. Gene transfer and elimination in bacterial crosses with strain K-12 of Escherichia coli. J Bacteriol. 1960 Apr;79:558-63. doi: 10.1128/jb.79.4.558-563.1960. PMID: 13843554; PMCID: PMC278731.
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Dynamic Changes of Intracellular Monomer Levels Regulate Block Sequence of Polyhydroxyalkanoates in Engineered Escherichia coli.

Biomacromolecules

Matsumoto K, Hori C, Fujii R, Takaya M, Ooba T, Ooi T, Isono T, Satoh T, Taguchi S.
PMID: 29323923
Biomacromolecules. 2018 Feb 12;19(2):662-671. doi: 10.1021/acs.biomac.7b01768. Epub 2018 Jan 22.

Biological polymer synthetic systems, which utilize no template molecules, normally synthesize random copolymers. We report an exception, a synthesis of block polyhydroxyalkanoates (PHAs) in an engineered Escherichia coli. Using an engineered PHA synthase, block copolymers poly[(R)-2-hydroxybutyrate(2HB)-b-(R)-3-hydroxybutyrate(3HB)] were produced in...

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Matsumoto K, Hori C, Fujii R, et al. Dynamic Changes of Intracellular Monomer Levels Regulate Block Sequence of Polyhydroxyalkanoates in Engineered Escherichia coli. Biomacromolecules. 2018;19(2):662-671doi: 10.1021/acs.biomac.7b01768.
Matsumoto, K., Hori, C., Fujii, R., Takaya, M., Ooba, T., Ooi, T., Isono, T., Satoh, T., & Taguchi, S. (2018). Dynamic Changes of Intracellular Monomer Levels Regulate Block Sequence of Polyhydroxyalkanoates in Engineered Escherichia coli. Biomacromolecules, 19(2), 662-671. https://doi.org/10.1021/acs.biomac.7b01768
Matsumoto, Ken'ichiro, et al. "Dynamic Changes of Intracellular Monomer Levels Regulate Block Sequence of Polyhydroxyalkanoates in Engineered Escherichia coli." Biomacromolecules vol. 19,2 (2018): 662-671. doi: https://doi.org/10.1021/acs.biomac.7b01768
Matsumoto K, Hori C, Fujii R, Takaya M, Ooba T, Ooi T, Isono T, Satoh T, Taguchi S. Dynamic Changes of Intracellular Monomer Levels Regulate Block Sequence of Polyhydroxyalkanoates in Engineered Escherichia coli. Biomacromolecules. 2018 Feb 12;19(2):662-671. doi: 10.1021/acs.biomac.7b01768. Epub 2018 Jan 22. PMID: 29323923.
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Cellular determinants of metabolite concentration ranges.

PLoS computational biology

Küken A, Eloundou-Mbebi JMO, Basler G, Nikoloski Z.
PMID: 30677015
PLoS Comput Biol. 2019 Jan 24;15(1):e1006687. doi: 10.1371/journal.pcbi.1006687. eCollection 2019 Jan.

Cellular functions are shaped by reaction networks whose dynamics are determined by the concentrations of underlying components. However, cellular mechanisms ensuring that a component's concentration resides in a given range remain elusive. We present network properties which suffice to...

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Küken A, Eloundou-Mbebi JMO, Basler G, et al. Cellular determinants of metabolite concentration ranges. PLoS Comput Biol. 2019;15(1):e1006687doi: 10.1371/journal.pcbi.1006687.
Küken, A., Eloundou-Mbebi, J. M. O., Basler, G., & Nikoloski, Z. (2019). Cellular determinants of metabolite concentration ranges. PLoS computational biology, 15(1), e1006687. https://doi.org/10.1371/journal.pcbi.1006687
Küken, Anika, et al. "Cellular determinants of metabolite concentration ranges." PLoS computational biology vol. 15,1 (2019): e1006687. doi: https://doi.org/10.1371/journal.pcbi.1006687
Küken A, Eloundou-Mbebi JMO, Basler G, Nikoloski Z. Cellular determinants of metabolite concentration ranges. PLoS Comput Biol. 2019 Jan 24;15(1):e1006687. doi: 10.1371/journal.pcbi.1006687. eCollection 2019 Jan. PMID: 30677015; PMCID: PMC6345444.
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CRISPR EnAbled Trackable genome Engineering for isopropanol production in Escherichia coli.

Metabolic engineering

Liang L, Liu R, Garst AD, Lee T, Nogué VSI, Beckham GT, Gill RT.
PMID: 28216108
Metab Eng. 2017 May;41:1-10. doi: 10.1016/j.ymben.2017.02.009. Epub 2017 Feb 16.

Isopropanol is an important target molecule for sustainable production of fuels and chemicals. Increases in DNA synthesis and synthetic biology capabilities have resulted in the development of a range of new strategies for the more rapid design, construction, and...

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Liang L, Liu R, Garst AD, et al. CRISPR EnAbled Trackable genome Engineering for isopropanol production in Escherichia coli. Metab Eng. 2017;41:1-10doi: 10.1016/j.ymben.2017.02.009.
Liang, L., Liu, R., Garst, A. D., Lee, T., Nogué, V. S. I., Beckham, G. T., & Gill, R. T. (2017). CRISPR EnAbled Trackable genome Engineering for isopropanol production in Escherichia coli. Metabolic engineering, 411-10. https://doi.org/10.1016/j.ymben.2017.02.009
Liang, Liya, et al. "CRISPR EnAbled Trackable genome Engineering for isopropanol production in Escherichia coli." Metabolic engineering vol. 41 (2017): 1-10. doi: https://doi.org/10.1016/j.ymben.2017.02.009
Liang L, Liu R, Garst AD, Lee T, Nogué VSI, Beckham GT, Gill RT. CRISPR EnAbled Trackable genome Engineering for isopropanol production in Escherichia coli. Metab Eng. 2017 May;41:1-10. doi: 10.1016/j.ymben.2017.02.009. Epub 2017 Feb 16. PMID: 28216108.
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Systematic analysis of Type I-E Escherichia coli CRISPR-Cas PAM sequences ability to promote interference and primed adaptation.

Molecular microbiology

Musharova O, Sitnik V, Vlot M, Savitskaya E, Datsenko KA, Krivoy A, Fedorov I, Semenova E, Brouns SJJ, Severinov K.
PMID: 30875129
Mol Microbiol. 2019 Jun;111(6):1558-1570. doi: 10.1111/mmi.14237. Epub 2019 Apr 06.

CRISPR interference occurs when a protospacer recognized by the CRISPR RNA is destroyed by Cas effectors. In Type I CRISPR-Cas systems, protospacer recognition can lead to «primed adaptation» - acquisition of new spacers from in cis located sequences. Type...

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Musharova O, Sitnik V, Vlot M, et al. Systematic analysis of Type I-E Escherichia coli CRISPR-Cas PAM sequences ability to promote interference and primed adaptation. Mol Microbiol. 2019;111(6):1558-1570doi: 10.1111/mmi.14237.
Musharova, O., Sitnik, V., Vlot, M., Savitskaya, E., Datsenko, K. A., Krivoy, A., Fedorov, I., Semenova, E., Brouns, S. J. J., & Severinov, K. (2019). Systematic analysis of Type I-E Escherichia coli CRISPR-Cas PAM sequences ability to promote interference and primed adaptation. Molecular microbiology, 111(6), 1558-1570. https://doi.org/10.1111/mmi.14237
Musharova, Olga, et al. "Systematic analysis of Type I-E Escherichia coli CRISPR-Cas PAM sequences ability to promote interference and primed adaptation." Molecular microbiology vol. 111,6 (2019): 1558-1570. doi: https://doi.org/10.1111/mmi.14237
Musharova O, Sitnik V, Vlot M, Savitskaya E, Datsenko KA, Krivoy A, Fedorov I, Semenova E, Brouns SJJ, Severinov K. Systematic analysis of Type I-E Escherichia coli CRISPR-Cas PAM sequences ability to promote interference and primed adaptation. Mol Microbiol. 2019 Jun;111(6):1558-1570. doi: 10.1111/mmi.14237. Epub 2019 Apr 06. PMID: 30875129; PMCID: PMC6568314.
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Metabolic Engineering of .

Journal of agricultural and food chemistry

Hou Y, Liu X, Li S, Zhang X, Yu S, Zhao GR.
PMID: 32627549
J Agric Food Chem. 2020 Aug 05;68(31):8370-8380. doi: 10.1021/acs.jafc.0c02949. Epub 2020 Jul 21.

Betalains are emerging natural pigments with high tinctorial strength and stability, physiological activities, and fluorescent properties for potential application in food, cosmetic, and pharmaceutical industries. Betalains including yellow betaxanthins and red betacyanins are mainly restricted in the Caryophyllales plants....

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Hou Y, Liu X, Li S, et al. Metabolic Engineering of . J Agric Food Chem. 2020;68(31):8370-8380doi: 10.1021/acs.jafc.0c02949.
Hou, Y., Liu, X., Li, S., Zhang, X., Yu, S., & Zhao, G. R. (2020). Metabolic Engineering of . Journal of agricultural and food chemistry, 68(31), 8370-8380. https://doi.org/10.1021/acs.jafc.0c02949
Hou, Yanan, et al. "Metabolic Engineering of ." Journal of agricultural and food chemistry vol. 68,31 (2020): 8370-8380. doi: https://doi.org/10.1021/acs.jafc.0c02949
Hou Y, Liu X, Li S, Zhang X, Yu S, Zhao GR. Metabolic Engineering of . J Agric Food Chem. 2020 Aug 05;68(31):8370-8380. doi: 10.1021/acs.jafc.0c02949. Epub 2020 Jul 21. PMID: 32627549.
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CRISPR-Cas9/CRISPRi tools for cell factory construction in E. coli.

World journal of microbiology & biotechnology

Hashemi A.
PMID: 32583135
World J Microbiol Biotechnol. 2020 Jun 25;36(7):96. doi: 10.1007/s11274-020-02872-9.

The innovative CRISPR-Cas based genome editing technology provides some functionality and advantages such as the high efficiency and specificity as well as ease of handling. Both aspects of the CRISPR-Cas9 system including genetic engineering and gene regulation are advantageously...

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Hashemi A. CRISPR-Cas9/CRISPRi tools for cell factory construction in E. coli. World J Microbiol Biotechnol. 2020;36(7):96doi: 10.1007/s11274-020-02872-9.
Hashemi, A. (2020). CRISPR-Cas9/CRISPRi tools for cell factory construction in E. coli. World journal of microbiology & biotechnology, 36(7), 96. https://doi.org/10.1007/s11274-020-02872-9
Hashemi, Atieh. "CRISPR-Cas9/CRISPRi tools for cell factory construction in E. coli." World journal of microbiology & biotechnology vol. 36,7 (2020): 96. doi: https://doi.org/10.1007/s11274-020-02872-9
Hashemi A. CRISPR-Cas9/CRISPRi tools for cell factory construction in E. coli. World J Microbiol Biotechnol. 2020 Jun 25;36(7):96. doi: 10.1007/s11274-020-02872-9. PMID: 32583135.
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Improving flux predictions by integrating data from multiple strains.

Bioinformatics (Oxford, England)

Long MR, Reed JL.
PMID: 27998937
Bioinformatics. 2017 Mar 15;33(6):893-900. doi: 10.1093/bioinformatics/btw706.

MOTIVATION: Incorporating experimental data into constraint-based models can improve the quality and accuracy of their metabolic flux predictions. Unfortunately, routinely and easily measured experimental data such as growth rates, extracellular fluxes, transcriptomics and even proteomics are not always sufficient...

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Long MR, Reed JL. Improving flux predictions by integrating data from multiple strains. Bioinformatics. 2017;33(6):893-900doi: 10.1093/bioinformatics/btw706.
Long, M. R., & Reed, J. L. (2017). Improving flux predictions by integrating data from multiple strains. Bioinformatics (Oxford, England), 33(6), 893-900. https://doi.org/10.1093/bioinformatics/btw706
Long, Matthew R, and Reed, Jennifer L. "Improving flux predictions by integrating data from multiple strains." Bioinformatics (Oxford, England) vol. 33,6 (2017): 893-900. doi: https://doi.org/10.1093/bioinformatics/btw706
Long MR, Reed JL. Improving flux predictions by integrating data from multiple strains. Bioinformatics. 2017 Mar 15;33(6):893-900. doi: 10.1093/bioinformatics/btw706. PMID: 27998937.
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Gene expression profiling and the use of genome-scale in silico models of Escherichia coli for analysis: providing context for content.

Journal of bacteriology

Lewis NE, Cho BK, Knight EM, Palsson BO.
PMID: 19363119
J Bacteriol. 2009 Jun;191(11):3437-44. doi: 10.1128/JB.00034-09. Epub 2009 Apr 10.

No abstract available.

Cite
Lewis NE, Cho BK, Knight EM, et al. Gene expression profiling and the use of genome-scale in silico models of Escherichia coli for analysis: providing context for content. J Bacteriol. 2009;191(11):3437-44doi: 10.1128/JB.00034-09.
Lewis, N. E., Cho, B. K., Knight, E. M., & Palsson, B. O. (2009). Gene expression profiling and the use of genome-scale in silico models of Escherichia coli for analysis: providing context for content. Journal of bacteriology, 191(11), 3437-44. https://doi.org/10.1128/JB.00034-09
Lewis, Nathan E, et al. "Gene expression profiling and the use of genome-scale in silico models of Escherichia coli for analysis: providing context for content." Journal of bacteriology vol. 191,11 (2009): 3437-44. doi: https://doi.org/10.1128/JB.00034-09
Lewis NE, Cho BK, Knight EM, Palsson BO. Gene expression profiling and the use of genome-scale in silico models of Escherichia coli for analysis: providing context for content. J Bacteriol. 2009 Jun;191(11):3437-44. doi: 10.1128/JB.00034-09. Epub 2009 Apr 10. PMID: 19363119; PMCID: PMC2681886.
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Network motifs modulate druggability of cellular targets.

Scientific reports

Wu F, Ma C, Tan C.
PMID: 27824147
Sci Rep. 2016 Nov 08;6:36626. doi: 10.1038/srep36626.

Druggability refers to the capacity of a cellular target to be modulated by a small-molecule drug. To date, druggability is mainly studied by focusing on direct binding interactions between a drug and its target. However, druggability is impacted by...

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Wu F, Ma C, Tan C. Network motifs modulate druggability of cellular targets. Sci Rep. 2016;6:36626doi: 10.1038/srep36626.
Wu, F., Ma, C., & Tan, C. (2016). Network motifs modulate druggability of cellular targets. Scientific reports, 636626. https://doi.org/10.1038/srep36626
Wu, Fan, et al. "Network motifs modulate druggability of cellular targets." Scientific reports vol. 6 (2016): 36626. doi: https://doi.org/10.1038/srep36626
Wu F, Ma C, Tan C. Network motifs modulate druggability of cellular targets. Sci Rep. 2016 Nov 08;6:36626. doi: 10.1038/srep36626. PMID: 27824147; PMCID: PMC5100546.
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Transfer and expression of genetic information in Escherichia coli K12.

Experimental cell research

JACOB F.
PMID: 14406328
Exp Cell Res. 1959;51-68.

No abstract available.

Cite
JACOB F. Transfer and expression of genetic information in Escherichia coli K12. Exp Cell Res. 1959;51-68
JACOB, F. (1959). Transfer and expression of genetic information in Escherichia coli K12. Experimental cell research, 51-68.
JACOB, F. "Transfer and expression of genetic information in Escherichia coli K12." Experimental cell research vol. (1959): 51-68.
JACOB F. Transfer and expression of genetic information in Escherichia coli K12. Exp Cell Res. 1959;51-68. PMID: 14406328.
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Observations on two types of genetic instability in Escherichia coli.

Acta pathologica et microbiologica Scandinavica

JYSSUM K.
PMID: 14408281
Acta Pathol Microbiol Scand. 1960;48:113-20. doi: 10.1111/j.1699-0463.1960.tb04747.x.

No abstract available.

Cite
JYSSUM K. Observations on two types of genetic instability in Escherichia coli. Acta Pathol Microbiol Scand. 1960;48:113-20doi: 10.1111/j.1699-0463.1960.tb04747.x.
JYSSUM, K. (1960). Observations on two types of genetic instability in Escherichia coli. Acta pathologica et microbiologica Scandinavica, 48113-20. https://doi.org/10.1111/j.1699-0463.1960.tb04747.x
JYSSUM, K. "Observations on two types of genetic instability in Escherichia coli." Acta pathologica et microbiologica Scandinavica vol. 48 (1960): 113-20. doi: https://doi.org/10.1111/j.1699-0463.1960.tb04747.x
JYSSUM K. Observations on two types of genetic instability in Escherichia coli. Acta Pathol Microbiol Scand. 1960;48:113-20. doi: 10.1111/j.1699-0463.1960.tb04747.x. PMID: 14408281.
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Showing 1 to 12 of 55 entries