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Metabolic Regulation and Coordination of the Metabolism in Bacteria in Response to a Variety of Growth Conditions.

Advances in biochemical engineering/biotechnology

Shimizu K.
PMID: 25712586
Adv Biochem Eng Biotechnol. 2016;155:1-54. doi: 10.1007/10_2015_320.

Living organisms have sophisticated but well-organized regulation system. It is important to understand the metabolic regulation mechanisms in relation to growth environment for the efficient design of cell factories for biofuels and biochemicals production. Here, an overview is given...

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Shimizu K. Metabolic Regulation and Coordination of the Metabolism in Bacteria in Response to a Variety of Growth Conditions. Adv Biochem Eng Biotechnol. 2016;155:1-54doi: 10.1007/10_2015_320.
Shimizu, K. (2016). Metabolic Regulation and Coordination of the Metabolism in Bacteria in Response to a Variety of Growth Conditions. Advances in biochemical engineering/biotechnology, 1551-54. https://doi.org/10.1007/10_2015_320
Shimizu, Kazuyuki. "Metabolic Regulation and Coordination of the Metabolism in Bacteria in Response to a Variety of Growth Conditions." Advances in biochemical engineering/biotechnology vol. 155 (2016): 1-54. doi: https://doi.org/10.1007/10_2015_320
Shimizu K. Metabolic Regulation and Coordination of the Metabolism in Bacteria in Response to a Variety of Growth Conditions. Adv Biochem Eng Biotechnol. 2016;155:1-54. doi: 10.1007/10_2015_320. PMID: 25712586.
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The BioCyc collection of microbial genomes and metabolic pathways.

Briefings in bioinformatics

Karp PD, Billington R, Caspi R, Fulcher CA, Latendresse M, Kothari A, Keseler IM, Krummenacker M, Midford PE, Ong Q, Ong WK, Paley SM, Subhraveti P.
PMID: 29447345
Brief Bioinform. 2019 Jul 19;20(4):1085-1093. doi: 10.1093/bib/bbx085.

BioCyc.org is a microbial genome Web portal that combines thousands of genomes with additional information inferred by computer programs, imported from other databases and curated from the biomedical literature by biologist curators. BioCyc also provides an extensive range of...

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Karp PD, Billington R, Caspi R, et al. The BioCyc collection of microbial genomes and metabolic pathways. Brief Bioinform. 2019;20(4):1085-1093doi: 10.1093/bib/bbx085.
Karp, P. D., Billington, R., Caspi, R., Fulcher, C. A., Latendresse, M., Kothari, A., Keseler, I. M., Krummenacker, M., Midford, P. E., Ong, Q., Ong, W. K., Paley, S. M., & Subhraveti, P. (2019). The BioCyc collection of microbial genomes and metabolic pathways. Briefings in bioinformatics, 20(4), 1085-1093. https://doi.org/10.1093/bib/bbx085
Karp, Peter D, et al. "The BioCyc collection of microbial genomes and metabolic pathways." Briefings in bioinformatics vol. 20,4 (2019): 1085-1093. doi: https://doi.org/10.1093/bib/bbx085
Karp PD, Billington R, Caspi R, Fulcher CA, Latendresse M, Kothari A, Keseler IM, Krummenacker M, Midford PE, Ong Q, Ong WK, Paley SM, Subhraveti P. The BioCyc collection of microbial genomes and metabolic pathways. Brief Bioinform. 2019 Jul 19;20(4):1085-1093. doi: 10.1093/bib/bbx085. PMID: 29447345; PMCID: PMC6781571.
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Multi-omics integration accurately predicts cellular state in unexplored conditions for Escherichia coli.

Nature communications

Kim M, Rai N, Zorraquino V, Tagkopoulos I.
PMID: 27713404
Nat Commun. 2016 Oct 07;7:13090. doi: 10.1038/ncomms13090.

A significant obstacle in training predictive cell models is the lack of integrated data sources. We develop semi-supervised normalization pipelines and perform experimental characterization (growth, transcriptional, proteome) to create Ecomics, a consistent, quality-controlled multi-omics compendium for Escherichia coli with...

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Kim M, Rai N, Zorraquino V, et al. Multi-omics integration accurately predicts cellular state in unexplored conditions for Escherichia coli. Nat Commun. 2016;7:13090doi: 10.1038/ncomms13090.
Kim, M., Rai, N., Zorraquino, V., & Tagkopoulos, I. (2016). Multi-omics integration accurately predicts cellular state in unexplored conditions for Escherichia coli. Nature communications, 713090. https://doi.org/10.1038/ncomms13090
Kim, Minseung, et al. "Multi-omics integration accurately predicts cellular state in unexplored conditions for Escherichia coli." Nature communications vol. 7 (2016): 13090. doi: https://doi.org/10.1038/ncomms13090
Kim M, Rai N, Zorraquino V, Tagkopoulos I. Multi-omics integration accurately predicts cellular state in unexplored conditions for Escherichia coli. Nat Commun. 2016 Oct 07;7:13090. doi: 10.1038/ncomms13090. PMID: 27713404; PMCID: PMC5059772.
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Finding elementary flux modes in metabolic networks based on flux balance analysis and flux coupling analysis: application to the analysis of Escherichia coli metabolism.

Biotechnology letters

Tabe-Bordbar S, Marashi SA.
PMID: 24078125
Biotechnol Lett. 2013 Dec;35(12):2039-44. doi: 10.1007/s10529-013-1328-x.

Elementary modes (EMs) are steady-state metabolic flux vectors with minimal set of active reactions. Each EM corresponds to a metabolic pathway. Therefore, studying EMs is helpful for analyzing the production of biotechnologically important metabolites. However, memory requirements for computing...

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Tabe-Bordbar S, Marashi SA. Finding elementary flux modes in metabolic networks based on flux balance analysis and flux coupling analysis: application to the analysis of Escherichia coli metabolism. Biotechnol Lett. 2013;35(12):2039-44doi: 10.1007/s10529-013-1328-x.
Tabe-Bordbar, S., & Marashi, S. A. (2013). Finding elementary flux modes in metabolic networks based on flux balance analysis and flux coupling analysis: application to the analysis of Escherichia coli metabolism. Biotechnology letters, 35(12), 2039-44. https://doi.org/10.1007/s10529-013-1328-x
Tabe-Bordbar, Shayan, and Marashi, Sayed-Amir. "Finding elementary flux modes in metabolic networks based on flux balance analysis and flux coupling analysis: application to the analysis of Escherichia coli metabolism." Biotechnology letters vol. 35,12 (2013): 2039-44. doi: https://doi.org/10.1007/s10529-013-1328-x
Tabe-Bordbar S, Marashi SA. Finding elementary flux modes in metabolic networks based on flux balance analysis and flux coupling analysis: application to the analysis of Escherichia coli metabolism. Biotechnol Lett. 2013 Dec;35(12):2039-44. doi: 10.1007/s10529-013-1328-x. PMID: 24078125.
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[Studies on conditions of genetic metabolism in E. coli].

Postepy biochemii

OPARA Z.
PMID: 13601386
Postepy Biochem. 1958;4(2):213-21.

No abstract available.

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OPARA Z. Badania nad warunkami wymian genetycznych u E. coli. [Studies on conditions of genetic metabolism in E. coli]. Postepy Biochem. 1958;4(2):213-21
OPARA, Z. (1958). Badania nad warunkami wymian genetycznych u E. coli. [Studies on conditions of genetic metabolism in E. coli]. Postepy biochemii, 4(2), 213-21.
OPARA, Z. "Badania nad warunkami wymian genetycznych u E. coli." [Studies on conditions of genetic metabolism in E. coli]. Postepy biochemii vol. 4,2 (1958): 213-21.
OPARA Z. Badania nad warunkami wymian genetycznych u E. coli. [Studies on conditions of genetic metabolism in E. coli]. Postepy Biochem. 1958;4(2):213-21. Polish. PMID: 13601386.
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Modeling a minimal cell.

Methods in molecular biology (Clifton, N.J.)

Shuler ML, Foley P, Atlas J.
PMID: 22639227
Methods Mol Biol. 2012;881:573-610. doi: 10.1007/978-1-61779-827-6_20.

One important aim of synthetic biology is to develop a self-replicating biological system capable of performing useful tasks. A mathematical model of a synthetic organism would greatly enhance its value by providing a platform in which proposed modifications to...

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Shuler ML, Foley P, Atlas J. Modeling a minimal cell. Methods Mol Biol. 2012;881:573-610doi: 10.1007/978-1-61779-827-6_20.
Shuler, M. L., Foley, P., & Atlas, J. (2012). Modeling a minimal cell. Methods in molecular biology (Clifton, N.J.), 881573-610. https://doi.org/10.1007/978-1-61779-827-6_20
Shuler, Michael L, et al. "Modeling a minimal cell." Methods in molecular biology (Clifton, N.J.) vol. 881 (2012): 573-610. doi: https://doi.org/10.1007/978-1-61779-827-6_20
Shuler ML, Foley P, Atlas J. Modeling a minimal cell. Methods Mol Biol. 2012;881:573-610. doi: 10.1007/978-1-61779-827-6_20. PMID: 22639227.
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[Contribution to the study of metabolism in "D" colonies of Escherichia coli].

Glasnik. Belgrad. Higijenski institut NR Srbije

GAJIC S.
PMID: 13896282
Glas Belgrad Hig Inst NR Srb. 1961 Jan-Jun;10:21-33.

No abstract available.

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GAJIC S. [Contribution to the study of metabolism in "D" colonies of Escherichia coli]. Glas Belgrad Hig Inst NR Srb. 1961;10:21-33
GAJIC, S. (1961). [Contribution to the study of metabolism in "D" colonies of Escherichia coli]. Glasnik. Belgrad. Higijenski institut NR Srbije, 1021-33.
GAJIC, S. "[Contribution to the study of metabolism in "D" colonies of Escherichia coli]." Glasnik. Belgrad. Higijenski institut NR Srbije vol. 10 (1961): 21-33.
GAJIC S. [Contribution to the study of metabolism in "D" colonies of Escherichia coli]. Glas Belgrad Hig Inst NR Srb. 1961 Jan-Jun;10:21-33. Undetermined. PMID: 13896282.
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The potential for the formation of a biosynthetic enzyme in Escherichia coli.

Biochimica et biophysica acta

GORINI L, MAAS WK.
PMID: 13445749
Biochim Biophys Acta. 1957 Jul;25(1):208-9. doi: 10.1016/0006-3002(57)90450-x.

No abstract available.

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GORINI L, MAAS WK. The potential for the formation of a biosynthetic enzyme in Escherichia coli. Biochim Biophys Acta. 1957;25(1):208-9doi: 10.1016/0006-3002(57)90450-x.
GORINI, L., & MAAS, W. K. (1957). The potential for the formation of a biosynthetic enzyme in Escherichia coli. Biochimica et biophysica acta, 25(1), 208-9. https://doi.org/10.1016/0006-3002(57)90450-x
GORINI, L, and MAAS, W K. "The potential for the formation of a biosynthetic enzyme in Escherichia coli." Biochimica et biophysica acta vol. 25,1 (1957): 208-9. doi: https://doi.org/10.1016/0006-3002(57)90450-x
GORINI L, MAAS WK. The potential for the formation of a biosynthetic enzyme in Escherichia coli. Biochim Biophys Acta. 1957 Jul;25(1):208-9. doi: 10.1016/0006-3002(57)90450-x. PMID: 13445749.
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Metabolic assessment of E. coli as a Biofactory for commercial products.

Metabolic engineering

Zhang X, Tervo CJ, Reed JL.
PMID: 26850742
Metab Eng. 2016 May;35:64-74. doi: 10.1016/j.ymben.2016.01.007. Epub 2016 Feb 02.

Metabolic engineering uses microorganisms to synthesize chemicals from renewable resources. Given the thousands of known metabolites, it is unclear what valuable chemicals could be produced by a microorganism and what native and heterologous reactions are needed for their synthesis....

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Zhang X, Tervo CJ, Reed JL. Metabolic assessment of E. coli as a Biofactory for commercial products. Metab Eng. 2016;35:64-74doi: 10.1016/j.ymben.2016.01.007.
Zhang, X., Tervo, C. J., & Reed, J. L. (2016). Metabolic assessment of E. coli as a Biofactory for commercial products. Metabolic engineering, 3564-74. https://doi.org/10.1016/j.ymben.2016.01.007
Zhang, Xiaolin, et al. "Metabolic assessment of E. coli as a Biofactory for commercial products." Metabolic engineering vol. 35 (2016): 64-74. doi: https://doi.org/10.1016/j.ymben.2016.01.007
Zhang X, Tervo CJ, Reed JL. Metabolic assessment of E. coli as a Biofactory for commercial products. Metab Eng. 2016 May;35:64-74. doi: 10.1016/j.ymben.2016.01.007. Epub 2016 Feb 02. PMID: 26850742.
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Robust Analysis of Fluxes in Genome-Scale Metabolic Pathways.

Scientific reports

MacGillivray M, Ko A, Gruber E, Sawyer M, Almaas E, Holder A.
PMID: 28325918
Sci Rep. 2017 Mar 21;7(1):268. doi: 10.1038/s41598-017-00170-3.

Constraint-based optimization, such as flux balance analysis (FBA), has become a standard systems-biology computational method to study cellular metabolisms that are assumed to be in a steady state of optimal growth. The methods are based on optimization while assuming...

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MacGillivray M, Ko A, Gruber E, et al. Robust Analysis of Fluxes in Genome-Scale Metabolic Pathways. Sci Rep. 2017;7(1):268doi: 10.1038/s41598-017-00170-3.
MacGillivray, M., Ko, A., Gruber, E., Sawyer, M., Almaas, E., & Holder, A. (2017). Robust Analysis of Fluxes in Genome-Scale Metabolic Pathways. Scientific reports, 7(1), 268. https://doi.org/10.1038/s41598-017-00170-3
MacGillivray, Michael, et al. "Robust Analysis of Fluxes in Genome-Scale Metabolic Pathways." Scientific reports vol. 7,1 (2017): 268. doi: https://doi.org/10.1038/s41598-017-00170-3
MacGillivray M, Ko A, Gruber E, Sawyer M, Almaas E, Holder A. Robust Analysis of Fluxes in Genome-Scale Metabolic Pathways. Sci Rep. 2017 Mar 21;7(1):268. doi: 10.1038/s41598-017-00170-3. PMID: 28325918; PMCID: PMC5427939.
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Bottom-up proteome analysis of E. coli using capillary zone electrophoresis-tandem mass spectrometry with an electrokinetic sheath-flow electrospray interface.

Proteomics

Yan X, Essaka DC, Sun L, Zhu G, Dovichi NJ.
PMID: 23798545
Proteomics. 2013 Sep;13(17):2546-51. doi: 10.1002/pmic.201300062. Epub 2013 Aug 01.

The Escherichia coli proteome was digested with trypsin and fractionated using SPE on a C18 SPE column. Seven fractions were collected and analyzed by CZE-ESI-MS/MS. The separation was performed in a 60-cm-long linear polyacrylamide-coated capillary with a 0.1% v/v...

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Yan X, Essaka DC, Sun L, et al. Bottom-up proteome analysis of E. coli using capillary zone electrophoresis-tandem mass spectrometry with an electrokinetic sheath-flow electrospray interface. Proteomics. 2013;13(17):2546-51doi: 10.1002/pmic.201300062.
Yan, X., Essaka, D. C., Sun, L., Zhu, G., & Dovichi, N. J. (2013). Bottom-up proteome analysis of E. coli using capillary zone electrophoresis-tandem mass spectrometry with an electrokinetic sheath-flow electrospray interface. Proteomics, 13(17), 2546-51. https://doi.org/10.1002/pmic.201300062
Yan, Xiaojing, et al. "Bottom-up proteome analysis of E. coli using capillary zone electrophoresis-tandem mass spectrometry with an electrokinetic sheath-flow electrospray interface." Proteomics vol. 13,17 (2013): 2546-51. doi: https://doi.org/10.1002/pmic.201300062
Yan X, Essaka DC, Sun L, Zhu G, Dovichi NJ. Bottom-up proteome analysis of E. coli using capillary zone electrophoresis-tandem mass spectrometry with an electrokinetic sheath-flow electrospray interface. Proteomics. 2013 Sep;13(17):2546-51. doi: 10.1002/pmic.201300062. Epub 2013 Aug 01. PMID: 23798545; PMCID: PMC4121376.
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CLCA: maximum common molecular substructure queries within the MetRxn database.

Journal of chemical information and modeling

Kumar A, Maranas CD.
PMID: 25412255
J Chem Inf Model. 2014 Dec 22;54(12):3417-38. doi: 10.1021/ci5003922. Epub 2014 Dec 01.

The challenge of automatically identifying the preserved molecular moieties in a chemical reaction is referred to as the atom mapping problem. Reaction atom maps provide the ability to locate the fate of individual atoms across an entire metabolic network....

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Kumar A, Maranas CD. CLCA: maximum common molecular substructure queries within the MetRxn database. J Chem Inf Model. 2014;54(12):3417-38doi: 10.1021/ci5003922.
Kumar, A., & Maranas, C. D. (2014). CLCA: maximum common molecular substructure queries within the MetRxn database. Journal of chemical information and modeling, 54(12), 3417-38. https://doi.org/10.1021/ci5003922
Kumar, Akhil, and Maranas, Costas D. "CLCA: maximum common molecular substructure queries within the MetRxn database." Journal of chemical information and modeling vol. 54,12 (2014): 3417-38. doi: https://doi.org/10.1021/ci5003922
Kumar A, Maranas CD. CLCA: maximum common molecular substructure queries within the MetRxn database. J Chem Inf Model. 2014 Dec 22;54(12):3417-38. doi: 10.1021/ci5003922. Epub 2014 Dec 01. PMID: 25412255.
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Showing 13 to 24 of 56 entries