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Informatics for Metabolomics.

Advances in experimental medicine and biology

Kusonmano K, Vongsangnak W, Chumnanpuen P.
PMID: 27807745
Adv Exp Med Biol. 2016;939:91-115. doi: 10.1007/978-981-10-1503-8_5.

Metabolome profiling of biological systems has the powerful ability to provide the biological understanding of their metabolic functional states responding to the environmental factors or other perturbations. Tons of accumulative metabolomics data have thus been established since pre-metabolomics era....

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Kusonmano K, Vongsangnak W, Chumnanpuen P. Informatics for Metabolomics. Adv Exp Med Biol. 2016;939:91-115doi: 10.1007/978-981-10-1503-8_5.
Kusonmano, K., Vongsangnak, W., & Chumnanpuen, P. (2016). Informatics for Metabolomics. Advances in experimental medicine and biology, 93991-115. https://doi.org/10.1007/978-981-10-1503-8_5
Kusonmano, Kanthida, et al. "Informatics for Metabolomics." Advances in experimental medicine and biology vol. 939 (2016): 91-115. doi: https://doi.org/10.1007/978-981-10-1503-8_5
Kusonmano K, Vongsangnak W, Chumnanpuen P. Informatics for Metabolomics. Adv Exp Med Biol. 2016;939:91-115. doi: 10.1007/978-981-10-1503-8_5. PMID: 27807745.
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Curating a comprehensive set of enzymatic reaction rules for efficient novel biosynthetic pathway design.

Metabolic engineering

Ni Z, Stine AE, Tyo KEJ, Broadbelt LJ.
PMID: 33662575
Metab Eng. 2021 May;65:79-87. doi: 10.1016/j.ymben.2021.02.006. Epub 2021 Mar 01.

Enzyme substrate promiscuity has significant implications for metabolic engineering. The ability to predict the space of possible enzymatic side reactions is crucial for elucidating underground metabolic networks in microorganisms, as well as harnessing novel biosynthetic capabilities of enzymes to...

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Ni Z, Stine AE, Tyo KEJ, et al. Curating a comprehensive set of enzymatic reaction rules for efficient novel biosynthetic pathway design. Metab Eng. 2021;65:79-87doi: 10.1016/j.ymben.2021.02.006.
Ni, Z., Stine, A. E., Tyo, K. E. J., & Broadbelt, L. J. (2021). Curating a comprehensive set of enzymatic reaction rules for efficient novel biosynthetic pathway design. Metabolic engineering, 6579-87. https://doi.org/10.1016/j.ymben.2021.02.006
Ni, Zhuofu, et al. "Curating a comprehensive set of enzymatic reaction rules for efficient novel biosynthetic pathway design." Metabolic engineering vol. 65 (2021): 79-87. doi: https://doi.org/10.1016/j.ymben.2021.02.006
Ni Z, Stine AE, Tyo KEJ, Broadbelt LJ. Curating a comprehensive set of enzymatic reaction rules for efficient novel biosynthetic pathway design. Metab Eng. 2021 May;65:79-87. doi: 10.1016/j.ymben.2021.02.006. Epub 2021 Mar 01. PMID: 33662575.
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[Current status and future perspectives of metabolic network models of industrial microorganisms].

Sheng wu gong cheng xue bao = Chinese journal of biotechnology

Zhang C, Wu Y, Xu X, Lv X, Li J, Du G, Liu L.
PMID: 33783155
Sheng Wu Gong Cheng Xue Bao. 2021 Mar 25;37(3):860-873. doi: 10.13345/j.cjb.200640.

Genome-scale metabolic network model (GSMM) is an extremely important guiding tool in the targeted modification of industrial microbial strains, which helps researchers to quickly obtain industrial microbes with specific traits and has attracted increasing attention. Here we reviewe the...

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Zhang C, Wu Y, Xu X, et al. [Current status and future perspectives of metabolic network models of industrial microorganisms]. Sheng Wu Gong Cheng Xue Bao. 2021;37(3):860-873doi: 10.13345/j.cjb.200640.
Zhang, C., Wu, Y., Xu, X., Lv, X., Li, J., Du, G., & Liu, L. (2021). [Current status and future perspectives of metabolic network models of industrial microorganisms]. Sheng wu gong cheng xue bao = Chinese journal of biotechnology, 37(3), 860-873. https://doi.org/10.13345/j.cjb.200640
Zhang, Chenyang, et al. "[Current status and future perspectives of metabolic network models of industrial microorganisms]." Sheng wu gong cheng xue bao = Chinese journal of biotechnology vol. 37,3 (2021): 860-873. doi: https://doi.org/10.13345/j.cjb.200640
Zhang C, Wu Y, Xu X, Lv X, Li J, Du G, Liu L. [Current status and future perspectives of metabolic network models of industrial microorganisms]. Sheng Wu Gong Cheng Xue Bao. 2021 Mar 25;37(3):860-873. doi: 10.13345/j.cjb.200640. Chinese. PMID: 33783155.
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Browsing metabolic and regulatory networks with BioCyc.

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

Latendresse M, Paley S, Karp PD.
PMID: 22144155
Methods Mol Biol. 2012;804:197-216. doi: 10.1007/978-1-61779-361-5_11.

The BioCyc database collection at BioCyc.org integrates genome and cellular network information for more than 1,100 organisms. This method chapter describes Web-based tools for browsing metabolic and regulatory networks within BioCyc. These tools allow visualization of complete metabolic and...

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Latendresse M, Paley S, Karp PD. Browsing metabolic and regulatory networks with BioCyc. Methods Mol Biol. 2012;804:197-216doi: 10.1007/978-1-61779-361-5_11.
Latendresse, M., Paley, S., & Karp, P. D. (2012). Browsing metabolic and regulatory networks with BioCyc. Methods in molecular biology (Clifton, N.J.), 804197-216. https://doi.org/10.1007/978-1-61779-361-5_11
Latendresse, Mario, et al. "Browsing metabolic and regulatory networks with BioCyc." Methods in molecular biology (Clifton, N.J.) vol. 804 (2012): 197-216. doi: https://doi.org/10.1007/978-1-61779-361-5_11
Latendresse M, Paley S, Karp PD. Browsing metabolic and regulatory networks with BioCyc. Methods Mol Biol. 2012;804:197-216. doi: 10.1007/978-1-61779-361-5_11. PMID: 22144155; PMCID: PMC3549617.
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Reconciliation of metabolites and biochemical reactions for metabolic networks.

Briefings in bioinformatics

Bernard T, Bridge A, Morgat A, Moretti S, Xenarios I, Pagni M.
PMID: 23172809
Brief Bioinform. 2014 Jan;15(1):123-35. doi: 10.1093/bib/bbs058. Epub 2012 Nov 19.

Genome-scale metabolic network reconstructions are now routinely used in the study of metabolic pathways, their evolution and design. The development of such reconstructions involves the integration of information on reactions and metabolites from the scientific literature as well as...

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Bernard T, Bridge A, Morgat A, et al. Reconciliation of metabolites and biochemical reactions for metabolic networks. Brief Bioinform. 2012;15(1):123-35doi: 10.1093/bib/bbs058.
Bernard, T., Bridge, A., Morgat, A., Moretti, S., Xenarios, I., & Pagni, M. (2014). Reconciliation of metabolites and biochemical reactions for metabolic networks. Briefings in bioinformatics, 15(1), 123-35. https://doi.org/10.1093/bib/bbs058
Bernard, Thomas, et al. "Reconciliation of metabolites and biochemical reactions for metabolic networks." Briefings in bioinformatics vol. 15,1 (2014): 123-35. doi: https://doi.org/10.1093/bib/bbs058
Bernard T, Bridge A, Morgat A, Moretti S, Xenarios I, Pagni M. Reconciliation of metabolites and biochemical reactions for metabolic networks. Brief Bioinform. 2014 Jan;15(1):123-35. doi: 10.1093/bib/bbs058. Epub 2012 Nov 19. PMID: 23172809; PMCID: PMC3896926.
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Cancer metabolism - An update.

Molecular metabolism

Schulze A.
PMID: 31831354
Mol Metab. 2020 Mar;33:1. doi: 10.1016/j.molmet.2019.11.017. Epub 2019 Nov 29.

No abstract available.

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Schulze A. Cancer metabolism - An update. Mol Metab. 2019;33:1doi: 10.1016/j.molmet.2019.11.017.
Schulze, A. (2020). Cancer metabolism - An update. Molecular metabolism, 331. https://doi.org/10.1016/j.molmet.2019.11.017
Schulze, Almut. "Cancer metabolism - An update." Molecular metabolism vol. 33 (2020): 1. doi: https://doi.org/10.1016/j.molmet.2019.11.017
Schulze A. Cancer metabolism - An update. Mol Metab. 2020 Mar;33:1. doi: 10.1016/j.molmet.2019.11.017. Epub 2019 Nov 29. PMID: 31831354; PMCID: PMC7056917.
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The great hairball gambit.

PLoS genetics

Flint J, Ideker T.
PMID: 31770365
PLoS Genet. 2019 Nov 26;15(11):e1008519. doi: 10.1371/journal.pgen.1008519. eCollection 2019 Nov.

No abstract available.

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Flint J, Ideker T. The great hairball gambit. PLoS Genet. 2019;15(11):e1008519doi: 10.1371/journal.pgen.1008519.
Flint, J., & Ideker, T. (2019). The great hairball gambit. PLoS genetics, 15(11), e1008519. https://doi.org/10.1371/journal.pgen.1008519
Flint, Jonathan, and Ideker, Trey. "The great hairball gambit." PLoS genetics vol. 15,11 (2019): e1008519. doi: https://doi.org/10.1371/journal.pgen.1008519
Flint J, Ideker T. The great hairball gambit. PLoS Genet. 2019 Nov 26;15(11):e1008519. doi: 10.1371/journal.pgen.1008519. eCollection 2019 Nov. PMID: 31770365; PMCID: PMC6910700.
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Endogenous toxic metabolites and implications in cancer therapy.

Oncogene

Lee N, Spears ME, Carlisle AE, Kim D.
PMID: 32709924
Oncogene. 2020 Aug;39(35):5709-5720. doi: 10.1038/s41388-020-01395-9. Epub 2020 Jul 24.

It is well recognized that many metabolic enzymes play essential roles in cancer cells in producing building blocks such as nucleotides, which are required in greater amounts due to their increased proliferation. On the other hand, the significance of...

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Lee N, Spears ME, Carlisle AE, et al. Endogenous toxic metabolites and implications in cancer therapy. Oncogene. 2020;39(35):5709-5720doi: 10.1038/s41388-020-01395-9.
Lee, N., Spears, M. E., Carlisle, A. E., & Kim, D. (2020). Endogenous toxic metabolites and implications in cancer therapy. Oncogene, 39(35), 5709-5720. https://doi.org/10.1038/s41388-020-01395-9
Lee, Namgyu, et al. "Endogenous toxic metabolites and implications in cancer therapy." Oncogene vol. 39,35 (2020): 5709-5720. doi: https://doi.org/10.1038/s41388-020-01395-9
Lee N, Spears ME, Carlisle AE, Kim D. Endogenous toxic metabolites and implications in cancer therapy. Oncogene. 2020 Aug;39(35):5709-5720. doi: 10.1038/s41388-020-01395-9. Epub 2020 Jul 24. PMID: 32709924; PMCID: PMC7452860.
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PANEV: an R package for a pathway-based network visualization.

BMC bioinformatics

Palombo V, Milanesi M, Sferra G, Capomaccio S, Sgorlon S, D'Andrea M.
PMID: 32028885
BMC Bioinformatics. 2020 Feb 06;21(1):46. doi: 10.1186/s12859-020-3371-7.

BACKGROUND: During the last decade, with the aim to solve the challenge of post-genomic and transcriptomic data mining, a plethora of tools have been developed to create, edit and analyze metabolic pathways. In particular, when a complex phenomenon is...

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Palombo V, Milanesi M, Sferra G, et al. PANEV: an R package for a pathway-based network visualization. BMC Bioinformatics. 2020;21(1):46doi: 10.1186/s12859-020-3371-7.
Palombo, V., Milanesi, M., Sferra, G., Capomaccio, S., Sgorlon, S., & D'Andrea, M. (2020). PANEV: an R package for a pathway-based network visualization. BMC bioinformatics, 21(1), 46. https://doi.org/10.1186/s12859-020-3371-7
Palombo, Valentino, et al. "PANEV: an R package for a pathway-based network visualization." BMC bioinformatics vol. 21,1 (2020): 46. doi: https://doi.org/10.1186/s12859-020-3371-7
Palombo V, Milanesi M, Sferra G, Capomaccio S, Sgorlon S, D'Andrea M. PANEV: an R package for a pathway-based network visualization. BMC Bioinformatics. 2020 Feb 06;21(1):46. doi: 10.1186/s12859-020-3371-7. PMID: 32028885; PMCID: PMC7006390.
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DNAplotlib: Programmable Visualization of Genetic Designs and Associated Data.

ACS synthetic biology

Der BS, Glassey E, Bartley BA, Enghuus C, Goodman DB, Gordon DB, Voigt CA, Gorochowski TE.
PMID: 27744689
ACS Synth Biol. 2017 Jul 21;6(7):1115-1119. doi: 10.1021/acssynbio.6b00252. Epub 2016 Oct 27.

DNAplotlib ( www.dnaplotlib.org ) is a computational toolkit for the programmable visualization of highly customizable, standards-compliant genetic designs. Functions are provided to aid with both visualization tasks and to extract and overlay associated experimental data. High-quality output is produced...

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Der BS, Glassey E, Bartley BA, et al. DNAplotlib: Programmable Visualization of Genetic Designs and Associated Data. ACS Synth Biol. 2016;6(7):1115-1119doi: 10.1021/acssynbio.6b00252.
Der, B. S., Glassey, E., Bartley, B. A., Enghuus, C., Goodman, D. B., Gordon, D. B., Voigt, C. A., & Gorochowski, T. E. (2017). DNAplotlib: Programmable Visualization of Genetic Designs and Associated Data. ACS synthetic biology, 6(7), 1115-1119. https://doi.org/10.1021/acssynbio.6b00252
Der, Bryan S, et al. "DNAplotlib: Programmable Visualization of Genetic Designs and Associated Data." ACS synthetic biology vol. 6,7 (2017): 1115-1119. doi: https://doi.org/10.1021/acssynbio.6b00252
Der BS, Glassey E, Bartley BA, Enghuus C, Goodman DB, Gordon DB, Voigt CA, Gorochowski TE. DNAplotlib: Programmable Visualization of Genetic Designs and Associated Data. ACS Synth Biol. 2017 Jul 21;6(7):1115-1119. doi: 10.1021/acssynbio.6b00252. Epub 2016 Oct 27. PMID: 27744689.
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Identifying Cell-Type-Specific Metabolic Signatures Using Transcriptome and Proteome Analyses.

Current protocols

Gebert N, Rahman S, Lewis CA, Ori A, Cheng CW.
PMID: 34516047
Curr Protoc. 2021 Sep;1(9):e245. doi: 10.1002/cpz1.245.

Studies in various tissues have revealed a central role of metabolic pathways in regulating adult stem cell function in tissue regeneration and tumor initiation. The unique metabolic dependences or preferences of adult stem cells, therefore, are emerging as a...

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Gebert N, Rahman S, Lewis CA, et al. Identifying Cell-Type-Specific Metabolic Signatures Using Transcriptome and Proteome Analyses. Curr Protoc. 2021;1(9):e245doi: 10.1002/cpz1.245.
Gebert, N., Rahman, S., Lewis, C. A., Ori, A., & Cheng, C. W. (2021). Identifying Cell-Type-Specific Metabolic Signatures Using Transcriptome and Proteome Analyses. Current protocols, 1(9), e245. https://doi.org/10.1002/cpz1.245
Gebert, Nadja, et al. "Identifying Cell-Type-Specific Metabolic Signatures Using Transcriptome and Proteome Analyses." Current protocols vol. 1,9 (2021): e245. doi: https://doi.org/10.1002/cpz1.245
Gebert N, Rahman S, Lewis CA, Ori A, Cheng CW. Identifying Cell-Type-Specific Metabolic Signatures Using Transcriptome and Proteome Analyses. Curr Protoc. 2021 Sep;1(9):e245. doi: 10.1002/cpz1.245. PMID: 34516047; PMCID: PMC8722675.
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Showing 25 to 35 of 35 entries