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Computational approaches for systems metabolomics.

Current opinion in biotechnology

Krumsiek J, Bartel J, Theis FJ.
PMID: 27135552
Curr Opin Biotechnol. 2016 Jun;39:198-206. doi: 10.1016/j.copbio.2016.04.009. Epub 2016 Apr 30.

Systems genetics is defined as the simultaneous assessment and analysis of multi-omics datasets. In the past few years, metabolomics has been established as a robust tool describing an important functional layer in this approach. The metabolome of a biological...

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Krumsiek J, Bartel J, Theis FJ. Computational approaches for systems metabolomics. Curr Opin Biotechnol. 2016;39:198-206doi: 10.1016/j.copbio.2016.04.009.
Krumsiek, J., Bartel, J., & Theis, F. J. (2016). Computational approaches for systems metabolomics. Current opinion in biotechnology, 39198-206. https://doi.org/10.1016/j.copbio.2016.04.009
Krumsiek, Jan, et al. "Computational approaches for systems metabolomics." Current opinion in biotechnology vol. 39 (2016): 198-206. doi: https://doi.org/10.1016/j.copbio.2016.04.009
Krumsiek J, Bartel J, Theis FJ. Computational approaches for systems metabolomics. Curr Opin Biotechnol. 2016 Jun;39:198-206. doi: 10.1016/j.copbio.2016.04.009. Epub 2016 Apr 30. PMID: 27135552.
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Metabolite damage and repair in metabolic engineering design.

Metabolic engineering

Sun J, Jeffryes JG, Henry CS, Bruner SD, Hanson AD.
PMID: 29030275
Metab Eng. 2017 Nov;44:150-159. doi: 10.1016/j.ymben.2017.10.006. Epub 2017 Oct 10.

The necessarily sharp focus of metabolic engineering and metabolic synthetic biology on pathways and their fluxes has tended to divert attention from the damaging enzymatic and chemical side-reactions that pathway metabolites can undergo. Although historically overlooked and underappreciated, such...

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Sun J, Jeffryes JG, Henry CS, et al. Metabolite damage and repair in metabolic engineering design. Metab Eng. 2017;44:150-159doi: 10.1016/j.ymben.2017.10.006.
Sun, J., Jeffryes, J. G., Henry, C. S., Bruner, S. D., & Hanson, A. D. (2017). Metabolite damage and repair in metabolic engineering design. Metabolic engineering, 44150-159. https://doi.org/10.1016/j.ymben.2017.10.006
Sun, Jiayi, et al. "Metabolite damage and repair in metabolic engineering design." Metabolic engineering vol. 44 (2017): 150-159. doi: https://doi.org/10.1016/j.ymben.2017.10.006
Sun J, Jeffryes JG, Henry CS, Bruner SD, Hanson AD. Metabolite damage and repair in metabolic engineering design. Metab Eng. 2017 Nov;44:150-159. doi: 10.1016/j.ymben.2017.10.006. Epub 2017 Oct 10. PMID: 29030275.
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Metabonomics: methods and protocols: preface.

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

Bjerrum JT.
PMID: 25821869
Methods Mol Biol. 2015;1277:v.

No abstract available.

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Bjerrum JT. Metabonomics: methods and protocols: preface. Methods Mol Biol. 2015;1277:v
Bjerrum, J. T. (2015). Metabonomics: methods and protocols: preface. Methods in molecular biology (Clifton, N.J.), 1277v.
Bjerrum, Jacob Tveiten. "Metabonomics: methods and protocols: preface." Methods in molecular biology (Clifton, N.J.) vol. 1277 (2015): v.
Bjerrum JT. Metabonomics: methods and protocols: preface. Methods Mol Biol. 2015;1277:v. PMID: 25821869.
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Gut microbiota: The difference diet makes to metabolites and microbiota.

Nature reviews. Gastroenterology & hepatology

Patman G.
PMID: 25511528
Nat Rev Gastroenterol Hepatol. 2015 Feb;12(2):63. doi: 10.1038/nrgastro.2014.227. Epub 2014 Dec 16.

No abstract available.

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Patman G. Gut microbiota: The difference diet makes to metabolites and microbiota. Nat Rev Gastroenterol Hepatol. 2014;12(2):63doi: 10.1038/nrgastro.2014.227.
Patman, G. (2015). Gut microbiota: The difference diet makes to metabolites and microbiota. Nature reviews. Gastroenterology & hepatology, 12(2), 63. https://doi.org/10.1038/nrgastro.2014.227
Patman, Gillian. "Gut microbiota: The difference diet makes to metabolites and microbiota." Nature reviews. Gastroenterology & hepatology vol. 12,2 (2015): 63. doi: https://doi.org/10.1038/nrgastro.2014.227
Patman G. Gut microbiota: The difference diet makes to metabolites and microbiota. Nat Rev Gastroenterol Hepatol. 2015 Feb;12(2):63. doi: 10.1038/nrgastro.2014.227. Epub 2014 Dec 16. PMID: 25511528.
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[Metabolomic study of flower buds of Tussilago farfara in different development stages by GC-MS].

Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica

Xue SY, Wang XJ, Sun HF, Zhang LZ, Qin XM, Li ZY.
PMID: 23270222
Zhongguo Zhong Yao Za Zhi. 2012 Oct;37(19):2863-9.

OBJECTIVE: Plant metabolomics combined with GC-MS was used to investigate metabolic fingerprinting of Tussilago farfara at different growth stages.METHOD: Dried Samples were extracted by two-phase solvent system to obtain polar and nonpolar parts, which were subjected to GC-MS analysis....

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Xue SY, Wang XJ, Sun HF, et al. [Metabolomic study of flower buds of Tussilago farfara in different development stages by GC-MS]. Zhongguo Zhong Yao Za Zhi. 2012;37(19):2863-9
Xue, S. Y., Wang, X. J., Sun, H. F., Zhang, L. Z., Qin, X. M., & Li, Z. Y. (2012). [Metabolomic study of flower buds of Tussilago farfara in different development stages by GC-MS]. Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica, 37(19), 2863-9.
Xue, Shui-yu, et al. "[Metabolomic study of flower buds of Tussilago farfara in different development stages by GC-MS]." Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica vol. 37,19 (2012): 2863-9.
Xue SY, Wang XJ, Sun HF, Zhang LZ, Qin XM, Li ZY. [Metabolomic study of flower buds of Tussilago farfara in different development stages by GC-MS]. Zhongguo Zhong Yao Za Zhi. 2012 Oct;37(19):2863-9. Chinese. PMID: 23270222.
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The KEGG databases and tools facilitating omics analysis: latest developments involving human diseases and pharmaceuticals.

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

Kotera M, Hirakawa M, Tokimatsu T, Goto S, Kanehisa M.
PMID: 22130871
Methods Mol Biol. 2012;802:19-39. doi: 10.1007/978-1-61779-400-1_2.

In this chapter, we demonstrate the usability of the KEGG (Kyoto encyclopedia of genes and genomes) databases and tools, especially focusing on the visualization of the omics data. The desktop application KegArray and many Web-based tools are tightly integrated...

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Kotera M, Hirakawa M, Tokimatsu T, et al. The KEGG databases and tools facilitating omics analysis: latest developments involving human diseases and pharmaceuticals. Methods Mol Biol. 2012;802:19-39doi: 10.1007/978-1-61779-400-1_2.
Kotera, M., Hirakawa, M., Tokimatsu, T., Goto, S., & Kanehisa, M. (2012). The KEGG databases and tools facilitating omics analysis: latest developments involving human diseases and pharmaceuticals. Methods in molecular biology (Clifton, N.J.), 80219-39. https://doi.org/10.1007/978-1-61779-400-1_2
Kotera, Masaaki, et al. "The KEGG databases and tools facilitating omics analysis: latest developments involving human diseases and pharmaceuticals." Methods in molecular biology (Clifton, N.J.) vol. 802 (2012): 19-39. doi: https://doi.org/10.1007/978-1-61779-400-1_2
Kotera M, Hirakawa M, Tokimatsu T, Goto S, Kanehisa M. The KEGG databases and tools facilitating omics analysis: latest developments involving human diseases and pharmaceuticals. Methods Mol Biol. 2012;802:19-39. doi: 10.1007/978-1-61779-400-1_2. PMID: 22130871.
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MassTRIX reloaded: combined analysis and visualization of transcriptome and metabolome data.

PloS one

Wägele B, Witting M, Schmitt-Kopplin P, Suhre K.
PMID: 22815716
PLoS One. 2012;7(7):e39860. doi: 10.1371/journal.pone.0039860. Epub 2012 Jul 06.

Systems Biology is a field in biological science that focuses on the combination of several or all "omics"-approaches in order to find out how genes, transcripts, proteins and metabolites act together in the network of life. Metabolomics as analog...

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Wägele B, Witting M, Schmitt-Kopplin P, et al. MassTRIX reloaded: combined analysis and visualization of transcriptome and metabolome data. PLoS One. 2012;7(7):e39860doi: 10.1371/journal.pone.0039860.
Wägele, B., Witting, M., Schmitt-Kopplin, P., & Suhre, K. (2012). MassTRIX reloaded: combined analysis and visualization of transcriptome and metabolome data. PloS one, 7(7), e39860. https://doi.org/10.1371/journal.pone.0039860
Wägele, Brigitte, et al. "MassTRIX reloaded: combined analysis and visualization of transcriptome and metabolome data." PloS one vol. 7,7 (2012): e39860. doi: https://doi.org/10.1371/journal.pone.0039860
Wägele B, Witting M, Schmitt-Kopplin P, Suhre K. MassTRIX reloaded: combined analysis and visualization of transcriptome and metabolome data. PLoS One. 2012;7(7):e39860. doi: 10.1371/journal.pone.0039860. Epub 2012 Jul 06. PMID: 22815716; PMCID: PMC3391204.
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Ethnic differences in body composition and the associated metabolic profile: a comparative study between Asians and Caucasians.

Maturitas

Wulan SN, Westerterp KR, Plasqui G.
PMID: 20079586
Maturitas. 2010 Apr;65(4):315-9. doi: 10.1016/j.maturitas.2009.12.012. Epub 2010 Jan 15.

It is estimated that Asia will be the home of more than 100 million people with type 2 diabetes by the year of 2025. This region combines a high proportion of the world's population with rapidly rising diabetes prevalence...

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Wulan SN, Westerterp KR, Plasqui G. Ethnic differences in body composition and the associated metabolic profile: a comparative study between Asians and Caucasians. Maturitas. 2010;65(4):315-9doi: 10.1016/j.maturitas.2009.12.012.
Wulan, S. N., Westerterp, K. R., & Plasqui, G. (2010). Ethnic differences in body composition and the associated metabolic profile: a comparative study between Asians and Caucasians. Maturitas, 65(4), 315-9. https://doi.org/10.1016/j.maturitas.2009.12.012
Wulan, S N, et al. "Ethnic differences in body composition and the associated metabolic profile: a comparative study between Asians and Caucasians." Maturitas vol. 65,4 (2010): 315-9. doi: https://doi.org/10.1016/j.maturitas.2009.12.012
Wulan SN, Westerterp KR, Plasqui G. Ethnic differences in body composition and the associated metabolic profile: a comparative study between Asians and Caucasians. Maturitas. 2010 Apr;65(4):315-9. doi: 10.1016/j.maturitas.2009.12.012. Epub 2010 Jan 15. PMID: 20079586.
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Fragment recruitment on metabolic pathways: comparative metabolic profiling of metagenomes and metatranscriptomes.

Bioinformatics (Oxford, England)

Desai DK, Schunck H, Löser JW, Laroche J.
PMID: 23303511
Bioinformatics. 2013 Mar 15;29(6):790-1. doi: 10.1093/bioinformatics/bts721. Epub 2013 Jan 09.

MOTIVATION: The sheer scale of the metagenomic and metatranscriptomic datasets that are now available warrants the development of automated protocols for organizing, annotating and comparing the samples in terms of their metabolic profiles. We describe a user-friendly java program...

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Desai DK, Schunck H, Löser JW, et al. Fragment recruitment on metabolic pathways: comparative metabolic profiling of metagenomes and metatranscriptomes. Bioinformatics. 2013;29(6):790-1doi: 10.1093/bioinformatics/bts721.
Desai, D. K., Schunck, H., Löser, J. W., & Laroche, J. (2013). Fragment recruitment on metabolic pathways: comparative metabolic profiling of metagenomes and metatranscriptomes. Bioinformatics (Oxford, England), 29(6), 790-1. https://doi.org/10.1093/bioinformatics/bts721
Desai, Dhwani K, et al. "Fragment recruitment on metabolic pathways: comparative metabolic profiling of metagenomes and metatranscriptomes." Bioinformatics (Oxford, England) vol. 29,6 (2013): 790-1. doi: https://doi.org/10.1093/bioinformatics/bts721
Desai DK, Schunck H, Löser JW, Laroche J. Fragment recruitment on metabolic pathways: comparative metabolic profiling of metagenomes and metatranscriptomes. Bioinformatics. 2013 Mar 15;29(6):790-1. doi: 10.1093/bioinformatics/bts721. Epub 2013 Jan 09. PMID: 23303511.
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Solving the differential biochemical Jacobian from metabolomics covariance data.

PloS one

Nägele T, Mair A, Sun X, Fragner L, Teige M, Weckwerth W.
PMID: 24695071
PLoS One. 2014 Apr 02;9(4):e92299. doi: 10.1371/journal.pone.0092299. eCollection 2014.

High-throughput molecular analysis has become an integral part in organismal systems biology. In contrast, due to a missing systematic linkage of the data with functional and predictive theoretical models of the underlying metabolic network the understanding of the resulting...

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Nägele T, Mair A, Sun X, et al. Solving the differential biochemical Jacobian from metabolomics covariance data. PLoS One. 2014;9(4):e92299doi: 10.1371/journal.pone.0092299.
Nägele, T., Mair, A., Sun, X., Fragner, L., Teige, M., & Weckwerth, W. (2014). Solving the differential biochemical Jacobian from metabolomics covariance data. PloS one, 9(4), e92299. https://doi.org/10.1371/journal.pone.0092299
Nägele, Thomas, et al. "Solving the differential biochemical Jacobian from metabolomics covariance data." PloS one vol. 9,4 (2014): e92299. doi: https://doi.org/10.1371/journal.pone.0092299
Nägele T, Mair A, Sun X, Fragner L, Teige M, Weckwerth W. Solving the differential biochemical Jacobian from metabolomics covariance data. PLoS One. 2014 Apr 02;9(4):e92299. doi: 10.1371/journal.pone.0092299. eCollection 2014. PMID: 24695071; PMCID: PMC3977476.
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Reverse engineering the cancer metabolic network using flux analysis to understand drivers of human disease.

Metabolic engineering

Badur MG, Metallo CM.
PMID: 29199104
Metab Eng. 2018 Jan;45:95-108. doi: 10.1016/j.ymben.2017.11.013. Epub 2017 Dec 02.

Metabolic dysfunction has reemerged as an essential hallmark of tumorigenesis, and metabolic phenotypes are increasingly being integrated into pre-clinical models of disease. The complexity of these metabolic networks requires systems-level interrogation, and metabolic flux analysis (MFA) with stable isotope...

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Badur MG, Metallo CM. Reverse engineering the cancer metabolic network using flux analysis to understand drivers of human disease. Metab Eng. 2017;45:95-108doi: 10.1016/j.ymben.2017.11.013.
Badur, M. G., & Metallo, C. M. (2018). Reverse engineering the cancer metabolic network using flux analysis to understand drivers of human disease. Metabolic engineering, 4595-108. https://doi.org/10.1016/j.ymben.2017.11.013
Badur, Mehmet G, and Metallo, Christian M. "Reverse engineering the cancer metabolic network using flux analysis to understand drivers of human disease." Metabolic engineering vol. 45 (2018): 95-108. doi: https://doi.org/10.1016/j.ymben.2017.11.013
Badur MG, Metallo CM. Reverse engineering the cancer metabolic network using flux analysis to understand drivers of human disease. Metab Eng. 2018 Jan;45:95-108. doi: 10.1016/j.ymben.2017.11.013. Epub 2017 Dec 02. PMID: 29199104; PMCID: PMC5927620.
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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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Showing 1 to 12 of 110 entries