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Integrative analysis of human omics data using biomolecular networks.

Molecular bioSystems

Robinson JL, Nielsen J.
PMID: 27510223
Mol Biosyst. 2016 Oct 20;12(10):2953-64. doi: 10.1039/c6mb00476h. Epub 2016 Aug 11.

High-throughput '-omics' technologies have given rise to an increasing abundance of genome-scale data detailing human biology at the molecular level. Although these datasets have already made substantial contributions to a more comprehensive understanding of human physiology and diseases, their...

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Robinson JL, Nielsen J. Integrative analysis of human omics data using biomolecular networks. Mol Biosyst. 2016;12(10):2953-64doi: 10.1039/c6mb00476h.
Robinson, J. L., & Nielsen, J. (2016). Integrative analysis of human omics data using biomolecular networks. Molecular bioSystems, 12(10), 2953-64. https://doi.org/10.1039/c6mb00476h
Robinson, Jonathan L, and Nielsen, Jens. "Integrative analysis of human omics data using biomolecular networks." Molecular bioSystems vol. 12,10 (2016): 2953-64. doi: https://doi.org/10.1039/c6mb00476h
Robinson JL, Nielsen J. Integrative analysis of human omics data using biomolecular networks. Mol Biosyst. 2016 Oct 20;12(10):2953-64. doi: 10.1039/c6mb00476h. Epub 2016 Aug 11. PMID: 27510223.
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Computational identification of cellular networks and pathways.

Molecular bioSystems

Markowetz F, Troyanskaya OG.
PMID: 17579773
Mol Biosyst. 2007 Jul;3(7):478-82. doi: 10.1039/b617014p. Epub 2007 May 14.

In this article we highlight recent developments in computational functional genomics to identify networks of functionally related genes and proteins based on diverse sources of genomic data. Our specific focus is on statistical methods to identify genetic networks. We...

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Markowetz F, Troyanskaya OG. Computational identification of cellular networks and pathways. Mol Biosyst. 2007;3(7):478-82doi: 10.1039/b617014p.
Markowetz, F., & Troyanskaya, O. G. (2007). Computational identification of cellular networks and pathways. Molecular bioSystems, 3(7), 478-82. https://doi.org/10.1039/b617014p
Markowetz, Florian, and Troyanskaya, Olga G. "Computational identification of cellular networks and pathways." Molecular bioSystems vol. 3,7 (2007): 478-82. doi: https://doi.org/10.1039/b617014p
Markowetz F, Troyanskaya OG. Computational identification of cellular networks and pathways. Mol Biosyst. 2007 Jul;3(7):478-82. doi: 10.1039/b617014p. Epub 2007 May 14. PMID: 17579773.
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Systems biology meets synthetic biology: a case study of the metabolic effects of synthetic rewiring.

Molecular bioSystems

Noirel J, Ow SY, Sanguinetti G, Wright PC.
PMID: 19756311
Mol Biosyst. 2009 Oct;5(10):1214-23. doi: 10.1039/b904729h. Epub 2009 Jul 07.

We present a systems biology approach to study the global metabolic effects of the insertion of synthetic circuits in a cellular chassis. Our approach combines high-throughput proteomics with the MMG probabilistic tool, which integrates the data with the metabolic...

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Noirel J, Ow SY, Sanguinetti G, et al. Systems biology meets synthetic biology: a case study of the metabolic effects of synthetic rewiring. Mol Biosyst. 2009;5(10):1214-23doi: 10.1039/b904729h.
Noirel, J., Ow, S. Y., Sanguinetti, G., & Wright, P. C. (2009). Systems biology meets synthetic biology: a case study of the metabolic effects of synthetic rewiring. Molecular bioSystems, 5(10), 1214-23. https://doi.org/10.1039/b904729h
Noirel, Josselin, et al. "Systems biology meets synthetic biology: a case study of the metabolic effects of synthetic rewiring." Molecular bioSystems vol. 5,10 (2009): 1214-23. doi: https://doi.org/10.1039/b904729h
Noirel J, Ow SY, Sanguinetti G, Wright PC. Systems biology meets synthetic biology: a case study of the metabolic effects of synthetic rewiring. Mol Biosyst. 2009 Oct;5(10):1214-23. doi: 10.1039/b904729h. Epub 2009 Jul 07. PMID: 19756311.
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Searching for a needle in a stack of needles: challenges in metaproteomics data analysis.

Molecular bioSystems

Muth T, Benndorf D, Reichl U, Rapp E, Martens L.
PMID: 23238088
Mol Biosyst. 2013 Apr 05;9(4):578-85. doi: 10.1039/c2mb25415h.

In the past years the integral study of microbial communities of varying complexity has gained increasing research interest. Mass spectrometry-driven metaproteomics enables the analysis of such communities on the functional level, but this fledgling field still faces various technical...

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Muth T, Benndorf D, Reichl U, et al. Searching for a needle in a stack of needles: challenges in metaproteomics data analysis. Mol Biosyst. 2013;9(4):578-85doi: 10.1039/c2mb25415h.
Muth, T., Benndorf, D., Reichl, U., Rapp, E., & Martens, L. (2013). Searching for a needle in a stack of needles: challenges in metaproteomics data analysis. Molecular bioSystems, 9(4), 578-85. https://doi.org/10.1039/c2mb25415h
Muth, Thilo, et al. "Searching for a needle in a stack of needles: challenges in metaproteomics data analysis." Molecular bioSystems vol. 9,4 (2013): 578-85. doi: https://doi.org/10.1039/c2mb25415h
Muth T, Benndorf D, Reichl U, Rapp E, Martens L. Searching for a needle in a stack of needles: challenges in metaproteomics data analysis. Mol Biosyst. 2013 Apr 05;9(4):578-85. doi: 10.1039/c2mb25415h. PMID: 23238088.
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Early self-reproduction, the emergence of division mechanisms in protocells.

Molecular bioSystems

Murtas G.
PMID: 23232904
Mol Biosyst. 2013 Feb 02;9(2):195-204. doi: 10.1039/c2mb25375e. Epub 2012 Dec 11.

Synthetic Biology approaches are proposing model systems and providing experimental evidences that life can arise as spontaneous chemical self-assembly process where the ability to reproduce itself is an essential feature of the living system. The appearance of early cells...

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Murtas G. Early self-reproduction, the emergence of division mechanisms in protocells. Mol Biosyst. 2012;9(2):195-204doi: 10.1039/c2mb25375e.
Murtas, G. (2013). Early self-reproduction, the emergence of division mechanisms in protocells. Molecular bioSystems, 9(2), 195-204. https://doi.org/10.1039/c2mb25375e
Murtas, Giovanni. "Early self-reproduction, the emergence of division mechanisms in protocells." Molecular bioSystems vol. 9,2 (2013): 195-204. doi: https://doi.org/10.1039/c2mb25375e
Murtas G. Early self-reproduction, the emergence of division mechanisms in protocells. Mol Biosyst. 2013 Feb 02;9(2):195-204. doi: 10.1039/c2mb25375e. Epub 2012 Dec 11. PMID: 23232904.
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PLS/OPLS models in metabolomics: the impact of permutation of dataset rows on the K-fold cross-validation quality parameters.

Molecular bioSystems

Triba MN, Le Moyec L, Amathieu R, Goossens C, Bouchemal N, Nahon P, Rutledge DN, Savarin P.
PMID: 25382277
Mol Biosyst. 2015 Jan;11(1):13-9. doi: 10.1039/c4mb00414k. Epub 2014 Nov 10.

Among all the software packages available for discriminant analyses based on projection to latent structures (PLS-DA) or orthogonal projection to latent structures (OPLS-DA), SIMCA (Umetrics, Umeå Sweden) is the more widely used in the metabolomics field. SIMCA proposes many...

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Triba MN, Le Moyec L, Amathieu R, et al. PLS/OPLS models in metabolomics: the impact of permutation of dataset rows on the K-fold cross-validation quality parameters. Mol Biosyst. 2014;11(1):13-9doi: 10.1039/c4mb00414k.
Triba, M. N., Le Moyec, L., Amathieu, R., Goossens, C., Bouchemal, N., Nahon, P., Rutledge, D. N., & Savarin, P. (2015). PLS/OPLS models in metabolomics: the impact of permutation of dataset rows on the K-fold cross-validation quality parameters. Molecular bioSystems, 11(1), 13-9. https://doi.org/10.1039/c4mb00414k
Triba, Mohamed N, et al. "PLS/OPLS models in metabolomics: the impact of permutation of dataset rows on the K-fold cross-validation quality parameters." Molecular bioSystems vol. 11,1 (2015): 13-9. doi: https://doi.org/10.1039/c4mb00414k
Triba MN, Le Moyec L, Amathieu R, Goossens C, Bouchemal N, Nahon P, Rutledge DN, Savarin P. PLS/OPLS models in metabolomics: the impact of permutation of dataset rows on the K-fold cross-validation quality parameters. Mol Biosyst. 2015 Jan;11(1):13-9. doi: 10.1039/c4mb00414k. Epub 2014 Nov 10. PMID: 25382277.
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Correction: Elucidating time-dependent changes in the urinary metabolome of renal transplant patients by a combined .

Molecular bioSystems

Muhrez K, Nadal-Desbarats L, Halimi JM, Dieme B, Büchler M, Emond P, Blasco H, Le Guellec C.
PMID: 28102871
Mol Biosyst. 2017 Jan 31;13(2):432. doi: 10.1039/c7mb90005h.

Correction for 'Elucidating time-dependent changes in the urinary metabolome of renal transplant patients by a combined

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Muhrez K, Nadal-Desbarats L, Halimi JM, et al. Correction: Elucidating time-dependent changes in the urinary metabolome of renal transplant patients by a combined . Mol Biosyst. 2017;13(2):432doi: 10.1039/c7mb90005h.
Muhrez, K., Nadal-Desbarats, L., Halimi, J. M., Dieme, B., Büchler, M., Emond, P., Blasco, H., & Le Guellec, C. (2017). Correction: Elucidating time-dependent changes in the urinary metabolome of renal transplant patients by a combined . Molecular bioSystems, 13(2), 432. https://doi.org/10.1039/c7mb90005h
Muhrez, Kienana, et al. "Correction: Elucidating time-dependent changes in the urinary metabolome of renal transplant patients by a combined ." Molecular bioSystems vol. 13,2 (2017): 432. doi: https://doi.org/10.1039/c7mb90005h
Muhrez K, Nadal-Desbarats L, Halimi JM, Dieme B, Büchler M, Emond P, Blasco H, Le Guellec C. Correction: Elucidating time-dependent changes in the urinary metabolome of renal transplant patients by a combined . Mol Biosyst. 2017 Jan 31;13(2):432. doi: 10.1039/c7mb90005h. PMID: 28102871.
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Molecular BioSystems--celebrations, growth and development.

Molecular bioSystems

Boone C, Minhas H, Smith M.
PMID: 20024061
Mol Biosyst. 2010 Jan;6(1):13-5. doi: 10.1039/b923136f. Epub 2009 Dec 08.

No abstract available.

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Boone C, Minhas H, Smith M. Molecular BioSystems--celebrations, growth and development. Mol Biosyst. 2009;6(1):13-5doi: 10.1039/b923136f.
Boone, C., Minhas, H., & Smith, M. (2010). Molecular BioSystems--celebrations, growth and development. Molecular bioSystems, 6(1), 13-5. https://doi.org/10.1039/b923136f
Boone, Charlie, et al. "Molecular BioSystems--celebrations, growth and development." Molecular bioSystems vol. 6,1 (2010): 13-5. doi: https://doi.org/10.1039/b923136f
Boone C, Minhas H, Smith M. Molecular BioSystems--celebrations, growth and development. Mol Biosyst. 2010 Jan;6(1):13-5. doi: 10.1039/b923136f. Epub 2009 Dec 08. PMID: 20024061.
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LC-MS-based metabolomics.

Molecular bioSystems

Zhou B, Xiao JF, Tuli L, Ressom HW.
PMID: 22041788
Mol Biosyst. 2012 Feb;8(2):470-81. doi: 10.1039/c1mb05350g. Epub 2011 Nov 01.

Metabolomics aims at identification and quantitation of small molecules involved in metabolic reactions. LC-MS has enjoyed a growing popularity as the platform for metabolomic studies due to its high throughput, soft ionization, and good coverage of metabolites. The success...

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Zhou B, Xiao JF, Tuli L, et al. LC-MS-based metabolomics. Mol Biosyst. 2011;8(2):470-81doi: 10.1039/c1mb05350g.
Zhou, B., Xiao, J. F., Tuli, L., & Ressom, H. W. (2012). LC-MS-based metabolomics. Molecular bioSystems, 8(2), 470-81. https://doi.org/10.1039/c1mb05350g
Zhou, Bin, et al. "LC-MS-based metabolomics." Molecular bioSystems vol. 8,2 (2012): 470-81. doi: https://doi.org/10.1039/c1mb05350g
Zhou B, Xiao JF, Tuli L, Ressom HW. LC-MS-based metabolomics. Mol Biosyst. 2012 Feb;8(2):470-81. doi: 10.1039/c1mb05350g. Epub 2011 Nov 01. PMID: 22041788; PMCID: PMC3699692.
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Do protein-protein interaction databases identify moonlighting proteins?.

Molecular bioSystems

Gómez A, Hernández S, Amela I, Piñol J, Cedano J, Querol E.
PMID: 21677976
Mol Biosyst. 2011 Aug;7(8):2379-82. doi: 10.1039/c1mb05180f. Epub 2011 Jun 16.

One of the most striking results of the human (and mammalian) genomes is the low number of protein-coding genes. To-date, the main molecular mechanism to increase the number of different protein isoforms and functions is alternative splicing. However, a...

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Gómez A, Hernández S, Amela I, et al. Do protein-protein interaction databases identify moonlighting proteins?. Mol Biosyst. 2011;7(8):2379-82doi: 10.1039/c1mb05180f.
Gómez, A., Hernández, S., Amela, I., Piñol, J., Cedano, J., & Querol, E. (2011). Do protein-protein interaction databases identify moonlighting proteins?. Molecular bioSystems, 7(8), 2379-82. https://doi.org/10.1039/c1mb05180f
Gómez, Antonio, et al. "Do protein-protein interaction databases identify moonlighting proteins?." Molecular bioSystems vol. 7,8 (2011): 2379-82. doi: https://doi.org/10.1039/c1mb05180f
Gómez A, Hernández S, Amela I, Piñol J, Cedano J, Querol E. Do protein-protein interaction databases identify moonlighting proteins?. Mol Biosyst. 2011 Aug;7(8):2379-82. doi: 10.1039/c1mb05180f. Epub 2011 Jun 16. PMID: 21677976.
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Proliferating dynamic modeling in systems biology studies.

Molecular bioSystems

Blinov ML.
PMID: 21874191
Mol Biosyst. 2011 Oct;7(10):2735-6. doi: 10.1039/c1mb90033a. Epub 2011 Aug 26.

No abstract available.

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Blinov ML. Proliferating dynamic modeling in systems biology studies. Mol Biosyst. 2011;7(10):2735-6doi: 10.1039/c1mb90033a.
Blinov, M. L. (2011). Proliferating dynamic modeling in systems biology studies. Molecular bioSystems, 7(10), 2735-6. https://doi.org/10.1039/c1mb90033a
Blinov, Michael L. "Proliferating dynamic modeling in systems biology studies." Molecular bioSystems vol. 7,10 (2011): 2735-6. doi: https://doi.org/10.1039/c1mb90033a
Blinov ML. Proliferating dynamic modeling in systems biology studies. Mol Biosyst. 2011 Oct;7(10):2735-6. doi: 10.1039/c1mb90033a. Epub 2011 Aug 26. PMID: 21874191.
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In-cell protein dynamics.

Molecular bioSystems

Johnston JE.
PMID: 18198529
Mol Biosyst. 2007 Dec;3(12):862. doi: 10.1039/b716807c.

No abstract available.

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Johnston JE. In-cell protein dynamics. Mol Biosyst. 2007;3(12):862doi: 10.1039/b716807c.
Johnston, J. E. (2007). In-cell protein dynamics. Molecular bioSystems, 3(12), 862. https://doi.org/10.1039/b716807c
Johnston, Julie E Bryant. "In-cell protein dynamics." Molecular bioSystems vol. 3,12 (2007): 862. doi: https://doi.org/10.1039/b716807c
Johnston JE. In-cell protein dynamics. Mol Biosyst. 2007 Dec;3(12):862. doi: 10.1039/b716807c. PMID: 18198529.
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Showing 1 to 12 of 36 entries