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A comparison of aggregate p-value methods and multivariate statistics for self-contained tests of metabolic pathway analysis.

PloS one

Mitchell MW.
PMID: 25927705
PLoS One. 2015 Apr 30;10(4):e0125081. doi: 10.1371/journal.pone.0125081. eCollection 2015.

For pathway analysis of genomic data, the most common methods involve combining p-values from individual statistical tests. However, there are several multivariate statistical methods that can be used to test whether a pathway has changed. Because of the large...

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Mitchell MW. A comparison of aggregate p-value methods and multivariate statistics for self-contained tests of metabolic pathway analysis. PLoS One. 2015;10(4):e0125081doi: 10.1371/journal.pone.0125081.
Mitchell, M. W. (2015). A comparison of aggregate p-value methods and multivariate statistics for self-contained tests of metabolic pathway analysis. PloS one, 10(4), e0125081. https://doi.org/10.1371/journal.pone.0125081
Mitchell, Matthew W. "A comparison of aggregate p-value methods and multivariate statistics for self-contained tests of metabolic pathway analysis." PloS one vol. 10,4 (2015): e0125081. doi: https://doi.org/10.1371/journal.pone.0125081
Mitchell MW. A comparison of aggregate p-value methods and multivariate statistics for self-contained tests of metabolic pathway analysis. PLoS One. 2015 Apr 30;10(4):e0125081. doi: 10.1371/journal.pone.0125081. eCollection 2015. PMID: 25927705; PMCID: PMC4415974.
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Single-cell Digital Twins for Cancer Preclinical Investigation.

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

Filippo MD, Damiani C, Vanoni M, Maspero D, Mauri G, Alberghina L, Pescini D.
PMID: 31893381
Methods Mol Biol. 2020;2088:331-343. doi: 10.1007/978-1-0716-0159-4_15.

Laboratory models derived from clinical samples represent a solid platform in preclinical research for drug testing and investigation of disease mechanisms. The integration of these laboratory models with their digital counterparts (i.e., predictive mathematical models) allows to set up...

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Filippo MD, Damiani C, Vanoni M, et al. Single-cell Digital Twins for Cancer Preclinical Investigation. Methods Mol Biol. 2020;2088:331-343doi: 10.1007/978-1-0716-0159-4_15.
Filippo, M. D., Damiani, C., Vanoni, M., Maspero, D., Mauri, G., Alberghina, L., & Pescini, D. (2020). Single-cell Digital Twins for Cancer Preclinical Investigation. Methods in molecular biology (Clifton, N.J.), 2088331-343. https://doi.org/10.1007/978-1-0716-0159-4_15
Filippo, Marzia Di, et al. "Single-cell Digital Twins for Cancer Preclinical Investigation." Methods in molecular biology (Clifton, N.J.) vol. 2088 (2020): 331-343. doi: https://doi.org/10.1007/978-1-0716-0159-4_15
Filippo MD, Damiani C, Vanoni M, Maspero D, Mauri G, Alberghina L, Pescini D. Single-cell Digital Twins for Cancer Preclinical Investigation. Methods Mol Biol. 2020;2088:331-343. doi: 10.1007/978-1-0716-0159-4_15. PMID: 31893381.
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MetaNET--a web-accessible interactive platform for biological metabolic network analysis.

BMC systems biology

Narang P, Khan S, Hemrom AJ, Lynn AM.
PMID: 25779921
BMC Syst Biol. 2014;8:130. doi: 10.1186/s12918-014-0130-2. Epub 2014 Dec 05.

BACKGROUND: Metabolic reactions have been extensively studied and compiled over the last century. These have provided a theoretical base to implement models, simulations of which are used to identify drug targets and optimize metabolic throughput at a systemic level....

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Narang P, Khan S, Hemrom AJ. MetaNET--a web-accessible interactive platform for biological metabolic network analysis. BMC Syst Biol. 2014;8:130doi: 10.1186/s12918-014-0130-2.
Narang, P., Khan, S., Hemrom, A. J., & Lynn, A. M. (2014). MetaNET--a web-accessible interactive platform for biological metabolic network analysis. BMC systems biology, 8130. https://doi.org/10.1186/s12918-014-0130-2
Narang, Pankaj, et al. "MetaNET--a web-accessible interactive platform for biological metabolic network analysis." BMC systems biology vol. 8 (2014): 130. doi: https://doi.org/10.1186/s12918-014-0130-2
Narang P, Khan S, Hemrom AJ, Lynn AM. MetaNET--a web-accessible interactive platform for biological metabolic network analysis. BMC Syst Biol. 2014;8:130. doi: 10.1186/s12918-014-0130-2. Epub 2014 Dec 05. PMID: 25779921; PMCID: PMC4267457.
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Systems biology: current status and challenges.

Cellular and molecular life sciences : CMLS

Zupanic A, Bernstein HC, Heiland I.
PMID: 31932855
Cell Mol Life Sci. 2020 Feb;77(3):379-380. doi: 10.1007/s00018-019-03410-z. Epub 2020 Jan 13.

We put together a special issue on current approaches in systems biology with a focus on mathematical modeling of metabolic networks. Mathematical models have increasingly been used to unravel molecular mechanisms of complex dynamic biological processes. We here provide...

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Zupanic A, Bernstein HC, Heiland I. Systems biology: current status and challenges. Cell Mol Life Sci. 2020;77(3):379-380doi: 10.1007/s00018-019-03410-z.
Zupanic, A., Bernstein, H. C., & Heiland, I. (2020). Systems biology: current status and challenges. Cellular and molecular life sciences : CMLS, 77(3), 379-380. https://doi.org/10.1007/s00018-019-03410-z
Zupanic, Anze, et al. "Systems biology: current status and challenges." Cellular and molecular life sciences : CMLS vol. 77,3 (2020): 379-380. doi: https://doi.org/10.1007/s00018-019-03410-z
Zupanic A, Bernstein HC, Heiland I. Systems biology: current status and challenges. Cell Mol Life Sci. 2020 Feb;77(3):379-380. doi: 10.1007/s00018-019-03410-z. Epub 2020 Jan 13. PMID: 31932855.
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A note on the complexity of finding and enumerating elementary modes.

Bio Systems

Acuña V, Marchetti-Spaccamela A, Sagot MF, Stougie L.
PMID: 19962421
Biosystems. 2010 Mar;99(3):210-4. doi: 10.1016/j.biosystems.2009.11.004. Epub 2009 Dec 03.

In the context of the study into elementary modes of metabolic networks, we prove two complexity results. Enumerating elementary modes containing a specific reaction is hard in an enumeration complexity sense. The decision problem if there exists an elementary...

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Acuña V, Marchetti-Spaccamela A, Sagot MF, et al. A note on the complexity of finding and enumerating elementary modes. Biosystems. 2009;99(3):210-4doi: 10.1016/j.biosystems.2009.11.004.
Acuña, V., Marchetti-Spaccamela, A., Sagot, M. F., & Stougie, L. (2010). A note on the complexity of finding and enumerating elementary modes. Bio Systems, 99(3), 210-4. https://doi.org/10.1016/j.biosystems.2009.11.004
Acuña, Vicente, et al. "A note on the complexity of finding and enumerating elementary modes." Bio Systems vol. 99,3 (2010): 210-4. doi: https://doi.org/10.1016/j.biosystems.2009.11.004
Acuña V, Marchetti-Spaccamela A, Sagot MF, Stougie L. A note on the complexity of finding and enumerating elementary modes. Biosystems. 2010 Mar;99(3):210-4. doi: 10.1016/j.biosystems.2009.11.004. Epub 2009 Dec 03. PMID: 19962421.
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Analyzing methods for path mining with applications in metabolomics.

Gene

Tagore S, Chowdhury N, De RK.
PMID: 24230973
Gene. 2014 Jan 25;534(2):125-38. doi: 10.1016/j.gene.2013.10.056. Epub 2013 Nov 12.

Metabolomics is one of the key approaches of systems biology that consists of studying biochemical networks having a set of metabolites, enzymes, reactions and their interactions. As biological networks are very complex in nature, proper techniques and models need...

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Tagore S, Chowdhury N, De RK. Analyzing methods for path mining with applications in metabolomics. Gene. 2013;534(2):125-38doi: 10.1016/j.gene.2013.10.056.
Tagore, S., Chowdhury, N., & De, R. K. (2014). Analyzing methods for path mining with applications in metabolomics. Gene, 534(2), 125-38. https://doi.org/10.1016/j.gene.2013.10.056
Tagore, Somnath, et al. "Analyzing methods for path mining with applications in metabolomics." Gene vol. 534,2 (2014): 125-38. doi: https://doi.org/10.1016/j.gene.2013.10.056
Tagore S, Chowdhury N, De RK. Analyzing methods for path mining with applications in metabolomics. Gene. 2014 Jan 25;534(2):125-38. doi: 10.1016/j.gene.2013.10.056. Epub 2013 Nov 12. PMID: 24230973.
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Editorial for the directed issue: "Metabolic pathways in cancer".

The international journal of biochemistry & cell biology

Atzori L, Griffin JL, Scherer PE, Irminger-Finger I.
PMID: 21554975
Int J Biochem Cell Biol. 2011 Jul;43(7):948-9. doi: 10.1016/j.biocel.2011.04.018. Epub 2011 Apr 29.

No abstract available.

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Atzori L, Griffin JL, Scherer PE, et al. Editorial for the directed issue: "Metabolic pathways in cancer". Int J Biochem Cell Biol. 2011;43(7):948-9doi: 10.1016/j.biocel.2011.04.018.
Atzori, L., Griffin, J. L., Scherer, P. E., & Irminger-Finger, I. (2011). Editorial for the directed issue: "Metabolic pathways in cancer". The international journal of biochemistry & cell biology, 43(7), 948-9. https://doi.org/10.1016/j.biocel.2011.04.018
Atzori, Luigi, et al. "Editorial for the directed issue: "Metabolic pathways in cancer"." The international journal of biochemistry & cell biology vol. 43,7 (2011): 948-9. doi: https://doi.org/10.1016/j.biocel.2011.04.018
Atzori L, Griffin JL, Scherer PE, Irminger-Finger I. Editorial for the directed issue: "Metabolic pathways in cancer". Int J Biochem Cell Biol. 2011 Jul;43(7):948-9. doi: 10.1016/j.biocel.2011.04.018. Epub 2011 Apr 29. PMID: 21554975.
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From metabolic reactions to networks and pathways.

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

Arita M.
PMID: 22144150
Methods Mol Biol. 2012;804:93-106. doi: 10.1007/978-1-61779-361-5_6.

Enzymatic reactions form a hypergraph structure and their translation into a graph structure accompanies an information loss. This chapter introduces well-known topological transformations from metabolic reactions to a graph, and discusses their advantages and disadvantages. Also discussed is the...

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Arita M. From metabolic reactions to networks and pathways. Methods Mol Biol. 2012;804:93-106doi: 10.1007/978-1-61779-361-5_6.
Arita, M. (2012). From metabolic reactions to networks and pathways. Methods in molecular biology (Clifton, N.J.), 80493-106. https://doi.org/10.1007/978-1-61779-361-5_6
Arita, Masanori. "From metabolic reactions to networks and pathways." Methods in molecular biology (Clifton, N.J.) vol. 804 (2012): 93-106. doi: https://doi.org/10.1007/978-1-61779-361-5_6
Arita M. From metabolic reactions to networks and pathways. Methods Mol Biol. 2012;804:93-106. doi: 10.1007/978-1-61779-361-5_6. PMID: 22144150.
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CAMND: Comparative analysis of metabolic network decomposition based on previous and two new criteria, a web based application.

Bio Systems

Yassaee Meybodi F, Emdadi A, Rezvan A, Eslahchi C.
PMID: 31838143
Biosystems. 2020 Mar;189:104081. doi: 10.1016/j.biosystems.2019.104081. Epub 2019 Dec 13.

Metabolic networks can model the behavior of metabolism in the cell. Since analyzing the whole metabolic networks is not easy, network modulation is an important issue to be investigated. Decomposing metabolic networks is a strategy to obtain better insight...

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Yassaee Meybodi F, Emdadi A, Rezvan A, et al. CAMND: Comparative analysis of metabolic network decomposition based on previous and two new criteria, a web based application. Biosystems. 2019;189:104081doi: 10.1016/j.biosystems.2019.104081.
Yassaee Meybodi, F., Emdadi, A., Rezvan, A., & Eslahchi, C. (2020). CAMND: Comparative analysis of metabolic network decomposition based on previous and two new criteria, a web based application. Bio Systems, 189104081. https://doi.org/10.1016/j.biosystems.2019.104081
Yassaee Meybodi, Fatemeh, et al. "CAMND: Comparative analysis of metabolic network decomposition based on previous and two new criteria, a web based application." Bio Systems vol. 189 (2020): 104081. doi: https://doi.org/10.1016/j.biosystems.2019.104081
Yassaee Meybodi F, Emdadi A, Rezvan A, Eslahchi C. CAMND: Comparative analysis of metabolic network decomposition based on previous and two new criteria, a web based application. Biosystems. 2020 Mar;189:104081. doi: 10.1016/j.biosystems.2019.104081. Epub 2019 Dec 13. PMID: 31838143.
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Mathematical Modeling of Cellular Metabolism.

Recent results in cancer research. Fortschritte der Krebsforschung. Progres dans les recherches sur le cancer

Berndt N, Holzhütter HG.
PMID: 27557541
Recent Results Cancer Res. 2016;207:221-32. doi: 10.1007/978-3-319-42118-6_10.

Cellular metabolism basically consists of the conversion of chemical compounds taken up from the extracellular environment into energy (conserved in energy-rich bonds of organic phosphates) and a wide array of organic molecules serving as catalysts (enzymes), information carriers (nucleic...

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Berndt N, Holzhütter HG. Mathematical Modeling of Cellular Metabolism. Recent Results Cancer Res. 2016;207:221-32doi: 10.1007/978-3-319-42118-6_10.
Berndt, N., & Holzhütter, H. G. (2016). Mathematical Modeling of Cellular Metabolism. Recent results in cancer research. Fortschritte der Krebsforschung. Progres dans les recherches sur le cancer, 207221-32. https://doi.org/10.1007/978-3-319-42118-6_10
Berndt, Nikolaus, and Holzhütter, Hermann-Georg. "Mathematical Modeling of Cellular Metabolism." Recent results in cancer research. Fortschritte der Krebsforschung. Progres dans les recherches sur le cancer vol. 207 (2016): 221-32. doi: https://doi.org/10.1007/978-3-319-42118-6_10
Berndt N, Holzhütter HG. Mathematical Modeling of Cellular Metabolism. Recent Results Cancer Res. 2016;207:221-32. doi: 10.1007/978-3-319-42118-6_10. PMID: 27557541.
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Showing 1 to 10 of 10 entries