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Metabolic pathways for the whole community.

BMC genomics

Hanson NW, Konwar KM, Hawley AK, Altman T, Karp PD, Hallam SJ.
PMID: 25048541
BMC Genomics. 2014 Jul 22;15:619. doi: 10.1186/1471-2164-15-619.

BACKGROUND: A convergence of high-throughput sequencing and computational power is transforming biology into information science. Despite these technological advances, converting bits and bytes of sequence information into meaningful insights remains a challenging enterprise. Biological systems operate on multiple hierarchical...

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Hanson NW, Konwar KM, Hawley AK, et al. Metabolic pathways for the whole community. BMC Genomics. 2014;15:619doi: 10.1186/1471-2164-15-619.
Hanson, N. W., Konwar, K. M., Hawley, A. K., Altman, T., Karp, P. D., & Hallam, S. J. (2014). Metabolic pathways for the whole community. BMC genomics, 15619. https://doi.org/10.1186/1471-2164-15-619
Hanson, Niels W, et al. "Metabolic pathways for the whole community." BMC genomics vol. 15 (2014): 619. doi: https://doi.org/10.1186/1471-2164-15-619
Hanson NW, Konwar KM, Hawley AK, Altman T, Karp PD, Hallam SJ. Metabolic pathways for the whole community. BMC Genomics. 2014 Jul 22;15:619. doi: 10.1186/1471-2164-15-619. PMID: 25048541; PMCID: PMC4137073.
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Virtual pathway explorer (viPEr) and pathway enrichment analysis tool (PEANuT): creating and analyzing focus networks to identify cross-talk between molecules and pathways.

BMC genomics

Garmhausen M, Hofmann F, Senderov V, Thomas M, Kandel BA, Habermann BH.
PMID: 26467653
BMC Genomics. 2015 Oct 14;16:790. doi: 10.1186/s12864-015-2017-z.

BACKGROUND: Interpreting large-scale studies from microarrays or next-generation sequencing for further experimental testing remains one of the major challenges in quantitative biology. Combining expression with physical or genetic interaction data has already been successfully applied to enhance knowledge from...

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Garmhausen M, Hofmann F, Senderov V, et al. Virtual pathway explorer (viPEr) and pathway enrichment analysis tool (PEANuT): creating and analyzing focus networks to identify cross-talk between molecules and pathways. BMC Genomics. 2015;16:790doi: 10.1186/s12864-015-2017-z.
Garmhausen, M., Hofmann, F., Senderov, V., Thomas, M., Kandel, B. A., & Habermann, B. H. (2015). Virtual pathway explorer (viPEr) and pathway enrichment analysis tool (PEANuT): creating and analyzing focus networks to identify cross-talk between molecules and pathways. BMC genomics, 16790. https://doi.org/10.1186/s12864-015-2017-z
Garmhausen, Marius, et al. "Virtual pathway explorer (viPEr) and pathway enrichment analysis tool (PEANuT): creating and analyzing focus networks to identify cross-talk between molecules and pathways." BMC genomics vol. 16 (2015): 790. doi: https://doi.org/10.1186/s12864-015-2017-z
Garmhausen M, Hofmann F, Senderov V, Thomas M, Kandel BA, Habermann BH. Virtual pathway explorer (viPEr) and pathway enrichment analysis tool (PEANuT): creating and analyzing focus networks to identify cross-talk between molecules and pathways. BMC Genomics. 2015 Oct 14;16:790. doi: 10.1186/s12864-015-2017-z. PMID: 26467653; PMCID: PMC4606501.
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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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Two-stage dynamic deregulation of metabolism improves process robustness & scalability in engineered E. coli.

Metabolic engineering

Ye Z, Li S, Hennigan JN, Lebeau J, Moreb EA, Wolf J, Lynch MD.
PMID: 34600151
Metab Eng. 2021 Nov;68:106-118. doi: 10.1016/j.ymben.2021.09.009. Epub 2021 Sep 30.

We report that two-stage dynamic control improves bioprocess robustness as a result of the dynamic deregulation of central metabolism. Dynamic control is implemented during stationary phase using combinations of CRISPR interference and controlled proteolysis to reduce levels of central...

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Ye Z, Li S, Hennigan JN, et al. Two-stage dynamic deregulation of metabolism improves process robustness & scalability in engineered E. coli. Metab Eng. 2021;68:106-118doi: 10.1016/j.ymben.2021.09.009.
Ye, Z., Li, S., Hennigan, J. N., Lebeau, J., Moreb, E. A., Wolf, J., & Lynch, M. D. (2021). Two-stage dynamic deregulation of metabolism improves process robustness & scalability in engineered E. coli. Metabolic engineering, 68106-118. https://doi.org/10.1016/j.ymben.2021.09.009
Ye, Zhixia, et al. "Two-stage dynamic deregulation of metabolism improves process robustness & scalability in engineered E. coli." Metabolic engineering vol. 68 (2021): 106-118. doi: https://doi.org/10.1016/j.ymben.2021.09.009
Ye Z, Li S, Hennigan JN, Lebeau J, Moreb EA, Wolf J, Lynch MD. Two-stage dynamic deregulation of metabolism improves process robustness & scalability in engineered E. coli. Metab Eng. 2021 Nov;68:106-118. doi: 10.1016/j.ymben.2021.09.009. Epub 2021 Sep 30. PMID: 34600151.
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mmnet: An R Package for Metagenomics Systems Biology Analysis.

BioMed research international

Cao Y, Zheng X, Li F, Bo X.
PMID: 26421278
Biomed Res Int. 2015;2015:167249. doi: 10.1155/2015/167249. Epub 2015 Sep 03.

The human microbiome plays important roles in human health and disease. Previous microbiome studies focused mainly on single pure species function and overlooked the interactions in the complex communities on system-level. A metagenomic approach introduced recently integrates metagenomic data...

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Cao Y, Zheng X, Li F, et al. mmnet: An R Package for Metagenomics Systems Biology Analysis. Biomed Res Int. 2015;2015:167249doi: 10.1155/2015/167249.
Cao, Y., Zheng, X., Li, F., & Bo, X. (2015). mmnet: An R Package for Metagenomics Systems Biology Analysis. BioMed research international, 2015167249. https://doi.org/10.1155/2015/167249
Cao, Yang, et al. "mmnet: An R Package for Metagenomics Systems Biology Analysis." BioMed research international vol. 2015 (2015): 167249. doi: https://doi.org/10.1155/2015/167249
Cao Y, Zheng X, Li F, Bo X. mmnet: An R Package for Metagenomics Systems Biology Analysis. Biomed Res Int. 2015;2015:167249. doi: 10.1155/2015/167249. Epub 2015 Sep 03. PMID: 26421278; PMCID: PMC4573221.
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Guiding the Refinement of Biochemical Knowledgebases with Ensembles of Metabolic Networks and Machine Learning.

Cell systems

Medlock GL, Papin JA.
PMID: 31926940
Cell Syst. 2020 Jan 22;10(1):109-119.e3. doi: 10.1016/j.cels.2019.11.006. Epub 2020 Jan 08.

Mechanistic models explicitly represent hypothesized biological knowledge. As such, they offer more generalizability than data-driven models. However, identifying model curation efforts that improve performance for mechanistic models is nontrivial. Here, we develop a solution to this problem for genome-scale...

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Medlock GL, Papin JA. Guiding the Refinement of Biochemical Knowledgebases with Ensembles of Metabolic Networks and Machine Learning. Cell Syst. 2020;10(1):109-119.e3doi: 10.1016/j.cels.2019.11.006.
Medlock, G. L., & Papin, J. A. (2020). Guiding the Refinement of Biochemical Knowledgebases with Ensembles of Metabolic Networks and Machine Learning. Cell systems, 10(1), 109-119.e3. https://doi.org/10.1016/j.cels.2019.11.006
Medlock, Gregory L, and Papin, Jason A. "Guiding the Refinement of Biochemical Knowledgebases with Ensembles of Metabolic Networks and Machine Learning." Cell systems vol. 10,1 (2020): 109-119.e3. doi: https://doi.org/10.1016/j.cels.2019.11.006
Medlock GL, Papin JA. Guiding the Refinement of Biochemical Knowledgebases with Ensembles of Metabolic Networks and Machine Learning. Cell Syst. 2020 Jan 22;10(1):109-119.e3. doi: 10.1016/j.cels.2019.11.006. Epub 2020 Jan 08. PMID: 31926940; PMCID: PMC6975163.
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Assessing Escherichia coli metabolism models and simulation approaches in phenotype predictions: Validation against experimental data.

Biotechnology progress

Costa RS, Vinga S.
PMID: 30294889
Biotechnol Prog. 2018 Nov;34(6):1344-1354. doi: 10.1002/btpr.2700. Epub 2018 Oct 09.

Over the last years, several genome-scale metabolic models (GEMs) and kinetic models of Escherichia coli were published. Their predictive performance varies according to the evaluation metric considered, the computational simulation methods used, and the type/quality of experimental data available....

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Costa RS, Vinga S. Assessing Escherichia coli metabolism models and simulation approaches in phenotype predictions: Validation against experimental data. Biotechnol Prog. 2018;34(6):1344-1354doi: 10.1002/btpr.2700.
Costa, R. S., & Vinga, S. (2018). Assessing Escherichia coli metabolism models and simulation approaches in phenotype predictions: Validation against experimental data. Biotechnology progress, 34(6), 1344-1354. https://doi.org/10.1002/btpr.2700
Costa, Rafael S, and Vinga, Susana. "Assessing Escherichia coli metabolism models and simulation approaches in phenotype predictions: Validation against experimental data." Biotechnology progress vol. 34,6 (2018): 1344-1354. doi: https://doi.org/10.1002/btpr.2700
Costa RS, Vinga S. Assessing Escherichia coli metabolism models and simulation approaches in phenotype predictions: Validation against experimental data. Biotechnol Prog. 2018 Nov;34(6):1344-1354. doi: 10.1002/btpr.2700. Epub 2018 Oct 09. PMID: 30294889.
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Reconstructing genome-scale metabolic models with merlin.

Nucleic acids research

Dias O, Rocha M, Ferreira EC, Rocha I.
PMID: 25845595
Nucleic Acids Res. 2015 Apr 30;43(8):3899-910. doi: 10.1093/nar/gkv294. Epub 2015 Apr 06.

The Metabolic Models Reconstruction Using Genome-Scale Information (merlin) tool is a user-friendly Java application that aids the reconstruction of genome-scale metabolic models for any organism that has its genome sequenced. It performs the major steps of the reconstruction process,...

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Dias O, Rocha M, Ferreira EC, et al. Reconstructing genome-scale metabolic models with merlin. Nucleic Acids Res. 2015;43(8):3899-910doi: 10.1093/nar/gkv294.
Dias, O., Rocha, M., Ferreira, E. C., & Rocha, I. (2015). Reconstructing genome-scale metabolic models with merlin. Nucleic acids research, 43(8), 3899-910. https://doi.org/10.1093/nar/gkv294
Dias, Oscar, et al. "Reconstructing genome-scale metabolic models with merlin." Nucleic acids research vol. 43,8 (2015): 3899-910. doi: https://doi.org/10.1093/nar/gkv294
Dias O, Rocha M, Ferreira EC, Rocha I. Reconstructing genome-scale metabolic models with merlin. Nucleic Acids Res. 2015 Apr 30;43(8):3899-910. doi: 10.1093/nar/gkv294. Epub 2015 Apr 06. PMID: 25845595; PMCID: PMC4417185.
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Modeling cancer metabolism on a genome scale.

Molecular systems biology

Yizhak K, Chaneton B, Gottlieb E, Ruppin E.
PMID: 26130389
Mol Syst Biol. 2015 Jun 30;11(6):817. doi: 10.15252/msb.20145307.

Cancer cells have fundamentally altered cellular metabolism that is associated with their tumorigenicity and malignancy. In addition to the widely studied Warburg effect, several new key metabolic alterations in cancer have been established over the last decade, leading to...

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Yizhak K, Chaneton B, Gottlieb E, et al. Modeling cancer metabolism on a genome scale. Mol Syst Biol. 2015;11(6):817doi: 10.15252/msb.20145307.
Yizhak, K., Chaneton, B., Gottlieb, E., & Ruppin, E. (2015). Modeling cancer metabolism on a genome scale. Molecular systems biology, 11(6), 817. https://doi.org/10.15252/msb.20145307
Yizhak, Keren, et al. "Modeling cancer metabolism on a genome scale." Molecular systems biology vol. 11,6 (2015): 817. doi: https://doi.org/10.15252/msb.20145307
Yizhak K, Chaneton B, Gottlieb E, Ruppin E. Modeling cancer metabolism on a genome scale. Mol Syst Biol. 2015 Jun 30;11(6):817. doi: 10.15252/msb.20145307. PMID: 26130389; PMCID: PMC4501850.
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Constraint-based metabolic control analysis for rational strain engineering.

Metabolic engineering

Tsouka S, Ataman M, Hameri T, Miskovic L, Hatzimanikatis V.
PMID: 33895366
Metab Eng. 2021 Jul;66:191-203. doi: 10.1016/j.ymben.2021.03.003. Epub 2021 Apr 22.

The advancements in genome editing techniques over the past years have rekindled interest in rational metabolic engineering strategies. While Metabolic Control Analysis (MCA) is a well-established method for quantifying the effects of metabolic engineering interventions on flows in metabolic...

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Tsouka S, Ataman M, Hameri T, et al. Constraint-based metabolic control analysis for rational strain engineering. Metab Eng. 2021;66:191-203doi: 10.1016/j.ymben.2021.03.003.
Tsouka, S., Ataman, M., Hameri, T., Miskovic, L., & Hatzimanikatis, V. (2021). Constraint-based metabolic control analysis for rational strain engineering. Metabolic engineering, 66191-203. https://doi.org/10.1016/j.ymben.2021.03.003
Tsouka, Sophia, et al. "Constraint-based metabolic control analysis for rational strain engineering." Metabolic engineering vol. 66 (2021): 191-203. doi: https://doi.org/10.1016/j.ymben.2021.03.003
Tsouka S, Ataman M, Hameri T, Miskovic L, Hatzimanikatis V. Constraint-based metabolic control analysis for rational strain engineering. Metab Eng. 2021 Jul;66:191-203. doi: 10.1016/j.ymben.2021.03.003. Epub 2021 Apr 22. PMID: 33895366.
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CRISPR-based metabolic pathway engineering.

Metabolic engineering

Zhao D, Zhu X, Zhou H, Sun N, Wang T, Bi C, Zhang X.
PMID: 33152516
Metab Eng. 2021 Jan;63:148-159. doi: 10.1016/j.ymben.2020.10.004. Epub 2020 Nov 02.

A highly effective metabolic pathway is the key for an efficient cell factory. However, the engineered homologous or heterologous multi-gene pathway may be unbalanced, inefficient and causing the accumulation of potentially toxic intermediates. Therefore, pathways must be constructed optimally...

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Zhao D, Zhu X, Zhou H, et al. CRISPR-based metabolic pathway engineering. Metab Eng. 2020;63:148-159doi: 10.1016/j.ymben.2020.10.004.
Zhao, D., Zhu, X., Zhou, H., Sun, N., Wang, T., Bi, C., & Zhang, X. (2021). CRISPR-based metabolic pathway engineering. Metabolic engineering, 63148-159. https://doi.org/10.1016/j.ymben.2020.10.004
Zhao, Dongdong, et al. "CRISPR-based metabolic pathway engineering." Metabolic engineering vol. 63 (2021): 148-159. doi: https://doi.org/10.1016/j.ymben.2020.10.004
Zhao D, Zhu X, Zhou H, Sun N, Wang T, Bi C, Zhang X. CRISPR-based metabolic pathway engineering. Metab Eng. 2021 Jan;63:148-159. doi: 10.1016/j.ymben.2020.10.004. Epub 2020 Nov 02. PMID: 33152516.
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[Metabolic regulation in constructing microbial cell factories].

Sheng wu gong cheng xue bao = Chinese journal of biotechnology

Liu Y, Mu Q, Shi Y, Yu B.
PMID: 34085442
Sheng Wu Gong Cheng Xue Bao. 2021 May 25;37(5):1541-1563. doi: 10.13345/j.cjb.200688.

The regulation of the expression of genes involved in metabolic pathways, termed as metabolic regulation, is vital to construct efficient microbial cell factories. With the continuous breakthroughs in synthetic biology, the mining and artificial design of high-quality regulatory elements...

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Liu Y, Mu Q, Shi Y, et al. [Metabolic regulation in constructing microbial cell factories]. Sheng Wu Gong Cheng Xue Bao. 2021;37(5):1541-1563doi: 10.13345/j.cjb.200688.
Liu, Y., Mu, Q., Shi, Y., & Yu, B. (2021). [Metabolic regulation in constructing microbial cell factories]. Sheng wu gong cheng xue bao = Chinese journal of biotechnology, 37(5), 1541-1563. https://doi.org/10.13345/j.cjb.200688
Liu, Yang, et al. "[Metabolic regulation in constructing microbial cell factories]." Sheng wu gong cheng xue bao = Chinese journal of biotechnology vol. 37,5 (2021): 1541-1563. doi: https://doi.org/10.13345/j.cjb.200688
Liu Y, Mu Q, Shi Y, Yu B. [Metabolic regulation in constructing microbial cell factories]. Sheng Wu Gong Cheng Xue Bao. 2021 May 25;37(5):1541-1563. doi: 10.13345/j.cjb.200688. Chinese. PMID: 34085442.
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Showing 13 to 24 of 35 entries