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Optimization of multi-omic genome-scale models: Methodologies, hands-on tutorial, and perspectives

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter (peer-reviewed) › peer-review

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Optimization of multi-omic genome-scale models: Methodologies, hands-on tutorial, and perspectives. / Vijayakumar, S.; Conway, M.; Lió, P. et al.
Metabolic Network Reconstruction and Modeling: Methods and Protocols. ed. / Marco Fondi. New York, NY: Humana Press, 2018. (Methods in Molecular Biology).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter (peer-reviewed) › peer-review

Harvard

Vijayakumar, S, Conway, M, Lió, P & Angione, C 2018, Optimization of multi-omic genome-scale models: Methodologies, hands-on tutorial, and perspectives. in M Fondi (ed.), Metabolic Network Reconstruction and Modeling: Methods and Protocols. Methods in Molecular Biology, Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7528-0_18

APA

Vijayakumar, S., Conway, M., Lió, P., & Angione, C. (2018). Optimization of multi-omic genome-scale models: Methodologies, hands-on tutorial, and perspectives. In M. Fondi (Ed.), Metabolic Network Reconstruction and Modeling: Methods and Protocols (Methods in Molecular Biology). Humana Press. https://doi.org/10.1007/978-1-4939-7528-0_18

Vancouver

Vijayakumar S, Conway M, Lió P, Angione C. Optimization of multi-omic genome-scale models: Methodologies, hands-on tutorial, and perspectives. In Fondi M, editor, Metabolic Network Reconstruction and Modeling: Methods and Protocols. New York, NY: Humana Press. 2018. (Methods in Molecular Biology). Epub 2017 Dec 9. doi: 10.1007/978-1-4939-7528-0_18

Author

Vijayakumar, S. ; Conway, M. ; Lió, P. et al. / Optimization of multi-omic genome-scale models : Methodologies, hands-on tutorial, and perspectives. Metabolic Network Reconstruction and Modeling: Methods and Protocols. editor / Marco Fondi. New York, NY : Humana Press, 2018. (Methods in Molecular Biology).

Bibtex

@inbook{3e479fec61d44ba1b123ef7937f3652d,
title = "Optimization of multi-omic genome-scale models: Methodologies, hands-on tutorial, and perspectives",
abstract = "Genome-scale metabolic models are valuable tools for assessing themetabolic potential of living organisms. Being downstream of gene expression,metabolism is being increasingly used as an indicator of the phenotypic outcomefor drugs and therapies. We here present a review of the principal methods used for constraint-based modelling in systems biology, and explore how the integration of multi-omic data can be used to improve phenotypic predictions of genome-scale metabolic models. We believe that the large-scale comparison of the metabolic response of an organism to different environmental conditions will be an important challenge for genome-scale models. Therefore, within the context of multi-omic methods, we describe a tutorial for multi-objective optimisation using the metabolic and transcriptomics adaptation estimator (METRADE), implemented in MATLAB. METRADE uses microarray and codon usage data to model bacterial metabolic response to environmental conditions (e.g. antibiotics, temperatures, heat shock). Finally, we discuss key considerations for the integration of multi-omic networks into metabolic models, towards automatically extracting knowledge from such models.",
author = "S. Vijayakumar and M. Conway and P. Li{\'o} and C. Angione",
year = "2018",
doi = "10.1007/978-1-4939-7528-0_18",
language = "English",
isbn = "9781493975273",
series = "Methods in Molecular Biology",
publisher = "Humana Press",
editor = "Marco Fondi",
booktitle = "Metabolic Network Reconstruction and Modeling",

}

RIS

TY - CHAP

T1 - Optimization of multi-omic genome-scale models

T2 - Methodologies, hands-on tutorial, and perspectives

AU - Vijayakumar, S.

AU - Conway, M.

AU - Lió, P.

AU - Angione, C.

PY - 2018

Y1 - 2018

N2 - Genome-scale metabolic models are valuable tools for assessing themetabolic potential of living organisms. Being downstream of gene expression,metabolism is being increasingly used as an indicator of the phenotypic outcomefor drugs and therapies. We here present a review of the principal methods used for constraint-based modelling in systems biology, and explore how the integration of multi-omic data can be used to improve phenotypic predictions of genome-scale metabolic models. We believe that the large-scale comparison of the metabolic response of an organism to different environmental conditions will be an important challenge for genome-scale models. Therefore, within the context of multi-omic methods, we describe a tutorial for multi-objective optimisation using the metabolic and transcriptomics adaptation estimator (METRADE), implemented in MATLAB. METRADE uses microarray and codon usage data to model bacterial metabolic response to environmental conditions (e.g. antibiotics, temperatures, heat shock). Finally, we discuss key considerations for the integration of multi-omic networks into metabolic models, towards automatically extracting knowledge from such models.

AB - Genome-scale metabolic models are valuable tools for assessing themetabolic potential of living organisms. Being downstream of gene expression,metabolism is being increasingly used as an indicator of the phenotypic outcomefor drugs and therapies. We here present a review of the principal methods used for constraint-based modelling in systems biology, and explore how the integration of multi-omic data can be used to improve phenotypic predictions of genome-scale metabolic models. We believe that the large-scale comparison of the metabolic response of an organism to different environmental conditions will be an important challenge for genome-scale models. Therefore, within the context of multi-omic methods, we describe a tutorial for multi-objective optimisation using the metabolic and transcriptomics adaptation estimator (METRADE), implemented in MATLAB. METRADE uses microarray and codon usage data to model bacterial metabolic response to environmental conditions (e.g. antibiotics, temperatures, heat shock). Finally, we discuss key considerations for the integration of multi-omic networks into metabolic models, towards automatically extracting knowledge from such models.

U2 - 10.1007/978-1-4939-7528-0_18

DO - 10.1007/978-1-4939-7528-0_18

M3 - Chapter (peer-reviewed)

SN - 9781493975273

SN - 9781493985111

T3 - Methods in Molecular Biology

BT - Metabolic Network Reconstruction and Modeling

A2 - Fondi, Marco

PB - Humana Press

CY - New York, NY

ER -