Studying metabolic flux adaptations in cancer through integrated experimental-computational approaches

The study of tumorigenic rewiring of metabolic flux is at the heart of cancer metabolic research. Here, we review two widely used computational flux inference approaches: isotope tracing coupled with Metabolic Flux Analysis (13C-MFA) and COnstraint-Based Reconstruction and Analysis (COBRA). We descr...

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Autores principales: Lagziel, Shoval, Lee, Won Dong, Shlomi, Tomer
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Review
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6609376/
https://www.ncbi.nlm.nih.gov/pubmed/31272436
http://dx.doi.org/10.1186/s12915-019-0669-x
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author Lagziel, Shoval
Lee, Won Dong
Shlomi, Tomer
author_facet Lagziel, Shoval
Lee, Won Dong
Shlomi, Tomer
author_sort Lagziel, Shoval
collection PubMed
description The study of tumorigenic rewiring of metabolic flux is at the heart of cancer metabolic research. Here, we review two widely used computational flux inference approaches: isotope tracing coupled with Metabolic Flux Analysis (13C-MFA) and COnstraint-Based Reconstruction and Analysis (COBRA). We describe the applications of these complementary modeling techniques for studying metabolic adaptations in cancer cells due to genetic mutations and the tumor microenvironment, as well as for identifying novel enzymatic targets for anti-cancer drugs. We further highlight the advantages and limitations of COBRA and 13C-MFA and the main challenges ahead.
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spelling pubmed-66093762019-07-16 Studying metabolic flux adaptations in cancer through integrated experimental-computational approaches Lagziel, Shoval Lee, Won Dong Shlomi, Tomer BMC Biol Review The study of tumorigenic rewiring of metabolic flux is at the heart of cancer metabolic research. Here, we review two widely used computational flux inference approaches: isotope tracing coupled with Metabolic Flux Analysis (13C-MFA) and COnstraint-Based Reconstruction and Analysis (COBRA). We describe the applications of these complementary modeling techniques for studying metabolic adaptations in cancer cells due to genetic mutations and the tumor microenvironment, as well as for identifying novel enzymatic targets for anti-cancer drugs. We further highlight the advantages and limitations of COBRA and 13C-MFA and the main challenges ahead. BioMed Central 2019-07-04 /pmc/articles/PMC6609376/ /pubmed/31272436 http://dx.doi.org/10.1186/s12915-019-0669-x Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Review
Lagziel, Shoval
Lee, Won Dong
Shlomi, Tomer
Studying metabolic flux adaptations in cancer through integrated experimental-computational approaches
title Studying metabolic flux adaptations in cancer through integrated experimental-computational approaches
title_full Studying metabolic flux adaptations in cancer through integrated experimental-computational approaches
title_fullStr Studying metabolic flux adaptations in cancer through integrated experimental-computational approaches
title_full_unstemmed Studying metabolic flux adaptations in cancer through integrated experimental-computational approaches
title_short Studying metabolic flux adaptations in cancer through integrated experimental-computational approaches
title_sort studying metabolic flux adaptations in cancer through integrated experimental-computational approaches
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6609376/
https://www.ncbi.nlm.nih.gov/pubmed/31272436
http://dx.doi.org/10.1186/s12915-019-0669-x
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