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

Abstract 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)....

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Main Authors: Shoval Lagziel, Won Dong Lee, Tomer Shlomi
Format: Article
Language:English
Published: BMC 2019-07-01
Series:BMC Biology
Subjects:
Online Access:http://link.springer.com/article/10.1186/s12915-019-0669-x
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author Shoval Lagziel
Won Dong Lee
Tomer Shlomi
author_facet Shoval Lagziel
Won Dong Lee
Tomer Shlomi
author_sort Shoval Lagziel
collection DOAJ
description Abstract 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 doaj.art-25f499bfa48e4595b52348f8d1073ff12022-12-21T17:50:05ZengBMCBMC Biology1741-70072019-07-0117111110.1186/s12915-019-0669-xStudying metabolic flux adaptations in cancer through integrated experimental-computational approachesShoval Lagziel0Won Dong Lee1Tomer Shlomi2Faculty of Computer Science, TechnionFaculty of Biology, TechnionFaculty of Computer Science, TechnionAbstract 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.http://link.springer.com/article/10.1186/s12915-019-0669-xCancer metabolismMetabolic network modelingMetabolic flux analysisConstraint-based modelingCOBRAIsotope tracing
spellingShingle Shoval Lagziel
Won Dong Lee
Tomer Shlomi
Studying metabolic flux adaptations in cancer through integrated experimental-computational approaches
BMC Biology
Cancer metabolism
Metabolic network modeling
Metabolic flux analysis
Constraint-based modeling
COBRA
Isotope tracing
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 Cancer metabolism
Metabolic network modeling
Metabolic flux analysis
Constraint-based modeling
COBRA
Isotope tracing
url http://link.springer.com/article/10.1186/s12915-019-0669-x
work_keys_str_mv AT shovallagziel studyingmetabolicfluxadaptationsincancerthroughintegratedexperimentalcomputationalapproaches
AT wondonglee studyingmetabolicfluxadaptationsincancerthroughintegratedexperimentalcomputationalapproaches
AT tomershlomi studyingmetabolicfluxadaptationsincancerthroughintegratedexperimentalcomputationalapproaches