The evolution of genome-scale models of cancer metabolism
Open accessAbstract
The importance of metabolism in cancer is becoming increasingly apparent with the identification of metabolic enzyme mutations and the growing awareness of the influence of metabolism on signaling, epigenetic markers, and transcription. However, the complexity of these processes has challenged our ability to make sense of the metabolic changes in cancer. Fortunately, constraint-based modeling, a systems biology approach, now enables one to study the entirety of cancer metabolism and simulate basic phenotypes. With the newness of this field, there has been a rapid evolution of both the scope of these models and their applications. Here we review the various constraint-based models built for cancer metabolism and how their predictions are shedding new light on basic cancer phenotypes, elucidating pathway differences between tumors, and dicovering putative anti-cancer targets. As the field continues to evolve, the scope of these genome-scale cancer models must expand beyond central metabolism to address questions related to the diverse processes contributing to tumor development and metastasis.
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Metabolic flux analysis, flux balance analysis, and metabolic pathway analysis are among the most popular tools in stoichiometric network analysis and are used extensively to study (cancer) metabolism.
“Metabolic flux analysis, flux balance analysis, and metabolic pathway analysis are among the most popular tools in stoichiometric network analysis and are used extensively to study (cancer) metabolism (284, 285).”
From Metabolic Flexibility as an Adaptation to Energy Resources and Requirements in Health and Disease (2018) · cites this paper as [285]
Genome-scale models of cancer metabolism can help comprehend the complexity of human cancer heterogeneity in relation to substrate preference and therefore expedite the discovery of putative anticancer targets.
“For instance, genome-scale models of cancer metabolism can help us comprehend the complexity of human cancer heterogeneity in relation to substrate preference and therefore expedite the discovery of putative anticancer targets (285).”
From Metabolic Flexibility as an Adaptation to Energy Resources and Requirements in Health and Disease (2018) · cites this paper as [285]
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Metabolic Flexibility as an Adaptation to Energy Resources and Requirements in Health and Disease
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Self-citation
- Authors
- Lewis, N. E., & Abdel-Haleem, A. M.
- Affiliations
- Brigham Young University (US)
- Corresponding author
- Lewis, Nathan E.
- ORCID
- Lewis, Nathan E.
- Venue
- Frontiers in Physiology
- Publisher
- Frontiers Media SA
- Published
- 2013
- Volume
- 4
- Pages
- 237-237
- ISSN
- 1664-042X (ISSN-L)
- PMC
- PMC3759783
- OpenAlex
- W2062581381
- Cited by
- 87
- References
- 70
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 7 · 2023: 4 · 2022: 9 · 2021: 11 · 2020: 5 · 2019: 10 · 2018: 4 · 2017: 11 · 2016: 8 · 2015: 9 · 2014: 3 · 2013: 1
- FWCI
- 0.66
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Frontiers in Physiology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Greater South Information System
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer, Hypoxia, and Metabolism
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- cancer metabolism, genome-scale metabolic models, constraint-based modeling, tumor heterogeneity, anticancer drug targets, tumor phenotype, metastasis, central carbon metabolism
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