The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
Modeling cancer metabolism on a genome scale
Open accessAbstract
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 the recognition that altered tumor metabolism is one of the hallmarks of cancer. Deciphering the full scope and functional implications of the dysregulated metabolism in cancer requires both the advancement of a variety of omics measurements and the advancement of computational approaches for the analysis and contextualization of the accumulated data. Encouragingly, while the metabolic network is highly interconnected and complex, it is at the same time probably the best characterized cellular network. Following, this review discusses the challenges that genome-scale modeling of cancer metabolism has been facing. We survey several recent studies demonstrating the first strides that have been done, testifying to the value of this approach in portraying a network-level view of the cancer metabolism and in identifying novel drug targets and biomarkers. Finally, we outline a few new steps that may further advance this field.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Signatures of mutational processes in human cancer
Initiative, A. P. C. G., Alexandrov, L. B., ICGC Breast Cancer Consortium, ICGC MMML-Seq Consortium, PedBrain, I., Nik-Zainal, S., Wedge, D. C., Aparicio, S. A. J. R., Behjati, S., Biankin, A. V., et al. (2013) · Nature · doi:10.1038/nature12477 · Cited by 9,660
An Integrated Genomic Analysis of Human Glioblastoma Multiforme
Parsons, D. W., Jones, S., Zhang, X., Lin, J. C.-H., Leary, R. J., Angenendt, P., Mankoo, P., Carter, H., Siu, I.-M., Gallia, G. L., et al. (2008) · Science · doi:10.1126/science.1164382 · Cited by 5,857
Mutational heterogeneity in cancer and the search for new cancer-associated genes
Lawrence, M. S., Stojanov, P., Polak, P., Kryukov, G. V., Cibulskis, K., Sivachenko, A., Carter, S. L., Stewart, C., Mermel, C. H., Roberts, S. A., et al. (2013) · Nature · doi:10.1038/nature12213 · Cited by 5,461
Regulation of cancer cell metabolism
Cairns, R. A., Harris, I. S., & Mak, T. W. (2011) · Nature Reviews Cancer · doi:10.1038/nrc2981 · Cited by 4,744
Cancer-associated IDH1 mutations produce 2-hydroxyglutarate
Dang, L., White, D. W., Gross, S., Bennett, B. D., Bittinger, M. A., Driggers, E. M., Fantin, V. R., Jang, H. G., Jin, S., Keenan, M. C., et al. (2009) · Nature · doi:10.1038/nature08617 · Cited by 4,154
The Biology of Cancer: Metabolic Reprogramming Fuels Cell Growth and Proliferation
DeBerardinis, R. J., Lum, J. J., Hatzivassiliou, G., & Thompson, C. B. (2008) · Cell Metabolism · doi:10.1016/j.cmet.2007.10.002 · Cited by 4,088
What is flux balance analysis?
Orth, J. D., Thiele, I., & Palsson, B. Ø. (2010) · Nature Biotechnology · doi:10.1038/nbt.1614 · Cited by 3,903
Hopkins, A. L., & Groom, C. R. (2002) · Nature Reviews Drug Discovery · doi:10.1038/nrd892 · Cited by 3,305
Metabolic Reprogramming: A Cancer Hallmark Even Warburg Did Not Anticipate
Ward, P. S., & Thompson, C. B. (2012) · Cancer Cell · doi:10.1016/j.ccr.2012.02.014 · Cited by 3,233
Discovery and saturation analysis of cancer genes across 21 tumour types
Lawrence, M. S., Stojanov, P., Mermel, C. H., Robinson, J. T., Garraway, L. A., Golub, T. R., Meyerson, M., Gabriel, S. B., Lander, E. S., & Getz, G. (2014) · Nature · doi:10.1038/nature12912 · Cited by 3,096
Cited by
Eelen, G., de Zeeuw, P., Treps, L., Harjes, U., Wong, B. W., & Carmeliet, P. (2018) · Physiological Reviews · doi:10.1152/physrev.00001.2017 · Cited by 572
Nielsen, J. (2017) · Annual Review of Biochemistry · doi:10.1146/annurev-biochem-061516-044757 · Cited by 252
Applications of Genome-Scale Metabolic Models in Biotechnology and Systems Medicine
Zhang, C., & Hua, Q. (2016) · Frontiers in Physiology · doi:10.3389/fphys.2015.00413 · Cited by 150
Genome-scale models in human metabologenomics
Mardinoglu, A., & Palsson, B. Ø. (2025) · Nature Reviews Genetics · doi:10.1038/s41576-024-00768-0 · Cited by 54
Metadata
- Authors
- Yizhak, K., Chaneton, B., Gottlieb, E., & Ruppin, E.
- Venue
- Molecular Systems Biology
- Published
- 2015-06
- Volume
- 11
- Issue
- 6
- Pages
- 817-817
- ISSN
- 1744-4292 (ISSN-L)
- OpenAlex
- W2099291727
- Cited by
- 183
- References
- 43
- FWCI
- 9.01
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Modeling cancer metabolism on a genome scale. https://app.cochat.ai/papers/10.15252/msb.20145307
- The paper
- Yizhak, K., Chaneton, B., Gottlieb, E., & Ruppin, E. (2015). Modeling cancer metabolism on a genome scale. Molecular Systems Biology. https://doi.org/10.15252/msb.20145307
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
