The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Cancer systems biology: a network modeling perspective
Open accessAbstract
Cancer is now appreciated as not only a highly heterogenous pathology with respect to cell type and tissue origin but also as a disease involving dysregulation of multiple pathways governing fundamental cell processes such as death, proliferation, differentiation and migration. Thus, the activities of molecular networks that execute metabolic or cytoskeletal processes, or regulate these by signal transduction, are altered in a complex manner by diverse genetic mutations in concert with the environmental context. A major challenge therefore is how to develop actionable understanding of this multivariate dysregulation, with respect both to how it arises from diverse genetic mutations and to how it may be ameliorated by prospective treatments. While high-throughput experimental platform technologies ranging from genomic sequencing to transcriptomic, proteomic and metabolomic profiling are now commonly used for molecular-level characterization of tumor cells and surrounding tissues, the resulting data sets defy straightforward intuitive interpretation with respect to potential therapeutic targets or the effects of perturbation. In this review article, we will discuss how significant advances can be obtained by applying computational modeling approaches to elucidate the pathways most critically involved in tumor formation and progression, impact of particular mutations on pathway operation, consequences of altered cell behavior in tissue environments and effects of molecular therapeutics.
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Cites
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Self-citationCancer: A Systems Biology disease
Hornberg, J. J., Bruggeman, F. J., Westerhoff, H. V., & Lankelma, J. (2006) · Biosystems · doi:10.1016/j.biosystems.2005.05.014 · Cited by 381
A Logical Model Provides Insights into T Cell Receptor Signaling
Saez-Rodriguez, J., Simeoni, L., Lindquist, J. A., Hemenway, R., Bommhardt, U., Arndt, B., Haus, U.-U., Weismantel, R., Gilles, E. D., Klamt, S., et al. (2007) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0030163 · Cited by 352
Systems-level interactions between insulin–EGF networks amplify mitogenic signaling
Borisov, N., Aksamitiene, E., Kiyatkin, A., Legewie, S., Berkhout, J., Maiwald, T., Kaimachnikov, N. P., Timmer, J., Hoek, J. B., & Kholodenko, B. N. (2009) · Molecular Systems Biology · doi:10.1038/msb.2009.19 · Cited by 222
Distinct mechanisms act in concert to mediate cell cycle arrest
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Cited by
Oncogenic K‐Ras decouples glucose and glutamine metabolism to support cancer cell growth
Gaglio, D., Metallo, C. M., Gameiro, P. A., Hiller, K., Danna, L. S., Balestrieri, C., Alberghina, L., Stephanopoulos, G., & Chiaradonna, F. (2011) · Molecular Systems Biology · doi:10.1038/msb.2011.56 · Cited by 492
Flavonoids: Potential Wnt/beta-catenin signaling modulators in cancer
Amado, N. G., Fonseca, B. F., Cerqueira, D. M., Neto, V. M., & Abreu, J. G. (2011) · Life Sciences · doi:10.1016/j.lfs.2011.05.003 · Cited by 125
Friedrich, C. (2016) · CPT: Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12056 · Cited by 87
Exploiting Conformational Ensembles in Modeling Protein–Protein Interactions on the Proteome Scale
Kuzu, G., Gursoy, A., Nussinov, R., & Keskin, O. (2013) · Journal of Proteome Research · doi:10.1021/pr400006k · Cited by 47
Tumor stratification by a novel graph-regularized bi-clique finding algorithm
Ahmadi Adl, A., & Qian, X. (2015) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2015.02.010 · Cited by 3
Metadata
- Authors
- Kreeger, P. K., & Lauffenburger, D. A.
- Venue
- Carcinogenesis
- Publisher
- Oxford University Press (OUP)
- Published
- 2009-10-27
- Volume
- 31
- Issue
- 1
- Pages
- 2-8
- ISSN
- 0143-3334 (ISSN-L)
- 1460-2180
- OpenAlex
- W2106353254
- Cited by
- 368
- References
- 8
- FWCI
- 11.52
- Type
- journal article
- Language
- en
- Open access
- hybrid
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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.
