Cancer systems biology encompasses the application of systems biology approaches to cancer research, in order to study the disease as a complex adaptive system with emerging properties at multiple biological scales.[1][2][3] Cancer systems biology represents the application of systems biology approaches to the analysis of how the intracellular networks of normal cells are perturbed during carcinogenesis to develop effective predictive models that can assist scientists and clinicians in the validations of new therapies and drugs. Tumours are characterized by genomic and epigenetic instability that alters the functions of many different molecules and networks in a single cell as well as altering the interactions with the local environment. Cancer systems biology approaches, therefore, are based on the use of computational and mathematical methods to decipher the complexity in tumorigenesis as well as cancer heterogeneity.
[4]
Cancer systems biology encompasses concrete applications of systems biology approaches to cancer research, notably (a) the need for better methods to distill insights from large-scale networks, (b) the importance of integrating multiple data types in constructing more realistic models, (c) challenges in translating insights about tumorigenic mechanisms into therapeutic interventions, and (d) the role of the tumor microenvironment, at the physical, cellular, and molecular levels.[5] Cancer systems biology therefore adopts a holistic view of cancer[6] aimed at integrating its many biological scales, including genetics, signaling networks,[7] epigenetics,[8] cellular behavior, mechanical properties,[9] histology, clinical manifestations and epidemiology. Ultimately, cancer properties at one scale, e.g., histology, are explained by properties at a scale below, e.g., cell behavior.
Cancer systems biology merges traditional basic and clinical cancer research with “exact” sciences, such as applied mathematics, engineering, and physics. It incorporates a spectrum of “omics” technologies (genomics, proteomics, epigenomics, etc.) and molecular imaging, to generate computational algorithms and quantitative models[10] that shed light on mechanisms underlying the cancer process and predict response to intervention. Application of cancer systems biology include but are not limited to- elucidating critical cellular and molecular networks underlying cancer risk, initiation and progression; thereby promoting an alternative viewpoint to the traditional reductionist approach which has typically focused on characterizing single molecular aberrations.
^Wang, Edwin. Cancer Systems Biology. Chapman & Hall, 2010
^Liu & Lauffenburger. Systems Biomedicine: Concepts and Perspectives. Academic Press, 2009.
^Barillot, Emmanuel; Calzone, Laurence; Hupe, Philippe; Vert, Jean-Philippe; Zinovyev, Andrei (2012). Computational Systems Biology of Cancer. Chapman & Hall/CRC Mathematical & Computational Biology. p. 461. ISBN 978-1439831441.
^Werner, HM; Mills, GB; Ram, PT (March 2014). "Cancer Systems Biology: a peek into the future of patient care?". Nature Reviews. Clinical Oncology. 11 (3): 167–76. doi:10.1038/nrclinonc.2014.6. PMC 4321721. PMID 24492837.
^Gentles, AJ; Gallahan, D (15 September 2011). "Systems biology: confronting the complexity of cancer". Cancer Research. 71 (18): 5961–4. doi:10.1158/0008-5472.CAN-11-1569. PMC 3174325. PMID 21896642.
^Kreeger, PK; Lauffenburger (2010). "Cancer systems biology: A network modeling perspective". Carcinogenesis. 31 (1): 2–8. doi:10.1093/carcin/bgp261. PMC 2802670. PMID 19861649.
^Huang, YW; Kuo, Stoner; Huang, Wang (2011). "An overview of epigenetics and chemoprevention". FEBS Lett. 585 (13): 2129–2136. doi:10.1016/j.febslet.2010.11.002. PMC 3071863. PMID 21056563.
^Spill, Fabian; Bakal, Chris; Mak, Michael (2018). "Mechanical and Systems Biology of Cancer". Computational and Structural Biotechnology Journal. 16: 237–245. arXiv:1807.08990. Bibcode:2018arXiv180708990S. doi:10.1016/j.csbj.2018.07.002. PMC 6077126. PMID 30105089.
^Lewis, NE; Abdel-Haleem, AM (2013). "The evolution of genome-scale models of cancer metabolism". Front. Physiol. 4: 237. doi:10.3389/fphys.2013.00237. PMC 3759783. PMID 24027532.
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