• Systems Biology
  • Hofest├Ądt, R., Kolchanov, N. eds: Computational Systems Biology: German/Russian Network of Computational Systems Biology. (uni-bielefeld.de)
  • 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. (wikipedia.org)
  • 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. (wikipedia.org)
  • 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. (wikipedia.org)
  • 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, (b) 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. (wikipedia.org)
  • Cancer systems biology therefore adopts a holistic view of cancer aimed at integrating its many biological scales, including genetics, signaling networks, epigenetics, cellular behavior, histology, (pre)clinical manifestations and epidemiology. (wikipedia.org)
  • Cancer systems biology merges traditional basic and clinical cancer research with "exact" sciences, such as applied mathematics, engineering, and physics. (wikipedia.org)
  • Cancer systems biology finds its roots in a number of events and realizations in biomedical research, as well as in technological advances. (wikipedia.org)
  • Cancer Systems Biology is steadily improving our ability to organize information on cancer, in order to fill these gaps. (wikipedia.org)
  • The Cancer Genome Atlas data collection Computational algorithms to extract drivers of cancer progression from existing datasets Statistical and mechanistic modeling of signaling networks Quantitative modeling of cancer evolutionary processes Mathematical modeling of cancer cell population growth Mathematical modeling of cellular responses to therapeutic intervention Mathematical modeling of cancer metabolism The practice of Cancer Systems Biology requires close physical integration between scientists with diverse backgrounds. (wikipedia.org)
  • At the translational level, Cancer Systems Biology should engender precision medicine application to cancer treatment. (wikipedia.org)