<unk>  encompasses the application of<unk><unk>  approaches to cancer research, in order to study the disease as a complex adaptive<unk> with emerging properties at multiple<unk> cal scales.<unk>  represents the application of<unk><unk>  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.<unk>  approaches, therefore, are based on the use of computational and mathematical methods to decipher the complexity in tumorigenesis as well as cancer heterogeneity.<unk>  encompasses concrete applications of<unk><unk>  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.<unk>  therefore adopts a holistic view of cancer aimed at integrating its many<unk> cal scales, including genetics, signaling networks, epigenetics, cellular behavior, mechanical properties, 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.<unk>  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 that shed light on mechanisms underlying the cancer process and predict response to intervention. Application of<unk>  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.
Cancer systems biology
Cancer research
Comparative oncology
HIV/AIDS research
Cellular neuroscience
Clinical engineering
Spinal cord injury research
Predictive medicine