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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.〔Wang, Edwin. ''Cancer Systems Biology''. Chapman & Hall, 2010〕〔Liu & Lauffenburger. ''Systems Biomedicine: Concepts and Perspectives''. Academic Press, 2009.〕 More explicitly, because cancer spans multiple biological, spatial and temporal scales, communication and feedback mechanisms across the scales create a highly complex dynamic system. The relationships between scales is not simple or necessarily direct, and sometimes become combinatorial, so that systems approaches are essential to evaluate these relationships quantitatively and qualitatively. 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. Ultimately, cancer properties at one scale, e.g., histology, are explained by properties at a scale below, e.g., cell behavior. Likewise, a higher scale, e.g., epidemiology, can encroach on a lower scale, e.g., genetics. The fundamental concept is that percolation of properties across scales must be measured and taken into account in order to fully understand etiology, progression and dynamics of cancer. The systems biology approach relies heavily on the successes of decades of reductionism, which has clarified the component parts and mechanistic principles of living organisms, as well as their key alterations in cancer, especially at the genetic/genomic scale, to deep detail. Basic researchers and clinicians have progressively recognized the complexity of cancer and of its interaction with the micro- and macro-environment, since putting together the components to provide a cohesive view of the disease has been challenging and hampered progress. Cancer Systems Biology transcends the “reductionist” approach to cancer that typically produces causative explanations focused on a single gene or mutation, with little emphasis on inter-scale relationships. 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 that shed light on mechanisms underlying the cancer process and predict response to intervention. == History == Cancer systems biology finds its roots in a number of events and realizations in biomedical research, as well as in technological advances. Historically cancer was identified, understood, and treated as a monolithic disease. It was seen as a “foreign” component that grew as a homogenous mass, and was to be best treated by excision. Besides the continued impact of surgical intervention, this simplistic view of cancer has drastically evolved. In parallel with the exploits of molecular biology, cancer research focused on the identification of critical oncogenes or tumor suppressor genes in the etiology of cancer. These breakthroughs revolutionized our understanding of molecular events driving cancer progression. Targeted therapy may be considered the current pinnacle of advances spawned by such insights. Despite these advances, many unresolved challenges remain, including the dearth of new treatment avenues for many cancer types, or the unexplained treatment failures and inevitable relapse in cancer types where targeted treatment exists. Such mismatch between clinical results and the massive amounts of data acquired by omics technology highlights the existence of basic gaps in our knowledge of cancer fundamentals. Cancer Systems Biology is steadily improving our ability to organize information on cancer, in order to fill these gaps. Key developments include: * The generation of comprehensive molecular datasets (genome, transcriptome, epigenomics, proteome, metabolome, etc.) * 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. Critical large-scale efforts are also underway to train a new workforce fluent in both the languages of biology and applied mathematics. At the translational level, Cancer Systems Biology should engender precision medicine application to cancer treatment. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Cancer systems biology」の詳細全文を読む スポンサード リンク
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