OCT 17, 2013 12:00 PM PDT

Embracing the Complexity of Cancer: Seeing the Forest and the Trees

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  • Director of Computational Sciences and Informatics program for Complex Adaptive Systems and Professor in the School of Life Sciences, Arizona State University
      Dr. Ken Buetow is a human genetics and genomics researcher who leverages computational tools to understand complex traits such as cancer, liver disease, and obesity. Dr. Buetow currently serves as Director of Computational Sciences and Informatics program for Complex Adaptive Systems at Arizona State University (CAS@ASU) and is a professor in the School of Life Sciences in ASU's College of Liberal Arts and Sciences. CAS@ASU applies systems approaches that leverage ASU's interdisciplinary research strengths to address complex global challenges. The Computational Sciences and Informatics program is developing and applying information technology to collect, connect, and enhance trans-disciplinary knowledge both within ASU and across the broader knowledge-generating ecosystems. CAS@ASU is creating a Next Generation Cyber Capability to address the challenges and opportunities afforded by "Big Data" and the emergence of 4th Paradigm Data Science. This capability brings state-of-the-art computational approaches to CAS@ASU's transdisciplinary, use-inspired research efforts Dr. Buetow previously served as the Director of the Center for Biomedical Informatics and Information Technology within the National Institutes of Health's National Cancer Institute (NCI). In that capacity he initiated and oversaw the NCI's efforts to connect the global cancer community through community-developed, standards-based, interoperable informatics capabilities that enable secure exchange and use of biomedical data. Buetow designed and built one of the largest biomedical computing efforts in the world. He was responsible for coordinating biomedical informatics and information technology at the NCI. The NCI center he led focused on speeding scientific discovery and facilitated translational research by coordinating, developing and deploying biomedical informatics systems, infrastructure, tools and data in support of NCI research initiatives.


    Personalized medicine is transforming biomedical research and healthcare service delivery. Disease definition, diagnosis, treatment, and prevention are being fundamentally altered by the capacity to routinely perform comprehensive molecular characterization. Nowhere is this change happening faster than in the field of cancer. Increasingly sophisticated technology provides the capacity to describe, in multiple molecular dimensions, the tumor and the individual in which it has developed. These technologies identify the millions of variants present in normal individuals and thousands of alterations that occur during the course of the disease process. This systems-wide molecular analysis of constitutional and somatic tissues has identified a complex cacophony of inherited and acquired variation. Coherence emerges from these data when evaluated using biologic networks as analytic frameworks. These networks account for the individual heterogeneity in underlying etiology as well as the diversity of events necessary to generate a complex phenotype such as cancer. Emerging collections of analytic approaches permit analysis using genome-wide data sets and established biologic networks as models. The generation of this unprecedented amount of data presents us with the challenge contextualizing that data and converting into actionable information. The integration and interpretation of this complex multidimensional information into the evidence necessary to support clinical care exceeds the raw human cognitive capacity. Information systems have the capacity to provide the needed tool to tackle this challenge to generate the necessary evidence to support the delivery of personalized medicine. Arizona State Universitys (ASU) Complex Adaptive Systems team is building such an Evidence Engine in its Next Generation Cyber Capability (NGCC). The ASU NGCC composed of networks, hardware, software, and people transforms Big Data to information and creates the evidence necessary to enable personalized medicine. Learning Objectives: *Understand that cancer displays the properties of a complex adaptive system. *Learn that the chaotic molecular patterns observed in cancer obtain coherence when examined in the context of biologic networks.

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