OCT 01, 2015 1:30 PM PDT

Data, Data, Everywhere - Using Complexity Analysis to find coherence in Cancer Big Data

Speaker
  • Director of Computational Sciences and Informatics program for Complex Adaptive Systems and Professor in the School of Life Sciences, Arizona State University
    Biography
      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.

    Abstract

    Multidimensional molecular characterization has led to a tsunami of cancer data. Precision Medicine assumes that new understanding and better interventions will flow from this “Big Data”. However, the cacophony of data has challenged many of the founding paradigms of cancer and when viewed through these simplifying lenses appears chaotic. Cancer has been observed to not only complicated, but also complex. Coherence emerges from the apparent disorder when the molecular cancer Big Data is examined through methods of complex adaptive systems. For example, complex phenotypes such as susceptibility to cancer and response to interventions have regular, reproducible patterns when the interaction between components is taken into account through molecular networks. Examination of molecular networks offers a means to capture gene-centric concepts and complex, emergent behavior. More specifically, in analysis of breast cancer and liver cancer data networks provide much stronger signals of disease susceptibility than individual constitutional variants or the expression of collections of individual genes. These analyses show emergent, higher-level association of gene variation.


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