MAY 09, 2018 10:30 AM PDT

Finding Coherence in Chaos by Embracing Evolution and Complexity

C.E. Credits: CEU
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

    Complex disease phenotypes - obesity, type II diabetes, and cancer challenge simple models in both evolution and biology.   Examination of molecular networks and their dynamic behavior offers a means to capture gene-centric concepts and complex, emergent behavior.    Examination of patterns of gene variation which appear chaotic and noisy when examined at the individual gene level show coherence when examined in biological network context.  More specifically, in liver cancer, networks provide much stronger signals of disease susceptibility than individual variants. These analyses show emergent, higher-level association of non-syntenic gene variation. When examining multiple obesity and type II diabetes data sets, consistent, recurrent collections of networks predict susceptibility where previous single gene analysis found no overlap.  More provocatively, common susceptibility pathways underpin obesity, type II diabetes, and liver cancer giving clues into disease progression. These interactions demonstrate the emergence of the underlying complex processes important in determining phenotype.  Evolutionary concepts and their related modeling promise to provide critical insight into biologic systems and disease.  In liver cancer, these concepts help explain paths of progression and suggest a novel candidate intervention.


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    MAY 09, 2018 10:30 AM PDT

    Finding Coherence in Chaos by Embracing Evolution and Complexity

    C.E. Credits: CEU

    Specialty

    Antibodies

    Virology

    Immunology

    Dna Sequencing

    Personalized Medicine

    Clinical Diagnostics

    Immunity

    Cancer Diagnostics

    Immunotherapy

    Bioinformatics

    Flow Cytometry

    Immuno-Oncology

    Gene Expression

    Dna

    Big Data

    Geography

    Europe100%

    Registration Source

    Website Visitors100%

    Job Title

    Student100%

    Organization

    Academic Institution100%


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