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

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


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