Using Networks to Understand Cancer Risk and Therapies

C.E. Credits: P.A.C.E. CE Florida CE
Speaker
  • Professor of Computational Biology and Bioinformatics, Chair of the Department of Biostatistics, Harvard University, Dana-Farber Cancer Institute
    BIOGRAPHY

Abstract

One of the central tenants of biology is that our genetics—our genotype—influences the physical characteristics we manifest—our phenotype. But with more than 25,000 human genes and more than 6,000,000 common genetic variants mapped in our genome, finding associations between our genotype and phenotype is an ongoing challenge. Indeed, genome-wide association studies have found thousands of small effect size genetic variants that are associated with phenotypic traits and disease. The simplest explanation is that these genetic variants work synergistically to help define phenotype and to regulate processes that are responsible for phenotypic state transitions. We will use gene expression and genetic data to explore gene regulatory networks, to study phenotypic state transitions, to analyze the connections between genotype, gene expression, and phenotyope, and to explore how cancer-risk SNPs exert an influence on the disease risk that extends beyond one locus. We will show how networks can lead us to new approaches to identifying therapies and therapeutic targets. 

Learning Objectives:

1. Discuss gene regulatory networks and tools for their inference that use biological constraints.
2. Explain how changes in network structure correlate with the sex of the subject and their clinical outcomes.


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