MAY 12, 2016 12:00 PM PDT
Learning Biology from Networks and their Structure
Presented at the Genetics and Genomics Virtual Event
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  • Postdoctoral Research Fellow, Dana Farber Cancer Institute/Harvard University
      John Platig received his PhD in Physics from the University of Maryland. His thesis focused on the applications of complex network methods to biological data sets, with an emphasis on understanding how errors in edge identification affect network properties. In conjunction with his physics training, he was a Cancer Research Training Fellow at the National Cancer Institute, working to identify potential therapeutic targets from a reconstructed gene regulatory network in Diffuse Large B Cell Lymphoma. In 2013 John started as postdoctoral fellow with John Quackenbush at the Dana-Farber Cancer Institute. He is currently working on network inference and clustering methods to better understand genetic and other factors that drive phenotypes.


    Network models are an invaluable tool for integrating multiple data types and for modeling interactions between biological elements. One common question that arises, however, is what to do with such a network beyond making a pretty picture. In this talk, I will describe two applications in which we have used network structure to explain features of biological systems. In the first, we construct bipartite eQTL networks from genotype and gene expression data collected by the GTEx consortium. In the second, we explore the response of gene regulatory networks in Mycobacterium tuberculosis to a targeted drug treatment. In both cases, we find that the network structural properties reflect constraints operating on each biological system, and that each network has its own unique, informative feature set. For example, in the eQTL networks, we find the global hubs are not very informative but degree is very useful in interpreting the tuberculosis regulatory network. I will also discuss statistical testing and validation of these networks using functional annotation and wet lab experiments.

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