Tuesday, November 12 2019
8:30am - 9:30am
Parker H. Petit Institute for Bioengineering and Bioscience, Room 1128
For more information:

Colly Mitchell, Petit Events Manager

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Breakfast Club Seminar Series - NEW Two-Speaker Format!


@ 8:30 A.M.

Sam Brown, Ph.D.
Associate Professor
School of Biological Sciences
Georgia Tech

"Conditional Strategies for Effective and Evolutionarily Robust Infection Control"

I will discuss recent theory and experimental work to address the antibiotic resistance crisis via treatment strategies that are conditioned on diagnostics. I will show that reversing the rise in AMR is possible, but requires diagnostic information during both point-of-care and periodic microbiome surveillance (e.g. as part of an annual health check). 

Microbial model systems offer unparalleled access to the molecular mechanisms and ecological forces that together shape evolution, making them powerful systems for addressing fundamental behavioural, ecological and evolutionary questions. In our work, these questions include – why cooperate? Why communicate? Why kill your host? At the same time, a continual lab challenge is to consider – what are the practical implications of our discoveries? Can they drive novel therapeutics or diagnostics? Do they point to unappreciated epidemiological or evolutionary risks? Can we develop sustainable, evolution-proof therapeutics?

@ 9:00 A.M.

Cassie Mitchell, Ph.D. @ 9:00 a.m.
Assistant Professor
Wallace H. Coulter Department of Biomedical Engineering
Georgia Tech and Emory University

"Literature Mining Strategies for Predictive Medicine"

This talk will focus on newer approaches and tools for text mining of biomedical relationships and concepts from the 28+ million PubMed publications.  A real case study will illustrate how literature mining using biomedical concept graphs can be used to derive new actionable insights for disease etiology, treatment discovery, clinical care support, and research prioritization.

Cassie Mitchell’s research goal centers around expediting clinical translation from bench to bedside using data-enabled prediction. Akin to data-based models used to forecast weather, Cassie’s research integrates disparate, multi-scalar experimental and clinical data sets to dynamically forecast disease.  Cassie is the principal investigator of the Laboratory for Pathology Dynamics, which uses a combination of big data, machine learning, biostatistics, and informatics-based techniques to identify complex disease etiology, predict new therapeutics, and optimize current interventions.  Cassie’s research has predominantly targeted neuropathology, but her research applications in predictive medicine expand across all clinical specialties, including cancer, pediatrics, and cardiovascular medicine.


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