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NEW TWO-SPEAKER FORMAT for 2019-2020!
@ 8:30 A.M.
Steve Diggle, Ph.D.
School of Biological Sciences
"Diversity Shapes Community Function in Evolving Pseudomonas Aeruginosa Populations"
Bacteria communicate, cooperate and compete, resulting in a wide range of behaviors such as biofilm formation, chemical warfare (bacteriocins) and quorum sensing. Microbiologists have made huge strides forward, using molecular and genomic approaches, in determining ‘how’ certain behaviors function. Despite this, answers are still lacking to adaptive questions, such as ‘why’ do such behaviors evolve? How are they maintained in natural populations? What role do they play during infection?
We are interested in understanding microbial interactions and social behaviors, and the implications for virulence, disease and antimicrobial resistance. The main organism that we focus on is the antibiotic resistant superbug Pseudomonas aeruginosa. The CDC has identified P. aeruginosa as a 'serious threat' in healthcare settings. It is also the key pathogen in cystic fibrosis lungs and is commonly isolated from non-healing chronic wounds.
@ 9:00 A.M.
Cassie Mitchell, Ph.D. @ 9:00 a.m.
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.