Combining the Power of Biobanks and System Biology Approaches for Better Infectious Disease Prevention and Treatment
Guest Speaker: Dr. Samira Asgari, Assistant Professor of Genetics and Genomic Sciences at the Icahn School of Medicine at Mount Sinai, NY
Moderated by Dr. Reza Rawassizadeh, Associate Prof of CS
This virtual event will be held on
Friday, November 15, 2024 at 10:00 AM EST
Abstract: The clinical outcome of any infectious disease can vary widely, ranging from asymptomatic cases to fatal outcomes, depending on a complex interplay between the pathogen, host, and environment. Traditionally, the study of infectious diseases has focused primarily on the pathogen, which has limited our understanding of the environmental and host-specific factors that influence disease susceptibility and severity. Addressing this gap is crucial for developing effective strategies and treatments for infectious disease control. In this talk, I will present our research on the role of human genetic factors and social determinants of health in infectious disease outcomes. I will also demonstrate how we can harness the power of large-scale biobanks and systems biology approaches to gain deeper insights into the complex mechanisms that determine the clinical outcomes of infectious diseases.
Bio: Dr. Asgari is an Assistant Professor of Genetics and Genomic Sciences at the Icahn School of Medicine at Mount Sinai, NY. She completed her M.Sc. in stem cell biology at the University of Tehran, Iran, her Ph.D. in human genomics of infectious diseases at the Lausanne Federal Institute of Technology, Switzerland, and her postdoctoral training in statistical genomics at Brigham and Women’s Hospital, USA. The overarching goal of the Asgari lab is to understand how human genetic, demographic, and environmental diversity translates to phenotypic diversity in the immune system and how these variations impact the clinical outcomes of infectious diseases. The lab achieves this goal by integrating electronic health records, multi-omics datasets, in vitro and in silico experimental models, statistical inference, and machine learning approaches.