Abstract: Proteomics has witnessed rapid technological advances in the past decade and a half. Despite its obvious promise, proteomics still poses a considerable barrier of entry for the average life scientist, and the adoption of this powerful technology across different areas has been uneven. In this talk, I will present two rather distinct stories that have the same underlying goal, namely, to suggest a roadmap for proteomics to become truly ubiquitous in all life science research. The first is in computational method development. Proteomics is a prototypical "Big Data" field, but it may be surprising to some that despite the rapid accumulation of LC-MS/MS data in public repositories, for the most part all these data have not been meaningfully reused. This unfortunate situation can be traced to how we are used to analyze data in proteomics in isolation without prior knowledge, and how we store and organize the data. I will discuss our ongoing efforts to change this by organizing proteomics data by spectral similarity, and our vision for a simpler and more user-friendly way to analyze proteomics data.  The second is in the application of proteomics to an important problem in microbiology -- the fascinating but annoying ability of bacterial pathogens to survive antibiotic treatment.  Antibiotic tolerance, defined as the ability of a bacterial population to outlive lethal doses of antibiotics in a non-proliferating and non-specific way, is distinct from, and less understood, than antibiotic resistance. I will discuss our recent work in applying proteomics and adaptive laboratory evolution to map the "tolerome" (the set of genes and proteins that affect tolerance) in two organisms, Escherichia coli and Staphylococcus aureus, and some hard-learned lessons of how to reduce distraction when dealing with hundreds of differentially expressed proteins. In doing so, I hope to show an example of what proteomics can and cannot do, and how it enables one to make progress towards addressing open-ended biological questions. 

Bio: Prof. Henry Lam is currently Professor in the Department of Chemical and Biological Engineering in the Hong Kong University of Science and Technology (HKUST). He obtained his Ph. D. (Chemical Engineering) from Massachusetts Institute of Technology in 2005, and received postdoctoral training in the Aebersold group at the Institute for Systems Biology (ISB) in Seattle, Washington. At ISB, he participated in developing data analysis workflows in the then-nascent field of proteomics, some of which have become standard practice today. He has a longstanding interest in making the powerful technology of proteomics more reliable and more accessible to life scientists. Current research in his group focuses on method development in proteomics and metabolomics, particularly in algorithm design and data organization, and applications of proteomics in biological research, particularly in microbiology. 

~BME Host: Young Kim~

NOTE:Students registered for the seminar are expected to attend in-person. 

Zoom link: https://purdue-edu.zoom.us/j/7731446991?pwd=RHdkZTVnRkxTM3J3dnRvY1VLWTlYUT09