The Leiser lab is interested in the biology of aging, in order to both understand the fundamental processes that lead to deterioration in health over time and to mitigate this deterioration and thus improve health in our world. This research strategy allows us to answer questions about why organisms age, and at the same time to study and alter many diseases at once, instead of focusing on just one disease at a time. Because aging is complicated, our research involves multiple areas of data science, including significant -omics-based strategies (transcriptomics, metabolomics, and proteomics), metabolic modeling, and behavioral analyses. These approaches are interwoven with basic genetic and phenotypic approaches that define aging.
We have a project studying the role of environmental perception in influencing lifespan. This project shows that positive environmental cues, like the smell, taste, or feel of food, leads to internal signals that limit lifespan, even when food is scarce. The project is mapping the signals involved so that we might block the pathway to slow aging. A second project in the lab focuses on the bacterial microbiome, modifying the bacteria to make it resist stress, and testing how that affects the host organism. Thus far, we have found that resistant bacteria make resistant and long-lived hosts in the nematode E. coli model.
I graduated college from UC Davis with limited lab experience but a string desire to study the aging process. I subsequently attended U of M for my PhD studies, where I was trained in mammalian stress, metabolism, and aging by the highly respected scientist and now colleague, Dr. Richard Miller. After wpending 6 years as a fellow at the University of Washington, mostly studying the hypoxic response and aging in C. elegans, I returned to Michigan in 2016 to start my own lab, which remaines today.
I would like to train and mentor a group of highly talented scientists that will make more impact on science than I ever could.
I think the potential to identify complex patterns and detect signals that were once missed leaves the potential for learning from data already put there and producing more useful data on complex phenotypes in the future.
I am a huge sports fan, having once played college athletics. Despite this, I only learned how to swim at age 45.