Martin S. Zand, M.D., Ph.D.

My research is focused in two areas: Understanding how B cells respond to vaccines and organ transplants to produce antibodies, and using graph theory to understand how we can understand and improve population health and healthcare delivery. My groups use a similar core set of analytic methods in both areas, including high dimensional clustering methods, graph theory, differential equation and stochastic branching process modeling.

My laboratory studies how B cells and plasma cells participate in the adaptive immune response, through antibody production, antigen presentation, and modulation of immune responses by other immune cells. Our goal is to understand how B cell differentiation and antibody production is regulated if we are to enhance B cell anti-viral and vaccine responses, or to prevent transplant rejection. We use innovative mathematical modeling and statistical analyses to gain insights from complex and large experimental data sets. Our experiments often involve daily analysis of gene expression and cellular phenotypes from single subjects over 5-10 days after vaccination. The scientific questions we are focused on include:

1. What is the timing of B cell differentiation and antibody secretion, and how can we alter the molecular and cellular events to improve vaccine responses?
2. What are the molecular gene expression signatures of a successful B cell vaccine response?
3. How can we use mathematical models to create individualized treatment protocols for kidney transplant patients with antibody mediated rejection or allosensitization?

My Health Informatics Group uses advanced data science analytics to understand how people make their way through the healthcare system, and how larger factors such as employment, mental health, community engagement, and socio-economic status contribute to population health. We use network theory to investigate connections between these factors, and turn those insights into population health based research initiatives that we refer to as the "living healthcare laboratory". We collaborate with groups in the Institute for Data Science, the University of Rochester Medical Center, and numerous community and population health groups within the greater Rochester community.