This is a collaborative team between Georgia Tech and Rutgers, which consists of two statisticians and one biomedical engineer. It can serve as a role model on how rigorous statistical methods are used to tackle important problems in biology. The proposed research will provide a better understanding of a complex biological signaling process called cell adhesion, which can pave the way to future clinical interventions. The proposed statistical approaches are readily applicable to a variety of scientific disciplines and will have immediate impact on accelerating discoveries in numerous fields involving complex experiments. This research will facilitate a new mode of intellectual interaction between statistics and cell biology. Some outreach programs are proposed for educating the next generation of mathematical biologists and biometricians. The team is committed to creating a diverse environment in their laboratories in terms of race, gender and national origin. The research will also provide an excellent opportunity to recruit students from underrepresented groups to participate in projects at the interface between biology and statistics. Cells use their surface receptors to sense the environment by engaging ligands on neighboring cells or in the extracellular matrix. This research focuses on understanding how receptor-ligand engagement induces cellular response, which is critical to unraveling many disease pathologies and can provide the groundwork for clinical intervention. With a few exceptions, the mechanisms behind signaling initiation remain elusive for most receptors. The innovation of this project is in combining the single-molecule experiments with statistical modeling to extract new readouts required for understanding the complex signaling processes. New frameworks based on novel modifications to Gaussian process (GP) models and new regime-switching models are proposed to quantify the memory effect in receptor/ligand binding. To rigorously quantify effects of different putative triggering parameters on cell signaling, a new varying-coefficient Cox model is proposed. The proposed statistical models will be validated with experimental data from the lab and modified if warranted. The proposed studies are significant because the sophisticated statistical modeling will greatly empower the understanding of the impact of mechanical forces on two biologically important and clinically relevant receptors in the human body: the platelet glycoprotein Ib and the T cell receptor. From the statistical point of view, the proposed GP model for binary data provides an analogy to the standard GP models with interpolation property, which can potentially lead to further advances in spatial statistics. The new regime-switching models borrow strength across different time series, which can have significant impacts on longitudinal study. The new Cox model allows the effects of covariates to vary over time and incorporates the between subject variation. It can open up new avenues for studying problems in various fields involving survival or failure analysis, and energize both theoretical and applied research.
|Effective start/end date||8/1/17 → 7/31/21|
- National Science Foundation (NSF)
Data storage equipment