The Tilton Laboratory utilizes molecular and computational approaches to understand mechanisms of susceptibility from combined dietary and environmental factors in lung disease with the goal of identifying signatures and early biomarkers that are predictive of disease outcome. We are also interested in understanding mechanisms of toxicity from complex environmental mixtures compared to their individual components. We are currently developing a mechanism-based approach for the classification, prediction and integration of dose and time-dependent transcriptomic and proteomic data to model carcinogenic potential of chemical mixtures compared to traditional measurements (adducts, RPFs, etc.) using an in vitro 3D human lung tissue model. Our goal is to predict long-term effects of chemical carcinogens and environmental mixtures based on short-term markers. Another ongoing area of research for our lab is to understand the role of non-coding RNAs during carcinogenesis and whether miRNAs or their downstream targets can be used as early markers predictive of cancer outcome.
Application of omics-based technologies and multivariate data analysis to understand mechanisms of toxicity and identify novel biomarkers
Use of machine learning to predict carcinogenic risk for environmentally relevant mixtures of PAHs
Assessment of short-term markers and bioactivity profiles in primary human in vitro 3D bronchial epithelial model
Development of pathway and network-based approaches for data integration to improve mechanistic characterization of toxicity
Role of miRNAs in regulating pulmonary inflammation associated with combined environmental factors of diet-induced obesity and chemical exposure