The ability of cells to detect, process, and react specifically to various stress signals is a key property of life. To characterize these complex signal transduction pathways, quantitative experiments can be synergistically combined with predictive modeling and simulation. This project focuses on the network underlying the stress response of the p53 protein. The p53 protein is a key a biomolecule involved in cancer. This research will advance the understanding of the dynamics between the tumor suppressor p53 protein and the nucleic acid biomolecule, microRNA, that regulates gene expression. Combining experimental, mathematical, and computational approaches, the single-cell and cell-population behavior of these cellular networks will be explored. This project will enhance the educational infrastructure at the University of Texas at Dallas by integrating mentoring, teaching, and research. Through this interdisciplinary project, undergraduate and graduate students will be trained at the interface of mathematics, engineering, and molecular biology. As an outreach component, summer internships will be offered to local high school students. Members of underrepresented groups will be encouraged to explore their interests and build self-confidence in pursuing cross-disciplinary career paths.
The cellular stress response is crucial for maintaining homeostasis. The p53 tumor suppressor protein plays a critical role under cellular stress conditions. In response to a variety of stress signals, p53 transactivates genes to trigger cancer-preventing functions. A class of regulators, termed microRNAs, is directly associated with the p53-mediated cellular stress networks. While many of the molecular interactions among p53 and microRNAs have emerged, a systems-level understanding of the regulatory mechanism remains elusive. Furthermore, the significant heterogeneity across a cell population in p53 stress response poses an additional challenge. The single-cell and cell-population behavior of the microRNA-p53 networks will be studied by integrating a range of mathematical, engineering, and molecular biology techniques. A predictive mathematical model will be developed that accounts for the feedback loops mediated by microRNAs, and importantly, the diverse noise sources originating from intra- and inter-cellular sources will be integrated and investigated. A range of experimental techniques will be applied to assay the p53 and microRNA activity at single-cell and cell-population levels. Experiments in human breast cancer epithelial cells will be used to test and inform model-driven hypotheses in an iterative manner.
