Cells are bombarded with external signals, and respond based on the dynamics of their internal signaling networks. Genetic variability between species, individuals, and even tissue types can result in divergent responses to the same external signal: humans and mice may respond differently to a drug treatment, and certain cancer cells exhibit resistance to a chemotherapeutic agent while others are sensitive. By manipulating the underlying signalling networks using experimental and theoretical techniques, this project addresses the question of how cells from different species or individuals may respond in a dissimilar manner to the same external chemical signal. By demonstrating that directed perturbations of a cell's internal signaling network can lead to specific physiological changes, this research will improve medical research by suggesting improvements to the efficacy of therapeutic interventions and enhancements to animal models of human diseases.
Despite the importance of genetic variability in explaining disparate responses in varied cell types, very little is known about which genetic alterations are responsible. To address this knowledge gap, this project utilizes computational modeling and targeted experimentation on a well-characterized signaling network in nematodes of the Caenorhabditis genus. The work focuses on the relationship between the EGF, Wnt and Notch signaling, pathways that are known to be important in human development and diseases such as cancer. The project goal is to identify the directed perturbations of a cell's internal signaling network that can lead to specific physiological changes. The project will define a rational approach to evaluating networks and intentionally engineering perturbations to improve animal models of human disease, and to broaden the utility or otherwise enhance the efficacy of therapeutic agents. The project also supports training in interdisciplinary applications of mathematics and biology, with an emphasis on undergraduate students and students from underrepresented groups.
