Identifying signaling pathway architectures using model-driven experimental design

All cells rely on intracellular signaling pathways to respond to environmental cues. These pathwas are comprised of tiers of signaling molecules, such as enzymes or metabolites, that direct specific subcellular events. How information is transmitted throughout can be complex and can depend on many tightly-controlled non-linear interactions. In this work, we combine computational modeling and bench experimentation to understand and identify signaling mechanisms driving complex behavior. We apply “model-driven experimental design” in which we use competing models trained on our experimental data to predict new signaing behavior that we then use to validate the models with experimental results. Using this approach, we identified a novel positive feedback mechanism in the High Osmolarity Glycerol (HOG) pathway in yeast. We then use a similar model to compare the difference between activation and nuclear translocation behaviors of the key HOG pathway Mitogen-Activated Protein Kinase (MAPK). In contrast to the current literature, we find that these two dynamics are indeed distinct. We suspect that these dynamics are responsible for driving differential cytosolic and nuclear responses and propose computational and bench experiments for follow up. Finally, we discuss the importance of this work in both the contexts of understanding MAPK signaling regulation and using model-driven experimental design.Doctor of Philosoph