Systems Analysis of Signal Transduction Networks Through R-Based Programming

Signal transduction is where external signals stimulate several biochemical pathways and result in a cellular response. Anomalies within these pathways can greatly affect a cell’s function and can result in disease. Ap-proaching these anomalies from a systems approach means studying the cell as a whole rather than its con-stituents in isolation. Computer models have been used to study complex biological/biochemical processes, including signal transduction networks because of their ability to be simulated under thousands of environ-mental conditions, including diseased states, which can result in novel and improved drug targets. My re-search focuses on how single mutations with in a network affect it as a whole. The goal is to identify potential drug targets not previously considered. To accomplish this, I have written an R program that analyzes models simulated under healthy and hundreds of mutagenic conditions, where each component within the model is either permanently activated or inactivated to simulated pathogenic mutations. The power behind this pro-gram lays in its ability to rapidly analyze high activity areas of any model, specifically, components that are most influential or most often affected on the model. At this time, the fibroblast cell network has been ana-lyzed under growth, death, motility, and quiescence conditions. Results have indicated that if mutated perma-nently inactive, most influential nodes typically are embryonic lethal, while most influential and most often affected nodes when mutated active are unique to the model’s external conditions. The insights these results provide have the capability of new explorations in more effective drug therapies