Phenotype switching during TNFa signaling
Tumor necrosis factor alpha (TNFa), a pleiotropic cytokine capable of exhibiting pro-survival, apoptosis, or necrotic phenotypes, is implicated in several cancers. Signal flow leading to these multitude of context-specific responses is orchestrated by underlying molecular network consisting of nodes such as proteins, genes connected by interactions between them. This leads to a question as to what are the topological properties of the network that dictate the context-specific responses and how to modulate these to favour a desired phenotype. The goal of this project is to employ graph theory based clustering and Boolean dynamic modelling approaches to identify specific characteristics of the network that govern its responses under different conditions. The developed network-level model with be used to identify the node-cover and/or edge-cover that may permit phenotype switching, such as from pro-survival to apoptosis, a desired response in cancer cells. The project will involve computations on MATLAB and other computing platforms.