When dealing with large compartment models it can often be challenging to track how a small component of the model affects the overall system. This is an issue when trying to determine if a model is in its “simplest form” or if there are components that can be removed without significantly affecting the model’s behavior. I will present three tools that address this problem: a global sensitivity approach looking at parameters within the model, an optimization approach developed by Linda Petzold, and an approach we developed using global sensitivity upon “switch variables” that are introduced to the model.