  Systems biology models are useful models of complex biological systems that
may require a large amount of experimental data to fit each model's parameters
or to approximate a likelihood function. These models range from a few to
thousands of parameters depending on the complexity of the biological system
modeled, potentially making the task of fitting parameters to the model
difficult - especially when new experimental data cannot be gathered. We
demonstrate a method that uses structural biology predictions to augment
systems biology models to improve systems biology models' predictions without
having to gather more experimental data. Additionally, we show how systems
biology models' predictions can help evaluate novel structural biology
hypotheses, which may also be expensive or infeasible to validate.
