  We present a method for rejecting competing models from noisy time-course
data that does not rely on parameter inference. First we characterize ordinary
differential equation models in only measurable variables using differential
algebra elimination. Next we extract additional information from the given data
using Gaussian Process Regression (GPR) and then transform the differential
invariants. We develop a test using linear algebra and statistics to reject
transformed models with the given data in a parameter-free manner. This
algorithm exploits the information about transients that is encoded in the
model's structure. We demonstrate the power of this approach by discriminating
between different models from mathematical biology.
