Using AICc distributions to assess goodness-of-fit and model selection

I have a couple of ordinary differential equation models that I'm trying to fit to time-dependent biological data ($y_n$). One model is more complex than then other as it has more free parameters.

I would like to test, which model is better overall for fitting the data and estimate which model is more 'likely' to be a better reflection of the dynamics (as much as models can reflect the dynamics in biological systems)

I could use a simple AIC (corrected as there's only ~25 points) comparing the two fits and calculate the Aikaike weights, but how this performs is dependent on the uncertainty in the data, as any effect could well be small and swamped by technical noise in the data.

To combat this I have generated bootstrap time-series samples using the Gaussian process-based bootstrap (as in http://www.ncbi.nlm.nih.gov/pubmed/19289448) giving ($\hat{y}^i_n$), and fitted the two models to each bootstrapped dataset. This then gives me a residual sum of squares (RSS) and an AIC(c) value for each bootstrap sample.

Can I use the distributions of the AIC(c)s as a measure of which model is more likely? The distributions are not independent, so an individual bootstrap-sample-based AIC(c) comparison could give me the information I want (scoring each model based on the number of times its AIC(c) value is the lower of the two), but I wonder if there's anything more formal.