Changes

* a) # Find an initial diffusion tensor estimate (you can do this in relax by only using model m0). This requires all non-mobile residues and side chain spins to be excluded, and this can be problematic. See the [d'Auvergne and Gooley, 2008b] paper for an example of the catastrophic failure that this initial estimate can result in. Or the bacteriorhodopsin fragment of [Orekhov et al., 1999] where this complete failure was earlier demonstrated.

* b) # Optimise all of the model-free models from m0 to m9. This requires high precision optimisation, for a comparison of all the softwares see the [d'Auvergne and Gooley, 2008a] model-free optimisation paper. Only relax and Dasha implement the full range of model-free models, though the models m6, m7, and m8 cannot be used if only single field strength data is used (m6 is the original 2-time scale motion model of Clore et al., 1990).

* c) # Eliminate failed models (this is only available in relax). See the [d'Auvergne and Gooley, 2006] model elimination paper.

* d) # Select the best model-free model for each spin system. This again requires precision modern techniques, with the best being AIC model select (see the [d'Auvergne and Gooley, 2003] model-free model selection paper). If you are unaware that ANOVA statistics for model selection (hypothesis testing via chi-squared, F- and t-tests) was abandoned by the field of model selection over 100 years ago (a field which makes the NMR field look very, very small), then you should really look at that paper.

* e) # Optimise the global model. This is the diffusion tensor plus the model-free models for all spin systems.

* f) # Check for convergence (identical chi-squared values to a previous iteration, and not necessarily the last one). If no, then go back to b) and repeat. Note that the chi-squared value can go up significantly between iterations, but this is because the model is simplifying itself at a much faster rate by loosing parameters - it's Occam's razor at work. Again see the [d'Auvergne and Gooley, 2008b] paper for figures demonstrating this. The concept as to what is happening during this combined model-free optimisation and model selection algorithm is described in the [d'Auvergne and Gooley, 2007] paper. It can take up to 20 iterations or more to reach convergence, depending upon the quality of the relaxation data and the 3D structure or the system in study.

* g) # Once steps a-f have been completed for all global models (characterised by the spheroid, prolate spheroid, oblate spheroid, and ellipsoid diffusion tensors), then model selection between the different global models needs to be performed.

* h) # Monte Carlo simulations for error analysis must be performed at the end.

* i) # Elimination of failed Monte Carlo simulations is essential for keeping the errors to reasonable values for certain spin systems. This is also a relax-only feature (see the [d'Auvergne and Gooley, 2007] model elimination paper).