Could we just set a slightly more restrictive prior on F_PL?

[sonyahanson]

Tested the effect of setting F_PL using values taken from the mean after equilibration from a previous run of assaytools, and dF_PL=0.50, which was approximated from the standard deviation. Tested this for Src:Erlotinib and Src: Gefitinib.

F_PL_def = {'Src-Erlotinib-EF': 59601584903.0,
            'Src-Gefitinib-GH': 81835891154.8}

Similar to issue #106 we see an expected removal of long tails and better reproducibility compared to without setting the F_PL. Note here, we are still looking at a dPstated = 0.35 * inputs['Pstated'].

Without setting F_PL:

Setting F_PL and dF_PL:

Without setting F_PL:

Setting F_PL and dF_PL:

Also we no longer see any sort of correlation between F_PL and DeltaG:

Without setting F_PL:

Setting F_PL and dF_PL:

While this is nice, I'm worried this is maybe too restrictive? Maybe we can just have a slightly more informed prior instead of this (link):

F_PL_guess = (Fmax - Fmin) / min(Pstated.max(), Lstated.max())
model['F_PL'] = pymc.Uniform('F_PL', lower=0.0, upper=2*Fmax/min(Pstated.max(),Lstated.max()), value=F_PL_guess)

[jchodera]

Wow!

Can you show this to Nick? We might ask his thoughts on using an estimate of F_PL and its uncertainty from our spectra assay and/or a separate cuvette assay.