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Tested the effect of changing dPstated = 0.35 * inputs['Pstated'] to dPstated = 0.15 * inputs['Pstated']. Interestingly, this removed the long tails for the Src-Erlotonib fits, changing them from this (dPstated = 0.35 * inputs['Pstated']):
to this (dPstated = 0.15 * inputs['Pstated']):
Similarly for the same number of iterations, running the same job three times gives more consistent answers with dPstated at 0.15, changing this (dPstated = 0.35 * inputs['Pstated']):
to this (dPstated = 0.15 * inputs['Pstated']):
The text was updated successfully, but these errors were encountered:
dPstate =0.15 can still be a realistic number. I wonder what would happen if you make uncertainty in protein concentration absurdly low. Something like dPstate =0.01 or 0.001. I wonder if fitting the model will get more difficult or will DeltaG uncertainties get narrower and narrower making the model look better (perhaps wrongly).
Tested the effect of changing
dPstated = 0.35 * inputs['Pstated']
todPstated = 0.15 * inputs['Pstated']
. Interestingly, this removed the long tails for the Src-Erlotonib fits, changing them from this (dPstated = 0.35 * inputs['Pstated']
):to this (
dPstated = 0.15 * inputs['Pstated']
):Similarly for the same number of iterations, running the same job three times gives more consistent answers with dPstated at 0.15, changing this (
dPstated = 0.35 * inputs['Pstated']
):to this (
dPstated = 0.15 * inputs['Pstated']
):The text was updated successfully, but these errors were encountered: