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  • Aug 03 10:31
    topazand starred openworm/ChannelWorm
  • Mar 31 2019 13:39
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Stephen Larson
@slarson
OIC so that's a subsequent step within the fitter framework!
Thanks for filling me in & sorry for my ignorance!
I think I got confused because you aren't writing them out into the repo
So that would be an option to add, potentially, right?
even though, hmm, looks like you are intending to do something like that here: https://github.com/openworm/ChannelWorm/blob/master/channelworm/fitter/examples/SLO-2.py#L277
Richard C Gerkin
@rgerkin
@VahidGh Looking at that code, are you just extracting channel parameters reported in the papers and then building NeuroML files with those parameters?
Vahid Ghayoomie
@VahidGh
@slarson, exactly :D
@rgerkin, Nope, I'm extracting the parameters from those figures
Richard C Gerkin
@rgerkin
Oh, but are they directly determinable from the figures or is there a modeling step there?
Things would be much easier if all data was reported in tables (in addition to figures).
Vahid Ghayoomie
@VahidGh
I'm getting the digitized data, then starting the optimization and testing different parameters, then storing the best model within a nml file
Richard C Gerkin
@rgerkin
"Best" in reproducing the figures from which you extracted the parameters, or some other figures?
Vahid Ghayoomie
@VahidGh
Yeah, there are some TODOs that was supposed to be completed by some "helping hands" :D
Richard C Gerkin
@rgerkin
@slarson @VahidGh Alright, I'm going to dive into an SLO-2 channel figure, maybe by analogy to the EGL-19 one I already did, and try to write a testing notebook for it.
Vahid Ghayoomie
@VahidGh
I'm really busy with the other milestone, and had some great progress, but lacking some super computer for the tasks such as grid searches I'm doing for feature selection made the work a bit slow. Universities are getting open by this week in Iran, and I hope I could improve the speed by having some powerful computers in our lab
Stephen Larson
@slarson
@rgerkin ok awesome!
Vahid Ghayoomie
@VahidGh
I'm trying to find some relationship between biophysical and biochemical features (like these of ion channel proteins and their kinetics
So far I could reach an estimation of the kinetics with the MAE of about ~9 mV
And decreasing the error day by day by playing with those features
The Scikit learn is a cool package, and getting master on it :D
Richard C Gerkin
@rgerkin
@VahidGh Scikit-learn is pretty great!
Vahid Ghayoomie
@VahidGh
@rgerkin, @slarson, Oh I forgot to mention this great work, recently launched: http://icg.neurotheory.ox.ac.uk/
And thanks to Dr. Crook for the clue
I talked to the guys behind this
To see if we can include my idea
They have some similar idea based on clustering techniques
And their publication is under review
My plan is to find those parameters from protein sequences and then for each channel, find the most similar model, and then include my parameters into the channel model
Vahid Ghayoomie
@VahidGh
unfortunately they don't have models for C. elegans, but we can use my approach to find out the best model for the channels we don't have patch clamp data for
Stephen Larson
@slarson
@VahidGh Wow just been looking on that site
that seems extremely relevant!
Richard C Gerkin
@rgerkin
@VahidGh Oh wow, surprised I haven't seen that
Stephen Larson
@slarson
Me too!
Vahid Ghayoomie
@VahidGh
The first version launched just a month ago
@rgerkin, @pgleeson, is there any script to convert a NEURON model to the NeuroML file?
Richard C Gerkin
@rgerkin
@VahidGh I think NEURON (the program) has NeuroML export. I don't know how well it works. Padraig would have the best word on this
Vahid Ghayoomie
@VahidGh
That would be great
An interesting part is that they've provided a REST API for accessing raw data
Richard C Gerkin
@rgerkin
@VahidGh You mean the ion channel site you just linked?
Vahid Ghayoomie
@VahidGh
yep
This way we can estimate each channel model by the predictor I'm working on, then using those APIs, find the most similar model for the ion channel of interest, and then convert it to NeuroML
Vahid Ghayoomie
@VahidGh
A problem with ion channel kinetics (especially gating) is that there could be two channels from the same family and subfamily but with different kinetics in different species
And vice versa for channels with different families but the same kinetics
So, we can not just rely on families
By combining two approaches, we can estimate gating kinetics from the protein and structural properties, and then find the other parameters (such as activation/inactivation and their powers) from similarity/family based approaches, and then having the best estimated model
Stephen Larson
@slarson
Cool idea
Richard C Gerkin
@rgerkin
Yes, I like it
@VahidGh How would you search by model similarity? Does the API let you do that?
Vahid Ghayoomie
@VahidGh
@rgerkin, using the browser we can limit the models we are interested in, and then using the APIs getting the raw data for each plot, and then by comparing with our estimations, we can find the best match
I also am waiting for their publication to see what are the features for the kinetics similarity , etc