Thanks for pledging and encouraging others to pledge! Full disclosure: I'm the coordinator for the project. I've been having a look through the discussions on your references and I'd offer the following thoughts:
I think Hanson's three part break down (Computing power, brain scanning, cell modeling) is useful and I agree that cell modeling is an important research investment that has not had enough focus, either academically or industrially. Better cell models is one of the technological advances that OpenWorm helps to address due to its approach to model a complete organism with such few cells.
I would add that none of these discussions seem to pick up on the additional benefits of cell modeling outside of the context of brain emulation, which include advances in complexity science in general, increased potential for tissue regeneration and repair, and better diagnostics and therapies for diseases. Remember, all living things have cells, so advanced cell modeling could give us a debugger and an editor for biology unlike anything we've ever seen.
In terms of funding open science via crowd funding as a differential technological development strategy, I would also point out that the results are held in a public commons (GitHub in our case) and this transparency and open access may be an important factor. Work like this is likely going to be done at some point, but if it isn't publicly funded then it is likely to be privately funded and also privately held, and may add to asymmetrical control over these technologies. Personally, I prefer power to be distributed as a bulwark against tyranny. The more of these technological advances are out in the open, I think, the less likely the power of them will be concentrated in the hands of the few and used improperly.
There is a good review of strategies for building computational models of neurons here:
I think you are right on. I would extend your comment a bit which is to say we are not just missing a methodological breakthrough, but we are not even really attempting to develop the methods necessary. The problem is not just scientific but also what is considered to be science that is worth funding.
Modeling lobster stomach ganglion work is going on at Brandeis and what they are finding is important: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2913134&tool=pmcentrez&rendertype=abstract
Given the results they are finding, and building on their methods it is not inappropriate to start thinking one level up to c. elegans
I would respectfully disagree with Dr. Hayworth.
I would challenge him to show a "well characterized and mapped out part of the mammalian brain" that has a fraction of the detail that is known in c. elegans already. Moreover, the prospect of building a simulation requires that you can constrain the inputs and the outputs to the simulation. While this is a hard problem in c. elegans, its orders of magnitude more difficult to do well in a mammalian system.
There is still no retina connectome to work with (c. elegans has it). There are debates about cell types in retina (c. elegans has unique names for all cells). The gene expression maps of retina are not registered into a common space (c. elegans has that). The ability to do calcium imaging in retina is expensive (orders of magnitude easier in c. elegans). Genetic manipulation in mouse retina is expensive and takes months to produce specific mutants (you can feed c. elegans RNAi and make a mutant immediately).
There are methods now, along the lines of GFP (http://en.wikipedia.org/wiki/Green_fluorescent_protein) to "read the signs of synapses". There is just very little funding interest from Government funding agencies to apply them to c. elegans. David Hall is one of the few who is pushing this kind of mapping work in c. elegans forward.
What confuses this debate is that unless you study neuroscience deeply it is hard to tell the "known unknowns" apart from the "unknown unknowns". Biology isn't solved, so there are a lot of "unknown unknowns". Even with that, there are plenty of funded efforts in biology and neuroscience to do simulations. However, in c. elegans there are likely to be many fewer "unknown unknowns" because we have a lot more comprehensive data about its biology than we do for any other species.
Building simulations of biological systems helps to assemble what you know, but can also allow you to rationally work with the "known unknowns". The "signs of synapses" is an example of known unknowns -- we can fit those into a simulation engine without precise answers today and fill them in tomorrow. The statement that no one should start simulating the worm based on the current data has no merit when you consider that there is a lot to be done just to get to a framework that has the capacity to organize the "known unknowns" so that we can actually do something useful with them once they have them. More importantly, it makes the gaps a lot more clear. Right now, in the absence of any c. elegans simulations, data are being generated without a focused purpose of feeding into a global computational framework of understanding c. elegans behavior. I would argue that the field would be much better off collecting data in the context of adding to the gaps of a simulation, rather than everyone working at cross purposes.
That's why we are working on this challenge of building not just a c. elegans simulations, but a general framework for doing so, over at the Open Worm project (http://openworm.googlecode.com).
Hi all,
Glad there's excitement on this subject. I'm currently coordinating an open source project whose goal is to do a full simulation of the c. elegans (http://openworm.googlecode.com). More on that in a minute.
If you are surveying past c. elegans simulation efforts, you should be sure not to leave out the following:
A Biologically Accurate 3D Model of the Locomotion of Caenorhabditis Elegans, Roger Mailler, U. Tulsa http://j.mp/toeAR8
C. Elegans Locomotion: An integrated Approach -- Jordan Boyle, U. Leeds http://j.mp/fqKPEw
Back to Open Worm. We've just published a structural model of all 302 neurons (http://code.google.com/p/openworm/wiki/CElegansNeuroML) represented as NeuroML (http://neuroml.org). NeuroML allows the representation of multi-compartmental models of neurons (http://en.wikipedia.org/wiki/Biological_neuron_models#Compartmental_models). We are using this as a foundation to overlay the c. elegans connectivity graph and then add as much as we can find about the biophysics of the neurons. We believe this represents the first open source attempt to reverse-engineer the c. elegans connectome.
One of the comments mentioned Andrey Palyanov's mechanical model of the c. elegans. He is part of our group and is currently focused on moving to a soft-body simulation framework rather than the rigid one they created here: http://www.youtube.com/watch?feature=player_embedded&v=3uV3yTmUlgo Our first goal is to combine the neuronal model with this physical model in order to go beyond the biophysical realism that has already been done in previous studies. The physical model will then serve as the "read out" to make sure that the neurons are doing appropriate things.
Our roadmap for the project is available here: http://code.google.com/p/openworm/wiki/Roadmap
We have a mailing list here: http://groups.google.com/group/openworm
We have regular meetings on Google+ Hangout. If you want to help, we can surely find a way to include you. If you are interested, please let us know and we'll loop you in.
Cheers, Stephen
I did -- could you summarize what parts of this you take away to discourage you specifically? There's quite a few things you could be resonating with there.