I’m not sure how to put a reasonable number on it, especially the simulation part, but C. elegans is a very common model organism. It’s maybe not as well-known as mice or rats, but probably in the top ten most-studied organisms. Here’s a nice review (https://www.nature.com/articles/nrg2105); a quick glance at the WormBook might also give you a sense for the breadth and depth of what’s been done[0]. http://www.wormbook.org/

Neurotransmission in C. elegans is unusual. They use a different set of neurotransmitters; this isn’t that odd—-insects also use a slightly different set than humans, and their role even flips in many animals (including mammals) during development. The weirder part is what those neurotransmitters do. In other animals, neurons produce stereotyped all-or-none “spikes” of electrical activity. Until quite recently, it was unclear whether C elegans neurons did too. This News and Views (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3951993/#R29) does a nice job describing plateau potentials and the reasons that C. elegans neurons might differ (namely, they’re very small). A few years later Cori Bargmann’s group discovered that the AWA neuron fires something more akin to a “classical” spike—-sometimes. It also uses calcium instead of sodium. https://www.cell.com/cell/fulltext/S0092-8674(18)31034-1

This might complicate simulations a little bit, but these differences are also understood pretty well, and the much smaller nervous system more than offsets them.

[0] I work at the polar opposite end of neuroscience—-large animal neurophys—-but I’ve always been a little jealous of how friendly and tight-knit the C elegans community seems. They have a lot of great open resources.

May I ask you on your opinion on non-faithful simulations (as a layman with only superficial understanding of the topic)? Would not some heuristic or discrete signaling enough for approximating the actual working? Are non-direct effects as you have written in a previous comment significantly modify the responses?

Simulations and modelling are great! They are a powerful way to generate and explore hypotheses.

Neuroscience has a lot of success this way. The properties of cones, the cells that detect colored light, were accurately modeled from behavioral experiments (e.g., people matching paint chips) in the 1800s, even though we didn't have the technology to measure them until the 1960s-1980s. The Hodgkin-Huxely model of action potential generation from the 1950s is still incredibly useful and predicted aspects of ion channel structure that took decades to confirm. David Robinson measured the physical forces produced by eye movements and used that to predict, and then reverse engineer, huge aspects of the "oculomotor plant". Real neurons have incredibly complicated behaviors, and yet artificial neural network models, where those are reduced down to a sigmoid or ReLu, have been very informative, first in the 1980s and then again today.

On the other hand, attempts to produce highly realistic simulations haven't really panned out. The Blue Brain Project has spent tons of time, money, and compute on very detailed simulations, but I think the consensus is that we have not learned a ton from these efforts. One of the most interesting outcomes (IMO) is actually the atlas that was built to build the model. There are probably many reasons for this difference, ranging from technical things like uncertainty propagation to very human expectations about what a model "should" be able to do.

In the specific context of C elegans, there's some data showing that diffusing peptides are essential for certain worm behaviors (e.g., Chen et al, 2013: https://www.sciencedirect.com/science/article/pii/S089662731...). The other mechanisms I mentioned are certainly there too. How much they matter is still up in the air: even for very simple organisms, we're still at the stage of figuring out what we don't know!

Thank you for the really informative answer! It was a pleasure to read!