UConn coordinated a ton of work during the past two decades on mechanistic cell models. Mostly ODEs, PDEs, and stochastic ODEs. See The Virtual Cell at https://vcell.org.
It's interesting how high-throughput perturbation assays have led to data-driven whole cell models. But these are not yet good at making robust predictions.
Probably the future are hybrid neuro-symbolic models.
Yes, this. A lot of work in this field is missing from that timeline. Just circa 2010-2020, Les Loew's VCell 3D PDE approaches, Faeder et al.'s BioNetGen / ODE work, Luthey-Schulten Shulten's grid based cell models, the Pittsburgh supercomputing center's 3D monte-carlo MCell, the image-based deep learning models at the Allen Institute for Cell Science...
It's nice to see the idea of virtual cells make a comeback now, though the meaning seems to have shifted to transciptomics-based transformer / gpu-powered models (which have issues[0]), it's a fun field / problem, but I think it will make better progress if we take advantage of all the varied computational work that has come before.
[0] Benchmarking Transcriptomics Foundation Models for Perturbation Analysis : one PCA still rules them all https://arxiv.org/abs/2410.13956
oh that's interesting, I didn't come across these the way I was looking at it! thank you for informing us, I will read up on these and add it to the timeline!
What a strange web page. Scrolling is thoroughly broken.
I recently went to a two day workshop on whole cell modelling. I'm still trying to work out how much of the exercise is fantasy. I get that some of the chemistry is well enough understood to simulate from the ground up, but there's so much more to it.
The oddest thing to me is the level of satisfaction in being able to run the model. I would think the model has to be very very fast, because of all the work that needs to be done with it to fit it to data and fully understand its behavior.
Lol! Overriding basic scroll functionality on esoteric cell simulation software documentation pages is such a pointless gamble. Really calls the quality of the software itself into question.
It's interesting how high-throughput perturbation assays have led to data-driven whole cell models. But these are not yet good at making robust predictions.
Probably the future are hybrid neuro-symbolic models.