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.
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
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.