Like, in highschool/undergraduate Chemistry classes, students sometimes calculate chemical-concentrations for [equilibrium reactions](https://en.wikipedia.org/wiki/Determination_of_equilibrium_c... ). This paper's basically at the same level.
Unlike highschool modeling, though, this paper:
1. Uses dynamic models, where things evolve over time (rather than everything being at-equilibrium).
2. Involves a large number of interacting systems (rather than just one system by itself).
3. Involves a lot more species, reactions, and complexity than one'd generally see in a classroom.
4. Involves spatially-variable concentrations, where chemicals have different concentrations at different points in space, rather than being the same throughout (well-mixed). (Actually, they did this both ways: they did the simpler well-mixed model without needing a GPU at all, then they used a GPU in their non-mixed models.)
Each one of those generalizations can make the problem, say, an order-of-magnitude more complex, if not more. Combined, they create a much larger model.
However, it's still largely based around that phase-level scale that students might be familiar with.
“ Because of the large variation in timescales and concentrations, developing a whole-cell model that treats metabolism, genetic information processes, and growth can, at the moment, only be achieved by hybrid stochastic and deterministic simulations. Kinetics of the essential metabolic network (Breuer et al., 2019) are handled deterministically via ordinary differential equations (ODEs), and the kinetics of the genetic information processes are handled with stochastic simulations.”
This reminds me of a description of how different levels of realism were chosen and combined to simulate a whole person in a Greg Egan science fiction book (Permutation City - I think). IIRC there is a scene where a character spends some time in the shower knowing that the partial differential equations for simulation of the water will be driving up the costs of running the simulation.
Edit: added the bit about Greg Egan.
It's also telling how far this is from something that could plausibly originate from a pre-biotic soup. There's enormous complexity here. Origin of life is very far from a solved problem.
I tried to calculate how many molecules of water can fit into a sphere with radius of 500nm and I got 17 billions. Doesn't look like a big number for modern hardware.
And if modeling billions of atoms is impossible, cannot we model separate reactions? Put several virtual proteins and RNAs near each other and see how they interact?
Modelling billions of atoms is possible, but not scientifically fruitful.