> It would seem that we have a ways to go before attempting this. We not
> only need the 3D shapes, we need to know the chemical affinities of
> each portion, and the mechanical properties. As a trivial first step,
> we'd need to know exactly how ribosomes perform protein synthesis.
> Very little is known about this at the mechanochemical level. They only
> obtained a detailed 3D map of the ribosome within the past few months.
> It appears that it may be something of a clockwork mechanism, with
> internal moving parts. I can't wait until we can really understand this
> molecular machine in detail.
We seem to have a confusion of sorts here, about what it means to simulate a cell to be able to understand it.
In the field of molecular modelling for instance, there is a different degree of simulation detail, known as the level of theory. Typically, at a deeper level of theory, you tend achieve a higher accuracy at a much higher associated computational cost. For instance, because of how current QM codes scale, it is computationally infeasible to model anything but the active site of the enzyme at that low level of theory. So current most advanced models are hybrid, using classical molecular dynamics/mechanics for the rest of the enzyme, feeding the geometry into the quantum treatment of the active enzyme core.
Frequently, it is possible to abstract a given system, by stepping back, tilting your head, squinting a bit, and discarding its aspects we currently don't need. An enzyme can be thus seen as a molecular contraption, processing freely diffusing species A in a given cell compartment into other species B. Of course it is more complicated than a mere Michaelis-Menten kinetics plot: enzyme's activity can be modulated by other means, it may be immobilized at a surface, the educts/products will be most likely be actively transported, but you got the general idea. No need at all to go back to the molecular dynamics level, or even (godforbid) formidable Herr Schroedinger.
Notice that it is sometimes might be necessary to look down to a deeper level of theory to calibrate a higher level model. For instance, one might not only use experimental data to obtain parameters for a MD system, but try to extract them from QM. Currently, afaik this cannot be done purely automatically. However, I am confident that such automatically learning forcefield codes are feasible in principle. In fact solving the Protein Folding Problem (PFP) will probably require such codes, since, as I already mentioned, our current forcefields are not accurate enough to meet the challenge.
Similiarly, we can consider the ribosome as a black box which takes different inputs to produce a given output: an amino acid string.
In a sense, most vanilla cells also don't do anything magical, they might thus be modellable as a mechanical system with a rich inner state, capable of sending and reacting to signals.
Of course there is a special class of cells of dear interest to us all:
Today the molecules, tomorrow cell assemblies?
> As we learn more about these protein machines I think this will add
> further interest in the prospects for nanotech. If proteins are just
> enzymes, with specially-shaped active sites, that is old-fashioned
> chemistry. But if they are mechanical devices, Drexler's gears and
> cables don't look so outlandish.
True. In fact I had my mechanosynthetic epiphany when I tried to understand as to why life does so much better than the organic synthesis in the lab. It is about control, and machine-phase chemistry is about as much control as it gets (Rejoiceth, you control freaks! For your Kingdom is at hand ;)