Agreed, this would be much more exciting, but we decidedly do NOT have the data and do NOT have the processing power to do this in general. Nor will we for the forseeable future, even if Moore's law holds and we figure out quantum computers.

Still, thanks for asking: it's an important question that deserves a thorough answer. There are four separate "simulation domains" lying between simulations of subatomic particles and simulations of whole cells. In each domain, one might expect to answer a single targeted question through years of painstaking accumulation and application of expert knowledge.

(0) Let's start by ignoring the fifth domain (that I didn't count): simulating the behavior of the atomic nucleus. Nature has granted us a reprieve here in that biological activity is independent of what goes on at this scale.

(1) The first domain happens when you try to simulate an atom (not a molecule, a single dinky little atom) starting from a point-nucleus and an electron cloud. Time-stepping a Newtonian Mechanics simulation is typically between O(N) and O(N^2) in space and time. Unfortunately, newtonian mechanics and classical E&M can't tell you anything remotely connected to reality about an atom. The wheels come completely off: they predict that electrons radiate away their potential energy and crash into the nucleus, saying nothing about the "orbitals" we observe them stacking into. Quantum Mechanics is necessary, and the full equations are O(M^N) in both time and space, where M is the resolution along each axis of your simulation (every possible "universe" (set of particle locations) has a complex probability associated with it). There are decent approximations, but none of them suffice to answer all the relevant questions at once. Expert knowledge and experimental verification must be used to select the appropriate approximations.

(2) The second domain happens when you try to simulate a molecule from atoms. Nature grants us a huge reprieve in that we can safely ignore most atomic behavior: only the valence orbitals have interesting interactions, and nuclei are much heavier than electrons, so we can model the electrons' behavior independently. Still, the full problem is O(M^N) in time and space, and no single approximation works in all cases. A grad student with several years of training in quantum mechanics might spend months finding the appropriate set of assumptions to simplify and simulate a single chemical reaction.

(3) The third domain happens when you try to go from the scale of small molecules (dozens of atoms at most) to the scale of biological macromolecules (proteins, with tens of thousands to millions of atoms -- completely impractical even for heavily simplified QM models). Nature grants us a huge reprieve in that Netwonian mechanics becomes relevant at this scale. We can ignore most of the quantum mechanics most of the time and model it using struts, springs, and repulsive forces (at the price of ignoring chemical reactions, which we must separately account for if necessary). The difficulty here is the timescale: individual "wiggles" and collisions happen every femtosecond or so, while meaningful reactions often happen on the scale of milliseconds. If you let the width of a large pencil lead (1mm) stand for the time of a typical "wiggle," a single second of simulated time stretches from the Earth to the Sun. Cutting edge custom-ASIC supercomputers can simulate single small proteins for 1.5 milliseconds: http://en.wikipedia.org/wiki/Anton_(computer) . Last year's Noble Prize in chemistry went to people who spent their lives whittling away at the problem of integrating (2) with (3).

(4) The fourth domain happens when you try to go from the scale of a single molecule or macromolecule to the scale of a cell. Nature grants us a huge reprieve in that typical macromolecules usually only have a small number of functions and often only act in a statistical sense (they can be well characterized by their concentration and the concentration of their substrates in different compartments). Unfortunately, this data is very difficult to collect with certainty. How do you know that a given protein interacts with a given substrate in a given way? There are many ways to guess and many ways to measure, but for the most part we have to tackle proteins one-at-a-time. This is what biologists do, and after hundreds of years, billions of dollars per year, and countless underpaid PhDs plunking away at the task of characterizing individual pathways, there is enough of a picture to perform a very rough simulation of the whole thing by specifying a chemical kinetic ODE between various species in various compartments.

Note the pattern at each scale: A full simulation within the domain is impossible, but nature grants us a reprieve which lets experts answer specific questions with tremendous expenditure of effort and a quantity of data that grows dramatically in the number of the layer.

In a theoretical sense, we do have all the data we need: the only thing stopping us from going from 1-4 is computational power, however the gulf is so large it will almost certainly never be spanned by a single simulation. In a practical sense, where we want to ask high-level questions on the scale of (4) or higher, there is a great deal of data missing, since data plays the role of simplifying the lower layers of the simulation, or of allowing us to skip (4) altogether and ask a question on the scale of (3) or (2).