In response to falenas108's "Ask an X" thread. I have a PhD in experimental particle physics; I'm currently working as a postdoc at the University of Cincinnati. Ask me anything, as the saying goes.
This is an experiment. There's nothing I like better than talking about what I do; but I usually find that even quite well-informed people don't know enough to ask questions sufficiently specific that I can answer any better than the next guy. What goes through most people's heads when they hear "particle physics" is, judging by experience, string theory. Well, I dunno nuffin' about string theory - at least not any more than the average layman who has read Brian Greene's book. (Admittedly, neither do string theorists.) I'm equally ignorant about quantum gravity, dark energy, quantum computing, and the Higgs boson - in other words, the big theory stuff that shows up in popular-science articles. For that sort of thing you want a theorist, and not just any theorist at that, but one who works specifically on that problem. On the other hand I'm reasonably well informed about production, decay, and mixing of the charm quark and charmed mesons, but who has heard of that? (Well, now you have.) I know a little about CP violation, a bit about detectors, something about reconstructing and simulating events, a fair amount about how we extract signal from background, and quite a lot about fitting distributions in multiple dimensions.
I think their problems will be rather different from ours. We simulate particle collisions literally at the level of electrons (well, with some parametrisations for the interactions of decay products with detector material); I think it will be a while before we have the computer power to treat cells as anything but black boxes, and of course cells are huge on the scale of particle physics (as are atoms). That said, I suspect that the major issues will be in parallelising their simulation algorithms (for speed) and storing the output (so you don't have to run it again). Consider that at BaBar we used to think that ten times as much simulated data as real data was a good ratio, and 2 times was an informal minimum. But at BaBar we had an average of eleven tracks per event. At LHCb the average multiplicity is on the order of thousands, and it's become impossible to generate even as much simulated as real data, at least in every channel. You run out of both simulation resources and storage space. If you're simulating a whole brain, you've got way more objects, even taking atoms as the level of simulation. So you want speed so your grad students aren't sitting about for a week waiting for the current simulation to finish so they can tweak one parameter based on the result; and you get speed from parallelising and caching. "A week" is not hyperbole, by the way; for my thesis I parallelised fits because, with twenty CPUs crunching the same data, I could get a result overnight; at that rate I did graduate eventually. Running on one CPU, each fit would take two weeks or so, and I'd still be 'working' on it (that is, mainly reading webcomics), except of course that the funding would have run out some time ago.