Physics envy

In many parts of physics, theory drives experiment: theory is advanced, complete, and quantitative enough that it is able to lead the way into unknown territory, suggesting new experiments. However, this is not the case is almost all other fields that have a significant theoretical component. For example, in biology, theory is almost always retrodictive: the best it does, or even tries to do, is to help better explain or quantify things that are already qualitatively understood through experiment. While this is certainly an important task, it does lead biological theorists (meaning, in this case, me...but also others I've spoken to!) to a certain 'physics envy.'

Because this is the current state of biological theory, when developing a new model, I've found there is an awkward balance between my inner gung-ho theorist, which tells me I should make all kinds of counter-intuitive, out-there assertions based on a sort of reductio ad absurdum application of my model, and my more practical, engineering-like mindset, which insists that I focus on interpreting data that already exists, and only make predictions which can be experimentally verified without a ridiculous time and/or expense. Where they find common ground is that (1) the theory does need to agree with whatever data currently exists, and (2) it does need to make meaningful predictions, which are (at least in principle) testable experimentally.

(1) seems straightforward, but consider this. I'm in the final stages of building a model for the evolution of protein-protein interaction (PPI) networks. It agrees with the data pretty well for every standard network property I could think to measure -- degree, clustering, betweenness, eigenvalues, closeness, error tolerance...you name it! Altogether I've got 12 things I'm comparing, and it seems like my model nails pretty much all of them, in fruit flies, yeast, and humans. I've also confirmed that various other models do not capture all these features. So, off to a good start, right?

Here's the complication: these are all static features. My model builds a network which ends up looking, at least topologically, very much like present-day PPI networks. The model also makes specific predictions about the evolution of the network (which is, of course, the point of the thing in the first place). For example, it describes (at a very rough level) the evolution of the first cell. The thing is, there's no data against which I can validate these predictions, and I feel that it would be very strange to make any grand claims regarding evolution without being able to at least qualitatively verify them.

Ironically, my goal with this model was to see how simple a model I could build that would still accurately represent the PPI network's structure. However, it turns out that the model's excessive genericness works against it: it's hard to find things to test! This is my quandary at the moment: how do I make predictions about evolution which won't require millions of years to actually test? I am very interested in augmenting this model in various ways, including functional and environmental factors into it. But the first step is to verify what I've got already, and that's tricky, precisely because it's so simple at the moment.

One idea (which was suggested by my wife) is that I should try and apply the model to allopatric speciation events -- that is, to organisms' sudden evolutionary burst in response to geological or environmental changes. I think I can incorporate this kind of event into my model framework in a relatively natural way, and this has the great advantage that it happens on a rapid enough time-scale that there is real data out there to compare my predictions with.