Here’s Steve Dickman at Forbes with a look at “cloud biology” approaches to medicine and drug discovery. This is an area I’ve written about several times before, and I also recommend Wavefunction’s take on this. I particularly like the quote from Mark Murcko about having to extrapolate from biology that’s “half right and half wrong”.
That’s something I emphasized here as well – biology is not only inherently messy and complicated, but the data that we have to figure it out are in pretty squishy shape, too. There’s the whole reproducibility problem, for one, which has gotten a lot of ink in the last few years. (And there are weird little variables that might be kicking all sorts of assays around). But beyond that, there are a lot of reproducible but hard-to-interpret data sets out there. If you’re using tool compound X to draw conclusions about pathway Y, and (unbeknownst to you), compound X has off-target activity that has some sort of bounce-shot downstream effect on your pathway Y markers, well. . .other people will probably be able to reproduce those numbers, but no one’s going to be able to make sense of them very easily.
There’s also the interesting problem of Stuff We Just Don’t Know About. Twenty years ago, for example, nobody really knew that all these little RNA pathways existed (RNAi, miRNA, dsRNA, etc.) There are any number of other examples, important processes and pathways that we haven’t even noticed yet. They’re in there, making cells do things in our assays, but all we can do if we’re puzzled is throw up our hands and say “something else must be going on” or try to force interpretations based on what we actually know.
Computer hardware and computer software, I think it’s safe to say, don’t suffer nearly as much from this sort of thing, since (as I never tire of pointing out) humans built them. That’s not to say that weird bugs don’t happen (they certainly do), but you don’t suddenly discover the inverse of a weird bug – something you didn’t suspect and didn’t understand that is actually causing your program to function correctly. (Reminds me of the rarely-if-ever seen diagnosis of inverse paranoia: the irrational conviction that people are sneaking around behind your back to do you favors). Since when do programmers find whole classes of subroutines in their code that they didn’t know existed, or assembly-language-level steps that are functioning in their systems without them ever being aware of them? The very idea seems crazy and surreal to a coder or chip designer, but this sort of thing happens all the time in biology. In fact, it’s going on right now, in the background of those papers that are coming out this week, and we’re just going to have to learn more about what we’re doing before we can make some more sense out of it all.
Like Ash in his post, I still welcome the Valley types, and I welcome the big-data handling and the automation and all the rest of it. Some of this is surely going to help, and we need all the help we can get. But it’s worth remembering that sometimes the end result is going to be generating ever bigger piles of puzzling results, even more quickly than anyone ever has before. I just want folks to be braced for that, mentally, organizationally, and financially.