Systems biology – depending on your orientation, this may be a term that you haven’t heard yet, or one from the cutting edge of research, or something that’s already making you roll your eyes at its unfulfilled promise. There’s a good spread of possible reactions.
Broadly, I’d say that the field is concerned with trying to model the interactions of whole biological systems, in an attempt to come up with come explanatory power. It’s the sort of thing that you could only imagine trying to do with modern biological and computational techniques, but whether these are up to the job is still an open question. This gets back to a common theme that I stress around here, that biochemical networks are hideously, inhumanly complex. There’s really no everyday analogy that works to describe what they’re like, and if you think you really understand them, then you’re in the same position as all those financial people who thought they understood their exposure to mortgage-backed security risks.
You’ll have this enzyme, you see, that phosphorylates another enzyme, which increases its activity. But that product of that second enzyme inhibits another enzyme that acts to activate the first one, and each of them also interacts with fourteen (or forty-three) others, some of which are only expressed under certain conditions that we don’t quite understand, or are localized in the cell in patterns that aren’t yet clear, and then someone discovers a completely new enzyme in the middle of the pathway that makes hash out of what we thought we knew about
So my first test for listening to systems biology people is whether they approach things with the proper humility. There’s a good article in Nature on the state of the field, which does point out that some of the early big-deal-big-noise articles in the field alienated many potential supporters through just this effect. But work continues, and a lot of drug companies are putting money into it, under the inarguable “we need all the help we can get” heading.
One of the biggest investors has been Merck, a big part of that being their purchase a few years ago of Rosetta Inpharmatics. That group published an interesting paper earlier this year (also in Nature) on some of the genetic underpinnings of metabolic disease. A phrase from the article's abstract emphasizes the difficulties of doing this work: "Our analysis provides direct experimental support that complex traits such as obesity are emergent properties of molecular networks that are modulated by complex genetic loci and environmental factors." Yes, indeed.
But here’s a worrisome thing that didn’t make the article: Merck recently closed the Seattle base of the Rosetta team, in its latest round of restructuring and layoffs. One assumes that many of them are being transitioned to the Merck mothership, and that the company is still putting money into this approach, but there is room to wonder. Update: here's an article on this very subject). There is this quote from the recent overview:
Stephen Friend, Merck's vice-president for oncology, thinks that any hesitancy will be overcome when the modelling becomes so predictive that the toxicity and efficacy of a potential drug can be forecast very accurately even before an experimental animal is brought out if its cage. "The next three to five years will provide a couple such landmark predictions and wake everyone up," he says.
Well, we’ll see if he’s right about that timeframe, and I hope he is. I fear that the problem is one of those that appears large, and as you get closer to it, does nothing but get even larger. My opinion, for what it’s worth, is that it’s very likely too early to be able to come up with any big insights from the systems approach. But I can’t estimate the chances that I’m wrong about that, and the potential payoffs are large. For now, I think the best odds are in the smaller studies, narrowing down on single targets or signaling networks. That cuts down on the possibility that you’re going to find something revolutionary, but it increases the chance that anything you find is actually real. Talk of “virtual cells” and “virtual genomes” is, to my mind, way premature, and anyone who sells the technology in those terms should, I think, be regarded with caution.
But that said, any improvement is a big one. Our failure rates due to tox and efficacy problems are so horrendous that just taking some of these things down 10% (in real terms) would be a startling breakthrough. And we’re definitely not going to get this approach to work if we don’t plow money and effort into it; it’s not going to discover itself. So press on, systems people, and good luck. You’re going to need it; we all do.