I can’t speak for each and every one of the other biologist types in the house here at ScienceBlogs, but one comment on Chad’s post on highfalutin particle physicists struck a chord with me. It all starts with this quote getting back at people who think their research is the be all and end all of all science:
One thing that bugs the heck out of me, is when I hear particle physicists talk about their field as if it is all of physics. I have a great love of particle physics, so I’m not dissing the field at all, nor arguing that it isn’t more fundamental, but it rubs me the wrong way to disregard all of the rest of physics that is currently going on. This especially irritates me since it gives students the wrong impression that the only exciting physics is in particle physics.
From there, Chad asks us:
I sometimes wonder if people in other disciplines have the same problem. Not being dissed by particle physicists– everybody gets that– but some sub-field within the discipline acting as if it’s the only interesting thing going on. Do developmental biologists strut around bio meetings as if they’re the only ones doing worthwhile work? Do medical chemists divide chemistry into “drug development” and “stamp collecting?”
This comment hit the nail on the head:
There is some as well between cell biologists, biochemists, molecular geneticists and structural biologists. The lines between all of them though are becoming more and more blurred so it lessening. I do know a few structural biologists who are upset by biochemistry labs doing structural work. Wet labs v. computational labs has become a bigger one along with traditional v. systems (big) biology.
The previous distinctions between biology sub-disciplines have been blurred. Alex has tried to describe each of the types of biologists at his campus, but they are relics of an older time. Cell biologists use molecular genetics tools. Geneticists study the expression of genes as organisms develop. And they’re all evolutionary biologists. As I pointed out the real distinction is between people that generate their own data and those that analyze other people’s data. That would be the wet lab v. computational lab distinction that the commenter pointed out. The computational group can be further split into data miners and theorists.
As someone at the interface of both groups (I mine data and I generate my own), I realize that these distinctions need to be blurred. Computational techniques and technologies have become so powerful that it would be stubborn for an old school wet lab to not take advantage of them. These computational tools allow you to identify interesting problems, efficiently analyze data, and develop models that fit your observations.
On the other hand, many new biologists come from mathematics, computer science, or physics backgrounds. Some of them need a little bit of help learning biology before they can apply their wares to the life sciences, but they make invaluable contributions to the scientific community — whether it be data mining, developing computational tools, or creating new models. But their research is limited if they are unable to generate their own data. The purely computational groups depend on what data are available. If you realize that you need some new data to test your hypothesis, you should be able to generate those data. For example, you can perform some interesting analysis on publicly available sequence and expression data, but a lot of those data come from a single individual of a species. Like I’ve said before: if you want to study evolution, you need polymorphism data.
Before I get too far off on a tangent, let me go back to my original point. The new division is between computational biologists and wet labs. Neither group is doing service to their research goals if they don’t reach out and use the tools from the other community. Blurring the line between the traditional disciplines has worked quite well. There are many groups that straddle the border between research areas, but there are also a lot of wet labs that are reluctant to perform heavy computational work and vice versa. Ideally, you should be able to perform both the wet lab and computational work. But if you have a wet lab and don’t know jack shit about bioinformatics, you can still collaborate with programmers so that you can perform the analyses that have been missing from your research. And if you’re a computational biologist who needs that little bit of extra data to push your paper over the hump, borrow some space in a wet lab and learn how to pipette.