Biomedical Sciences, Two Approaches

Yes still in Italy. Looking back at this post, it looks like most of the small biologists (excluding structural biologists) who practiced the molecule-centric approach have been weeded out by the stagnation in NIH funding, but I still beleive that the temptation to perform such research is still there for many young scientists ... so here goes.

As time goes on my ability to cope with the rich experience of daily lab life requires me to rant every so often. So here is today's rant.

There are two approaches to small biology, studying molecules and studying processes. Stay away from the molecule-centric approach!

What do I mean by that?

An easy trap for biologists to fall into is to latch on to their favorite protein and attempt to explain how it functions within the context of a cell. This methodology can sometimes lead to advances, but more often ends up examining the most pedestrian aspects of biology. "Molecule X interacts with Y, molecule X has three domains, molecule X is involved in apoptosis, cell division and development." Yes that's very nice, but it's the context that counts, not molecule X.

Other scientist study processes. Such as ... How does a cell polarize? How does RNA get exported from the nucleus? These lines of thought are much more productive and tend to lead to insightful findings. Before the age of genomics, there was a tendency for some process-oriented biologists, such as the cytoskeletal field, to ignore how molecules regulated certain processes. But that time (I think) is well over. With the ease of obtaining cDNAs, sequences, strains and other tools, it is now trivial to find and study how molecules are directly regulating your process of interest.

So ... if you have set your goal on studying processes, the first question you should adress is "what molecules are involved?" (or as my thesis advisor would say "show me the molecules!") Having identified the various proteins involved in your favorite process you can now piece together the underlying molecular machinery.

Other alternatives? Well three have emerged recently.

1) Big biology. In many ways this method is as mind numbing as studying a molecule. On the surface there are three types of big biologists: those who study the impact of a single molecule on the cell's composition, those who study the effects of larger perturbations on a cell's composition, and those that apply a technique to study the cell's composition. (As an example of the last group would be a lab that catalogues every protein-protein interaction in a cell). These projects are very ambitious, yet tend not to provide any real insightful results. In the end we are swamped with tons of data of unknown quality. This combination (too much data + questionable reliability of the data) make it tough for small biologists to gain any insight. In the best scenarios, data generated from big biology has been used as a tool that can further the research of small biologists. Examples are the genome, databases and strain collections.

2) Synthetic Biology. The idea here is to reconstruct biological processes from scratch. Call it reverse bioengineering. This field is in its infancy and holds great promise. It is unclear however when we will be able to reconstruct complex mechanisms such as the cell cycle or cell migration. One major problem is that evolution is smarter than we are. But probably the exercise is well worth it.

3) Systems biology. We're not sure what this is. Understanding the system of protein networks on a higher level? Peter Soger described it as understanding how modules of proteins act in concert to form signaling modules and other higher order structures. This approach hold lots of promise ... however it could also go the way of other trendy but oversold "new approaches".

OK my time's up.

More like this

Both "systems biology" and "computational biology" are poorly-defined terms, but I don't think it's fair to call them oversold when their potential is just starting to be explored.

I mean, microarrays certainly aren't going anywhere, and their results are usually interpreted at a higher level than that of specific proteins -- people use them to describe "stress response programs," etc.

I think it's a very exciting time...there's a ton of data being made public between GEO, ENCODE, and all that, so I think these large-scale approaches will bear a lot of fruit in the future.

But I'm kind of biased being a computational person myself :)

I think small biology has some advantages for PhD students: They have a better chance to identify themselves with their project and publish their work in a way that it becomes clear that it is their work, their ideas, their expertise etc.
Big Biology will advance our knowledge but how is the situation for PhD students in Big Biology? They may do the very same experiments as some other PhD students in some other labs whith whom they don't get in contact because their bosses control all interactions. At the end they even may not have learned what hypothesis driven research is and get lost in an list of dozens of authors. Maybe they have a Nature or Science paper then but how much did they learn? Will they be able to plan and run their own projects later? What do they do when they find themselves in Small Biology later.

When I started my PhD I did not want to go the "my favorite molecule" route. I wanted to study a Process, to explain a System, and uncover Great Truths About Science.

Yeah, I'm not done yet.

Many is the day I ruefully think, "What's wrong with just picking one goddamn protein and figuring out how that works?" Because, it seems, to answer the big questions, it's almost inevitable to have to get really intimate with one or two little widgets in the system (e.g. "protein X is critical for system Y; explain how protein X does Z, Q, and L"), which is about all that one grad student is able to accomplish in 4-5 years.

That said, I am still interested in Big Questions, but now am in the process of working out how to strike the balance between the large and the small foci that can 1) lead to those interesting systemsy answers but 2) not drive grad student crazy; i.e. how to keep my brain functioning on two very different scales at the same time (or at least in quick succession).

This discussion brings up an interesting related point -- how and when do you confront your PI if your project isn't bearing fruit?

One of my labmates worked for an entire year on a project she wasn't thrilled with, and ended up with fuzzy results that didn't really go anywhere. When she graduated, the PI passed the project on to the next newcomer...I guess the lesson is, sometimes you need to confront your PI about the prospects of your project. Maybe they weren't aware of your doubts and they'll fix things, or maybe they'll pull rank and shut you down. It's better than being frustrated for an entire year and not having said anything though.