P.C. Shannon

The usual trajectory for graduate school in science is that you spend the first year or so taking classes. Then in the second year, you take a preliminary or qualifying exam in order to become a doctoral candidate. After candidacy, you do some research, write a dissertation, defend it, and thus earn your PhD. There’s no Master’s degree involved; it sort of a consolation prize if you muster out after some point (I’m not sure exactly when one becomes eligible for the Master’s and when quitting is just quitting).

Presently, I find myself in the early stages of the preliminary exam — prelims. In my program (and I think this is pretty common in the life sciences), the exam basically consists of preparing an NIH-style grant on an original research topic. This is the proposal. Then you have to give a presentation on the proposal to a committee of three faculty members whose identities you don’t know until you walk into the room. You have to defend your proposed research, and the committee gets to quiz you over any general science knowledge they like — basically, they will back you into a corner in order to figure out what you don’t know. This is the oral exam.

At this early stage, I don’t even have a research topic yet. Instead, we have to prepare a pair of abstracts on proposed research topics. Then the committee will choose one of the two topics for the proposal. This is no small feat as the topics can’t be related to the research we’re already doing in the lab, but they have to related to our tracks within the program. In my case, this is “Biosensors, Biotechnology, and Bioinformatics.” Plus there are all sorts of other recommendations and guidelines about the experiments that frequently lead towards good, basic science. Unfortunately, basic science is not one of the three B’s in the BBB track, so it’s difficult to make it all come together.

On the other hand, I really appreciate what the grant-writing style of the exam is trying to accomplish. Obviously it’s practical experience for writing grants, especially since NIH fund so much of the life science research in this country. Prelims have also gotten me into the library, pouring through journals, exploring other topics outside of my little microcosm of bioinformatics. It’s broadened my horizons and opened my eyes to what practicing scientists actually do. It may be hell, but like so many hellish things in life, it’s also a good experience.

I have a lot of anxiety over what I’m going to do after I finish grad school. Even though I’m only starting my second year, I’ve still spent some time browsing job listings online at sites like Nature Jobs. This is not necessarily representative because in my field, I think a lot of recruiting is done at conferences and via word-of-mouth. In fact, I think it’s only fueled my anxiety because most of the bioinformatics lobs listed are for coders and technicians — people with Bachelors and Masters degrees — which is not what I want to do.

Strangely, it took me a while to figure out that rather than being paranoid about limited opportunities available to me (as listed online), I need to position myself as a bioinformatics researcher as opposed to a mere technician. This is similar to the difference in the software industry between a coder and an engineer. What I need to be doing, I realized, is not just taking one bioinformatics course and farting around on my computer. I need to be developing my toolbox so that I have a variety of ways of solving new problems rather than just getting headaches while trying to reinvent the computer science wheel. (Much of the foundation of computer science was set out years ago by people who are much smarter than me, so I couldn’t possibly recapitulate it all.)

This curriculum was driven home recently by an editorial in the journal Bioinformatics by Pavel Pevzner, a name I recognized from browsing bioinformatics books on Amazon.com. Pevzner’s argument is that undergraduates in molecular biology need to take an introductory algorithms class, but his broader point is that biologists need to understand the logic behind bioinformatics and not just “cookbook” their way thru computational biology.

The bioinformatics class that I’m currently taking does a good job of explaining the algorithms that are used. The students in there certainly understand sequence alignments better than most practicing biologists. The professor is not just giving us a recipe, he takes us through the algorithms. The problem for my educational goals is that, out of necessity and aim, he only hits a few algorithms that are currently employed. He can’t build the toolkit that I want to have. He does a good job of explaining the current state of the science, but he’s not preparing us to advance that state. It’s just beyond the scope of his course.

The class is a good starting point, a solid foundation that I have already applied to my research. But from here, I definitely want to take at least a basic algorithms class and I’d like to take something more advanced, too. But I also want to take some high level statistics and math courses, too, because a lot of bioinformatics is statistical modeling and such, so I need that background as well. The only problem is that I don’t want to have to subject myself to taking a slew of undergraduate classes if I can avoid it. So maybe I’ll just audit all of the class, we’ll see. Certainly having this plan has alleviated much of my anxiety.

I’m off to Duke University today to visit the lab of Homme Hellinga. Homme has this cool program called “Dezymer” that can be used to computationally design new binding proteins and enzymes. This is really cool stuff, and I’m super excited about it. If you want the nitty-gritty technical details, see this paper in Nature or this one that just came out in Science. For anyone keeping score at home, these are the top journals in biology if not all of science. It’s a big deal.

I’ve got two projects going in lab at the moment. One is the same thing I’ve been working on for a while: mining the structural data in AANT. You can read the paper describing the database (PubMed or PDF) for more background.

I have also been drafted, at least temporarily, by the selection facility to operate the Tecan robot workstation and do some aptamer selections. There’s a paper in review that describes the process. I’ll let you know when it gets published. Sadly, it doesn’t have my name on it.

…away from home, anyway. I joined the Ellington Lab this week. I have a slew of computational projects stemming from my rotation project. I’m also supposed to get another wet lab project because there’s currently no funding for the computational stuff so I need to “pay the bills” somehow until new grants come in. I’m excited about it, but it’s very overwhelming. I think I was handed about twenty journal articles in the first hour, and I have yet to really sift through them. I’m still trying to get my feet under me in the lab. I’m so accustomed to the corporate world where there’s a fair amount of supervision (and also someone looking after you) that it’s a bit of a shock to be thrown in open water and forced to swim. I’m still just doggy paddling, but before too long I should find my pace.

For my third rotation, I am in Alan Lambowitz’s lab working on the group II intron stuff. It’s nice to be finally doing, you know, actual molecular biology after more than six months in grad school. I’d kinda forgotten how much I enjoy pipetting DNA and setting up reactions. Maybe the novelty will wear off before too long. The biggest complication at this point is that the post-doc with whom I was assigned to work went home to the Czech Republic for two weeks. That was four weeks ago. When he tried to come back, his papers weren’t in order or something and they sent him back to Prague. I’ve heard horror stories about Chinese students being stuck out of the country for four months. Welcome to Bush’s America. So it goes. In the meantime, I’ll do my best to get by without the post-doc and vote Democrat.

I’m in the process of applying for an IGERT fellowship, and part of the application calls for a 1-2 page summary of research interests and goals. I wrote a first draft as best I could, having never attempted such a thing before. I felt like it was the biggest turd I’d ever written, but I showed it to my adviser and he said it was a good start. I guess I often forget that most people aren’t as concerned with wordcraft as I am (I did want to be a writer for a number of years). Moreover, scientists tend to think of writing as peripheral to their work, so being good with words gives me a leg up in a competitive field.