Wednesday, August 09, 2006

Year II: lessons learned

The last couple of weeks have been full of milestones: the second anniversary of our move to Boston, my qualifying exam, Christina's birthday a couple of days ago, and 2 years since my first blog post. And, of course, our very own little person will hopefully arrive in the next few days. With that, I figured it might be worth to put together another summary post of the past year, though this one will be much shorter than the multi-installment summary [1, 2, 3, 4] that I did last year. So, here 'tis.

"Shhh, be vewwy, vewwy quiet, I'm hunting theses"
and I had almost as much trouble finding a thesis topic as Elmer has with that wascally wabbit.
I looked into working on microRNAs, tried to come up with a good reason to rebuild a yeast chromosome, started working on investigating transcriptional feedback in the yeast pheromone response, and finally settled on working on T7.

One obvious question is why I had such a hard time finding a topic. The answer is also fairly obvious: my first choice [working on malaria] wasn't an option, and it took me a while to find an acceptable alternative that coupled my other two main interests, synthetic biology and high-throughput [HT] data generation and analysis. Those two areas are not natural bedfellows because most work in synthetic biology is currently focused on building single instances of relatively simple systems [which don't generate much very much data]. This meant that there were relatively few examples of research
that I could use as inspiration, and so it took me a while to formulate a topic that fit my desires, addressed interesting questions, and fit into the overall research direction of Drew's lab.

In retrospect, I thought finding a thesis topic would be one of the easier parts of grad school. Guess I was wrong.

"It's what you don't know that's important"
A large part of getting a grip on a given field is having an idea of what the current limits of the field are ie what isn't known, what the interesting questions are, what's difficult etc. These aspects are, of course, the very bits that aren't covered in class: you're told in excruciating detail what is known, but nobody dwells on the "Hic sunt dracones" areas, even in graduate classes. I think a large part of the point of grad school is precisely that you figure out where the boundaries of knowledge are in your attempts to push them a bit further.

In any case, when I started school, I lacked the basic "this is what we know"-type knowledge and had no big-picture perspective of the current challenges in biology either, beyond a fuzzy "Biology is hard, because, well ... people tell me that it is. If it were easy, we would have created a dragon by now." Lacking this mental scaffold made it difficult to get more than a superficial understanding of the papers I read and talks that I attended -- not only did I struggle to fully understand the claims/results contained in the paper/talk, I also didn't have a framework in which to evaluate the overall impact of what I was being told. The end result was that my takeaway was generally "Uhm, ok, if you say so", and a few isolated facts that didn't really connect to the bigger picture.

The biggest intellectual transition for me over the last year was moving beyond the "just the facts, ma'am" stage, and towards a better meta-level understanding of at least a couple of the sub-areas of my new field. The largest contributor to that increased understanding was actually my thesis odyssey. In the process of trying to formulate a thesis topic, I had to think really hard about various areas of systems/computational biology -- what questions were being asked [and why], what tools were available to answer them etc. Doing so helped me tie together a lot of the loose strands waving around in my head into a semi-coherent overall picture.

The negative aspect of all this cogitatin' and meditatin', of course, is that I'm not as far along as I would have liked to be after two years of school -- I haven't done anything yet. That said, probably the main reason that I'm [theoretically, at least] "behind schedule" is that my envisioned timeline radically underestimated the switching costs of moving from computer science into biology -- remnants of the instinctive "Oh, how hard can it be ?" programmer's reflex, possibly.

Integrating over everything
At the end of the day [or year], though, the high-order bit is that I [still] like what I'm doing and think I made the right choice in leaving MS. That's not to say I didn't have a few "It'd be so nice and easy to go back to software" moments during and after our trip to Seattle -- things like hanging out with friends and family, going by the motorcycle shop where I spent many a dollar buying unnecessary stuff for my bike, and checking up on our house in Seattle served as sharp reminders of everything we gave up when we moved here. I'm sure I'll have similar sentiments each time we go back, but hopefully/presumably their intensity will decrease as I get closer to the end of grad school and can see the light at the end of the tunnel.

Next up, year III: try to get some real work done while being dominated by a tiny lifeform halfway based on me.


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