<p>minkyboodle: Okay, one infodump coming up. :-)</p>
<p>You probably know this, but UCSF just got a chunk of $$$ from the NSF for systems and synthetic bio. They are going to hire three fellows in that area.</p>
<p>As for current researchers, Lim has a couple of students doing straight-up computational work, on what he calls a “periodic table of circuits”. This is with Tang and El-Samad; one of these (adaptation circuits) was published in Cell a couple years ago, but there’s similar analyses going on for other behaviors. (Tang is a modeler, and El-Samad is a control theorist; they both have students collaborating with various experimental labs.)</p>
<p>There’s a healthy emphasis on using high-resolution measurement techniques to constrain models - this was a priority for me, because I’m into tools development, and I think this type of approach will be increasingly common/useful. For instance, Tang has a student working with Morgan, doing microfluidic imaging studies and modeling on the cell cycle. Li recently did a time-course study to figure out the regulation points in a biosynthetic pathway - I believe he made fluorescent reporter strains, stimulated the culture, and popped it on a FACS to get induction over time. I also hear the new virologist, Weinberger, uses similar methods.</p>
<p>Also, El-Samad is particularly interested in noise/stochastics, and has some nice projects on this with Walter (unfolded protein response) and Madhani (yeast mating). I met with Madhani on the interview; they did a high-throughput screen, and found a gene that increases noise in a pathway-specific fashion. They chased it to a rather funky phenotype (weird enough to get a purely descriptive paper into a Nature-baby). I don’t think he knows what to make of it, and I don’t either, but it was interesting enough that I might rotate there.</p>
<p>I think you <em>could</em> do that kind of work at Berkeley. Arkin was in the first wave of people to integrate modeling with biological research (he’s a good guy, too), and the Voigt/Salis RBS calculator was one of the things that really pushed me to consider tools as the future of synthetic biology. (And if you want to do pure theory, you have a much wider selection at Berkeley as well.) But I ended up not applying, because I only tried a few schools (last-minute decision to apply…) and I wasn’t sure that there was an intellectual community for systems/synthetic.</p>
<p>What there IS an intellectual community for is Keasling-style synthetic biology. And here’s where I have to lay out my biases a bit. You’ve probably figured out that there are a few quasi-religious debates in synbio…among them is how fast the field is really going to progress. There are the realists/incrementalists (type specimen: Keasling), and there are the optimists (type specimen: Endy). If you’re a realist, you will be interested in simple(-ish) genetic engineering: move this pathway from one critter to another, break out the optimization hacks to boost production, maybe find and domesticate a new part, but you’re working one project at a time. If you’re an optimist, you’re going for universal principles: let’s build this parts library, these CAD tools, and why not try to build some crazy phenotype while we’re at it.</p>
<p>My problem with Keasling-style is that being good at it doesn’t help you very much. The success formula is highly weighted towards persistence and blind luck. Not only does this work directly against my comparative advantage (don’t want to outwork 'em if I can outsmart 'em…), it also makes it harder to get a PhD-level job. Yes, in academia, it’s normal to spend a large chunk of your time doing tasks that could be outsourced to a reasonably smart Golden Retriever. In industry, they aren’t going to pay PhD wages for that - if a couple PhDs and a herd of minions can run a project, then that’s what they will hire.</p>
<p>I went to industry after I graduated (BS Bioengineering); looking at my options, I could go to grad school in synthetic biology, or I could do the same work in industry, with double the salary and much better resources. (Granted, this was before the economy did a swan-dive.) I still think I made the right decision. Now that I’m going back, I am taking a hard look at where I can add value to my skills - if I kept on, and became the best darn genetic engineer in the whole world, I wouldn’t be all that much more valuable than I am right now. So I’m taking a detour; I’m going to go off with the systems people and work on building better output tools.</p>
<p>I’ll still be at SynBERC, though. (I’ve been there awhile…at the last one, somebody asked me if I was applying for faculty positions. Sigh.)</p>