PhizzleDizzle Is Right

Python is a lot more intuitive than Perl. Perl is all twisted up in contractions and brackets and weird contractions whereas Python has just a few punctuation sets and very clear functional parsing. I spent 3h on a Python tutorial and have managed to write scripts that I haven't been able to write even after a couple months of casually studying Perl. The only non-intuitive thing I've found in Perl so far has been trying to link an operation inside of a "while" loop to a condition set outside the loop. With this, it took me about 40min to figure out what was wrong with my script to ask for numbers one at a time until the sum was 100 or greater, because the loop kept quitting after 2 numbers. I think I've got it now.

Bonus: Python can do more than just parse text files. This has been one of my frustrations with Perl so far, I could write a little script to change all the "ae" in a text document to "ä", but when it came to actual utility it was kind of limited. I have no doubt that Perl is useful for large-scale Internet applications, I now think that Python might be a more useful skill to develop. I'm not going to give up completely on Perl, but for the moment Python suits my curiosities much better. There's even a serial.py module that let's Python talk to micro-controllers!

So yes, Phizzle Dizzle was right. Python seems to be just as powerful, more intuitive, and offer a much shallower and less painful learning curve. Also, the IDLE Python GUI is quite nice.

Our Shrinking Cloister

I've got a beefy diatribe to spleen out.

I have seen on the Interwebz in the past several months a great deal of discussion about DIY biology research and whether or not it's safe. I've seen big-wiggy biomedical researchers and computer sciencers weigh in on this. I suspect both are missing the point.

Currently, biomedical scientists are arcane magicians working quiet miracles in the shadowy corners of public consciousness. We're nerds of such deep inaccessibility that we become artificers of mythical proportion and prophetic comprehensibility. The public does not understand what we are doing or why it takes so long for progress to be made; we're supposed to produce flash-bang-holy-shit magic tricks on demand even as we dread being questioned why our research matters. The terms we use, the depth of the knowledges that we plumb, the datasets that we construct, the very equipment that we use all serve to enforce an idiosyncratic barrier that requires intelligence, time, and flinty-nosed mule-like stubborness to break into.

This cloister which we've inherited does indeed have its advantages. We can go to work in the lab with other fully competent people whom we may easily assume are familiar with basic genetics and protein dogma. We can develop our presentations with great detail, sculpting each to the level of specialization of each audience. We can dive deeply into a heady rush of informational give-and-take, intellectual play, and experimental thrill. However, I find that this cloister is slowly suffocating us.

When our predecessors enshrined themselves and their disciplines into ivory monoliths of thought, they also shut out nonconformists, radicals, and fundamental innovators. Those that don't deign play the game of academia or lie out the sides of their mouths about career intentions are inevitably shanked with a glass pipette and thrown under the steam roller of their committee's disapproval. An entire industry has birthed itself in response to the standardized demands of academic science, and has made massive profits while doing so (and while it's important to have data standards, it is also true that standards become economic commodities that eventually get more and more expensive to maintain, and are also exclusive of other ways of generating data). By setting the bar for entry to biomedical science so very high, we've effectively shut out everyone who cannot pay to play. This includes independent hobbyists, start-up biomedical firms, and newly minted PIs.

This is due to 2 structural features of academic science:

1) Physical plant/equipment. Think about the sheer variety of specialized equipment that a molecular biology laboratory has. Gel docks, power sources, block heaters, incubators, freezers, microcentrifuges, flow cytometers, UV photoimagers, flow hoods, Bunsen burners, shaking warm water baths, RNase-free chambers, etc. etc. etc.. All of this stuff costs huge sums of money, effectively making the entry costs of biomedical science so very high that no one but the most affluent, in private funds or grants, can hope to even try to play.

This parallels the initial development of computer science. The first computers were so massive, specialized, and expensive that computer science and the study and application thereof were but a fringe niche. Today both are ubiquitous. The fundamentals of computing science haven't changed. But the entry costs have. With the advent of the personal computer costing less and less, more and more nerds have been able to pour into computer science, coding and hacking and building the very environment in which Internet-users dwell. Lowering the entry fees has allowed more people to play and succeed in an arena previously dominated by UNIVACs and their attendant haughty acolytes.

Now, we as biomedical researchers are the high priests of Biology. The knowledge for a talented hobbyist to make real progress exists, as does the capability. The textbooks are written, the databases are public, yet we remain skeptical and the equipment remains prohibitively pricey*.

2) Computer scientists tell their computers what to do and hope they didn't make a mistake. Biologists grope around in the darkness of genetics with enzyme fingers and break something, hope it was what they meant to break, and then hope that it has a measurable outcome. Essentially: they start out knowing what they're doing and we hope we know what we did when we're done.

This also hinders inclusivity in the biological sciences inasmuch as detailed and deep knowledge is required to interpret data or plan a meaningful experiment. Computer science can tinker with code components for a couple hours and a bright person will eventually figure out that $x assigns a variable while @y assigns an array. Meanwhile, the same person could spend weeks, years even, tinkering with E. coli before figuring out that LacI is a protein that acts upon lacZ genetic material.

Unlike the first obstacle, however, this one is much much more difficult to overcome. We could easily rig a water-jacketed incubator by using a styrofoam cooler, Tupperware, and a tropical fish aquarium pump. We can't rig knowledge nearly so easily. To a degree, this is what textbooks are for. Yet at the same time, we've got the most valuable information hidden behind an elaborate series of paywalls.

This also hinders biomedical sciences being relevant. Yes, we know it's important because we work out of compassion, trying to improve knowledge to ease suffering and improve lives. But society doesn't necessarily know that. Sending rockets to the moon was concrete and real and it beat the Ruskies. Growing dynein engines in Petri dishes isn't nearly so concrete. So we're back to being shadowy nerds, toiling on nerdy stuff that doesn't matter. It's here where our cloister begins to strangle us, because when the public does not understand what, why, or how, even to the most shallow degree, what we are doing and what it's potential payoffs are, we have to go on the defensive automatically to keep justifying our labor and its expense.

We'd better serve ourselves by spending more time on the offensive, engaging the public and demystifying what we do, maybe even helping out the DIYers. By embiggening the pool of people thinking about biomedical research issues and increasing the hunger to do something about it we'd be setting up a positive feedback loop, even if some of the greybeards may fear the potential for nontraditional competition. In the end it can only help us, you know?

*To this end I favor the development of biological hacker spaces, similar to those featured at www.hackerspaces.org/wiki. In these, members pay a monthly fee for access to common equipment. This collectivism may be the only viable way to surmount the high entry costs of biomedical research.