Wednesday, December 29, 2004

Go read the "Hookie awards" essays

The "Hookie awards", Part 1 and Part 2, have a set of links to very interesting, wide-ranging essays -- prescription drug prices, the war in Iraq as part of World War IV, the global decline in fertility rates etc. The most interesting ones, to me, are the ones dealing with [how to respond to] radical Islam, especially the essays that claim that the fight against radical Islam is similar to the cold war [and that Bush recognized this and acted accordingly] and that Democrats need to come up with a coherent answer to radical Islam in order to remain relevant. I'm not sure I buy all that's in them, but they're definitely thought-provoking.

Monday, December 27, 2004

Turning points in biology

Over the years, I've read a lot of books about the "history" of physics and mathematics ["Men of Mathematics", by ET Bell, is a great book, btw] ie who the major figures were and what their discoveries/contributions were. That's given me some context so that when I read a math/physics textbook I have an idea of where the material I'm reading originated. As part of my "re-education", I've been trying to do something similar for biology, getting to a historical understanding beyond "Crick and Watson discovered the double helix structure of DNA".

One of the interesting bits I've come across is that a lot of the "founding fathers" of modern molecular biology were actually physicists by training who converted over to biology. Examples include:

-Leo Szilard came up with the initial idea of an atomic chain reaction which led to the atomic bomb, and then went on to contribute to the analysis of gene regulation in bacteria
-George Gamow, one of the originators of the "big bang" theory of the creation of the universe, proposed a code explaining the relationship between the sequence of bases in DNA and the amino acids in proteins. The code was wrong, but he was the first person to propose it, days after Watson and Crick announced their discovery.
- Erwin Schroedinger, one of the giants of quantum physics, wrote a book called "What is life ?" which apparently served to attract lots of young physicists to biology.
- Max Delbruck got his PhD in theoretical physics, was attracted to biology by some of Niels Bohr's thoughts on the subject and then became the unofficial leader of the "phage group", a well-known group of scientists who worked on bacteriophage ie viruses that infect bacteria.

The book I'm currently reading, "A History of Molecular Biology", by Michael Morange, has an interesting theory, attributed to Francois Jacob, about why so many physicists moved over to biology. The gist of it is that before WW II, some young physicists felt like physics was at a point where it was mostly a matter of refining existing models/theories, and required large collaborative efforts. In addition, during the war, many physicists were drafted to assist their countries' war efforts and came away feeling a bit sullied by the fact that their efforts contributed to killing people. In contrast, biology appeared far away from political and military uses, and a field which had lots of open fundamental questions and thus room for revolutionary contributions by individual scientists. Over time, though, the number of physicists [and other kinds of scientists, like chemists] switching over to biology slowed down, because the profile of biology had become high enough that young people interested in science headed straight for biology instead of 'detouring' through another science.

So, in summary, there was a curious sort of "inflection point" in biology in the 30's and 40's, when a lot of scientists from other disciplines switched into biology. It feels like we're at a similar inflection point right now, with biology benefitting from the interdisciplinary efforts of people originally trained in "computational" subjects like physics, mathematics, computer science etc. I think some of the causes are the same. Biology is once again a "hot" subject, with lots of excitement about the possibilities for biologically-based technological advances, attracting folks from other disciplines who like the idea that their expertise can be put to good use in investigating biological phenomena. I also expect that, over time, the number of people who are explicitly changing tracks will once again decrease, as more universities introduce interdisciplinary educational programs that combine biology with math/engineering/computer science [ie Bioengineering and Computational Biology departments and majors] that allow people to integrate different disciplines right from the start, rather than having to do it later in their careers.

When the first "cross-over" occurred, part of the hope was that the tools of quantum physics could be applied towards understanding biology, ie that biology could become a more quantitative science, but this effort met with limited success. My impression/suspicion is that the idea was simply ahead of its time in that we didn't know enough about the actual molecular mechanisms at play, and didn't have the right experimental tools, to really be able to apply computational methods. In any case, what's happening today is a resurrection of this desire to "quantify" biology. There is an increasingly widespread belief that biology will benefit from the application of computational methods, in the form of mathematical models of biological processes, computer-based analysis of data etc. This time, I think we have a much better shot at being successful -- we know a lot more about certain areas of biology and have much better experimental techniques and tools than were available 60 years ago. So, from a "pure research" perspective, biology is well on its way to becoming more quantitative. However, the jury is still out on how long it'll take before some aspects of these computational approaches become relevant to industry.

What I find most exciting about all this is that I feel like I'm getting a chance to participate in a period that will [hopefully =)] be viewed as a turning point in biology in the coming years, a period when our understanding of, and ability to manipulate, biological systems started to increase exponentially. Quite apart from my inherent interest in the subject matter, the notion of being present at a crucial point in history is, in many ways, what consoles me when I consider that I gave up an insanely cushy corporate job for the grind and relative poverty of graduate school and, in many ways, having to start all over again.

Ask me again in a few years how that's working out for me ;-)

- Francis Crick was also a physicist by training before turning to biology.
- I was under the impression that notions like "information content", "programs", "feedback loops" etc that are commonly associated with engineering/computer science were relatively recent ways of looking at biology. Apparently, though, these viewpoints been around since the 40's, so I'm left wondering what caused their new rise to prominence.

Back when -I- was a kid, the world was a better place ... right ?

While wandering around the web looking at various reports on the earthquake that hit SE Asia yesterday, I came across a phrase along the lines of "... and in 2 weeks, everybody will have forgotten about this". That sentence made me start thinking about something that's been bugging me for a while, namely that I seem to have a really poor memory for momentous "world" events. The only major event I can think of in 2003 was the US invasion of Iraq, but there must have been a lot more things that happened on the world stage that I thought were noteworthy [alas, the oracle of Google isn't helping me -- a few searches haven't found a list of the major events of 2003], yet somehow I have no recollection of any of them. It seems like so many insane things are happening nowadays [Yushenko being poisoned, a 20 million pound bank robbery in Ireland, the 8.9 quake in SE Asia etc] that there isn't time for any of them to sink in enough that you can actually remember them for more than a couple of weeks.

The question I have is: are there really more crazy things happening than used to be the case or do we just have much better media coverage, so we're getting news from all over the world [that we weren't getting before]? I'd like to think it's the latter, because it would mean that things aren't going to hell in a handbasket any more so than they were before, it's just that we've gotten better at reporting the speed, heading and contents of the handbasket. However, I fear it's the former.

... and that's it for this installment of "End-of-the-year doom and gloom". Tune in next week for a discussion on "Today's teenagers: should we shoot them all and start over ?"

Sunday, December 26, 2004

The little machine that could: the ZX Spectrum

This song about the ZX Spectrum [song link off Scoble's blog] brought back a lot of memories. The ZX Spectrum was my first computer; I initially bought it with 16K of RAM and then upgraded [oooh, aaah !] to 48K. I mostly played games on it but the only games I made it all the way through were "The Hobbit", an adventure game based on Tolkien's book, and the unfortunately-named "Penetrator", one of the venerable side-scrolling-and-jumping-and-or-shooting games. Manic Miner [check out this site for some screenshots -- I think I'm going to download the game and try it =)] also rocked, but somehow I never spent enough time at it to get past the first 4-5 levels. The problem was that pretty much all my games were, ahem, "second-hand", with no instruction manuals [so I didn't know what the point of the game was] and I was in Ghana, so I didn't have anybody to ask.

That little machine, with its rubbery keyboard, reliance on loading from cassette and whopping 48K of memory, rocked.

[Adam, I believe you also have some fond memories of this little machine, no ?]

I must have been a good boy this year ...

... because Christmas this year was pretty cool. My brother Victor and cousin Dominique were [are] here; I haven't spent Christmas with Victor in several years [I don't actually remember how many years it's been; maybe since I left Ghana in '92 ?] and I hadn't seen Dominique in over 6 years, so it was a nice little "family" Christmas. Thanks to the superb cooking skills of Christina, we had a great Christmas dinner [pork, sauerkraut, spaetzle and apple sauce ... super-German] as well as a cake that was the caloric equivalent of an atomic bomb. All goodness.

... and now it's snowing quite heavily, for that super-authentic Christmas feeling. Better late than never, I suppose.

[By the way, did you know that those wacky Puritans officially banned Christmas for 22 years in Boston, in the mid-17th century ?]

Thursday, December 23, 2004

More model disenchantment

I happened to spend a few minutes yesterday talking to one of the professors around here about what folks in the biotech industry were looking for in people coming out of grad school. That conversation actually served to further my current skeptical stance on [certain types of] mathematical models in biology, at least in terms of their current practical utility.

It turns out that the folks trying to come up with therapeutics [ie drugs] don't really care very much about mechanistic models of what's going on in a particular biological process. By "mechanistic" models, I mean models that explicitly describe molecule A reacting with molecule B to produce C, which in turn reacts with molecule D etc. Apparently, the reason they don't care about them is that

a. In order to create and refine these computer models, you have to do a lot of quantitative biochemistry that allows you to measure how fast certain reactions are, how much of a particular compound gets produced etc. Obtaining that sort of data is a lot of work because there are no good high-throughput [ie fast], accurate ways of getting it, so you end up having to do a lot of painful grunt work.
b. Even if you get all this data, chances are that you measured it all in your little petri dish ["in vitro"] ie not under the actual physiological conditions that exist inside an animal ["in vivo"]. That makes it questionable how applicable the computer simulation is to what you're really interested in, namely how an animal/person would react to the drug. So, in the end you have to go do the majority of the experimental work [that a computer model was supposed to save you from] anyway.

What industry folks actually do care about is being able to analyze large data sets coming from high-throughput measurement methods and seeing what they can figure out that way.

An analogy based on cars and car engines goes something like this: people building mechanistic models are basically taking the engine apart, figuring out what the carburetor does, how it's connected to the combustion chamber, how the spark plugs work etc, and then building up a picture of how the various components all fit together. In contrast, the folks analyzing large data sets are doing the equivalent of stepping on the brake, the accelerator, changing gears etc and seeing how the car reacts. If all you care about in the short/medium term is driving the car in a particular direction, the second set of actions produces results much more quickly, and is more useful than the first.

Definitely a useful reality check to have early on in my graduate career.

Wednesday, December 22, 2004

The software industry ain't what it used to be ...

I'm sure Michael Kanello's article on CNET is going to lead to lots of rebuttals along the lines of "You're crazy, innovation in software is just beginning !", but I have to agree with him when he says

"By comparison, information technology as a field in itself has become somewhat less interesting. Maybe not dull, but it seems to have hit a plateau of excitement after three decades of stupendous accomplishments. [...] Don't get me wrong. IT companies will continue to generate interesting products, but the spotlight seems to be moving elsewhere."

I guess my main issue with IT/software industry at the moment is that while I believe they will continue to produce some redefining-how-a-lot-of-the-world-operates technologies [think the Web, email, Google, Ebay], my gut feel is that those will be relatively few and far between. And it's not obvious at all to me what the next such technology will be. Things like collaboration software, better user interfaces, devices [phones, handhelds, music players] etc are cool, but they're not exactly earth-shattering.

On the other hand, you have fields like nanotech and biotech that are still so full of promise that you could pretty much blindfold yourself and throw darts at a list of things being pursued and have an excellent chance of hitting something that, if it works out, will have an enormous impact. Given my desire to work on something that knocks people's socks off, I like those odds a lot better than hoping I happen to be at exactly the right place at the right time in the software industry to participate in one of those category-redefining shifts.

Of course, I'm also the guy who said "Why on earth would anybody buy a book about the Internet ? That's the dumbest idea I've ever heard !" back in '94 so my track record for technology prognostication isn't something a smart betting man would put money on.

Tuesday, December 21, 2004

Seattle vrs Boston: not even close

Right after undergrad, I spent a year in Boston, working for Sapient. And I actually liked Boston. When I moved to Seattle, I often thought "Gee, this place would be perfect if you could replace the city of Seattle with the city of Boston, but keep the surroundings ie the lakes, mountains etc".

Now that I'm back in Boston, I realize that my affection for it was probably based a lot on the fact that
I was getting a real paycheck for the first time, I didn't really see much of it [because I was working a lot], and when I did go out, I spent a lot of time in bars, hanging out with the hard-drinkin' folks at Sapient. In other words, there wasn't much about the city itself that I liked, it was more that I liked my lifestyle, independent of the city and surroundings per se.

I find myself missing Seattle a lot. A lot of this is stuff that I didn't consciously recognize but that bubbled to the top when Christina pointed it out [not surprisingly, she's much more perceptive about feelings than I am ;-)]. We miss the wide-open views, seeing mountains in the distance, and lots of open water; seeing lots of green, living plants even in the middle of winter; living in a city where people aren't rude, where leaning on your car's horn is viewed as a last resort, not the first; where the cost of living isn't insane and which just isn't as grimy as many East Coast cities seem to be. Personally, I miss driving across Lake Washington in the morning and watching the fog rise off the lake; in the summer, it was always fun to ride across the bridge on my motorcycle, feel the breeze coming off the lake, smell the water and watch waterskiers throw up roostertails of sparkling water. In summary, we both don't understand why the whole nation doesn't just pack up and move to Seattle =) . It's much more of a rude shock for Christina, giving that it's the first time she's lived anywhere else. She's mostly grinning and bearing it, though, trooper that she is, but she's definitely agitating for getting out of here as quickly as possible and I'm with her all the way.

Halfway through my first year and we're already tired of Boston. It's going to be a long 4-5 years ;-)

[Of course we miss friends and family as well, but that'd be the case regardless of where we moved to ...]

First semester braindump

A recap of what happened over the last 3.5 months as far as my edu-ma-cation goes.


CSB 100: "Topics in Computational and Systems Biology". This was a "literature-based" class ie we read a bunch of papers and discussed them. Good class in terms of looking at a very broad range of topics eg DNA microarrays, high-throughput protein phosphorylation measurements, Bayesian networks, robustness and modularity in biological systems etc. My main problem with it was that it wasn't entirely clear what we were supposed to get out of each paper ie what the take-home message from each one was supposed to be.

Biological Engineering 420: "Biomolecular Kinetics and Cellular Dynamics". This was a class that really had two parts. The first part concentrated on applying the principles of chemical kinetics [ie how fast chemical reactions occur] and equilibria ["given compounds A and B, how much compound C do I get when the reaction is complete ?"] to the chemical reactions that occur in biological systems. Examples of such reactions are molecules (like drugs) binding to receptors on cells, the chemical reactions that occur inside cells to turn genes on and off etc.

The second, and in some ways more valuable, part was taking scientific papers that contained a mathematical model of some biological process and getting us to reproduce the results of the model, as well as extend the model in some way. Having to do this really forces you to understand where all the equations and graphs in a paper come from so you can reproduce them [by writing a bunch of Matlab code]. In that respect, it was a good way of instilling the skills required to analyze a model, figure out what assumptions it makes and decide for yourself whether you think it's a good model or not.

The main downside to this course was the amount of work required -- I spent an average of 15-20 hours a week on the homework for it.

7.81: "Systems Biology". Another model-building class, but what made this different is that it covered models at various scales, starting from modeling what occurs inside a single cell all the way up to how different cells 'talk' to each other in order to produce a whole animal. It also relied very heavily on analyzing these models via something called "linear stability analysis" -- basically, you write down a set of differential equations that you think the system obeys, which then allows you to analyze how the system behaves. For example, you can have three genes that interact in such a way that gene A turns off gene B, gene B turns off gene C and gene C turns off gene A. Depending on how quickly the genes turn each other off, you can get a continuous oscillation ie the amount of gene A goes up, goes down, goes back up, goes down etc. Linear stability analysis allows you to figure out how fast the reactions have to go for this to be the case. [For the detail-oriented: yes, I'm playing fast and loose here with the distinction between a gene and the protein it encodes. Deal.] Overall, a great class; even the problem sets were interesting, once you actually figured out what the hell the question meant =)

Historical side note from this class: Alan Turing, one of the fathers of modern computer science, actually did some
theoretical work on pattern formation in biological systems [eg the stripes on a zebra's skin], way back in the 1950s. That was one smart man ...

So, basically it's been a semester of model building -- lots of differential equations, both ordinary and partial, and finding mostly numerical solutions to them. I must admit that I'm a bit disappointed, in an "Is that all there is to these models ?" sort of way ... I'm not sure what I expected, but somehow this wasn't quite it. Haven't quite put my finger on what exactly is bothering me yet.


described elsewhere, I did one rotation in Drew Endy's lab, building some Biobrick parts. My current rotation is in Doug Lauffenburger's lab, where I'm working on applying Bayesian networks to analyzing some T-cell signaling data. Sounds fancy, but so far, it's pretty much consisted of figuring out how to get some existing code running and massaging the data from an Excel spreadsheet into a suitable XML format. In other words, pretty mundane computer stuff. Some of that is an artefact of the timing of the rotation -- it started a week and a half before Thanksgiving [just enough time to start ramping up], then there was Thanksgiving, after which came the last week of classes [ie last set of homework the professors needed to cram in], finals week and now the holiday season. In other words, I haven't exactly devoted a lot of time to it, or to figuring out how to make it more interesting. We'll see how far I get before my third rotation starts in the middle of January ...

Things I've figured out this semester:

1. I'm an engineer, not a scientist

This means that I want to actually build something, not just understand how it works. If, in order to build what I want to build, I have to figure out how something works, great, but that's not my main motivation. What this means is that a lot of the research going on in MIT's biology department leaves me rather cold, because it's very much "pure science" -- figure out how system X in organism Y works, in excruciating detail, with no obvious practical application other than the oft-invoked "... and this research may help us understand how the process works in humans, which will lead to treatments for [insert your favorite disease here]". I definitely understand that argument -- working on model organisms like E. coli, mice, fruit flies etc has been a great way to figure out how many biological systems work, but where things fall down for me is in not going beyond just understanding the system, and actually trying to manipulate it.

My statement that I like to build stuff may strike some of you who know about my lack of interest in really hands-on things like tinkering with engines etc as rather funny. I've thought about that apparent disparity a lot, and the conclusion that I've come to is that I like building abstract entities -- computer code is the perfect example. You can construct really complex artifacts, tinker with them and have the satisfaction of seeing your creations run, all without getting your hands dirty or requiring much hand-eye coordination =) More about this later on.

2. I'm not much of a [mathematical] theorist

I already kind of knew this based on my likes and dislikes in computer science. I was never very interested in algorithm design and coming up with things like the absolutely quickest sorting algorithm; instead, what's always interested me more has been "systems"-level stuff ie tinkering with operating systems and networks. Essentially, figure out the base-level algorithm needed for your system to not totally suck, build the system, measure it and then improve it based on what you've measured. That's in contrast to the approach of first coming up with a really sophisticated mathematical model that allows you to prove all kinds of cool things and only then trying to write code that actually implements your insanely-complicated algorithm.

From personal observation, I know what happens when you take the second approach: the software is pretty much impossible to get right, when it breaks only the person who wrote it can fix it and you keep discovering corner cases that your fancy algorithm doesn't handle very well. But the real killer is that as soon as your code encounters the real world, you find out that your system doesn't work as anticipated because the real world doesn't match all those nice simplifying assumptions you made. And then you're right back to where you would have been if you'd taken the first approach -- measuring what's going on and trying to fix your code accordingly, except that you're actually worse off: now you have to try to fix something really complicated.

In terms of biology, this realization was reinforced by my reaction to some of the papers I've had to read that contain mathematical models of biological processes. Basically, beyond a certain level of mathematical complexity, my mind just switched off and I skipped that section. Part of this is likely due to the fact that sometimes I didn't actually understand the math, but I also think that a lot of the theoretical models in papers go a bit too far -- at a certain point, it feels like they're just making stuff up so that their model fits the experimental evidence, with little physical evidence.

I suppose I'm a bit disillusioned about mathematical models of biological processes in general. When I started the semester, my stance was something along the lines of "Models should be as detailed as possible" and I thought that if you built in enough complexity, you could predict just about anything. Now, it's more like "Models should be as detailed as needed, and as allowed by experimental evidence ... and in the end you're going to have to do the experiment anyway, so don't overdo it". In other words:

- only try to model something at, say, the level of individual molecules if what you really want is knowledge at the molecular level, don't do it if what you're really interested in is something higher-level, like behavior of a whole tissue.
"Of Exactitude In Science" (thanks to Drew Endy for pointing this out) makes the point more poetically =)
- only put things into your model for which you have experimental evidence, don't just make up some terms so that your model produces pretty graphs which look like the experimental data
- ultimately, you're going to have to perform the experimental work to verify your model anyway, so don't go insane building in lots of fancy math

In thinking about the issue of mathematical models of biological processes, I've come across a couple of links that I haven't fully digested yet, but that I present here for anybody who is interested in thinking about this some more:

Eugene Wigner [a Physics Nobel laureate] wrote an essay on "The Unreasonable Effectiveness of Mathematics in the Natural Sciences", the gist of which is
a) beyond elementary arithmetic and geometry, math is just something that somebody made up; in other words, there's nothing in the real, physical world that corresponds to mathematical constructs like imaginary numbers, non-Euclidean geometry etc.
b)despite the fact that a lot of math is "invented", math is amazingly good at allowing us to describe and predict physical phenomena
c) what's up with that ?

- Apparently, Richard Feynman had some thoughts about the correspondence between models and nature as well, according to Werner Vogels' post about
Feynman & REST.

What's next ?

As I said earlier, this semester was mostly about learning how to build mathematical models. Next semester, my main focus is going to be what most people think of when they hear "bioinformatics" [to the extent that they think of anything ;-)]: analysing various sorts of biological data. I plan to take:

BE 490 "Foundations of Computational and Systems Biology": this deals with questions like "I have the sequence of a gene or protein, is it similar to any known genes/proteins ? If not, how different is it ?", "I know the sequence of a protein, can I predict into what 3-dimensional shape it will fold ?", "I have the equivalent of the pieces of a jigsaw puzzle of DNA fragments, how can I put them back together ?"

6.874 "Computational functional genomics": concentrates on analyzing genomic data via statistics, machine learning etc.

7.56 "Foundations of Cell Biology": the title says it all. My first actual biology course in 16 years ;-)

From a research perspective, I still think the notion of engineering biological systems to perform computation is the most interesting thing going on; this includes not only things like synthetic biology [on which there is another article in the Jan 2005 edition of Wired; you know stuff is cool when it's in Wired ;-)], but also other efforts that can be grouped under
"How to build a computer out of DNA" [see also the "International Meetings on DNA-based computers"]. I think these efforts have incredible long-term potential for radically changing our technological abilities. They also appeal to my "build something" instincts mentioned earlier -- it's basically the biological equivalent of writing code. In contrast, other research efforts to engineer biological systems, like the various tissue engineering efforts going on, are already a bit too hands-on/physical for me.

The main problem with this set of technologies is that they're still very, very early in their development; even though there are already some efforts that are close to being practically relevant [like Jay Keasling's work on
engineering bacteria to produce malaria drugs], I doubt the technology as a whole will become practically relevant in the next 10 years [although I'd be happy to be proven wrong]. This matters to me because I want to go into industry, not into academia, so I need to acquire a skillset that's relevant to industry. A quick-and-dirty survey of job postings looking for "computational biologists" reveals that most of the positions are for people who know about analysis of large biological data sets, basically the sort of stuff that will be covered in my courses next semester. I don't expect that that will really change over the next few years -- the glut of data is just going to get worse, so people who know how to extract something useful out of it will continue to be in demand. And just taking a course in this area doesn't exactly count as expertise -- you need to have actually used the techniques in your research to be able to claim that. So if I want to pursue a thesis topic centered around synthetic biology, I need to figure out a way to tie in some computation-intensive data analysis bits so I can say something more intelligent than "Well, I know how to spell 'microarray' ... " when a prospective employer asks me what I know about analyzing gene expression data. Still trying to figure that one out. [In other words, I'm still pretty much where I was 4 months ago. Somewhat depressing.]

and that's all I have to say about that.

Wednesday, December 15, 2004

I'm not a true geek Boy Scout

Yet another lesson learned today: a prepared geek always carries his own calculator. About 15 mins before my final exam today, I realized I hadn't brought along my calculator, so I sprinted around campus trying to borrow one before the exam started. One of the folks in the Endy lab was kind enough to lend me his, a high-falutin' graphing calculator, namely a TI-89. My misgivings started when I pulled it out of its sleeve -- it looked insanely complicated, with way more buttons than I really needed, but beggars can't be choosers, so off I ran to the exam.

About 30 mins into the exam, I needed to use a calculator and that's when the trouble began. I had to figure out something like the square root of 2000, so I typed in '2000', 'sqrt', '=', which produced the output '2000sqrt('. Not what I was looking for. Hmm, ok, looks like I need to type in 'sqrt','2000', ')','=' ie give it a well-formed expression 'sqrt(2000)'. Doing that gave me the incredibly useful message 'Syntax error. Hit Esc to cancel'. I tried a couple more variations, none of which worked, so in the end I resorted to good ol' memory [for some odd reason, I know that the square root of 2 is 1.414 and the square root of 10 is 3.162 so the square root of 2000 is 1.414*31.62], and got an answer that way.

Unfortunately, the next hurdle wasn't so easily surmounted -- I had to calculate the natural log of a number. I'm a geek, but memorizing natural logs is a bit much even for me, so I tried extracting the answer from the calculator again, with a similar lack of success. In desperation, I threw dignity to the winds, went up to the professors and asked 'Uhm ... do you know how to use this calculator ?'. That provided a bit of comic relief -- they went through all the same steps I'd gone through and finally had to admit defeat as well, which made me feel like less of a retard. Luckily, at this point one of the TA's offered me her calculator, which was a nice simple one that just did what you asked and didn't try to get fancy, so I used that for the rest of the exam.

It turns out that the calculator tries to do everything symbolically [ie it doesn't evaluate anything] unless you tell it to quit being so smart and use real numbers, which can be done by pressing the one button on the entire friggin' thing that doesn't have a label on it [believe me, I looked for a 'Just do it !' key ...].

The good news is that I'm now truly done with my first semester at The Institute, with all my limbs intact. [Random aside, on the subject of intact limbs: some people have asked the question "Is suicide at MIT a Poisson process ?", with inconclusive results. Other articles have claimed
that MIT's suicide rate is higher than at other schools, but this has also been debated.]

Monday, December 13, 2004

Short, and to the point

As I mentioned in my last post, I just sent off my application for an NSF Graduate Fellowship. Part of the application process was having to write essays answering awkward, impolite questions like "Tell us what you'll do with our money if we give it to you". The nerve of these people ;-)

My answer to that particular question ended with me describing why MIT was a good place to conduct the research I'm interested in, namely that there are several professors here who can help me out. A friend of mine read my essay and had this to say:

I like the conclusion, which could also be titled "Why I'm not just some punk off the street asking for loot":

1. I'm at MIT, fool
2. There are some really bad, bad cats who have my back on this

... and that's about as good a summary as one could wish for =)

Thursday, December 09, 2004

Quotable quotes, insane fandom, science news and the Zen wisdom of reality TV

Whew. Just had my last class of the semester, handed in a take-home final [whose main goal seemed to be to make sure that some people in the class can justifiably be given Bs, by making the last question worth half the score and stupidly difficult =)] and submitted my NSF Graduate Fellowship application. Now all that stands between me and 6 blissful weeks of not stepping into a classroom is a final next Wednesday. Of course, since that final is for a class whose notes I haven't looked at since the beginning of the semester because I've been too busy doing homework [paradoxical, isn't it ?], I can't just totallly slack off, but, nonetheless, no more homework for a while. Very exciting.

I'm going to write a summary of all the stuff I've figured out about myself, MIT and this whole computational biology business sometime in the next couple of weeks, but until then, here are a few more random items for your entertainment:

- Another funny line from van Oudenaarden, of "It's not hard, you just have to know how to do it" fame, while he was trying to explain something about the biology of fruitfly development: "Ok, is it clear now why this is confusing ?"

- The truth about why mice are used in so many research studies: scientists just don't like them.

- Laird Desmarais, the son of my friend Ron, is this week's Raiders Fan of the Week. While I may not take it to quite such extremes, I suspect that our kids will sport a certain amount of Valentino Rossi, Troy Bayliss, Ducati and MotoGP paraphernalia, at least until they're old enough to protest.

- It had to happen sometime: the genetically-enhanced cocaine plant, which produces eight times more cocaine and is more resistant to herbicides than the original. I guess if I can't find a job anywhere else once I graduate, I can always consider selling my services to the Medellin Cartel. I've always wanted to learn Spanish anyway.

- Christina and I got sucked into watching an episode of the reality-TV show "Trading Spouses". The set up is pretty simple: husband or wife from family A goes to spend a week with family B and husband or wife from family B in return spends a week with family A. Both families end up getting $50K at the end of it. Obviously, to make for good TV, the families tend to be radically different from each other, so as to produce the maximum amount of drama. This week, family A are the super-crunchy, Taoist, no-shoe-wearing, no-furniture-having, all-in-the-same-room-sleeping Abbotts from Santa Cruz. Family B is a black family from Tennessee that's rather, uhm, unrestrained -- loud, boisterous and crude.

It turns out that family A doesn't have to work because the husband's father left them a boatload of real-estate, so they're "free to pursue their desired lifestyle". Right off the bat, that kind of strikes me as cheating -- it's easy to be hippy-dippy, let's-just-all-chill-out-and-commune-with-nature when you don't have to worry about paying bills etc. The second bit I don't get is -- why would a family that pursues Taoism, with its tenets of detachment from material excess etc, participate in something as crass as reality-TV ?

Anyhoo. The wives from the two families exchange places and the predictable mismatches happen. Vickie from the Lowe family is not really down with getting up to do Yoga at 5:30am, not having any furniture, doing Tai-chi on the beach and speaking only Chinese at Chinese restaurants. However, she makes a game effort to just go along with the natural rhythms of the Abbotts. Leslie from the Abbott family, however, tries to impose her lifestyle on the Lowes, trying to get them to do Yoga, sing bluegrass etc., and then gets upset when they're not super-enthusiastic about it.

What struck me most about this was that Leslie, the person ostensibly living a lifestyle devoted to the Taoist "Wu-wei" principle of adaptability, was unable to do so once she was removed from her usual environment, where she didn't have to adapt to anything, and exposed to a different one that actually required adaptation [which ties in with my gut reaction to the fact that they didn't have to work and so lived in a hot-house, fairy tale environment]. Vickie, on the other hand, seems to have found the answer to the question "What Would Lao-Tse Do ?", quite possibly without ever having heard of him.

Moral of the story: Grasshopper, you must learn when to stay on The Way and when to leave it. Only then will you truly have the strength required to flow like water and bend like grass yet stay true to yourself.