The Futile Cycle

One of the amusing things to look at when I have time in between experiments is to look at what kind of search phrases people use to find the site. The best ones are from the long tail of search phrases that only one person would ever type:

“earthworm hammer dissection”. I don’t know about you, but generally I don’t use hammers when dissecting things as small as earthworms…

“vitameatavegamin medicine”. I don’t really want to know how many people are taking medical advice from “I Love Lucy”.

“is well done steak higher in water than rare”. I’m not really sure what this means. Are they asking whether well done steak has more water? (It doesn’t.) Are they asking whether it floats compared to rare? (I have no idea.)

“how in the hell does a person create a ‘table of contents’ in microsoft word 2007″. Someone is really frustrated! I don’t know. I’ve sworn off using Word unless I absolutely have to.

“should we treat hospitals differently than we treat farms, car”. There are lots of things wrong with the current health system, but that doesn’t mean we should take it out to the pasture. (cricket) Is this asking about subsidies? I hope we treat our hospitals differently from cars…

“when i hear biochemistry, i think of”. Well, I think of lots of pipetting. And blots. Lots of blots, lots of buffer. And sometimes some nifty, but really expensive, machines.

“what is a parafilm in chemistry?” I’m a big fan of parafilm. It’s a form of wax in a sheet, with some additives that make it more elastic. It’s like the sophisticated man’s plastic wrap, the scientist’s duct tape. More praises cannot be poured over parafilm.

This year’s “Genius Grants” (MacArthur Foundation Fellowships) have been awarded, and one of the fellows is Michael Elowitz, a biologist at Caltech. He’s done some really amazing work on a subject near and dear to my own heart, which is on how genes interact, one branch of the so-called “Systems Biology.”

Some of his most famous works include the “repressilator”, work on competence in bacteria (PDF), and the theory (PDF) and measurement (PDF) of intrinsic vs. extrinsic noise in gene expression. All these major contributions, and he’s only 37!

Although systems biology is “hot”, not that many “systems biologists” work at the level of really fundamental, mechanistic biological questions (more common is a branch-off of bioinformatics and microarray analysis), so it’s great to see this kind of wonderful recognition for a really prominent figure in that arena.

I’ve kept this back for a long time, but sometimes I just can’t help myself to a bad pun or two (or three), so here it is, the world’s worst biology/programming combo joke. Please, look away, it isn’t pretty. (Prerequisites: Perl, some knowledge of biology)

So, what is this Perl subroutine?

sub p53
{
  my @n_mer = @_;
  if (scalar(@n_mer) == 2) {
    return ();
  }
  return @n_mer;
}

It’s a “two-mer” suppressor!

I warned you.

A new psychology paper is out in Intelligence claiming that differences in academic achievement by siblings of opposite sex are due to differences in variance of ability. I don’t know about the merits or demerits of the paper (I only skimmed the abstract and some of the rest of it), but the topic reminds me of remarks two years ago by a certain university president…

In other news, Dscam in flies is alternatively spliced to almost 18000 different forms so that each neuron can randomly have a unique form of the protein, allowing them to recognize themselves. This reminds me a lot of GUIDs (Globally Unique Identifiers)!

Biology (and science in general) has some jargon that sticks around long past the point where they should be buried. “Sense”, for example, is a really popular one. It gave rise to all sorts of derivative words (”anti-sense”, “nonsense”, “mis-sense”), but really those words reflect what geneticists thought of the “DNA code” back in the mid-twentieth century, when they were just beginning to realize that changes in the DNA sequences of genes led to changes in the proteins that were translated from them. The scientists thought of the DNA sequence as the “code” for the protein sequence, but they didn’t know exactly how the two corresponded with each other, which was why all their jargon was very metaphorical: “nonsense”, “commas”, and so on.

“Nonsense” is actually easier to explain than “sense”, so we’ll start with that. Researchers were studying how mutations in genes affected the proteins they coded. Certain mutations, they figured, might scramble the DNA into a sequence that wouldn’t make any “sense” if you tried to translate its code into a protein sequence. Hence, they called these scrambles “nonsense” mutations. Later, however, they found that the “nonsense” mutations didn’t really scramble or garble the instructions for the protein, but that they were just a code for “Stop! This is the end of the protein!” “Nonsense” sequences were naturally occuring “periods” at the end of code “sentences,” so to speak, and the mutations were just putting these periods into the middle of the sentences and truncating them. The cell knew exactly what they meant; they weren’t nonsense at all! But the name stuck, and even today, the particular codons that mark the end of a protein are sometimes referred to as “nonsense codons,” even though the cell knows exactly what they mean.

Of course, scientists, being tinkerers, started to change the endings of the names in order to get new words with related meanings. So if a stop code is called “nonsense”, then everything aside from that is called “sense.” Since there are two strands of DNA, the one that has the code is called “sense”, the other is called “anti-sense” (since “nonsense” is already taken). And if a “nonsense” code mutates to “sense” (or if one “sensible” code mutates to another), then that’s called “missense.” And so on and so forth.

So here we are with this arcane jargon that dates back to the 1950, from before we figured out how genes worked, and yet we’re still stuck with it for historical reasons. It could be worse. We could all still use the word “cistron” instead of “gene.”

The Ph in a Bioinformatics PhD, from Bioinformatics Zen. Look at the graphs at the end. Quite wonderful!

Suicyte Note expresses an opinion that I share very much. It’s just an opinion, but one sorely overlooked.

Via Pharmagossip, a dying professor’s last lecture. It’s very good and touching; go watch it! It seems that the lecture is part of a series, in which professors give what they would (hypothetically) consider the “last lecture of their life”; this particular scenario isn’t so hypothetical. The “Last Lecture” series itself seems like a wonderful idea. I hope that more schools try to start similar lecture series (well, hopefully without a need for real last lectures).

From S. Sarkar (1991), Genetics, 127, 257-261. (PDF):

“[The talk on the "Statistical Theory of Bacterial Mutations"] was attended, Delbruck (1946) observed, ‘by those who took the phage course this year and by a few outsiders, mostly people to whom algebra is more strange than Chinese.’”

It seems the paucity of mathematical prowess in biology was evident even back then.

After the first week of classes in graduate school, I’m a bit wrung out. I’ve taken graduate classes before, but not too many focused on paper-reading; the other classes had maybe two or three papers per week, but the current course I’m taking assigns two or three papers per class, which means around 10 hours of reading a week, assuming a little more than an hour per paper (we have to read them inside and out, understanding them in deep, experimental detail). With having around 6 or 7 seminar talks a week, two classes, and lab work to be done, and fellowships to write, I’m glad the weekend is here.

On the other hand, though, all the classwork means that I’ve been getting a lot of exposure to some very classic papers. The “fluctuation test” paper by Luria and Delbruck, for example, is from 1943, ten years before the structure of DNA was deciphered by Watson and Crick. Luria and Delbruck try to figure out whether bacteria become immune to viruses by mutations or because by chance some survive and acquire an immunity which is heritable, and they use some pretty clever math to do it.

The basic idea they use is that of the “jackpot”. Imagine that you’re playing a slot machine, and you win very rarely, but when you do, it’s a huge, huge payout: $100 million. Let’s say you pull the lever a million times, enough that you have some chance to actually win once or twice, but not enough that you’re sure to win. Now, let’s say that you have 20 people who all go to this machine and pull the lever a million times. Because one person might win three times, and another might not win at all, each person’s winnings will vary a huge amount from the others’. That’s the “jackpot” idea, that small differences get amplified a whole lot, because the payout is huge.

Now back to bacteria. There are two possible ideas, that bacteria all have a small chance of surviving the virus randomly, but that once they survive they’re immune (and pass that immunity on to their children), or that a small fraction of bacteria have a mutation that makes them immune, but most bacteria are susceptible.

The thing is, mutations are kind of like jackpots. If you start with one cell and it divides, acquiring mutations along the way, then in the early stages where there still aren’t that many cells, one cell might get a mutation. Since bacteria multiply quickly, that cell will then have lots of descendents that are also mutated, and so there’s a huge “payoff” for having an early mutation, because that change gets amplified. The number of mutations early on is random, so in two different experiments, there’s a good chance that the number of mutations you find in one is very different from the number in the other.

On the other hand, in the surviving and adapting viewpoint, there’s a low chance of surviving no matter how you go about it, and that characteristic doesn’t get amplified when bacteria have descendents. There’s no “jackpot” effect, and so the level of immune bacteria wouldn’t vary as much from experiment to experiment.

So, Luria and Delbruck did the experiment a lot of times. They took a small number of bacteria, grew them up, and tested them for immunity against a virus; and they saw the jackpot effect. Thus, mutations are the underlying cause for the resistance to virus. Bingo!

From Jacob and Wollman (1958), Symposia of the Society for Experimental Biology, 12, 75-92.

“One is thus led to dispose all known genetic characters on a circle….It seems unnecessary to emphasize that this diagrammatic representation, which is the simplest one that will account for the observed results at the present time, is not meant to imply that the bacterial chromosome is actually circular.”

(It is circular.)