We’ve mentioned the Playstation 3′s enormous computational potential in the past, and so we’re sad to see this capability disappear. It’s easy to understand the need for the changes from Sony’s perspective; Linux installation allowed for a lot more video game piracy, which is especially important considering that Sony makes very little money from the PS3 unit itself. The PS3 is most likely a loss-leader for Sony’s much more lucrative game licensing business, so Sony decided the cost of supporting the relatively tiny community of researchers and hobbyists just isn’t worth the hit in revenue from allowing video game piracy.

In any case, the newest PS3s don’t have support for general Linux installation in any case, so the overall impact of this software update will probably be limited. Existing users won’t need to install the software unless they really want to play games online on the Playstation Network, and newer users won’t be missing much anyway. Still, the older PS3 was a relatively inexpensive way to obtain a test-machine that had IBM’s Cell processor, since Sony could aggressively price their PS3s through large economies of scale. It’s just too bad that the newest PS3s and older PS3s with the new software won’t be able to contribute to massive scientific computational projects for the betterment of mankind.

In the last decade or two, chemists have started to experiment with chemical, non-biological systems that are self-replicating, by using catalysts that make more of themselves. This autocatalysis, as it’s called, can lead to some surprising findings, such as the one published this week in Science magazine.

Some molecules can come in two mirror image forms called enantiomers that behave exactly the same way, except one is left-handed and the other is right-handed. Not all molecules have a “thumb” that makes them have the hand-like asymmetry, but by tweaking a symmetric molecule, one can add a thumb to make them have an enantiomer. The “thumb” that breaks the molecule’s symmetry can be anything from a huge cluster of atoms, in which asymmetries are easily detectable, to a tiny substitution for a different isotope, in which asymmetries are nearly undetectable.

The authors constructed a catalyst that makes more of itself from a pool of “fuel” molecules. The key thing here is that these fuel molecules are asymmetric; they each have on Carbon-12 isotope on one side, and one Carbon-13 isotope on the other side. There’s just slightly more of one enantiomer than the other. Surprisingly, the catalyst, because it makes more of itself, biases new copies of itself to one mirror form, which causes more bias in the newer generations of copies. At the end of the reaction, when all the fuel is spent, the catalyst is dramatically enriched in one mirror form over another, even though the system that started was only ever-so-slightly, almost undetectably biased in one form.

One of the big questions about the origins of life is about things like asymmetry. All organisms have bias in their molecules for one particular mirror version, but where this asymmetry came from is hard to analyze. One theory that’s growing in popularity is about autocatalytic systems: a small initial bias for one mirror form got amplified over time by self-replicating chemistry, until finally when life started, the molecules were all asymmetric in the same way. As a sort of modern confirmation of that theory, this study shows that even the smallest, most trivial of asymmetries can be amplified by self-replicating systems. Whatever the real history of life is, we do know that nature can pull off some amazing feats that still boggle our minds.

One relatively new journal that breaks pretty much every norm of scientific publishing, however, is Rejecta Mathematica, which only accepts manuscripts that have been rejected by peer-review elsewhere. (via Marginal Revolution) It’s tag-line is, hilariously, “Caveat Emptor,” and its logo is an amusing “not an element of” symbol. Now all we need is the Journal of Russell’s Paradoxes: it only contains papers that have been rejected from its own peer review process.

Although much of this movement towards open science has focused on journals and their large subscription fees, there’s another area of open science that’s drawn my attention: Gene Ontology (GO) annotations, which are a set of standardized annotations to classify genes according to their biological, such as “amino acid metabolism.” These annotations are, as of now, curated by experts. What I’ve noticed in particular is that GO has thrived in one community, and withered in another, and I’m curious as to why.

The yeast community is famous amongst all the molecular biology communities as being open and collaborative, to the extent that almost all gene names have been systematized, annotations for genes are very extensive and well-structured, a strain is available for the deletion of every gene, many genes are available fused to a fluorescent marker for easy microscopy, and so on. Just go to the Saccharomyces Genome Database, and there’s a wealth of all this sort of information at your fingertips, centralized, standardized, interconnected, and easy to use. In particular, the Gene Ontology annotations are considered superb and accurate, allowing for easy computational interpretation of large-scale experiments involving hundreds and thousands of genes and their interactions. Yeast genomicists use GO all the time, and contribute to its development very often.

In contrast, the human Gene Ontology annotations are considered sparse and relatively uninformative, and generally they aren’t quite as useful for interpreting things like gene expression microarrays. Instead, one of the most successful and popular sets of biological function annotations is called Ingenuity, which is a commercial software package, well developed by the large amount of money poured into it by pharmaceutical companies and other health science research and development.

Why did the two communities end up going in two directions, one towards a more collaborative, “open science”-friendly annotation system, and the other towards a proprietary, commercial annotation platform? Undoubtedly, part of the reason is the structure of financial incentives; human biology has unique opportunities for direct commercialization via drug or health research, and so people would naturally focus their efforts on things that can win them fortune. But the first yeast biology research done by Louis Pasteur was probably related to budding (pun intended) commercial R&D on reproducible bread/wine/beer recipes, so what prevented the yeast community from, say, balkanizing yeast research because of incentives from the beer brewing and bread-making industries?

Perhaps it is because the yeast community arrived at common standards and nomenclature for information sharing long before it got very large. After all, yeast doesn’t nearly have the same problem of having multiple names for the same genes that humans do (just look at the gene RANKL, which is also known as OPGL, ODF, CD254, TNFSF11, TRANCE, and hRANKL2). They also don’t have nearly as much of a problem with the explosion of gene database IDs (humans have, as a small sample: RefSeq, HGNC, Ensembl, EMBL/GenBank, Entrez, MIM, Unigene, UniProt/SwissProt, and UCSC). Perhaps having a common, universal standards-making institution is the answer, to make sure all the railroad tracks are the same width, to use an analogy.

Or perhaps its the size of the community. There are many, many more labs studying human biology than yeast biology, not only because of the financial incentives, but also because of the huge size of the human genome (1000 times bigger than the yeast genome). Maybe it’s just easier to coordinate fewer people into one community.

I think as the scientific community moves forward, especially in embracing new collaborative methods on the internet, we should closely examine what’s worked so far and what hasn’t, so that we don’t end up fording through endless patents, fees, and proprietary, non-interoperable data structures to get what we need.

There’s an imaging technique that’s been emerging over the last ten or so years called imaging mass spectrometry, which can tell you where a whole bunch of different molecules are in tissue all in one go! Basically, you take a slice of the tissue you want to image, apply a chemical resin to it (which is called a “matrix”), and then you shine a fairly strong laser at the tissue. This causes a physical reaction in which the resin absorbs the laser, ionizes, and causes molecules from the tissue to get ejected into the air. The mass spectrometer then sucks those ejected molecules into its cavernous depths and weighs them, breaks them apart, and then weighs their fragments to try to identify what the molecules are. For proteins, this identification process is actually quite easily done.

What you end up with is a really remarkable picture, a 2D map of where a range of different molecules are in the slice of tissue. You can look at the distribution of molecules of different specific weights across the entire slice. By stacking images of adjacent slices, you can then reconstruct an approximate 3D map of where molecules are in a tissue, like a brain or a tumor. Pretty neat!

A section of a mouse brain imaged for molecules of various weight (from Stoeckli et al. (2001) Nature Medicine 7, 493–496.)

Up until now, there have been two main ways to see macromolecules like proteins and RNA in biological tissue. First, you can look at where a specific macromolecule is by sticking a fluorescent dye to it and looking for that “tag.” It’s a great technique, but it’s also pretty laborious and limited in scope, because you can only examine at most three or four things at a time or the colors will start overlapping together a little too much to reliably distinguish what you’re looking at. In addition, you have to know what molecules you’re interested in first in order to tag them.

Second, you can look at cells as a whole using high energy techniques like transmission electron microscopy (TEM), which uses beams of electrons to “X-ray” cells. Since electrons have a very short wavelength, that means that they can be blocked and scattered even by very small structures, including groups of proteins, which means that a TEM image can look at a lot of things with an insanely high amount of detail. Unfortunately, electron microscopy won’t really tell you what you’re looking at, though there are a few techniques (like sticking gold beads to stuff you’re interested in) that try to get around that. Nonetheless, it doesn’t tell you much besides the overall “visual” picture.

Imaging mass spectrometry gets over a lot of these hurdles of traditional microscopy by directly associating molecular weights with specific coordinates in a tissue sample. And because the mass spectrum measures a wide range of weights, a computer algorithm can potentially tease out patterns that might not have occurred to a researcher or doctor looking for a few things at a time. The amount of information that can be obtained from a simple mass spectrum image is tremendous. With newer, cheaper super-accuracy mass spectrometers coming out, such as the orbitrap, this technology is growing in popularity outside of specialized physics labs, and it promises to revolutionize the way a lot of science and medicine is done. In the clinic, for example, a ton more information could come out of a biopsy all at once using this technology, from identifying cancer markers to positively identifying infectious agents and inflammation. I look forward to what’s coming in the future!

First, a bit of context. I’m an experimental biologist, not a computational biologist (at least, not primarily computational), so I have to deal with the fact that not everything in my research can be “electronified.” I’ve got samples in the freezer, vials of things in the fridge, cultures going in the incubators, and so on. Thus, I’m not looking for a notebook where I can keep every single bit of my research; maybe when the Matrix Google finally digitizes all of reality, I can finally just plug into my computer and never leave my desk.

On the other hand, though I’m not a computational biologist, I am pretty comfortable with computers. I program, I know HTML, and I can use Photoshop pretty quickly. I’m fine with cobbling together my own electronic lab notebook of sorts from the tools I can find on the internet (such as making a wiki); others (such as some in my lab) might find even formatting a wiki post to be an intimidating prospect. So what I do might not work for you.

Right now, my lab has an internal wiki using the MediaWiki engine, and I’ve been using my wiki space as my lab notebook. The wiki is backed up regularly, is password-protected, lets me view and edit the wiki from any internet-connected computer, has full-text search built-in, and has an editing format lets me put in cross-references to other entries; this is all I generally need for a lab notebook.

How do I manage the offline stuff, like gel pictures, data sheets, and so on? Well, I can put a surprising number of things on the wiki (for example, the lab next door lets me use their UV transilluminator, which has a CCD camera that I use to save TIFF images of my gels onto my network account), but basically I keep everything in a giant binder, numbered in order by date, and then put a reference to it in the online notebook. That way, I can easily find a result; just search the wiki for the entry I’m looking for, look up the reference number, and then find it in my binder of results. I do something similar with my experimental samples; my initials, followed by an experiment number, and then a vial number (such as “EJS-109-10″).

Unlike some people, my note-taking philosophy is that the lab notebook shouldn’t necessarily be a dirty log of absolutely everything that I do or think about while I’m in the lab; I don’t put in routine calculations or procedures, such as cell culture maintenance, making common reagents, and so on. The whole point of a lab notebook is so that I can keep track of what I did, so if I, or someone else, needs to look up what I did, or what’s in a vial in the freezer, I don’t have to spend hours trying to remember what I meant with “RfMQ2-3a-4-5-07″ on a tiny tube cap.

Sure, it’s sometimes nice to have everything in one lab notebook, gels pasted in and so on, but frankly, I find that even just the organizational benefits of being able to read my own handwriting and being able to search and cross-reference my posts to be worth giving up the all-in-one solution. When I kept a paper notebook, I spent so much time flipping back and forth between pages trying to remember where I’d written the concentration for the vial in my hand. Now, I just search for the vial number and voila!

Not only that, but I like to organize my lab notebook by project, rather than chronologically, because I generally have more than one thing going on at the same time. Organizing that way is doable on paper if you use a binder and loose-leaf paper, but still quite a hassle, especially since some experiments don’t always fit cleanly in one project or another, making it hard to find later (“Did I file it under this project or the other one?”). I prefer the electronic notebook, which lets me organize by both time and project, and lets me put experiments under more than one lab notebook by simply putting a link to it from both project pages. Not only that, but if I’m repeating an experiment with slightly different conditions, I can simply copy-and-paste a previous experiment and change just a few things.

Paper just doesn’t cut it for me anymore. If I were keeping a paper notebook, I’d basically be doing all the same things as I do on my electronic notebook: a loose-leaf notebook organized by projects, numbered experiments, separate binder of raw data, cross-referencing based on page numbers or experiment numbers, and so on. My online notebook does all this and adds remote access, readability, copy-and-paste, and searching to boot.

Not only that, but because my lab notebook is online, if I’m writing a paper or making slides for a presentation, I can be lazy and work from home. Win for the web!