Bench Press

One thing that biologists have learned quickly is that evolution can easily solve problems that we can only dream about understanding. A key part of the power of evolution comes from the fact that biological systems are self-replicating; cells divide and make copies of themselves, organisms give rise to offspring, and so on. Biochemists have been using so-called “directed” evolution in order to engineer really cool new proteins and molecules, such as a whole spectrum of new fluorescent proteins that Roger Tsien (2008 Nobel in Chemistry) made.

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.

Many problems in science require computing power which goes beyond mere number crunching and extends into the realm of “artificial intelligence.” For years, what researchers considered to be the ultimate test of artificial intelligence was the ability to defeat a chess pro (something which was formally resolved in the favor of our machine overlords when IBM’s Deep Blue beat out reigning chess champ Gary Kasparov). I’ve always been confused by this for, as complex as chess is, there is a game which exists which is so much more complex than chess it makes chess look like a game of tic-tac-toe.

That game is Go. Invented in China and reaching the West through Japan, Go is a game with relatively simple rules, but requires a depth of understanding to master (personal note: one of the brains behind Bench Press, Eric, is an avid fan of the game) given the intricacies of gameplay and the structure of the board. This makes it exceedingly difficult for a computer using brute-force methods to defeat even a moderately skilled Go player. For instance:

• In chess, different pieces have specific limitations on where they can move. There are very few limitations on where Go pieces can be placed on a board.
• In chess, pieces can be removed via capture, and those pieces can never return. No such limitation exists in Go. A piece can be removed, but it can just as easily come back in a later turn.
• The Go board is a 19 x 19 grid compared to a chess board which merely has 8 x 8 squares. This translates into ~100-200 possible moves each turn of Go compared with ~30-40 in chess.

What does this mean? A quick Wikipedia search shows that an estimated 10¹⁷⁰ possible end-states and 10³⁶⁰-10⁷⁰⁰ possible games (compared to a measly 10⁵⁰ end-states and 10¹²⁰ games for chess). To give a sense of how large these numbers are, there are an estimated 10⁸⁰ atoms in the observable universe!

The sheer complexity of the game and the ability of human masters to intuitively understand and visualize the board (as Dartmouth artificial intelligence professor Bob Hearn puts it in an interview with Wired Science, “Go is a game of living things, and you talk about it that way, as if the patterns might be alive”) led many to believe that developing a program capable of beating humans at Go would thus be a high sign of artificial intelligence. After all, what computer can possibly brute force search far enough ahead in a game of Go to beat a human?

Well, as it turns out, creating a computer algorithm that can understand a game of Go is exceedingly difficult. But, while ingenuity and intuition are difficult for computers, simulating it with number-crunching on carefully conducted statistical simulations is in a computer’s list of tricks. New programs based on compiling the results of millions of Monte Carlo simulations (a computational technique revolving around crunching the results of many random tests) have succeeded where dozens of previous attempts at introducing human-pattern-recognition heuristics failed. Instead of attempting to analyze every possible move or feably trying to understand the layout of a game, these Monte Carlo Go programs crunch through the results of their random games to determine quick statistical rules of play which help guide still further Monte Carlo simulations – the result of which is a computer which gets more and more knowledgeable about how Go games may result.

The result? On August 7, 2008, for the first time in history, the Monte Carlo Go program MoGo beat 8-dan (the second highest ranking possible) professional Go player Kim Myungwang. In all fairness to Kim, the program had a 9-stone handicap (a lead you give a beginner). But, I think the key takeaway that can be learned here is the power of statistical algorithms to mimick (and potentially surpass) human ingenuity.

And, with new methods making supercomputer power much more accessible like crowdsourcing, distributed computing, and alternative chip architectures, that’s something which scientists, doctors, and engineers may all hopefully benefit from in the near future.

(Image Credit) (Image Credit)

What do the LHC and cloud computing have to do with each other? Read and find out.

Not two phrases that generally go with one another. However, with the high demand of calculations required to do large ion simulations, it seemed only natural that CERN physicists in Switzerland start employing the benefits of cloud computing. Before using cloud computing, CERN physicists relied on its own pool of distributed computing which was managed by a scheduler called AliEn.

However, the need for more resources suggested a push towards cloud computing and inspired the birth of the Nimbus Project. Sponsored by Google’s Summer of Code, the Nimbus Project worked on providing integration between cloud computing’s dynamically allocated resources along with the already existing infrastructure at CERN. Thus, to tackle this problem, the Nimbus Project needed to create a virtual machine capable of supporting CERN’s heavy ion calculations to deploy on computers outside of their distributed computing pool. Luckily, the CernVM technology already had existing support for supplying “portable development environments” in case scientists needed to crunch data on their personal laptops and desktops.

However, the snag with this approach was that these virtual machines still needed to be integrated with AliEn for it to be of any use. In order to do that, CERN scientists needed computers in the cloud to have context-specific information involving certain configurations and security. But thanks to the Nimbus Context Broker, software created by the Department of Energy’s Argonne National Laboratories and the University of Chicago, this context-specific information was able to be provided securely without much overhead. In the end, the endeavor was a success and support for Xen images used by Amazon’s EC2 and Science Cloud’s has already been added.

“Commercial cloud providers such as EC2 allow users to deploy groups of unconnected virtual machines, whereas scientists typically need a ready-to-use cluster whose nodes share a common configuration and security context. The Nimbus Context Broker bridges that gap,” said Kate Keahey, a computer scientist at Argonne and head of the Nimbus project.

For me, this breakthrough is another testament to how new technologies are being used in different fields to further research and science. Modeling and cloud computing, both topics we’ve discussed before on Benchpress, are paving the way for the future and illustrates technology’s wide-reaching benefits to all aspects of science.

The internet’s enabled a lot of innovation in models of scientific publishing, from the age-old arXiv pre-print depository, to open access journals like PLoS Biology and PLoS ONE, and journals like JoVE (the Journal of Visualized Experiments).

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.

Sorry for the late post everyone between lab and March Madness (UCLA ftw!) it’s been a hectic couple days. Despite all that I wanted to write a quick post about a news feature on science journalism over at Nature by Geoff Brumfiel. Brumfiel’s article discusses the rapid decline of science journalism and questions whether science blogging can step in to fill the role.

It’s very well written and brings up several interesting points which are already being discussed all over the blogosphere. One idea in Brumfiel’s article really caught my attention and that is that since science journalism is atrocious to begin with, we’re better off without it. Larry Moran’s comment that “[m]ost of what passes for science journalism is so bad we will be better off without it” is sentiment that’s apparently shared by many bloggers and while I don’t disagree that a lot of what passes as science journalism is poor (thanks to a variety of issues e.g. dwindling budgets, lack of writers with legitimate science backgrounds) I can’t agree with the sentiment that society would be better off without some form of mainstream science journalism. Regardless of their failures, mainstream science journalism at it’s worst raises awareness of scientific endeavors within the general public and at it’s best ought to serve as a legitimate watchdog for scientific misconduct.

Even if hype and marketability play a major role in the presentation of science news stories, the exposure, discussion, and potential inspiration from scientific breakthroughs presented in the mainstream media outweigh much of the typical issues (e.g. inaccuracies, oversimplification, and generalizations) that scientists have with scientific journalism. I became curious about science by getting a taste from mainstream scientific journalism as a young student and I’d hate to see that possibility disappear.

The mainstream media’s science coverage is definitely flawed but that does not provide a necessary and sufficient justification for getting rid of mainstream science journalism in it’s entirety. Improvements can and should be made, however as discussed by Brumfiel’s article this will ultimately require a give and take between journalists and scientists. The editorial introduction to Brumfiel’s article puts it best:

[I]n today’s overstressed media market, scientists must change these attitudes if they want to stay in the public eye. They must recognize the contributions of bloggers and others, and they should encourage any and all experiments that could help science better penetrate the news cycle. Even if they are reluctant to talk to the press themselves, they should encourage colleagues who do so responsibly. Scientists are poised to reach more people than ever, but only if they can embrace the very technology that they have developed.

In the end as Bora Zivkovic astutely states “[s]omebody has to actually be paid to write about things as they come out”. There will always be a need for a “professional” science journalist of some sort and I think scientists can play a large role in helping these journalists be science journalists. The decline of mainstream journalism in it’s current incarnation provides a grand opportunity for scientists to help fix the problems that we currently see. The movement of bloggers into print media and John Timmer’s work at Ars Technica are just two examples of how scientists can begin making an impact on the scientific journalism establishment. Participation in the discussion and providing new ideas will ultimately help more than happily dancing on the grave of that drivvle most scientists view scientific journalism as.

A lot of scientific circles are talking about how best to spur collaboration, and that’s spawned a number of movements, such as “open access” and “open science” — both inspired by the “open source” movement in programming — that fight to end the fencing of science into proprietary, commercial enclaves that require fees to access. Clearly, in terms of fostering the trade of knowledge, an open, free highway is better than a highway with a large toll.

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.

CO₂ + 2 H₂O + light –> (CH₂O)_n + H₂O + O₂

The equation above was the first thing I ever learned about photosynthesis. A simple equation that stated that the input of water, carbon dioxide, and light would allow a plant to produce sugar, water, and oxygen. The equation is just a simple overview of the impressive chain of events that take place within each cell of a plant undergoing photosynthesis. While scientists have studied and admired photosynthesis in great detail; producing a cost-effective artificial system for harnessing light for energy has proven to be a difficult proposition.

Today, much of the research being done focuses on finding ways to improve efficiency of solar cells thereby making them more cost effective. Some research is even being done to produce artificial “trees” that contain solar cells in the leaves as well as piezoelectric elements to harness kinetic energy from the wind and rain. While all these different approaches are promising and are obviously photosynthesis inspired none of them truly imitate the basic chemical reaction that is the crux of photosynthesis. That’s why I was really impressed when I read about researchers, at the U.S. Department of Energy’s Lawrence Berkeley National Laboratory, who’ve discovered nanocrystals of cobalt oxide are capable of splitting water with only the application of visible light.

An excerpt from Physorg.com’s article:

Green plants perform the photooxidation of water molecules within a complex of proteins called Photosystem II, in which manganese-containing enzymes serve as the catalyst. Manganese-based organometallic complexes modeled off Photosystem II have shown some promise as photocatalysts for water oxidation but some suffer from being water insoluble and none are very robust. In looking for purely inorganic catalysts that would dissolve in water and would be far more robust than biomimetic materials, Frei and Jiao turned to cobalt oxide, a highly abundant material that is an an important industrial catalyst. When Frei and Jiao tested micron-sized particles of cobalt oxide, they found the particles were inefficient and not nearly fast enough to serve as photocatalysts. However, when they nano-sized the particles it was another story.

“The yield for clusters of cobalt oxide (Co₃O₄) nano-sized crystals was about 1,600 times higher than for micron-sized particles,” said Frei, “and the turnover frequency (speed) was about 1,140 oxygen molecules per second per cluster, which is commensurate with solar flux at ground level (approximately 1,000 Watts per square meter).”

Frei and Jiao hope to tie this breakthrough into a liquid fuel producing system that’s renewable and scrubs the atmosphere of CO₂ in the process. With their work on cobalt oxide they’ve made an important first step in producing a viable artificial photosynthetic system. I sure hope nature’s ok with us taking a page from her playbook.

(Image Credit – Simple Photosynthesis , Image Credit – Aritifical photosynthesis concept , Complete Physorg.com article)

*edited the photosynthesis formula meant to use the general one, but instead I used some wack combination of the two.

We’ve posted a couple of times before about revolutions in scanning and probing technology allowing scientists to study and detect intricate molecular detail (e.g lab-on-a-chip, nanoscale MRI, and imaging mass spectrometry).

But what if you’re trying to study something much larger? Say a Mayan temple? Or a cave? Good luck running a imaging mass spec on that!

Seismic imaging techniques might give you some clues, but they run the risk of damaging what you’re trying to study and are generally not precise enough to necessarily be able to detect hidden rooms. What you want is to be able to X-ray the temple – without the hassle of an X-ray. Well, researchers at the University of Texas in Austin are using just such a method: muons.

For those of you who aren’t particle physicists, muons are negatively charged subatomic particles that are about 200 times larger than electrons and are very good at penetrating substances, a property which makes muon tomography possible. Much as X-rays can be used to image the insides of a person because of their ability to penetrate the skin, muons can be used to image Mayan pyramids because of their ability to penetrate the rock which makes up the walls of the pyramid. And, instead of being absorbed by bone like X-rays are, muons are deflected, and the amount of deflection is dependent on the density of the substance that they encounter.

So, the method sounds great on paper, but where do you get these muons? Does one need to carry a giant muon machine analogous to the large X-ray devices that you may find at the doctor’s or dentist’s? Well, that would be one approach, except the energy necessary to create muons is only achieved inside a high energy particle accelerator. Unless someone is intending to move the Large Hadron Collider to Central America, that approach hardly seems viable.

Luckily, there is a readily accessible source of muons that is far more powerful than CERN’s famed Large Hadron Collider: the universe.

Every moment, thanks to cosmic rays, we are being bombarded by muons, and since they pretty much come from everywhere in the cosmo’s, that gives a decent “baseline” from which to measure muon deflection.

All that’s needed is to plant a couple muon detectors (pictured on the left) in strategic locations, some detection techniques borrowed from particle physics, and sophisticated computer-aided tomography technology, the self-dubbed UT Maya Muon group is able to image at meter-resolution at a radius of almost 100 meters from the detector – enabling the researchers to create a 3D model of the pyramid.

And, I bet if you listen carefully, you’ll hear someone say “my muon generator is bigger than your muon generator!”

(Image Credit – Mayan temple) (Image Credit – Muon detector)

The thesis of this  blog has always been that technology can play a valuable and meaningful role in science. We’ve shown that it can:

• Change how you image/detect things
• Enable you to cheaply pool large amounts of computing power
• Help you reach out and connect with the general public

And, with the power of YouTube, social media, and advanced computer graphics and modeling techniques, it can make something as mundane as PCR look super cool (HT: my Benchpress partner-in-crime Anthony for showing this to me and Roche for making it):

Or as Anthony noted, “if RT-PCR was this cool, I’d do it everyday”. Now, how do I get my PCR machine to fight evil?

Thankfully food scares like the Salmonella contamination of peanut products from the Peanut Corporation of America are fairly rare in the US the health risk posed by contaminated food and water must be taken extremely seriously and effective means to test and certify food and water safety are of the utmost importance. Despite this even with modern advances some tests can currently take days to verify the safety of food and water samples.

This lag time can ultimately prove disastrous in certain scenarios. For example, the rural
community of Walkerton, Canada experienced E. coli contamination in May, 2000 resulting in seven deaths and hundreds sickened by the contaminated water. Therefore, testing of critical supplies like water ought to be as near real time as possible in order to minimize potential harm.

This brings me to research being conducted by Dr. Shacham-Diamand’s group at Tel Aviv University. Speaking about the dangers of water poisoning Dr. Shacham-Diamand warns “You don’t want hospitals to be sensors for toxicity. That’s too late”. This desire to provide a more rapid and effective testing apparatus propelled Shacham-Diamand’s group to design a “lab on a chip” capable of accurately detecting a wide range of contaminants in water within minutes of simply adding water to the chip.

This “lab on a chip” is built upon genetically engineered E. coli in reaction chambers on a chip as seen in the diagram below (Subfigures A + B). When exposed to nL samples the E. coli luminesce in the presence toxins which are then detected and quantified by the signal strength. Initial experiments done with E. coli containing the lac promoter (activated by IPTG) fused to lux-CDABE genes of V. Fischeri proved the feasibility of utilizing whole cell bacteria in order to generate luminescent signal that could be detected utilizing a solid-state photodetector¹. Other experiments conducted by Dr. Shacham-Diamond’s group have proved the feasibility of detecting a variety of contaminants with genetically engineered E. coli².

Currently, Dr. Shacham-Diamand’s group has worked on further modeling of their chip design (above Subfigure C) in order to optimize the detection of the luminescent signal. The flexibility generated by using genetically engineered E. coli has Dr. Shacham-Diamand’s group looking into alternative applications of their chip such as screening potential cancer drugs.

In the end innovative nanoscale devices like this “lab on a chip” and the DNA-coated nanowire device we blogged about previously show tremendous promise for improving our ability to detect and diagnose a wide range of problems be they contaminated water or diseases.

(Sources: Tel Aviv University , 1 – Towards toxicity detection using a lab-on-chip based on the integration of MOEMS and whole-cell sensors , 2 - Novel Integrated Electrochemical Nano-Biochip for Toxicity Detection in Water)