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.

Happy belated 200th, Charles Darwin.

Charles Darwin brought to the field of biology a revolutionary insight into how species came about. But beyond merely synthesizing a  coherent theory of evolution, Darwin was also responsible for popularizing a way of conceptualizing evolution which continues to be used today: the evolutionary tree.

Darwin’s first (and quite possibly the first ever) evolutionary tree is depicted to the left (dated 1837, from Darwin’s First Notebook on Transmutation of Species on view at the Museum of Natural History in Manhattan) and is, almost ceremonially, directly underneath the words “I think”.

The tree depicts the relationships between a number of species in terms of how they are all related. And, while the concept itself is relatively simple, the actual tree itself is highly complex for three reasons.

First, as the number of species increases, it becomes much harder to generate complete and accurate evolutionary trees to cover all the possible evolutionary relationships. Knowing that A and B are related does not tell you if A and B are more closely related to each other than they are to C, for instance.

Secondly, Darwin’s tree only considered the possibility of branches – it never considered the possibility that branches may merge or that relationships other than branching out could exist. For instance, bacteria have at least three ways of obtaining genetic material through a method other than replicating:

• Transformation: a bacterium absorbs genetic material from its surroundings
• Conjugation: a bacterium exchanges genetic material with a neighboring bacterium
• Transduction: a virus inserts genetic material into the bacterium’s genetic code

Third, evidence linking two species in terms of an evolutionary tree is difficult to interpret. The challenge is that evolution occurs over millions of years – what we can see are end-results and fossils. What we don’t see is that evolution can change “directions” or affect different genes and characteristics differently over time.

So, what do we do? How do we present the richness of information encoded in an evolutionary tree, but updated with a contemporary understanding of evolutionary biology and genetics, and yet presented in a way which is actually useful and meaningful to scientists?

Enter Tree 2.0. Scientists (including Michael Sanderson’s group at the University of Arizona who developed the Paloverde software to visualize dense “tree of life”’s) have apparently partnered with web technology super-specialists Google and Adobe (HT: New York Times) to develop a new way of visualizing and storing the massive amounts of information needed to fully characterize the tree of life.

While very little in the way of details of the partnership have emerged, I think this is a clear example of where advances in computational and visualization technology can play in helping biologists better understand evolution, on three levels:

• First, sophisticated software can be used to help scientists crunch through the myriads of genetic data that needs to be understood in order to synthesize a proper evolutionary tree; it’s simply too difficult for humans and non-automated systems to sift through data from thousands of species at once to pick out all the relevant relationships; we posted before about how algorithms used to detect cheating may help in picking out evolutionary relationships – we probably need many more to detect and assemble the total tree of life structure
• Second, new technology and standards are needed to store the information within the tree to depict the types of relationships which can emerge between species (e.g. horizontal gene transfer, how certain genes have converged between species while others have diverged, etc)
• Third, visualization technology akin to what is used by Google Earth to depict rich and complex information quickly can help scientists quickly query and use to advance our understanding of biology – not to mention it would just look cool and be something everyone could easily browse

I can’t wait for what comes out of the collaboration with Google – and I do believe that this is the best birthday present Charles Darwin could ask for – even if he has to wait a little after his 200th birthday to get it.

(Image credit – Darwin’s tree: Wikipedia) (Image credit – Circle Tree of Life: Hillis and Bull laboratory)

Berkeley researchers are on the heels of settling debates over taxonomy using innovative computing methods.

Apparently, whoever came up with the saying “nothing good ever comes from cheating” didn’t go to Berkeley. Recently, a team of researchers at the University of California, Berkeley took a little bit of inspiration from plagiarism-detecting software and created a program which can compare the entire genomes of two distinct organisms. Using what are known as feature frequency profiles (FFP), professor of chemistry Sung-Hou Kim and his team were able to successfully compare the genomes of several different organisms, and in a sense, “map” the organisms’ evolutionary family tree.

Traditionally, methods of determining how closely related two species are focus on a very specific subset of genes that the organisms have in common. The differences and similarities in the genetic code are then counted up and a computer program constructs the family tree. The more differences in the genes, the more distantly related the organisms are. However, the drawback of this technique is that it relies on organisms having these specific genes in common. What may end up happening is that an organism may not have a “homologous” gene to compare it with. Additionally, two genes that are used for comparison oftentimes conflict with each other; one gene says two organisms should be closely related while another one says they shouldn’t. With this innovative new approach of using FFP’s, the entire genome is sequenced and compared, as opposed to several different genes, allowing scientists to view differences in a larger scope.

Maybe even more surprising, this idea of using FFP’s as a means of comparison transcends the realm of genetics. When applied to literary works, this algorithm was able to better detect similarities between texts by the same author, of the same genre, and of the same historical era, than other conventional methods.

I was just stunned when I saw this,” Kim said. One of the reasons this method works better, he said, may be that, while word frequency analysis treats each word independently, feature frequency analysis picks up syntax.

Armed with this technique, these researchers have successfully segregated the proteomes of bacteria, Archaea, and eukaryotes with genomes of varying complexity into distinct groups and domains. Whenever disagreement occurred between their findings and common scientific belief, there was generally some kind of ongoing debate over that particular organism’s taxonomy. Professor Kim and the rest of his colleagues hope that they can use this algorithm to classify some enigmatic viral genomes and possibly utilize it in other fronts, such as literature and electronic encoding.

To me, this breakthrough in comparative genomics is a chance to reflect on how marvelous and miraculous life and evolution are. To think that the entire human civilization arose from single celled organisms, and that the building blocks and impulses which act upon bacteria and micro-organisms to keep them alive are the very same mechanisms that we live on.