Bench Press

Two weeks ago a large wildfire broke out north of Los Angeles within the Angeles National Forest. It grew quickly becoming the largest wildfire in Los Angeles County’s history. A suspected case of arson, it has burned over 160,000 acres as of today and is only 62 percent contained. The immediate impact of the Station Fire is illustrated dramatically by this image produced by NASA’s Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument on the Terra satellite.

Even having lived in San Diego during the 2007 wildfires, images like that one are just incredible. In addition to this image NASA’s Aqua satellite monitored carbon monoxide concentration within the atmosphere over the first seven days of the fire. Click through for the full animation.Thanks to NASA’s satellites some potentially useful scientific data can be gleaned from this disaster. Too bad they don’t have a satellite to help us catch those responsible.

(Station Fire Image)(Carbon monoxide measurements)

In case you weren’t aware, biology is really complicated.

It’s complicated not only because understanding specific proteins and pathways is difficult, but because understanding how a biological system of many proteins and pathways functions is even more difficult. In recent years, however, computer technology has made it possible to convert vast databases of biological information into an understanding not only of how individual genes and pathways work, but of how those individual genes and pathways work together.

The Diseasome project is one such project (and one of many -ome/omics words that you’ll encounter in the field of biology) which has converted human gene-disease relationship data from NCBI’s Online Mendelian Inheritance in Man (OMIM) into an annotated network exploring the genetic relationships between all the diseases covered in OMIM. The picture above is a small piece of the full poster (warning the file size is 20 MB) available at the site. An interactive version of the map is also available at the web page (and includes many links to Wikipedia entries explaining each of the genes and diseases) and is a fascinating browse-through for anyone who’s even remotely interested in how new network analysis techniques may be used in understanding human disease.

The Barbabasi Lab at Northeastern published an interesting paper in PNAS about how the data was compiled and analyzed for insights into how diseases and disease genes are connected and how that differs from how “normal” genes are connected. Although the work is subject to the standard Garbage-in-Garbage-Out criticism (if the data being entered is facetious or not necessarily relevant to the study then the results aren’t necessarily relevant or good) and the conclusions thus far are still relatively generic, two conclusions immediately stood out to me.

The first conclusion that jumped out at me was a very interesting analysis done where the researchers shuffled the precise disease-to-gene relationships while keeping the total number of relationships per gene and per disease the same (and repeated this 10,000 times). The finding from that analysis was that the “network clusters” from a random shuffle tended to be much larger than the actual network cluster size or, in other words, diseases which are genetically related to one another tend to be 8 times more related to one another than one would expect at random. This suggests that there are probably types of disease gene profiles with which most diseases tend to cluster around, something which ties to the groups finding that genes which are “shared” by multiple diseases tend to encode proteins which interact with one another!

The second striking conclusion was that disease-associated genes tend to interact and associate with fewer genes than non-disease associated genes. This is in sharp contrast to diseases like cancer which arise from somatic mutations (mutations which happen after birth and are not passed down from past generations) which almost always affect genes which interact with many other genes. The reasoning the paper gives, while speculative, rings true to me:

“This unexpected peripherality of most disease genes can be best explained by using an evolutionary argument. Mutations in topologically central, widely expressed genes are more likely to result in
severe impairment of normal developmental and/or physiological function, leading to lethality in utero or early extrauterine life and to eventual deletion from the population. Only mutations compatible with survival into  the reproductive years are likely to be maintained in a population.”

I’m sure I’m not the only one who eagerly awaits what other insights can arise from mining large biological databases for network information: the holy grail, of course, being enhancements to the quality of medical diagnoses and treatments.

(Image credit – Diseaseome poster)

Computer-in-a-petri-dish?

So much is made about man vs machine in science fiction, that maybe we’ve neglected to pay attention to the entity which may trump us both: bacteria. A team of US Researchers have recently published an article in the Journal of Biological Engineering that suggests an innovative use of synthetic biology to perform complex computations.

Consider a classical computer science problem: the Hamiltonian Path. Imagine that you’re a tourist, and you have a map of all the cities you wish to visit along with all the roads which connect those cities. Finding a Hamiltonian Path, in this case, would mean traveling to each city you want to visit without having to go through any city more than once. While this may seem like simple map work, computer scientists have so far been unable to find a way of efficiently finding a Hamiltonian Path that does not go through an exhaustive list of paths.

However, a team of scientists from across four academic institutions (Missouri Western State University, Davidson College, North Carolina Central University, and Johnson C. Smith University) have cleverly engineered a solution using Escherichia coli bacteria to solve a basic form of the Hamiltonian Path with only three cities.

The cities were represented by a combination of genes causing the bacteria to glow red or green, and the possible routes between the cities were explored by the random shuffling of DNA. Bacteria producing the correct answer glowed both colours, turning them yellow.

The magic behind the bacterial solution is that, through sheer brute force made possible by bacterial exponential multiplication, the bacteria can explore a wide range of possible solutions to the Hamiltonian Path Problem in parallel! 1000 bacteria = 1000 test runs! So, if the Hamiltonian Path Problem is harder than you expect, then simply let the bacteria divide. If they divide three more times, you’ll have increased the “computational” power available to you by a factor of 8!

But what makes the Hamiltonian Path Problem so interesting? If you’re a computer science buff, you’d know that the Hamiltonian Path is classified as NP complete, something which describes the most difficult search problems out there. More formally, an NP complete problem is a search problem that has an answer that can be checked/verified in a quick and efficient manner, but, as far as we know, does not have a quick and efficient way to solve. Given this very precise and esoteric definition, you would think the number of NP complete problems would be very limited. In actuality, NP complete problems show up everywhere. These problems range from esoteric ones like the Knight’s tour problem, the Travelling salesman problem, to challenging scheduling and traffic routing questions in telecommunications networks and multicore processors, and even to games like Sudoku and Battleship! (A list of some of the more well-known NP complete problems is listed on this Wikipedia page)

What makes this particular result even more interesting is the fact that NP complete problems are reducible to each and every other NP complete problem. In other words, a method that can quickly and efficiently solve one NP complete problem can be used to solve every other NP complete problem quickly and efficiently as well. In a sense, all NP complete problems are inherently the same problem. Thus, creating a bacterial solution to solving one NP complete problem provides a potential path to solve every other NP complete problem, proving that synthetic biology can do a whole lot more than just simple biological circuits, they may even provide an interesting tool to solve some of the most challenging computational problems.

So perhaps the judgment day the Terminator series envisioned won’t originate from Cyberdyne, but from a synthetic biology lab experimenting with some “smart” bacteria. I’ll be keeping my eyes open.

(Image Credit)

As technology continues to advance the ubiquitous nature of certain devices prompts innovative people to come up with amazing new uses for everyday items. A perfect example of this is the cell phone. We’ve already shown you smartphones that can take and record ultrasound images as well as a nifty Android application that makes stargazing easy for the amateur astronomer in all of us. Now a team led by Dr. Daniel Fletcher at UC Berkeley in collaboration with researchers at UCSF have turned the smartphone into an incredibly effective microscopy device.

Light microscopy is a vital tool for the diagnosis and screening of various diseases. Unfortunately in many regions of the world access is limited due to availability or lack of portability. Dr. Fletcher’s group looked to solve this problem by taking off the shelf components and building a solution that would be cheap and effective. Fletcher’s group built a mobile-phone mounted light microscope, dubbed the CellScope, capable of providing images detailed enough to help diagnose diseases like malaria and tuberculosis. Using a mobile phone as the platform for the microscope also allows images to be saved and transmitted to clinical experts for further analysis.

The CellScope was put through it’s paces by Dr. Fletcher’s team as they tested it in various applications. As seen in the figure below sickle shaped red blood cells are clearly visible within the image of a blood smear sample allowing the diagnosis of malaria.

In addition to taking diagnostically clear images of blood smears, Dr. Fletcher’s group tested the CellScope with fluorescent filters to see if the CellScope could be utilized in an increasingly popular tuberculosis screening and monitoring assay.

As seen above the fluorescent staining of tuberculosis bacilli in spittum is remarkably clear for a microscope attachment on a mobile phone. In the C panel of the above figure, Dr. Fletcher’s group also attempted to harness the computational power of the mobile phone by developing software to automatically count and process the fluorescent image.

The CellScope’s effectiveness, portability, and low cost make it an incredible tool for health care providers throughout the world. More details available at PLoS ONE.

(PLoS ONE: Mobile Phone Based Clinical Microscopy for Global Health Applications)

The heartbeat of the news.

Ever since June 25, 2009, Michael Jackson’s death has been the talk of the nation, his face plastered over web articles, newspapers, and television stations. His death broke the record for the number of users on Yahoo news at any one point in time, topping even President Barack Obama’s inauguration, and even Google believed its servers were under attack due to the sudden spike in web searches for the moon-walking legend. However, have you ever wondered why the news of the King of Pop’s untimely death has stayed in the media for so long, while other news topics, such as the death of another cultural icon, Farrah Fawcett, quickly died out?

Jon Kleinberg, Jure Leskovec, and Lars Backstroma, from the computer science department at Cornell, sought to answer these types of questions by tracking the life-cycle of news articles for a three month period during 2008. Their research included 20,000 mainstream media sites and over 90 million articles. Using a complex algorithm which could identify certain phrases in different news articles such that the computer could mark them as being of the same subject (a task that has proven to be very difficult time and time again), the team tracked the movement of news using across blogs and news sites across the Internet. Armed with an extensive pool of data to sift through and analyze, the three researchers discovered an astounding pattern that was shared throughout most news topics.

They found a consistent rhythm as stories rose into prominence and then fell off over just a few days, with a “heartbeat” pattern of handoffs between blogs and mainstream media. In mainstream media, they found, a story rises to prominence slowly then dies quickly; in the blogosphere, stories rise in popularity very quickly but then stay around longer, as discussion goes back and forth. Eventually though, almost every story is pushed aside by something newer.

Before research like this was done, many editors and journalists perceived something they described to be a “news cycle.” However, with no quantifiable data, there was no way to be confident whether this was just their perceptions or an actual phenomenon. With the information collected by these Cornell researchers, they believe the latter to be the case and have started to create mathematical models which would accurately describe the life-cycle of news.

The slow rise of a new story in the mainstream, the researchers suggest, results from imitation – as more sites carried a story, other sites were more likely to pick it up. But the life of a story is limited, as new stories quickly push out the old. A mathematical model based on the interaction of imitation and recency predicted the pattern fairly well, the researchers said, while predictions based on either imitation or recency alone couldn’t come close.

This type of news excites me because it shows how technology and the Internet have produced a tangible result (in this case, a physical model to the life cycle of a news article) to a question that would have been unsolvable 20 years ago. Truly the capabilities of technology to solve even the most abstract problems are limitless.

(Image Credit)

A lot of the “hot” research occurs at the edges of disciplines. In my humble opinion, this is a natural effect stemming from the fact that the research that occurs at those edges typically involves the use of new equipment/techniques (e.g. applying physical techniques to biology) or because of the need for experts in different fields to come together and exchange thoughts and perspectives (e.g. astrophysics requires an appreciation of the very large [traditional astronomy], the very small [quantum mechanics, statistical mechanics], and the very weird [relativity, ok so it’s not that weird, but from the perspective of someone who’s never moved at relativistic velocities, I think it’s weird]).

But, of course, interdisciplinary research, has its limits (HT: Abstruse Goose):

Anyone else have any equally bad ideas for “interdisciplinary research”?

(Image Credit – Abstruse Goose)

As genomes have been sequenced over the past few decades scientists have looked for new ways to analyze and interpret the wealth of information. They’ve developed numerous algorithms with goals ranging from organizing evolutionary family trees (inspired by plagiarism detecting software) to aligning genetic sequences. All of this to answer the numerous questions that can now be asked thanks to sequence databases. One of the many things scientists have attempted to study is positive selection in protein-coding genes.

Positive selection of advantageous gene mutation is particularly interesting to scientists as it can provide insight into the function of new genes. However, positive selection is difficult to detect and analyze as neutral and deleterious mutations predominate advantageous mutations in frequency. Initially scientists looked for positive selection by simply comparing the ratio (/omega) of nonsynonymous nucleotide substitutions (d_N) to the number of synonymous nucleotide substitutions (d_S) between homologous protein-coding gene sequences while utilizing Fisher exact tests to accept or reject a null hypothesis of neutral selection¹.

Over the years scientists developed additional statistical analyses to infer positive selection. Two of the most popular methods are the branch-site method (BSM) and site-specific method. The BSM utilizes a likelihood ratio test to detect positive selection within a given phylogenic branch. The site-specific method on the other hand utilizes /omega to look for specific amino acid substitutions that are positively selected. Both of these methods have been utilized in hundreds of papers and seemingly provided a great deal of insight into potential points of positive selection within various genomes. What would you say then when told that both of these methods contain significant flaws which provide an inordinate number of false positives?

Bovine Rhodopsin protein with predicted sites in red and experimentally determined in blue. (Adapted from Yokoyama et al. 2008 PNAS)

That’s exactly what Masatoshi Nei and his group believe to have shown in a recent paper evaluating the reliability of the branch-site and site-specific methods. Nei’s group utilized several controlled computer simulations as well as data collected by Shozo Yokoyama, at Emory University, on dim-light vision opsins in vertebrates² in their studies determining that both the branch-site and site-specific methods yielded far too many false positives. Nei and his group contend:

This low rate of predictability occurs because most of the current statistical methods are designed to identify codon sites with high /omega values, which may not have anything to do with functional changes. The codon sites showing functional changes generally do not show a high /omega value. To understand adaptive evolution, some form of experimental confirmation is necessary.

From this paper it looks like scientists looking for high /omega values may have been chasing ghosts by assuming that amino acid changes result in functional changes indicating proof of positive selection. The potential impact this will have on hundreds of papers is stunning. In the end the take home message is that statistical analyses, no matter how elegant, have their limits and ought to be utilized in conjunction with experimental data as much as possible.

(Sources: 1 – Reliabilities of identifying positive selection by the branch-site and the site-prediction methods , 2 – Elucidation of phenotypic adaptations: Molecular analyses of dim-light vision proteins in vertebrates )

updated: Had to change all the &omega to /omega because WordPress kept changing it into ? for some reason…bah

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.

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.

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)