Evolved Art Competition: Evolve the new BEACON logo!

Want to experience evolution first-hand?

We are happy to announce that the NSF BEACON Center for the Study of Evolution in Action is hosting an evolved art competition running from March 1st through May 31st, 2013. The goal of this competition is to evolve an alternative lighthouse to the BEACON lighthouse on the collaborative art evolution web site Picbreeder. The top three lighthouses submitted will receive monetary awards of $200 (1st place), $100 (2nd place), and $50 (3rd place). With control over the creative power of evolution, can you evolve BEACON’s new logo?

BEACON logo

How will the competition work?

You can join the competition by creating an account on Picbreeder. Make sure to provide a valid email address on your Picbreeder profile. We will be contacting the winners via the email address provided on their Picbreeder profile. This competition is open to the general public, not just BEACON members!

After you have created a profile, start evolving art! Picbreeder provides thorough instructions for how to evolve art on the web site: http://picbreeder.org/gettingstarted.php

Once you are ready to make a submission to the competition, use the Publish feature to make the image publicly available. Tag the image with the text “BEACON.” If you do not publish the image with the “BEACON” tag, it will not be considered for the competition! You may enter as many images as you like.

On June 1st, we will collect all of the images submitted to the competition and organize a committee of BEACON scientists to select the top three images. Only images published on or before May 31st will be considered for the competition. We will publicly announce the winners and present awards by June 14th.

AWARDS:
First place: $200 Visa gift card
Second place: $100 Visa gift card
Third place: $50 Visa gift card

Good luck! We’re looking forward to seeing everyone’s creativity!

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Lamprey genome sequenced

This blog post is reposted with permission from BEACON faculty member C. Titus Brown’s blog, Living in an Ivory Basement.

LampreyThe lamprey is a jawless vertebrate that diverged from the jawed vertebrate lineage around 550 mya. Lampreys, together with hagfish, represent the last extant vestiges of the evolutionary lineage bounded by invertebrate chordates — organisms without vertebrate features — and jawed vertebrates, which include fish, frogs, and mammals.

The genome was a gigantic pain in the butt. We (and by “we” I mean Jeramiah Smith, the first author) could only assemble 800 Mbp, a maximum of 2/3 of the estimated complete genome (which is in the range of 1.2-1.6 Gbp, depending on which estimates you believe). This is partly because the genome has a bunch of really annoying GC-rich repeats that confounded much of our BAC sequencing and hence much of our scaffolding.

The other reason for the incompleteness of the genome is much less common and more problematic: we constructed our sequencing libraries from liver, which, in the lamprey means that we’re missing 20% or more of the genome. This is because the lamprey genome undergoes lineage-specific loss of genomic DNA. (At this point you should say “WHAT? WHY!?” and/or lament the cost of sequencing and analyzing a subset of the germ line genome :).

Remember, genomics is out to get you.

What’s the single most interesting take-home observation?

The main section to read, I think, is “Duplication structure of the genome.” Here, we (again, mostly Jeramiah, with a lot of input from others) argue that synteny analysis shows

“the most recent (two-round) whole-genome duplication event likely occurred in the common ancestral lineage of lampreys and gnathostomes.”

Other things discussed are

we (i) provide genome-wide evidence for two whole-genome duplication events in the common ancestral lineage of lampreys and gnathostomes, (ii) identify new genes that evolved within this ancestral lineage, (iii) link vertebrate neural signaling features to the advent of new genes, (iv) uncover parallels in immune receptor evolution and (v) provide evidence that a key regulatory element in limb development evolved within the gnathostome lineage.

So, overall, pretty cool.

My main involvement in the nitty gritty of this paper was a sadly failed attempt to use protein domain alignment to determine the duplication structure of the genome. Because the initial assembly we had was not very good (it was considerably worse than the one that finally got published!), I tried to develop a novel approach using PFAM models to drive gene/domain alignment, followed by automatic tree examination. This approach unambiguously indicated that there had been no 2R. However, a few months later, after I did some QC and ran some models, it turned out that the approach was extraordinarily sensitive to gene loss. This occasioned a very embarrassing e-mail to the lamprey genome list, sigh.

(Genomics really is out to get you.)

The syntenic 2R analysis on a new Jeramiah-generated assembly turned out to be much better and argued for the pre-divergence 2R scenario.

Are you still working on lamprey?

The lamprey genome is one of two projects that launched my research into assembly; digital normalization was, in large part, driven by the desire to assemble approximately 5 billion mRNAseq reads produced by Weiming Li’s lab. We were driven to do this by the poor quality of the initial lamprey genome, and the newer revelation that large portions of the genome are simply missing. (In general, it seems like the genomics research community is starting to realize that mRNAseq is a complementary approach to genome assembly, which is often quite hard.

Our paper on assembling massive mRNAseq is still in the process of being written. Preliminary results from that work indicate that we do see about 20-30% of transcribed & conserved genes missing from the lamprey genome, but we’re still nailing down the numbers — large transcriptome assemblies turn out to be really messy!

You can see the paper here  and the Michigan State University press release here. For more information about Titus’ work, you can contact him at ctb at msu dot edu.

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Ex Una Plures! Happy 25th birthday to the Long Term Evolution Experiment

This blog post is by MSU postdoc Zachary Blount.

25th birthday party hatOnce upon a time, at a university far from MSU, Richard Lenski, my boss, founded twelve populations of E. coli from a single clone called REL606. And so began the E. coli Long-Term Evolution Experiment, or LTEE. Those twelve, initially identical populations have since been evolving under carefully controlled conditions in which, each day, 1% of each population is transferred to a fresh flask of medium. Under this regimen, each population goes through about 6.67 generations a day, and after more than 8,500 transfers, the populations have experienced more than 57,000 generations of evolution. That in itself is amazing, but this past Sunday the LTEE reached another, more human, milestone that more tangibly testifies to the patience and fortitude that has gone into the experiment. Rich performed the very first transfer of the experiment, the LTEE’s official beginning, on February 24, 1988, making the LTEE now 25 years old.

25 years! I don’t know about you, but I find that simply remarkable. In other labs, I have heard of experiments that go on for a week or two as being “long-term.” The LTEE redefines the term. 25 years. A quarter century. It is striking to consider that the experiment is now older than all the undergrads who help us in the lab, and even older than one of the graduate students! How amazing is that? At some point, we will need to start adding “venerable” to the name, as in The Venerable Long-Term Evolution Experiment!

Anniversaries offer, by their nature, both times and excuses to reflect and wax philosophical, and so it has been for me at this anniversary. Among other things, it underscores something that continues to provide me with a sense of both amazement and deep meaning. This is an experiment that, for all of us in the lab save Rich, was here before we came, and which will continue long after we leave. It is a constant thread running through the times of all the changing members of the Lenski lab. All of us, in one way or another, are participants in a grander story that is written by each of us in turn. (Indeed, because more than one LTEE worker was inspired to study experimental evolution by the LTEE itself, this is a story that creates its own writers!) I have taken great power and solace from that fact. In times of frustration with my work, when I might otherwise be tempted to just give up on a line of inquiry, I have known that it isn’t just me that I am working for, and thereby found new resolve. Yes, that is always true, or at least it should be, in science, where we are all in a sense working for humanity, but in the Lenski lab, working on the LTEE, there is a greater sense of the immediacy of this fact. I know that I am a part of a line of great workers on a grand experiment that is daily shedding light on hidden corners of evolution that might not otherwise be illuminated. I know that my work adds to the accumulated findings of that experiment, and that it will provide further foundation to those who come after me. This sense of continuity and communal effort adds a great deal to the wonderful experience of working in this great lab.

Zack's calligraphyIn 2005, about a year after I joined the lab, we celebrated the LTEE’s “Sweet 16,” based on the years of transfers that had been made. I decided to indulge a hobby of mine to celebrate the event, and so I made a calligraphy that I designed to express the community and continuity of the experiment. The calligraphy listed those who had worked in the lab since the experiment began to express the community. It also featured a graphic depiction of the experiment, with twelve lines extending from a circle representing REL606, and streaming down the page, the years ticked off to the left, and the generations of evolution experienced to the right. While I was coming up with this design, I realized that both the lab and the experiment really embodied the inverse of one of the mottos on the Great Seal of the United States: “E pluribus unum,” which is Latin for “One from many.” Being one who bears deep love for ancient history, and consequent respect for Latin mottos, I unilaterally decided on a motto of the LTEE for me to put on the calligraphy: “Ex una plures,” or “Many from one.”

As time has gone by, I have come to see this motto as appropriate on levels I had not originally considered. Originally, of course, it came from how there were twelve lines that came from a single cell, but it is really more than that. From one person, Rich, came many students, both graduate students and undergrads, many postdocs, and many associates in collaborating labs. One mind’s product led to the products of many minds. One idea led to many. One experiment yielded many others. One set of questions led to the asking of many others. One paper led to many. Even more so, I think “Ex una plures” is also that it is a good motto for evolution, so that those simple words and the concept behind them describe both the greater phenomenon, and the microcosm meant to allow its study. From one population of identical cells, existing under a single environmental condition, cycling predictably, though the chance and contingency infused process of evolution, came many different cell lines existing in their own little tangled banks, each with its own quirks and unique features. It echoes the great and poetic line with which Darwin closed On the Origin of Species

“There is grandeur in this view of life, with its several powers, having been originally breathed into a few forms or into one; and that, whilst this planet has gone cycling on according to the fixed law of gravity, from so simple a beginning endless forms most beautiful and most wonderful have been, and are being, evolved.”

I am waxing lyrical, I know. What I am trying to say is that this anniversary has made me reflect on just how wonderful, profound, elegant, and, yes, incredibly beautiful this experiment is! I consider myself so very lucky, indeed blessed to work on and be a part of it, and for that I am profoundly grateful. So happy birthday, Long-Term Lines! May your days continue onward uncounted, and your story never end!

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BEACON Researchers at Work: A Tiny Teal Tale

This week’s BEACON Researchers at Work blog post is by MSU graduate student Jessica Caton.

Jessica CatonBirds are everywhere! With 10,000 species living on every continent, you are bound to have come across a bird in your lifetime with possibly a unique story to go along with this sighting. From children’s games to TV shows and commercials, birds have become social icons: Aflac, Big Bird, Donald Duck… to name a few!

PenguinsI have always wanted to work with animals, but only started to become interested in birds in my undergraduate degree. I became fascinated with the complexities of animal behavior, especially when learning about bird behavior.  I was fortunate enough to gain work experience with bird species in zoo, laboratory, rehabilitation, and field settings. Two past experiences focused my future interests on working with waterbirds. In my senior year, I completed a study abroad through MSU to travel to Antarctica. The trip to Antarctica offered me the chance to be able to see and observe pelagic seabirds like albatross, petrels, and penguin species; it was amazing to see penguins that spend most of their life at sea, now nesting in a colony with a population larger than some U.S. cities! It was a truly life-changing trip that made me interested in studying marine birds. After graduation, I completed an internship at the Kellogg Bird Sanctuary in Augusta, MI. I was in charge of caring for captive waterfowl, game birds, and raptors along with educating visitors about the birds. Through my short time there, I gained a fond interest in wild and captive waterfowl. I became interested in migration after observing waterfowl (e.g. Mallard Ducks and Canada Geese) that never seemed to migrate versus the migratory waterfowl at the edge of the lake that would only stopover for a short time.

geese and ducks

Birds are remarkable in many ways; one is their ability to migrate long distances. An extreme example is the Arctic Tern; only weighing half a pound, this bird migrates from the Arctic to the Antarctic (~22,000 miles roundtrip)! Bird migration studies date back to Aristotle, 3000 years ago. The first ornithologist known to describe bird migration was Holy Roman Emperor Frederick II in 1240 who made the connection between the movement of birds with lack of food and cold temperatures. Since then, our understanding of migratory behavior of birds has been furthered by numerous experiments. It is now known that birds increase food intake to gain weight to prepare for the energetic demands of migration. Birds also become restless nearing the time to migrate.  

One way to study migratory behavior is by housing birds in captivity. In the late 18th century, Johann Andreas Naumann was the first to document migratory restlessness (called Zugunruhe) in captive birds. He observed birds perch hopping, wing flapping, foraging, and flying. By manipulating photoperiod (daylength), scientists are able to artificially initiate migration and observe if the bird housed in captivity becomes restless or increases activity as if they were actually migrating. There are many techniques to study captive bird migration; infrared video is the most recent technique to study nocturnal activity in birds. There is an abundance of literature and studies on migratory restlessness and behavior in captive landbirds; however, there are relatively few studies on marine and aquatic birds.

Teal nestlings

I am a Master’s student in the Avian Health and Disease Ecology Lab at MSU, led by Dr. Jen Owen. My Master’s research is studying the migratory behavior of captive Blue-winged Teals in both fall and spring migratory periods. According to Birds of North America (2012), they are considered the second most abundant duck in North America. Blue-winged Teals are dabbler ducks that breed in Canada and northern US and are found around marshy ponds and mudflats. They are considered trans-continental migrants as their wintering grounds range from the southern U.S. to South America. They are the first to migrate south for the fall and the last to migrate north for spring. Unlike many waterfowl species, Blue-winged Teals are obligate migrants, which means the urge to migrate is innate; because of this, they make for a fitting focal species to study migratory behavior in captivity.

Adult tealMy research began with Blue-winged Teal eggs that were collected from nests in North Dakota wetlands and hatched in an incubator in Michigan. At one month of age, they were individually housed in custom built cages (6’L x 3’W x 2H’) that were equipped with an infrared motion sensor and an infrared video surveillance camera that recorded all nocturnal activity. Vocalizations were captured by audio recorders placed in each room.

Cage with monitoring equipment
Cages

I used these three forms of data collection to answer three questions:

  1. If we keep teals on a natural photoperiod during fall migration, will the birds exhibit migratory behaviors? 
  2. If we increase daylength during in spring, will the birds gain weight and become more active in preparation for spring migration? 
  3. Do these forms of data collection capture migratory restlessness in captivity specific to Blue-winged Teals?

I am currently coding the videos to assign position (sitting, flying, swimming, etc), behavior (foraging, preening, resting, wing activity), and resource (location of duck in cage).  This coding will be analyzed in conjunction with the motion sensors to see if migratory restlessness or active heightened behavior was observed at night. In addition, audio recordings will be used to see if there is a correlation between this heightened activity and an increase in vocalizations. 

Examples of video clips of behaviors and audio recordings: 

 Audio 1 – BWTE

Using these techniques, I will be able to describe and to define migratory
behaviors of captive waterfowl in a way that has never been done before! Not only does this research embrace a novel form of technology, it helps to create detailed data for researchers to witness behaviors hard to capture in the field. This foundational research is considered a baseline study, which can possibly be used for future studies addressing the impact of zoonotic diseases, rehabilitation/reintroduction programs, and further understanding specific behaviors.

And this, my friends, is my Tiny Teal Tale!

Teals

For more information about Jessica’s work, you can contact her at catonjes at msu dot edu. 

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BEACON Researchers at Work: What do evolution, cancer, and optical character recognition have in common?

This week’s blog post is by MSU postdoc David Knoester.

Photo of Dave KnoesterIn 2012, cancer accounted for about 1 of every 4 deaths in the United States. That’s 1,500 people each day. By 2020, annual cancer deaths are expected to increase to 40 million people worldwide. (That’s right — 40 Million per year.) The economic ramifications of cancer are similarly staggering: In 2007, the overall cost of cancer in the US is pegged at $227 billion. Odds are good that cancer will, in some way, affect your life. (All statistics: Cancer facts and figures.)

One of the many factors contributing to cancer survival is early detection. Early detection, of course, requires frequent screening. This sounds like a simple thing — But it’s not. Malignant and benign masses tend to look remarkably similar when viewed in X-Ray (see images below), and they can both be difficult to distinguish from normal tissue. For example, even in the case of breast cancer (one of the “easier” cancers), between 10-30% of cancers are not detected (see here, here, and here), and 70% of biopsies following a suspicious mammogram come back negative. More frequent screening also increases physician workload, which in turn raises the cost of health care.

fig2
Example mammograms showing benign mass (left) and malignant tumor (right). Images are quite similar (delineations of masses were added manually). Images from Nandi et al 2006.

I’m researching the use of evolutionary algorithms (EAs) to help detect and diagnose breast cancer. Why do I think EAs could help? Well, for the past few months I’ve been using EAs to detect and identify handwritten characters in raw image data (also known as optical character recognition, or OCR). This is similar to what the post office has to do in order to route mail, but a bit more general. Using a novel EA that discovers Markov networks, we’ve been able to perfectly discriminate among different characters, and correctly classify those images with high accuracy.

However, one of the nice things about using an evolutionary algorithm for a data mining task like this is that we can apply the same technique to different datasets. For example, with few changes to the evolutionary algorithm itself, we’re able to swap out the database of handwritten numerals for Chinese characters and even for human faces.

fig3Example images from the MNIST database of handwritten numerals (A), the CASIA Chinese character database (B), and the ATT Olivetti face database (C). The actual databases contain many more images than those shown here.

In a BEACON project that spans four different departments at MSU, including investigators from Computer Science, Microbiology and Molecular Genetics, Clinical and Translational Sciences, and Radiology, we hope to extend this approach to detect and diagnose breast cancer in digital mammograms. We’ve just started this project, and are excited about the potential benefits to society, both in survival rates and reduced health care cost, not to mention sparing people anxiety, discomfort, and expense.

For more information about Dave’s work, you can contact him at dk at msu dot edu.

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BEACON Researchers at Work: Why do men and women exist?

This week’s BEACON Researchers at Work blog post is by MSU graduate student Rohan Maddamsetti. 

Rohan MaddamsettiIn his treatise on love, Symposium, Plato tells a myth of a time when men and women were one. People used to have one head with two faces, and four hands and four feet. Terrible was our might and strength, and the thoughts in our hearts were great; so Zeus punished us for our hubris by dividing each of us into two halves. After this trauma, each half, desiring to heal his or her state, came together, and throwing their arms about one another, entwined in mutual embraces, longing to grow back into one.

Why do men and women exist? Finding love is difficult, and since males need to find females (and vice-versa) to reproduce, the dating pool is half as large as it could be if we were hermaphroditic, like leopard snails. Reproduction would be even easier, if we could simply bud off clones of ourselves whenever we felt like it, as Hydra do. Why is there such diversity in how living things reproduce?

Evolutionary biologists have struggled to answer these questions, ever since Charles Darwin and Alfred Russel Wallace first originated our discipline. I certainly don’t know the full answer—however, there’s a lot of exciting research these days that I think will resolve these questions in the years to come. One theme of my research is the origin of sexual reproduction, which I’m defining here as combining genes from two parents to make an offspring. However, I’m attacking this problem from an unusual direction.

I largely study how genomes change over time in bacteria. I’m interested in both long, geological timescales (Escherichia coli and its close relative Salmonella enterica shared a common ancestor 120-160 million years ago) as well as short timescales that I can observe in the laboratory. A fascinating difference between bacteria and complex animals (cats, people, squid) is that bacteria exchange genes willy-nilly with each other, even without having to reproduce. This process, called horizontal gene transfer, is akin to being able to grow bark-like skin after picking up the necessary genes by touching a tree. Many dangerous pathogens have acquired antibiotic resistance from unrelated species this way. Horizontal gene transfer is pervasive. In one study, the authors examined the genomes of 20 different Escherichia coli strains and found that in sum, these bacteria contained a grand total of 18,000 different genes, but had only 2,000 genes in common! Even though bacteria divide asexually, they still exchange massive numbers of genes.

The reproduction of viruses and parasitic genes called transposons drive much horizontal gene transfer. Transposons are so named, because they copy and paste themselves in genomes. Hence, they appear to “jump around,” or in other words, transpose themselves. When these selfish genes reproduce by copying and pasting themselves, sometimes they carry along some extra genetic baggage, say genes allowing Japanese people to metabolize the carbohydrates found in seaweed. Based on this fact, I have been working on understanding how a gene-centered view of evolution in bacteria can explain how genomes are put together. In my current view, genomes are made up of cooperative networks of genes that co-evolve. In some sense, all living things are not simple entities, but communities made up of genes with their own individual interests. Usually, the genes in a genome work together nicely. Sometimes, however, the selfish reproductive interests of a few genes prevail, to the detriment of the entire organism. For instance, more than 10% of the human genome consists of a particular kind of jumping gene called an Alu element. When Alu elements spread, by copying and pasting themselves throughout the human genome, they can cause many different kinds of disease, including breast cancer, hemophilia, and type II diabetes. Bacteria often profit from the free exchange of genes. In more complicated organisms, like humans, perhaps the risk of breaking something complicated (such as brain development) outweighs the potential benefits of gaining a gene that can do something new.

Lothar von Seebach, Adam et Eve

Lothar von Seebach, Adam et Eve

So, perhaps sexual reproduction originated as a mechanism for preventing the genome from being hacked by rogue genes, or viral sequences. By finding a partner to share genes in order to produce an offspring, maybe complex organisms can find a partner with fewer parasitic genes, or can recombine genetic material in such a way to minimize the spread of parasitic genes. The alternative might be the gradual accumulation of genetic parasites over the generations, eventually leading to a “mutational meltdown,” when the load of genetic parasites overwhelms rest of the community of cooperative genes in the genome.

Why are there men and women? Why do some creatures duplicate themselves, while others have to put on spectacular displays to find mates? I am one of the many biologists—including several BEACON researchers—working to find the answers to these fundamental questions.

For more information about Rohan’s work, you can contact him at maddamse at msu dot edu. 

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Evolve & Conquer: Teaching Evolution via an Engaging Multiplayer Video Game

Cross-posted from the Adami Lab website.

Teaching with games has been one of the buzz topics in pedagogy lately. Although video games aren’t a formal teaching method by any means, many education scientists suggest games increase intrinsic (self-) motivation to learn, augment information retention, and enable students to learn for longer periods of time beyond the normal attention span (of about 40 minutes). As one of its stated missions, BEACON wishes to educate the public about evolution. A possible means to accomplish this would be a full-fledged, engaging video game that is fun to continually play while teaching evolution. (Which, admittedly, can be a difficult task!)

Thus, a few members of Dr. Chris Adami’s lab at Michigan State University started the “Evolve and Conquer” (E&C) game project. In short, E&C will be a multiplayer video game (similar in play style to Starcraft and Warcraft), which allows players to experience “evolution in action” first-hand. Instead of directly teaching evolution, we make it a part of the video game such that players who wish to excel in the game would benefit by learning the concepts of evolution; for example, maintaining variation in your population (“clan”) of warriors will make it easier to adapt to and defeat a new enemy clan of warriors. In the following demonstration, we will give you a sneak peek at some of the core game mechanics.

Unit control and navigation

Players control the nanobots (“warriors”) with standard Real-Time Strategy controls. Left-clicking on units selects them, then right-clicking on the terrain gives them the order to move. Here, the nanobot is commanded to collect resources while automatically avoiding obstacles in the terrain.

Realistic physics

E&C will feature NVIDIA’s PhysX physics engine, which comes packaged with Unity3D. In E&C, physics will play a large role in the nanobots’ interactions in the world. Nanobots will have to adapt to challenging physical obstacles such as physical barriers, wind, mud, rockslides, and even meteor showers.

Combat / “survival of the fittest”

Beyond the physical environment, nanobots may be forced into direct conflict with alien species. This video demonstrates the combat system, where the nanobots are ordered to attack the hostile alien species. In battle, only the strongest species survive. Thus, the nanobots experience survival of the fittest.

Reproduction

Of course, the nanobots must be able to reproduce in order to evolve. This video shows how nanobots reproduce. Whenever the nanobot consumes three resource cubes, it is able to produce an offspring.

Inheritance with variation

Here, an army of nanobots are producing offspring en masse. Each offspring inherits the traits of its parent, though some of the offspring experience slight changes (“mutations”) and become larger, faster, stronger, etc.

Limited resource competition

Not only do nanobots have to weather a hostile physical environment and survive combat with alien species, they have to compete for limited resources to reproduce as well. In this video, two nanobots forage for resources and reproduce after they have gathered 3 resource cubes. In this case the larger, faster nanobot outcompetes the smaller, slower nanobot.

Automatic foraging and reproduction

If you don’t feel like micro-managing the forage-and-reproduce process of your nanobots, you can order them to automatically forage and reproduce by themselves. In this video, a smaller, faster nanobot competes for limited resources with a larger, slower nanobot. If we let this run for an extended period of time, the faster nanobot would sweep the population. Thus, E&C is a full-fledged simulation of evolution by natural selection.

Feel free to leave feedback on our lab page. We hope to have a public playable demo soon, so keep an eye out!

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BEACON Researchers at Work: Making and breaking species

This week’s BEACON Researchers at Work blog post is by MSU graduate student Alycia Lackey.

Alycia LackeyThe world’s biodiversity represents a balance between the formation and extinction of species. To understand what drives diversity, scientists study what generates, maintains, and degrades species. This work is particularly important in the light of global change and human impacts on the environment.

Although we want to know how new species form, it is really hard to study this process because it often takes an extremely long time – millions of years from start to finish! Even when species form relatively quickly, in just a few thousand years, this process is still far longer than a single human’s lifetime (note: there are some exciting exceptions, e.g., microbes). Given the typical timeframe needed for most species to evolve, how can scientists study this process without a time machine?

One approach is to look at snapshots of the “speciation process”. You can imagine speciation as a continuum with a single population at one end and two distinct species at the other. To get snapshots of this process, you can study populations that represent different stages along this continuum.

The speciation continuum. Point A represents a single population. Point B represents two populations that interbreed but are somewhat different. Point C represents two distinct species. Scientists might determine that these are distinct species by looking for differences in genes or physical characteristics and by determining whether members of each species mate with each other.

The speciation continuum. Point A represents a single population. Point B represents two populations that interbreed but are somewhat different. Point C represents two distinct species. Scientists might determine that these are distinct species by looking for differences in genes or physical characteristics and by determining whether members of each species mate with each other.

I work with Dr. Jenny Boughman to study the speciation process in threespine stickleback fish, which are small, minnow-sized fish that live in freshwater lakes in British Columbia, Canada. The name “stickleback” comes from the spines along the backs of these fish. There are two species of threespine stickleback fish that have recently separated into distinct species in the past 15,000 years.

Two species of threespine stickleback fish: a benthic male (top) and a limnetic male (bottom). Photo credit: J. Boughman.

Two species of threespine stickleback fish: a benthic male (top) and a limnetic male (bottom). Photo credit: J. Boughman.

Studying these fish gives us a good idea of what helps populations split into distinct species (Point C on the figure above). For these fish, different feeding and mating habitats have been major players favoring forward progress along the continuum.

Stickleback fish are also interesting for studying speciation because human disturbance in one lake likely caused distinct species to become populations that interbreed in just 30 years. (These fish moved from Point C to Point B on the figure above, though this backward movement may not be exactly what happened in the forward direction.) In many cases, human disturbance causes species to go extinct, so this rare case provides exciting insight into how species might break down.

Sometime in the 1980s, someone dumped bait with crayfish into Enos Lake. These crayfish are native in British Columbia, but they are very invasive, meaning they can survive and reproduce much better than the animals that already live in that area. After the crayfish were introduced to Enos Lake, there was a major decrease in the number of aquatic plants. Soon after this environmental change, scientists working on the fish in this lake noticed lots of individuals that looked like intermediate forms of the two distinct species. You would expect to find such intermediate fish if members of the two species were interbreeding to make hybrid offspring. Much of my work is focused on asking if this environmental change could have caused changes in fish mating behavior and how that might have happened.

Before this environmental change, researchers thought plants were important for how mating occurs in stickleback fish. The two species used different mating habitats: one species mates in open areas and the other species mates in dense plants. So, it’s possible that two mating habitats made mating between members of the two species unlikely.

My work has tested how these mating habitats might be important to two aspects of mating in stickleback fish: male competition and female mate choice. Males compete fiercely with each other for territories. Males with territories then build nests and try to attract females with a courtship dance. Females visit multiple males and are very picky about which males they choose. A female deposits her eggs in the nest of the male of her choice, and then the male cares for the offspring. Male-only parental care is actually pretty common in fish, unlike in birds and mammals. This means that males have to put in a lot of energy to pass on their genes to the next generation; they have to compete for territories, court mates, and raise offspring.

So far I have found that male competition can help populations to split into two distinct species, but only when both mating habitats are present. In two habitats, male competition favors two types of males. However, when only one habitat is present, like in the case of fish in Enos Lake, male competition favors one type of male. Thus, changes to the environment could have favored fish in Enos Lake to move backward along the speciation continuum from distinct species to populations that interbreed. I also found that female mate choice depends on habitats most when populations are still interbreeding and not so much once species are distinct.

Figuring out how the environment affects how species evolve and what happens when they break down gives us great insight into what generates and maintains biodiversity. This is useful for basic biology, so we can understand evolutionary processes that favor new species to form. This work also has very important implications for conservation. It helps us understand what we can do minimize detrimental impacts on environments and help to maintain the biodiversity that exists today.

For more information about Alycia’s work, you can contact her at reyno340 at msu dot edu.

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Goldilocks and the Three Mutators

This blog post is by MSU graduate student Mike Wiser.

Many things in life seem to follow the Goldilocks principle: both too much and too little of something can be worse than striking a balance somewhere in between. Goldilocks saw this in heat of porridge, size of chairs, and softness of beds. In a recent paper, we show that this applies to mutations in an adapting population: there are times when the mutation rate is too high, and others where it’s too low.

Mutations are tricky things to think about. At the core, they are merely differences between a new copy and its template. In biological systems, the majority of mutations are deleterious – that is, they harm the mutant. Many of the remaining mutations are neutral, and have no net effect. Only a small percentage of mutations are beneficial, in that they make the organism better at something. This is because cells (and their protein components) are complicated things that generally already work; changing things at random in a complex machine is more likely to break it than improve it. But if mutations stop occurring, no new variants come into a population, and without variation, adaptation by natural selection grinds to a halt.

I work with the Long Term Evolution Experiment, an ongoing experiment in the lab of BEACON researcher Dr. Richard Lenski. The LTEE consists of a set of 12 populations of the bacteria E. coli, each diluted by a factor of 100 into fresh growth media daily. Frozen samples are collected every 75 transfers, roughly every 500 generations, and stored for future analysis. Because bacteria can be stored indefinitely at very low temperatures, and then revived later, it is as if we have a frozen fossil record of each of these 12 populations, going all the way back to the beginning of the experiment.

My work with the LTEE focuses on how fitness – essentially, a measure of the bacteria’s ability to turn resources into more bacteria – changes over time. At broad scales, fitness over time follows a very smooth progression. For the population that is the focus of this paper, that fitness over time is the green line, tracing the dark green circles high on the graph below. However, the mutation rate in this project follows a very different pattern – a very slow rise at first (the blue line and circles), then a very rapid rise (the red line and triangles), and then two separate reductions (the light green diamonds, and the purple squares).

Figure 1 from Wielgoss et al 2013, PNAS (see text for full citation information)

Figure 1 from Wielgoss et al 2013, PNAS (see text for full citation information)

Beginning very early in the LTEE, some of the populations began mutating much more often than the ancestor, generally a 10- to 100-fold increase. It happened in this focal population sometime between 20,000 and 24,000 generations into the experiment. This switch to being a hypermutator can happen when any of the genes involved in finding or fixing errors itself becomes less effective due to a mutation. Even though most individual mutations are harmful, about half of the populations have these increased mutation rates, showing that there is some benefit to an increased mutation rate, at least for a short time in this environment. This benefit is suspected to be because the hypermutator, producing so many more mutants, is more likely to also get a rare beneficial mutation, where the combination of all the mutations it has is still beneficial compared to a bacteria with none of these mutations. In a sense, the beneficial mutation is what’s increasing in frequency, while every other mutation on the same genome – including the mutation that raised the mutation rate in the first place – hitchhikes along with it. Once a mutation rate increases, though, it’s much harder to lower it again later; there are more ways to mess up the repair process than fix a broken one.

Our research team took advantage of this frozen fossil record for one of these populations, sequencing 22 total clones from a variety of time points. Reconstructing the relationships between these clones, we were able to determine that the hypermutators became established in the population between 20,000 and 24,000 generations. Interestingly, though, the increased mutation rate didn’t stay uniformly as high as it started out. Our analysis shows that two different subpopulations subsequently arose between 25,000 and 33,000 generations with different mutations which lowered the mutation rate in the hypermutator, though not all the way back to the ancestral rate. These subpopulations persisted to at least 40,000 generations, and the benefit of this lowered mutation rate is calculated to be right in line with the typical beneficial mutations seen in this general timeframe.

Like an increasingly large amount of science these days, this project spanned a large team of researchers in multiple locations. This paper involved a collaboration between two BEACON institutions, including the Lenski lab at MSU and Jeff Barrick’s lab at UT Austin, along with Prof. Dominique Schneider (Rich’s long-time collaborator) in Grenoble and a sequencing team at the Genoscope lab in Paris. The NSF supports not only the BEACON Center but also Lenski’s long-term evolution experiment through the Long Term Research in Environmental Biology.

 

S. Wielgoss, J. E. Barrick, O. Tenaillon, M. J. Wiser, W. J. Dittmar, S. Cruveiller, B. Chane-Woon-Ming, C. Médigue, R. E. Lenski & D. Schneider 2013. Mutation rate dynamics in a bacterial population reflect tension between adaptation and genetic load. Proceedings of the National Academy of Sciences 110: 222-227.

For more information about Mike’s work, you can contact him at mwiser at msu dot edu. 

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BEACON Researchers at Work: In Search of The Perfect Password

This week’s BEACON Researchers at Work post is by NC A&T graduate student Joshua Adams.

Passwords are a problem. They are easy to forget and if you write them down, they can be stolen. If you’re like me, you probably have more passwords than you could ever hope to remember. Not only do you have too many passwords, you are required to change them almost weekly and you can’t reuse them. They have to be more than 8 characters long, and they have to include uppercase characters, lowercase characters, symbols, and numbers. If you think this is bad, just wait… As computers get faster, our passwords will get longer and more complex. Is this the price of security in the digital age? Is there a better way? Before we get to that, a little background about me and how I got here.

CIMG2096My name is Joshua Adams. I am a graduate student majoring in computer science at NC A&T State University. Growing up, I loved to build things. It started with Lincoln Logs and then Legos as I got a little older. As I approached my teenage years, I started to build less and play more. Most of my attention turned to video games and computers. Much to my parents’ surprise, some good came out of it: I started a business in high school building and selling computers. Fortunately, the business didn’t work out. Costs were too high and profits were too low. Why is this fortunate? All of this helped me to realize that I wanted to go to college to learn how to develop software.

I was lucky to get involved with research as an undergraduate. As chance would have it, I started doing research with biometrics (unique physical and/or behavioral identifiers). Different biometric modalities are featured in TV shows and movies all of the time. Crime shows frequently use fingerprints, face recognition, and DNA to catch criminals. In the latest OO7 movie, James Bond was given a gun that used palm recognition and would only fire if he was holding it. These are all good examples of physical biometrics. Could biometrics like these be the solution to our password problem? That question is a major part of my research.

[Reference: http://www.sans.org/security-resources/policies/Password_Policy.pdf]

[Reference: http://www.sans.org/security-resources/policies/Password_Policy.pdf]

Physical biometric systems are great because they deal with who you are. Unlike passwords, they can’t be guessed or forgotten. They also can’t be lost or stolen like RFID cards and USB keys. Best of all, these biometric recognition techniques have made their way from the realm of sci-fi to reality. Many laptops now have fingerprint readers than can be used to log in. Newer Android phones can use face recognition to be unlocked. So what’s the catch?

While these techniques are a step in the right direction, we don’t have the solution yet. One disadvantage is that many physical biometric modalities (fingerprint, iris, face, etc…) require additional hardware that can be quite expensive. Another disadvantage is that additional hardware can increase failure rates. Because they sometimes fail, many current biometric systems allow you to bypass them completely and continue to use a password. Is there a way we can use biometric recognition techniques without these shortcomings?

Behavioral biometrics could be a solution. Behavioral biometric techniques involve measuring a user’s actions with respect to time such as movement or language choice. There have recently been techniques developed to identify someone based on how they use a computer. Unfortunately, these techniques haven’t matured enough to be used on a large scale.

Is there a way to improve these biometric techniques so they can completely replace passwords? In my research, I use a form of artificial intelligence called genetic and evolutionary computations (GECs) to solve this question. These techniques simulate the process of natural selection to find previously unknown solutions, essentially evolving biometric systems. Not only can we improve an individual biometric system, we can evolve ways to combine both physical and behavioral biometric techniques so that they work together. By creating new biometric recognition techniques, we can start moving away from our current password predicament.

The ultimate goal of this research is to create a system that automatically authenticates you without the need for passwords. Imagine logging into a computer by typing a simple phrase or moving your mouse. Not only could this be used to log you in, it could be used to log you out as well. If someone were to start using your computer while you were away, your computer could automatically log you out. There is still a lot of work to be done, but the future is bright.

For more information about Joshua’s work, you can contact him at jcadams2 at aggies dot ncat dot edu.

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