BEACON Researchers at Work: Cheaters never win

This week’s BEACON Researchers at Work blog post is by University of Washington graduate student Adam Waite.

Adam cooperating with some friends to get to the top of a boulder.

Adam cooperating with some friends to get to the top of a boulder.

Why do we cooperate? It’s easy enough to understand the benefits of cooperation. When we pay taxes, for example, we are contributing to the maintenance of the roads we bike on, the parks we enjoy, and universal access to education. However, no one enjoys paying taxes. Some people go through all kinds of complicated accounting schemes — which, somewhat ironically, cost them quite a bit of money — to minimize the amount they pay. Some people try to get away with not paying any at all, despite the potentially harsh punishments they will face if caught. Before they’re caught, these “cheaters” still drive to work on the roads and enjoy the parks maintained by the money of others.

For most of us, the threat of jail time is enough to deter the desire to “get something for nothing.” But what if there were no such thing as the IRS and no way to punish cheaters? No matter how unrealistically optimistic your opinion is on the general morality of the human race, I would feel comfortable betting a large sum of money that these roads and parks would not last very long.

So maybe we cooperate because of the fear of being punished somehow — in the form of fines or jail time for large offenses, but also because we want other people to like and respect us. We are incredibly social creatures, endowed with highly sophisticated cognitive systems that associate positive emotions with trust and friendship, and negative emotions with deception and treachery. And, for the most part, this “social sense” is all we need to remain cooperators and avoid becoming cheaters.

But I am an evolutionary biologist, and while this provides something of an answer as to why we cooperate right now, I am compelled to wonder, “But where did this sense come from?” Being biological creatures, our cognitive systems are the product of evolution by natural selection. And natural selection acts at the level of individuals, rewarding those who make the most use of resources available to them, even (and especially) if this comes at a cost to others. Imagine some pre-cooperative organisms, lacking any means of punishment or social sensibilities. Even if they did better when they worked together, the appearance of a “cheater” — through mutation of a previously-cooperating individual or invasion from outside — should quickly destroy them. Thus, the continued existence of cooperation seems to require the impossible: the maintenance of parks and roads by individuals acting purely in their own self-interest, with no possibility of being punished.

Although we are most familiar with cooperation as occurring among humans, it is actually found at all levels of biological complexity. Genes “cooperate” in the form of genomes; individual cells cooperate to form multicellular organisms; and cooperation within and between species is commonplace. As expected, all of these systems have their cheaters. For example, transposable elements cheat on genomes by selfishly replicating, often to the detriment of the host organism. Cells in multicellular organisms that ignore signals to cease dividing are responsible for cancer.

My interest lies in answering this seemingly impossible question: How does cooperation survive cheating? This is a big problem, and was recognized by Darwin as potentially fatal to his theory. Thus, decades of theoretical and experimental research have been focused on this problem. We now know that any mechanism allowing cooperators to preferentially associate with other cooperators can allow cooperation to survive cheating. This association can be achieved in simple ways, such as clustering with other cooperators, or it can be as complex as recognizing and remembering which organisms to cooperate with and which to avoid. And, everywhere we look, we find that these successful cooperative systems have one or more of these mechanisms at their disposal.

But what about cooperative systems — such as motile microorganisms — that exist in well-mixed environments and lack sophisticated methods of recognition? Presumably this type of cooperative system had to survive long enough to allow the appearance of more sophisticated mechanisms of cheater control. In order to study this question, I needed a cooperative system that had a clearly defined way of cooperating and was known to lack any mechanisms of cheater control. Since the existence of such a system is not known to exist, we made our own. My research group genetically engineered the commonly used species of baker’s yeast, Saccharomyces cerevisae, to cooperate and cheat.

Our system, which is called “CoSMO” (Cooperation that is Synthetic and Mutually Obligitory), cooperates through the exchange of essential metabolites: each cooperator produces a nutrient required by its counterpart. Cheaters require one of the nutrients but do not produce anything and, because they don’t pay the metabolic “tax” of production, they can divide a little bit faster than their cooperative counterpart.

Thus, my expectation was that, when mixed together, cheaters would deterministically displace their cooperative counterpart. Eventually, I reasoned, the amount of nutrient would not be sufficient to support the population, and the growth of the entire co-culture would slow down and eventually stop. To my astonishment, however, a fraction of the co-cultures continued to grow! Not only that, they were growing at a rate I would expect if cheaters were not present at all. Because we had fluorescently labelled each of the strains with a different color, I could quickly determine that, in fact, the growing co-cultures were almost completely devoid of cheaters. What was going on?

By isolating individual members from the cooperator-dominated co-cultures, I discovered that now they could always beat the ancestor cheater. The cooperators had evolved to become better cooperators! I could also isolate the few remaining cheaters from this population and they were always able to beat the ancestor cooperators… so they had improved, as well. However, when I mixed these improved cooperators with the improved cheaters, the cooperators always won. How strange! It was as if the cooperators and cheaters were in a race with one another to obtain the best mutations. The “winner” of this “adaptive race” would determine whether cooperators dominated the co-culture and continued to grow, or whether cheaters would dominate and destroy it.

But what was the selective pressure to which they were adapting? Luckily, yeast have small genomes and we know all their genes. So, to answer this question, I could sequence their entire genomes and look for mutations. I found mutations in both cooperators and cheaters that allowed them to grow at much lower concentrations of their required nutrient. In other words, the benefit obtained by adapting to the nutrient-limited cooperative environment was much greater than the disadvantage of being a cooperator, and allowed cooperators to outcompete cheaters.

So here was a way that cooperators could defeat cheaters without any mechanisms of cheater control, and it was through adaptation by natural selection, the very process that was supposed to destroy cooperation in the first place! And while in our system adaptation was to the nutrient-limited cooperative environment, any stressful environment — such as a change in temperature or salt concentration, or the presence of antibiotics — could potentially provide the fuel to drive the “adaptive race” and give cooperators a chance to drive cheaters extinct. Since changing — and therefore stressful — environments are an inescapable reality of biology, the “adaptive race” is a simple but effective mechanism to buy time for cooperation, allowing a fraction of cooperators to persist long enough to evolve more sophisticated mechanisms of cheater control.

For more information about Adam’s work, you can contact him at nodice at uw dot edu.

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BEACON Researchers at Work: The not-so-inscrutable HIV

This week’s BEACON Researchers at Work post is by MSU postdoc Aditi Gupta.

Aditi GuptaIt all started in 1981. A few patients suffering from unusual opportunistic infections, that immune system normally easily takes care of, walked into doctors’ offices and nobody could understand why their immune systems were not working. Something was wiping out their T-lymphocytes, the cells that regulate the immune response to infection. With the number of cases rapidly increasing, no treatment or cure in sight, and biggest of all, no knowledge about what is causing this deadly disease and how it is spreading, HIV became synonymous with fear and death. Scientists at The Pasteur Institute in Paris finally isolated the Human Immunodeficiency Virus (HIV) in 1983, and a blood test was soon developed. Almost three decades later, more than 34 million people worldwide are currently living with HIV and more than 2 million new infections were reported in 2012. This rapid spread of HIV is in part due to its long incubation period, where an HIV-positive person does not show any symptoms for years, and thus may pass on the virus to someone else without even knowing it. However, HIV infection is not a death sentence anymore. There are treatments available that can keep the viral load (the amount of virus in blood) in a person at such a low level that the immune system can fight off opportunistic infections as it normally does.

HIV first caught my attention when I saw entire families dying from AIDS in my hometown in India circa early 2000. Even today, more than a million people die from AIDS every year, mostly in resource-poor countries where patients cannot afford treatment. For those who can afford treatment, strict adherence to treatment for the rest of their lives is essential. Timely and regular HIV-testing is encouraged in at-risk individuals, as early detection positively impacts treatment outcome. Thus, years of research have found ways to make HIV infection quite manageable, but we still don’t have a permanent cure or vaccines for HIV. To better understand why, let’s review what we know about HIV.

HIV is a retrovirus, meaning that once it gets inside a cell that it can infect, it takes over the host-cell system to make more copies of itself. The following video gives a nice overview of the HIV life-cycle:

The current drugs target the reverse transcriptase and the protease proteins of the HIV. Reverse transcriptase makes a DNA copy of the viral RNA genome that then gets integrated into the host-cell genome for making multiple new copies of the HIV. Protease plays an important role in maturation of new viruses, rendering them infectious. HIV-infected individuals take medicines every day that target both reverse transcriptase and protease proteins of the virus, to minimize the production of new viruses and thus protect the immune system. However, the reverse transcriptase of HIV frequently makes errors (mutations) when it makes the DNA copy of the HIV genome, which allows the virus to rapidly evolve. Thus, any lapses in taking the drugs give the virus enough room to make drug-resistant copies of itself, which leads to treatment failure. Tests to find the drug-resistance causing mutations are available and routinely used in clinical practice to design new drug-regimens for patients. However, there are limited alternatives to these drugs, requiring a better understanding of how drug resistance develops in HIV, and how it can be prevented.

My research focuses on just that: How does the HIV population in a patient becomes drug-resistant? While error-prone reverse transcription of HIV genome is one contributing factor, it is the large population size of the virus that allows HIV to try multiple mutations simultaneously, and select the ones that can escape the effect of the drugs. That’s why keeping the viral load low is so important in treatment. Computational simulations show that large populations of rapidly evolving “computer organisms” are very robust as they can adapt faster to adverse changes in their environment. When I joined Dr. Chris Adami’s lab at Michigan State University as a postdoc, we decided to study the evolutionary dynamics of HIV populations in vitro. This started a collaboration with Dr. Yong-hui Zheng’s lab, also at Michigan State University, to do experiments where T-lymphocytes are infected with HIV in lab, and after a few days the HIV RNA is extracted from the T-cells for sequencing. We also collaborate with Dr. C. Titus Brown’s lab at MSU for analysis of next-generation sequencing data.

Photo of test tubesHere, the two small tubes (pink solution) contain two different strains of HIV, that we used to infect a fresh batch of T-lymphocytes (yellowish solution).

Once the HIV RNAs in the infected T-cells are extracted and sequenced, we can identify the mutations that appear in the HIV populations. By repeating this experiment at several time-points, we can basically observe HIV evolution in action. Drugs can be added to the infected T-cells as well, to see what mutations arise specifically in response to treatment.

This research will further uncover the mysteries that surround HIV, and hopefully will take us one step closer towards finding a cure. In my opinion, rapid evolution is the biggest weapon in HIV’s arsenal, and therein might lay its biggest weakness. Decades of research has unearthed valuable knowledge, not only about HIV, but also about how our immune system works, and the pieces of the puzzle are slowly but surely being assembled together.

For more information about Aditi’s work, contact her at agupta at msu dot edu.

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BEACON Introductory Video

We are very pleased to share our new video introducing BEACON, some of our members, and examples of the work we do. Enjoy!

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Animal Athletes: BEACONites introduce kids to biomechanics

Kids see how much weight/force it takes to knock over animals with large vs. small bases of support by adding marbles to a bucket at Melissa Thompson’s station.

Kids see how much weight/force it takes to knock over animals with large vs. small bases of support by adding marbles to a bucket at Melissa Thompson’s station.

‘How do animals jump so high?’, ‘Why are some animals so fast?’, and ‘How do kangaroos hop?’ were just a few of the questions kids and their families had a chance to explore at the ‘Animal Athletes’ Science Saturday program at the Palouse Science Discovery Center in Pullman, WA on December 8, 2012. The ‘Animal Athletes’ program was presented by members and friends of the University of Idaho’s Comparative Neuromuscular Biomechanics Lab including Dr. Craig McGowan (PI and BEACON member), Dr. Anne Gutmann (BEACON postdoctoral fellow), Melissa Thompson, Catherine Shine, Travis Morgan, Shannon McGowan, and Kenneth Burns.

Kids see how much force it takes for animal to jump by having a toy frog jump on a force plate at Craig McGowan’s station.

Kids see how much force it takes for animal to jump by having a toy frog jump on a force plate at Craig McGowan’s station.

The mission of the Palouse Discovery Science Center is bringing hands-on science and learning experiences to people of all ages. The ‘Science Saturday’ programs provide an opportunity for members of the local science community to share their expertise with the public in a fun, informal manner. One of the main focuses of the Comparative Neuromuscular Biomechanics Lab is understanding how evolution affects the biomechanics of animal movement, so the lab chose the ‘Animal Athletes’ theme as a way to introduce kids and their families to the basics of biomechanics.  Hands-on activities and games were used to show kids and their families how simple biomechanical principles enable animals to accomplish amazing athletic feats.

Kids see how adjusting the length of lever arms at the joints changes how easy it is for muscles to exert force on bones at Travis Morgan’s station.

Kids see how adjusting the length of lever arms at the joints changes how easy it is for muscles to exert force on bones at Travis Morgan’s station.

At one of the more popular activity stations, kids filled buckets with marbles to see how much more weight/force it takes to knock over an animal with a large base of support (e.g., a mountain goat) than a small base of support (e.g., a heron). At another station, kids got to see how much more force animals need to generate to jump than to sit or stand by comparing the force generated by a toy frog jumping to the force generated by a toy frog sitting on a force plate. And at yet another station, kids got to see how adjusting the length of lever arms at the joints changes how easy it is for muscles to exert force on bones. Kids also got a chance to test their own athletic abilities at the balance beam and jump height stations.

Everyone involved had a great time, and the Palouse Discovery Science Center is eager to have the Comparative Neuromuscular Biomechanics Lab back for a repeat performance.

Kids test their balance on both narrow and wide balance beams.

Kids test their balance on both narrow and wide balance beams.

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BEACON Researchers at Work: A Tyrannosaurus and a virus walk into a bar…

This week’s BEACON Researchers at Work post is by MSU graduate student Alita Burmeister.

Dinosaur skeleton

… the scientist asks “Hey, what do you two have in common?”

This summer I met Sue the T. rex. Her fossil remains are the largest, most complete of her species, discovered after 65 million years preserved in rock. While Sue was one of earth’s largest predators, I research one of the smallest:  a virus. Most people are all-too-familiar with viruses that affect human cells, for example the influenza virus and HIV. Viruses themselves are not cell-based life forms, which means a virus must infect a cell to grow. Influenza virus infects cells in the respiratory tract. HIV infects cells of the immune system. This virus I work with infects bacterial cells. Like us, bacteria are cell-based life forms, and like us, bacteria are susceptible to their own viral infections. Like a T. rex, a virus leads to a violent end for its prey. This type of relationship is called a “predator-prey interaction.”

Predator prey comic

My research at Michigan State University studies how viruses coevolve with the cells they infect. Coevolution is the process in which a population of organisms adapts to other organisms. For example, cheetahs and gazelles coevolved to catch and escape one another, respectively. Unlike cheetahs, gazelles, and T. rex, microorganisms and viruses mutate and grow quickly, so they can evolve in the laboratory and be kept frozen and revived indefinitely. In the lab, I use such a frozen “fossil bank” of viruses and the cells they infect to study coevolution. To do this, I thaw a bit of a frozen “fossil record” to revive the viruses. I then sequence the virus DNA to find mutations and test how the viruses interact with cells.

Photo of AlitaAs a scientist, my main job is asking questions and seeking answers. Most people are familiar with questions like, “Was T. rex a scavenger or a hunter?” To answer this question, paleontologists consider the evidence. They don’t know for sure because they can’t study T. rex behavior directly, but the evidence suggests that T. rex was likely part scavenger and part-predator. Paleontologists hypothesize that T. rex‘s backward curving teeth helped prevent live prey from escaping, indicating a predatory lifestyle. While paleontologists have questions for their favorite species, I have questions for the viruses and cells I work with in the lab. Do mutations help the viruses? Do mutations help the cells? As a grad student of microbiology and evolution, I get to do the tests to find out!

As a teacher, I work with college students in a laboratory classroom. My students are preparing for jobs in medicine, food research, agriculture, and the pharmaceutical industry. To do these jobs, students need to understand genetics and how to work with molecules like DNA. In our class, the students use bacteria to study how DNA works as the genetic code. The classroom experiments involve moving genes from one bacterium to another using standard genetic techniques. In all of these labs, the key genes code for “antibiotic resistance.” These genes make bacteria able to survive antibiotic treatments. These genes are the major reason why pathogens like MRSA and TB are becoming more dangerous, and why your doctor will tell you to not take antibiotics unless you really need them. In nature and medical settings, bacterial populations evolve by changing their DNA – often that includes antibiotic resistance genes. As a teacher of microbiology and evolution, I get to teach students the details of how these genes move among strains – and the students themselves get to perform the experiments demonstrating these evolutionary mechanisms.

While genetic engineering is interesting, observing bacteria naturally evolving is even more fascinating. Collaborating with MSU Professor of Microbiology Dr. Michael Bagdasarian, we are working on changing the course’s curriculum to include an evolution experiment in the lab. Refocusing the course around real-time evolution of antibiotic resistance, we want students to experience evolution for themselves. After graduation, our students will get jobs where antibiotics are widely used – for example in human medicine, agriculture, and the pharmaceutical industry. Our students will be responsible for using antibiotics wisely, in order to prevent the evolution of antibiotic resistant pathogens. The good news for our class is this: evolution is simple! A straightforward experiment could involve evolution of bacterial cells in the lab. The cells would be exposed to an antibiotic during evolution, and students would test how antibiotic resistance and genes change throughout the semester.

While evolution can be simple, bacterial genetics can be complicated. In the classroom we are limited to basic genetics methods and do not have time to characterize the effect of mutations in all 4,000 genes of a bacterial genome. To get around this problem, I am working with a simpler model of a genome. Models are used throughout science to make complex systems easier to understand. For example, lab rats are models of human biology. In the case of genomes, I am working with a model system based on computer code rather than DNA code. Before you check out at the sight of “computer code,” realize this: if you can fix a typu typo on your computer, you can mutate computer code. It’s that easy! I am developing an exercise to model genome mutations using simple computer programs called “digital organisms.”

Digital organisms are self-replicating computer programs. These programs have coded genomes and can be mutated by simple code changes. For example, here is the code a digital organism in AVIDA-ED:

rnzarpqgfcqppidffvcnpqbqnqppgcgiypzqfgpqputtsfqvkk

And here is a “knock-out mutant” of the genome.  Can you spot the “genetic” deletion?

rnarpqgfcqppidffvcnpqbqnqppgcgiypzqfgpqputtsfqvkk

Did you find it? (Sorry no Waldo-stripe giveaway.) In this code, the third letter “z” was deleted. My goal is to use these digital genomes to teach about DNA-based genomes. Using computer codes rather than DNA codes, my students will be able to test the effect of mutations on a digital organism’s health or “fitness.” To do this, students will use the application AVIDA-ED, in which they will be able to test both individual fitness and the evolution of a population of digital organisms. With AVIDA-ED, students will investigate two questions:

1) Are most mutations beneficial or harmful? 

Finding that most random mutations are harmful, they will ask:

2) Since most mutations are harmful, why does fitness increase during evolution? 

If you thought about the second question, you may have thought about the selection picking out the rare beneficial mutations and leaving behind the harmful mutations — if so, you’d be in agreement with most evolutionary scientists.

And if you thought that digital organisms sound a little like computer viruses, evolutionary scientists would agree with you there as well, with one key exception:  computer viruses, luckily for us, do not mutate.

For more information about Alita’s work, you can contact her at alita dot burmeister at gmail dot com.

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BEACON Researchers at Work: Going Viral

This week’s BEACON Researchers at Work blog post is by University of Washington graduate student Sonia Singhal.

Photo of SoniaAs a junior in college, I fell in love with viruses.

That September, I joined Dr. Paul Turner’s virus evolution lab. I had chosen to work there in particular because I thought the research would allow me to combine ecology and molecular biology. The Turner lab used viruses to study topics from host jumping to the evolution of sex. Before entering the lab, I had never considered that viruses, like any other creature on the planet, might have their own ecological niches or evolutionary trajectories. I had never considered that we might be able to use patterns of changes in viruses—which, after all, may or may not actually be alive—to infer what might happen in higher organisms.

Instead, I found myself becoming more and more enamored with the viruses themselves. Viruses are full of paradoxes. Although they are not considered “living” by textbook definitions, they have genomes, they hijack other cells (hosts) to propagate themselves, and their genomes change rapidly over time. Such rapid change comes at a high cost: Viruses need to change often enough to find the few helpful mutations, but not so much that they then lose that helpful alteration or become overloaded with harmful mutations.

Fittingly, my project in the Turner lab also involved a paradox. Researchers in the lab had previously evolved a set of viruses to be either genetically robust or genetically non-robust. A virus that is genetically robust will generally not be impacted by mutations—if its genome changes, it can usually still survive. In contrast, a genetically non-robust virus that gains a mutation in its genome will probably not survive.

Now, one of the debates surrounding genetic robustness is whether robustness would help or hinder evolvability, the ability of the virus to adapt to new conditions. As viruses replicate, errors (mutations) accumulate in the genomes of their offspring. Sometimes, these changes lead to small differences in how well the virus can infect its host, replicate its genetic material, and exit the host. Viruses that do slightly better at any or all of those things are favored, and their numbers accumulate over time. But in the case of a genetically robust population of viruses, mutations will not cause any difference in how well each virus performs. If all viruses are performing equally well, no one lineage of virus can dominate. For this reason, genetic robustness may prevent viruses from evolving (in the sense of changing how exactly they infect, replicate, and burst their host).

However, for the same reason—that mutations cause no difference in performance—the changes that accumulate in the viral genomes are also not removed from the population. Rather than having a population in which each virus’s genome resembles every other virus’s, each virus is genetically distinct. Suppose we then put this diverse population into a completely new environment, where the relationship between genome and survival is not the same as in the original environment. Now some of the viruses cannot infect their host, they cannot hijack its replication machinery, or they cannot burst it. These viruses die out. But other viruses that happen to have an advantageous mutation do very well. They propagate, and they start to gain additional mutations that increase their advantage. In this way, genetic robustness might actually lead to evolvability: The final population of viruses does not look or behave in the same way as the original population.

When the researchers in the Turner lab exposed their genetically robust and genetically non-robust viruses to a higher temperature (20 deg. C higher than what the viruses are used to), they found that the robust viruses adapted more quickly to that high temperature than the non-robust viruses. In fact, at moderately high temperatures, the robust viruses immediately survive better than the non-robust viruses. So in this type of environment, robustness seems to promote evolvability. I was curious whether the advantage of the robust viruses would hold in other environments. If I put the viruses on a new type of host, would the robust viruses initially do better than the non-robust viruses? Would they adapt to that new host more quickly than the non-robust viruses?

I only got to carry out the first question during my time with the Turner lab. I tested the viruses’ initial survival on different cell types. The short answer to my question was no, the robust viruses did not perform any better than the non-robust viruses. Their advantage is probably specific to changes in temperature. However, by that point, my love affair was already well underway. I was hooked by the power of these microscopic particles to teach us about deep principles of evolution. I applied to do graduate work in experimental evolution—to use microbes and viruses to study evolution in real time—and am currently part of Dr. Ben Kerr’s lab at the University of Washington, where I hope to continue exploring questions on robustness and evolvability in viruses. 

My love affair with virus evolution has taken me places that, as an undergraduate, I never imagined I would go. I feel that the evolutionary trajectory of my academic career hinged on my work in the Turner lab. After completing my undergraduate degree, I lived for a year among the Roman ruins in Southern France determining the genome sequence of a plant virus. I spent this past summer in the supreme silence of the San Juan Islands learning about marine viruses. As a result of that course, I traveled to the beaches of Búzios, Brazil, where cactuses grow next to the ocean on volcanic rock, to present some of the research we did. But most exciting is the rich intellectual pasture I have found in the complicated concepts that virus evolution can help start to answer.

My evolutionary journey is still young, but I am eager to see where it will take me.

For more information about Sonia’s work, you can contact her at singhal at uw dot edu. 

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BEACON Researchers at Work – Teaching Evolution: The Ladybug Game

This week’s BEACON Researchers at Work blog post is by University of Idaho graduate student Joshua Rubini.

Hi, my name is Joshua Rubini, and I am a graduate (master’s) student in computer science at the University of Idaho. I’m a “non-traditional” student, returning to my education after 12 years in the US Navy, part of which was spent here. I work with Dr. Terence Soule and Dr. Robert Heckendorn on evolved swarm robotics research in the Laboratory for Artificial Intelligence and Robotics.

My interest in artificial intelligence dates back to the summer of 1987, when I played a game called Wizardry, produced by Sir-Tech Software. I eagerly worked my way through the game to the “final” encounter (which was repeatable) and decided that the game just wasn’t smart enough, and that I needed to make a smarter computer game. My father purchased a book on BASIC programming, and at the age of 12 I had a functioning, discreet-room dungeon crawl game, which was the result of some 800 hours and 20000 lines of BASIC code! Needless to say, it was not “smarter” than Wizardry…

Here at the University of Idaho, I took every class I could find relating to AI, but it wasn’t until the Fall of 2008 that I enjoyed my first real exposure to AI research. During our department’s professional seminar series, I listened to Dr. Soule present the research he and Dr. Heckendorn were doing, investigating new methods of evolving teams of AI agents in order to encourage more efficient cooperation solving an exploration problem. I was completely fascinated, and afterward went up and told them how interested I was. The response I received, to my amusement, was, “When can you start?!?” We quickly identified an interesting extension of that experiment that I could work on, and 3 months later it was accepted for publication at GECCO 2009! It was a great experience, and I was hooked. I’ve been working on evolved swarm cooperation and communication ever since.

Earlier this week I was at our department’s professional seminar, which was a talk given by Dr. Carol Taylor, one of my professors years ago. Her talk was about research methodology and the scientific method, and during it she posed the question to the audience, “Is anthropogenic global warming a serious problem?” I looked around, and seeing no one else willing to answer, I raised my hand and said, “I believe it is.” I gave several reasons for my position, after which a couple of other people weighed in, one agreeing with me and one disagreeing. However, both used the word “believe” in their responses.

At this point, the moderator of the seminar series, Dr. Axel Krings, cut in and made an observation. He said,”What is interesting and disturbing about all of your responses is you all used the word ‘believe.’ Belief has no place in a scientific discussion!”

After a minute’s or so reflection on this, I realized that this, in fact, is what my entire semester’s work has really been about. I have been working with Dr. Soule, Travis DeVault, and Melissa Kjelvik on a BEACON-funded project that focuses on educating children about evolution. As a nation, we are sadly lacking in public acceptance of evolution as the driving, central mechanism of biological diversity. Our difficulties here can really be summed up by what Dr. Krings said above; we allow too much “belief” to influence our choices in what we teach students about biology. The other major hurdle is that evolution, as a process, is difficult to “see” working.

Our program attempts to tackle the second problem directly, with a fun, engaging application that allows student to not only see evolution taking place in front of them, but also allows them to “tinker” with the mechanisms and forces that drive evolution. Students can see how these processes work together to allow populations to evolve and adapt to changing conditions.

The program is called “The Ladybug Game” and shows a simulated leaf with a ladybug and a bunch of aphids. The ladybug runs around the leaf, eating aphids that it sees in front of it. Each time an aphid is eaten, another is spawned, inheriting traits from its parent and mutating those traits slightly. The inherited traits are color and strategy, where the “strategy” of each aphid is defined by a simple neural network which decides how much to change its heading and speed. The chance that an aphid is “seen” by the ladybug is dependent on how closely that aphid matches the background leaf color. In this way, the ladybug acts as the “selection” function, eating aphids that haven’t blended in well enough into the background.

Start screen for Ladybugs and Aphids gameLadybugs and Aphids game

The program focuses on three primary facets of evolution: selection, inheritance, and variation. In three of the lessons, students get to watch two separate populations, one with all of the evolutionary machinery running, and one with a single mechanism removed. For example, in lesson 3, one of the populations has variation removed entirely, such that new aphids are exact copies of their parent. It quickly becomes apparent that these aphids cannot adapt at all if the background color of the leaves is changed, since there is no way to introduce any sort of random change once the population converges on a single color. Similar to this lesson, two more lessons each show what happens if you remove color selection or inheritance.

Working on this program has been a very rewarding experience for me, as I feel that its use could make a very real impact on how the next generation of investigators understands the fundamentals of evolution. I have very much enjoyed working with Terry and Melissa on this, and look forward to seeing it in classrooms in the future.

Current version of the program can be found at:
www2.cs.uidaho.edu/~rubi4714/LBapp/index.html

A detailed description of each lesson is at:
www2.cs.uidaho.edu/~rubi4714/Teaching.html#Ladybug

For more information about Josh’s work, you can contact him at rubi4714 at vandals dot uidaho dot edu.

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BEACON Distinguished Postdoctoral Fellows Program

Reminder: The deadline is approaching for BEACON’s Distinguished Postdoctoral Fellows Program.

BEACON is an NSF Science and Technology Center headquartered at Michigan State University with partners at North Carolina A&T State University, University of Idaho, University of Texas at Austin, and University of Washington. BEACON brings together biologists, computer scientists, and engineers to study evolutionary dynamics using biological and computational techniques and to apply evolutionary principles to engineering problems. We seek outstanding post-doctoral scholars to pursue interdisciplinary research on evolution in action with BEACON faculty members, in the fields of biology, computer science, and/or engineering.

Applicants will propose a research project within the scope of BEACON’s mission and must have two BEACON faculty sponsors who will serve as research mentors should the fellowship be awarded. At least one sponsor must be from the MSU faculty; the other sponsor may be from any of the five BEACON institutions. Preference is given for interdisciplinary research. The postdoc fellow will be based at Michigan State University in East Lansing. Please see our website (http://www.beacon-center.org) for information about BEACON’s mission, participants and ongoing research projects.

Applicants must submit the following, in a single PDF, to BEACON Managing Director Danielle Whittaker via email (djwhitta@msu.edu):

  1. CV
  2. A two-page description of their research plan
  3. A one-page summary of their doctoral research

Also required (may be sent separately):

  • Letters of support from two BEACON sponsors (at least one must be from MSU)
  • Two additional letters of recommendation

Fellowships are for two years and include a salary of $50,000/year and modest funds to support research and travel. The successful applicant will help foster collaborations among faculty and disciplines and serve as a professional model for pre-doctoral trainees.

A Ph.D. in biology, computer science, engineering or related fields is required. Current MSU graduate students or postdocs are not eligible for this fellowship. Minority applicants are especially encouraged to apply. MSU is an Equal Opportunity/Affirmative Action Employer.

The deadline for applications is December 15 of each year. Finalists will be invited to give research seminars in January/February, and the award will be announced in late February.

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BEACON Researchers at Work: Journey from Microbiology to Microsoft

This week’s BEACON Researchers at Work post is by MSU graduate student Michelle Vogel.

Michelle Vogel rock climbingSince this is the tale of how I ended up as an intern and future employee of Microsoft, I guess I should tell you a bit about myself.  My name is Michelle Vogel, and I am currently a Masters Student at Michigan State University in Computer Science Engineering (CSE). If you couldn’t tell from the title, I did not start out in computer science engineering and I never thought in a million years I would end up working for one of the largest tech companies in the world.

I discovered CSE in Dr. Punch’s Introduction to Computer Science course, my last semester of my senior year of my undergraduate degree, making me a late bloomer compared to most in the field.  The course taught basic programing in Python, and I decided I had to learn more. I was fascinated by the challenges posed to me and the ability to use programming to solve biological problems. I spent the next year taking classes and learning all I could about CSE. I also spent a considerable amount of time performing research in conjunction with Drs. Heather Goldsby, Aaron Wagner and Charles Ofria. The next year I started the master’s program in CSE and began a project with Dr. Ofria.  In the process of working on my research (an algorithm for tree of life reconstruction) I realized I was more interested in the computer aspect than I was in the biological question I was attempting to solve with my program. Dr. Ofria encouraged me to try and obtain a summer internship in order to see what industry was like as I was uncertain if I wanted to continue on to a PhD program at another university.

Through hard work, perseverance and some luck I was offered an internship position at Microsoft. I knew prior to driving to Seattle that I would be working in Windows as an SDET – Software Development Engineer in Test.  SDET’s are responsible for testing all of the production code to try and find and remove as many bugs as possible before the software ships. SDET’s code is run in “lights out” automation labs, which run fully automated tests that require no interaction with a human once the test has been written.

Upon arriving in Redmond I started working with the Device Foundation Team. This team is part of the Hardware Developer Experience in the Windows division of Microsoft.  My team is responsible for testing the OS side of drivers.  Everyone has dealt with drivers at one point in time or another, usually for installing a new mouse or the dreaded printer driver.  What many people are unaware of is that drivers in their most basic form are the interface between the OS and hardware. This means that there are drivers for your hard drive, RAM, processor and virtually every hardware component in your computer. When these critical drivers are not working, your computer is unable to turn on.

My team gave me a unique experience. Instead of assigning me a project on my first day, they provided me a list of possibilities to pick from. I spent the next week learning how to schedule meetings with my coworkers to learn about the projects and setting up my system.  I learned more TLAs (three letter acronyms) in that first week than I could count and it would only get worse as I selected what I would work on for the summer and dived into it.  After giving the options some serious thought, I decided to work on a set of tools that enable testing of driver specific scenarios in an automated way.

One of the problems I addressed with my project is that drivers are software programs that reside in the same memory space as the software program responsible for running the computer (the kernel). When installation of a driver package goes wrong, the memory space that the operating system occupies may be overwritten or changed by the driver.  If this happens, the computer may not turn on, or if the driver malfunctions once it is installed and tries to access the kernel memory, the user may experience a bug check (commonly referred to as the “blue screen of death”).  If there isn’t enough memory, or the driver tries to use memory it doesn’t have permission to use, the program fails and the computer and operating system appropriately handle the error and crash to protect the rest of the system.

In order to ensure that drivers are installed correctly we need a safe way to test them, what computer scientists call a sandbox, a place where you can try building something without fear of getting hurt or destroying a working system. I designed my sandbox by installing driver packages onto the operating system software before it is installed on a machine.  The system then installs that same, altered, operating system software on a virtual machine (VM).  A virtual machine is a simulated computer that runs a full operating system as a program on an existing operating system. The VMs gave me flexibility in terms of not taking an entire machine out of commission to test. If something goes wrong when I perform my test, I can scrap the VM and start again, similar to deleting a program on your computer that was running poorly and reinstalling it.  I also can hand off a copy of the machine to a fellow team member to inspect. In addition to creating a safe way to test the installation process, virtual machines introduce scalability for the test teams. A single server can run multiple VMs at that same time and test different configurations.  This in turn reduces the number of machines required to test numerous scenarios and allows for easy parallelization of the tests.  There are also benefits to manageability and control that the system offer, but those are discussions for another day. The system is complex, but useful for testing various many OS problems, not just driver package installation. By the end of my twelve weeks, I was able to demo my prototype to my team. The system presented exciting possibilities, and team members started coming up with new scenarios that the system might be used to test in the future once it is fully functioning.

At the end of the summer I had a project I was proud of and that was useful to my team. I have since returned to MSU to finish my Master’s degree, but I am excited to say that I will be starting at Microsoft as a full time employee next summer.

For more information about Michelle’s work, you can contact her at vogelmi3 at msu dot edu.

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How a heavy metal musician became a scientist

Jeff Morris used to play in a heavy metal band but is now a BEACON postdoctoral researcher in Rich Lenski’s lab. As he explains in this video, “Being an independent musician pre-adapts you for what you’re going to have to do as a scientist… there’s nobody standing over your shoulder making you work, and you’re going to have to work very very hard to get anywhere.”

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