BEACON Researchers at Work: Linking microbial interactions to disease

Posted on June 11, 2012 by Danielle Whittaker

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

I am interested in microbial communities for a number of reasons. First, microbial communities are found nearly everywhere, from soil to the surfaces of everyday objects. Moreover they interact profoundly with other organisms, even establishing complex communities on the skin and in the guts of animals. Additionally, microbial communities do important things such as playing an important role in nutrient cycling in the soil. They can even affect human health, for example aiding in digestion and playing an important role in disease.

However, while microbial communities are common, they are difficult to study. There are huge numbers of individual microbes in a community and microbes are very diverse. The number of microbial cells in a single gram of soil is estimated to be on the order of 10^8. On top of that, hundreds or thousands of different microbe types may inhabit a single community. But this complexity is only the first hurdle to studying microbial communities. We are not yet able to grow many microbes in the lab. Even if such growth was possible, microbes may behave differently when grown in isolation compared to when they are a part of a complex community.

Luckily, new sequencing technologies are allowing us to study microbial communities in new and dynamic ways. Specifically, the development of two basic techniques allows us to accommodate many of these challenges. Bar code DNA sequencing lets us estimate the relative number of each type of microbe in the community. Similarly, metagenomic sequencing can show which genes are present in the community, allowing us to detect potential biochemical pathways. These data are giving us a new view of microbial communities. It also allows us to study the microbial communities without needing to grow the microbes in the lab. These techniques have made it possible to research microbial communities in new ways.

My research is focused on microbial communities that may be related to bacterial vaginosis (BV). BV is a disease characterized by changes in the microbial community in the vagina. It is remarkably prevalent, with estimates as high as nearly 30% of all women affected. Moreover BV is particularly interesting because it appears to be linked to the type of microbial community, rather than individual types of microbes.

Specifically my research is to determine which parts of the microbial community in the vagina are associated with BV. If BV is not linked to a specific microbe, perhaps it is linked to interactions between the microbes. Moreover, interactions between the microbes and environmental factors may be important. Researchers studying genetic interactions have developed a number of tools that aim to detect interactions between genes that may lead to disease, which is remarkably similar to the search for microbial interactions. Because I wish to detect interactions between microbes that are associated with BV, I am applying many of the same tools as researchers studying genetic interactions. Some of these tools include logistic regression, multifactor dimensionality reduction, and genetic programming.

All of these techniques work in similar ways. First, models are generated that relate aspects of the microbial community to BV. These aspects may be the relative abundance of microbe types or various environmental factors. After these models are generated, I evaluate them based on how well they are able to classify microbial communities into BV and non-BV categories. I then analyze the models to determine which aspects of the microbial community are being strongly linked to the classification. The parts of the community identified by the models as important become strong candidates for further study.

But this is just the beginning! In particular, I hope to extend these tools to new types of data. If these techniques work to detect microbial interactions related to BV, then they may also be able to detect interactions in other types of microbial communities. In addition, these same tools may be applied to more detailed metagenomic data to look for interactions between genes in microbial communities.

It is an exciting time to be a graduate student studying microbial communities. It often seems like every answer leads to many more questions about how microbial communities work. New types of data and analysis techniques are allowing us slowly begin to understand the incredibly complex world of microbes.

For more information about Daniel’s work, you can contact him at danlbek at gmail dot com.

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BEACON Researchers at Work: Applying evolution to telecommunications

Posted on June 4, 2012 by Danielle Whittaker

This week’s BEACON Researchers at Work blog post is by MSU postdoc Matt Rupp.

Matt RuppAs a researcher, I have never been completely comfortable dedicating myself to a single field of study. My formal background in both computer science and evolutionary biology reflects this desire to stay diverse. The products of cross-pollination between the methods and ideas of different disciplines are where I believe innovation and novelty lie. It is likely that these interdisciplinary collaborations will open new avenues for testing hypotheses that are otherwise intractable. The project that I am currently working on exemplifies this blending of disciplines to produce a new way of testing hypotheses in the field of telecommunications. 

I work with a group of researchers from multiple disciplines to synthesize knowledge from economics, telecommunications, computer science, and evolutionary biology to understand how telecommunication regulation affects the growth and development of the Internet. Combining our expertise, my colleagues and I are creating a virtual telecommunication market: a digital economic ecosystem with selfish individuals that often evolve their behavior in an open-ended manner. By examining how these individuals evolve under different regulations, we hope to better understand how specific regulatory decisions will affect the real world. 

Regulation should strike a balance between the often-opposing forces that encourage growth and innovation and those that support competition and fairness. If there is too much or poorly-focused regulation, investment is stifled; if regulation is absent or too limited, there is the potential for oligopolies or monopolies of choice, lack of fairness, and market fragmentation. There is no lack of theory about striking a good regulatory balance, but there is little in the way of empirical testing. As a result, regulation often leads to unintended consequences. This is because market participants, like natural organisms, evolve to exploit their environment. Market participants will often find loopholes and strategies to leverage the regulatory environment to their advantage in ways that were not envisioned by policy makers. 

The telecommunication industry is a rapidly changing area where empirical testing of regulation prior to implementation will be beneficial. Questions of how neutrally network traffic should be treated, whether or not voice over IP (VoIP) telephone service providers should be treated the same as traditional providers, and to what extent content and access should be bundled are only a few of many areas of regulatory concern. Answers to these questions will shape the cost and availability of telecommunication-based services to consumers as well as investment and deployment of future technologies. By examining in advance the consequences of specific regulations on existing and future telecommunication technology or services, my colleagues and I seek to help policy makers make better-informed decisions, potentially avoiding costly or undesired outcomes.

Figure 1: A caricature of our virtual market highlighting the three types of market participants and their interactions.

The virtual market my colleagues and I are creating encompasses the fundamental elements of the telecommunication industry in a highly configurable manner. It currently consists of three different types of market participants: consumers, network providers, and application providers as shown in Figure 1. Although the behavior of some participants is fully determined by us, other participants in the virtual market use genetic representations (such as GP-tree sets) to interpret and respond to market conditions. For example, a network provider’s genetic representation might take into consideration the number of customers, the amount of competition, and the congestion on its network for a particular location in the market to make a decision as to whether or not to invest in additional bandwidth for that location. Over the course of the experiments, the evolvable participants improve their competency through selection based upon their performance in a series of market competitions. By comparing the behavior of well-adapted participants evolved under different regulations, we hope to understand the consequences particular regulations have on the development of the market.

Part of my contribution to the project is make it easier to translate behaviors of real-world market participants, the information required to inform those behaviors, and the regulatory policies we want to test into the software and configuration of our virtual market experiments. I also work to make the interface between the virtual market and the genetic representation systems work together without the need for much intervention when we modify one side or the other. This is important because configuration tractability and a low cost of adding or removing properties of the market are important to our group to create an understandable and flexible experimental system.

Figure 2: An abstraction of how genetic representations, market participants, and concerns work together to produce changes in the market.

One of the tools I worked with my colleagues to achieve these twin goals separates an agent’s possible behaviors into a set of concerns. Each concern provides a configurable way to transmit information to the participant’s genetic representation without having to write a new interface between the genetic- and market-sides of our system when we add something new to the market or want a participant to have a new behavior. Figure 2 shows how concerns relate to market participants and the genetic representations. Concerns are very flexible; they provide the genetic representation a set of file-system like paths to access specific information about the market. By restricting which paths a representation is allowed to use, we can create different views of the market for each representation. Concerns also provide a generic means for a representation to react to the information they provide by causing the participant to perform actions in the market. In combination with our evaluation tools, I believe the concerns utilities have the potential to help other researchers more quickly develop and test similar types of hypotheses in different domains. 

It is a challenge at times to combine our group’s different domain knowledge into one experimental system, but I believe the insight we gain from our experiments will help us understand how different regulations affect the growth and development of our increasingly networked world. Our research is especially important now, as the lines between traditional telephone providers, broadband providers, and content providers are beginning to blur. Poor regulatory choices threaten to fragment our current interconnectivity, limit consumer choice, and create improper investment incentives for the next generation of infrastructure that will be neede

d. Further, I believe our work is a good example of researchers from different disciplines coming together to create new tools to study complex hypotheses. It is my hope that what we’re learning from our experiments not only helps policy makers but also enables other researchers to test similarly challenging hypotheses in different domains.

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

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BEACON Researchers at Work: Brainy birds and fish

Posted on May 28, 2012 by Danielle Whittaker

This week’s BEACON Researchers at Work post is by MSU postdoc Jason Keagy.

Jason Keagy and a bowerbirdLike many BEACONites, I am involved in several projects. Liliana Lettieri has already written an excellent post about the project I spend most of my time on. That project is focused on understanding the traits and genes involved in stickleback speciation. You can read more about this work on a blog we’ve started called campstickleback. Jianxun Wang wrote another post worth reading on his work designing a robotic fish, which Liliana and I will use to interact with sticklebacks in various behavioral experiments. So today I am going to discuss something that I have been interested in since my days as an undergraduate: cognition.

What do I mean when I use the word “cognition”? This is important, because we all use it in everyday speech, but it can have very different meanings depending on the research context. Actually, defining cognition could be its own blog post! A definition commonly used by behavioral ecologists (people who study animal behavior) states that cognition is a set of processes concerned with the acquisition, processing, retention, or use of information, including perception, learning, memory, and decision-making.

I am specifically interested in the evolution of cognition. I think about questions such as: Why did complex brains evolve? What are the selective forces influencing or promoting cognitive evolution? Are there constraints to cognitive evolution? Why are some animals better at certain cognitive tasks than others? These sorts of questions led me to take all kinds of different courses as an undergraduate across the Biology, Anthropology, Computer Science, and Psychology departments.

There are two main approaches that scientists have taken to study the evolution of cognition. First, researchers test for a relationship between individual variation in cognitive ability (or a neuroanatomical correlate) and a measure of their fitness. Second, researchers look across species to see if there is a relationship between species differences in cognitive ability (or neuroanatomical correlates) and potential agents of selection (e.g. social group size, diet, ecological variables). As a graduate student I focused on the first approach. Now I am using the second.

I ended up leaving college completely enthralled with the idea of studying a different topic, sexual selection (although I seriously considered studying cognition in primates instead). Soon after graduation, I set out to Australia to study a fascinating species of bird called the satin bowerbird, Ptilonorhynchus violaceus. The males of this species build elaborate bachelorpads out of sticks and then decorate them with flowers, feathers, snail shells, and other objects, solely to convince a female to mate with him. Then he kicks her out and she raises the kids on her own. For researchers interested in sexual selection, there are a couple features that make this species worth studying. First, the structure of the bachelorpad (called a bower) makes it impossible for males to force copulate with females. This means she has complete control over her mating decisions. Second, since she gets nothing from him but genes for her offspring, there aren’t alternative strategies available for males who aren’t flashy (such as giving her food treats or caring for the babies). Third, the bowers are on the ground and all courtship and copulations occur there, so we can monitor them using automated video cameras.

After observing the birds for some time, it occurred to me that there might be differences in males’ cognitive ability and that this might have an impact on their displays and thus mating success. In other words, there might be a relationship between two of my biggest interests: sexual selection and cognition. I gave male bowerbirds puzzles to solve to assess their problem solving ability. Male bowerbirds keep other males away from their bower sites and they don’t like red objects on their bower. These two facts allowed me to present problems in the wild to individual males that they were highly motivated to solve. One test involved a large clear container covering three red objects. The male had to remove the container to move the red objects, which you can see in the video below.

Picture of experimental setup. On the bottom are pictures from four different males showing the range of abilities in covering the red object.

The other was a bit more devious on my part. I superglued a red tile (as well as control green and blue ones) to very long screws and screwed them into the ground. Now the bowerbird male couldn’t pull them out. But if he was clever, he could figure out how to cover the red object with other things, such as leaf litter or decorations. It turns out that males that were better problem solvers did have higher mating success ! In other words, smart is sexy!

 

 

 

Graph showing relationship between problem solving ability and mating success. The table on the bottom explains the correlation between each variable and the respective factor (problem solving ability or mating success).

When I came to MSU, I switched to studying sticklebacks, a type of fish. In British Columbia, there are three lakes where you can find two species of stickleback, called “benthics” and “limnetics.” However, there are several pieces of evidence suggesting that evolution of these two species from marine ancestors has occurred independently in each lake (in other words, there has been natural replication of this divergence). Also this evolution has been very rapid, because glaciers covered these lakes ~10,000 years ago. What is the difference between benthic and limnetic sticklebacks? Limnetic sticklebacks live in open water and are planktivorous, more social, and more visually oriented, whereas benthics feed on littoral invertebrates, live in complex spatially structured vegetated habitats, and are more dependent on smell. Limnetic and benthic sticklebacks also differ in body size, shape, antipredator behavior, mating traits, and spatial learning abilities. The differences between the species imply possible differences in cognitive abilities and structures in the brain. Next year I’ll be able to tell you about results of work I plan on doing using magnetic resonance imaging (MRI) and histology to examine the volume and shape of different brain regions across stickleback species from multiple different lakes. For now, I’ll tell you about a cognition experiment that is currently underway.

My cognition
experiment is looking at whether stickleback species vary in their ability to use what is called “public information.” A video explanation of the methods can be seen below.

Almost all Paxton fish chose the correct side. Enos fish chose the side randomly.

Basically an observer fish can see two groups of fish, one group receives food, the other doesn’t (but does receive water the food was in to control for odor). The observer can’t see the food, but can see demonstrator fish try to eat the food. Then the demonstrator fish are removed and I see which side the observer fish chooses. If the observer fish (who is hungry) chooses the side where food was, then this implies the fish learned that side had food just a little while ago. My hypothesis is that benthics, who are less social, will be poorer at this task. So far I have data on both species from one lake, Enos. In that lake, all fish choose sides randomly, and so do not appear to be using social information. In the other lake I have tested so far, Paxton, I only have data on benthics, but they are all very good at choosing the correct side. The next step is to see whether limnetics in this lake are also good at choosing the correct side, and to test benthics and limnetics from a third lake, Priest. Then I can see whether Enos fish are the only ones bad at this task or whether there are different patterns in every lake. Interestingly, Enos lake has undergone rapid environmental change. What if this environmental change has impacted their social cognitive abilities? That would tie my interest in cognition back to another interest of mine from my undergraduate days: human effects on other species through environmental change.

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

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BEACON Researchers at Work: The evolution of simplicity and the Black Queen Hypothesis

Posted on May 21, 2012 by Danielle Whittaker

This week’s BEACON Researchers at Work blog post is by Michigan State University postdoc Jeff Morris.

Jeff MorrisWe could probably agree that humans are a lot more complicated than bacteria. At a first glance, we have more moving parts, lots of different encapsulated regions (organs) that do different things, and then there’s that wonderful casserole-sized lump of computer sitting in our heads. If we take a closer look, we find that our genomes are also monstrously more complex. In comparison to bacteria, our chromosomes are full of genetic spam, our genes are regulated by convoluted networks of interacting proteins, and in between transcription and translation there are a battery of modifications that must take place, not to mention active transport of RNA across the nuclear membrane.

So what does all this complexity do for us? One might point to our large, dramatic bodies; but in reality single-celled eukaryotes have genomes just as complex as humans, and sometimes more so. Also, our metabolisms aren’t that much different than bacteria. In fact, bacteria can do lots of reactions no eukaryote can do. Surely, then, our complexity gives us some profound fitness advantage. But the bacteria still comprise the huge majority of Earth’s biomass. They grow faster, are more versatile, more resistant to virtually all stresses, and can occupy environments off-limits to almost all eukaryotes. In this light, our complexity makes us look like Rube Goldberg devices – impressive but inefficient machines comprised of countless bells and whistles of dubious utility. 

Figure 1

In his 1995 book Full House, Stephen Jay Gould proposed that complexity should accumulate over the course of evolution by a purely neutral “drunkard’s walk” mechanism if species were just as likely to become more simple as more complex with any given mutational event. Because there is a “wall” of minimum complexity at one end of the spectrum, one would expect to see simple organisms as the dominant “mode” of life, with a long “tail” of increasingly complex (and rare) organisms that gets longer as life goes on (Fig. 1). Of course, this is indeed what one sees; there are orders of magnitude more bacteria than eukaryotes in the world.

Despite Gould’s neutral hypothesis, evolutionary biologists seem determined to prove that natural selection underlies the expansion of complexity in the living world. There have been some very compelling studies by BEACON scientists to this end (for instance, this classic by a 4-piece BEACONite supergroup), but comparatively few that look at selective pressures for simplification. A major focus of my research is on the other side of the coin. We’re all aware of cases of reductive evolution, where simple organisms have more complex ancestors – think tapeworms, which have no digestive tracts. Indeed, reductive evolution is a hallmark of the parasitic lifestyle, driven by the low effective population sizes of parasites and the fact that they are largely isolated from gene flow that could restore lost or damaged genes by recombination. These processes are examples of genetic drift, a random evolutionary process similar to Gould’s drunkard’s walk.

Figure 2

But natural selection can also simplify organisms, and not just parasites. Consider Prochlorococcus (Fig. 2), the world’s most abundant photosynthetic organism. These tiny cyanobacteria are the dominant “algae” in much of the ocean. At ~100,000,000 cells per liter of seawater they are far too populous to be subject to drift. And yet the genomes of Prochlorococcus are roughly half the size of their closest relatives and are missing many important pathways. Some of these missing genes actually make Prochlorococcus dependent on its neighbors for vital functions. For instance, compared to almost all other aerobic bacteria (and most of its cyanobacterial relatives), Prochlorococcus has very little ability to protect itself from hydrogen peroxide. Yet this toxin is continuously produced by sunlight acting on seawater, and would kill off Prochlorococcus if it were living by itself. Fortunately, there are many other bacteria in the ocean that are able to destroy this toxin, and because it is freely membrane-permeable (i.e., it’s a “leaky” function), when they protect themselves they also protect Prochlorococcus and all their other neighbors.

Metabolic pathways like peroxide degradation are expensive, and resources are scarce in the open ocean where Prochlorococcus lives. It is thus reasonable to suspect that the loss of this function gave Prochlorococcus’ ancestor a fitness advantage – but only if its neighbors kept the function. Notably, other common bacteria in the ocean also don’t have the genes necessary to break down peroxide, and these cells also have reduced genomes. Thus, we suspected that there was some common evolutionary process at work here, actively eliminating these genes and making these cells dependent on their neighbors.

We proposed the “Black Queen Hypothesis” (BQH) to explain this phenomenon. The name comes from the card game “Hearts,” where the object is to get the lowest possible score. The queen of spades, however, is worth as much as all the other cards combined, so a primary strategy is to not win her. We propose that there are functions in nature that are like this black queen – they are very costly, so you get a benefit if you aren’t holding her, but nevertheless somebody has to get stuck with her or else you wouldn’t be playing Hearts. For instance, imagine an ocean where no one degraded peroxide – game over, everybody dies. But it doesn’t take everybody working to make the environment safe, so cells can evolve to stop breaking down peroxide up to the point where any further function loss would lead to a level of peroxide that would offset the advantage of the gene loss. This is an example of what evolutionary biologists call negative frequency dependence, and it is striking because it allows the co-existence of very similar organisms. We also suspect that BQH evolution isn’t limited to peroxide and oceanic bacteria, but can be extended to any function that is both leaky and costly.

Nothing in the BQH is truly novel. It’s trivially obvious that complexity can be problematic. For instance, anyone who has written computer code knows that unnecessary lines mean extra clock cycles and slow, sloppy programs. But there hasn’t been much serious systematic thought about what the consequences are for natural selection on genomic and/or functional reduction. With the BQH we show how simple, selfish evolution

toward genomic efficiency can create complicated ecological interactions. It can even explain the evolution of mutualistic interactions in well-mixed environments, which was thought to be a virtual impossibility by theorists.

For more information about Jeff’s work, contact him at jmorris at msu dot edu.

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BEACON Researchers at Work: Exploring the evolution of navigation

Posted on May 14, 2012 by Danielle Whittaker

This week’s BEACON Researchers at Work blog post is by MSU postdoc Frank Bartlett.

Frank BartlettOver the years, my research has focused primarily on understanding mechanisms of navigation behavior. My interest in navigation probably stems in part from my complete inability to navigate, often getting lost on campus or in my own back yard. Up until the last two years my investigations have addressed questions about visual navigation in hymenopteran insects -questions such as “What are the contents of visual spatial memory? “ and “How are these memories used to revisit familiar locations?” Such questions have a rich scientific history with published experiments dating back nearly a century. Much is now known about how a variety of animal taxa, ranging from roundworms to humans, learn about and move through their environments.

As much as we currently know about how navigation behavior works, very little is known about how it evolves. What constitutes the early beginnings of complex navigation strategies such as path integration and landmark guidance? What kinds of selective pressures influenced the evolution of navigation behavior? Questions of this nature have gone largely unasked. This is not surprising since the adaptive nature of directed movement often appears self-evident. In the case of attraction or repulsion in response to some environmental stimulus such as light or gravity, it is rather easy to imagine how and why such an ability evolved. But how do we go from simple oriented movement, such as phototaxis, to more sophisticated behavior such as path integration, which allows an organism to compute the direct homeward path from anywhere along the outbound route? (This ability has been demonstrated in a variety of animals ranging from insects to humans.) Organisms such as honeybees, ants, rats, pigeons, etc. display elegant behavioral solutions to deal with navigation in complex environments. However, it is very difficult to use biological systems to track the evolution of these abilities.

In order to formulate and test ideas about the evolution of navigation behavior, I have packed away my video camera and sunscreen and loaded Avida onto my laptop. The digital organisms in Avida provide an opportunity to examine the evolution of behavior over the course of hours and days rather than decades. It also provides explicit control over the environment allowing us to carefully define the behavioral task organisms must solve.

The first obstacle in my pursuit was to determine the physical and sensory capabilities of the organism. If one wishes to observe navigation, the organisms must first be able to move. To evolve any kind of interesting movement behavior the organism should have the ability to assess its environment including current position in the environment, condition or quality of the current position, the direction of movement, etc. To accommodate these requirements, we equipped Avidians with instructions that allowed forward movement and rotation as well as instructions to sense their current position in the environment. In addition we added an instruction that allowed the Avidians to sense their current directional heading.

Having settled on the organisms’ sensory and locomotor capabilities it was time to define the environment and the task. Avidians live in a grid world where they compete for space. They also compete for cycling time on a common processor, which allows them to execute their behavior. Under standard circumstances, all Avidians live and behave in a common grid. In our experiments the organisms lived and competed for space in a population grid but performed their behavior in a separate “state-grid.” The state grid was implemented to remove collisions and other complicating interactions among neighbors. We set up a rather simple foraging task defined by a single boundary. The state grid was divided into a northern resource half and a southern reproductive half. If an Avidian occupied a pink, northern grid cell and performed the sense instruction, it received a reward in the form of extra processor cycles. This extra processing power allows an Avidian to execute its behavior faster. A faster organism can move and reproduce more quickly than its neighbors and gain a competitive advantage. Collecting resources, however, was not enough to be successful at our task. Here is the rub: resource cells did not allow for reproduction. In order to successfully divide and generate offspring the Avidian had to occupy a southern, white grid cell. These cells provided no resource. So, the task required the organisms to evolve a move/sense behavioral strategy that put them into the pink zone to collect resource followed by traveling to the white zone to reproduce. This task is a simplified version of central-place-foraging seen in many biological organisms where the critter has to venture out to collect food and return home. Rather than “home” being a single point in the environment it is south of a “line in the sand”. Such a task mimics a intertidal zone where aquatic animals have to travel on shore to collect food but return to the water to avoid desiccation. Of course our digital organisms neither swim nor get wet!

We started the population with a single Avidian, placed facing north at the boundary of white and pink. This organism was equipped with a simple strategy (see Movie 1, above). In the movie the Avidian is represented with a dot for a head and a line for its body. When it flashes red it is executing a sense instruction. Our starting organism simply moved across the boarder and sensed to receive resource. It then turned around and moved to the white zone to divide. We turned this organism loose in the environment for 60,000 generations of mutation and natural selection. This took about 6 hours to complete, equating to about 1.5 million years in human generations. Movie 2 (below) shows the most successful behavior from this evolutionary run. This Avidian exhibits what folks here call “cockroach” behavior; following the walls of its environment and occasionally moving across the diagonal. Although this appears similar to the edge following behavior seen in many biological organisms the Avidian performs this strategy by simply moving without using a sense of direction.

It was instantly clear to us that these Avidians evolved a very simple strategy that exploited the geography of the state grid. To counter this chicanery, we modified the environment to provide no resource or reproductive area along the edges and diagonals of the state grid. The south is still the reproductive zone, represented by grey regions, while resource remains pink. White areas are a no-mans-land where the organism could neither collect resource nor reproduce (Movie 3, below). In addition, Avidians started their lives from random locations in the grid with a random facing. We seeded the evolutionary run with the cockroach from the last experiment and another 60,000 generations later a more interesting Avidian evolved. The dominant organism from this run evolved to use a sense of direction and ha
d multiple dist
inct behaviors. Movie 3 shows the behavioral trace of this organism. Every time the Avidian flashes red, it is trying to collect resource. When it flashes green, it is sensing its current direction. How many separate movement behaviors can you categorize? (This organism evolved 4 separate behavioral modules)

In addition to the short generation time, another advantage of studying digital organisms is our ability to open up the hood and figure out exactly how they work. For example, even though our organism shown in Movie 3 appears to seek the northeastern corner of the state grid, it has no internal representation of the corner of its world. It simply executes a loop that moves it in the northeastern direction exactly 50 times. Although there is information from the environment via the directional sensor that could indicate when it reached the corner, this Avidian did not evolve to use it. Instead, both the corner seeking behavior and the resource collection behavior, which it spends the bulk of its time performing, are timed by a mechanism that monitors the continuous growth of its offspring. Each behavior is terminated when the Avidian’s offspring has grown to a particular length. So, rather than orchestrating behavior using cues from the environment, many of the Avidians evolved to make navigational decisions based on their life history.

These experiments are the beginning exploration of how complex navigation behavior might evolve. In these cases, both the organism’s sensory/motor capabilities are quite minimal and the environments of evolution are oversimplified. Currently we are developing more elaborate sensing and more intricate environments to develop hypotheses about how simple behavioral systems may evolve to solve more complicated tasks.

To learn more about Frank’s work, contact him at bartle47 at msu dot edu.

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BEACON Researchers at Work: A computer scientist, but also a biologist

Posted on May 7, 2012 by Danielle Whittaker

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

“But wait – aren’t you getting your Ph.D. in Computer Science?” That’s a question that I have gotten used to hearing in my matriculation through graduate school. The short answer is yes, I am getting my Ph.D. in Computer Science, but I am also part of the Quantitative Biology program, and part of BEACON Center for the Study of Evolution in Action. With that said, I’m not your traditional computer scientist.

During my undergraduate career, I participated in several summer research experiences, one of which introduced me to the field of computational biology. My research project was to identify residues in proteins that are important for structural and functional purposes. The more important residues are usually conserved from one organism to the next. This conservation can be observed by mathematically modeling the protein and identical or similar proteins in other organisms. At that point in time, I had one biology class under my belt, and it wasn’t doing me any good. I started off with no idea of the biological implications of the data. That summer proved to be a pivotal point in my future research career because I developed an interest in the field of biology. As I was developing code to analyze biological data, I found it a bit tedious because I did not completely understand the problem. This lack of understanding fueled my interest in biology. I wanted to better understand the problem so that I could better process and analyze the data.

After that summer, I wanted a lab where I could work on computational science, as well as wet lab biological science. Michigan State University turned out to be a great fit. I found an adviser, Dr. C. Titus Brown, with joint appoints in Computer Science and Microbiology and Molecular Genetics. Since joining the Brown lab I have had the opportunity to learn of the growing pains of working in a wet lab, and it is a world of difference. There is no print function to determine if I set my parameters correctly, or to see if a block of my code is being executed. There are controls in biological experiments, but the margin for error is a lot smaller, and I learned this the hard way. However, once the balance shifts from “learning” to actually successfully completing an experiment, the sense of accomplishment makes everything worth it. 

Although there are long days and occasionally sleepless nights, I wouldn’t trade what I’m doing for anything. I’ve found that job that I would do for free – well, not free; I still have bills to pay. I get to go to “work” everyday and do something I enjoy. Currently I’m working on a cross-disciplinary evolution and development research project where I get to use my computational skills to analyze transcriptome data, and my wet lab skills to answer biological questions. There are two closely related species of Molgula, which are believe to be some of the closest related invertebrates to all vertebrates. The two species—M. oculata, and M. occulta—look very similar in their adult life, they sit on the bottom of the ocean and filter feed using their two siphons. Molgula fall under the phylum Tunicates. During development tunicates have a tadpole like stage that is similar to all vertebrates. The tailed tadpole phase is one of the key features that group tunicates and vertebrates into a group known as chordates. One of the species, M. occulta, has loss one of the key features that make it a chordate, and we want to find out why. In lab conditions the two species can be cross-fertilized and a hybrid forms with a tail about half the length of the M. oculata. Using the two species and the hybrid, we’re analyzing gene expression levels computationally and experimentally, in hopes of finding the gene regulatory network behind this loss of tail.

Another great opportunity that has come out of this project is the ability to travel and collaborate with other labs. I spend my winters in Michigan, which is great, seeing how much I love snow, and freezing cold weather, and I spend my summers in Washington or France. I have the privilege of working with Dr. Billie Swalla during the summer months, which has been a tremendous asset. I have learned a lot working under Dr. Swalla. The training I received in the Swalla lab was important to my development as a scientist, seeing that I come from a computer science background and wasn’t formally trained as a wet lab experimental scientist.

The way research is conducted has evolved. There is a strong push for collaboration and interdisciplinary research. I feel that I am being well prepared for this shift, and much of my preparation can be attributed to BEACON in some shape or form.  The experiences that I’ve had are not ones that an intercity kid from a public school typically gets, but because of my desire to uncover the unknown, I’m living a life I never dreamt of.

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

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BEACON Researchers at Work: Walk This Way

Posted on April 30, 2012 by Danielle Whittaker

This week’s BEACON Researchers at Work blog post is by University of Idaho postdoc Anne Gutmann.

Anne Gutmann

In the classic Monty Python skit “Ministry of Silly Walks”,  the comedian John Cleese demonstrates a series of hilariously weird and wacky walks while maintaining the prim and proper demeanor of a British government official.  One reason Cleese’s “silly walks” seem so strange and comical is that although humans (and other animals) are capable of a wide variety of movements, they mostly use just a few typical gaits such as walking and running to move from place to place. But why do animals generally choose to use only certain common gaits and not a multitude of other uncommon, “silly” gaits?

As a scientist in the field of locomotion biomechanics my research addresses this question on two main levels: 1) the evolutionary level and 2) the muscular level. On the evolutionary level, I am interested in understanding how natural selection shapes animals’ bodies and gaits, and on the muscular level I am interested in understanding how the mechanical properties of muscles and tendons determine which gaits are possible and preferable for a given animal. For example, I might hypothesize that natural selection should favor animals that use energy-efficient gaits because animals that cannot obtain enough food to meet their energy needs risk starvation. However, to understand why certain gaits are more energy efficient than others, I must also examine how muscles and tendons function during locomotion. The most energy-efficient gaits might provide energy saving opportunities by allowing the tendons to store and return energy like springs or by allowing the muscles to function at the most efficient contraction velocities.

Desert kangaroo rat, Dipodomys deserti, in its natural habitat in Nevada

Currently, I am studying the evolution of bipedal hopping as a postdoc in Craig McGowan’s lab at the University of Idaho. My work is part of a collaborative project between the McGowan and McKinley labs (University of Idaho and Michigan State University respectively) which is funded by a BEACON seed grant. Our goal is to understand why animals as diverse as kangaroos, wallabies, kangaroo rats, and jerboas all hop. These animals span a surprisingly wide range of body sizes and habitats, but all have the same basic leg design and hop on two legs to move from place to place. One hypothesis is that hopping evolved as a means of producing the high accelerations needed to escape predators. However, differences in muscle-tendon architecture suggest that some hopping animals have evolved for energy efficiency rather than high acceleration. We will use an interdisciplinary approach that integrates biomechanics, computation, and physics-based simulation to understand how selective pressures shape the evolution of leg design and gait in these animals. Graduate students in the McKinley lab will use a physics-based simulator and evolutionary algorithms to determine which selective pressures produce bipedal hopping. I will develop a detailed musculoskeletal model of a kangaroo rat to determine the effects of muscle-tendon architecture on hopping dynamics. This process will include using micro CT scans to create a 3-D model of a kangaroo rat skeleton and doing careful dissections of kangaroo rats to determine the points at which the muscles attach to the skeleton. I will also collect mechanics data from real kangaroo rats to allow me to develop realistic simulations of hopping. Once I have a detailed musculoskeletal model of a normal kangaroo rat up and running, I will adjust the leg design of this model to match the designs that emerge in the physics-based simulator. I can then use these modified models to compare the effect of different limb designs on muscle-tendon dynamics. This integrated approach will provide novel insight into why and how the musculoskeletal system of certain animals evolved for hopping.

Results from our study can be applied design biologically-inspired robots and prosthetic devices. Currently, most legged robots must move slowly and carefully to avoid falling over and have high energy requirements. Similarly, amputees often are forced to move more slowly than non-amputees because they must use more energy to walk and run. This can deter amputees from engaging in physical activity and reduce their overall quality of life.  Developing a better understanding of how kangaroos, wallabies, kangaroo rats, and jerboas hop will allow us to design agile legged robots that can navigate rough terrain quickly and efficiently and less-tiring prosthetic devices that will allow the wearer to walk and run at high speeds with ease.

For more information about Anne’s work, you can contact her at agutmann at uidaho dot edu.

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BEACON Researchers at Work: Teaching Kids about Evolution

Posted on April 23, 2012 by Danielle Whittaker

This week’s BEACON Researchers at Work blog post is by University of Washington postdoc Heather Goldsby.

Heather GoldsbyInitially, I was planning on chatting with all of you about my actual research into studying division of labor using digital organisms. I’m fascinated by the questions and our exciting results. However, as I was sitting to write this post, I realized I really wanted to tell you about my recent outreach activities.

As scientists, we’re familiar with the sobering realities  — many kids don’t understand evolution, many kids think science is memorizing facts, many kids think computers are for geeks, many kids think scientists and, especially computer scientists, are all men. Nothing could be further from the truth (except for maybe the last one… but we should really work to change that), but how can we convince the average kid of that? Many of them have lost interest in science and computers long before freshman year when we have a chance to talk with them.

Think back, when did you become interested in science or computer science? For me, it was in high school. I went to a small high school and took a computer science class when I ran out of history classes to take, and my deplorable lack of musical talent made band or choir less than appealing. I immediately became addicted to the joys of problem solving with computers, and when the time came, enrolled in computer science as a college major. My interest in science arose much, much later when I learned science was about discovery, not just the names of rocks. I wish I would have discovered both at an earlier age.

Researcher with kids and laptopTo expose kids to the wonders of science and computers, we are partnering with teachers to bring an “evolution in action” program to elementary school and middle school kids. The program is simple. We spend the first few minutes discussing how computers are everywhere — in their (or their parents’) phones, gaming systems, household electronics — and are used for everything from school work to angry birds. The focus is on kids realizing that computers are extremely integrated into their lives and are used for more than geeky things. Next, we chat about how computers can be used to address some of the challenges that arise in studying evolution. Drawing upon the motivation for my own work, we talk about the tremendous amount of time it can take to watch evolution in the wild and how evolution in a computer works far more rapidly.  We also discuss how evolution produces animals that are more fit for their environment. We use examples the kids are familiar with (e.g., cheetahs, monkeys, etc.) to help them understand.

Abstract image

Figure 1: Ancestor image to evolve in Picbreeder

For the rest of the time, we split the kids into small groups where they work with a scientist to evolve either pictures (using http://picbreeder.org) or 3D objects (using http://endlessforms.com). I’ll describe the basic process using a picbreeder example, although endlessforms is similar. First, the group selects an ancestor picture to evolve (for example, Figure 1).  A panel of pictures appears. From this panel, the kids collectively select one or more parents for the next generation. Clicking the ‘evolve’ button produces the next generation. As part of this process, they can change the mutation rate from small to large and observe how similar or different the pictures are to their parents. The scientist working with the group uses this process to explain evolutionary concepts such as mutation, recombination, and selection. Because the kids themselves are interacting with the process by selecting the parents, the mutation rate, and letting things evolve, they gain a more intuitive feel for how evolution works. Some future twists we are adding to this process is having all the groups start with the same picture and then at the end comparing final products to see what selection under divergent environments (different groups) might look like.

Researcher with kids looking at laptopThus far, we’ve worked with first graders and third graders as part of a yearly event where students can sign up to visit different labs on campus. Some of the surprising things for us were: (1) The students are incredibly smart! During the discussion portion of the event, first graders have explained to us what predators and prey are. One student also informed us that the ancestor of a whale was a  land mammal that looked something like a hippo. (2) The kids have had tons of fun. Both years we’ve done this event our participants have groaned when they had to leave and have asked their sponsors if they could stay longer. Apparently, evolution in action won them over. (3) We, the volunteers, have enjoyed the event nearly as much as the students. There is something incredibly refreshing about sharing the wonders of science and computers with such a young audience.

In the upcoming weeks, we are expanding our outreach program to go visit the local schools working with 4th graders and 6th graders. This expansion lets us reach more kids close to the ages where they lose interest in science and computer science.

Next year, we hope to expand the program to include two additional sessions that target different aspects of the BEACON mission. Specifically, we’d like to expand our outreach to have three parts, where the first part is dedicated to traditional science, the second part is computer science used to address questions in science (the evolving pictures part I just described), and the third is using evolutionary computation to address engineering challenges, such as robots. I’m always looking for volunteers and would love to be able to share this program with other BEACON representatives who are interested in reaching kids in their own local area!

For more information, you can contact Heather at goldsby at uw dot edu.

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BEACON Researchers at Work: Understanding spatial genetic structure of martens and their prey

Posted on April 16, 2012 by Danielle Whittaker

This week’s BEACON Researchers at Work blog post is by Michigan State University graduate student Paige Howell.

Paige HowellUnderstanding the processes that influence the spatial distribution of diversity is a long-standing goal in ecology and evolutionary biology. It has also always been an area of interest for me, both in the classroom and in my own research. A major theme of my research is identifying the processes that are most influential to the spatial genetic structure of individual species and communities of co-distributed species. In most taxa, dispersal is correlated with gene flow and consequently, it is a major contributor to population genetic differentiation. Dispersal ability and behavior are largely determined by the variety and spatial configuration of landscapes. Consequently, to understand the processes influencing how populations, species and communities are genetically structured requires consideration of the landscape features in which individuals exist and through which they disperse.

The emergence of landscape genetics as a synthesis of population genetics and landscape ecology has provided important advancements to our understanding of how dispersal and spatial genetic patterns are influenced by environmental features. To date, most research has focused on identifying physical landscape features (e.g., habitat matrix) or barriers (e.g., roads, waterfalls) that are correlated with genetic discontinuities. Identifying the environmental factors that most strongly affect gene flow and the spatial genetic patterns of different organisms has implications for basic research as well as conservation and management. For example, because of the potential influence of gene flow on the evolution of adaptation, evolutionary biologists are often concerned with understanding the processes governing gene flow. The dispersal ability of individuals between local sites may also impact the persistence of populations and so is an important study area for managers.

Unfortunately, I have found that most landscape genetic studies have ignored the potential impact of species interactions (e.g., density of heterospecifics, competition for food resources) on patterns of spatial genetic structure. Co-distributed species may respond differently or at different scales to the structure and spatial arrangement of landscapes. However, the dynamics of each species within the greater community are linked via ecological processes such as competition or predation. Ecological processes that are influential to the structure and function of biological communities are dependent on the intrinsic properties of each species as well as underlying landscape characteristics. Thus, incorporating measures of species interactions and physical landscape features at multiple spatial scales is crucial to understanding the spatial distribution of genetic variation for single species and communities of co-distributed species. A better understanding of the factors contributing to patterns of genetic diversity will enhance our ability to predict the probability of population, species, and community persistence in the face of changing landscapes.

Map showing distribution of marten and roads that impede gene flowThe community I’m working with now is a predator-prey system in the upper peninsula (UP) of Michigan composed of the American marten (Martes americana) and their small mammal prey (such as grey squirrels, voles). Previous research in my lab has developed genotypic data sets for American marten using neutral, microsatellite markers. Based on these data, three geographically distinct genetic clusters of American marten have been identified. Although individuals are continuously distributed across the landscape, the presence of genetic discontinuities suggests there are barriers to dispersal limiting the interaction between genetic groups. Based on all data from the UP, I am currently developing single species models of the associations between landscape (e.g., land cover, roads) and climatic (e.g., snow depth) features and measures of spatial genetic structure. In one such model, I tested whether a pattern of isolation by distance could explain the distribution of genetic variation in this species. Isolation by distance refers to the phenomenon that gene flow decreases with increasing geographic distance between groups and this results in higher genetic differentiation. Using a simple Mantel test to compare pair-wise geographic distances based on Euclidean distance and pairwise genetic distances based on inter-individual relatedness, I have detected a significant pattern of isolation by distance for marten over the entire study.

Because there exist a number of putative barriers to dispersal for marten in the UP, I was curious to see whether including any of these barriers in my model would improve my ability to explain genetic variation. The first barrier I have investigated is the presence of state roads and whether it contributes to the maintenance of genetic discontinuities within the population. Similar to models of isolation by distance, the spatial genetic structure of marten appears to be correlated with the presence of state roads as a factor influencing resistance to movement. However, the amount of genetic variation explained by either of these models is relatively low and incorporating other habitat features (e.g., landcover type, size of suitable habitat patches) may improve model fit.

While neutral markers like microsatellites serve as one measure of population genetic structure, I’m interested in looking at non-neutral markers as well. Certain phenotypic traits may be selected for in a population based on advantages they provide during dispersal through a complex mosaic of habitats. Phenotypic traits with high heritability may be used in addition to neutral markers as a proxy to evaluate the spatial distribution of genetic variation at non-neutral loci. Using a geometric morphometric approach, I will investigate whether spatial genetic structure in these animals is paralleled by morphological differences in skull shape.

As I mentioned earlier, I think it is critical to consider the species interactions that may be strongly contributing to the patterns of diversity of different species within a community. With marten, the diversity (e.g., beta-diversity in an ecological sense and intraspecific genetic variation) in their prey species may be one important factor. In some studies of community genetics, the genetic variation of one species has been found to predict the genetic structure of the other. The correlation of genetic variation in one species with another will depend on the strength of the ecological interactions linking each species and the shared responses of each species to environmental heterogeneity. At a more local scale where ecological processes, such as predation, are operating between marten and their prey, what factors are driving the observed patterns in spatial genetic structure? Is it the composition and configuration of habitat features, the spatial genetic structure of their prey, or a combination?

For more information about Paige’s work, you can contact her at howellp

at msu dot edu.

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BEACON exhibit and workshop draws teachers interested in Evolution in Action!

Posted on April 11, 2012 by Danielle Whittaker

The Michigan Science Teachers Association held their 59th Annual Conference, Pure Michigan Science, in Lansing March 8-10, 2012 and a number of BEACONites attended, informing regional teachers about the exciting educational programs in development across our consortium.  

Melissa Kjelvik at the BEACON table

The BEACON Exhibit highlighted games and software being developed to help teach basic and advanced evolutionary concepts in the K-16 classroom. Melissa Kjelvik, a graduate student working with Drs. Getty, Hayes, and Soule, told teachers about the Lady Bug and Aphid game developed by Terence Soule from the University of Idaho. Melissa shared lessons she is developing for the K-5 classroom. Teachers also interacted with two EvoAPPS, Variation and Selection.  A collaboration between Stephen Thomas and Adventure Club Games, these games were designed for museum kiosk interactions, and specifically target common misconceptions and work to clarify how variation and selection are requirements for evolutionary change. Amy Lark, a graduate student working with the Avida-ED team, told teachers about this digital platform, showing the advantages of working with a program that shows evolution in action, and where students can carry out their own experiments.

Wendy Johnson talks about Avida-ED

In addition to the BEACON exhibit, Wendy Johnson, a teacher at Lansing Catholic, and part of the RET program through the College of Engineering last summer, gave a workshop on her use of Avida-ED in AP Biology. The high school teachers attending her workshop showed great enthusiasm for and interest in Avida-ED. The meeting room was over capacity and the participants were very engaged, and perhaps more importantly, excited about being able to give their students the opportunity to observe and test evolution in action rather than just lecturing. We anticipate holding a longer workshop for science educators in late August here at BEACON!

 

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