BEACON Researchers at Work: Poisons and Microbes

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

Photo of PatricFew animals can strut around the woodlands of the Pacific Northwest with as much poise and confidence as the rough-skinned newt (Taricha granulosa). While opportunistic predators lurk in every bush and stream, most avoid encountering this intrepid salamander as it ambles from one pond to the next. Fearless and unwavering, this small amphibian possesses one of the most deadly poisons found in nature: tetrodotoxin or TTX. While the newt does not use the toxin as venom, that is, it does not incorporate the poison into its bite, it does exude TTX when faced with predators as a defense mechanism.

In antiquity, TTX was most widely known as the poison of toxic puffer fishes within the family Tetrodontidae, from which the toxin derives its name. However, in recent decades TTX has been detected in an enormous diversity of animals including flatworms, nematodes, crabs, starfishes, octopuses, toads, and newts (see photos below). The wide distribution of TTX among evolutionarily unrelated animal lineages has led to the hypothesis that animals accumulate TTX from their environment, as the repeated evolution of the biosynthetic pathway that produces such a structurally complex molecule (see figure below at left) in all of these organisms is highly unlikely.

Three representative tetrodotoxic animals. Clockwise from top: Rough-skinned newt, puffer fish, and blue-ringed octopus. (sources: newt http://ih3.redbubble.net/image.5493116.7106/flat,550x550,075,f.jpg, puffer fish http://images.nationalgeographic.com/wpf/media-live/photos/000/006/cache/pufferfish_673_600x450.jpg, and octopus http://marinebio.org/upload/_cephs/Hapalochlaena-maculosa/3.jpg)

Three representative tetrodotoxic animals. Clockwise from top: Rough-skinned newt, puffer fish, and blue-ringed octopus.

Molecular structure of tetrodotoxin (TTX). (source: http://0.tqn.com/d/chemistry/1/0/J/Q/1/Tetrodotoxin.jpg)

Molecular structure of tetrodotoxin (TTX). (source)

Indeed, in nearly all tetrodotoxic animals, the accumulation of TTX has been associated with toxin-synthesizing bacteria present within their organs or upon their skin. These relationships are generally referred to as mutualisms, as the bacteria are provided with nutrients and a nice place to live, and the animal gains protection from the toxin secreted by the bacteria. In other cases, animals can accumulate TTX through their diet, but even then the fundamental source of the toxin is still attributed to toxic bacteria.

However, the ultimate origin of TTX in our intrepid salamander, the rough-skinned newt, remains to be elucidated, and this is the subject of my graduate research at Michigan State University.

Witch’s Brew: Don’t forget the Eye of Newt

Even though Shakespeare was likely referring to a seed or fruit when the witches added eye of newt to their mysterious potion in The Tragedy of Macbeth, the real thing would have been just as suitable. Present in every organ of the newt including the eyes, TTX is a potent neurotoxin that inhibits the electrical signaling underlying neurotransmission in the nerves and muscles of nearly all animals. Specifically, TTX binds to the voltage-gated sodium channels in excitable cells, preventing the influx of positive Na+ ions necessary to generate an action potential. The consequences of ingesting TTX are severe: numbness, nausea, paralysis, convulsions, and death—and there is no antidote. Fortunately, death can be avoided if the victim is placed on a respirator to prevent suffocation, as is the strategy implemented when a restaurant patron ingests improperly prepared puffer fish sushi, or Fugu.

So why spend my time investigating the production of TTX in newts? Well, they don’t float like a butterfly or sting like a bee, but rough-skinned newts are the heavy weight champions of tetrodotoxicity in nature, and they are easily the most poisonous vertebrate on the North American continent. They possess high enough concentrations of the toxin to make a puffer fish nauseous, and easily enough to kill several adult humans. One half milligram of toxin can kill a 165 lb. human, and the highest total TTX measured in a newt was fourteen milligrams. Okay, wait, what’s the deal with that? A newt is never going to encounter a predator as large as one human, so why would they possess enough toxin to kill 28 people?

Newts do have one natural enemy slithering in the woodlands, a predator that has evolved the ability to withstand the neurotoxic effects of TTX. In populations that overlap with newts, garter snakes in the genus Thamnophis have overcome the toxic defense of newts. In most cases, the levels of TTX in newts are matched by the levels of resistance in their snake predators, as determined by mutations in their sodium channels. Consequently, newts and snakes are locked in a coevolutionary arms race, where increasing toxicity in the newt prey is matched by increasing resistance in the predator, leading to an asymmetric escalation of extreme traits in these lineages.

Poison or Perfume?

Electro-olfactogram recording of newt olfactory neurons in the nose. Electrical response to positive control amino acid solution (top) and TTX (bottom).

Electro-olfactogram recording of newt olfactory neurons in the nose. Electrical response to positive control amino acid solution (top) and TTX (bottom).

The Eisthen lab at MSU originally became interested in TTX and rough-skinned newts because of a claim that male puffer fishes could smell TTX released from females, and that the toxin acted as a pheromone in these animals. Furthermore, later reports found that larval newts seek shelter when low concentrations of TTX are present, suggesting that larvae use TTX as an olfactory cue for the presence of adult newts that may potentially cannibalize them. Our lab has since conducted behavioral and electrophysiological experiments to decipher the roles that TTX might play in chemical communication. The figure at right shows an electro-olfactogram recording, made by former graduate student Justin Schroeder, from a newt nose presented with amino acids (positive control) and TTX. The recordings show that an electrical signal is generated in olfactory neurons when presented with these two stimuli, demonstrating that newts are capable of smell

ing the otherwise deadly neurotoxin.

We Are Not Alone

Cultured bacterial isolates from newt skin.

Cultured bacterial isolates from newt skin.

For nearly 600 million years, animals have evolved in a world dominated by microorganisms, and through this time we have remained in close association with microbial life. In humans, for example, nearly 100 trillion microbes inhabit our skin and gastrointestinal tract, where they help digest our food and produce vitamins that we cannot synthesize ourselves. In tetrodotoxic animals, mutualistic relationships with toxic bacteria have resulted in enormous benefits to the hosts, who become unpalatable to virtually all predators. Newts present an interesting situation, because the source of TTX remains unknown despite the fact that newts possess the highest concentrations of the toxin, and TTX is central to their coevolutionary arms race with garter snakes. There is tenuous evidence that newts may be synthesizing the toxin themselves, and have thus evolved biosynthesis of the neurotoxin in parallel with the various genera of bacteria that produce it. However, these data do not reject a microbial origin. Through BEACON, our lab has established collaborations with Dr. Kevin Theis at MSU, and the Foster and Harmon labs at the University of Idaho to investigate if the newt microbiome contains TTX-producing bacteria (see photo above). The elucidation of this elusive microbe(s) may provide early evidence that the microbiota inhabiting an animal can respond to natural selection upon the host (in this case predation), suggesting that in the evolution of animals, including ourselves, we are not alone.

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

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Discussing evolution on reddit: Interview with Bjørn Østman

Cross-posted from Randal Olson’s blog. 

As a followup to my previous blog post about using reddit AMAs as a form of science outreach, I thought it’d be helpful to interview a few scientists who are already doing science outreach on reddit and discuss their experiences. If you’re on the fence about whether reddit is a good place to do science outreach, give these interviews a read and see if it sounds like your cup of tea.

My first interview is with Bjørn Østman, a 4th year postdoc and computational evolutionary biologist at Michigan State University. Bjørn holds a M.S. in Astrophysics from the University of Copenhagen, a M.A. in Biology from UC Santa Barbara, and a Ph.D. in Computational Biology from the Keck Graduate Institute. During his free time, Bjørn runs the Carnival of Evolution, a popular monthly review of the latest research on evolution. He also blogs about evolution and his interactions with creationists on his personal blog.


reddit IAmA

Last Sunday afternoon, Bjørn ran an impromptu reddit IAmA to talk about his research and evolution in general. Following standard IAmA practice, he posted a picture verifying that he was the actual person answering the questions.

ostman-ama-pic

He also provided a brief introduction to himself and his research so people knew what kinds of questions to ask. He wanted to make it clear that he was giving the IAmA to discuss evolution and careers studying evolution.

ostman-ama-intro

Within 5 hours of starting, the IAmA had reached the front page of reddit, “the front page of the internet.” That means that around 2.8 million people around the world logged into reddit that day and saw his IAmA (estimated from these stats).

ostman-ama-frontpage

After 24 hours, the IAmA was still active with users asking questions and discussing evolution. If you look at the second picture I included, users had posted over 2,000 comments focused on Bjørn, his career, and his research. A simple post and a few hours of his time had inspired thousands of users to learn more about evolution — and some even to pursue a career studying it.

Below, I interviewed Bjørn about his experience giving a reddit IAmA. My questions are bolded.

Interview with Bjørn

What motivated you to use reddit as a science outreach tool?

This post did, actually. I really like talking about evolution, and figured that a Reddit AMA would solicit a few questions. I was overwhelmed by the interest.

What are the pros and cons of using reddit as a science outreach tool?

One pro is that it apparently has a lot of users and that they are particularly inquisitive.

What was your best experience engaging with the public on reddit? Have you had any negative experiences while engaging with the public on reddit?

I most enjoyed answering questions about my career, actually. That seemed to be helpful, I hope, to a lot of people who want to be a scientist. No negative experience with his AMA.

What other science outreach programs have you participated in? How would you compare science outreach on reddit to other science outreach programs you have participated in?

I blog, I do Carnival of Evolution, and I have participated in some school and high school programs through BEACON. I really like that Reddit has so many users that can be reached so easily.

What was it like giving a reddit AMA as a scientist?

It was intense. I answered questions for 8 hours straight, and still go back there to answer a few questions. It was a genuinely positive experience, and I’d like to do a more focused one again soon.

Do you have any advice for scientists interested in engaging with the public through reddit?

Yes, just do it already, but be prepared to spend a long time on it. I can only imagine how disappointing it is for people with burning questions, and then they don’t get an answer.

Bjørn added that creating a Frequently Asked Questions page (FAQ) as he encountered the same questions repeatedly was a major time saver.

Anything else you would like to add?

If I had known about Reddit AMAs before, I would have done it a long time ago. This way I can reach many more people than via blog, Twitter, Facebook, and G+ combined.

Interested in running your own reddit IAmA?

If a reddit IAmA sounds like something you’d like to do, make sure to read this guide on how to run one. Like any online community, reddit has its own culture and customs that are important to know about before you wade into the community.

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BEACON Researchers at Work: BEACON on the Beach

This week’s BEACON Researchers at Work post is by University of Washington graduate student Bryan Bartley.

Like some kind of planktonic larva, I drifted through my formative years, until I finally settled in Seattle 14 years ago. Seattle belongs to a great metropolis situated amidst mountain ranges, evergreen forests, and a great maze of waterways called Puget Sound. For those of us who call this place home, Puget Sound is a natural wonder and source of vitality. We share our home with diverse species of wildlife, fish, seabirds, and marine mammals. Marine invertebrates shaped like aliens are strategically poised to invade Seattle from the city’s shorelines, their invasion checked only by the rising tide.

There are signs the world inhabited by these creatures is rapidly changing. Temperatures around Puget Sound are warming faster than the global average, while a number of human-caused factors, including carbon emissions, are impacting water quality and habitat. Critical species of salmon and shellfish, which are central to the cultural and economic identity of our region, are already threatened. The citizens of Puget Sound, regardless which branch they occupy on the tree of life, must learn to recognize, manage, and adapt to this rapidly changing world.

No one can deny that our world is rapidly changing. Stocks are traded at the speed of light. Planes span the globe in a few hours. Everywhere you look, the pace of life is speeding up. Given how easily the effects of our actions spread across the globe, we must consider the larger consequences of our actions. We must begin to think like citizens of a planet.

I want the citizens of tomorrow to think like scientists as they face the future. That is why I helped organize a collaboration between citizen scientists at the Seattle Aquarium and educators from BEACON. The Aquarium’s Citizen Science program actively involves high school students in surveying biodiversity and intertidal habitat around Puget Sound for long term conservation management. The intertidal ecosystem becomes a classroom in which students learn how to ask scientific questions, systematically collect observations about the natural world, and construct arguments based on their observations. Together with BEACON we are enhancing the educational impact of this program by disseminating and developing curricular resources, training teachers at workshops, and creating research opportunities for high school interns in marine biology labs at UW.

In order to characterize plankton communities in Puget Sound water samples, we size-fractionated, concentrated, and collected them on filters.  DNA was extracted from the filter and the genetic sequence used to ID organisms.

In order to characterize plankton communities in Puget Sound water samples, we size-fractionated, concentrated, and collected them on filters. DNA was extracted from the filter and the genetic sequence used to ID organisms.

As a graduate student, I started volunteering with the Citizen Science program because I wanted to find better ways to promote scientific thinking. I enjoy the camaraderie with students while conducting beach surveys and working together toward a scientific objective. While on the beach, we also get to witness the dramatic interplay between landscape, elements, and life on the edge in the intertidal zone. Some of our high schoolers become excited about science during their experience in the Aquarium’s beach monitoring program. Last summer, I and some fellow BEACON graduate students mentored some of these students in summer research projects. For their project, my summer interns isolated microscopic plankton from the Sound, sequenced their DNA, and used their genetic sequences to build phylogenetic trees showing how they are evolutionarily related.

But my biggest reward is the impact the program has on attitudes and values in my community. The Citizen Science program promotes community ecoliteracy and activism through its network of students, families, teachers, adult volunteers, and aquarium staff. Even the most jaded students find it hard to be bored by the diversity of life forms they discover on our shores. These encounters are opportunities to teach kids about the ecological and evolutionary relationships that connect all life on earth. I believe that experiences like this cultivate a connection to and respect for life that transcends cultural boundaries.

In my graduate research, I use science to explore my connection with life. From what you’ve read, you might think that I am probably studying to be a marine biologist, so you might be surprised to discover that I am actually training to be an engineer. While I certainly enjoy getting outside and studying organisms in their natural habitat, I spend more time in the lab or at a computer than I do outside! In my research, I use computers to model complex living systems, such as the biochemical reactions inside a cell.

Is it possible that living systems on all scales, from cellular metabolism all the way up to ecosystems, share some common principles? Though still a controversial topic, I think so! I like to study the principles of living systems, whatever their scale, and think about how those principles might be applied to engineer better artificial systems. For example, economists and business leaders could learn a lot about “sustainability” by studying the famous Krebs cycle of primary metabolism, which you might have studied in an introductory biology course. This cycle of biochemical reactions is so useful that it is found universally among oxygen-respiring life on earth, and it can flexibly juggle diverse inputs and demands under very different conditions in order to maintain a stable equilibrium. I imagine it as a logistical hub that coordinates a complex network of supply chains and manufacturing pipelines. It is interesting to think about analogies like this across different scales of living systems.

Aggregations of intertidal snails like these have been hypothesized to be self-organizing systems.

Aggregations of intertidal snails like these have been hypothesized to be self-organizing systems.

I am eager to find ways to collaborate with others in BEACON who see an opportunity to teach kids the principles of living systems in the context of an ongoing, ecological field study. For example, it would be fun to use AVIDA-Ed in classroom activities to explore concepts related to ecological resiliency and sustainability, along themes discussed by Fortuna, et al in their paper “Evolving Digital Ecological Networks.” This kind of systems-level thinking is promoted in Washington state’s educational standards, but so far there aren’t very good curricula available for teachers. Not only that, but I also believe that this kind of systems-level perspective is extremely important as kids grow up in a world full of complex issues, including climate change.

Another interesting question about living systems that I have been considering recently is what’s called a bi-level optimization problem. It asks, can we find ways to use the resources from a living system that maximizes our own personal good but also benefits the overall fitness of the system? I have found at least one answer to that question. From my perspective, the opportunity to work with the students in the Citizen Science program, my BEACON partners, and the Seattle aquarium has been a win-win all around.

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

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BEACON Researchers at Work: Evolving Bio-Inspired Robots

This week’s BEACON Researchers at Work post is by Michigan State University graduate student Jared Moore(@j_redmmoore).

Jared MooreIf you had asked me during my undergrad years what a computer scientist did, I’m not sure I would’ve been able to give you a clear answer. I might have said something like, “Write computer programs as part of a team.” Of course there are computer scientists who do write programs, and I must admit that I write a great deal of code in my day-to-day work, but there is so much more than just code! Currently, I am working in the field of evolutionary robotics, which applies principles from natural evolution to the development of robots.

While one might associate robots with R2-D2, Cylons or Rosie from the Jetsons, a new generation is on the horizon. These bio-inspired robots borrow structures and behaviors observed in animals. All around us, we see animals and plants that are the product of millions of years of evolution. Many display remarkable features: the acceleration of a cheetah, the careful silent walk of a deer, or the agility and schooling abilities of fish. These and other animals provide inspiration for the development of robots.

Clown fish exhibit station keeping against ocean currents while staying close to sea anemones for food and protection.

Clown fish exhibit station keeping against ocean currents while staying close to sea anemones for food and protection.

Some of my recent work has focused on station keeping or station holding, wherein an aquatic robot must maintain its position around a point in space in flowing water. This behavior is seen in many fish. Maybe you’re familiar with clown fish swimming around a sea anemone, as in the movie “Finding Nemo,” exhibiting station keeping for protection as well as feeding. In robots, this behavior is highly desirable for environmental monitoring and inspection of underwater structures. As demonstrated in the past few years, humans can wreak havoc on ecosystems. The Deepwater Horizon spill resulted in large patches of oil dispersing throughout the Gulf of Mexico, while the Fukushima Reactor failure has led to continual radiation leakage into the Pacific Ocean. In both cases, scientists and cleanup crews have had to monitor large swaths of ocean to track the spread of contaminants. Robots help in this monitoring task, providing near constant data streams that enable rapid response to unexpected events. Of course, developing these robots comes with challenges.

Controllers capable of station keeping require integration of multiple sensory modalities including body position, orientation and ability to make sense of external sensory input such as the speed and direction of water flow. The specific “brains” that I work with are called artificial neural networks (ANNs), which draw inspiration from biological brains. These ANNs can be thought of as a collection of mathematical functions that evolve to receive sensory input, combine multiple different measurements in internal functions and provide outputs to govern motors on a robot. An ANN for station keeping must then not only display the ability to swim, but also recognize the robot’s position in the environment and actively maintain station in the face of flows. It’s not uncommon for these evolved ANNs to contain hundreds of neurons and thousands of connections.

Evolutionary computation approaches use a high number of individual simulations to test many possible solutions to a problem.

Evolutionary computation approaches use a high number of individual simulations to test many possible solutions to a problem.

Evolutionary computation is one approach to developing such complex structures. A population of individuals is used to represent many solutions to a problem and evolve over generations. Starting with an initial randomly generated population, each individual contains a genetic code representing one possible solution. Each individual is then evaluated in a physics simulation engine. Individuals are then ranked by their performance, with the more successful ones selected to populate the next generation. Borrowing another concept from natural evolution, the next-generation of solutions are created through a process of recombination, where two parents’ genetic information is mixed to create a new genetic code, and mutation, where individual elements of the genetic code are altered randomly. These new children form the next generation and the process of simulation, selection and recombination/mutation is repeated for many generations. How many generations depend on many factors, but it isn’t uncommon to have a population of 200 individuals evolve for 2000 generations. That’s 400,000 individuals to simulate, which can require over 60 hours of computing time!

While the development process can be quite complex, how do we know if a solution will be successful? In station keeping, we look for robots that start at the station point and then attempt to stay at or near that position while swimming against a simulated flow. Poor solutions simply drift away from the station point, but successful individuals demonstrate a variety of unique behaviors. The following videos show evolved solutions in a variety of different flow situations.

First, in the video above, this individual faces a flow coming from the direction of the camera. It is pushed away from the station before reorienting itself and swimming back to the station point (the white cross and sphere). Here, the evolved controller demonstrates the ability to swim against a simulated flow. What I find most interesting is the type of solutions evolutionary approaches often come up with. Many times the solutions exhibit behaviors that solve a problem in completely unexpected ways.

In the next video (see also the sequence of screen capturess in the image below the video), the evolved robot attempts to hold station against a flow coming from the rear. Rather than use its pectoral flippers, it instead flips completely upside down to bring the tail in a position to swim effectively against the flow.

An evolved individual swims against a flow coming from the rear. Here, the controller flips the robot over to bring the caudal fin into an effective position to swim against the flow.

An evolved individual swims against a flow coming from the rear. Here, the controller flips the robot over to bring the caudal fin into an effective position to swim against the flow.

These simulated robots are the result of many hours of computer simulatio

n and experimentation. Evolutionary computation allows us to harness the process of evolution and the power of high performance computing to produce effective robots. By using processes based on natural evolution, we hope to someday develop robots that rival the complexity of natural organisms. (Hopefully I’ve provided a little bit more information of what a computer scientist does!)

For more information about Jared’s work, you can contact him at moore112 at msu dot edu or visit his website at www.jaredmmoore.com.

Notes: 

Clown Fish Source: Wikipedia  (Creative Commons License)

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BEACON Researchers at Work: What's a Genetic Algorithm?

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

PhotoA genetic algorithm is a method often used for optimization, or finding the best solution to a particular problem. When I first heard of genetic algorithms, I was working toward my Bachelors in Mechanical Engineering at MSU. Part of the senior capstone course included weekly guest lectures, the most memorable of which was a presentation by Dr. Ron Averill and Dr. Erik Goodman. They explained the basics of genetic algorithms and demonstrated how effective it could be when applied to engineering optimization problems. The work fascinated me enough that it was still on my mind several years later when I returned to MSU to pursue a PhD in Mechanical Engineering. Today I am lucky to be working with Dr. Averill and Dr. Goodman, along with Dr. Kalyanmoy Deb, on genetic algorithms.

Part of what drew me toward genetic algorithms, and optimization in general, is its multidisciplinary applications. Optimization is an important step of the design or decision process of any field. Even many day-to-day decisions can be thought to have gone through an optimization process. For example, when you go to school or work each morning there are a number of possible routes you can take. Each route will have different qualities, some will be shorter, some will be less busy, or some may be more scenic. The route that you choose is what you consider to be the most optimal route based on your preferences.

Optimization of engineering problems is not much different. Each problem will have a number of design variables that can be changed and criteria that should be met. For example, consider the design of a car body. You may want to ensure the crashworthiness of the car is as high as possible while also keeping the mass and cost of the body below a certain limit. The goal of the optimization method is to determine the values of the design variables that best meet this goal.

The idea of choosing an optimal solution from a set of all possible solutions is easily understood. However, what many people do not realize is that it is almost impossible to consider all possible solutions. If each possible solution needs to be evaluate—for example when designing the car—then each possible design would need to be run in a simulator in order to determine crashworthiness. A single design may contain tens, hundreds, or even thousands, of design variables. On top of that, each variable can have a large range of possible values. This is where the particular method of optimization comes in. Its job is to generate new, and hopefully better, solutions based on the information known. The measure of an optimization method is not just the quality of its best solutions but how many tries it took to get there.

There are many different types of optimization methods; depending on the problem some types will perform better than others. Genetic algorithms are optimization methods that take inspiration from natural selection. All organisms have their own DNA that controls their development and eventual traits. The organisms best fit for their environment will also be the most likely to survive and reproduce, passing their DNA on to the next generation.

Genetic algorithms operate in the same way. Each possible solution is given its own genome, just like how each organism has its own DNA. This genome contains all the design variables for that particular solution. This solution is evaluated (for example, by using the car crash simulator) and its performance is considered to be its “fitness.” The solutions with the best fitness will be used to recombine, or reproduce, with other solutions to produce new child solutions. The hope is that these children will perform better than either parent by combining the design traits that made them successful. Due to their simplicity, flexibility, and ease of operation, they are increasingly being used in many different optimization problems in sciences, engineering and commerce.

The placement and strength of the sensors (blue) are determined such that the cover the domain as efficiently as possible.

The placement and strength of the sensors (blue) are determined such that the cover the domain as efficiently as possible.

Our current work with genetic algorithms looks at a certain class of problems. Most problems contain a fixed number of design variables. Our work considers problems where the number of design variables may change from solution to solution. For example, one of our test problems involves placing sensors onto a field. Each sensor covers a circular area, we can control the position and sensing radius of each sensor. We want to cover the field as extensively as possible while minimizing the total cost of the sensors. The size of the genome will be different for solutions that contain different numbers of sensors. This introduces new questions of how to recombine solutions to produce offspring solutions; genetic algorithms typically only consider genomes of a fixed length.

Using traditional genetic algorithm methods for recombination almost always fails to produce children with finesses similar to that of the parents. What we’ve found is that additional care needs to be taken when exchanging information between the two parent solutions. First, parts of the two parent genomes that perform similar functions should be identified. In the sensor placement problem, for example, sensors that cover similar regions of the field in parent solutions should be identified. Using this information allows for a more controlled recombination of parent solutions and can reliably produce quality children, even when the parents contain different numbers of design variables.

Additionally, we’ve found that to produce solutions with the optimal number of sensors we have to try to protect solutions that contain a different numbers of sensors than their peers. For example, all possible solutions up until a point in the optimization run might contain 30 or more sensors. If a solution appears that contains only 29 sensors it is likely to be considered a poor solution as it will likely leave a portion of the field uncovered. However, 29 sensors may be closer to the optimal number of sensors. By giving these solutions some additional time to improve themselves, we may find that they will be the most optimal in the end, even if they perform poorly in the beginning. 

For more information about Matt’s work, you can contact him at matt dot ryerkerk at gmail dot com. 

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Kalyanmoy Deb receives the World Academy of Sciences Prize in Engineering Sciences

Kalyanmoy Deb (center), with (left) Mr. Bai Chunli, President of TWAS and President of Chinese Academy of Sciences, and (right) Mr. Lino Barañao, Argentinian Minister of Science, Technology and Innovation.

Kalyanmoy Deb (center), with (left) Mr. Bai Chunli, President of TWAS and President of Chinese Academy of Sciences, and (right) Mr. Lino Barañao, Argentinian Minister of Science, Technology and Innovation.

Prof. Kalyanmoy Deb, a Beacon researcher and Koenig Endowed Chair Professor of Department of Electrical and Computer Engineering, Michigan State University, received the TWAS Prize in Engineering Sciences from The World Academy of Sciences in a meeting held in Buenos Aires, Argentina on 1 October 2013. Prof. Deb received this prize “for his contribution to the development of efficient evolutionary multi-objective optimization techniques for scientific and applied problem-solving tasks.”

Prof. Deb has been working on evolutionary computation since 1987. He has recently moved to Michigan State University from Indian Institute of Technology Kanpur, India. His NSGA-II algorithm — an evolutionary based multi-criterion optimization method — is a highly-cited work with more than 4,200 Web of Science citations alone and has been adopted by a number of commercial software companies. His newly established Computational Optimization and Innovation (COIN) Laboratory at MSU plans to promote research and application in this area by collaborating with academia and industries. More information about Prof. Deb’s research on evolutionary optimization can be found on his website.

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BEACON Researchers at Work: Electrical Life

This week’s BEACON Researchers at Work blog post is by University of Texas at Austin graduate student Ben Liebeskind. 

BenAll life stores energy in the form of electrochemical gradients. These gradients drive nutrient uptake in bacteria, water uptake in plants, and power the formation of ATP in our mitochondria (which are actually derived bacteria). A few life forms have turned these gradients to a new purpose: electrical signaling. Electrical signals, like those in the brain, are well known in animals, but in fact many organisms control behavior using electrical signaling.  The sparks emitted in the ocean by phosphorescent algae are set off by an electrical impulse called an action potential which largely resembles those that occur in our nervous systems. The closing of the Venus fly trap is also initiated by an action potential. Life, and behavior in particular, is electrical.

Ion Channels – the Transistors of the Brain

Ion channels turn environmental conditions, like those listed here, into electrical signals by only opening under certain environmental conditions.  Once the channels are open, charged ions can flow across the cell’s membrane, creating a regenerative electrical spike.

Ion channels turn environmental conditions, like those listed here, into electrical signals by only opening under certain environmental conditions. Once the channels are open, charged ions can flow across the cell’s membrane, creating a regenerative electrical spike.

The Zakon Lab at the University of Texas studies the evolution and function of the proteins that give rise to these electrical impulses. These proteins, called ion channels, are like tiny holes in the membrane of the cells in which they reside; holes so small that only charged elements, called ions, can pass through. The movement of these tiny charges through ion channels can create electrical impulses so small we can barely measure them, or so large that, in the case of the electric eel, they can stun or even kill us.  Ion channels propagate the electrical signals in nerves, and they also transduce the sensory signals of the world, like light or odorant molecules, into the electrical language of the nervous systems. The Hillis lab, also at the University of Texas, studies molecular evolution, phylogenetics, and broad questions about the systematics and diversity of life. Being co-advised by Drs. Zakon and Hillis, I approach the study of ion channels from an evolutionary perspective.

Evolution of Ion Channels – Lessons from the Deep Past

So when did these magical proteins evolve? Because all life must maintain electrochemical gradients, ion channels are found in the genomes of every type of organism, even viruses, which, not being cellular life forms themselves, have to steal the host cell’s lipid membrane before they can use the channels. So if all organisms have them, why is animal electrical signaling so complex relative to that of other organisms? This is a complex question, but I think we can start to formulate an answer by looking at ion channel evolution.

Many organisms use their electrical signals for two purposes at once: to stimulate the neighboring areas of the cell, thereby setting off a traveling electrical wave; and also as a means of rapidly delivering one key ion to the cell: Calcium. Calcium ions serve many purposes in cells. They trigger muscle contraction, regulate gene expression, trigger cell growth, and contribute to learning and memory in the brain. This great power is also a danger – too much calcium can trigger the cellular pathways that trigger cell death. Calcium overload is thought to be a major contributor to neuron death during a stroke.  Organisms avoid this catastrophe by tightly limiting calcium intake. It is therefore hard to see how the nervous system could maintain its electrical activity using calcium ions alone. In fact, it doesn’t. Most electrical signals in our nervous system are carried by a different ion: Sodium – the same element that resides on your table as a salt. Sodium cannot trigger cell death; its sole purpose is to carry charges across the membrane.  It also has the advantage of being abundant in the ocean, where early animals evolved.  This is why the body maintains a salty internal environment, effectively carrying the ocean with us on land so we can maintain the environment in which our physiology first evolved. By employing sodium in this way, the nervous system can maintain a complex neural code that doesn’t poison its cells.

So when did sodium, rather than calcium channels, evolve? Now the story gets interesting. There appears to be no physiological evidence for sodium channels outside the animal kingdom, consistent with their specialized role in nervous systems. We have been able to show that the gene family actually arose long before the evolution of animals, but their function may initially have been different – they may perhaps have been calcium channels initially, and only later gained sodium selectivity in the animal lineage. By co-opting these genes for a new purpose, animals may have been able to develop their complex nervous sytems by running them on sodium.

Ion Channel Toxins – Terrible, Beneficial, Tasty?

Because of their centrality to nervous system function, ion channels are the targets of some of nature’s deadliest toxins. Organisms can create these toxins to quickly immobilize their prey or to cause searing pain in would-be predators. Many of these naturally occurring toxins have such a unique affinity for ion channels that they are used as research tools to selectively block or activate certain channel types. Several are used in medicine as well, for instance in the treatment of chronic pain. Perhaps the most famous ion channel toxin is the sodium channel blocker tetrodotoxin (TTX), named for the Tetraodontid pufferfish from which it was first isolated. This toxin occurs at low levels in pufferfish sushi, called Fugu – low levels, that is, if the chef is properly trained. Although pufferfish get the naming rights to this powerful toxin, the premier producer of TTX on land is the rough-skinned newt, Taricha granulosa. The newts, and the pufferfish as well, appear to be insensitive to their toxins, and several researchers study naturally occurring TTX resistance by looking at the sodium channels that are blocked by it. Interestingly, the newts also appear to be able to smell TTX in the water. The Zakon lab is involved in a collaboration with the Eisthen lab at Michigan State University to figure out how the newts have evolved this remarkable ability.

Ion channels create the language of the nervous system, and so we must understand them in order to understand ourselves. Not only that, but we they can give us insight into the private worlds of unique organisms, like the newts, or into organisms that existed in the deep past.  This is only possible if we take a unique, interdisciplinary approach that combines molecular biology, neurobiology and biophysics, bioinformatics and computer science, and classical organismal biology. An interdisciplinary approach usually requires the input of many different scientists with different backgrounds, and BEACON facilitates this interaction by funding exploratory, interdisciplinary research, like ou

r newt study.  Only in this integrative way can we hope to tackle the fascinating questions related to electrical life.

For more information about Ben’s work, you can contact him at bliebeskind at austin dot utexas dot edu.

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BEACON Researchers at Work: Ecology and evolution of scent production in PNW Sasquatch

This week’s BEACON Researchers at Work blog post is by University of Washington students Sarah Hammarlund and Katie Dickinson.

Sarah Hammarlund and Katie DickinsonThe existence of the Sasquatch (also referred to as Bigfoot) has been debated for centuries. Alleged witnesses have described the forest-dwelling ape-like creature as being covered in hair, with large eyes, a pronounced brow, and a large, low-set forehead. Most scientists discount the existence of Bigfoot and consider it to be a combination of folklore, misidentification, and hoax, rather than a living animal, because of the lack of physical evidence and the large numbers of creatures that would be necessary to maintain a breeding population. However, indigenous groups in the Pacific Northwest have long told legends of the creature, and more recently researchers in the PNW may have identified what makes Sasquatch such an elusive creature.

A Sasquatch sighting in the lab

A Sasquatch sighting in the lab

Deep in the Hoh rainforest on the Olympic peninsula in Washington State, researchers have discovered a remote population of Sasquatches. Interestingly, the population can actually be divided into two types: Sasquatches that produce a strong unpleasant odor, and those that do not produce any odor. The odor appears to have the amazing ability to obscure the population from humans by both repelling and disorienting the humans. Scientists stumbled upon this remarkable phenomenon when a team of researchers studying the Olympic marmot included a scientist who is anosmic, lacking the ability to perceive odor. While all other members of the research team became confused and retreated from the area, the anosmic researcher came upon a family of scent-producing individuals, and began studying them. Recent evidence suggests that rare mushrooms must be gathered in order for Sasquatches to produce the oily foul-smelling compounds. In addition, it has been argued that the odor can be considered a “public good” – the Sasquatch population as a whole can benefit from the protection provided by odor producers because non-producers in the neighborhood of producers are also shielded. Researchers therefore consider scent producers to be “cooperators.” Sasquatch sightings have occurred periodically for decades and it has been observed that these are always solo creatures. It is now believed that all of the Bigfoot sightings are of “social cheats” – types that do not incur the cost of producing the scent, and are protected as long as they are in the vicinity of odor producer. However, when the cheaters become isolated, they are vulnerable to Bigfoot hunters and researchers. It is suggested that the cooperative Sasquatch stench has allowed large populations of the creatures to exist for years and yet remain a mystery.

Investigating the social context of odor-production

According to social evolution theory, a fundamental first step in the study of any trait is to demonstrate its fitness consequences in a social context (West et al., 2006). If the Sasquatch odor is a cooperative public good and odor-negative individuals are social cheaters, then the following predictions (Zhang & Rainey, 2013) should hold:

  1. Populations of odor producing Sasquatches should be more successful (less likely to be captured by scientists or hunters) than populations of non-producers when types are separated.
  2. Non-producers should outperform producers in mixed populations.
  3. Populations of non-producers should perform better than populations of producers when environmental conditions change such that scent production is not required (i.e. no scientists/hunters present).
  4. When rare, producers should not invade a population of non-producers.

Unfortunately, the discoverer of the Sasquatch population has since disappeared, but a notebook was found at a field site containing some interesting observations. One hypothesis is that the stress due to habitat fragmentation in the rainforest may actually favor odor producers. This may be because population fragmentation can isolate cooperators and cheaters from one another, allowing only cooperators to benefit from public good production. Additional anosmic scientists have been contacted to aid in the investigation of this hypothesis in the natural Sasquatch population.

Using microbes to explore public good production

Pseudomonas aeruginosa grown in minimal media. The green culture is the wild-type ‘cooperator’ and the white is our ‘cheater’ strain.

Pseudomonas aeruginosa grown in minimal media. The green culture is the wild-type ‘cooperator’ and the white is our ‘cheater’ strain.

Sarah Hammarlund and Katie Dickinson in the Kerr lab at the University of Washington are using microbes as a model system to address some of the ecological and evolutionary factors involved in the public good production reported in the Sasquatch population. We are using two strains of the bacterium Pseudomonas aeruginosa: a quorum sensing “cooperator” that produces protease (a public good) and a quorum sensing-defective “cheater” which benefits from public good production without incurring the cost of production. We are currently exploring how the environment affects the relationship between the cooperator and cheater. Specifically, we are investigating whether stress can benefit cooperation. In a spatially structured environment, stress may favor cooperation, since public goods become localized around cooperators (Hamilton, 1964; Fletcher & Doebeli, 2009). Furthermore, when public good producers are isolated from cheaters, this may allow for further adaptation to the stressful environment in the cooperator background.

Pseudomonas aeruginosa colonies on skim milk agar. The protease-producing wild-type strain has large ‘halos’ where casein proteins have been broken down.

Pseudomonas aeruginosa colonies on skim milk agar. The protease-producing wild-type strain has large ‘halos’ where casein proteins have been broken down.

Cooperation has puzzled evolutionary biologists since Darwin. We are interested in understanding not only how cooperative behaviors evolve but also how they are maintained, particularly in the face of cheaters. Recently, with the help of fellow BEACONite Brian Connelly, we entered the world of computer modeling to create a simulation of our system. In addition, Sarah has applied for

a Fulbright scholarship with the hopes of researching the evolution of cooperation between species at Oxford with Kevin Foster before beginning graduate school. Katie, in addition to her BEACON administrative work, plans to continue studying cooperation and altruism in the Kerr lab while debating the costs and benefits of grad school. We both enjoy photography and the great PNW outdoors, but despite being camera-at-the-ready hikers we have yet to cross paths with a Sasquatch. 

For more information about Sarah and Katie’s work, you can contact Sarah at sarahham at uw dot edu and Katie at katiejd8 at uw dot edu. BEACON would like to caution that Sarah and Katie are not a reliable source for information about Sasquatches.

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BEACON Researchers at Work: The Networked Brain

This week’s BEACON Researchers at Work blog post is by University of Texas at Austin graduate student Sean Maguire.

Sean MaguireThe Networked Brain

The brain has long been viewed as being composed of isolated regions controlling specific functions. Modern neuroscience has revealed that the brain is in fact a highly distributed and interconnected system. The brain can be viewed as a network of networks that span different spatial and temporal scales: from molecular networks within a single neuron, to synaptic connections between neurons, to local and regional circuitry, to long range interregional circuits and finally to social (brain to brain) networks (See Figure 1). The really interesting thing is that each level can affect the levels below it, as well as the levels above it. For example, the activity of a single neuron is affected by its gene expression, and its activity can then affect the other neurons that it synapses with. Likewise the neuron is part of a circuit and is receiving signals from other neurons that can modulate its gene expression. The Hofmann lab at The University of Texas does integrative studies across every level of the system shown in Figure 1. My research has focused specifically on the top two levels: social networks and interregional brain circuits, and how those two levels may interact.

Figure 1: The brain can be viewed as a network of networks. Depicted from top to bottom: Social (brain to brain) Networks, interregional brain networks, local and regional synaptic circuits and finally intercellular molecular networks.

Figure 1: The brain can be viewed as a network of networks. Depicted from top to bottom: Social (brain to brain) Networks, interregional brain networks, local and regional synaptic circuits and finally intercellular molecular networks.

Social networks affect health and behavior

Social connections are extremely important for human health and behavior. Using large observational studies of human social networks, researchers have shown that serious health problems like obesity and depression correlate with the disease status of each individual’s friends. One interpretation of these data is that if your friends are, for example, obese you are more likely to become obese in the future. The direct effect that people can have on their friends has been shown using experiments in large online social networks. In one experiment (which many of us may have unknowingly participated in) researchers displayed a banner to every user that logged into Facebook during the 2010 congressional election. Some users received a social message showing that their friends had voted along with information about how to vote. The control group received a non-social message with just the information on how to vote. The results showed that users that received the social message were much more likely to vote themselves. In fact, the researchers estimate that the “Facebook effect” accounted for about 340,000 extra votes!

How do social interactions affect physiology and behavior?

These effects must occur via individuals affecting the physiology and behavior of their social partners; however, the mechanisms of these effects are still unclear. In order to study these mechanisms, my collaborators and I are using the African cichlid fish, Astatotilapia burtoni, a model system in social neuroscience. A. burtoni live in social groups and display different phenotypes depending on where they are in the dominance hierarchy. Dominant (DOM) males are brightly colored, aggressive and territorial while subordinate (SUB) males are dull and spend their time schooling with females. These phenotypes are entirely determined by social interactions and SUBs can start transitioning to DOMs within minutes given an opportunity.

It may seem like a far-fetched proposition to study fish in order to learn about how social interactions affect brain and behavior more generally; however, the neural circuitry controlling social behavior (the Social Decision Making Network) has a deep evolutionary history that is conserved over at least 350 million years of vertebrate evolution. The Hofmann lab has recently synthesized evidence from 88 species across all vertebrate lineages to infer the ancestry and conservation of this brain network.

Social networks in A. burtoni 

Figure 2: A. burtoni can be either dominant or subordinate and can transition back and forth between these phenotypes.

Figure 2: A. burtoni can be either dominant or subordinate and can transition back and forth between these phenotypes.

We have reconstructed the social networks of replicate groups of A. burtoni and have found that social network position predicts many aspects of individual behavior and physiology. For example, one of the most important predictors of testosterone levels in males is not social status alone but community membership. In some communities DOM and SUB males have nearly equal testosterone levels while in others DOMs have much higher levels. This can be explained by the stability of the social networks in the community. In stable social networks DOM males have higher levels and in unstable communities DOMs and SUBs have similar levels.

How does social network position affect the brain?

Behavior is ultimately determined, not by the activity of any one region of the Social Decision Making Network, but rather by the patterns of activity across these nodes. My hypothesis is that social network position affects this neural patterning thereby modulating behavioral decision making. I predict that DOM males higher in the social hierarchy will have a different pattern of activity across these nodes compared to SUBs and that this will directly correlate with their behavior.

My current experiments aim to test this by giving SUB males and opportunity to transition to DOM status and observing their neural patterning (using gene expression markers that correlate to neural activity) at different stages of their transition.

Keep your friends close…

Ultimately my research is part of a growing body of evidence showing that the social environment has very important effects on individual behavior, physiology and gene expression. I think that it is important to be mindful of ones own social environment and to try and curate friendships that are engaging and positive, which has been shown to correlate with overall life satisfaction as well as success in things like adopting healthy lifestyle changes.

For more about Sean’s work, you can contact him at smmaguire at gmail dot com.

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Teaching computing to biologists

This week at BEACON, two different computing workshops are being taught:

These workshops are supported by an NSF supplement to the BEACON grant awarded to Titus Brown, who is collaborating with SESYNC (The National Socio-Environmental Synthesis Center) to improve computational training for scientists. The video below summarizes a recent meeting at SESYNC discussing cyberinfrastructure education in biology.

These workshops will be offered again, so if you missed out this time, stay tuned for future announcements!

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