For Robust Robots, Let Them Be Babies First

Or at least that's not too far off from what University of Vermont roboticist Josh Bongard has discovered, as he reports in the January 10 online edition of theProceedings of the National Academy of Sciences.

In a first-of-its-kind experiment, Bongard created both simulated and actual robots that, like tadpoles becoming frogs, change their body forms while learning how to walk. And, over generations, his simulated robots also evolved, spending less time in"infant" tadpole-like forms and more time in"adult" four-legged forms.

These evolving populations of robots were able to learn to walk more rapidly than ones with fixed body forms. And, in their final form, the changing robots had developed a more robust gait -- better able to deal with, say, being knocked with a stick -- than the ones that had learned to walk using upright legs from the beginning.

"This paper shows that body change, morphological change, actually helps us design better robots," Bongard says."That's never been attempted before."

Robots are complex

Bongard's research, supported by the National Science Foundation, is part of a wider venture called evolutionary robotics."We have an engineering goal," he says"to produce robots as quickly and consistently as possible." In this experimental case: upright four-legged robots that can move themselves to a light source without falling over.

"But we don't know how to program robots very well," Bongard says, because robots are complex systems. In some ways, they are too much like people for people to easily understand them.

"They have lots of moving parts. And their brains, like our brains, have lots of distributed materials: there's neurons and there's sensors and motors and they're all turning on and off in parallel," Bongard says,"and the emergent behavior from the complex system which is a robot, is some useful task like clearing up a construction site or laying pavement for a new road." Or at least that's the goal.

But, so far, engineers have been largely unsuccessful at creating robots that can continually perform simple, yet adaptable, behaviors in unstructured or outdoor environments.

Which is why Bongard, an assistant professor in UVM's College of Engineering and Mathematical Sciences, and other robotics experts have turned to computer programs to design robots and develop their behaviors -- rather than trying to program the robots' behavior directly.

His new work may help.

To the light

Using a sophisticated computer simulation, Bongard unleashed a series of synthetic beasts that move about in a 3-dimensional space."It looks like a modern video game," he says. Each creature -- or, rather, generations of the creatures -- then run a software routine, called a genetic algorithm, that experiments with various motions until it develops a slither, shuffle, or walking gait -- based on its body plan -- that can get it to the light source without tipping over.

"The robots have 12 moving parts," Bongard says."They look like the simplified skeleton of a mammal: it's got a jointed spine and then you have four sticks -- the legs -- sticking out."

Some of the creatures begin flat to the ground, like tadpoles or, perhaps, snakes with legs; others have splayed legs, a bit like a lizard; and others ran the full set of simulations with upright legs, like mammals.

And why do the generations of robots that progress from slithering to wide legs and, finally, to upright legs, ultimately perform better, getting to the desired behavior faster?

"The snake and reptilian robots are, in essence, training wheels," says Bongard,"they allow evolution to find motion patterns quicker, because those kinds of robots can't fall over. So evolution only has to solve the movement problem, but not the balance problem, initially. Then gradually over time it's able to tackle the balance problem after already solving the movement problem."

Sound anything like how a human infant first learns to roll, then crawl, then cruise along the coffee table and, finally, walk?

"Yes," says Bongard,"We're copying nature, we're copying evolution, we're copying neural science when we're building artificial brains into these robots." But the key point is that his robots don't only evolve their artificial brain -- the neural network controller -- but rather do so in continuous interaction with a changing body plan. A tadpole can't kick its legs, because it doesn't have any yet; it's learning some things legless and others with legs.

And this may help to explain the most surprising -- and useful -- finding in Bongard's study: the changing robots were not only faster in getting to the final goal, but afterward were more able to deal with new kinds of challenges that they hadn't before faced, like efforts to tip them over.

Bongard is not exactly sure why this is, but he thinks it's because controllers that evolved in the robots whose bodies changed over generations learned to maintain the desired behavior over a wider range of sensor-motor arrangements than controllers evolved in robots with fixed body plans. It seem that learning to walk while flat, squat, and then upright, gave the evolving robots resilience to stay upright when faced with new disruptions. Perhaps what a tadpole learns before it has legs makes it better able to use its legs once they grow.

"Realizing adaptive behavior in machines has to date focused on dynamic controllers, but static morphologies," Bongard writes in his PNAS paper"This is an inheritance from traditional artificial intelligence in which computer programs were developed that had no body with which to affect, and be affected by, the world."

"One thing that has been left out all this time is the obvious fact that in nature it's not that the animal's body stays fixed and its brain gets better over time," he says,"in natural evolution animals bodies and brains are evolving together all the time." A human infant, even if she knew how, couldn't walk: her bones and joints aren't up to the task until she starts to experience stress on the foot and ankle.

That hasn't been done in robotics for an obvious reason:"it's very hard to change a robot's body," Bongard says,"it's much easier to change the programming inside its head."

Lego proof

Still, Bongard gave it a try. After running 5000 simulations, each taking 30 hours on the parallel processors in UVM's Vermont Advanced Computing Center --"it would have taken 50 or 100 years on a single machine," Bongard says -- he took the task into the real world.

"We built a relatively simple robot, out of a couple of Lego Mindstorm kits, to demonstrate that you actually could do it," he says. This physical robot is four-legged, like in the simulation, but the Lego creature wears a brace on its front and back legs."The brace gradually tilts the robot," as the controller searches for successful movement patterns, Bongard says,"so that the legs go from horizontal to vertical, from reptile to quadruped.

"While the brace is bending the legs, the controller is causing the robot to move around, so it's able to move its legs, and bend its spine," he says,"it's squirming around like a reptile flat on the ground and then it gradually stands up until, at the end of this movement pattern, it's walking like a coyote."

"It's a very simple prototype," he says,"but it works; it's a proof of concept."

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Robotic Ghost Knifefish Is 'Born'

The robot -- created after observing and creating computer simulations of the black ghost knifefish -- could pave the way for nimble robots that could perform underwater recovery operations or long-term monitoring of coral reefs.

Led by Malcolm MacIver, associate professor of mechanical and biomedical engineering at Northwestern's McCormick School of Engineering and Applied Science, the team's results are published in the Journal of the Royal Society Interface.

The black ghost knifefish, which works at night in rivers of the Amazon basin, hunts for prey using a weak electric field around its entire body and moves both forward and backward using a ribbon-like fin on the underside of its body.

MacIver, a robotics expert who served as a scientific consultant for"Tron: Legacy" and is science advisor for the television series"Caprica," has studied the knifefish for years. Working with Neelesh Patankar, associate professor of mechanical engineering and co-author of the paper, he has created mechanical models of the fish in hopes of better understanding how the nervous system sends messages throughout the body to make it move.

Planning for the robot -- called GhostBot -- began when graduate student Oscar Curet, a co-author of the paper, observed a knifefish suddenly moving vertically in a tank in MacIver's lab.

"We had only tracked it horizontally before," said MacIver, a recent recipient of the Presidential Early Career Award for Scientists and Engineers."We thought, 'How could it be doing this?'"

Further observations revealed that while the fish only uses one traveling wave along the fin during horizontal motion (forward or backward depending on the direction on the wave), while moving vertically it uses two waves. One of these moves from head to tail, and the other moves tail to head. The two waves collide and stop at the center of the fin.

The team then created a computer simulation that showed that when these"inward counterpropagating waves" are generated by the fin, horizontal thrust is canceled and the fluid motion generated by the two waves is funneled into a downward jet from the center of the fin, pushing the body up. The flow structure looks like a mushroom cloud with an inverted jet.

"It's interesting because you're getting force coming off the animal in a completely unexpected direction that allows it to do acrobatics that, given its lifestyle of hunting and maneuvering among tree roots, makes a huge amount of sense," MacIver said.

The group then hired Kinea Design, a design firm founded by Northwestern faculty that specializes in human interactive mechatronics, and worked closely with its co-founder, Michael Peshkin, professor of mechanical engineering, to design and build a robot. The company fashioned a forearm-length waterproof robot with 32 motors giving independent control of the 32 artificial fin rays of the lycra-covered artificial fin. (That means the robot has 32 degrees of freedom. In comparison, industrial robot arms typically have less than 10.) Seven months and$200,000 later, the GhostBot came to life.

The group took the robot to Harvard University to test it in a flow tunnel in the lab of George V. Lauder, professor of ichthyology and co-author of the paper. The team measured the flow around the robotic fish by placing reflective particles in the water, then shining a laser sheet into the water. That allowed them to track the flow of the water by watching the particles, and the test showed the water flowing around the biomimetic robot just as computer simulations predicted it would.

"It worked perfectly the first time," MacIver said."We high-fived. We had the robot in the real world being pushed by real forces."

The robot is also outfitted with an electrosensory system that works similar to the knifefish's, and MacIver and his team hope to next improve the robot so it can autonomously use its sensory signals to detect an object and then use its mechanical system to position itself near the object.

Humans excel at creating high-speed, low-maneuverability technologies, like airplanes and cars, MacIver said. But studying animals provides a platform for creating low-speed, high-maneuverability technologies -- technologies that don't currently exist. Potential applications for such a robot include underwater recovery operations, such as plugging a leaking oil pipe, or long-term monitoring of oceanic environments, such as fragile coral reefs.

While the applied work on the robot moves ahead in the lab, the group is pursuing basic science questions as well."The robot is a tool for uncovering the extremely complicated story of how to coordinate movement in animals," MacIver said."By simulating and then performing the motions of the fish, we're getting insight into the mechanical basis of the remarkable agility of a very acrobatic, non-visual fish. The next step is to take the sensory work and unite the two."

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Cellular Traffic: Factors Beyond Crowding Affect How Molecules Interact Within Cells, Modeling Shows

A detailed understanding of the interactions inside cells -- where macromolecules can occupy as much as 40 percent of the available space -- could provide important information to the developers of therapeutic drugs and lead to a better understanding of how disease states develop. Ultimately, researchers hope to have a complete simulation of these cellular processes to help them understand a range of biological issues, from metabolism to cell division.

Sponsored by the National Institutes of Health, the research was reported Oct. 11 in the early online edition of the journalProceedings of the National Academy of Sciences.

"We found that hydrodynamics -- perturbation of the solvent with eddies and wakes created by molecules in this crowded environment -- may be the dominant effect in intermolecular dynamics within cells," said Jeffrey Skolnick, director of the Center for the Study of Systems Biology at Georgia Tech."The correlations created between molecules through this process have a lot of functional consequences for how collections of these molecules interact."

The motion of macromolecules within cells is normally random, occurring through Brownian motion that causes the molecules to diffuse through the cellular cytoplasm, which has viscosity similar to that of water. Researchers have studied the movement of fluorescent protein molecules injected into E. coli cells, but don't yet understand the forces affecting that motion. However, the measurements show that the fluorescent molecules move about 15 times more slowly inside the cell than they do in a test tube.

Using simulations that allowed them to adjust the impacts of natural forces, Skolnick and collaborator Tadashi Ando analyzed the activity of 15 different molecules in a portion -- just one one-thousandth -- of an E. coli cell. By altering those simulated forces in the computer, they attempted to determine what may cause the reduction in diffusion speed.

The most logical reason for that slowed movement is the crowded nature of cells, but Skolnick and Ando found that bumping into other molecules accounted for only a portion of the reduced molecular diffusion.

"If you are in a crowded room and want to walk to the bar, the other people slow you down," explained Skolnick, who is Georgia Research Alliance eminent scholar in computational systems biology."In biological processes, if there are a lot of large molecules in the way, these protein molecules can't move as quickly. But our model showed that this crowding accounted for only about a third of the reduction measured experimentally."

The researchers also studied the hydrodynamic forces exerted by molecules on one another. These forces are comparable to the way in which the wake of a large boat on a lake affects smaller boats, or how a swimming whale might effect a school of small fish. The interaction causes correlated motion, which was known to be important in the movement of polymers and colloids studied earlier by chemists.

By turning off the other forces at work in their silicon world, the Georgia Tech researchers found that this correlated motion accounted for much more of the diffusion reduction than did the crowding.

"The hydrodynamic interactions create cooperative motion between the molecules," Skolnick explained."We see long-lived correlations between the molecules, independent of size, in space and time. This suggests that these correlated motions may be extremely important in the dynamics of molecules."

The researchers also studied other possible causes for the slow-down but found that repulsion between molecules, variations in molecular shape and"stickiness" between molecules could not account for the dramatic reduction in diffusion rate.

Though the findings are interesting in themselves, their real importance may be in setting the stage for larger studies that would include the thousands of molecules known to be important to cellular operations. Researchers ultimately hope to model everything happening in the cell, including interactions with the cell membrane.

"This is the beginning of what will be a very complicated effort to develop the tools and approaches that will allow us to simulate a sufficiently useful caricature of a cell," Skolnick said."From that, we will be able to learn the biological principles at work, and then study some 'what if' scenarios."

Those"what if" questions might one day help drug designers better understand how therapeutic compounds work within cells, for instance, or allow cancer researchers to see how cells change from a healthy state to a disease state.

"It would be great if we could study new drugs in a model set of cells to very quickly see what might be the side-effects and cross interactions to understand how we might minimize these problems," Skolnick noted."The nice thing about a computer simulation is that if it is a reasonably faithful caricature, you can ask a lot of questions -- and get answers that help you understand what's going on."

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Fruit Fly Nervous System Provides New Solution to Fundamental Computer Network Problem

With a minimum of communication and without advance knowledge of how they are connected with each other, the cells in the fly's developing nervous system manage to organize themselves so that a small number of cells serve as leaders that provide direct connections with every other nerve cell, said author Ziv Bar-Joseph, associate professor of machine learning at Carnegie Mellon University.

The result, the researchers report in the Jan. 14 edition of the journalScience, is the same sort of scheme used to manage the distributed computer networks that perform such everyday tasks as searching the Web or controlling an airplane in flight. But the method used by the fly's nervous system to organize itself is much simpler and more robust than anything humans have concocted.

"It is such a simple and intuitive solution, I can't believe we did not think of this 25 years ago," said co-author Noga Alon, a mathematician and computer scientist at Tel Aviv University and the Institute for Advanced Study in Princeton, N.J.

Bar-Joseph, Alon and their co-authors -- Yehuda Afek of Tel Aviv University and Naama Barkai, Eran Hornstein and Omer Barad of the Weizmann Institute of Science in Rehovot, Israel -- used the insights gained from fruit flies to design a new distributed computing algorithm. They found it has qualities that make it particularly well suited for networks in which the number and position of the nodes is not completely certain. These include wireless sensor networks, such as environmental monitoring, where sensors are dispersed in a lake or waterway, or systems for controlling swarms of robots.

"Computational and mathematical models have long been used by scientists to analyze biological systems," said Bar-Joseph, a member of the Lane Center for Computational Biology in Carnegie Mellon's School of Computer Science."Here we've reversed the strategy, studying a biological system to solve a long-standing computer science problem."

Today's large-scale computer systems and the nervous system of a fly both take a distributive approach to performing tasks. Though the thousands or even millions of processors in a computing system and the millions of cells in a fly's nervous system must work together to complete a task, none of the elements need to have complete knowledge of what's going on, and the systems must function despite failures by individual elements.

In the computing world, one step toward creating this distributive system is to find a small set of processors that can be used to rapidly communicate with the rest of the processors in the network -- what graph theorists call a maximal independent set (MIS). Every processor in such a network is either a leader (a member of the MIS) or is connected to a leader, but the leaders are not interconnected.

A similar arrangement occurs in the fruit fly, which uses tiny bristles to sense the outside world. Each bristle develops from a nerve cell, called a sensory organ precursor (SOP), which connects to adjoining nerve cells, but does not connect with other SOPs.

For three decades, computer scientists have puzzled over how processors in a network can best elect an MIS. The common solutions use a probabilistic method -- similar to rolling dice -- in which some processors identify themselves as leaders, based in part on how many connections they have with other processors. Processors connected to these self-selected leaders take themselves out of the running and, in subsequent rounds, additional processors self-select themselves and the processors connected to them take themselves out of the running. At each round, the chances of any processor joining the MIS (becoming a leader) increases as a function of the number of its connections.

This selection process is rapid, Bar-Joseph said, but it entails lots of complicated messages being sent back and forth across the network, and it requires that all of the processors know in advance how they are connected in the network. That can be a problem for applications such as wireless sensor networks, where sensors might be distributed randomly and all might not be within communication range of each other.

During the larval and pupal stages of a fly's development, the nervous system also uses a probabilistic method to select the cells that will become SOPs. In the fly, however, the cells have no information about how they are connected to each other. As various cells self-select themselves as SOPs, they send out chemical signals to neighboring cells that inhibit those cells from also becoming SOPs. This process continues for three hours, until all of the cells are either SOPs or are neighbors to an SOP, and the fly emerges from the pupal stage.

In the fly, Bar-Joseph noted, the probability that any cell will self-select increases not as a function of connections, as in the typical MIS algorithm for computer networks, but as a function of time. The method does not require advance knowledge of how the cells are arranged. The communication between cells is as simple as can be.

The researchers created a computer algorithm based on the fly's approach and proved that it provides a fast solution to the MIS problem."The run time was slightly greater than current approaches, but the biological approach is efficient and more robust because it doesn't require so many assumptions," Bar-Joseph said."This makes the solution applicable to many more applications."

This research was supported in part by grants from the National Institutes of Health and the National Science Foundation.

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Quantum Quirk Contained

"We have demonstrated, for the first time, that a crystal can store information encoded into entangled quantum states of photons," says paper co-author Dr. Wolfgang Tittel of the University of Calgary's Institute for Quantum Information Science."This discovery constitutes an important milestone on the path toward quantum networks, and will hopefully enable building quantum networks in a few years."

In current communication networks, information is sent through pulses of light moving through optical fibre. The information can be stored on computer hard disks for future use.

Quantum networks operate differently than the networks we use daily.

"What we have is similar but it does not use pulses of light," says Tittel, who is a professor in the Department of Physics and Astronomy at the University of Calgary."In quantum communication, we also have to store and retrieve information. But in our case, the information is encoded into entangled states of photons."

In this state, photons are"entangled," and remain so even when they fly apart. In a way, they communicate with each other even when they are very far apart. The difficulty is getting them to stay put without breaking this fragile quantum link.

To achieve this task, the researchers used a crystal doped with rare-earth ions and cooled it to -270 Celsius. At these temperatures, material properties change and allowed the researchers to store and retrieve these photons without measurable degradation.

An important feature is that this memory device uses almost entirely standard fabrication technologies."The resulting robustness, and the possibility to integrate the memory with current technology such as fibre-optic cables is important when moving the currently fundamental research towards applications."

Quantum networks will allow the sending of information without one being afraid of somebody listening in.

"The results show that entanglement, a quantum physical property that has puzzled philosophers and physicists since almost hundred years, is not as fragile as is generally believed," says Tittel.

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Couch Potatoes Beware: Too Much Time Spent Watching TV Is Harmful to Heart Health

Data show that compared to people who spend less than two hours each day on screen-based entertainment like watching TV, using the computer or playing video games, those who devote more than four hours to these activities are more than twice as likely to have a major cardiac event that involves hospitalization, death or both.

The study -- the first to examine the association between screen time and non-fatal as well as fatal cardiovascular events -- also suggests metabolic factors and inflammation may partly explain the link between prolonged sitting and the risks to heart health.

"People who spend excessive amounts of time in front of a screen -- primarily watching TV -- are more likely to die of any cause and suffer heart-related problems," said Emmanuel Stamatakis, PhD, MSc, Department of Epidemiology and Public Health, University College London, United Kingdom."Our analysis suggests that two or more hours of screen time each day may place someone at greater risk for a cardiac event."

In fact, compared with those spending less than two hours a day on screen-based entertainment, there was a 48% increased risk of all-cause mortality in those spending four or more hours a day and an approximately 125% increase in risk of cardiovascular events in those spending two or more hours a day. These associations were independent of traditional risk factors such as smoking, hypertension, BMI, social class, as well as exercise.

The findings have prompted authors to advocate for public health guidelines that expressly address recreational sitting (defined as during non-work hours), especially as a majority of working age adults spend long periods being inactive while commuting or being slouched over a desk or computer.

"It is all a matter of habit. Many of us have learned to go back home, turn the TV set on and sit down for several hours -- it's convenient and easy to do. But doing so is bad for the heart and our health in general," said Dr. Stamatakis."And according to what we know so far, these health risks may not be mitigated by exercise, a finding that underscores the urgent need for public health recommendations to include guidelines for limiting recreational sitting and other sedentary behaviors, in addition to improving physical activity."

Biological mediators also appear to play a role. Data indicate that one fourth of the association between screen time and cardiovascular events was explained collectively by C-reactive protein (CRP), body mass index, and high-density lipoprotein cholesterol suggesting that inflammation and deregulation of lipids may be one pathway through which prolonged sitting increases the risk for cardiovascular events. CRP, a well-established marker of low-grade inflammation, was approximately two times higher in people spending more than four hours of screen time per day compared to those spending less than two hours a day.

Dr. Stamatakis says the next step will be to try to uncover what prolonged sitting does to the human body in the short- and long-term, whether and how exercise can mitigate these consequences, and how to alter lifestyles to reduce sitting and increase movement and exercise.

The present study included 4,512 adults who were respondents of the 2003 Scottish Health Survey, a representative, household-based survey. A total of 325 all-cause deaths and 215 cardiac events occurred during an average of 4.3 years of follow up.

Measurement of"screen time" included self-reported TV/DVD watching, video gaming, as well as leisure-time computer use. Authors also included multiple measures to rule out the possibility that ill people spend more time in front of the screen as opposed to other way around. Authors excluded those who reported a previous cardiovascular event (before baseline) and those who died during the first two years of follow up just in case their underlying disease might have forced them to stay indoors and watch TV more often. Dr. Stamatakis and his team also adjusted analyses for indicators of poor health (e.g., diabetes, hypertension).

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Played by Humans, Scored by Nature, Online Game Helps Unravel Secrets of RNA

The game, called EteRNA harnesses game play to uncover principles for designing molecules of RNA, which biologists believe may be the key regulator of everything that happens in living cells. But the game doesn't end with the highest computer score. Rather, players are scored and ranked based on how well their virtual designs can be rendered as real, physical molecules. Each week's top designs are synthesized in a biochemistry laboratory so researchers can see if the resulting molecules fold themselves into the three-dimensional shapes predicted by computer models.

"Putting a ball through a hoop or drawing a better poker hand is the way we're used to winning games, but in EteRNA you score when the molecule you've designed can assemble itself," said Adrien Treuille, an assistant professor of computer science at Carnegie Mellon, who leads the EteRNA project with Rhiju Das, an assistant professor of biochemistry at Stanford."Nature provides the final score -- and nature is one tough umpire."

Because EteRNA is crowdsourcing the scientific method -- enlisting non-experts to uncover still-mysterious RNA design principles -- it is essential that scoring be rigorous.

"Nature confounds even our best computer models," said Jeehyung Lee, a computer science Ph.D. student at Carnegie Mellon who led the game's development."We knew that if we were to truly tap the wisdom of crowds, our game would have to expose players to every aspect of the scientific process: design, yes, but also experimentation, analysis of results and incorporation of those results into future designs."

The complex, three-dimensional shape of an RNA molecule is critical to its function. The goal of the EteRNA project is to design RNA knots, polyhedra and other shapes never seen before.

"We want to understand how RNA folds in a test tube and eventually in viruses and living cells," Das said."We also want to create a toolkit of basic building blocks that could be used to construct sensors, therapeutic agents and tiny machines."

By synthesizing a design generated by game play, researchers will learn quickly whether the resulting molecule folds into the predicted shape, or something close to it, or if it even folds at all. Even designs that are not synthesized will be scored by nature, in that their scores will be based on the performance of similar designs previously synthesized.

"These experiments are the first-line strategy for validating a design and a crucial part of the scientific method," said Das, whose lab at Stanford synthesizes the molecules."This makes EteRNA similar to what goes on in my lab on a daily basis: You make a prediction, do an experiment, make adjustments and start again." Initially, Das' lab is synthesizing eight designs each week, but is ramping up to synthesize about 100 a week.

RNA, or ribonucleic acid, long has been recognized as a messenger for genetic information, yet its role usually was overshadowed by DNA, which encodes genes, and by proteins, which do the work of the cell. But biologists now suspect RNA plays a much broader role as the regulator of cells, acting much like the operating system of a computer. Understanding RNA design could prove useful for treating or controlling such diseases as HIV, for creating RNA-based sensors and even for building computers out of RNA.

The game employs state-of-the-art simulation software that players use to generate designs. It includes training exercises and challenge puzzles for honing skills, as well as challenges for designing molecules that will be synthesized.

In its use of game play to generate results of scientific interest, EteRNA is similar to other online games such as Foldit, an online protein-folding game that Treuille helped create while at the University of Washington. In fact, Treuille and Das met when they sat at adjacent desks in the Washington biochemistry lab of David Baker, where Treuille was working on Foldit and Das was studying RNA and protein folding and occasionally offering advice.

Both men recognized that the lack of real-world feedback was a limitation of these games. They realized an RNA design game could solve this problem because RNA, unlike many biological molecules, can be readily synthesized in a matter of hours.

RNA consists of long, double strands of four bases -- adenine, guanine, cytosine and uracil -- with the shape determined by the sequence of the bases. The rules controlling shape are relatively simple, but the sheer size of the molecules greatly complicates the design process.

"We've already found it's better not to use regularly repeating sequences of bases because they prove unstable," Treuille said, based on play by beta testers."We're trying to build things that work in nature, and nature favors solutions that are robust."

The game is integrated with Facebook, so players can post accomplishments to their Facebook wall automatically and can create groups that talk about play and compete with each other.

The first challenges are relatively simple, arbitrary shapes, Das said, but will soon begin to incorporate designs of scientific relevance, such as RNA switches that could be used to sense and respond to other molecules in living cells.

Ultimately, players may end up creating designs and making discoveries of their own."They're already beginning to act like a scientific community," Treuille said."One player solved a puzzle that a widely used algorithm could not. Another player has written a strategy guide that proposes an algorithm for solving design problems that is different and simpler than anything in the scientific literature."

The EteRNA project is funded by a grant from the National Science Foundation.

For more information on EterRNA watch these video clips:

• What is the EteRNA game? (1:19)http://wms.andrew.cmu.edu:81/nmvideo/eterna_1.mov
• What have we learned from EteRNA? (1:57)http://wms.andrew.cmu.edu:81/nmvideo/eterna_2.mov 

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