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Researchers use engineered viruses to provide quantum-based enhancement of energy transport:MIT Research

Quantum physics meets genetic engineering

Researchers use engineered viruses to provide quantum-based enhancement of energy transport.

By David Chandler


CAMBRIDGE, Mass.–Nature has had billions of years to perfect photosynthesis, which directly or indirectly supports virtually all life on Earth. In that time, the process has achieved almost 100 percent efficiency in transporting the energy of sunlight from receptors to reaction centers where it can be harnessed — a performance vastly better than even the best solar cells.

One way plants achieve this efficiency is by making use of the exotic effects of quantum mechanics — effects sometimes known as “quantum weirdness.” These effects, which include the ability of a particle to exist in more than one place at a time, have now been used by engineers at MIT to achieve a significant efficiency boost in a light-harvesting system.

Surprisingly, the MIT researchers achieved this new approach to solar energy not with high-tech materials or microchips — but by using genetically engineered viruses.

This achievement in coupling quantum research and genetic manipulation, described this week in the journal Nature Materials, was the work of MIT professors Angela Belcher, an expert on engineering viruses to carry out energy-related tasks, and Seth Lloyd, an expert on quantum theory and its potential applications; research associate Heechul Park; and 14 collaborators at MIT and in Italy.

Lloyd, a professor of mechanical engineering, explains that in photosynthesis, a photon hits a receptor called a chromophore, which in turn produces an exciton — a quantum particle of energy. This exciton jumps from one chromophore to another until it reaches a reaction center, where that energy is harnessed to build the molecules that support life.

But the hopping pathway is random and inefficient unless it takes advantage of quantum effects that allow it, in effect, to take multiple pathways at once and select the best ones, behaving more like a wave than a particle.

This efficient movement of excitons has one key requirement: The chromophores have to be arranged just right, with exactly the right amount of space between them. This, Lloyd explains, is known as the “Quantum Goldilocks Effect.”

That’s where the virus comes in. By engineering a virus that Belcher has worked with for years, the team was able to get it to bond with multiple synthetic chromophores — or, in this case, organic dyes. The researchers were then able to produce many varieties of the virus, with slightly different spacings between those synthetic chromophores, and select the ones that performed best.

In the end, they were able to more than double excitons’ speed, increasing the distance they traveled before dissipating — a significant improvement in the efficiency of the process.

The project started from a chance meeting at a conference in Italy. Lloyd and Belcher, a professor of biological engineering, were reporting on different projects they had worked on, and began discussing the possibility of a project encompassing their very different expertise. Lloyd, whose work is mostly theoretical, pointed out that the viruses Belcher works with have the right length scales to potentially support quantum effects.

In 2008, Lloyd had published a paper demonstrating that photosynthetic organisms transmit light energy efficiently because of these quantum effects. When he saw Belcher’s report on her work with engineered viruses, he wondered if that might provide a way to artificially induce a similar effect, in an effort to approach nature’s efficiency.

“I had been talking about potential systems you could use to demonstrate this effect, and Angela said, ‘We’re already making those,’” Lloyd recalls. Eventually, after much analysis, “We came up with design principles to redesign how the virus is capturing light, and get it to this quantum regime.”

Within two weeks, Belcher’s team had created their first test version of the engineered virus. Many months of work then went into perfecting the receptors and the spacings.

Once the team engineered the viruses, they were able to use laser spectroscopy and dynamical modeling to watch the light-harvesting process in action, and to demonstrate that the new viruses were indeed making use of quantum coherence to enhance the transport of excitons.

“It was really fun,” Belcher says. “A group of us who spoke different [scientific] languages worked closely together, to both make this class of organisms, and analyze the data. That’s why I’m so excited by this.”

While this initial result is essentially a proof of concept rather than a practical system, it points the way toward an approach that could lead to inexpensive and efficient solar cells or light-driven catalysis, the team says. So far, the engineered viruses collect and transport energy from incoming light, but do not yet harness it to produce power (as in solar cells) or molecules (as in photosynthesis). But this could be done by adding a reaction center, where such processing takes place, to the end of the virus where the excitons end up.

The research was supported by the Italian energy company Eni through the MIT Energy Initiative. In addition to MIT postdocs Nimrod Heldman and Patrick Rebentrost, the team included researchers at the University of Florence, the University of Perugia, and Eni.

Source:MIT News Office

Longstanding problem put to rest:Proof that a 40-year-old algorithm is the best possible will come as a relief to computer scientists.

By Larry Hardesty

CAMBRIDGE, Mass. – Comparing the genomes of different species — or different members of the same species — is the basis of a great deal of modern biology. DNA sequences that are conserved across species are likely to be functionally important, while variations between members of the same species can indicate different susceptibilities to disease.

The basic algorithm for determining how much two sequences of symbols have in common — the “edit distance” between them — is now more than 40 years old. And for more than 40 years, computer science researchers have been trying to improve upon it, without much success.

At the ACM Symposium on Theory of Computing (STOC) next week, MIT researchers will report that, in all likelihood, that’s because the algorithm is as good as it gets. If a widely held assumption about computational complexity is correct, then the problem of measuring the difference between two genomes — or texts, or speech samples, or anything else that can be represented as a string of symbols — can’t be solved more efficiently.

In a sense, that’s disappointing, since a computer running the existing algorithm would take 1,000 years to exhaustively compare two human genomes. But it also means that computer scientists can stop agonizing about whether they can do better.

“This edit distance is something that I’ve been trying to get better algorithms for since I was a graduate student, in the mid-’90s,” says Piotr Indyk, a professor of computer science and engineering at MIT and a co-author of the STOC paper. “I certainly spent lots of late nights on that — without any progress whatsoever. So at least now there’s a feeling of closure. The problem can be put to sleep.”

Moreover, Indyk says, even though the paper hasn’t officially been presented yet, it’s already spawned two follow-up papers, which apply its approach to related problems. “There is a technical aspect of this paper, a certain gadget construction, that turns out to be very useful for other purposes as well,” Indyk says.

Squaring off

Edit distance is the minimum number of edits — deletions, insertions, and substitutions — required to turn one string into another. The standard algorithm for determining edit distance, known as the Wagner-Fischer algorithm, assigns each symbol of one string to a column in a giant grid and each symbol of the other string to a row. Then, starting in the upper left-hand corner and flooding diagonally across the grid, it fills in each square with the number of edits required to turn the string ending with the corresponding column into the string ending with the corresponding row.

Computer scientists measure algorithmic efficiency as computation time relative to the number of elements the algorithm manipulates. Since the Wagner-Fischer algorithm has to fill in every square of its grid, its running time is proportional to the product of the lengths of the two strings it’s considering. Double the lengths of the strings, and the running time quadruples. In computer parlance, the algorithm runs in quadratic time.

That may not sound terribly efficient, but quadratic time is much better than exponential time, which means that running time is proportional to 2N, where N is the number of elements the algorithm manipulates. If on some machine a quadratic-time algorithm took, say, a hundredth of a second to process 100 elements, an exponential-time algorithm would take about 100 quintillion years.

Theoretical computer science is particularly concerned with a class of problems known as NP-complete. Most researchers believe that NP-complete problems take exponential time to solve, but no one’s been able to prove it. In their STOC paper, Indyk and his student Artūrs Bačkurs demonstrate that if it’s possible to solve the edit-distance problem in less-than-quadratic time, then it’s possible to solve an NP-complete problem in less-than-exponential time. Most researchers in the computational-complexity community will take that as strong evidence that no subquadratic solution to the edit-distance problem exists.

Can’t get no satisfaction

The core NP-complete problem is known as the “satisfiability problem”: Given a host of logical constraints, is it possible to satisfy them all? For instance, say you’re throwing a dinner party, and you’re trying to decide whom to invite. You may face a number of constraints: Either Alice or Bob will have to stay home with the kids, so they can’t both come; if you invite Cindy and Dave, you’ll have to invite the rest of the book club, or they’ll know they were excluded; Ellen will bring either her husband, Fred, or her lover, George, but not both; and so on. Is there an invitation list that meets all those constraints?

In Indyk and Bačkurs’ proof, they propose that, faced with a satisfiability problem, you split the variables into two groups of roughly equivalent size: Alice, Bob, and Cindy go into one, but Walt, Yvonne, and Zack go into the other. Then, for each group, you solve for all the pertinent constraints. This could be a massively complex calculation, but not nearly as complex as solving for the group as a whole. If, for instance, Alice has a restraining order out on Zack, it doesn’t matter, because they fall in separate subgroups: It’s a constraint that doesn’t have to be met.

At this point, the problem of reconciling the solutions for the two subgroups — factoring in constraints like Alice’s restraining order — becomes a version of the edit-distance problem. And if it were possible to solve the edit-distance problem in subquadratic time, it would be possible to solve the satisfiability problem in subexponential time.

Source: MIT News Office

The rise and fall of cognitive skills:Neuroscientists find that different parts of the brain work best at different ages.

By Anne Trafton

CAMBRIDGE, Mass–Scientists have long known that our ability to think quickly and recall information, also known as fluid intelligence, peaks around age 20 and then begins a slow decline. However, more recent findings, including a new study from neuroscientists at MIT and Massachusetts General Hospital (MGH), suggest that the real picture is much more complex.

The study, which appears in the XX issue of the journal Psychological Science, finds that different components of fluid intelligence peak at different ages, some as late as age 40.

“At any given age, you’re getting better at some things, you’re getting worse at some other things, and you’re at a plateau at some other things. There’s probably not one age at which you’re peak on most things, much less all of them,” says Joshua Hartshorne, a postdoc in MIT’s Department of Brain and Cognitive Sciences and one of the paper’s authors.

“It paints a different picture of the way we change over the lifespan than psychology and neuroscience have traditionally painted,” adds Laura Germine, a postdoc in psychiatric and neurodevelopmental genetics at MGH and the paper’s other author.

Measuring peaks

Until now, it has been difficult to study how cognitive skills change over time because of the challenge of getting large numbers of people older than college students and younger than 65 to come to a psychology laboratory to participate in experiments. Hartshorne and Germine were able to take a broader look at aging and cognition because they have been running large-scale experiments on the Internet, where people of any age can become research subjects.

Their web sites, gameswithwords.org and testmybrain.org, feature cognitive tests designed to be completed in just a few minutes. Through these sites, the researchers have accumulated data from nearly 3 million people in the past several years.

In 2011, Germine published a study showing that the ability to recognize faces improves until the early 30s before gradually starting to decline. This finding did not fit into the theory that fluid intelligence peaks in late adolescence. Around the same time, Hartshorne found that subjects’ performance on a visual short-term memory task also peaked in the early 30s.

Intrigued by these results, the researchers, then graduate students at Harvard University, decided that they needed to explore a different source of data, in case some aspect of collecting data on the Internet was skewing the results. They dug out sets of data, collected decades ago, on adult performance at different ages on the Weschler Adult Intelligence Scale, which is used to measure IQ, and the Weschler Memory Scale. Together, these tests measure about 30 different subsets of intelligence, such as digit memorization, visual search, and assembling puzzles.

Hartshorne and Germine developed a new way to analyze the data that allowed them to compare the age peaks for each task. “We were mapping when these cognitive abilities were peaking, and we saw there was no single peak for all abilities. The peaks were all over the place,” Hartshorne says. “This was the smoking gun.”

However, the dataset was not as large as the researchers would have liked, so they decided to test several of the same cognitive skills with their larger pools of Internet study participants. For the Internet study, the researchers chose four tasks that peaked at different ages, based on the data from the Weschler tests. They also included a test of the ability to perceive others’ emotional state, which is not measured by the Weschler tests.

The researchers gathered data from nearly 50,000 subjects and found a very clear picture showing that each cognitive skill they were testing peaked at a different age. For example, raw speed in processing information appears to peak around age 18 or 19, then immediately starts to decline. Meanwhile, short-term memory continues to improve until around age 25, when it levels off and then begins to drop around age 35.

For the ability to evaluate other people’s emotional states, the peak occurred much later, in the 40s or 50s.

More work will be needed to reveal why each of these skills peaks at different times, the researchers say. However, previous studies have hinted that genetic changes or changes in brain structure may play a role.

“If you go into the data on gene expression or brain structure at different ages, you see these lifespan patterns that we don’t know what to make of. The brain seems to continue to change in dynamic ways through early adulthood and middle age,” Germine says. “The question is: What does it mean? How does it map onto the way you function in the world, or the way you think, or the way you change as you age?”

Accumulated intelligence

The researchers also included a vocabulary test, which serves as a measure of what is known as crystallized intelligence — the accumulation of facts and knowledge. These results confirmed that crystallized intelligence peaks later in life, as previously believed, but the researchers also found something unexpected: While data from the Weschler IQ tests suggested that vocabulary peaks in the late 40s, the new data showed a later peak, in the late 60s or early 70s.

The researchers believe this may be a result of better education, more people having jobs that require a lot of reading, and more opportunities for intellectual stimulation for older people.

Hartshorne and Germine are now gathering more data from their websites and have added new cognitive tasks designed to evaluate social and emotional intelligence, language skills, and executive function. They are also working on making their data public so that other researchers can access it and perform other types of studies and analyses.

“We took the existing theories that were out there and showed that they’re all wrong. The question now is: What is the right one? To get to that answer, we’re going to need to run a lot more studies and collect a lot more data,” Hartshorne says.

The research was funded by the National Institutes of Health, the National Science Foundation, and a National Defense Science and Engineering Graduate Fellowship.

Source: MIT News Office

Neuroscientists reverse memories’ emotional associations

MIT study also identifies the brain circuit that links feelings to memories.

By Anne Trafton

Most memories have some kind of emotion associated with them: Recalling the week you just spent at the beach probably makes you feel happy, while reflecting on being bullied provokes more negative feelings.

A new study from MIT neuroscientists reveals the brain circuit that controls how memories become linked with positive or negative emotions. Furthermore, the researchers found that they could reverse the emotional association of specific memories by manipulating brain cells with optogenetics — a technique that uses light to control neuron activity.

The findings, described in the Aug. 27 issue of Nature, demonstrated that a neuronal circuit connecting the hippocampus and the amygdala plays a critical role in associating emotion with memory. This circuit could offer a target for new drugs to help treat conditions such as post-traumatic stress disorder, the researchers say.

“In the future, one may be able to develop methods that help people to remember positive memories more strongly than negative ones,” says Susumu Tonegawa, the Picower Professor of Biology and Neuroscience, director of the RIKEN-MIT Center for Neural Circuit Genetics at MIT’s Picower Institute for Learning and Memory, and senior author of the paper.

 This image depicts the injection sites and the expression of the viral constructs in the two areas of the brain studied: the Dentate Gyrus of the hippocampus (middle) and the Basolateral Amygdala (bottom corners). Credits Image courtesy of the researchers/MIT

This image depicts the injection sites and the expression of the viral constructs in the two areas of the brain studied: the Dentate Gyrus of the hippocampus (middle) and the Basolateral Amygdala (bottom corners).
Credits: Image courtesy of the researchers/MIT

The paper’s lead authors are Roger Redondo, a Howard Hughes Medical Institute postdoc at MIT, and Joshua Kim, a graduate student in MIT’s Department of Biology.

Shifting memories

Memories are made of many elements, which are stored in different parts of the brain. A memory’s context, including information about the location where the event took place, is stored in cells of the hippocampus, while emotions linked to that memory are found in the amygdala.

Previous research has shown that many aspects of memory, including emotional associations, are malleable. Psychotherapists have taken advantage of this to help patients suffering from depression and post-traumatic stress disorder, but the neural circuitry underlying such malleability is not known.

In this study, the researchers set out to explore that malleability with an experimental technique they recently devised that allows them to tag neurons that encode a specific memory, or engram. To achieve this, they label hippocampal cells that are turned on during memory formation with a light-sensitive protein called channelrhodopsin. From that point on, any time those cells are activated with light, the mice recall the memory encoded by that group of cells.

Last year, Tonegawa’s lab used this technique to implant, or “incept,” false memories in miceby reactivating engrams while the mice were undergoing a different experience. In the new study, the researchers wanted to investigate how the context of a memory becomes linked to a particular emotion. First, they used their engram-labeling protocol to tag neurons associated with either a rewarding experience (for male mice, socializing with a female mouse) or an unpleasant experience (a mild electrical shock). In this first set of experiments, the researchers labeled memory cells in a part of the hippocampus called the dentate gyrus.

Two days later, the mice were placed into a large rectangular arena. For three minutes, the researchers recorded which half of the arena the mice naturally preferred. Then, for mice that had received the fear conditioning, the researchers stimulated the labeled cells in the dentate gyrus with light whenever the mice went into the preferred side. The mice soon began avoiding that area, showing that the reactivation of the fear memory had been successful.

The reward memory could also be reactivated: For mice that were reward-conditioned, the researchers stimulated them with light whenever they went into the less-preferred side, and they soon began to spend more time there, recalling the pleasant memory.

A couple of days later, the researchers tried to reverse the mice’s emotional responses. For male mice that had originally received the fear conditioning, they activated the memory cells involved in the fear memory with light for 12 minutes while the mice spent time with female mice. For mice that had initially received the reward conditioning, memory cells were activated while they received mild electric shocks.

Next, the researchers again put the mice in the large two-zone arena. This time, the mice that had originally been conditioned with fear and had avoided the side of the chamber where their hippocampal cells were activated by the laser now began to spend more time in that side when their hippocampal cells were activated, showing that a pleasant association had replaced the fearful one. This reversal also took place in mice that went from reward to fear conditioning.

Altered connections

The researchers then performed the same set of experiments but labeled memory cells in the basolateral amygdala, a region involved in processing emotions. This time, they could not induce a switch by reactivating those cells — the mice continued to behave as they had been conditioned when the memory cells were first labeled.

This suggests that emotional associations, also called valences, are encoded somewhere in the neural circuitry that connects the dentate gyrus to the amygdala, the researchers say. A fearful experience strengthens the connections between the hippocampal engram and fear-encoding cells in the amygdala, but that connection can be weakened later on as new connections are formed between the hippocampus and amygdala cells that encode positive associations.

“That plasticity of the connection between the hippocampus and the amygdala plays a crucial role in the switching of the valence of the memory,” Tonegawa says.

These results indicate that while dentate gyrus cells are neutral with respect to emotion, individual amygdala cells are precommitted to encode fear or reward memory. The researchers are now trying to discover molecular signatures of these two types of amygdala cells. They are also investigating whether reactivating pleasant memories has any effect on depression, in hopes of identifying new targets for drugs to treat depression and post-traumatic stress disorder.

David Anderson, a professor of biology at the California Institute of Technology, says the study makes an important contribution to neuroscientists’ fundamental understanding of the brain and also has potential implications for treating mental illness.

“This is a tour de force of modern molecular-biology-based methods for analyzing processes, such as learning and memory, at the neural-circuitry level. It’s one of the most sophisticated studies of this type that I’ve seen,” he says.

The research was funded by the RIKEN Brain Science Institute, Howard Hughes Medical Institute, and the JPB Foundation.