Author Archives: Catherine Zandonella

Beautiful but strange: The dark side of cosmology (Science)

By Catherine Zandonella, Office of the Dean for Research

It’s a beautiful theory: the standard model of cosmology describes the universe using just six parameters. But it is also strange. The model predicts that dark matter and dark energy – two mysterious entities that have never been detected — make up 95% of the universe, leaving only 5% composed of the ordinary matter so essential to our existence.

In an article in this week’s Science, Princeton astrophysicist David Spergel reviews how cosmologists came to be certain that we are surrounded by matter and energy that we cannot see. Observations of galaxies, supernovae, and the universe’s temperature, among other things, have led researchers to conclude that the universe is mostly uniform and flat, but is expanding due to a puzzling phenomenon called dark energy. The rate of expansion is increasing over time, counteracting the attractive force of gravity. This last observation, says Spergel, implies that if you throw a ball upward you will see it start to accelerate away from you.

The components of our universe

The components of our universe. Dark energy comprises 69% of the mass energy density of the universe, dark matter comprises 25%, and “ordinary” atomic matter makes up 5%. Three types of neutrinos make up at least 0.1%, the cosmic background radiation makes up 0.01%, and black holes comprise at least 0.005%. (Source: Science/AAAS)

A number of experiments to detect dark matter and dark energy are underway, and some researchers have already claimed to have found particles of dark matter, although the results are controversial. New findings expected in the coming years from the Large Hadron Collider, the world’s most powerful particle accelerator, could provide evidence for a proposed theory, supersymmetry, that could explain the dark particles.

But explaining dark energy, and why the universe is accelerating, is a tougher problem. Over the next decade, powerful telescopes will come online to map the structure of the universe and trace the distribution of matter over the past 10 billion years, providing new insights into the source of cosmic acceleration.

Yet observations alone are probably not enough, according to Spergel. A full understanding will require new ideas in physics, perhaps even a new theory of gravity, possibly including extra dimensions, Spergel writes. “We will likely need a new idea as profound as general relativity to explain these mysteries.”

When that happens, our understanding of the dark side of cosmology will no longer accelerate away from us.

Read the article

Citation: Spergel, David. The dark side of cosmology: Dark matter and dark energy. Science, 6 March 2015: Vol. 347 no. 6226 pp. 1100-1102 DOI: 10.1126/science.aaa0980.

–David Spergel is the Charles A. Young Professor of Astronomy on the Class of 1897 Foundation, a professor of astrophysical sciences, and chair of Princeton’s Department of Astrophysical Sciences. His research is supported by the National Science Foundation and NASA.

Pennies reveal new insights on the nature of randomness (PNAS)

By Tien Nguyen, Department of Chemistry

The concept of randomness appears across scientific disciplines, from materials science to molecular biology. Now, theoretical chemists at Princeton have challenged traditional interpretations of randomness by computationally generating random and mechanically rigid arrangements of two-dimensional hard disks, such as pennies, for the first time.

‘It’s amazing that something so simple as the packing of pennies can reveal to us deep ideas about the meaning of randomness or disorder,” said Salvatore Torquato, professor of chemistry at Princeton and principal investigator of the report published on December 30 in the journal Proceedings of the National Academy of Sciences.

In two dimensions, conventional wisdom held that the most random arrangements of pennies were those most likely to form upon repeated packing, or in other words, most “entropically” favored. But when a group of pennies are rapidly compressed, the most probable states are actually highly ordered with small imperfections—called a polycrystalline state.

“We’re saying that school of thought is wrong because you can find much lower density states that have a high degree of disorder, even if they are not seen in typical experiments,” Torquato said.

Torquato and coworkers proposed that randomness should be judged from the disorder of a single state as opposed to many states. “It’s a new way of searching for randomness,” said Morrel Cohen, a senior scholar at Princeton and the editor assigned to the article.

Using a computer algorithm, the researchers produced so-called maximally random, jammed (rigid) states as defined by a set of “order metrics.” These measurements reflect features of a single configuration, such as the fluctuations of density within a system and the extent to which one penny’s position can be used to predict another’s.

The algorithm generated random states that have never been seen before in systems with up to approximately 200 disks. Theoretically, these maximally random states should exist for even larger systems, but are beyond the computational limits of the program.

These findings hold promise especially for the physics and chemistry of surfaces. Randomly dispersed patterns can be relayed to a 3D printer to create materials with unique properties. This may be desirable in photonics—analogous to electronics, but with photons instead of electrons—where the orientation of particles affects light’s ability to travel through a material.

This work also provides a tool for measuring degrees of order that may be applied to broadly to other fields. For example, the degree of disorder in the spatial distribution of cancer cells versus healthy cells could be measured and compared for possible biological links. The next challenge in this line of research will be for experimentalists to replicate these findings in the laboratory.

Read the article.

Atkinson, S.; Stillinger, F. H.; Torquato, S. “Existence of isostatic, maximally random jammed monodisperse hard-disk packings,” Proc. Natl. Acad. Sci., 2014, 111, 18436.

This work was supported in part by the National Science Foundation under Grants DMR- 0820341 and DMS-1211087. This work was partially supported by Simons Foundation Grant in Theoretical Physics 231015.

Genome-wide search reveals new genes involved in long-term memory (Neuron)

By Catherine Zandonella, Office of the Dean for Research

Whole genome expression data reveals new genes involved in long-term memory formation in worms. (Image source: Murphy lab)

Whole genome expression data reveals new genes involved in long-term memory formation in worms. (Image source: Murphy lab)

A new study has identified genes involved in long-term memory in the worm as part of research aimed at finding ways to retain cognitive abilities during aging.

The study, which was published in the journal Neuron, identified more than 750 genes involved in long-term memory, including many that had not been found previously and that could serve as targets for future research, said senior author Coleen Murphy, an associate professor of molecular biology and the Lewis-Sigler Institute for Integrative Genomics at Princeton University.

“We want to know, are there ways to extend memory?” Murphy said. “And eventually, we would like to ask, are there compounds that could maintain memory with age?”

Long-term memory training in worms (left) led to induction of the transcription factor CREB in AIM neurons (shown by arrows in right). CREB-induced genes were shown to be involved in forming long-term memories in worm neurons. (Image source: Murphy lab)

Long-term memory training in worms (left) led to induction of the transcription factor CREB in AIM neurons (shown by arrows in right). CREB-induced genes were shown to be involved in forming long-term memories in worm neurons. (Image source: Murphy lab)

The newly pinpointed genes are “turned on” by a molecule known as CREB (cAMP-response element-binding protein), a factor known to be required for long-term memory in many organisms, including worms and mice.

“There is a pretty direct relationship between CREB and long-term memory,” Murphy said, “and many organisms lose CREB as they age.” By studying the CREB-activated genes involved in long-term memory, the researchers hope to better understand why some organisms lose their long-term memories as they age.

To identify the genes, the researchers first instilled long-term memories in the worms by training them to associate meal-time with a butterscotch smell. Trained worms were able to remember that the butterscotch smell means dinner for about 16 hours, a significant amount of time for the worm.

The researchers then scanned the genomes of both trained worms and non-trained worms, looking for genes turned on by CREB.

The researchers detected 757 CREB-activated genes in the long-term memory-trained worms, and showed that these genes were turned on primarily in worm cells called the AIM interneurons.

They also found CREB-activated genes in non-trained worms, but the genes were not turned on in AIM interneurons and were not involved in long-term memory. CREB turns on genes involved in other biological functions such as growth, immune response, and metabolism. Throughout the worm, the researchers noted distinct non-memory (or “basal”) genes in addition to the memory-related genes.

The next step, said Murphy, is to find out what these newly recognized long-term memory genes do when they are activated by CREB. For example, the activated genes may strengthen connections between neurons.

Worms are a perfect system in which to explore that question, Murphy said. The worm Caenorhabditis elegans has only 302 neurons, whereas a typical mammalian brain contains billions of the cells.

“Worms use the same molecular machinery that higher organisms, including mammals, use to carry out long-term memory,” said Murphy. “We hope that other researchers will take our list and look at the genes to see whether they are important in more complex organisms.”

Murphy said that future work will involve exploring CREB’s role in long-term memory as well as reproduction in worms as they age.

The team included co-first-authors Postdoctoral Research Associate Vanisha Lakhina, Postdoctoral Research Associate Rachel Arey, and Associate Research Scholar Rachel Kaletsky of the Lewis-Sigler Institute for Integrative Genomics. Additional research was performed by Amanda Kauffman, who earned her Ph.D. in Molecular Biology in 2010; Geneva Stein, who earned her Ph.D. in Molecular Biology in 2014; William Keyes, a laboratory assistant in the Department of Molecular Biology; and Daniel Xu, who earned his B.A. in Molecular Biology in 2014.

Funding for the research was provided by the National Institutes of Health and the Paul F. Glenn Laboratory for Aging Research at Princeton University.

Read the abstract

Citation: Vanisha Lakhina, Rachel N. Arey, Rachel Kaletsky, Amanda Kauffman, Geneva Stein, William Keyes, Daniel Xu, and Coleen T. Murphy. Genome-wide Functional Analysis of CREB/Long-Term Memory-Dependent Transcription Reveals Distinct Basal and Memory Gene Expression Programs, Neuron (2015), http://dx.doi.org/10.1016/j.neuron.2014.12.029.

Dirty pool: Soil’s large carbon stores could be freed by increased CO2, plant growth (Nature Climate Change)

By Morgan Kelly, Office of Communications

Soil carbon

Researchers based at Princeton University report that an increase in human-made carbon dioxide in the atmosphere could initiate a chain reaction between plants and microorganisms that would unsettle one of the largest carbon reservoirs on the planet — soil. The researchers developed the first computer model to show at a global scale the complex interaction between carbon, plants and soil. The model projected changes (above) in global soil carbon as a result of root-soil interactions, with blue indicating a greater loss of soil carbon to the atmosphere. (Image by Benjamin Sulman, Princeton Environmental Institute)

An increase in human-made carbon dioxide in the atmosphere could initiate a chain reaction between plants and microorganisms that would unsettle one of the largest carbon reservoirs on the planet — soil.

Researchers based at Princeton University report in the journal Nature Climate Change that the carbon in soil — which contains twice the amount of carbon in all plants and Earth’s atmosphere combined — could become increasingly volatile as people add more carbon dioxide to the atmosphere, largely because of increased plant growth. The researchers developed the first computer model to show at a global scale the complex interaction between carbon, plants and soil, which includes numerous bacteria, fungi, minerals and carbon compounds that respond in complex ways to temperature, moisture and the carbon that plants contribute to soil.

Although a greenhouse gas and pollutant, carbon dioxide also supports plant growth. As trees and other vegetation flourish in a carbon dioxide-rich future, their roots could stimulate microbial activity in soil that in turn accelerates the decomposition of soil carbon and its release into the atmosphere as carbon dioxide, the researchers found.

This effect counters current key projections regarding Earth’s future carbon cycle, particularly that greater plant growth could offset carbon dioxide emissions as flora take up more of the gas, said first author Benjamin Sulman, who conducted the modeling work as a postdoctoral researcher at the Princeton Environmental Institute.

“You should not count on getting more carbon storage in the soil just because tree growth is increasing,” said Sulman, who is now a postdoctoral researcher at Indiana University.

On the other hand, microbial activity initiated by root growth could lock carbon onto mineral particles and protect it from decomposition, which would increase long-term storage of carbon in soils, the researchers report.

Whether carbon emissions from soil rise or fall, the researchers’ model depicts an intricate soil-carbon system that contrasts starkly with existing models that portray soil as a simple carbon repository, Sulman said. An oversimplified perception of the soil carbon cycle has left scientists with a glaring uncertainty as to whether soil would help mitigate future carbon dioxide levels — or make them worse, Sulman said.

“The goal was to take that very simple model and add some of the most important missing processes,” Sulman said. “The main interactions between roots and soil are important and shouldn’t be ignored. Root growth and activity are such important drivers of what goes on in the soil, and knowing what the roots are doing could be an important part of understanding what the soil will be doing.”

The researchers’ soil-carbon cycle model has been integrated into the global land model used for climate simulations by the National Oceanic and Atmospheric Administration’s (NOAA) Geophysical Fluid Dynamics Laboratory (GFDL) located on Princeton’s Forrestal Campus.

Read the abstract

Benjamin N. Sulman, Richard P. Phillips, A. Christopher Oishi, Elena Shevliakova, and Stephen W. Pacala. 2014. Microbe-driven turnover offsets mineral-mediated storage of soil carbon under elevated CO2. Nature Climate Change. Arti­cle pub­lished in December 2014 print edition. DOI: 10.1038/nclimate2436

The work was supported by grants from NOAA (grant no. NA08OAR4320752); the U.S. Department of Agriculture (grant no. 2011-67003-30373); and Princeton’s Carbon Mitigation Initiative sponsored by BP.

 

Computational clues into the structure of a promising energy conversion catalyst (J. Physical Chemistry Letters)

Mosaic structure

Representation of the mosaic texture of β-NiOOH and its possible structures.

By Tien Nguyen, Department of Chemistry

Hydrogen fuel is a promising source of clean energy that can be produced by splitting water into hydrogen and oxygen gas with the help of a catalyst, a material that can speed up the process. Although most known catalysts are inefficient, one called iron-doped nickel oxide is promising but not well understood.

Now researchers at Princeton University have reported new insights into the structure of an active component of the nickel oxide catalyst, known as β-NiOOH, using theoretical calculations. Led by Annabella Selloni, professor of chemistry at Princeton, the findings were published in The Journal of Physical Chemistry Letters on October 28.

“Understanding the structure is the basis for any further study of the material’s properties. If you don’t know the material’s structure you can’t know what it’s doing,” Selloni said. Nickel oxide’s exact structure has been difficult to determine experimentally because it is constantly changing during the reaction.

The research team took a theoretical approach and employed a “genetic algorithm” to search for the structure. Genetic algorithms operate under a set of parameters that draw inspiration from evolution by creating generation after generation of structures to arrive at the most “fit” or most likely candidates.

Taking the results of the genetic algorithm search in combination with computational techniques known as hybrid density functional theory calculations—which estimate a molecule’s electronic structure—co-author Ye-Fei Li, a former postdoctoral researcher at Princeton who is now at Fudan University, and Selloni were able to identify structures of nickel oxide that supported existing observations.

One such observation is the material’s mosaic texture, composed of tiny grain-like microstructures. The researchers propose that these microstructures are stable tunnel structures that relieve stress between layers. Another observed feature is the doubling of the distance between layers made of the same material, referred to as its c axis periodicity, which represents the alternating layers of Ni(OH)2 and NiO2 formed during the reaction.

Armed with a better understanding of the material’s structure, the scientists hope to further map out its activity in the reaction. “I’m interested in the microscopic mechanisms, what are the electrons and atoms doing?” Selloni said.

Read the abstract

Li, Ye-Fei and Annabella Selloni. “Mosaic Texture and Double c-Axis Periodicity of β–NiOOH: Insights from First-Principles and Genetic Algorithm Calculations.” J. Phys. Chem. Lett. 2014, 5, 3981.

This work was supported by the US Department of Energy, Division of Chemical Sciences, Geosciences and Biosciences under award DE-FG0212ER16286.

Caught in the act: Video system for mapping behaviors (Royal Society: Interface)

By Catherine Zandonella, Office of the Dean for Research

Studies of animal behavior have come a long way from the days when scientists followed their subjects around with pen and notepad. But although cameras have replaced clipboards, evaluating the resulting videos is still a cumbersome process.

To make that job easier and more comprehensive, researchers at Princeton have built a computerized system that analyzes videos to reveal what animals do, how often and for how long, and then generates an easy-to-understand map of the behaviors.

By looking at the map, the researchers can tell what sorts of movements the animal did without having to spend time watching the video. They used the system to track the behavior of fruit flies (Drosophila melanogaster) and found that it accurately detected behaviors such as grooming a leg or waggling a wing.

Map that indicates the type of movements of a fly.

Princeton researchers have developed a computerized system for evaluating animal behavior. A video system records an animal, in this case a fruit fly, for one hour. Then a computer program analyzes the video to create a map of the behaviors. The map can reveal, for example, the differences in movements between male and female fruit flies. (Image source: Joshua Shaevitz)

The researchers’ goal was a system that could create accurate records of behaviors for use in studies of the mechanisms behind those behaviors – in other words, the genes and brain circuits that govern movements. Knowing which genes and neural circuits govern behavior will help answer basic scientific questions and could shed light on the mechanisms behind conditions such as autism.

Described in the journal The Royal Society Interface, the system was built for monitoring Drosophila flies, which are commonly used in studies of genes, but the researchers say that the system can also analyze the movements of other creatures, including worms, mice and humans.

The new system is a significant advance over current approaches because it takes note of all the animal’s activities, not just behaviors that seem important to researchers, said Joshua Shaevitz, associate professor of physics and the Lewis-Sigler Institute for Integrative Genomics, who led the study. “We don’t know which behaviors are important to a fruit fly, or a mouse,” he said. “Instead, we ask the computer to find behaviors that are frequently repeated, which tend to be the ones that are worth studying.”

Researchers need reliable ways to catalog behaviors in studies of how genes and neural pathways control behavior. A common experiment involves deleting a gene in an organism such as a fruit fly or worm to look for any resulting loss of function or change in the organism’s behavior. But without reliable behavioral data, researchers cannot make firm conclusions about the role of the deleted gene.

With the new system, a high-speed camera records the animal for a given period of time, and the computer sorts the resulting video frames. For the current study, the researchers recorded individual fruit flies for a period of one hour, resulting in an enormous amount of data. The challenge was how to sort the frames and translate the images into data that could be evaluated.

“We have to figure out how the body parts of the fly are positioned in relation to each other at each point in time, and then find a way to tell how those body parts are moving, while keeping the amount of data manageable using the available computing power,” said Gordon Berman, an associate research scholar in the Lewis-Sigler Institute for Integrative Genomics. “Then you have to project the information into some form that allows you to understand the data.”

The researchers accomplished this goal by writing a computer program that converts the information about a fly’s position and movements into mathematical descriptions that are then grouped according to their similarities. The computer places these groupings on a two-dimensional map, so that different regions of the map represent different types of movements.

A computerized system for evaluating animal behavior.

Individual fruit flies were filmed for about one hour, resulting in over 300,000 video frames. Then, a computer program converts the fly’s position in each frame into mathematical descriptions that represent movements. The computer then groups the movements on a map, so that one region of the map represents leg movements while another represents wing movements, and so on. By looking at the map, the researchers can tell what sorts of movements the fly did, such as grooming its front leg, grooming its head, or waggling its wing. (Image source: Joshua Shaevitz)

To verify that the system worked, the researchers collected data from 50 male and 50 female flies. They identified hundreds of behaviors, including ones that had never been documented and others that were consistent with findings from human observers. For example, some of the findings consistent with previous observations were that male fruit flies kick out their legs when grooming, whereas females don’t, and that young females are more active than young males. “Males and females do things slightly differently, and we can pick up on that right away,” said Shaevitz.

Male fruit flies kick out with their legs when grooming:

whereas females do not:

The team is now expanding the capabilities of the method, Shaevitz said. “The technique that we have developed can also be used to study patterns of behavior in humans,” Shaevitz said. “There are several conditions that are diagnosed on the basis of behavior, such as autism. Having a more quantitative way of measuring behavior could improve the accuracy of diagnoses.”

The study co-authors included William Bialek, Princeton’s John Archibald Wheeler/Battelle Professor in Physics and the Lewis-Sigler Institute for Integrative Genomics, and Daniel Choi, a graduate student working with Shaevitz.

The work was funded through awards from the National Institutes of Health (GM098090, GM071508), the National Science Foundation (PHY 0957573, PHY 1066293), the Pew Charitable Trusts, the Swartz Foundation and the Alfred P. Sloan Foundation.

Read the abstract

Gordon J. Berman, Daniel M. Choi, William Bialek, Joshua W. Shaevitz. Mapping the stereotyped behaviour of freely moving fruit flies. The Journal of the Royal Society Interface (2014). DOI: 10.1098/rsif.2014.0672 Published online 20 August 2014 http://rsif.royalsocietypublishing.org/content/11/99/20140672

Quantum mechanical calculations reveal the hidden states of enzyme active sites (Nature Chemistry)

[2Fe–2S] cluster in front of a leaf. (Image by C. Todd Reichart)

Researchers at Princeton University have reported the first direct observation of the electronic states of iron-sulfur clusters, common to many enzyme active sites. Iron-sulfur cluster in front of a leaf. (Image by C. Todd Reichart)

By Tien Nguyen, Department of Chemistry

Enzymes carry out fundamental biological processes such as photosynthesis, nitrogen fixation and respiration, with the help of clusters of metal atoms as “active” sites. But scientists lack basic information about their function because the states thought to be critical to their chemical abilities cannot be experimentally observed.

Now, researchers at Princeton University have reported the first direct observation of the electronic states of iron-sulfur clusters, common to many enzyme active sites. Published on August 31 in the journal Nature Chemistry, the states were revealed by computing the complicated quantum mechanical behavior of the electrons in the clusters.

“These complexes were thought of as impossible to model, due to the complexity of the quantum mechanics,” said Garnet Chan, the A. Barton Hepburn Professor of Chemistry and corresponding author on the paper.

Iron-sulfur clusters

Caption: (a) [2Fe–2S] clusters (b) [4Fe–4S] (c) Area-law entanglement of the physical states can be used to reduce the complexity of quantum calculations (d) Wavefunctions with area-law entanglement can be written compactly as a tensor network where each tensor (represented here by a circle) denotes an active space orbital and the bonds between adjacent orbitals introduce local entanglement between them. (Source: Garnet Chan)

In these systems, the electrons interact strongly with each other, their movements resembling a complicated dance. To reduce the complexity, the researchers drew on a new understanding, gained from fundamental work in quantum information theory, that the motion of the electrons had a special pattern.

“At first glance, the electrons appear to move in a complicated way, but eventually you realize that they only care about what their immediate neighbors are doing, similar to being in a crowded room. This restriction on their behavior leads to important simplifications: the calculations become very difficult rather than impossible — it’s just on the edge of what can be done,” Chan said.

Using their new method, Chan and coworkers found that iron-sulfur clusters possess an order of magnitude more accessible electronic states than previously reported. The researchers suggested that this unusual richness might explain their ubiquity in biological processes.

This finding, that there are many more available electronic states than previously thought, presents many different chemical possibilities. What if these clusters simultaneously used a combination of mechanisms, instead of the accepted chemical idea that there is one distinct electronic pathway, Chan wondered. To test that idea and learn more about the clusters’ behavior, the researchers plan to extend their calculations to observe a chemical transformation in action.

“If you want to understand why iron-sulfur clusters are a ubiquitous biological motif and how we can create even better synthetic analogs, then you need to know what the electrons are doing,” Chan said. “Now we’ve caught a first glimpse as to what they are getting up to.”

Read the abstract.

Sharma, S.; Sivalingam, K.; Neese, F.; Chan, K.-L. G. “Low-energy spectrum of iron sulfur clusters directly from many-particle quantum mechanics.Nat. Chem. 2014, 6, 927.

This work was supported by the US National Science Foundation (CHE-1265277) and used software developed with the support of OCI-1265278. F.N. and K.S acknowledge financial support from the Max Planck Society, the University of Bonn and the SFB 813 “Chemistry at Spin Centers.”