Showing posts with label complex. Show all posts
Showing posts with label complex. Show all posts

Thursday, 1 December 2011

Trillions served: Massive, complex projects for DOE JGI 2012 Community Sequencing Program

ScienceDaily (Nov. 3, 2011) — According to roadside signs, the number of burgers served has eclipsed the billion mark, while the U.S. Department of Energy (DOE) Joint Genome Institute (JGI) will now serve up trillions of nucleotides of information from scores of newly-selected projects geared to feed the data-hungry worldwide research community.

The 2012 Community Sequencing Program (CSP) call invited researchers to submit proposals for projects that advance capabilities in fields such as plant-microbe interactions, microbes involved in carbon capture and greenhouse gas emission, and metagenomics -- the characterization of complex collections of microbes from particular environmental niches. The total allocation for the coming year's CSP portfolio will exceed 30 trillion bases (terabases or Tb), a 100-fold increase compared with just two years ago, when just a third of a terabase was allocated to more than 70 projects. This amounts to the equivalent of at least 10,000 human genomes in data.

"These selections truly take advantage of the DOE JGI's massive-scale sequencing and data analysis capabilities," said Eddy Rubin, DOE JGI Director. "The projects span the globe and the unexplored branches of the tree of life, and promise to yield a better understanding of the interplay between climate, ecosystem and organism. Still other projects are targeting improvements in biofuel feedstock production, focusing on the potential of microorganisms to improve feedstock growth and prevent devastating diseases that hinder yields."

A total of 41 CSP proposals were approved from the 152 submitted, culled from the 188 letters of intent originally received. The projects were then reviewed and approved by an outside review panel before being vetted by the DOE.

One of single largest project comes from Jeff Dangl at the University of North Carolina and his colleagues and focuses on the rhizosphere, that narrow region where microbes in the soil colonize and interact with plant roots. The importance of rhizosphere microbial communities for plant growth and success cannot be overstated. "The distinctive 'terroir' that flavors wine, the yield of maize and other crops, and the productivity of any plant community rely in part on the respective rhizosphere microbiome," they wrote in their proposal. "The microbiome is most simply viewed as an extension of each plant's genome; we do not know any plant genome's full functional capacity until we also know the functional capacity and the drivers governing assembly of its associated microbiome." The Dangl team seeks to apply the genetic and genomic information toward applications in bioenergy and carbon cycling research. They propose to study the rhizosphere microbiomes of maize, Arabidopsis and a mustard relative known commonly as Drummond's rockcress, as well as potential biofuel crop Miscanthus and wild prairie grasses, to understand the plant genetics involved in determining the microbial communities associated with plant species.

Another plant project involves Casuarina trees, which are able to tolerate soils laden with salt and heavy metals due to Casuarina symbiosis with Frankia bacteria. As these trees have the potential to serve as biomass sources in tropical and subtropical regions of the world, the team led by Laurent Laplaze from the French institute IRD-Montpellier proposes to use deep sequencing to analyze gene expression changes in the roots and nodules of the Casuarina trees and learn more about how these plant-microbe interactions influence nitrogen fixation and carbon sequestration.

The first genomic characterization of a microbial community resulted from a collaboration between the DOE JGI and Jill Banfield at the University of California, Berkeley and her colleagues and involved samples from a U.S. Environmental Protection Agency Iron Mountain Superfund site in Northern California. Now Banfield and her colleagues propose to recover genomes from subsurface microbial communities at the DOE Integrated Field Research Challenge bioremediation research site at Rifle, Colorado. They will generate, initially, a terabase of sequence to identify novel rare microbes that might be useful for the environmental cleanup of metals and radionuclides, and which may also lend insight into subsurface carbon sequestration.

"One of the challenges that has held back environmental genomics, has been the inability to sample adequately and assemble genomes from complex environments," Rubin said. "With recent advances in sequencing technologies and assembly algorithms developed by DOE JGI and others, it has become tractable."

Another terabase-sized metagenome project comes from Craig Cary at the University of Delaware and Charles Lee at the University of Waikato in New Zealand. Their plan focuses on the microbial communities in the McMurdo Dry Valley system of Antarctica. Considered to be among the harshest and most extreme environments on the planet with low water and nutrient levels paired with high salt and ultraviolet radiation levels, the team seeks to understand how the carbon cycle plays out in this ecosystem. Additionally, the microbes in this environment may offer insight into bioremediation applications for cold ecosystems.

Moving away from studies set along the equator and at the poles, a team led by Michael Pester from the University of Vienna in Austria proposed one of the CSP 2012 portfolio's smaller sequencing projects. They propose to work with roughly 100 Gb of metagenome and metatranscriptome (the complex region of the complete genetic code that is transcribed into RNA molecules and provides information on gene expression and gene function) sequence to study the greenhouse gas emissions from the microbial communities that reside in peatlands, a significant carbon sink. The researchers noted that peat soil contains methane-producing organisms whose emissions are mitigated by poorly-studied sulfate reducing microbes.

Just as the ongoing Genomic Encyclopedia of Bacteria and Archaea project aims to fill in gaps in microbial knowledge, a team led by Joseph Spatafora at Oregon State University and Jason Stajich at University of California at Riverside is planning to fill in gaps in the Fungal Tree of Life by sequencing 1,000 fungal genomes over the next five years, providing at least two reference genomes for each of the 577 recognized families classified under Fungi. "This [project] has the core goal of providing reference information to leverage all subsequent studies relevant to fungal biology," Spatafora and his colleagues wrote in their proposal. "By providing a reference database of fungal genome diversity, it will be relevant to any sequencing study of plant-microbe interactions, of microbial emission and capture of greenhouse gasses, or of environmental metagenomic sequencing." Though more than 100 fungal genomes are now available, the team added, acknowledging the DOE JGI's role in these efforts, currently nearly three-quarters of these families have not yet been sequenced.

Another fungal project approved this year was proposed by U.S. Department of Agriculture researcher Jo Anne Crouch and involves several species of the grass-infecting fungus Colletotrichum. As more grasses are explored as candidate bioenergy feedstocks, strategies that defend against fungal pathogens that may reduce plant biomass and thus yields of cellulosic biofuels are of particular interest. Additionally, novel enzymes in these fungal genomes may have industrial applications in the biomass pretreatment processes.

In an indication of the increasing use of sequencing to study whole biological systems rather than individual organisms, more than half the approved proposals include sequencing of multi-organism samples either instead of or in addition to individual genomes. Of the proposals that include individual genome sequencing, nine address plant genomes; six are fungal projects; and eight are microbial projects, five of which involve sequencing the genomes of single cells.

"Through these diverse and challenging projects, the DOE JGI's Community Sequencing Program will continue to provide the scientific community with high-throughput sequencing for projects relevant to DOE's scientific and energy-related missions," DOE JGI Director Rubin said.

For the complete list of CSP 2012 sequencing projects, see: http://www.jgi.doe.gov/sequencing/cspseqplans2012.html.

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The above story is reprinted from materials provided by DOE/Joint Genome Institute.

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Saturday, 26 November 2011

Astronomers discover complex organic matter exists throughout the universe

ScienceDaily (Oct. 26, 2011) — Astronomers report in the journal Nature that organic compounds of unexpected complexity exist throughout the Universe. The results suggest that complex organic compounds are not the sole domain of life but can be made naturally by stars.

Prof. Sun Kwok and Dr. Yong Zhang of The University of Hong Kong show that an organic substance commonly found throughout the Universe contains a mixture of aromatic (ring-like) and aliphatic (chain-like) components. The compounds are so complex that their chemical structures resemble those of coal and petroleum. Since coal and oil are remnants of ancient life, this type of organic matter was thought to arise only from living organisms. The team's discovery suggests that complex organic compounds can be synthesized in space even when no life forms are present.

The researchers investigated an unsolved phenomenon: a set of infrared emissions detected in stars, interstellar space, and galaxies. These spectral signatures are known as "Unidentified Infrared Emission features." For over two decades, the most commonly accepted theory on the origin of these signatures has been that they come from simple organic molecules made of carbon and hydrogen atoms, called polycyclic aromatic hydrocarbon (PAH) molecules. From observations taken by the Infrared Space Observatory and the Spitzer Space Telescope, Kwok and Zhang showed that the astronomical spectra have features that cannot be explained by PAH molecules. Instead, the team proposes that the substances generating these infrared emissions have chemical structures that are much more complex. By analyzing spectra of star dust formed in exploding stars called novae, they show that stars are making these complex organic compounds on extremely short time scales of weeks.

Not only are stars producing this complex organic matter, they are also ejecting it into the general interstellar space, the region between stars. The work supports an earlier idea proposed by Kwok that old stars are molecular factories capable of manufacturing organic compounds. "Our work has shown that stars have no problem making complex organic compounds under near-vacuum conditions," says Kwok. "Theoretically, this is impossible, but observationally we can see it happening."

Most interestingly, this organic star dust is similar in structure to complex organic compounds found in meteorites. Since meteorites are remnants of the early Solar System, the findings raise the possibility that stars enriched the early Solar System with organic compounds. The early Earth was subjected to severe bombardments by comets and asteroids, which potentially could have carried organic star dust. Whether these delivered organic compounds played any role in the development of life on Earth remains an open question.

Prof. Sun Kwok is the Dean of Science and Chair Professor of Physics of the University of Hong Kong. He serves as Vice President of Division VI (interstellar matter) of the International Astronomical Union, and is the incoming Vice President of Commission 51 (bioastronomy) of the International Astronomical Union. He has published many books, including the recent book "Organic Matter in the Universe" (Wiley, 2011). Dr. Yong Zhang is a Research Assistant Professor at the University of Hong Kong. This work was supported by the Research Grants Council of Hong Kong.

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The above story is reprinted from materials provided by The University of Hong Kong.

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Journal Reference:

Sun Kwok, Yong Zhang. Mixed aromatic–aliphatic organic nanoparticles as carriers of unidentified infrared emission features. Nature, 2011; DOI: 10.1038/nature10542

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Monday, 21 November 2011

'Robot biologist' solves complex problem from scratch

ScienceDaily (Oct. 14, 2011) — First it was chess. Then it was Jeopardy. Now computers are at it again, but this time they are trying to automate the scientific process itself.

An interdisciplinary team of scientists at Vanderbilt University, Cornell University and CFD Research Corporation, Inc., has taken a major step toward this goal by demonstrating that a computer can analyze raw experimental data from a biological system and derive the basic mathematical equations that describe the way the system operates. According to the researchers, it is one of the most complex scientific modeling problems that a computer has solved completely from scratch.

The paper that describes this accomplishment is published in the October issue of the journal Physical Biology and is currently available online.

The work was a collaboration between John P. Wikswo, the Gordon A. Cain University Professor at Vanderbilt, Michael Schmidt and Hod Lipson at the Creative Machines Lab at Cornell University and Jerry Jenkins and Ravishankar Vallabhajosyula at CFDRC in Huntsville, Ala.

The "brains" of the system, which Wikswo has christened the Automated Biology Explorer (ABE), is a unique piece of software called Eureqa developed at Cornell and released in 2009. Schmidt and Lipson originally created Eureqa to design robots without going through the normal trial and error stage that is both slow and expensive. After it succeeded, they realized it could also be applied to solving science problems.

One of Eureqa's initial achievements was identifying the basic laws of motion by analyzing the motion of a double pendulum. What took Sir Isaac Newton years to discover, Eureqa did in a few hours when running on a personal computer.

In 2006, Wikswo heard Lipson lecture about his research. "I had a 'eureka moment' of my own when I realized the system Hod had developed could be used to solve biological problems and even control them," Wikswo said. So he started talking to Lipson immediately after the lecture and they began a collaboration to adapt Eureqa to analyze biological problems.

"Biology is the area where the gap between theory and data is growing the most rapidly," said Lipson. "So it is the area in greatest need of automation."

Software passes test

The biological system that the researchers used to test ABE is glycolysis, the primary process that produces energy in a living cell. Specifically, they focused on the manner in which yeast cells control fluctuations in the chemical compounds produced by the process.

The researchers chose this specific system, called glycolytic oscillations, to perform a virtual test of the software because it is one of the most extensively studied biological control systems. Jenkins and Vallabhajosyula used one of the process' detailed mathematical models to generate a data set corresponding to the measurements a scientist would make under various conditions. To increase the realism of the test, the researchers salted the data with a 10 percent random error. When they fed the data into Eureqa, it derived a series of equations that were nearly identical to the known equations.

"What's really amazing is that it produced these equations a priori," said Vallabhajosyula. "The only thing the software knew in advance was addition, subtraction, multiplication and division."

Beyond Adam

The ability to generate mathematical equations from scratch is what sets ABE apart from Adam, the robot scientist developed by Ross King and his colleagues at the University of Wales at Aberystwyth. Adam runs yeast genetics experiments and made international headlines two years ago by making a novel scientific discovery without direct human input. King fed Adam with a model of yeast metabolism and a database of genes and proteins involved in metabolism in other species. He also linked the computer to a remote-controlled genetics laboratory. This allowed the computer to generate hypotheses, then design and conduct actual experiments to test them.

"It's a classic paper," Wikswo said.

In order to give ABE the ability to run experiments like Adam, Wikswo's group is currently developing "laboratory-on-a-chip" technology that can be controlled by Eureqa. This will allow ABE to design and perform a wide variety of basic biology experiments. Their initial effort is focused on developing a microfluidics device that can test cell metabolism.

"Generally, the way that scientists design experiments is to vary one factor at a time while keeping the other factors constant, but, in many cases, the most effective way to test a biological system may be to tweak a large number of different factors at the same time and see what happens. ABE will let us do that," Wikswo said.

The project was funded by grants from the National Science Foundation, National Institute on Drug Abuse, the Defense Threat Reduction Agency and the National Academies Keck Futures Initiative.

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The above story is reprinted (with editorial adaptations by ScienceDaily staff) from materials provided by Vanderbilt University.

Journal Reference:

Michael D Schmidt, Ravishankar R Vallabhajosyula, Jerry W Jenkins, Jonathan E Hood, Abhishek S Soni, John P Wikswo, Hod Lipson. Automated refinement and inference of analytical models for metabolic networks. Physical Biology, 2011; 8 (5): 055011 DOI: 10.1088/1478-3975/8/5/055011

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Disclaimer: Views expressed in this article do not necessarily reflect those of ScienceDaily or its staff.


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Monday, 7 November 2011

'Robot biologist' solves complex problem from scratch

ScienceDaily (Oct. 14, 2011) — First it was chess. Then it was Jeopardy. Now computers are at it again, but this time they are trying to automate the scientific process itself.

An interdisciplinary team of scientists at Vanderbilt University, Cornell University and CFD Research Corporation, Inc., has taken a major step toward this goal by demonstrating that a computer can analyze raw experimental data from a biological system and derive the basic mathematical equations that describe the way the system operates. According to the researchers, it is one of the most complex scientific modeling problems that a computer has solved completely from scratch.

The paper that describes this accomplishment is published in the October issue of the journal Physical Biology and is currently available online.

The work was a collaboration between John P. Wikswo, the Gordon A. Cain University Professor at Vanderbilt, Michael Schmidt and Hod Lipson at the Creative Machines Lab at Cornell University and Jerry Jenkins and Ravishankar Vallabhajosyula at CFDRC in Huntsville, Ala.

The "brains" of the system, which Wikswo has christened the Automated Biology Explorer (ABE), is a unique piece of software called Eureqa developed at Cornell and released in 2009. Schmidt and Lipson originally created Eureqa to design robots without going through the normal trial and error stage that is both slow and expensive. After it succeeded, they realized it could also be applied to solving science problems.

One of Eureqa's initial achievements was identifying the basic laws of motion by analyzing the motion of a double pendulum. What took Sir Isaac Newton years to discover, Eureqa did in a few hours when running on a personal computer.

In 2006, Wikswo heard Lipson lecture about his research. "I had a 'eureka moment' of my own when I realized the system Hod had developed could be used to solve biological problems and even control them," Wikswo said. So he started talking to Lipson immediately after the lecture and they began a collaboration to adapt Eureqa to analyze biological problems.

"Biology is the area where the gap between theory and data is growing the most rapidly," said Lipson. "So it is the area in greatest need of automation."

Software passes test

The biological system that the researchers used to test ABE is glycolysis, the primary process that produces energy in a living cell. Specifically, they focused on the manner in which yeast cells control fluctuations in the chemical compounds produced by the process.

The researchers chose this specific system, called glycolytic oscillations, to perform a virtual test of the software because it is one of the most extensively studied biological control systems. Jenkins and Vallabhajosyula used one of the process' detailed mathematical models to generate a data set corresponding to the measurements a scientist would make under various conditions. To increase the realism of the test, the researchers salted the data with a 10 percent random error. When they fed the data into Eureqa, it derived a series of equations that were nearly identical to the known equations.

"What's really amazing is that it produced these equations a priori," said Vallabhajosyula. "The only thing the software knew in advance was addition, subtraction, multiplication and division."

Beyond Adam

The ability to generate mathematical equations from scratch is what sets ABE apart from Adam, the robot scientist developed by Ross King and his colleagues at the University of Wales at Aberystwyth. Adam runs yeast genetics experiments and made international headlines two years ago by making a novel scientific discovery without direct human input. King fed Adam with a model of yeast metabolism and a database of genes and proteins involved in metabolism in other species. He also linked the computer to a remote-controlled genetics laboratory. This allowed the computer to generate hypotheses, then design and conduct actual experiments to test them.

"It's a classic paper," Wikswo said.

In order to give ABE the ability to run experiments like Adam, Wikswo's group is currently developing "laboratory-on-a-chip" technology that can be controlled by Eureqa. This will allow ABE to design and perform a wide variety of basic biology experiments. Their initial effort is focused on developing a microfluidics device that can test cell metabolism.

"Generally, the way that scientists design experiments is to vary one factor at a time while keeping the other factors constant, but, in many cases, the most effective way to test a biological system may be to tweak a large number of different factors at the same time and see what happens. ABE will let us do that," Wikswo said.

The project was funded by grants from the National Science Foundation, National Institute on Drug Abuse, the Defense Threat Reduction Agency and the National Academies Keck Futures Initiative.

Recommend this story on Facebook, Twitter,
and Google +1:

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Story Source:

The above story is reprinted (with editorial adaptations by ScienceDaily staff) from materials provided by Vanderbilt University.

Journal Reference:

Michael D Schmidt, Ravishankar R Vallabhajosyula, Jerry W Jenkins, Jonathan E Hood, Abhishek S Soni, John P Wikswo, Hod Lipson. Automated refinement and inference of analytical models for metabolic networks. Physical Biology, 2011; 8 (5): 055011 DOI: 10.1088/1478-3975/8/5/055011

Note: If no author is given, the source is cited instead.

Disclaimer: Views expressed in this article do not necessarily reflect those of ScienceDaily or its staff.


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Wednesday, 29 June 2011

Making complex fluids look simple

ScienceDaily (June 1, 2011) — An international research team has successfully developed a widely applicable method for discovering the physical foundations of complex fluids for the first time. Researchers at the University of Vienna and University of Rome have developed a microscopic theory that describes the interactions between the various components of a complex polymer mixture. This approach has now been experimentally proven by physicists from Jülich, who conducted neutron scattering experiments in Grenoble.

The results have been published in the June issue of the journal Physical Review Letters.

Some important materials from technology and nature are complex fluids: polymer melts for plastics production, mixtures of water, oil and amphiphiles, which can be found in both living cells and in your washing machine, or colloidal suspensions such as blood or dispersion paints. They are quite different from simple fluids consisting of small molecules, such as water, because they are made of mixtures of particles between a nanometre and a micrometre in size, and have a large number of so-called degrees of freedom. The latter include vibrations, movements of the functional groups of molecules or joint movements of several molecules. They can appear on widely varied length, time, and energy scales. This makes experimental and theoretical studies difficult and, so far, has impeded understanding of the properties of these systems and the targeted development of new materials with improved properties.

A method developed and tested by physicists at Forschungszentrum Jülich, the Institut Laue-Langevin in Grenoble, and the Universities of Vienna and Rome now permits realistic modelling of complex fluids for the first time. "Our microscopic theory describes the interactions between the various components of a complex mixture and in turn, enables us to draw realistic conclusions about their macroscopic properties, such as their structure or their flow properties," said Prof. Christos Likos of the University of Vienna, an expert on theory and simulation.

The team from Vienna and Rome developed the theory model. Since the researchers were unable to include all the details of the real system -- a mixture of larger star-shaped polymers and smaller polymer chains -- they systematically eliminated the rapidly moving degrees of freedom and focused on the relevant slow degrees of freedom, a time-consuming and challenging task. "To do this, we use a relatively new method called coarse graining and replace each complex macromolecule with a sphere of the appropriate size. The challenge involves integrating the degrees of freedom that have been eliminated in the simplified systems as averages so that the characteristics of the substances are retained," Likos explained.

The team from Jülich used elaborate small angle neutron scattering experiments with the instrument D11 at the Institut Laue-Langevin in Grenoble to prove that the interactions between the spheres of the coarse-grained model realistically simulate the conditions in the real system. "We were faced with the proverbial challenge of visualizing the needle in a haystack," explained Dr. Jörg Stellbrink, a physicist and neutron scattering expert at the Jülich Centre for Neutron Science (JCNS). For neutrons, the individual polymers of the mixture cannot be readily distinguished. For this reason, the physicists "coloured" the components they were interested in, so that they stood out of the crowd. This is one of the Jülich team's specialities. In this way, they were able to selectively examine the structures and interactions on a microscopic length scale.

The physicists are especially proud of the excellent agreement between theoretical predictions and experimental results. The method will now open up a spectrum of possibilities for studying the physical properties of a whole range of different complex mixtures.

Story Source:

The above story is reprinted (with editorial adaptations by ScienceDaily staff) from materials provided by Helmholtz Association of German Research Centres, via EurekAlert!, a service of AAAS.

Journal Reference:

B. Lonetti, M. Camargo, J. Stellbrink, C. Likos, E. Zaccarelli, L. Willner, P. Lindner, D. Richter. Ultrasoft Colloid-Polymer Mixtures: Structure and Phase Diagram. Physical Review Letters, 2011; 106 (22) DOI: 10.1103/PhysRevLett.106.228301

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Disclaimer: Views expressed in this article do not necessarily reflect those of ScienceDaily or its staff.


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