Uncovering secrets of living cells
The Computational Biology Institute at CCS will develop software tools to enable understanding of the molecular interactions of protein networks in bacteria and in mice
Cellulose breakdown in plant cell walls.
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Probing microbes to determine what they are made of and what drives them requires more than mass spectrometers, microarrays, and microscopes. Computational models run on supercomputers have been key contributors to our growing understanding of these single-cell organisms.
How Microbes Help DOE
The Department of Energy seeks to understand the diverse range of biochemical pathways that enable microbes to survive under extreme conditions--high temperature, high radiation, and high concentrations of toxic chemicals. DOE is interested in harnessing the genes of microbes whose talents could help DOE meet its missions in environmental bioremediation, climate change, and energy production.
For example, the bacterium Deinococcus radiodurans can withstand high doses of radiation because its cells efficiently repair radiation damage. These bacteria also might be able to convert radioactive uranium in storage ponds from a soluble to an insoluble form so that this toxic metal stays put in the sediments instead of dissolving in water that may flow off-site. Thus, it might be possible to harness the genes of D. radiodurans for remediating sites with mixed wastes--combinations of radioactive materials and toxic metals. Use of genes with the right abilities from bacteria such as D. radiodurans and Shewanella oneidensi (studied at ORNL) could potentially save DOE billions of dollars in toxic waste cleanup activities.
Genes and other DNA sequences contain instructions on how and when the cell should build proteins. Proteins form complexes, or molecular machines, that do the work of the cell.
Certain bacteria in the ocean and on land absorb carbon dioxide from the atmosphere and perform photosynthesis.Harnessing the genes from these bacteria would help DOE achieve its goal of finding ways to halt the buildup of atmospheric carbon dioxide from energy production to counter global warming.
DOE is also interested in microbes that produce clean fuels, such as methane, methanol, and hydrogen. ORNL researchers are focusing on Rhodopseudomonas palustris, whose genes might be harnessed to produce hydrogen for possible use in power-producing fuel cells for cars and buildings in the envisioned hydrogen economy. ORNL researchers and their colleagues are studying these microbes as part of DOE's Microbial Genome Program and Genomes to Life (GTL) Program.
Enzyme (green) embedded in a synthetic membrane that increases the enzyme's stability and activity. The enzyme converts toxic materials (purple molecules at left) into harmless substances (yellow and red molecules at right). Courtesy of Pacific Northwest National Laboratory
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Computational biologists working with supercomputers at DOE's Center for Computational Sciences (CCS) at ORNL have a long history of contributing to an understanding of microbial genes. They have identified many genes in bacterial, mouse, and human genomes and have computationally analyzed the human genome using ORNL-developed gene-finding computer programs. ORNL researchers also have written and used assembly programs and analysis tools to produce draft sequences of the 300 million DNA base pairs in chromosomes 19, 16, and 5 for DOE's Joint Genome Institute (JGI) as part of DOE's Human Genome Project.
Some have analyzed 60 complete and draft microbial genomes containing 230,000 genes and used computers to keep up with JGI sequencing rates of a genome per day. Others have predicted the structures of proteins from amino-acid sequences using an ORNL-developed protein-threading computer program.
Bioinformatics specialists from ORNL and the University of Tennessee have written algorithms and developed other tools to make it easier for biologists to use com-puters to find genes and make sense out of the rising flood of biological data. These data are produced in studies of biochemical pathways and processes, cellular and developmental processes, tissue and organism physiology, and ecological processes and populations. Through ORNL's user-friendly Genome Channel web site, its Genomic Integrated Supercomputing Toolkit, and CCS supercomputers, the international biology community, including pharmaceutical industry researchers and academics, have easily obtained genetically meaningful interpretations of their DNA sequences and other data. ORNL's web site, especially the pages supporting the Human Genome Project, is the focus of approximately 150,000 sessions per month in the biological community.
ORNL computational scientists are now working with research partners in the GTL Program to develop high-throughput computational tools for rapidly analyzing, interpreting, and communicating the volumes of data on, for example, five novel proteins that the partners discovered during research on R. palustris. Analytical tools and algorithms will be needed to determine how proteins interact, stimulate chemical reactions, and move materials inside and out of cells when exposed to different conditions. Proteins turn genes on and off, regulating their activities. When a bacterial cell is moved from clean water to polluted water, proteins in cells capture environmental signals and turn on genes that make special proteins enabling the cell to adapt to a new environment. Computer models will be built to characterize this cascade of changes.
The Computational Biology Institute (CBI), led by Jeff Nichols, has been formed as a multidisciplinary partnership to develop and provide innovative computational algorithms, analysis tools, and data and hardware infrastructure to enable a scientific understanding of the molecular interactions typical of networks of proteins in complex microbial and metazoan systems-- primarily bacteria and mice. ORNL traditionally has conducted research on mice to determine the genetic effects of radiation and toxic chemicals on mammalian systems; radiation and toxic chemicals are byproducts of weapons development and energy production, which have long been missions of the U.S. government. CBI will analyze, model, and simulate molecular interactions and networks of interactions among proteins and cells from microbes and mice.
What are the CBI focus areas? One is microbial genome analysis--determining which genes are present in each genome. Another is mass spectrometry analysis-- modeling data from mass spec experiments to determine which proteins are made in the cell and when they are used. Another focus area is molecular interaction image analysis, investigates which proteins interact with each other, and when and where. CBI scientists will also use molecular machine modeling, docking, and dynamics to determine which molecular machines are made to do the work. A final focus area is molecular interaction networks modeling and simulation, which describes the web of interactions that transmit information to control the cell.
To precisely describe these bio-molecular interactions involving networks of cells and biochemical pathways, CBI will foster interactions and networking among researchers who need access to supercomputers to better understand data produced by experimentalists at ORNL and at universities. Most of the research is sponsored by DOE and the National Institutes of Health (NIH), which provides funding for neuroscience studies by members of the Tennessee Mouse Genome Consortium with whom ORNL mouse biologists work. Some small research projects at CBI involving single principal investigators are sponsored by the National Science Foundation.
CBI comprises researchers largely from ORNL's Life Sciences, Chemical Sciences, Environmental Sciences, and Computer Science and Mathematics divisions. These researchers also collaborate with researchers from the University of Tennessee and other universities.
Computational research planned for the future will require leadership-class scientific computing. This capability should enable researchers to simultaneously track 100 moving proteins in a live microbe with the help of imaging technology and to meet a GTL goal of completely characterizing a microbe in a year.
By combining experimenters' analytical capabilities with the mathematical and simulation capabilities of CBI, the biology community will have a better understanding of the function of large macromolecular complexes, the control of gene expression, cell membrane dynamics, metabolism, and signaling and environmental responses. Single cells are very small, but the complexities of their workings and interactions demand large networks of interacting researchers using very large computers.