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.
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.
![Click image for larger view.](graphics/article03_bio2.jpg)
Cellulose breakdown in plant cell walls.
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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.
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.
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![Click image for larger view.](graphics/article03_bio1.jpg)
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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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.
Institute Missions
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.
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