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Office of Science; Office of Science Financial Assistance Program Notice 01-21; Advanced Modeling and Simulation of Biological Systems
Note: EPA no longer updates this information, but it may be useful as a reference or resource.
[Federal Register: January 26, 2001 (Volume 66, Number 18)]
[Notices]
[Page 7890-7894]
From the Federal Register Online via GPO Access [wais.access.gpo.gov]
[DOCID:fr26ja01-21]
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DEPARTMENT OF ENERGY
Office of Science; Office of Science Financial Assistance Program
Notice 01-21; Advanced Modeling and Simulation of Biological Systems
AGENCY: U.S. Department of Energy (DOE).
ACTION: Notice inviting grant applications.
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SUMMARY: The Offices of Advanced Scientific Computing Research (ASCR)
and Biological and Environmental Research (OBER) of the Office of
Science (SC), U.S. Department of Energy, hereby announce interest in
receiving applications for grants in support of computational modeling
and simulation of biological systems. The goal of this program is to
enable the use of terascale computers to explore fundamental biological
processes and predict the behavior of a broad range of protein
interactions and molecular pathways in prokaryotic microbes of
importance to DOE. This goal will be achieved through the creation of
scientific simulation codes that are high performance, scalable to
hundreds of nodes and thousands of processors, and able to evolve over
time and be ported to future generations of high performance computers.
The research efforts being sought under this Program Notice will take
advantage of extensive information inferred from the complete DNA
sequence, such as the genetics and the biochemical processes available
for a well-characterized prokaryotic microbe; for example, Escherichia
coli (E. coli). This notice encourages applications from the
disciplines of applied mathematics and computer science in partnership
with microbiology, molecular biology, biochemistry and structural and
computational biology to combine information available on a well
[[Page 7891]]
characterized prokaryotic microbe with advanced mathematics and
computer science to enable this new understanding. This announcement is
being issued in parallel with Program Notice 01-20, the Microbial Cell
Project. Together, they represent a planned first step in an ambitious
effort to understand the functions of the proteins in a prokaryotic
microbial cell, to understand their interactions as they form pathways
that carry out DOE-relevant activities, and to eventually build
predictive models for microbial activities that address DOE mission
needs.
DATES: Preapplications referencing Program Notice 01-21 should be
received by February 21, 2001. Earlier submissions will be gladly
accepted. A response to timely preapplications will be communicated to
the applicant by March 9, 2001.
Formal applications in response to this notice should be received
by 4:30 p.m., E.D.T., April 24, 2001, to be accepted for merit review
and funding in FY 2001.
ADDRESSES: Preapplications referencing Program Notice 01-21 should be
sent to Dr. Walter M. Polansky, Office of Advanced Scientific Computing
Research, SC-32, Office of Science, U.S. Department of Energy, 19901
Germantown Road, Germantown, MD 20874-1290; e-mail is acceptable for
submitting preapplications using the following address:
walt.polansky@science.doe.gov.
Formal applications referencing Program Notice 01-21, should be
forwarded to: U.S. Department of Energy, Office of Science, Grants and
Contracts Division, SC-64, 19901 Germantown Road, Germantown, MD 20874-
1290, ATTN: Program Notice 01-21. This address must be used when
submitting applications by U.S. Postal Service Express Mail or any
commercial mail delivery service, or when hand-carried by the
applicant.
FOR FURTHER INFORMATION CONTACT: Dr. Walter M. Polansky, Office of
Advanced Scientific Computing Research, SC-32, Office of Science, U.S.
Department of Energy, 19901 Germantown Road, Germantown, MD 20874-1290;
telephone: (301) 903-5995, e-mail: walt.polansky@science.doe.gov.
Dr. John Houghton, Office of Biological and Environmental Research,
Office of Science, U.S. Department of Energy, 19901 Germantown Road,
Germantown, MD 20874-1290; telephone: (301) 903-8288, e-mail:
john.houghton@science.doe.gov.
The full text of Program Notice 01-21 is available via the World
Wide Web using the following web site address: http://www.sc.doe.gov/
production/grants/grants.html.
SUPPLEMENTARY INFORMATION: Extraordinary advances in computing
technology in the past decade have set the stage for a new era in
scientific computing. Within the next five to ten years, computers
running at 1 to 10 trillion floating point operations per second (Tops)
will become available. Using such computers, it will be possible to
dramatically extend explorations of fundamental processes as well as
advance the ability to predict the behavior of a broad range of complex
biological systems.
The primary mission of the Office of Advanced Scientific Computing
Research is to discover, develop, and deploy the computational and
networking tools that enable researchers in the scientific disciplines
to analyze, model, simulate and predict complex phenomena important to
the Department of Energy. In carrying out this mission, ASCR:
Maintains world leadership in areas of scientific
computing research relevant to the missions of the Department of
Energy;
Integrates the results of advanced scientific computing
research into the natural sciences and engineering;
Provides world class supercomputer and networking
facilities for scientists working on problems that are important to the
missions of the Department.
The primary mission of the Office of Biological and Environmental
Research is to advance environmental and biomedical knowledge connected
to energy production, development, and use. In carrying out this
mission, OBER:
Contributes to the environmental remediation and
restoration of contaminated environments at DOE sites through basic
research in bioremediation, microbial genomics, and ecological science;
Provides new knowledge that will widen DOE's options for
clean and affordable energy through research in microbial genomics and
bioinformatics;
Advances our understanding of and finds solutions for the
effects of energy production and use on the environment through
research in global climate modeling and simulation, the role of clouds
in climate change, carbon cycle and carbon sequestration, atmospheric
chemistry, and ecological science;
Helps protect the health of DOE workers and the public by
advancing our understanding of the health effects of energy production
and use through basic research in key areas of the life sciences
including functional genomics and structural biology as well as low
dose radiation research;
Seeks to develop new applications of radiotracers in
diagnosis and treatment and supports biomedical engineering research
focused on fundamental studies in medical imaging, biological and
chemical sensors, laser medicine, new biocompatible materials,
informatics, and artificial organs.
The scope and complexity of the proposed projects will likely
require close collaboration among researchers from the biological
sciences, computational sciences, computer science, and applied
mathematics disciplines. Accordingly, this solicitation calls for the
creation of scientific simulation teams, or collaborations, as the
organizational basis for a successful application. Partnerships among
universities, national laboratories, and industry are encouraged but
not required. A scientific simulation team is a multi-disciplinary, and
perhaps multi-institutional, group of people who will:
Create scientific simulation codes that take full
advantage of terascale computers,
Work closely with other research teams and centers to
ensure that the best available mathematical algorithms and computer
science methods are employed, and
Manage the work of the team in a way that will foster good
communication and decision making.
Biological systems and their regulatory and metabolic pathways are
complex. The details of many biological processes are not well
understood, and the resulting computations will require new algorithms,
computational biology tools, and extraordinary computing resources. The
successful development of the new tools will require the sustained
efforts of multi-disciplinary teams, and applications of these tools
will require Tops-scale and beyond supercomputers, as well as the
considerable expertise required to use them. Although forms of these
computational tools already exist, considerable research in mathematics
and computer science remains to be done in order to develop reliable,
robust, efficient, and widely applicable versions of these tools.
Data analysis, computational modeling and simulation will play
critical roles in the future of biological research. Large sets of
genomic data will be generated by the on-going DNA sequencing efforts
at large genome centers around the world. These data
[[Page 7892]]
will be analyzed and combined with different types of biological data,
including information on structure, expression, and function to develop
a more comprehensive understanding of biological systems. Homology-
based protein structure correlations identified by pattern searches
will be used to predict the structures of the proteins coded by the new
genome sequences and will be invaluable for ascertaining protein
function and for identifying more distant homologies than are possible
by simple sequence comparisons. For selected biochemical processes,
computational modeling will be used for a range of applications, from
elucidating the mechanisms of enzymatic reactions to identifying the
energetic principles underlying macromolecular interactions. Computer
models of entire cells and microbial ecosystems will also use the
understanding gained about biomolecular processes to predict likely
behaviors of organisms under different conditions.
A goal for the research solicited here is to develop a predictive
understanding of biological systems using a well characterized
prokaryotic microbial cell, for example, E. coli, as a model system.
Given the immense complexity of even the simplest microbes, fully
predictive models that provide quantitatively accurate estimates of
each chemical component of a cell will remain a challenge for
subsequent generations of researchers. Hence, in the foreseeable
future, the modeling of cellular processes will instead be performed at
a level beyond that of the individual chemical reactions, perhaps at
the level of functional building blocks that can be pieced together or
linked into higher order models. At this level, cellular pathways are
described either qualitatively as being present or absent, or
quantitatively, in terms of the average concentrations and rates of
activity derived from experimental data. Despite their lack of chemical
detail, such models will provide a powerful tool for integrating and
analyzing the very large new biological data sets and, under some
conditions, predicting cellular behavior under changing conditions.
Just as importantly, these high level models will provide a means of
inducing and testing the general principles of cellular function.
Three levels of modeling are included in this solicitation: (1)
Molecular simulations of protein function and macromolecular
interactions, (2) semi-quantitative simulations of metabolic networks
in whole cells, and (3) quantitative kinetic models of biochemical
pathways. The latter simulations are much more demanding in terms of
the empirical data and computer power required and therefore, will
initially be limited to relatively small, well characterized pathways.
Since both of these levels of modeling depend on having the (nearly)
complete parts lists provided by the fully annotated genome sequences,
combined with gene function, expression information and phenotypic data
about an organism, the focus of this solicitation will be on E. coli or
another well-characterized and studied prokaryotic microbe.
(1) Molecular simulations of protein function and macromolecular
interactions. The ultimate biological models would be molecular-level
simulations of each biochemical process. There are many challenges to
molecular-level simulations of biological processes, including the
large size of biomolecules and the wide range of time scales of many
biological processes, as well as the subtle energetics and complex
milieu of biochemical reactions. Moreover, many biochemical reactions
occur far from equilibrium and are regulated by both transport of the
reactants and subsequent processing of the products. Finally, there
remains a wide gulf between the detailed chemical data needed for
initiating and validating biomolecular simulations and the data
available on many biological processes and environments. Despite these
challenges, there are a vast number of biochemical processes for which
chemical simulations will have a major impact on our understanding.
These problems include the elucidation of the energetic factors
underlying protein-protein or protein-DNA interactions and the
dissection of the catalytic function of certain enzymes. The promise of
such modeling studies is rapidly growing as a result of the development
of linear-scaling computational chemical methods and molecular modeling
software for massively parallel computers. Additionally, molecular
modeling will be used to determine the principles that underlie
protein-protein interactions, and ultimately to predict likely protein
binding sites.
(2) Semi-quantitative simulations of metabolic networks. This
modeling approach follows the engineering tradition of making maximal
use of limited information by combining highly simplified models with
successive constraints to identify an ``envelope'' of expected
behaviors of the system under different conditions. A fundamental tenet
of such modeling is that the very complex molecular details of biology
combine to form robust and relatively simple rules for behavioral
responses. Such models are iteratively refined as more functional data
and constraints become available from experiments that are themselves
guided by the model's predictions.
Since such modeling depends only on the nature of the reactants and
products (i.e., the stoichiometry) of the metabolic transformations,
rather than the rates of these reactions (kinetics), most of the
necessary data for building the model can be derived directly from
annotated genomes, in some cases using artificial intelligence based
pathway synthesis algorithms. These data are typically encoded in a
``stoichiometry matrix'' relating specific reaction products to
metabolic reactions. Numerical analysis of this matrix can identify the
entire repertoire of theoretically possible metabolic capabilities of a
given genotype, for example, what nutrients are essential and what
metabolic pathways are non-redundant. Such information, although
qualitative, has enormous potential value. It will allow the inference
of phenotypic properties directly from the functionally annotated
genotype, help in the optimization of product yield in bio-reactors,
and provide a predictive basis for engineering organisms with novel
capabilities. Additionally, such analysis can be used to improve and
validate tentative functional annotations. Even in the absence of
stoichiometric data, mathematical analysis of metabolic networks can
shed light on overall biological function. A number of successful
models have already been developed for E. coli using both
stoichiometric data, based on a network analysis, and constraint-based
approaches.
Unlike the kinetic pathway described below, computing speed is not
typically a limiting factor in molecular pathway analysis. Instead, the
primary bottleneck to progress is the availability of functionally
annotated genomes and the human talent trained in both the biological
sciences and the art of developing and applying such mathematical
models. The choice of a well-characterized prokaryotic organism as a
model biological system for this solicitation minimizes the challenges
associated with the first bottleneck.
(3) Quantitative kinetic models of biochemical pathways. Although
the metabolic network modeling described above can provide useful
qualitative information on possible behavioral characteristics of
organisms, a fully predictive understanding of biological processes
will require quantitative information about the dynamics of each sub-
process. In other words, network
[[Page 7893]]
analysis can suggest what metabolic transformations may be possible,
but full kinetic details are required to determine which pathways are
most important under the given conditions. Such models will require
detailed empirical data, including in vivo reaction rates and substrate
concentrations for each step in the biological system to be simulated.
Additionally, these simulations are highly computationally demanding;
for example, the simulation of a regulatory circuit involving only
several dozen parameters required the use of a parallel supercomputer.
These experimental and computational requirements will prohibit such
quantitative simulations of whole cells in the foreseeable future.
Nevertheless, for selected critical cell subsystems, such simulations
offer the promise of quantitative predictions of cellular response and
will constitute a rigorous validation of the completeness of our
understanding the processes under investigation.
Kinetic models have been applied to a handful of specific cellular
pathways that demonstrate both the benefits and technical challenges of
such simulations. One of the most complex examples to date has been a
full kinetic analysis of the lytic versus lysogenic pathways in phage
infected E. coli cells. The heart of the decision circuitry
for this pathway contains only four promoter sites modulated by five
gene transcripts, yet the kinetic model required nearly forty empirical
rate constants and a number of other parameters. Additionally, to be
computationally tractable, this model involved a number of simplifying
assumptions, including approximating the cell as a well-stirred
homogeneous mixture. Despite these assumptions and the large number of
empirical parameters this model yielded reasonably accurate results for
the lytic/lysogenic fractions at different levels of viral infection.
An important outcome of this previous work is to highlight the
significant differences between the modeling methodologies necessary
for biochemical pathways and those used for macroscopic chemical
processes (e.g., in optimizing industrial chemical processes.) In the
latter the chemical concentrations can be assumed to be continuous and
therefore the kinetics can be simulated using ordinary differential
equations. In contrast, the very small numbers of individual signaling
molecules in biological regulatory pathways require the use of discrete
stochastic simulations. Indeed, a number of seemingly non-deterministic
features in gene expression have been ascribed to the inherently
stochastic fluctuations in the concentrations of very small numbers of
regulatory signals.
Overall, both the kinetic models and the metabolic network analysis
will provide a means of combining and evaluating the consistency of
large sets of biological data. Each requires detailed functional
annotation of whole genomes and well as phenotypic data under a wide
variety of conditions.
In a parallel solicitation, the Microbial Cell Project (see Program
Notice 01-20) supports key DOE missions by building on the successful
DOE Microbial Genome Program that has furnished microbial DNA sequence
information on microbes relevant to environmental remediation, global
carbon sequestration (e.g., CO2 fixation), complex polymer
degradation (e.g., cellulose and lignins), and energy production
(fuels, chemicals, and chemical feedstocks). These microbial genome
sequences provide a finite set of ``working parts'' for a cell and the
challenge now is to understand how these parts are assembled into
functional pathways and networks to accomplish activities of interest
to the DOE. The traditional reductionist experimental approach has
defined specific steps or stages within many physiological processes;
however, the availability of whole genomes affords the opportunity to
integrate these individual pathways into a larger physiological or
whole organism framework. The Microbial Cell Project seeks to integrate
available information about individual processes and regulatory
complexes to understand the intracellular environment, in which these
pathways and networks exist and function. The DOE Microbial Cell
Project is part of a coordinated Federal effort called the Microbe
Project involving elements from several other Federal agencies. The
long-term goal is that research funded in this program and in the
Microbial Cell Project will converge so that simulations and models can
be developed in organisms and for biochemical pathways important for
the DOE mission.
This notice takes advantage of decades of research on E. coli (or a
similarly well characterized prokaryotic microbe) providing much of the
biological information needed to begin developing more comprehensive
models of biological systems. It is anticipated that the applied
mathematicians and computer scientists will need to partner with
biologists in the initial phases of algorithm development, as well as
in the design of biological tests to validate models that are
developed, including predictions made using these models. Links to some
of the vast amount of information available on E. coli can be found at
http://genprotec.mbl.edu/start and http://web.bham.ac.uk/bcm4ght6/
res.html.
The mathematical and computer science challenges in this effort
span a broad range of the current research topics in both fields. A few
examples of possible areas include: advanced techniques for data
fusion; algorithms for solution of low dimensional dynamical systems in
the presence of uncertainty; applications of computational geometry and
topology to pattern recognition and analysis; advanced concepts in
discrete state machines; and control theory. It must, however, be
emphasized that the preceding list is only a list of possible examples
and does not reflect any prioritization of areas.
Collaboration and Coordination
Applicants are encouraged to collaborate with researchers in other
institutions, such as: universities, industry, non-profit
organizations, Federal laboratories and Federally Funded Research and
Development Centers (FFRDCs), including the DOE National Laboratories,
where appropriate, and to include cost sharing wherever feasible.
Further information on preparation of collaborative proposals is
available in the Application Guide for the Office of Science Financial
Assistance Program that is available via the World Wide Web at: http://
www.science.doe.gov/production/grants/Colab.html.
Preapplications
Potential applicants are strongly encouraged to submit a brief
preapplication that consists of two to three pages of narrative
describing the research objectives, the technical approach(es), and the
proposed team members and their expertise. The intent in requesting a
preapplication is to save the time and effort of applicants in
preparing and submitting a formal project application that may be
inappropriate for the program. Preapplications will be reviewed
relative to the scope and research needs outlined in the summary
paragraph and in the SUPPLEMENTARY INFORMATION. The preapplication
should identify, on the cover sheet, the title of the project, the
institution, principal investigator name, telephone, fax, and e-mail
address. No budget information or biographical data need be included,
nor is an institutional endorsement necessary. A response to each
timely preapplication will be communicated to the Principal
Investigator by March 9, 2001.
[[Page 7894]]
Program Funding
It is anticipated that up to $2 million will be available for all
awards in Fiscal Year 2001. Multiple year funding is expected, also
contingent on availability of funds and progress of the research;
pending the availability of future funding, it is anticipated that this
initiative will reflect a long term commitment to understanding the
workings of a microbial cell. Awards are expected to range from
$250,000 to $600,000 per year with terms of one to three years. The DOE
is under no obligation to pay for any costs associated with the
preparation or submission of an application. DOE reserves the right to
fund, in whole or in part, any, all, or none of the applications
submitted in response to this Notice. Applications received by the
Office of Science under its normal competitive application mechanisms
may also be deemed appropriate for consideration under this
announcement and may be funded under this program.
Merit Review
Applications will be subjected to scientific merit review (peer
review) and will be evaluated against the following evaluation criteria
which are listed in descending order of importance codified at 10 CFR
605.10(d):
1. Scientific and/or Technical Merit of the Project;
2. Appropriateness of the Proposed Method or Approach;
3. Competency of Applicant's Personnel and Adequacy of Proposed
Resources;
4. Reasonableness and Appropriateness of the Proposed Budget.
In addition to the above evaluation criteria, applications will
also be evaluated on the following:
5. The robustness of the organizational framework if a consortium
is proposed;
The evaluation under item 2, Appropriateness of the Proposed Method
or Approach, will also consider the following elements:
(a) clarity of the plan in detailing areas of work to be addressed
by biologists, computational scientists, applied mathematicians,
computer scientists and computer programmers;
(b) quality of the plan for effective collaboration among
participants;
(c) viability of the plan for verifying and validating the models
developed, including verification using experiment results; and
(d) quality and clarity of the proposed work schedule and project
deliverables.
The evaluation will include program policy factors such as the
relevance of the proposed research to the terms of the announcement and
the agency's programmatic needs. Note, external peer reviewers are
selected with regard to both their scientific expertise and the absence
of conflict-of-interest issues. Non-federal reviewers will often be
used, and submission of an application constitutes agreement that this
is acceptable to the investigator(s) and the submitting institution.
Submission Information
The Project Description must be 25 pages or less, exclusive of
attachments. It must contain an abstract or project summary on a
separate page with the name of the applicant, mailing address, phone,
FAX and E-mail listed. The application must include letters of intent
from collaborators (briefly describing the intended contribution of
each to the research), and short curriculum vitaes, consistent with NIH
guidelines, for the applicant and any co-PIs.
To provide a consistent format for the submission, review and
solicitation of grant applications submitted under this notice, the
preparation and submission of grant applications must follow the
guidelines given in the Application Guide for the Office of Science
Financial Assistance Program, 10 CFR Part 605. Access to SC's Financial
Assistance Application Guide is possible via the World Wide Web at:
http://www.sc.doe.gov/production/grants/grants.html.
DOE policy requires that potential applicants adhere to 10 CFR part
745 ``Protection of Human Subjects'' (if applicable), or such later
revision of those guidelines as may be published in the Federal
Register.
The Office of Science, as part of its grant regulations (10 CFR
605.11(b)) requires that a grantee funded by SC and performing research
involving recombinant DNA molecules and/or organisms and viruses
containing recombinant DNA molecules shall comply with the NIH
``Guidelines for Research Involving Recombinant DNA Molecules,'' which
is available via the World Wide Web at: http://www.niehs.nih.gov/odhsb/
biosafe/nih/rdna-apr98.pdf, (59 FR 34496, July 5, 1994), or such later
revision of those guidelines as may be published in the Federal
Register.
Other useful web sites include:
MCP Home Page--http://microbialcellproject.org
Microbial Genome Program Home Page--http://www.er.doe.gov/
production/ober/microbial.html
DOE Joint Genome Institute Microbial Web Page--http://
www.jgi.doe.gov/JGI_microbial/html/
GenBank Home Page--
http://www.ncbi.nlm.nih.gov/
Human Genome Home Page--
http://www.ornl.gov/hgmis
The Catalog of Federal Domestic Assistance Number for this
program is 81.049, and the solicitation control number is ERFAP 10
CFR Part 605.
Issued in Washington, D.C. on January 16, 2001.
John Rodney Clark,
Associate Director of Science for Resource Management.
[FR Doc. 01-2372 Filed 1-25-01; 8:45 am]
BILLING CODE 6450-01-P