[Federal Register Volume 66, Number 18 (Friday, January 26, 2001)]
[Notices]
[Pages 7890-7894]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 01-2372]


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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: 
[email protected].
    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: [email protected].
    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: 
[email protected].
    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

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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.

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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