Grantee Research Project Results
U.S. Environmental Protection Agency
Office of Research and Development
National Center for Environmental Research
Science to Achieve Results (STAR) Program
CLOSED - FOR REFERENCES PURPOSES ONLY
Computational Toxicology Centers: Development Of Predictive Environmental And Biomedical Computer-Based Simulations And Models
This is the initial announcement of this funding opportunity.
Funding Opportunity Number: EPA-G2007-STAR-D1
Catalog of Federal Domestic Assistance (CFDA) Number: 66.509
Solicitation Opening Date: February 7, 2007
Solicitation Closing Date: June 12, 2007 4:00 pm Eastern Time
| Eligibility Contact: Tom Barnwell (barnwell.thomas@epa.gov); phone: 202-343-9862 | |
| Electronic Submissions: Thomas O'Farrell (o'farrell.thomas@epa.gov); 202-343-9639 | |
| Technical Contacts: | |
| Pasky Pascual (pascual.pasky@epa.gov); phone: 202-343-9710 Deborah Segal (segal.deborah@epa.gov); phone: 202-343-9797 | |
Access Standard STAR Forms and Instructions
Research awarded under previous solicitations
SUMMARY OF PROGRAM REQUIREMENTS
Synopsis of Program:
The U.S. Environmental Protection Agency (EPA), as part of its Science to Achieve Results (STAR) program, is seeking applications proposing to develop in silico modeling applications of biological systems in areas as diverse as receptor–ligand interactions in cell signaling, simulated organ dysfunction (e.g., heart, liver, kidney), and systemic response to environmental toxicants and pollutants. The STAR program is issuing this request for applications (RFA) for research that will seek to apply high-performance computing technologies and theoretical mathematical techniques to facilitate the development of a predictive capacity for estimating outcomes or risk associated with particular toxicity processes as a result of environmental exposure to pollutants and toxicants. The development of predictive computational modeling of whole biological systems from cells to organs has the potential to address environmental and human health factors with broad scientific and environmental or economic impacts. The overall goal of the computational research effort supported by the U.S. EPA is to develop the use of computational approaches to provide tools for quantitative risk assessment and more efficient strategies for prioritizing chemicals for screening and testing. Through the support of the computational toxicology initiative, EPA will fund research that addresses data gaps in environmental and human health risk assessment and will strengthen the ability of predictive scientific data to guide sound future scientific policy, decisions, and research. To support the development of predictive mathematical models and simulations, the Computational Toxicology Centers will be funded for up to 4 years. A research Center is operated through a university, non-profit, or governmental entity to conduct complex, long-term, and collaborative research projects using multi-disciplinary approaches. A research Center is a consortium of investigators who will work together to address the investigational areas being solicited. (see eligibility information section for eligible applicants)
To obtain optimal impact from the STAR computational toxicology resources only two Centers that address problems and research needs facing the U.S. in human health and environmental risk assessment will be awarded. The Centers should be comprised of multiple scientists with different backgrounds and capabilities, from a single or a variety of institutions, working collaboratively. The Centers will also be expected to foster the professional development of junior faculty as well as the training of students and postdoctoral fellows in the use of high performance computing.
Award Information:
Anticipated Type of Award: Grant or Cooperative Agreement
Estimated Number of Awards: Approximately 2 awards
Anticipated Funding Amount: Approximately $6.8 million total for all awards
Potential Funding per Award: Up to a total of $3,400,000, including direct and indirect costs, with a maximum duration of 4 years. Cost-sharing is not required. Proposals with budgets exceeding the total award limit will not be considered.
Eligibility Information:
Public nonprofit institutions/organizations (includes public institutions of higher education and hospitals) and private nonprofit institutions/organizations (includes private institutions of higher education and hospitals) located in the U.S., state and local governments, Federally Recognized Indian Tribal Governments, and U.S. territories or possessions are eligible to apply. See full announcement for more details.
Application Materials:
You may submit either a paper application or an electronic application (but not both) for this announcement. The necessary forms for submitting a STAR application will be found on the National Center for Environmental Research (NCER) web site, https://www.epa.gov/research-grants/funding-opportunities-how-apply-and-required-forms. To apply electronically, you must use the application package available at Grants.gov (see “Submission Instructions for Electronic Applications” in Section IV). If your organization is not currently registered with Grants.gov, you need to allow approximately one week to complete the registration process to apply electronically. This registration, and electronic submission of your application, must be performed by an authorized representative of your organization.
| Eligibility Contact: Tom Barnwell (barnwell.thomas@epa.gov); phone: 202-343-9862 | |
| Electronic Submissions: Thomas O'Farrell (o'farrell.thomas@epa.gov); 202-343-9639 | |
| Technical Contacts: | |
| Pasky Pascual (pascual.pasky@epa.gov); phone: 202-343-9710 Deborah Segal (segal.deborah@epa.gov); phone: 202-343-9797 | |
I. FUNDING OPPORTUNITY DESCRIPTION
A. Introduction
The U.S. Environmental Protection Agency (EPA) is interested in the development and application of novel technologies, derived from computational chemistry, molecular biology, systems biology, and bioinformatics in toxicological risk assessment. In assessing risk associated with exposure to a chemical or other environmental stressor, a number of scientific uncertainties exist along a “source-to-adverse outcome” continuum, beginning with the presence of the chemical in the environment, the uptake and distribution of the chemical in the organism or environment, the presence of the active chemical at a systemic target site, and the series of biological events that lead to the manifestation of an adverse human health or ecological outcome that can be used for risk assessment. The “Human Health Research Strategy” (PDF) (67 pp, 1.50 MB) and “Ecological Research Strategy” (PDF) (130 pp, 1.3 MB) developed by EPA’s Office of Research and Development (ORD) describe the scientific uncertainties and some of the multidisciplinary approaches that are needed to build linkages between exposure, dose and effects.
ORD’s research program in Computational Toxicology seeks to use emerging technologies to improve risk assessment and reduce uncertainties in this source-to-adverse outcome continuum (https://www.epa.gov/comptox/comptox_framework.html). The strategic objectives of the Computational Toxicology Initiative are:
- Develop improved linkages across the source-to-outcome continuum, including the areas of chemical transformation and metabolism, better diagnostic/prognostic molecular markers, improved dose metrics, characterization of toxicity pathways, metabonomics, systems biology approaches, modeling frameworks, and uncertainty analysis.
- Provide improved predictive models for hazard identification, including the areas of Quantitative Structure Activity Relationships (QSARs) and other computational approaches, improved pollution prevention strategies, and high through-put screening approaches.
- Apply computational toxicology to enhance quantitative risk assessment in the areas of dose-response assessment, cross-species extrapolation, and chemical mixtures.
Computational modeling of whole biological systems from cells to organs is gaining momentum in cell biology and disease studies. Advancements in the ability to implement and develop advanced mathematical approaches to modeling biological systems are a key element in facilitating the development of a predictive capacity for estimating outcomes or risk associated with particular disease processes and environmental toxicants or pollutants. Therefore, we seek to fund research in computer science and mathematical research areas that address the environmental problems and research needs facing the U.S. by applying high-performance computing techniques and resources to in silico multi-scale modeling applications at the cellular, organ, and system-wide level.
B. Background
The mission of the U.S. Environmental Protection Agency is to safeguard public health and the environment from harmful effects that may be caused by exposure to pollutants in the air, water, soil, and food. Protecting human health and the environment carries with it the challenge of assessing the risk that is posed by tens of thousands of chemicals. The large number of chemicals that the Agency must evaluate and the many different statutes under which chemicals are regulated has traditionally made it impossible for the Agency to evaluate every chemical with the most rigorous testing strategies. Instead, standard toxicity tests have been conducted on only a small number of chemicals.
In November of 2005 the U.S. EPA funded the STAR Environmental Bioinformatics Research Centers to foster research that involves the design, development, and application of computer systems and software that enable scientists to explore high-throughput data from gene expression microarray experiments, mass spectrometry-based peptide and protein identification experiments, and various quantitative measures of metabolic states and metabolites.
Examples of potential modeling project topics of interest to environmental and human health scientists are listed below:
- Multiscale/multilevel modeling of biological processes and molecular interactions; development of models that combine components with different length and time scales (biochemical networks to cell behavior to tissue morphology). For instance; a "complete" cell model would track the time evolution levels of RNA, protein and other chemical species.
- Full system modeling /systems biology modeling; many computational models have been developed to cover a single subsystem or pathway (metabolic, regulatory, signaling...). However, toxic effects may arise downstream of the initial site of action of an input chemical. To recognize this situation more readily, it would be useful to create models that link unit networks into whole system models.
- Development of qualitative as well as quantitative system models / computational analysis methods; for systems modeling, research on qualitative models could be envisioned as a stage in model development where the topology of the model is specified but data are not available to parameterize the fully quantitative model. Development efforts could focus on determining the threshold at which the topology of the qualitative model is sufficient by itself to determine quantitative model behavior.
- Development of methods that create, validate and use both quantitative and qualitative models as a methodology for determining network or connectivity from experimental data.
The specific Strategic Goal and Objective from EPA’s Strategic Plan that relate to this solicitation are: Goal 4: Healthy Communities and Ecosystems, Objective 4.4: Enhance Science and Research
The EPA’s Strategic Plan can be found at www.epa.gov/ocfo/plan/2006/entire_report.pdf (184 pp, 11.56 MB).
C. Authority and Regulations
The authority for this RFA and resulting awards is contained in the Toxic Substances Control Act, Section 10, 15 U.S.C. 2609; Federal Insecticide, Fungicide, and Rodenticide Act, Section 20, 7 U.S.C. 136r; Clean Air Act, Section 103, 42 U.S.C. 7403; and the Clean Water Act, Section 104, 33 U.S.C.
For research with an international aspect, the above statutes are supplemented, as appropriate, by the National Environmental Policy Act, Section 102 (2)(F).
D. Specific Research Areas of Interest/Expected Outputs and Outcomes
Note to applicant: The term “output” means an environmental activity or effort, and associated work products, related to a specific environmental goal(s), (e.g., testing a new methodology), that will be produced or developed over a period of time under the agreement. The term “outcome” means the result, effect, or consequence that will occur from the above activit(ies) that is related to an environmental, behavioral, or health-related objective.
The U.S. EPA, as part of its STAR program, is seeking applications proposing to develop in silico simulation and modeling applications of biological systems in areas as diverse as receptor–ligand interactions in cell signaling, simulated organ dysfunction (e.g., heart, liver, kidney), and systemic response to environmental toxicants and pollutants. The STAR program is issuing this Request for Applications (RFA) for research that will seek to apply high-performance computing technologies and theoretical mathematical techniques to facilitate the development of a predictive capacity for estimating outcomes or risk associated with particular toxicity processes as a result of environmental exposure to pollutants and toxicants. Risk assessors need to better understand chemical behavior in natural and chemically-impacted ecosystems and in biological systems to carry out the increasingly complex array of exposure and risk assessments necessary to protect public health. Trends in decision making strategies also require greater reliance on predictive modeling. Therefore the development of mechanistic models for the purpose of building the ability of environmental and human health scientists to better assess and communicate risks to human health, risks to ecological systems, and risks to biological systems is a necessary outcome for this process to move forward.
This STAR Computational Toxicology RFA seeks to fund Computational Modeling Centers to facilitate the understanding of cell behavior by creating sophisticated mathematical- and computer-based models. For the purpose of this RFA, the term Computational Toxicology is defined as the application of mathematical and computer models to predict adverse effects and to better understand the mechanism(s) through which a chemical induces harm. EPA seeks to fund research that will develop complex computational models and simulations that use a broad range of biological information, including high-throughput genomics and proteomics data. Further development of advanced, mechanistic models will allow scientists to create new hypotheses as well as analyze the ever-expanding mass of biological data.
Through its application, computational modeling can provide validation as well as predictive analysis for experiments, which due to research limitations (whether as a result of time, funding, logistics, etc.) cannot otherwise be performed. In biomedicine, simulation models of biological systems now contain sufficient detail, not only to reconstruct normal functions, but also to reconstruct major disease states. As these models become more widely developed, simulation modeling will aid the targeting of current knowledge gaps, reveal insights that will bridge those gaps, and provide a research tool for selecting critical factors from multiple simulated experiments for real experimental design. Through network modeling of biological systems scientists will improve the understanding of how cells sense their environment and respond to environmental stimuli as well as guide scientific decision makers and statutory regulations. Therefore through the application of today's powerful computational platforms to biological research we are now able to begin to identify, analyze, and compare the fundamental biological components and processes that result from exposures to environmental toxicants and pollutants and their predicted impact on the human body and the ecosystem.
The outcome of this model development is the potential to increase the understanding and ability to predict the cumulative risk effects of, for example, the environmental exposure of an organism to multiple chemicals as opposed to a single chemical analysis. The need for multimedia, multistressor, multipathway assessments (from both the human and ecological perspectives) over broad spatial and temporal scales places a high priority both on the further elucidation of chemical behavior and on the development of new modeling tools. Because of the inherent complexity of real biological systems, development and analysis of computational models based directly on experimental data are necessary to achieve this understanding.
The output will include the creation of simulation models for predicting toxicity pathways, mechanisms, and health impacts as a result of environmental exposures. Ultimately, models will have an impact on understanding human health and strategies for environmental health risk management. The overall goal of the computational research effort supported by the U.S. EPA is to develop the use of computational approaches and to provide to the public tools for quantitative risk assessment and more efficient strategies for prioritizing chemicals for screening and testing.
Through this RFA, EPA seeks to fund centers to conduct research that will synthesize mathematical and computational simulations and models of biological systems. Models and simulations that capture knowledge through the explicit representation of dynamic biochemical and biophysical processes are desired. This research will also include the validation and application of these simulations and models in order to further our understanding of complex biological system functions in response to environmental exposures to toxicants. Proposals are expected to include multiple investigators working in collaboration. A proposed center must have a Center Director who will have the responsibility of ensuring that resources are utilized in an optimal way and that efforts of each team are focused on their proposed research project. The Center Director will also be responsible for reports and reviews of the teams’ research progress. Centers are obligated to engage the expertise of multidisciplinary teams focused on the basic and applied research projects needed to solve the Nation's environmental problems. The teams should each have their own unique research focus, for the purpose of this RFA denoted as an Investigational Area, and be balanced in such a manner to make the most effective use of available resources in accomplishing their proposed objectives.
Each Center should encompass multiple research teams, with each team’s Investigational Area focus detailed in a Research Plan (for general guidance see “Research Plan” in section IV.B.7.a.). To be responsive to the requirements of this RFA, a Center must be comprised of a minimum of two Investigational Areas, each with its own research team (2-5 proposed Investigational Areas are expected for each Center). For each Investigational Area proposed, successful applications will address each of the following:
- Development of Mathematical Models
- Priorities of Model Development
- Standard Model Formats and Computational Approaches
Researchers are encouraged to take advantage of existing biological data. However, limited data generation for the purpose of model validation is permissible under this RFA. Successful research grant applications will be those driven by computational and mathematical principle, model design, and validation.
Development of Mathematical Models
For each Investigational Area, a Center should propose research that develops in silico modeling applications for biological processes such as receptor–ligand interactions in cell signaling, downstream signaling networks, developmental processes, and simulated organ dysfunction (e.g., heart, liver, lungs), and be applicable to human and environmental toxicity studies and risk assessments. Such systems should seek to couple models across large ranges of length- and time-scales in describing complex biological systems through the use of hierarchical and hybrid multiscale modeling. Multiscale models that couple behavior at the molecular biological level to that at the cellular level are desired, to inform the process for calculating many unknown parameters as well as investigating the effects of small changes at the biomolecular level (e.g. network analysis of cell signaling, metabolic pathways, or genetic mutations due to the presence of an environmental pollutant or toxicant). Modeling methods proposed are expected to bring benefit to environmental and human health risk assessments.
Priorities of Model Development
Models should address issues of biological importance and be relevant to toxicity pathways or processes activated in response to exposures to environmental toxicants. Computational approaches to building models and simulations of biological systems may include, but are not limited to, the following:
- Models of biological networks including: signal transduction; biochemical networks; gene regulatory networks; metabolic networks; intracellular dynamics; cell structural dynamics; cell communication and tissue physiology.
- Models including finer scale details such as protein structure and behavior, and electronic effects.
- Development of algorithmic methods for specification of Physiologically Based Pharmacokinetic (PBPK) models that take into account the chemical structure of potentially toxic substances or stressors. This could include, for instance, development of algorithms for partition coefficients, metabolism, absorption through skin and from the GI tract, or pulmonary absorption.
For each Investigational Area, the proposal should address how each of the three components of model development below will be undertaken.
- Model Complexity:
- Multiscale/multilevel modeling of biological processes and molecular interactions.
- Full system modeling, i.e. the inclusion of multiple processes at the each length scale or level.
- Qualitative as well as quantitative system models and computational analysis methods.
- Methods that create, validate, and use both quantitative and qualitative models as a methodology for determining network structure or connectivity from experimental data.
- Model Parameterization:
- Model Parameterization / Validation; a key issue in using predictive models is the problem of determining models parameters such as rate constants, reaction orders, and initial conditions. Development of improved methods for determining these parameters from experimental data or detailed computations is needed.
- Model stability; many models can produce the same output behavior. Models may have different or identical topology but different parameters and still yield close to identical dynamic behavior. This is an issue related to model parameterization, and should be investigated because it will impact the ultimate validity and extensibility of a model.
- Research on characterization of the uncertainty in biologically based models and of models based on default assumptions. The purpose of this research would be to provide better tools for understanding in a rigorous, reproducible manner how to rank model uncertainty for models incorporating different amounts of biological and toxicological information. For example, given two or more models for the response to a toxicant, the preferred model is the one whose predictions are least uncertain (i.e., the model in whose predictions we have the most confidence).
- Validation / Prediction using Experimental Data:
- Integration of computational research with experimental data from ongoing experimental work, preferably on pathways/chemicals/endpoints relevant to toxicity risk assessment.
- Scripting/applying techniques for the rapid development, adaptation, and validation of the mathematical methods and models against experimental data.
- Computational evaluation of predicted pathways (e.g. approaches like sensitivity analysis) against known biological pathways.
In addition, as part of their research goals, for each Investigational Area, applicants are encouraged to address one or both of the following components:
- Informatics Issues: Development of informatics tools for integrating, organizing, managing and providing open access to disparate biological experimental and reference biological data (not database development).
- Visualization: Development of graphical interfaces to allow the visualization of the output of complex models and simulations.
Proposals should include not only method, simulation, and model development, but also demonstrate the link between proposed models and simulations to areas of interest including, but not limited to: developmental biology, metabolism, neurology, systems biology, and tumor initiation and growth. Proposed models and simulations should address toxicology issues which directly affect human and ecological health.
*Note to applicants: Each center should have, or obtain a commitment for, access to experimental data sets that will be used to parameterize and/or validate the models being developed. In conducting its research, the Center must demonstrate a willingness to use data generated by EPA and other institutions or organizations, as the basis for the development and application of computational models. It is not expected that the Center will be responsible for the generation of new biological data, but rather would be focused on advancing the science of ecological and human health risk assessment through the use of existing databases or data available through collaborators (such as academic institutions, EPA, NIEHS, DOE, CDC, etc.).
[Examples of several published models are given in Section E. (References) of this RFA (Albert and Othmer 2003; Alarcon, Byrne et al. 2004; Friedman 2004; King, Garrett et al. 2005; Bookout, Jeong et al. 2006)]
Standard Model Formats and Computational Approaches
For computational modeling to become more widely used in biological research, researchers must be able to exchange their results. Therefore, for each Investigational Area, the proposal should also address how teams will: (1) define agreed-upon standards for model curation, (2) define agreed-upon vocabularies for annotating models with connections to biological data resources, and (3) provide free publicly-accessible computational models.
- Data / model exchange: The center should be prepared to exchange data and models in standard (but still evolving) formats, such as SBML.
- In addition to the use of community developed model exchange formats, a strategy should be outlined for re-using existing public domain software and plans for public release of software modules, e.g. bioconductor, bioperl, biolisp, etc.
- Modeling and simulation of biomolecular systems is very computationally intensive; therefore the Center should detail allocations of computer time and identification of computational support for each Investigational Area.
Barbara Di Ventura, Caroline Lemerle, Konstantinos Michalodimitrakis, Luis Serrano (2006). “From in vivo to in silico biology and back.” Nature 443, 527-533. Available at: http://www.nature.com/nature/journal/v443/n7111/full/nature05127.html
Alarcon, T., H. M. Byrne, et al. (2004). "Towards whole-organ modelling of tumour growth." Prog Biophys Mol Biol 85(2-3): 451-72.
Albert, R. and H. G. Othmer (2003). "The topology of the regulatory interactions predicts the expression pattern of the segment polarity genes in Drosophila melanogaster." J Theor Biol 223(1): 1-18.
Bookout, A. L., Y. Jeong, et al. (2006). "Anatomical profiling of nuclear receptor expression reveals a hierarchical transcriptional network." Cell 126(4): 789-99.
Friedman, N. (2004). "Inferring cellular networks using probabilistic graphical models." Science 303(5659): 799-805.
King, R. D., S. M. Garrett, et al. (2005). "On the use of qualitative reasoning to simulate and identify metabolic pathways." Bioinformatics 21(9): 2017-26.
Agency policy prevents EPA technical staff and managers from providing individual applicants information that may create an unfair competitive advantage. Consequently, EPA employees will not review, comment, advise, provide technical assistance to applicants preparing applications in response to EPA RFA's, endorse an application or discuss in any manner how the Agency will apply the published evaluation criteria for this competition.
Groups of two or more eligible applicants may choose to form a consortium and submit a single application for this assistance agreement. The application must identify which organization will be the recipient of the assistance agreement and which organizations(s) will be sub-awardees of the recipient.
The application must include a plan (see “Data Plan” in section IV.B.7.c.) to make available to the public all data generated from observations, analyses, or model development (primary data) and any secondary (or existing) data used under an agreement awarded from this RFA. The data must be available in a format and with documentation such that they may be used by others in the scientific community.
It is anticipated that a total of approximately $6.8 million will be awarded under this announcement, depending on the availability of funds and quality of applications received. The EPA anticipates funding approximately two Grants or Cooperative Agreements under this RFA. Requests for amounts in excess of a total of $3,400,000, including direct and indirect costs, will not be considered. The total project period requested in an application submitted for this RFA may not exceed four years. The EPA reserves the right to reject all applications and make no awards, or make fewer awards than anticipated, under this RFA. The EPA reserves the right, consistent with Agency policy and without further competition, to make additional awards under this RFA if additional funding becomes available. Any additional selections for awards will be made no later than four months after the original selection decisions.
EPA may fund both grants and cooperative agreements under this announcement.
Under a grant, EPA scientists and engineers are not permitted to be substantially involved in the execution of the research. However, EPA encourages interaction between its own laboratory scientists and grant Principal Investigators after the award of an EPA grant for the sole purpose of exchanging information in research areas of common interest that may add value to their respective research activities. This interaction must be incidental to achieving the goals of the research under a grant. Interaction that is “incidental” does not involve resource commitments.
Where appropriate, based on consideration of the nature of the proposed project relative to the EPA’s intramural research program and available resources, the EPA will fund cooperative agreements under this announcement. When addressing a research question/problem of common interest, collaborations between scientists and the institution’s principal investigators are permitted under a cooperative agreement. These collaborations may include data and information exchange, providing technical input to experimental design and theoretical development, coordinating extramural research with in-house activities, the refinement of valuation endpoints, and joint authorship of journal articles on these activities. Proposals should not identify EPA cooperators or interactions; specific interactions between EPA’s investigators and those of the prospective recipient for cooperative agreements will be negotiated at the time of award.
A. Eligible Applicants
Public nonprofit institutions/organizations (includes public institutions of higher education and hospitals) and private nonprofit institutions/organizations (includes private institutions of higher education and hospitals) located in the U.S., state and local governments, Federally Recognized Indian Tribal Governments, and U.S. territories or possessions are eligible to apply. Profit-making firms are not eligible to receive assistance agreements from the EPA under this program.
Eligible nonprofit organizations include any organizations that meet the definition of nonprofit in OMB Circular A-122. However, nonprofit organizations described in Section 501(c) (4) of the Internal Revenue Code that lobby are not eligible to apply.
National laboratories funded by Federal Agencies (Federally-Funded Research and Development Centers, “FFRDCs”) may not apply. FFRDC employees may cooperate or collaborate with eligible applicants within the limits imposed by applicable legislation and regulations. They may participate in planning, conducting, and analyzing the research directed by the applicant, but may not direct projects on behalf of the applicant organization. The institution, organization, or governance receiving the award may provide funds through its assistance agreement from the EPA to an FFRDC for research personnel, supplies, equipment, and other expenses directly related to the research. However, salaries for permanent FFRDC employees may not be provided through this mechanism.
Federal Agencies may not apply. Federal employees are not eligible to serve in a principal leadership role on an assistance agreement, and may not receive salaries or augment their Agency’s appropriations in other ways through awards made under this program.
The applicant institution may enter into an agreement with a Federal Agency to purchase or utilize unique supplies or services unavailable in the private sector. Examples are purchase of satellite data, census data tapes, chemical reference standards, analyses, or use of instrumentation or other facilities not available elsewhere. A written justification for federal involvement must be included in the application. In addition, an appropriate form of assurance that documents the commitment, such as a letter of intent from the Federal Agency involved, should be included.
Potential applicants who are uncertain of their eligibility should contact Tom Barnwell (barnwell.thomas@epa.gov) in NCER, phone (202) 343-9862.
B. Cost-Sharing
Institutional cost-sharing is not required.
C. Other
Applications must substantially comply with the application submission instructions and requirements set forth in Section IV of this announcement or they will be rejected. In addition, where a page limitation is expressed in Section IV with respect to parts of the application, pages in excess of the page limit will not be reviewed. Applications must be received by the EPA, or Grants.gov, on or before the solicitation closing date and time in Section IV of this announcement or they will be returned to the sender without further consideration. Also, applications exceeding the funding limits or project period term described herein will be returned without review. Further, applications that fail to demonstrate a public purpose of support or stimulation (e.g., by proposing research which primarily benefits a Federal program or provides a service for a Federal agency) will not be funded.
To ensure that proposals address priority areas of research, studies will be considered non-responsive if they propose to conduct extensive molecular biology research or other laboratory-based research or data generation. Researchers are encouraged to form partnerships to take advantage of existing biological data, however limited data generation for the purpose of model validation is permissible under this RFA. Successful research grant applications will be those driven by computational and mathematical principle, model design, and validation.
In addition, each application must address the following:
- Computational Capabilities
- Describe the computational and network resources available as well as the specific types of statistical and bioinformatic approaches that exist within the organization and how such approaches can be applied to the selected Investigative Areas.
- Highlight what new developments in computational toxicology analysis, database development, and other areas of de novo programming are proposed, as well as areas where existing computational and database resources will be utilized.
- The Center must be composed of at least two Investigational Areas, each of which must be completely described according to the APPLICATION AND SUBMISSION INFORMATION section of this RFA (see "Research Plan" in section IV.B.7.a.). Each Area is permitted a 15-page description. Individual project descriptions must explain how the Investigational Area fits into the overall Center program and relates to other projects in the proposal.
- The Center must have a Center Director.
- The Center must have an Administrative Core Unit.
- The application must include a "Center Integration Plan."
- The Center must submit a Communication and Public Outreach Strategy.
- New Investigators - To attract new investigators into the application of computer science and mathematics research to biological systems, the Center must partially support at least one newly recruited Center scientist. Up to $70,000 per year, direct cost, may be used for each newly required Center scientist to provide up to 75 percent salary support, technical support, equipment, and supplies. Recruitment of women, minorities, and persons with disabilities is strongly encouraged. To the extent possible, the types of individuals sought and their expected roles should be described in the application if specific individuals have not been identified.
- Postdoctoral training - the Center must support a minimum of two postdoctoral scientists (at least one of whom is to be newly recruited) and provide a research environment in leading-edge informatics/computational tools or approaches, as well as a structured mentoring and career development opportunity (see "Center Description" in section IV.B.5.).
To be eligible for funding consideration, a project's focus must consist of activities within the statutory terms of EPA's financial assistance authorities; specifically, the statute(s) listed in I.C. above. Generally, a project must address the causes, effects, extent, prevention, reduction, and elimination of air pollution, water pollution, solid/hazardous waste pollution; toxic substances control; or pesticide control. These activities should relate to the gathering or transferring of information or advancing the state of knowledge. Proposals should emphasize this "learning" concept, as opposed to "fixing" an environmental problem via a well-established method. Proposals relating to other topics which are sometimes included within the term "environment" such as recreation, conservation, restoration, protection of wildlife habitats, etc., must describe the relationship of these topics to the statutorily required purpose of pollution prevention and/or control.
Applications deemed ineligible for funding consideration will be notified within fifteen calendar days of the ineligibility determination.
IV. APPLICATION AND SUBMISSION INFORMATION
You may submit either a paper application or an electronic application (but not both) for this announcement. Instructions for both types of submission follow. If not otherwise marked, instructions apply to both types of submissions.
A. Internet Address to Request Application Package
For paper applications, forms and instructions can be found on the NCER web site: https://www.epa.gov/research-grants/funding-opportunities-how-apply-and-required-forms.
For electronic applications, use the application package available at Grants.gov (see “Submission Instructions for Electronic Applications”). Note: With the exception of the Budget form (available at https://www.epa.gov/research-grants/funding-opportunities-how-apply-and-required-forms); all necessary forms are included in the electronic application package.
For both paper and electronic applications, an email will be sent by NCER to the Principal Investigator and the Administrative Contact (see below) to acknowledge receipt of the application and transmit other important information. The email will be sent from receipt.application@epa.gov; emails to this address will not be accepted. If you do not receive an email acknowledgment within 30 days of the submission closing date, immediately inform the Eligibility Contact shown in this solicitation. Failure to do so may result in your application not being reviewed. See “Submission Instructions for Electronic Applications” for additional information regarding acknowledgment of receipt of electronically submitted applications. Please note: Due to often-lengthy delays in delivery, it is especially important that you monitor NCER’s confirmation of receipt of your application when using regular mail.
B. Content and Form of Application Submission
The application is made by submitting the materials described below. It is essential that the application contain all information requested and be submitted in the formats described.
- Standard Form 424
The applicant must complete Standard Form 424. This form will be the first page(s) of the application. Instructions for completion of the SF424 are included with the form. (However, note that EPA requires that the entire requested dollar amount appear on the 424, not simply the proposed first year expenses.) The form must contain the original (or electronic) signature of an authorized representative of the applying institution.
Applicants are required to provide a "Dun and Bradstreet Data Universal Numbering System" (DUNS) number when applying for federal grants or cooperative agreements. Organizations may receive a DUNS number by calling 1-866-705-5711 or by visiting the web site at http://www.dnb.com.
Executive Order 12372, "Intergovernmental Review of Federal Programs," applies to most EPA programs and assistance agreements, unless the program or assistance agreement supports tribal, training/fellowships (other than Wastewater and Small Water Systems Operator training programs), and research and development (with some exceptions). The SF424 refers to this Executive Order requirement. National research programs are generally exempt from review unless the proposals (a) require an Environmental Impact Statement (EIS), or (b) do not require an EIS but will be newly initiated at a particular site and require unusual measures to limit the possibility of adverse exposure or hazard to the general public, or (c) have a unique geographic focus and are directly relevant to the governmental responsibilities of a State or local government within that geographic area. To determine whether their state participates in this process, and how to comply, applicants should consult http://www.whitehouse.gov/omb/grants/spoc.html.
- Key Contacts
The applicant must complete the "Key Contacts" form as the second page of the application: a Key Contacts continuation page is also available at https://www.epa.gov/research-grants/funding-opportunities-how-apply-and-required-forms. The Key Contacts form should also be completed for major sub-agreements (i.e., primary co-investigators). Please make certain that all contact information is accurate.
- Table of Contents
Provide a list of the major subdivisions of the application indicating the page number on which each section begins.
- Abstract (1 page)
The abstract is a very important document in the review process. Therefore, it is critical that the abstract accurately describes the research being proposed and conveys all the essential elements of the research. Also, the abstracts of applications that receive funding will be posted on the NCER web site.
The abstract should include the information described below (a-h). Examples of abstracts for current grants may be found on the NCER web site.
- Funding Opportunity Title and Number for this proposal.
- Project Title: Use the exact title of your project as it appears in the application. The title must be brief yet represent the major thrust of the project. Because the title will be used by those not familiar with the project, strike a balance between highly technical words and phrases and more commonly understood terminology. Do not use general phrases such as "research on."
- Investigators: List the Principal Investigator, then the names and affiliations of each co-investigator who will significantly contribute to the project. Provide a web site URL or an email contact address for additional information.
- Institution: In the same order as the list of investigators, list the name, city and state of each participating university or other applicant institution. The institution applying for assistance must be clearly identified.
- Project Period and Location: Show the proposed project beginning and ending dates, and the geographical location(s) where the work will be conducted.
- Project Cost: Show the total dollars requested from the EPA (include direct and indirect costs for all years).
- Project Summary: Provide three subsections addressing: (1) the objectives of the study (including any hypotheses that will be tested), (2) the experimental approach to be used (a description of the proposed project), and (3) the expected results of the project and how it addresses the research needs identified in the solicitation, including the estimated improvement in risk assessment or risk management that will result from successful completion of the proposed work.
- Supplemental Keywords: Without duplicating terms already used in the text of the abstract, list keywords to assist database searchers in finding your research. A list of suggested keywords may be found at: https://www.epa.gov/research-grants/funding-opportunities-how-apply-and-required-forms.
- Center Description (5 pages)
Applications should describe the overall goals, objectives, and approach for the Center, including how the Center will pursue a multidisciplinary and thematic approach to the problems to be investigated. The structure of the Center's proposed postdoctoral training and mentorship programs should be described herein; this must include a description of career development opportunities and training provided to the postdoctoral fellows in addition to their research activities.
This description must not exceed five (5) consecutively numbered (bottom center), 8.5x11-inch pages of single-spaced, standard 12-point type with 1-inch margins. While these guidelines establish the minimum type size requirements, applicants are advised that readability is of paramount importance and should take precedence in selection of an appropriate font for use in the proposal.
- Administrative Core Unit (10 pages)
Applications must have an Administrative Core Unit, which provides overall oversight, coordination and integration of the Center's activities. A Center Integration Plan describing how the program will be integrated internally must be submitted as part of the Administrative Core description. The Center's Integration Plan, at a minimum, should indicate how programmatic and sub-contracting decisions will be made; how investigators from different computational disciplines and Investigational Areas within the Center will communicate on a regular basis about the development and progress of each of the Areas; how progress will be monitored; who sets priorities, and who is responsible for implementing the Integration Plan, ensuring compliance with the plan, and evaluating its effectiveness in achieving integration within the Center.
In order to facilitate the dissemination to the scientific community of the computational models and simulations developed by the Center, the Center must develop and submit a Communication and Public Outreach Strategy. Publishing research results in scientific journals is essential; however, it is not sufficient. Plans for Center websites and other means of communicating results should be described. This communication strategy will address how the Center will work to disseminate the products of its research as well as identify envisioned applications of the models and simulations developed and the research community and public will benefit from this work.
The description of the Administrative Core Unit must not exceed ten (10) consecutively numbered (bottom center), 8.5x11-inch pages of single-spaced, standard 12-point type with 1-inch margins. While these guidelines establish the minimum type size requirements, applicants are advised that readability is of paramount importance and should take precedence in selection of an appropriate font for use in the proposal.
- Research Plan, Quality Assurance Statement and References
- Research Plan (15 pages per Investigational Area, with a minimum of 2 Areas)
Applications should focus on a limited number of research objectives that adequately and clearly demonstrate that they meet the RFA requirements. Explicitly state the main hypotheses that you will investigate, the data you will create or use, the analytical tools you will use to investigate these hypotheses or analyze these data, and the results you expect to achieve. Research methods must be clearly stated so that reviewers can evaluate the appropriateness of your approach and the tools you intend to use. A statement such as: "we will evaluate the data using the usual statistical methods" is not specific enough for peer reviewers.
This description must not exceed fifteen (15) consecutively numbered (bottom center), 8.5x11-inch pages of single-spaced, standard 12-point type with 1-inch margins. While these guidelines establish the minimum type size requirements, applicants are advised that readability is of paramount importance and should take precedence in selection of an appropriate font for use in the proposal.
The description must provide the following information:
- Objectives: List the objectives of the proposed research and the hypotheses being tested during the project, and briefly state why the intended research is important and how it fulfills the requirements of the solicitation. This section should also include any background or introductory information that would help explain the objectives of the study. If this application is to expand upon research supported by an existing or former assistance agreement awarded under the STAR program, indicate the number of the agreement and provide a brief report of progress and results achieved under it (one to two pages recommended).
- Approach/Activities: Outline the research design, methods, and techniques that you intend to use in meeting the objectives stated above (five to ten pages recommended).
- Expected Results, Benefits, Outputs, and Outcomes: Describe the results you expect to achieve during the project (outputs) and the potential benefits of the results (outcomes). This section should also discuss how the research results will lead to solutions to environmental problems and improve the public's ability to protect the environment and human health. A clear, concise description will help NCER and peer reviewers understand the merits of the research (one to two pages recommended).
- General Project Information: Discuss other information relevant to the potential success of the project. This should include facilities, personnel expertise/experience, project schedules, proposed management, interactions with other institutions, etc. Applications for multi-investigator projects must identify project management and the functions of each investigator in each team and describe plans to communicate and share data (one to two pages recommended).
- Appendices may be included but must remain within the 15-page limit.
- Quality Assurance Statement (1 to 3 pages in addition to the 15-page research plan)
For projects involving environmental data collection or processing, conducting surveys, modeling, method development, or the development of environmental technology (whether hardware-based or via new techniques), provide a Quality Assurance Statement (QAS) regarding the plans for processes that will be used to ensure that the products of the research satisfy the intended project objectives. Follow the guidelines provided below to ensure that the QAS describes a system that complies with ANSI/ASQC E4, Specifications and Guidelines for Quality Systems for Environmental Data Collection and Environmental Technology Programs. Do not exceed three consecutively numbered, 8.5x11-inch pages of single-spaced, standard 12-point type with 1-inch margins.
Address each section below by including the required information, referencing the specific location of the information in the Research Plan, or explaining why the section does not apply to the proposed research.
- Identify the individual who will be responsible for the quality assurance (QA) and quality control (QC) aspects of the research along with a brief description of this person's functions, experience, and authority within the research organization. Describe the organization's general approach for conducting quality research. (QA is a system of management activities to ensure that a process or item is of the type and quality needed for the project. QC is a system of activities that measures the attributes and performance of a process or item against the standards defined in the project documentation to verify that they meet those stated requirements.)
- Discuss project objectives, including quality objectives, any hypotheses to be tested, and the quantitative and/or qualitative procedures that will be used to evaluate the success of the project. Include any plans for peer or other reviews of the study design or analytical methods.
- Address each of the following project elements as applicable:
- Collection of new/primary data:
(Note: In this case the word "sample" is intended to mean any finite part of a statistical population whose properties are studied to gain information about the whole. If certain attributes listed below do not apply to the type of samples to be used in your research, simply explain why those attributes are not applicable.)- Discuss the plan for sample collection and analysis. As applicable, include sample type(s), frequency, locations, sample sizes, sampling procedures, and the criteria for determining acceptable data quality (e.g., precision, accuracy, representativeness, completeness, comparability, or data quality objectives).
- Describe the procedures for the handling and custody of samples including sample collection, identification, preservation, transportation, and storage, and how the accuracy of test measurements will be verified.
- Describe or reference each analytical method to be used, any QA or QC checks or procedures with the associated acceptance criteria, and any procedures that will be used in the calibration and perform
- Collection of new/primary data:
- Research Plan (15 pages per Investigational Area, with a minimum of 2 Areas)
The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.