Opportunity Information: Apply for RFA AG 17 004
Apply for RFA AG 17 004
- The HHS-NIH11 in the health sector is offering a public funding opportunity titled "Systems Biology of Aging (R01)" and is now available to receive applicants.
- Interested and eligible applicants and submit their applications by referencing the CFDA number(s): 93.866.
- This funding opportunity was created on Mar 09, 2016 and posted on Mar 09, 2016.
- Applicants must submit their applications by Oct 07, 2016. (Agency may still review applications by suitable applicants for the remaining/unused allocated funding in 2026.)
- Each selected applicant is eligible to receive up to $600,000.00 in funding.
- The number of recipients for this funding is limited to 4 candidate(s).
- Eligible applicants include: State governments, County governments, City or township governments, Special district governments, Independent school districts, Public and State controlled institutions of higher education, Native American tribal governments (Federally recognized), Public housing authorities/Indian housing authorities, Native American tribal organizations (other than Federally recognized tribal governments), Nonprofits having a 501(c)(3) status with the IRS, other than institutions of higher education, Nonprofits that do not have a 501(c)(3) status with the IRS, other than institutions of higher education, Private institutions of higher education, For profit organizations other than small businesses, Small businesses, Others (see text field entitled Additional Information on Eligibility for clarification).
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Opportunity Summary:
The Systems Biology of Aging (R01) grant opportunity (RFA-AG-17-004) is a National Institutes of Health (NIH) research funding announcement aimed at pushing aging research beyond single-gene explanations and toward systems-level models that can explain how aging emerges from complex biological interactions. The central idea is to build "aging networks" where lifespan is treated as the key measurable outcome (the observable phenotype), and then use those networks to understand why organisms age the way they do. Rather than focusing on one pathway at a time, the FOA prioritizes projects that integrate multiple types of biological information to map how genes, proteins, pathways, and cellular processes interact to influence longevity.
A major emphasis of the FOA is not only constructing these aging networks, but also analyzing how they behave over time. Applicants are expected to examine which properties of an aging network change across the lifespan, meaning the project should address aging as a dynamic process rather than a single endpoint comparison (young versus old). In practice, that means designing studies that can detect how network structure, connectivity, regulatory relationships, or system robustness shifts as an organism progresses through life, and tying those changes back to lifespan and aging phenotypes.
Another key goal is hypothesis generation and testing driven by systems biology. The FOA is looking for proposals that use network models to identify deeper, potentially more fundamental questions in the biology of aging that are difficult to resolve using traditional one-gene-at-a-time approaches. The expectation is that a systems framework will reveal emergent behaviors, interactions, or higher-order principles (for example, coordinated regulation, feedback loops, trade-offs between cellular programs, or network-level vulnerabilities) that help explain aging more convincingly than isolated molecular findings. Strong applications would typically include an iterative loop where data feed into model/network construction, the model generates predictions, and experiments are performed to validate or refute those predictions.
The eligible experimental organisms are restricted to two well-established laboratory models for aging research: Saccharomyces cerevisiae (budding yeast), representing a single-cell organism, and Caenorhabditis elegans, a multicellular nematode. The FOA deliberately limits the scope to these systems because they are highly tractable for genetic manipulation and are compatible with large-scale and high-throughput experimentation. Proposals are therefore expected to make serious use of the strengths of these organisms, such as rapid life cycles, powerful genetics, and the ability to collect large datasets across many perturbations or conditions that can support robust network inference.
Team composition is also a defined requirement. Applications must include a contact Program Director/Principal Investigator (PD/PI) with clear expertise in systems biology, reflecting the emphasis on computational, quantitative, and network-based approaches. In addition, the project leadership team should include other PD/PI(s) with expertise in the biology of aging and/or the high-throughput technologies needed to generate the kinds of datasets that systems biology depends on, specifically in yeast or C. elegans. This signals that NIH expects interdisciplinary projects where modeling and data generation are tightly integrated, rather than projects that are primarily descriptive or purely computational without strong experimental grounding.
From an administrative standpoint, this is an NIH discretionary grant using the R01 funding mechanism under the Health funding activity category, administered by HHS/NIH (AgencyName: HHS-NIH11), with CFDA number 93.866. The opportunity listed an award ceiling of $600,000 and anticipated making about 4 awards. The FOA was posted March 9, 2016, created the same date, and had an application closing date of October 7, 2016 (original and current closing dates match). Eligibility is broad and includes various levels of government entities, public and private institutions of higher education, nonprofit organizations (with or without 501(c)(3) status), for-profit organizations (including small businesses and other than small businesses), tribal governments and organizations, public housing authorities/Indian housing authorities, and other applicants as clarified in NIH eligibility text.
Overall, the opportunity is designed for research programs that can combine high-throughput experimental aging biology in yeast or C. elegans with strong systems biology methods to construct and analyze lifespan-linked networks, track how those networks change with age, and use the resulting models to generate testable, systems-level explanations for fundamental aging mechanisms.
Frequently Asked Questions (FAQ) - Systems Biology of Aging (R01) (RFA-AG-17-004)
What is the Systems Biology of Aging (R01) funding opportunity?
The Systems Biology of Aging (R01) opportunity (RFA-AG-17-004) is an NIH research funding announcement that supports projects using systems biology approaches to explain aging as an emergent property of complex biological interactions, rather than as the result of single genes or isolated pathways.
What is the main scientific goal of this FOA?
The main goal is to build and use "aging networks" where lifespan is treated as the key measurable outcome (observable phenotype), and to use these networks to understand how interactions among genes, proteins, pathways, and cellular processes influence longevity and aging phenotypes.
What does NIH mean by an "aging network" in this announcement?
In this FOA, an aging network refers to a systems-level model that integrates multiple types of biological information to map how components such as genes, proteins, pathways, and cellular processes interact in ways that affect lifespan and aging outcomes.
Why does the FOA emphasize moving beyond single-gene explanations?
The FOA is intended to push aging research toward systems-level models that can explain how aging emerges from complex interactions. The premise is that focusing on one gene or one pathway at a time can miss emergent behaviors and higher-order principles that become visible only when interactions are modeled as networks.
What role does lifespan play in the proposed research?
Lifespan is prioritized as the central measurable outcome (phenotype) around which network models are constructed and interpreted, with the goal of linking network properties and changes to longevity and aging-related phenotypes.
Does the FOA require studying aging as a dynamic process over time?
Yes. A major emphasis is analyzing how aging networks behave over time. Projects are expected to examine which network properties change across the lifespan, treating aging as a dynamic process rather than only comparing young versus old endpoints.
What kinds of network changes across lifespan are relevant under this FOA?
The FOA highlights studying changes in network structure, connectivity, regulatory relationships, and system robustness across the life course, and relating those changes back to lifespan and aging phenotypes.
What is meant by systems-biology-driven hypothesis generation and testing?
The FOA encourages an approach where network models are used to generate deeper, potentially more fundamental hypotheses about aging that may not be accessible through traditional one-gene-at-a-time methods, followed by experiments designed to validate or refute the model-driven predictions.
Is an iterative model-and-experiment loop expected?
Yes. Strong applications are expected to include an iterative workflow in which data inform model/network construction, the model generates predictions, and experiments are performed to test those predictions.
Which organisms are eligible for experimental work in this FOA?
Eligible experimental organisms are limited to two established laboratory models for aging research: Saccharomyces cerevisiae (budding yeast) and Caenorhabditis elegans (a multicellular nematode).
Why does the FOA restrict eligible organisms to yeast and C. elegans?
The scope is limited to these systems because they are highly tractable for genetic manipulation and compatible with large-scale, high-throughput experimentation. The FOA expects applicants to use the strengths of these organisms to generate robust datasets that support network inference.
What types of study strengths are expected when using yeast or C. elegans?
The FOA expects serious use of features such as rapid life cycles, powerful genetics, and the ability to collect large datasets across many perturbations or conditions suitable for systems-level analysis and network modeling.
What team expertise is required for the project leadership?
Applications must include a contact Program Director/Principal Investigator (PD/PI) with clear expertise in systems biology. The leadership team should also include other PD/PI(s) with expertise in the biology of aging and/or in high-throughput technologies needed to generate the datasets used in systems biology, specifically in yeast or C. elegans.
What does the FOA signal about the kind of collaboration NIH expects?
It signals NIH expects interdisciplinary projects where computational/quantitative modeling and experimental data generation are tightly integrated, rather than proposals that are primarily descriptive or purely computational without strong experimental grounding.
What is the funding mechanism for this opportunity?
This is an NIH discretionary grant using the R01 funding mechanism under the Health funding activity category, administered by HHS/NIH (AgencyName: HHS-NIH11).
What is the CFDA number for this opportunity?
The CFDA number listed for this opportunity is 93.866.
What is the award ceiling stated in the opportunity listing?
The opportunity listing states an award ceiling of $600,000.
How many awards were anticipated?
The opportunity anticipated making about 4 awards.
When was the FOA posted, and what was the application closing date?
The FOA was posted (and created) on March 9, 2016. The application closing date was October 7, 2016 (the original and current closing dates match).
What types of organizations are eligible to apply?
Eligibility is broad and includes various levels of government entities, public and private institutions of higher education, nonprofit organizations (with or without 501(c)(3) status), for-profit organizations (including small businesses and other than small businesses), tribal governments and organizations, public housing authorities/Indian housing authorities, and other applicants as clarified in NIH eligibility text.
Does the FOA prioritize projects that integrate multiple types of biological information?
Yes. The FOA prioritizes integrating multiple types of biological information to map interactions among genes, proteins, pathways, and cellular processes that influence longevity.
What kinds of explanations or outcomes is NIH hoping systems biology will reveal?
The FOA specifically points to the potential for systems approaches to reveal emergent behaviors and higher-order principles such as coordinated regulation, feedback loops, trade-offs between cellular programs, and network-level vulnerabilities that may explain aging more convincingly than isolated molecular findings.
Is the FOA intended for purely computational projects without experiments?
Based on the stated expectations, the FOA emphasizes tight integration of modeling with data generation and experimental validation. The leadership requirements and emphasis on high-throughput datasets in yeast or C. elegans indicate that strong experimental grounding is expected alongside systems biology methods.
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