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Integrated Biological and Computational Low-Dose Radiation Research
DOE SC program in Biological and Environmental Research (BER) hereby announces its interest in development of integrated biological and computational research to gain a mechanistic understanding of the effects of low dose radiation exposure on cellular functions.
The long-term goal is to build on the understanding of radiation effects on cellular function and establish comprehensive datasets amenable to incorporation into increasingly capable AI/ML models to identify both transient and persistent changes in cellular metabolism that may be linked to adverse health outcomes.
This announcement builds on BER’s past efforts in low dose radiation research but seeks to take advantage of rapid technological changes in high throughput genomics, ‘omics and computational (Artificial Intelligence and Machine Learning [AI/ML]) technologies in recent years to advance a more mechanistic understanding of low dose and low dose rate effects on cellular metabolism.
Developing a large compendium of high-quality datasets coupled with advances in AI/ML computational techniques trained on such data and integrated within the larger scientific literature on cellular processes affords the ability to explore a more mechanistic understanding of the impact low dose radiation has on cellular function.
Over the long-term, this approach can provide a mechanistic basis for identifying markers and/or patterns in altered cellular function that may be indicative of or associated with an adverse health outcome.
These approaches may also provide new insights into the uncertainties of health effects seen in epidemiological studies due to low dose radiation exposure and provide a basis to explore links to health effects other than cancer.
The long-term goal is to build on the understanding of radiation effects on cellular function and establish comprehensive datasets amenable to incorporation into increasingly capable AI/ML models to identify both transient and persistent changes in cellular metabolism that may be linked to adverse health outcomes.
This announcement builds on BER’s past efforts in low dose radiation research but seeks to take advantage of rapid technological changes in high throughput genomics, ‘omics and computational (Artificial Intelligence and Machine Learning [AI/ML]) technologies in recent years to advance a more mechanistic understanding of low dose and low dose rate effects on cellular metabolism.
Developing a large compendium of high-quality datasets coupled with advances in AI/ML computational techniques trained on such data and integrated within the larger scientific literature on cellular processes affords the ability to explore a more mechanistic understanding of the impact low dose radiation has on cellular function.
Over the long-term, this approach can provide a mechanistic basis for identifying markers and/or patterns in altered cellular function that may be indicative of or associated with an adverse health outcome.
These approaches may also provide new insights into the uncertainties of health effects seen in epidemiological studies due to low dose radiation exposure and provide a basis to explore links to health effects other than cancer.
Obtain Full Opportunity Text:
https://grants.nih.gov/grants/guide/pa-files/PAR-24-074.html
Additional Information of Eligibility:
All types of domestic applicants are eligible to apply, except nonprofit organizations described in section 501(c)(4) of the Internal Revenue Code of 1986 that engaged in lobbying activities after December 31, 1995.
Federally affiliated entities must adhere to the eligibility standards below: III.A.1.
DOE/NNSA National Laboratories DOE/NNSA National Laboratories are not eligible to submit applications under this FOA but may be proposed as subrecipients under another organization’s application.
If recommended for funding as a proposed subrecipient, the value of the proposed subaward will be removed from the prime applicant’s award and will be provided to the laboratory through the DOE Field-Work Proposal System and work will be conducted under the laboratory’s contract with DOE.
No administrative provisions of this FOA will apply to the laboratory or any laboratory subcontractor.
Additional instructions for securing authorization from the cognizant Contracting Officer are found in Section VIII of this FOA.
III.A.2.
Non-DOE/NNSA FFRDCs Non-DOE/NNSA FFRDCs are not eligible to submit applications under this FOA but may be proposed as subrecipients under another organization’s application.
If recommended for funding as a proposed subrecipient, the value of the proposed subaward may be removed from the prime applicant’s award and may be provided through an interagency agreement to the FFRDC’s sponsoring Federal Agency.
Additional instructions for securing authorization from the cognizant Contracting Officer are found in Section VIII of this FOA.
III.A.3.
Other Federal Agencies Other Federal Agencies are not eligible to submit applications under this FOA but may be proposed as subrecipients under another organization’s application.
If recommended for funding as a proposed subrecipient, the value of the proposed subaward may be removed from the prime applicant’s award and may be provided through an interagency agreement.
Additional instructions for providing statutory authorization are found in Section VIII of this FOA.
Full Opportunity Web Address:
http://grants.nih.gov/grants/guide/pa-files/PAR-24-074.html
Contact:
Agency Email Description:
SC.GrantsandContracts@science.doe.gov
Agency Email:
Date Posted:
2023-12-21
Application Due Date:
Archive Date:
2024-05-02
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