The synopsis for this grant opportunity is detailed below, following
this paragraph. This synopsis contains all of the updates to this
document that have been posted as of
09/14/2009
. If
updates have been made to the opportunity synopsis, update information
is provided below the synopsis.
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opportunity click
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Document Type:
Grants Notice
Funding Opportunity Number:
DE-FOA-0000143
Opportunity Category:
Discretionary
Posted Date:
Sep 14, 2009
Creation Date:
Sep 14, 2009
Original Closing Date for Applications:
Jan 29, 2010
Current Closing Date for Applications:
Jan 29, 2010
Archive Date:
Jan 14, 2011
Funding Instrument Type:
Grant
Category of Funding Activity:
Science and Technology and other Research and Development
Category Explanation:
Expected Number of Awards:
Estimated Total Program Funding:
Award Ceiling:
Award Floor:
CFDA Number(s):
81.049
--
Office of Science Financial Assistance Program
Cost Sharing or Matching Requirement:
No
Eligible Applicants
Unrestricted (i.e., open to any type of entity above), subject to any clarification in text field entitled "Additional Information on Eligibility"
Additional Information on Eligibility:
All types of entities are eligible to apply,
except other Federal agencies,
Federally Funded Research and Development Center (FFRDC) Contractors, and
nonprofit organizations described in section 501(c)(4) of the Internal
Revenue
Code of 1986 that engaged in lobbying activities after December 31,
1995.
Agency Name
Chicago Service Center
Description
The Office of Biological and Environmental Research
(BER) of the Office of
Science (SC), U.S. Department of Energy (DOE) advances world-class
biological
and environmental research and provides scientific facilities to support
DOE
missions in scientific discovery and innovation, energy security, and
environmental responsibility. BER hereby announces its interest in
receiving
applications for potential funding of computational biology and
bioinformatic
methods and analytics to enable the planned Systems Biology Knowledgebase.
The
goal of systems biology is to understand living systems, a simple microbe
or a
complex ecosystem, so well that we can develop predictive, computational
models
of the systems that ¿behave¿ the same way as the living system. Such
predictive
models would enable scientists to accurately predict an organism¿s or a
more
complex system¿s behavior.
