NSF Org:	DBI Division of Biological Infrastructure
Recipient:	UNIVERSITY OF NEW MEXICO
Initial Amendment Date:	December 4, 2002
Latest Amendment Date:	December 4, 2002
Award Number:	0302344
Award Instrument:	Standard Grant
Program Manager:	Manfred D. Zorn DBI  Division of Biological Infrastructure BIO  Directorate for Biological Sciences
Start Date:	December 1, 2002
End Date:	November 30, 2004 (Estimated)
Total Intended Award Amount:	$99,474.00
Total Awarded Amount to Date:	$99,474.00
Funds Obligated to Date:	FY 2003 = $99,474.00
History of Investigator:	Paul Helman (Principal Investigator) helman@cs.unm.edu Robert Veroff (Co-Principal Investigator) Stephanie Ruby (Co-Principal Investigator) Susan Atlas (Co-Principal Investigator)
Recipient Sponsored Research Office:	University of New Mexico 1 UNIVERSITY OF NEW MEXICO ALBUQUERQUE NM  US  87131-0001 (505)277-4186
Sponsor Congressional District:	01
Primary Place of Performance:	University of New Mexico 1 UNIVERSITY OF NEW MEXICO ALBUQUERQUE NM  US  87131-0001
Primary Place of Performance Congressional District:	01
Unique Entity Identifier (UEI):	F6XLTRUQJEN4
Parent UEI:
NSF Program(s):	ADVANCES IN BIO INFORMATICS
Primary Program Source:	app-0103
Program Reference Code(s):	BIOT, 9237, 9184, 9150
Program Element Code(s):	116500
Award Agency Code:	4900
Fund Agency Code:	4900
Assistance Listing Number(s):	47.074

ABSTRACT

A Bayesian framework will be used to compare interspecies gene expression data in order to reconstruct gene regulatory networks for the species considered. Considering multiple species simultaneously should lead to more accurate, complete network models and facilitate the understanding of evolutionary relationships between components sharing common function across species. The knowledge of yeast networks will be used to generate hypotheses for networks that model human networks. Global gene expression data in response to DNA damaging agents will be compared as a first instance of the analysis tools. Impact: The ability to compare expression data across species will contribute to our understanding of systems biology and can also be applied to the comparison of different cell and tissue types as well as species. This may have applications in development and health sciences.

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