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A Metabolomic Framework in Predicting Optimal Fitness and Performance

Award Information
Agency: Department of Defense
Branch: Army
Contract: W911SR-04-P-0103
Agency Tracking Number: A045-020-0024
Amount: $99,975.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: A04-T020
Solicitation Number: N/A
Solicitation Year: 2004
Award Year: 2004
Award Start Date (Proposal Award Date): 2004-09-29
Award End Date (Contract End Date): 2005-09-29
Small Business Information
400 West Cummings Park, Suite 2850
Woburn, MA 01801
United States
DUNS: 004704404
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Christos Hatzis
 VP, Technology
 (781) 938-3844
Business Contact
 Nandan Padukone
Title: President
Phone: (781) 938-3829
Research Institution
 Tufts University School of Medicine
 Abby Shevitz
150 Harrison Ave, Jaharis 2
Boston, MA 02111
United States

 (617) 636-6726
 Nonprofit College or University

The availability of the human genome and its potential in disease and health applications has spurred the development of novel methodologies in genomics, proteomics, and metabolomics. These technologies represent an untapped promise in optimizing nutrition and diet for achieving optimal health and fitness levels. Defense-based programs stand to benefit highly from such initiatives because of the criticality of personnel fitness and the importance of understanding of individualized metabolism for strategic planning. A key objective of this program is to demonstrate initial analytics to link an individual's metabolic state and fitness level using techniques of genomic and metabolomic profiling. We present a partnership between the Tufts School of Medicine and Silico Insights, a computationally-driven pathway discovery company. We propose to leverage data from existing clinical studies at Tufts on the syndrome lipodystrophy as a sub-optimal fitness state and establish molecular signatures that enhance well-being based on exercise or diet. Dr. Abby Shevitz at Tufts has been leading clinical research in disease management with nutritional and exercise regimens. Silico Insights brings proven expertise in multivariate statistical analysis and an established computational infrastructure. The collaboration will merge expertise in medical, nutritional and multivariate analysis disciplines to meet feasibility objectives of this proposal.

* Information listed above is at the time of submission. *

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