You are here

Integration of Biological High throughput Data with a Metabolic Model of a Liver Cell

Award Information
Agency: Environmental Protection Agency
Branch: N/A
Contract: EPD06042
Agency Tracking Number: B05D8-0284
Amount: $69,904.24
Phase: Phase I
Program: SBIR
Solicitation Topic Code: 05-NCER-D8
Solicitation Number: PR-NC-05-10246
Timeline
Solicitation Year: 2006
Award Year: 2006
Award Start Date (Proposal Award Date): 2006-03-01
Award End Date (Contract End Date): 2006-08-31
Small Business Information
5405 Morehouse Drive, Suite 210
San Diego, CA 92121
United States
DUNS: 071401090
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Tom Fahland, Sr.
 Senior Research Scientist
 (858) 824-1771
 tfahland@genomatica.com
Business Contact
 Chrostphie Schilling
Title: President & CSO
Phone: (858) 824-1771
Email: cschilling@genomatica.com
Research Institution
N/A
Abstract

A large number of potentially harmful chemicals and pollutants in the environment make comprehensive experimental chemical testing cost prohibitive and unrealistic. Methods that can decrease the required experimental work and aid in the streamlining of this process would provide a valuable tool in this area. Computational cellular modeling can provide a significant improvement in linking exposure of potentially harmful chemicals to the effects those chemicals have on the metabolism of a host. With the latest advances in biological high-throughput technologies, vast amounts of data exist that represent gene expression profiles, protein interactions, and metabolite concentrations. These multivariate data sets allow the construction of detailed genome-scale metabolic network models. By using a constraint-based approach for constructing a model of liver metabolism, the effect of compound exposure can be linked to detailed mechanisms in the metabolic network. Metabolism plays a central role in the toxicological realm from how a xenobiotic compound is metabolized to determining downstream side effects. The combination of linking the results of statistical analysis of high-throughout data with a comprehensive model of liver metabolism creates a powerful tool that can have wide market appeal in many areas. From pharmaceutical and biotechnological companies to government and military institutions, a product of this type can increase productivity significantly and aid in understanding the metabolic effects of toxicity.

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

US Flag An Official Website of the United States Government