Grantee Research Project Results

Final Report: Integration of Biological High-Throughput Data with a Metabolic Model of a Liver Cell

EPA Contract Number: EPD06042
Title: Integration of Biological High-Throughput Data with a Metabolic Model of a Liver Cell
Investigators: Fahland, Tom R.
Small Business: Geomatica, Inc.
EPA Contact: Richards, April
Phase: I
Project Period: March 1, 2006 through August 31, 2006
Project Amount: $69,784
RFA: Small Business Innovation Research (SBIR) - Phase I (2006) RFA Text |  Recipients Lists
Research Category: Computational Toxicology , SBIR - Computational Toxicology , Small Business Innovation Research (SBIR)

Description:

This purpose of this research project was to analyze high-throughput gene expression data relating to chemical and toxicity exposure and to construct a mouse liver metabolic network.

Summary/Accomplishments (Outputs/Outcomes):

Genomatica, Inc., obtained and analyzed several gene expression datasets using sophisticated algorithms to determine the most significantly regulated genes. For the second major research task, a mouse liver metabolic network, which consisted of many aspects of central metabolism, was constructed. The results of the gene expression analysis were coupled with the metabolic network to probe the phenotypic effects of exposure on the system. This project successfully identified the most regulated genes from the gene expression samples, constructed a preliminary metabolic liver network, and probed the metabolic network using the regulated genes from the gene expression analysis. A link demonstrated the feasibility of combining high-throughput experimental data with computer modeling to gain insight into phenotypic effects.

Conclusions:

Genomatica, Inc., successfully performed the stated objectives for this SBIR Phase I research project. Gene expression data relating to toxicity exposure were obtained and analyzed to determine the most regulated genes using statistical analysis techniques for replicated data. A preliminary mouse liver metabolic network based on literature, articles, and leveraging the adipoctye model was constructed. This network consisted of the main aspects of central metabolism as well as other required functions. Lastly, results of the gene expression analysis were linked with the metabolic network; this combines analysis of experimental high-throughput data with computer simulation of a cellular system. This work demonstrates the feasibility of using computer modeling to help understand metabolic systems for higher-level organisms such as mice. This project explores an area that Genomatica, Inc., believes will have a significant impact on biology and medicine.

Supplemental Keywords:

small business, SBIR, chemical testing, computational cellular modeling, chemical exposure, high-throughput technologies, liver metabolism, toxicity, toxicological metabolic effects, public health, health effects, EPA, systems biology, metabolism, liver metabolic network,, Health, Scientific Discipline, ENVIRONMENTAL MANAGEMENT, Risk Assessments, Biochemistry, Risk Assessment, metabolic study, toxicity, human exposure, chemical exposure, computational cellular modeling, biological high throughput technologies, human health risk, toxicologic assessment, biochemical research