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2006 Progress Report: Development of Computational Tools for Optimal Identification of Biological Networks

EPA Grant Number: R832721C003
Subproject: this is subproject number 003 , established and managed by the Center Director under grant R832721
(EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).
Center: New Jersey Research Center for Environmental Bioinformatics and Computational Toxicology
Center Director: Welsh, William J.
Title: Development of Computational Tools for Optimal Identification of Biological Networks
Investigators: Rabitz, Herschel
Current Investigators: Rabitz, Herschel , Androulakis, Ioannis , Floudas, Christodoulos , Georgopoulos, Panos G. , Ierapetritou, Marianthi , Tong, Weida , Welsh, William J.
Institution: Princeton University
Current Institution: Princeton University , Rutgers University , U.S. Food and Drug Administration , University of Medicine and Dentistry of New Jersey
EPA Project Officer: Mustra, David
Project Period: October 1, 2005 through September 30, 2010
Project Period Covered by this Report: October 1, 2005 through September 30, 2006
RFA: Computational Toxicology: Environmental Bioinformatics Research Center (2004)
Research Category: Computational Toxicology

Description:

Objective:

The aims of the project have not changed from the original application; original objectives for the report period have been met.

Progress Summary:

Results to Date

The SNIP method is the first algorithm to extract nonlinear, cooperative regulatory structure of general bionetworks directly from tailored laboratory data (i.e., without resorting to any specific kinetic/dynamic models). The SNIP-identified network structure is conveniently represented by a series of tensor coefficients that quantify the nonlinear hierarchical network relationships. These tensor coefficients, when determined from the laboratory data, can then be utilized to predict the network behavior under previously unsampled circumstances. The enabling capabilities of SNIP and its general applicability make it a wonderful tool for unraveling the complex architecture of environmentally significant bionetworks.

Future Activities:

Following the theoretical analysis and computer simulations, the next step for the development of SNIP will be a proof-of-principle laboratory application. Specifically, we have obtained a synthetic transcriptional cascade (harbored in Escherichia coli) containing two chemical inputs and one output (quantified by bicistronic fluorescent proteins) connected by nonlinear regulatory interactions. The goal is to see if SNIP can recover the nonlinear, cooperative input-output relationships and enable predictive power.

Journal Articles:

No journal articles submitted with this report: View all 1 publications for this subproject

Supplemental Keywords:

computational toxicology, toxicogenomics, bioinformatics, toxicoinformatics, proteomics, metabonomics, physiomics, cytomics, genomics, transcriptomics, enviroinformatics, cheminformatics, biologically based dose-response modeling, cross-species extrapolation, risk assessment, , ENVIRONMENTAL MANAGEMENT, Scientific Discipline, Health, Risk Assessment, Biology, Risk Assessments, Biochemistry, Environmental Monitoring, exposure assessment, biochemical research, chemical composition, ecological risk assessment, toxicologic assessment, bioinformatics, human health risk, biopollution, toxicology, environmental risks, risk, computational toxicology
Relevant Websites:

http://ebctc.org exit EPA

Progress and Final Reports:
Original Abstract
2007 Progress Report


Main Center Abstract and Reports:
R832721    New Jersey Research Center for Environmental Bioinformatics and Computational Toxicology

Subprojects under this Center: (EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).
R832721C001 Development and Application of the DORIAN (Dose-Response Information Analysis) System
R832721C002 Hepatocyte Metabolism Model for Xenobiotics
R832721C003 Development of Computational Tools for Optimal Identification of Biological Networks
R832721C004 Cheminformatics Tools for Toxicant Characterization
R832721C005 Optimization Tools for In Silico Structural Proteomics

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The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.

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