Impact/Purpose:

The Research Center will bring together a team of computational scientists, with diverse backgrounds in bioinformatics, cheminformatics and enviroinformatics, from UMDNJ, Rutgers, and Princeton Universities, and the USFDA’s Center for Toxicoinformatics. This team will address, in a systematic and integrative manner, multiple elements of the toxicant Source-to-Outcome sequence ( Investigational Area 1, as identified in the RFA) as well as develop cheminformatics tools for toxicant characterization ( Investigational Area 2, Predictive Models for Hazard Identification ). The computational tools to be developed through this effort will be extensively evaluated and refined through collaborative applications involving Center scientists as well as colleagues from the three universities and USEPA; particular emphasis will be on methods that enhance current quantitative risk assessment practices and reduce uncertainties.

The Research Center will bring together a team of computational scientists, with diverse backgrounds in bioinformatics, cheminformatics and enviroinformatics, from UMDNJ, Rutgers, and Princeton Universities, and the USFDA’s Center for Toxicoinformatics. This team will address, in a systematic and integrative manner, multiple elements of the toxicant Source-to-Outcome sequence ( Investigational Area 1, as identified in the RFA) as well as develop cheminformatics tools for toxicant characterization ( Investigational Area 2, Predictive Models for Hazard Identification ). The computational tools to be developed through this effort will be extensively evaluated and refined through collaborative applications involving Center scientists as well as colleagues from the three universities and USEPA; particular emphasis will be on methods that enhance current quantitative risk assessment practices and reduce uncertainties.

Description:

The Center activities focused on integrating developmental efforts from the various research projects of the Center, and collaborative applications involving scientists from other institutions and EPA, to enhance research in critical areas. A representative sample of specific accomplishments includes:

Data Analysis Methods and Computational Tools

• Implementation of the framework of ebTrack, that builds upon and expands the framework of ArrayTrack through the incorporation and/or linkage of new analysis components that will be available within an integrative data management environment. Implementated and tested ebTrack interfaces to open source databases (e.g., PostgreSQL) and to various "external" modeling tools for facilitating wider deployment of the ebTrack/ArrayTrack system for integrative analyses of various types of related genomic, proteomic, and metabonomic data.
• Development and demonstration of novel computational tools for peptide identification from tandem mass spectrometry data; development and demonstration of novel, optimized statistical and pattern recognition methods for clustering of gene expression data (these tools have been implemented as modules compatible with ebTrack).
• Application of novel techniques for analysis of time-series gene expression data and identification of informative genes to support risk analysis tasks: application to exposures to phthalates with identification of critical gene expression motifs, associated gene ontology functions, maximally affected pathways and subsequent cross-species extrapolation conservation of protein sequences between rat and human.

Diagnostic Analysis Methods and Computational Tools

• Enhancements to the Random-Sampling High Dimensional Model Representation (RS-HDMR) algorithm for sensitivity and uncertainty analysis: application to (a) toxicokinetic modeling of arsenic and of aromatic hydrocarbon mixtures; (b) allosteric regulation of aspartate transcarbamoylase (AtCase) by all four ribonucleotide triphosphates (NTPs).
• New capabilities of HDMR for cases with correlated input variables or physically constrained input space, with applications studying functional relationships in the human T-cell signaling network.
• Integration of HDMR with the optimal substituent reordering method for efficient molecular discovery in complex, high dimensional libraries, with applications to optimization of protein libraries, NMR chemical shift prediction, and protein inhibition prediction.
• Development of a diffeomorphic modulation under observable response preserving homotopy (D-MORPH) technique to optimal identification and analysis of bio-network models.
• Optimization and refinement of sensitivity analysis techniques for usage with PBPK modeling: application to novel models for aging organisms and populations.
• Development and evaluation of a Bayesian computational framework for exposure reconstruction from biomarker data using toxicokinetic models and numerical inversion methods: applications to the NHEXAS and NHANES datasets.
• Development of a strategy for efficiently optimizing the substituent combinations by iterative rounds of compound sampling, and property estimation over the landscape of molecular discovery. Application of this approach to a large pharmaceutical compound library demonstrating its ability to find active compounds.

Molecular Modeling Methods and Computational Tools

• Development of computational tools for de novo protein design and high resolution protein structure determination: applications to prediction of interhelical restraints for alpha helical proteins, and to prediction of three-dimensional structures of PXR.1 and PXR.2.
• Development of shape-based prioritization and classification approaches to predict human pregnane x receptor activators; inhibitors of acetylcholine esterase; ligands to hERG and 5HT2b receptors involved in cardiotoxicity; and to predict ligand blood-brain barrier properties.
• Development, enhancement and application of the Shape Signatures QSAR technology for chemical hazard identification: (a) demonstrations with applications involving conazoles, (b) development of a Shape Signatures database of ligands extracted from the Protein Data Bank (PDB), and (c) application of a multi-step screening procedure using Shape Signatures and clustering to identify previously unrecognized antiestrogenic chemicals.
• Molecular modeling studies of ligand-PXR interactions: applications to binding of conazoles, azoles, steroids and various other structural families to the AF-2 site.
• Development of customized metabolic engineering tools for identifying important pathways within the overall hepatocyte metabolism, and experimental verification of modeling results. Applications focused on (a) interactions between ethanol and other central hepatic pathways and xenobiotic pathways, (b) steroidogenesis pathways due to in utero exposure to phthalate esters, (c) effects of conazoles on liver metabolism and networks involved in conazole detoxification.

Integrative Toxicokinetic/Toxicodynamic Modeling for Biologically Based Dose-Response Analysis

• Development of algorithms for rapid assessment of risks from chronic and multiscale exposures to mixtures of contaminants: applications to halogenated organics.
• Developed the modular multiscale DORIAN (Dose-Response Information Analysis) framework to support mechanistic toxicity and—in conjunction with the Modeling Environment for Total Risk (MENTOR)—comprehensive risk assessment studies.
• Performed demonstration case studies with selected environmental toxicants: arsenic, TCDD, and TCE as "prototype" toxicants. Conducted additional applications focusing on the role of essential nutrients (e.g., tocopherol) and also on exposures to nanomaterials.

URLs/Downloads:

2006 Progress Report

2008 Progress Report

2007 Progress Report

Final Progress Report

Record Details:

Record Type:PROJECT( ABSTRACT )

Start Date:10/01/2005

Completion Date:09/30/2010

Record ID: 142147

Show Additional Record Data

Related Organizations:

Role :OWNER

Organization Name :PRINCETON UNIVERSITY

Organization Name :RCFEBACT



Role :OWNER

Organization Name :UNIVERSITY OF MEDICINE AND DENTISTRY OF NEW JERSEY

Organization Name :RCFEBACT



Role :OWNER

Organization Name :RUTGERS UNIVERSITY

Organization Name :RCFEBACT

Project Information:

Approach :

The proposed Center will address a wide range of issues in Investigational Areas 1 and 2 and, furthermore, will pursue complementary applications in risk assessment ( Investigational Area 3 ). This will be achieved with the requested resources, by building upon a variety of methods and software systems recently developed at UMDNJ, Rutgers, Princeton (with funding from USEPA, USDOE, NIH and NSF), and USFDA. Research activities over the proposed 5-year effort will be organized in five projects; each project will develop a set of “stand-alone" components addressing specific problems of computational toxicology. Furthermore, Research Project 1 will provide an integrative framework for Investigational Area 1 while Project 4 will address the core issues of Area 2. Extensive interaction as well as public outreach and training activities will constitute essential elements of the Center and will be tightly interwoven with the research activities.

Cost :$5,422,135.00

Research Component :Health Effects

Approach :

The proposed Center will address a wide range of issues in Investigational Areas 1 and 2 and, furthermore, will pursue complementary applications in risk assessment ( Investigational Area 3 ). This will be achieved with the requested resources, by building upon a variety of methods and software systems recently developed at UMDNJ, Rutgers, Princeton (with funding from USEPA, USDOE, NIH and NSF), and USFDA. Research activities over the proposed 5-year effort will be organized in five projects; each project will develop a set of “stand-alone" components addressing specific problems of computational toxicology. Furthermore, Research Project 1 will provide an integrative framework for Investigational Area 1 while Project 4 will address the core issues of Area 2. Extensive interaction as well as public outreach and training activities will constitute essential elements of the Center and will be tightly interwoven with the research activities.

Cost :$5,422,135.00

Research Component :Computational Toxicology

Project IDs:

ID Code :R832721

Project type :Center