2007 Progress Report: Development of Computational Tools for Optimal Identification of Biological Networks
EPA Grant Number: R832721C003Subproject: 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 , Androulakis, Ioannis , Floudas, Christodoulos , Georgopoulos, Panos G. , Ierapetritou, Marianthi , Tong, Weida , Welsh, William J.
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, 2006 through September 30, 2007
RFA: Computational Toxicology: Environmental Bioinformatics Research Center (2004)
Research Category: Computational Toxicology
Description:
Objective: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.
Progress Summary:- Continued development of the mathematical framework and operational procedure for the S-Space Network Identification Protocol (SNIP), which extracts the connectivity of complex nonlinear bionetworks directly from laboratory data, such as those from systematically designed “perturb-observe” experiments.
- Applied SNIP to identify the structure of a synthetic transcriptional cascade harbored in E.coli using data quantified by bicistronic fluorescent proteins.
- Investigated optimization of SNIP in order to maximize the reliability and efficiency of information extraction from minimal and noisy laboratory data.
- Applied SNIP to the single cell fluorescence data of the human T-cell signaling network to reveal its complex regulatory structure; investigated how the operation of SNIP can be optimized to maximize the reliability and efficiency of information extraction from minimal and noisy laboratory data.
- Improved the Random-Sampling High Dimensional Model Representation (RS-HDMR) algorithm through a regularization method for smoothing the RS-HDMR component functions and for improving prediction accuracy, especially for small sample sizes (e.g., of the order of a few hundred points).
- Applied the regularized RS-HDMR for analyzing biochemical laboratory data through linear, nonlinear, and high-order analysis of complex biochemical interactions. Case study focused on in vitro allosteric regulation of aspartate transcarbamoylase (AtCase) by all four ribonucleotide triphosphates (NTPs).
Currently planned and ongoing activities include the following:
- Continued implementation of existing and design of new ebTrack interfaces to open source databases (e.g. PostgreSQL) and to various “external” and Center-developed modeling tools for facilitating wider-deployment and applicability of the ebTrack/ArrayTrack system for integrative analyses of various types of genomic, proteomic, and metabonomic data. This will be pursued through further incorporation of novel, optimized statistical and pattern recognition methods for clustering of gene expression data as ebTrack components, and through further analysis of ongoing applications and initiation of additional applications of ArrayTrack for environmentally-relevant toxicants (e.g., dibutyl phthalate, Arsenic, etc.) and component-by-component evaluations of ArrayTrack applications.
- Refinement of the environmental bioinformatics Knowledge Base (ebKB) and making a public beta version of ebKB available.
- Continuing development and implementation of new techniques for incorporating biochemical data into the optimization and parameter estimation components of MENTOR-3P (Modeling Environment for Total Risk with Physiologically-based Pharmacokinetic modules for Populations), focusing on Bayesian tools in conjunction with optimization techniques.
- Refinements to the framework for DORIAN (Dose-Response Information Analysis) modules representing different scales of biological complexity ranging from molecule-molecule interactions to biochemical networks to virtual organs and systems.
- Implementation of a modular “Virtual Liver” with alternative levels of detail in describing physical structure of the liver with respect to toxicokinetic and toxicodynamic processes with case studies focusing on environmentally-relevant chemicals.
- Implementation of algorithms as DORIAN modules for rapid assessment of risks from chronic and multiscale exposures to mixtures of contaminants.
- Continuing development and incorporation of diagnostic tools as DORIAN modules for sensitivity and stability analysis of mechanistic models, and demonstration with case studies focusing on environmentally-relevant chemicals.
- Experimental verification of modeling results from network models of hepatocyte metabolism, and integration of regulatory rules within metabolic network models and constraining the model such that cell capabilities in the models become more realistic.
- Metabonomic case studies focusing on (a) pathways involved in steroidogenesis pathways due to in utero exposure to phthalate esters, (b) hepatocarcinogenic potential of exposure to triazole conazoles, and (c) experimental verification of the interactions between ethanol and other central hepatic pathways and xenobiotic pathways.
- Application of SNIP (S-space Network Identification Protocol) to larger networks with higher complexity and optimal design of perturbation experiments for improved efficiency and reliability of SNIP. Additional applications to other realistic bionetworks (e.g., metabolic networks) and optimizing the performance of SNIP.
- Further application of the RS-HDMR (Random-Sampling High Dimensional Model Representation) analysis of the mechanism of action on the cooperative inhibition of aspartate transcarbamoylase, which potentially can enable deeper understanding of many biological processes that this enzyme is involved in.
- Further incorporation of cheminformatic data in Shape Signatures classification models. Ongoing case studies focus on a blood-brain barrier model.
- Continuing development of structural models for liver nuclear receptors: PXR, FXR, LXR, VDR, etc., and, molecular modeling studies of xenobiotic-NR interactions, with emphasis on chemicals from the Toxcast database and nuclear receptors found in the liver. Ongoing case studies focus on the computational structural model of FXR for Ciona (sea squirt), for comparison with x-ray structural data of FXR for other species.
- Study of new approaches, including hybrid methods, for (a) de novo protein design, (b) understanding biological coherence in gene clustering, and (c) peptide identification.
- Definition of specific case studies for comprehensive source-to-outcome modeling and analysis, and further evaluation of approaches developed at the Center through collaborative efforts with external researchers.
No journal articles submitted with this report: View all 1 publications for this subproject
Supplemental Keywords:, 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
Progress and Final Reports:
2006 Progress Report
Original Abstract
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