What is ebCTC?
The environmental bioinformatics and Computational Toxicology Center (ebCTC) is a research consortium of UMDNJ-RWJ Medical School, Princeton University, Rutgers University, and USFDA’s Center for Toxicoinformatics. It was established with 6 years of base funding by the USEPA, to pursue innovative methodological research in Computational Toxicology and develop informatics tools to support both high throughput and high content analyses for environmental health risk assessments. During that initial USEPA-funded period, ebCTC researchers published jointly over 125 peer-reviewed articles and chapters; the interactions among the consortium teams have thus established a solid foundation for collaborative interdisciplinary research, which is indeed actively continuing today.
Environmental Bioinformatics, Envirogenomics, and Computational Systems Toxicology
Bioinformatics involves the use of computational methods in biological research to analyze or predict molecular events and evaluate changes to genes and proteins in an organism. ebCTC employs bioinformatics as a "toolbox" for a multidisciplinary "environmental systems toxicology" approach (ranging from "envirogenomics" to multiscale virtual tissue and organism modeling), that aims to provide a consistent integrative framework for mechanistic assessment of human health risks associated with exposures to environmental stressors.
This framework is based on the concept of coupled bionetworks that span multiple scales of "biological space" and on the study of their hierarchical structures and functional states, as those are perturbed by behavioral and environmental influences. At any given time, human health state reflects the dynamics of coupled
- signaling networks,
- regulatory networks and
- metabolic networks
which are potentially influenced by developmental and aging processes, as these interact with - or are "perturbed by" - extragenomic factors, such as presence of xenobiotics. Systematic study of bionetworks at each scale involves identification and quantitative characterization of
- network components (nodes),
- network interactions (links) and
- network dynamics (states).
For example, components of transcriptional regulatory networks are binding sites, transcription factor molecules, riboswitches, etc., while network links include DNA-protein, protein-protein and metabolite-RNA interactions.
Computational chemistry methods (e.g. QSARs) are being developed and utilized to quantitatively characterize molecular components and interactions at a "local" (e.g. ligand-receptor) scale. Deterministic and stochastic system process analysis and optimization techniques, are applied towards elucidation of "larger" network structures (e.g.interlinked signaling, regulatory and metabolic pathways). The latter process relies on interpretation of data from "network perturbation" experiments, that may include consideration of
- genetic perturbations (polymorphisms, gene knockouts, gene silencing, etc.),
- environmental perturbations (toxicant dosage, nutrient availability, etc.) and
- disease state (pathological vs. normal).
Outcomes provide information to improve understanding of
- molecular mechanisms of toxic responses,
- differences in responses between humans and model species (improved cross-species extrapolation), and
- interindividual variability in responses (improved consideration of genetic susceptibility to environmental disease).
Consideration of "individual-specific" toxicoinformatic data, within a systems toxicology framework, is expected to allow development of more accurate, and eventually even "personalized," risk assessments.
EPA Announced an Award of $9 million to Establish Two Cutting-Edge Environmental Bioinformatics Research Centers (11/2/2005)