The Environmental Bioinformatics Knowledge Base (ebKB) is an ongoing project of the Computational Chemodynamics Laboratory (CCL). ebKB development started for the Environmental Bioinformatics and Computational Toxicology Center (ebCTC), a research consortium of RW Johnson Medical School, Rutgers and Princeton Universities, and USFDA, and is currently continuing to support CEED (Center for Environmental Exposures and Disease) activities.
The Environmental Bioinformatics Knowledge Base evaluates, assembles, organizes, and provides information on resources that support the computational representation and analysis of environmental and biological systems and of their interactions. These resources span the fields of Cheminformatics, Enviroinformatics and Bioinformatics as well as of Computational Toxicology, Exposure and Risk Analysis, and Pattern Recognition and Analysis. Overall goal of ebKB is to facilitate and enhance mechanistic assessments of human and ecological health risks associated with exposures to environmental stressors.
Environmental bioinformatics provides an analysis framework based on the concept of coupled bionetworks, that span multiple scales of “biological space,” and on the study of bionetwork functional states, that are influenced by interactions with with co-existing behavioral and environmental networks.
At any given time, human health state reflects the dynamics of coupled
- signaling networks,
- regulatory networks and
- metabolic networks
which incorporate developmental and aging processes, as these interact with environmental networks, involving xenobiotics, nutrients, the microbiome, human activities, socioeconomic and other demographic factors, etc.
Systematic study of networks 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 bionetworks are binding sites, transcription factor molecules, riboswitches, etc., while network links include DNA-protein, protein-protein and metabolite-RNA interactions.
Cheminformatics (e.g. QSARs) are 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, exercise-driven energy expenditure, 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 support development of more accurate, and eventually “predictive, precise and personalized” risk assessments.