Systems toxicology: multiscale modeling of environmental impacts on bionetworks
W.J. Welsh, P.G. Georgopoulos
Environmental & Occupational Health Sciences Institute, Piscataway, NJ
Environmental systems toxicology 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, regulatory and metabolic bionetworks, 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 (i) network components (nodes), (ii) network interactions (links) and (iii) 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 characterizate 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(a) molecular mechanisms of toxic responses, (b) differences in responses between humans and model species (improved cross-species extrapolation), and (c) 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.
This work is funded in part by the USEPA through STAR Grant GAD R 832721-010 and Cooperative Agreement CR-83162501. Viewpoints expressed here are the responsibility of the authors and do not necessarily reflect views of the USEPA or its contractors.