Mechanistic studies of environmental causes of human disease: a bionetwork dynamics modeling approach
P.G. Georgopoulos, Environmental & Occupational Health Sciences Institute
Though it is well known that the majority of human diseases result from complex processes driven by combinations of genetic and environmental factors, the actual mechanisms underlying these processes are in general still mostly unknown. Improving the understanding of these mechanisms at the molecular, cellular, tissue, organ, etc. levels would offer multiple benefits from the perspective of quantifying and subsequently mitigating environmental health risks (screening of new chemicals for potential toxicity, design of "benign" alternatives, prioritization of exposure-oriented controls and methods, etc.) A general "reverse engineering" approach for the systematic study of environmental disease mechanisms is based on the concept of coupled bionetworks that span multiple scales of "biological space." Mechanism elucidation results from the study of network hierarchical structures and functional states, as those are perturbed by behavioral and environmental influences. Indeed, the health state of any individual at a given time reflects the dynamics of coupled signaling, regulatory and metabolic bionetworks, which are generally influenced by developmental and aging processes and by interactions with "environmental" factors, such as the presence of xenobiotic chemicals. Systematic study of bionetworks at each scale of biological organization involves the 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 corresponding network links include DNA-protein, protein-protein and metabolite-RNA interactions. Computational chemistry methods are being developed to quantitatively characterize network components and interactions at the molecular (e.g. ligand-receptor) scale, while deterministic and stochastic system process analysis and optimization techniques, are applied to determine "larger" network structures (e.g. interlinked signaling, regulatory and metabolic pathways). This process relies on interpretation of data (transcriptomic, proteomic, metabolomic, lipidomic, etc.) from "network perturbation" experiments, that may include (i) genetic perturbations (polymorphisms, gene knockouts, gene silencing, etc.), (ii) environmental perturbations (exposures to environmental toxicants, nutrient availability, etc.) and (iii) disease state (pathological vs. normal). Outcomes provide information that enhances the understanding of (a) molecular mechanisms of disease (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 is expected to allow development of more accurate, and eventually even "personalized," risk assessments.
This work is supported in part by the USEPA-funded environmental bioinformatics and Computational Toxicology Center (ebCTC) through STAR Grant GAD R 832721-010; and by the NIEHS sponsored UMDNJ Center for Environmental Exposures and Disease, Grant P30ES005022. Viewpoints expressed here do not necessarily reflect views of the funding agencies.