Academic journal article Environmental Health Perspectives

Fellowships, Grants, & Awards

Academic journal article Environmental Health Perspectives

Fellowships, Grants, & Awards

Article excerpt

Computational Toxicology and Endocrine Disruptors: Use of Systems Biology in Hazard Identification and Risk Assessment

The U.S. Environmental Protection Agency (EPA) is interested in the application of novel technologies that are derived from computational chemistry, molecular biology, and systems biology and used in toxicological risk assessment.

In assessing risk associated with exposure to a chemical or other environmental stressor, a number of scientific uncertainties exist along a "source-to-adverse-outcome" continuum, beginning with the presence of the chemical in the environment, the uptake and distribution of the chemical in the organism or environment, the presence of the active chemical at a systemic target site, and the series of biological events that lead to the manifestation of an adverse outcome that can be used for risk assessment. The EPA Office of Research and Development (ORD) Human Health Research Strategy (available online at http://www.epa. gov/sab/pdf/hhrs.pdf) describes these scientific uncertainties and some of the multidisciplinary approaches that are needed to build linkages between exposure, dose, and effects.

The ORD has initiated a new research program called Computational Toxicology (see http://www.epa.gov/nheerl/comptoxframework/ comptoxframeworkfinaldraft7_17_03.pdf) that will use emerging technologies to improve risk assessment and reduce uncertainties in this source-to-adverse-outcome continuum. This initiative, as well as work being conducted by other institutions and organizations, will provide a wealth of information on effects of toxicants at multiple levels of biological organization by using genomic, proteomic, and metabonomic techniques. In order to be most useful, this information must be integrated into a coherent picture.

One of the strategic objectives of the Computational Toxicology initiative is to develop improved linkages across the continuum, including the areas of chemical transformation and metabolism, better diagnostic/prognostic molecular markers, improved dose metrics, characterization of toxicity, pathways, metabonomics, systems biology, approaches, modeling frameworks, and uncertainty analysis. This solicitation for research proposals is focused on development of systems biology-based models for key components of adverse health outcomes induced by environmental contaminants.

Systems biology uses computational methods to reconstruct an integrated physiologic and biochemical model of an organism's or cell's biology. The approach is similar to developing a wiring diagram for a complicated electrical system or an engineering diagram, such as one that shows the function and interaction of different parts of an automobile. In this regard, systems biology, is targeted at studying how normal biological processes are governed, and how alterations can lead to diseases or other unwanted outcomes. Understanding the functions of a normal cell or organism is key to understanding how toxicants can exert effects.

A systems biology approach will enable the integration of disparate data developed by biologists, computer scientists, chemists, engineers, mathematicians, and physicists to construct models of organism function and response to toxic insult. For any model, a choice must be made about scale and level of detail. In this case, models useful to the EPA most likely will be built from individual subcomponents assembled into a larger system. Once these models are developed, then hypotheses can be developed and tested through virtual simulations prior to designing targeted experiments to validate and inform the models. An integral part of the Computational Toxicology initiative will be the use of relevant model organisms to expand our understanding of the regulation of biological processes and how toxicants can perturb these processes, with the goal of identifying the key mechanistic events for improved risk prediction.

The brain-pituitary-gonadal and brain-pituitary-thyroid axes represent two complex endocrine pathways important for reproduction and survival. …

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