| Call Number | 17232 |
|---|---|
| Day & Time Location |
M 8:30am-11:20am To be announced |
| Points | 3 |
| Grading Mode | Standard |
| Approvals Required | None |
| Instructor | Brandon Pearson |
| Type | LECTURE |
| Method of Instruction | In-Person |
| Course Description | We are exposed to thousands of chemicals in the air, on our food, and as part of consumer products with many hundreds more new chemicals brought to market every year. Yet, only a very small proportion of these have been comprehensively tested for safety. Existing toxicological methods are often insufficient to test every new or existing product due to various constraints including economics, relevance, politics, and ethics. The advent of computational strategies, with high-throughput in vitro and in vivo toxicology data, now permits predictive approaches to a priori, predict potential health risks of chemicals which have not be tested in the laboratory. These strategies range from predicting cellular toxicity based on similarities of chemical structure with chemicals of known toxicity, to forecasting human cellular toxicity from pesticides on food and other exposures using high-throughput cellular assays. Integrating publicly available “omics” data, environmental and personal monitoring data, and bioinformatics, is empowering innovative discovery about exposure-outcome relationships. The goal of this course will be to expose students to the various data sources and approaches that are used to predict toxicity and introduce innovative data manipulation and display strategies that are increasingly needed in data heavy disciplines. This is a hands-on course; students will be required to mine publicly accessible data and perform their own analyses, regularly presenting their work in the classroom. Students will be evaluated on their ability to integrate the material and apply it to real data in order to garner thoughtful, novel insight into predictive or integrative toxicity. |
| Web Site | Vergil |
| Department | Environmental Health Sciences |
| Enrollment | 17 students (35 max) as of 9:14PM Wednesday, November 20, 2024 |
| Subject | Environmental Health Sciences |
| Number | P8334 |
| Section | 001 |
| Division | School of Public Health |
| Open To | GSAS, Public Health |
| Section key | 20241EHSC8334P001 |