Research Project: This project aims to develop computational models to predict the safety of chemicals without running tests on whole animals. The tools to be used include high-throughput transcriptomic screening data across multiple cell lines, databases of previously run animal studies, data from other in vitro high-throughput screening experiments, data and models for in vitro to in vivo extrapolation of dose, and outputs of various predictive models of chemical action. The project integrates bioinformatics, data science, software engineering, applied statistics, and predictive mathematical modeling, with the broader goal of advancing safety assessment using new approach methodologies.
Under the guidance of a mentor, the research participant will develop novel bioinformatics and biostatistics applications to integrate multiple data streams relevant to toxicological testing, including transcriptomics, biochemical assays, and other high-throughput screening methods. Research activities may include:
Helping develop models to predict toxicological outcomes of animal testing from in vitro assays
Helping develop a data science infrastructure to simulate outcomes of tiered testing strategies that combine multiple in vitro screening methods
Curating and managing large-scale heterogeneous chemical safety screening data
Evaluating additional methods to integrate data across screening studies.
Learning Objectives: This is a research training opportunity where in the candidate will gain education and training in the general areas of bioinformatics data science, transcriptomics, computational toxicology, and mathematical modeling in preparation for future career opportunities across government, industry, and academic sectors.
The research participant may also author or co-author on peer-reviewed publications, and present at local and national meetings. The participant will be a member of a multi-disciplinary research team.
Mentor(s): The mentors for this opportunity are Katie Paul-Friedman (firstname.lastname@example.org) and Logan Everett (Everett.email@example.com). If you have questions about the nature of the research please contact the mentor(s).
The qualified candidate should have received a doctoral degree in one of the relevant fields, or be currently pursuing one of the degrees and will reach completion by November 2020. Degree must have been received within five years of the appointment start date.
+ Software development experience in R and/or Python
+ Strong written, oral and electronic communication skills
+ Experience in bioinformatics, and/or pharmacokinetics or mathematical modeling
+ Proficient in the use of MySQL and NoSQL database solutions
Applications must be submitted through ORAU/zintellect site here: www.zintellect.com/Opportunity/Details/EPA-ORD-CCTE-BCTD-2020-06