Developing Adverse Outcome Pathways from exposure dynamics in zebrafish embryos

Adverse outcome pathways (AOPs) are the conceptualization of knowledge into logical chains of events linking molecular initiating events via key events to adverse outcomes. Their applicability to regulatory and monitoring purposes has been demonstrated, however the development and discovery of AOPs still proves to be a challenge. To address this, data-driven approaches can leverage the power of computational biology and high-dimensional datasets to accelerate the discovery of AOPs. Using the newly established OECD test-guideline 236, the fish embryo toxicity test (FET), the PhD student will develop a large molecular dose response dataset and expand the currently under development Environmental-Prediction-Information-Connectivity Map (EPIC-map) to utilize the additional doses to develop quantitative AOPs.
The Student will have the opportunity of working closely with the Centre for environment fisheries and aquaculture science (Cefas), learning from their expertise in zebrafish biology and its application to regulation and environmental monitoring. Experiments with zebrafish embryos will be conducted at the Cefas Weymouth laboratory. Furthermore the student will have the opportunity of working with AstraZeneca and utilize their expertise in AOPs and their application in industry.
The PhD Student should be enthusiastic and ambitious and have some basic knowledge of statistics and its application to biological data. Knowledge in programming languages such as R, python, perl, C or other would be preferred but are not required. Training in computational biology, in particular predictive biology, will be provided. As this project encapsulates a strong experimental component it is advised that the student should have laboratory experience, ideally already have experience running TG236, and have worked with environmental species of interest.

Training:
This project will provide diverse training in computational biology approaches and associated programming languages. Specifically the student will be trained in the statistical programming language R and it diverse set of libraries and functions. In addition the student will learn data-driven approaches and how to generate and validate hypotheses from this vast data resource.
In addition to the project specific skills, the institute and University PhD programme will provide more generic skills and support the student via an excellent infrastructure providing access to experts on molecular biology, ecotoxicology and computational biology.