Post-Doc Project Description

Virtual Embryo (v-Embryo™) is a new EPA research project motivated by scientific and regulatory needs to understand mechanisms of developmental toxicity and to model how the embryo reacts as a complex system to environmental chemical exposure and tissue disruption (www.epa.gov/ncct/v-Embryo/). Understanding this complexity requires detailed knowledge of developmental processes and toxicities, and innovative computational models that predict dysmorphogenesis from cell-based data and molecular signaling networks at a systems-level.

The main idea of v-Embryo™ is to utilize simulated and empirical data to construct and validate autonomous computer models of embryonic tissues that can be used to unravel complex relationships in molecular and cellular networks during morphogenesis. Such computational models can become increasingly important to EPA efforts applying the latest scientific knowledge in quantitative models of dose-response relationships and uncertainty analysis of complex systems. The candidate will design and implement specialized software, tools, methods and models that can leverage pathway-based data for chemicals tested in mouse embryonic stem cells, zebrafish embryos, and ToxCast™ high-throughput screening research programs at the EPA. These innovative experimental-computational models will aim to: simulate key signaling pathways, interlocking genetic networks and cellular dynamics in developing tissues; model how embryonic cells react to chemical exposure individually, and collectively as a complex system; analyze emergent behaviors and canalizing influences following physiological stimulus or toxicant injury; and understand how this complexity contributes to the differential susceptibility of embryonic tissues across dose, stage and time.

1. Design of multi-scale biological models: conceptualizing key biological events and representing them as qualitative models; understanding the nature, sources and limitations of experimental data to implement useful models.
2. Integrate experimental-computational data: experience with relational databases or knowledgebase systems to extract relevant facts and data about the system; incorporate high-dimensional data and complex information; basic understanding of molecular networks and/or bioinformatics programming.
3. Model implementation: scientific computing experience (algorithms, computer code) for simulating dynamical systems and/or software engineering experience using different programming languages (e.g., R, Matlab, Perl, Common Lisp, Python); rapid prototyping of cell-based computational models.
4. Problem-solving and translation: experience or capacity to trouble-shoot computational models; work with software engineers for improved graphics and visualization; accept scientific feedback to make quantitative models more useful for scientific applications.