Key responsibilities Systems toxicology is emerging as key methodology to analyze and interpret omics data in the chemical safety: it mimics the modular and multi-factorial nature of diseases and perturbational statuses. In particular, Dr. Callegaro and colleagues in the PI group of Bob van de Water have previously successfully applied this concept and Weighted Gene Co-expression Network Analysis (WGCNA) to model liver and kidney toxicity with preclinical in vitro and in vivo test systems. In this project, we aim at fully unlocking the potential of systems toxicology by determining which gene regulatory networks are predictive of human adverse events and how specific they are for a certain target organ class. The prospective candidate will get access to uniquely large and extensive transcriptomics databases covering the whole pipeline of drug and chemical safety testing, ranging from in vitro test systems of increasing complexity (such as HepG2 cell line, Primary Human Hepatocytes, iPSC-induced hepatocyte like cells, human liver microtissues) the rat preclinical system and human clinical pathology samples. The research group consist of a one-of-a-kind mixture of computational and experimental scientists that foster the implementation of both state of the art and novel bioinformatic tools as well as experimental validation via high-throughput and cutting edge technologies executed in the same pipeline. This research will represent the next step to impact both drug development practices as well as regulatory practices.
The prospective candidate will join an enthusiastic research team of >25 researchers that conduct their work in the context of multiple European funded projects (EU-ToxRisk, RISK-HUNT3R, eTRANSAFE, TransQST, VHP4S, PARC, KWF, EFSA TD-TRAQ and TXG-MAP), where successful interactions between academia, industry and regulatory agencies assures excellent scientific and societal impact, as well as a strong support network.
The successful applicant will:
- Developing computational toxicology models, including co-expression models for several liver and neuronal test systems and patient biopsies, incorporating clinical information; ML and AI algorithm to predict adverse outcomes and contribute to the further developments the co-expression application TXG-MAPr tool (https://txg-mapr.eu/, R-Shiny);
- Integrate experimental data validating findings obtained computational work, or, depending on interest, carry out the experiments themself (stem cell culturing, CRISPR array screen to identify the role of each gene for cellular adversity or survival and morphology)
- Design and apply case studies to demonstrate the value of co-expression methods and the TXG-MAPr tool for safety assessment;
- Interface with multiple partners in collaborative projects;
- A MSc degree in experimental toxicology or biopharmaceutical sciences but with a strong affinity/interest for bioinformatics, or a MSc degree in bioinformatics but with a deep understanding for experimental work;
- Experience with transcriptomic analysis, knowledge of common bioinformatics tools.
- Proficiency in R, experience with Shiny app development;
- Experience with network approaches/WGCNA or stem cell culturing andCRISPR array is considered a plus;
- Strong analytical skills, critical and lateral thinking;
- Strong communication skills and team player;
- Strong ability to work independently and creative thinker;
- Entrepreneurship interest or disposition is considered a plus.
Information Enquiries can be made to Giulia Callegaro ( firstname.lastname@example.org ). If you have any questions about the procedure, please contact the secretariat of the Division of Drug Discovery & Safety, Marjolijn Lugthart ( email@example.com ).