Mots-Clés

Cancer, Single cell, Drug response

Description

Project description Anti-cancer drugs targeting signaling pathways often show great initial response, but resistance inevitably develops. One major reason for this is cell-state heterogeneity, where the response of a cell to a drug is influenced by the state the cell is in. In this project, we will use state-of-the-art single-cell multi-omics techniques (Radboud) and computational modelling (VU/NKI) to understand cellular responses to drugs over time, and develop strategies to rationally control cell behaviour to achieve better therapeutic efficacy.

This is a collaborative project between the Vrije Universiteit (VU), the Netherlands Cancer Institute (NKI) in Amsterdam, and the Radboud University in Nijmegen. It is funded by the Dutch Cancer Society. 

Responsibilities: Your role will be to develop and apply computational approaches to analyze the data generated in the project and build computational models of the signaling networks. You will use these models to understand how the targeted signaling pathways behave under the various perturbations and how this determines the response of the cells to treatment. You will formulate testable hypotheses that will be validated experimentally Finally, you will contribute to designing more effective drug treatment strategies. 
 

Research groups: This project will be jointly supervised by dr. Evert Bosdriesz (www.evertbosdriesz.org), assistant professor in the Bioinformatics group at the VU Amsterdam and prof. Lodewyk Wessels, who heads the Computational Cancer Biology group at the Netherlands Cancer Institute (ccb.nki.nl). The successful candidate will be employed at the VU and will spend at least two days per week at the NKI. The project is in close collaboration with the research group of prof. Klaas Mulder (https://www.ru.nl/personen/mulder-k-w) at the Radboud Universiteit Nijmegen.

REQUIREMENTS

• a degree in a quantitative discipline such as bioinformatics, computer science, physics, (applied) mathematics, or a related field
• proficiency in bioinformatics programming languages (e.g. R, Python) 
• good cross-disciplinary collaborative and communication skills
• experience in molecular and/or (cancer) biology and is a plus
• experience in statistics, machine learning and/or pattern recognition is a plus
• experience in analyzing high-throughput molecular data is a plus