Mapping cellular regulation from 130 genome-wide phenotype screens
Are you a computational biologist with a fascination for cellular regulation? This project is a unique opportunity to unravel cellular regulation and discover new biology and therapeutic strategies.
The Project: Human cells perform highly complex tasks but the map of cellular regulation that controls these processes is surprisingly poorly known. In diseases, these processes are disturbed and targeted interference is required to rebalance disease. Such interference can only result in effective disease eradication with minimal side effects when the map of cellular regulation is known in sufficient detail. We will employ Phenosaurus, a large collection ~130 genome-wide haploid screens and advanced computational inference to reconstruct this map. Phenosaurus is a genotype to phenotype map that contains the strength of association between every gene and 130 (phopho)protein readouts in HAP1 cells. We envisage charting the regulatory map using two approaches. First, we will identify functional units. We will determine gene-gene similarity based on Phenosaurus phenotypic profiles and fuse these similarities with independent co-data sources such as protein-protein interaction maps, co-expression maps and gene ontologies to identify high confidence functional units (complexes and pathways). Second, we will identify regulatory interactions between functional units and genes. As many associations in Phenosaurus are indirect i.e. mediated by e.g. ‘master regulators’, we will employ network inference approaches, such as the PC algorithm, guided by co-data sources to identify (master) regulators and their associated interactions. High confidence predictions emerging from these analyses, such as protein complex or pathway membership (first approach) and new putative regulatory interactions with master regulators (second approach) will be validated experimentally. Importantly, this project provides the opportunity to develop novel computational approaches associated with data fusion, network inference and effective application of co-data.
Your working environment: You will research and benchmark the latest algorithms, develop new algorithms and help design validation and follow-up experiments. You will be working closely with fellow computational scientists of the Wessels group (ccb.nki.nl) experimentalists in the Brummelkamp group and scientists of Scenic Biotech BV (scenicbiotech.com) a biotech startup. You will be part of the Netherlands Cancer Institute (NKI), a world-class cancer research institute residing under the same roof as the Antoni van Leeuwenhoek cancer hospital in the vibrant city of Amsterdam. The NKI provides an open, lively and stimulating environment with access to top-notch facilities, including high-performance computing and with many opportunities to interact with colleagues at social events, attend high-quality seminars and perform translational research.
What we are looking for: Highly motivated Postdocs with:
• A degree or advanced skills in bioinformatics, or a related discipline
• A degree or a high level of proficiency in (molecular) biology
• Proficiency in R, Python and data base management
• Experience in interacting with molecular biologists
Interested? Please send applications by email to Lodewyk Wessels (firstname.lastname@example.org) and use “#application #postdoc” in the subject line. Include a CV, motivation letter and the names and contact information of at least two references in your application.
Deadline: 20 January 2020