- Identify functionally relevant variants in cancer
- Understand how molecular pathways and variants drive drug response
- Develop methods to integrate cancer omics data across biological scales
- Develop tools for functional precision medicine
Research in the Brunk laboratory focuses on developing computational methods that accelerate the clarity and utility of omics data in biomedical science. Our aim is understanding the link between genetic/molecular variation and phenotype, both in natural and engineered cellular systems. We approach these topics through the lens of computational biology, machine learning and advanced data integration. Thanks to the growing body of cancer omics data, our methods borrow strength across genomics, transcriptomics, ribosome profiling, proteomics, structural genomics, metabolomics and phenotype variability data.
Our research has uncovered patterns in variants that co-occur in three-dimensional protein space and their downstream, molecular and clinical phenotypes. We also develop methods that explore how cells respond to drugs and genetic engineering. Broadly speaking, we aim to better understand how cells achieve regulation at multiple scales of complexity and which genetic and molecular variants influence this process.