Increasingly cohorts of tumors are being profiled that collect genetic data for thousands of tumor samples, with an ever-increasing level of detail and completeness. Such systems genetics datasets especially when combined with clinical information can help improving patient stratification and developing novel therapies. Currently most of the driver identification efforts focused on identifying drivers that frequently occur and/or that are located in the coding fraction of the genome. We want to apply network methods to also study the role of non coding drivers in driving cancer related phenotypes.

The composition of microbial populations/communities is driven by both extrinsic and intrinsic evolutionary forces that alter both the frequency and genomes of the composing strains (haplotypes). In microbial communities intrinsic evolutionary forces often involve interactions between the composing subclonal populations. Using our developed methods we want to gain an improved understanding of the molecular mechanisms that drive existing or evolved interactions between subclonal populations in microbial communities of medical and/or ecological relevance.

Example publication:

• adaptive pathways explaining community behavior