Abstract

Genes do not act in isolation, but rather in tight interaction networks. Maps of genetic interactions between pairs of genes are a powerful way to dissect these relationships. I will discuss two statistical approaches to better understand the functional content of genetic interaction networks and the mechanism underlying them.

First, I will present a method that adapt concepts of spatial statistics to assess the functional content of molecular networks. Based on the guilt-by-association principle, our approach (called SANTA ) quantifies the strength of association between a gene set and a network, and functionally annotates molecular networks like other enrichment methods annotate lists of genes.

Second, I will describe a method to understand genetic interactions based on high-dimensional phenotypes. I will present methodology we developed to test the hypothesis that complex relationships between a gene pair can be explained by the action of a third gene that modulates the interaction. Our approach to test this hypothesis builds on Nested Effects Models (NEMs), a probabilistic model tailored to inferring networks from gene perturbation data. We have extended NEMs with logical functions to model gene interactions and show in simulations and case studies that our approach can successfully infer modulators of genetic interactions and thus lead to a better understanding of an important feature of cellular organisation.

Related Links

• http://www.markowetzlab.org – Markowetz lab website