The main focus of our group is to determine how the genotype-to-phenotype map is modulated by both genetic and non-genetic heterogeneity. To do so we take a high-throughput quantitative systems biology approach to determine the genetic and non-genetic determinants of phenotypic variability, with a focus on evolution, gene expression, proliferation and drug resistance in single cells. The following are some examples of questions we’re working on:
- Why do identical mutations and drug treatments have different outcomes in different cells? Within an isogenic population not all microbes are killed by an antibiotic and not all cancer cells are killed by chemotherapy. In addition, the effect of a mutation varies across individuals; identical mutations often have no effect in some people but result in a severe disease phenotype in others. Why are only some individuals affected by a drug or mutation?
- Machine learning to predict mutational impacts in heterogeneous genetic backgrounds. The effect of each mutation depends on the genetic background in which it occurs. To discover fundamental principles that govern how genetic interactions determine phenotype we build large libraries containing millions of genetic variants and measure the phenotype of each genotype. To understand the resulting large multi-dimensional datasets, we develop novel machine-learning based approaches to quantify and predict the impact of each mutation on fitness.
- What mechanisms result in the predictable evolution of drug resistance during treatment? Some tumors, fungi and bacteria strains consistently and reproducibly acquire multidrug resistance in both patients and lab experiments, while others do not. Why? We are working to understand how the evolvability of various phenotypes is encoded in the genome.