Predicting the phenotypic impacts of a mutation is a major goal in biology and medicine. But the paths linking genotype to phenotype are difficult to navigate. For one, some phenotypes impact others, so the impacts of mutation can stretch out across networks of related traits, percolating from impacts at the molecular level to higher orders of biological organization. Moreover, the impacts of mutation, and the networks of traits through which they spread, change across contexts like the environment or genetic background. Comprehensively mapping genotype to phenotype is akin to untangling a ball of knotted strings, only to realize the task needs to be repeated many times.

We implement and develop high-throughput phenotyping methods to simultaneously quantify the phenotypic impacts of many mutations across many contexts. We interpret these data in different ways. In some projects, we quantify the correlations between phenotypes to infer the network through which a mutation’s influence travels and how that network changes across contexts. In other projects, we deconvolute these high-dimensional data into an abstract map that uses shared patterns of mutant behavior across contexts to make phenotypic predictions.

Some of our work is driven by mechanistic hypotheses about how basic properties of cells change the phenotypic impacts of mutation in predictable ways, while other work focuses on large collections of adaptive mutations generated by laboratory evolution experiments. Our overall goal is to build predictive maps from genotype to phenotype and, in so doing, to generate insights about the map’s structure (e.g., is it modular or interconnected?) and tools to study this map that will be broadly useful to the biology community.