A theme in many of our studies is our goal to predict some phenotypic responses from others, for example, we want to predict the toxicity of a misfolded protein from its degree of misfolding, or predict growth rate from ribosomal content. We also want to know the limits of these predictions and under what contexts they fail. Some of the most complex networks of related traits are gene regulatory networks. These often can reveal how the impacts of mutation fan out at the molecular level affecting the abundances of many transcripts. We’ve optimized an RNAseq method to report on correlated transcript levels across thousands of single yeast cells. We will use these data to infer regulatory networks and how they change across contexts. This work will uncover the paths linking genotype to phenotype to phenotype to phenotype and how these paths can change across contexts.

Relevant Papers:
-Inferring networks of related traits that predict which traits are jointly affected by mutation link
-Optimizing a single-cell RNA sequencing technology to investigate gene regulation in yeasts link