A continuous epistasis model for predicting growth rate given combinatorial variation in gene expression and environment

These data were collected in the Kimberly Reynolds lab at UT Southwestern Medical Center. This project contains all next-generation sequencing reads associated with Otto et al., "A continuous epistasis model for predicting growth rate given combinatorial variation in gene expression and environment".The ability to predict changes in cellular growth rate given variation in gene expression is critical to interpret disease-causing mutations, engineer biosynthetic pathways, and understand evolutionary constraints on mRNA abundance. However, the relationship between gene expression and growth rate is nonlinear and shaped by environmental and genetic context. As a consequence, genetic studies focused on discrete perturbations with strong effects like total knockouts or complete nutrient depletion miss critical information relevant to organismal variation, evolution, and human disease. To address these challenges, we developed a strategy in which we 1) experimentally measure the growth rate effects of titrated changes in enzyme expression under varied environmental and genetic contexts for E. coli and 2) use a subset of these data to train an interpretable, predictive model. Models trained using a sparse subset of our experimental data - sampling only pairwise perturbations over genes and environments - were sufficient to predict the effects of higher-order combinations of expression and environmental perturbations. This high-throughput, generalizable framework provides a strategy for characterizing the growth rate effects of altering gene expression across entire metabolic pathways, or even genomes, in varied environments using sparsely sampled, low-order measurements.