Predicting growth rate from gene expression
Significance Connecting genetic changes to organismal function has been a central problem of biology for decades. Understanding the genetic underpinnings of functional traits like growth rate remains incomplete despite efforts to uncover metabolic and gene-regulatory networks. Here, we leverage correlations derived from large-scale datasets of Escherichia coli and Saccharomyces cerevisiae to construct a mapping between gene expression and growth using the k-nearest neighbors technique. Our mapping can predict growth rate more accurately than previous methods, while compressing gene-expression data from thousands of genes to tens of features without requiring network structure identification. This model can be applied to generate hypotheses, design experiments, and reduce the amount of trial and error in research. Growth rate is one of the most important and most complex phenotypic characteristics of unicellular microorganisms, which determines the genetic mutations that dominate at the population level, and ultimately whether the population will survive. Translating changes at the genetic level to their growth-rate consequences remains a subject of intense interest, since such a mapping could rationally direct experiments to optimize antibiotic efficacy or bioreactor productivity. In this work, we directly map transcriptional profiles to growth rates by gathering published gene-expression data from Escherichia coli and Saccharomyces cerevisiae with corresponding growth-rate measurements. Using a machine-learning technique called k-nearest-neighbors regression, we build a model which predicts growth rate from gene expression. By exploiting the correlated nature of gene expression and sparsifying the model, we capture 81% of the variance in growth rate of the E. coli dataset, while reducing the number of features from >4,000 to 9. In S. cerevisiae, we account for 89% of the variance in growth rate, while reducing from >5,500 dimensions to 18. Such a model provides a basis for selecting successful strategies from among the combinatorial number of experimental possibilities when attempting to optimize complex phenotypic traits like growth rate.