Predicting growth rate from gene expression

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 116; no. 2; pp. 367 - 372
• Main Authors: Wytock, Thomas P., Motter, Adilson E.
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences 08.01.2019
• Subjects: Antibiotics; Biological Sciences; Bioreactors; Combinatorial analysis; Databases, Genetic; E coli; Escherichia coli; Escherichia coli - growth & development; Fungi; Gene expression; Gene Expression Regulation, Bacterial - physiology; Gene Expression Regulation, Fungal - physiology; Gene mapping; Genetics; Genotype & phenotype; Growth rate; Human growth; Learning algorithms; Machine learning; Mapping; Microorganisms; Models, Biological; Mutation; Physical Sciences; Predictive Value of Tests; Regression models; Saccharomyces cerevisiae; Saccharomyces cerevisiae - growth & development; Transcription; systems biology; metabolic networks; data science; machine learning; biological networks
• ISSN: 0027-8424; 1091-6490; 1091-6490
• DOI: 10.1073/pnas.1808080116

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.