# Predicting Growth Rate from Gene Expression

## TL;DR

This study develops a machine learning model that predicts microbial growth rates from gene expression data, achieving high accuracy and reducing feature complexity, which can guide experimental optimization.

## Contribution

It introduces a novel approach using k-nearest-neighbors regression to accurately predict growth rates from gene expression with significant feature reduction.

## Key findings

- Predicts E. coli growth rate with 81% variance explained
- Predicts S. cerevisiae growth rate with 89% variance explained
- Reduces gene expression features from thousands to fewer than twenty

## Abstract

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 paper, 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 over 4,000 to nine. In S. cerevisiae, we account for 89% of the variance in growth rate while reducing from over 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.