Data-driven discovery of the interplay between genetic and environmental factors in bacterial growth
Honoka Aida, Bei‐Wen Ying
Abstract
A complex interplay of genetic and environmental factors influences bacterial growth. Understanding these interactions is crucial for insights into complex living systems. This study employs a data-driven approach to uncover the principles governing bacterial growth changes due to genetic and environmental variation. A pilot survey is conducted across 115 Escherichia coli strains and 135 synthetic media comprising 45 chemicals, generating 13,944 growth profiles. Machine learning analyzes this dataset to predict the chemicals’ priorities for bacterial growth. The primary gene-chemical networks are structured hierarchically, with glucose playing a pivotal role. Offset in bacterial growth changes is frequently observed across 1,445,840 combinations of strains and media, with its magnitude correlating to individual alterations in strains or media. This counterbalance in the gene-chemical interplay is supposed to be a general feature beneficial for bacterial population growth. Genetic and environmental factors shaping bacterial growth are explored using profiles of E. coli. Machine learning revealed glucose-dependent gene-chemical hierarchies and negative epistasis, highlighting mechanisms that maintain growth homeostasis.