Growth against entropy in bacterial metabolism: the phenotypic trade-off behind empirical growth rate distributions in E. coli

TL;DR

This paper explains the distribution of bacterial growth rates by modeling a trade-off between the fitness advantage of fast growth and the entropy of slow-growing phenotypes, using genome-scale metabolic models of E. coli.

Contribution

It introduces a minimal evolutionary model capturing the phenotypic trade-off in bacterial growth, linking empirical distributions to metabolic network properties.

Findings

Empirical growth rate distributions can be explained by a fitness-entropy trade-off.

Scaling relationships reflect the distance from maximum growth rate and phenotypic change rate.

Genome-scale models provide insights into bacterial growth phenotypes.

Abstract

The solution space of genome-scale models of cellular metabolism provides a map between physically viable flux configurations and cellular metabolic phenotypes described, at the most basic level, by the corresponding growth rates. By sampling the solution space of E. coli's metabolic network, we show that empirical growth rate distributions recently obtained in experiments at single-cell resolution can be explained in terms of a trade-off between the higher fitness of fast-growing phenotypes and the higher entropy of slow-growing ones. Based on this, we propose a minimal model for the evolution of a large bacterial population that captures this trade-off. The scaling relationships observed in experiments encode, in such frameworks, for the same distance from the maximum achievable growth rate, the same degree of growth rate maximization, and/or the same rate of phenotypic change. Being grounded on genome-scale metabolic network reconstructions, these results allow for multiple implications and extensions in spite of the underlying conceptual simplicity.