Relationship between fitness and heterogeneity in exponentially growing microbial populations

TL;DR

This study introduces an inverse modeling framework linking microbial population fitness to metabolic variability, revealing a trade-off between growth and heterogeneity that aligns with theoretical optimal bounds in richer media.

Contribution

We develop a Maximum-Entropy based inverse modeling approach to connect population fitness with single-cell metabolic variability, providing new insights into bacterial metabolic strategies.

Findings

Fitness and variability approach theoretical bounds in rich media

Metabolic heterogeneity is shaped by a trade-off with growth

Population-level optimization influences bacterial metabolic behavior

Abstract

Despite major environmental and genetic differences, microbial metabolic networks are known to generate consistent physiological outcomes across vastly different organisms. This remarkable robustness suggests that, at least in bacteria, metabolic activity may be guided by universal principles. The constrained optimization of evolutionarily-motivated objective functions like the growth rate has emerged as the key theoretical assumption for the study of bacterial metabolism. While conceptually and practically useful in many situations, the idea that certain functions are optimized is hard to validate in data. Moreover, it is not always clear how optimality can be reconciled with the high degree of single-cell variability observed in experiments within microbial populations. To shed light on these issues, we develop an inverse modeling framework that connects the fitness of a population of cells (represented by the mean single-cell growth rate) to the underlying metabolic variability through the Maximum-Entropy inference of the distribution of metabolic phenotypes from data. While no clear objective function emerges, we find that, as the medium gets richer, the fitness and inferred variability for Escherichia coli populations follow and slowly approach the theoretically optimal bound defined by minimal reduction of variability at given fitness. These results suggest that bacterial metabolism may be crucially shaped by a population-level trade-off between growth and heterogeneity.