# Stochastic modelling reveals mechanisms of metabolic heterogeneity

## TL;DR

This paper introduces a stochastic model demonstrating that fluctuations in enzyme expression can lead to diverse metabolic states within genetically identical cell populations, revealing new mechanisms behind metabolic heterogeneity.

## Contribution

The study presents a novel stochastic modeling approach showing how enzyme fluctuations cause complex metabolic heterogeneity, including multimodal metabolite distributions.

## Key findings

- Metabolic heterogeneity can arise from enzyme expression fluctuations.
- Clonal populations can split into distinct metabolic subpopulations.
- Metabolite distributions can be bimodal or multimodal despite unimodal enzyme expression.

## Abstract

Phenotypic variation is a hallmark of cellular physiology. Metabolic heterogeneity, in particular, underpins single-cell phenomena such as microbial drug tolerance and growth variability. Much research has focussed on transcriptomic and proteomic heterogeneity, yet it remains unclear if such variation permeates to the metabolic state of a cell. Here we propose a stochastic model to show that complex forms of metabolic heterogeneity emerge from fluctuations in enzyme expression and catalysis. The analysis predicts clonal populations to split into two or more metabolically distinct subpopulations. We reveal mechanisms not seen in deterministic models, in which enzymes with unimodal expression distributions lead to metabolites with a bimodal or multimodal distribution across the population. Based on published data, the results suggest that metabolite heterogeneity may be more pervasive than previously thought. Our work casts light on links between gene expression and metabolism, and provides a theory to probe the sources of metabolite heterogeneity.