  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.
