  Metabolic heterogeneity is widely recognised as the next challenge in our
understanding of non-genetic variation. A growing body of evidence suggests
that metabolic heterogeneity may result from the inherent stochasticity of
intracellular events. However, metabolism has been traditionally viewed as a
purely deterministic process, on the basis that highly abundant metabolites
tend to filter out stochastic phenomena. Here we bridge this gap with a general
method for prediction of metabolite distributions across single cells. By
exploiting the separation of time scales between enzyme expression and enzyme
kinetics, our method produces estimates for metabolite distributions without
the lengthy stochastic simulations that would be typically required for large
metabolic models. The metabolite distributions take the form of Gaussian
mixture models that are directly computable from single-cell expression data
and standard deterministic models for metabolic pathways. The proposed mixture
models provide a systematic method to predict the impact of biochemical
parameters on metabolite distributions. Our method lays the groundwork for
identifying the molecular processes that shape metabolic heterogeneity and its
functional implications in disease.
