Stochastic modelling reveals mechanisms of metabolic heterogeneity

Mona Tonn, Philipp Thomas, Mauricio Barahona, Diego Oyarzun

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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.
Original languageEnglish
Article number108
Number of pages9
JournalCommunications Biology
Publication statusPublished - 21 Mar 2019


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