RT Journal Article
SR Electronic
T1 Stochastic modelling reveals mechanisms of metabolic heterogeneity
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 522425
DO 10.1101/522425
A1 Mona K. Tonn
A1 Philipp Thomas
A1 Mauricio Barahona
A1 Diego A. Oyarzún
YR 2019
UL http://biorxiv.org/content/early/2019/01/29/522425.abstract
AB 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.